Organizational Project Management Maturity

October/November 2016 Volume 47, Number 5

3 From the Editor Hans Georg Gemünden, Dr. rer. oec. habil., Dr. h.c. rer. oec. et soc., Professor of Project

Management, BI – Norwegian Business School Department of Leadership & Organization, Oslo, Norway

PAPERS

6 Project Networks: Governance Choices and Paradoxical Tensions Robert DeFillippi and Jörg Sydow

18 Disassembling and Reassembling Project Management Maturity Jan Christoph Albrecht and Konrad Spang

36 Practical Application and Empirical Evaluation of Reference Class Forecasting for Project Management Jordy Batselier and Mario Vanhoucke

52 Antecedents of Relationship Conflict in Cross-Functional Project Teams Xiaoyan Huo, Lianying Zhang, and Haiyan Guo

70 Closing the Stakeholder Expectation Gap: Managing Customer Expectations Toward the Process of Developing Information Systems Dirk Basten, Georgios Stavrou, and Oleg Pankratz

89 Lessons for IT Project Manager Efficacy: A Review of the Literature Associated with Project Success Chuck Millhollan and Michelle Kaarst-Brown

107 Organizational Design in Public Administration: Categorization of Project Management Offices

Monique Aubry and Maude Brunet

130 Calendar of Events

132 Project Management Journal ® Author Guidelines

The Book Review Section can be found online.

Cover to Cover—Book Reviews Kenneth H. Rose, PMP

T h e P r o f e s s i o n a l R e s e a r c h J o u r n a l o f t h e P r o j e c t M a n a g e m e n t I n s t i t u t e

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October/November 2016 ■ Project Management Journal 3

Project Management Journal, Vol. 47, No. 5, 3–5 © 2016 by the Project Management Institute Published online at www.pmi.org/PMJ

From the Editor Hans Georg Gemünden, Dr. rer. oec. habil., Dr. h.c. rer. oec. et soc., Professor of Project Management, BI – Norwegian Business School Department of Leadership & Organization, Oslo, Norway

Project Networks—An Important—But Still Under-Researched Topic in Project Management Research

We are living in an interconnected world and this has clear implications for project management. A recent trend study from Schoper, Gemünden, and Nguyen (2016) showed that experienced practitioners and academic researchers par- ticipating in this survey expected an increase in globally dispersed project teams until 2025, using new means for their virtual communication, and requiring a higher level of interpersonal and intercultural skills. According to this study, projects will become even more complex, leading to a higher level of professionalization of project manage- ment, an increasing need for better governance of publicly financed projects, and more project-oriented organizations with higher levels of individual and organizational project management competences. The study also indicates that projects will be assessed as project businesses, which are done together with other partners in complex business ecol- ogies, in order to develop and deliver innovative complex integrated solutions. This means that in an interconnected world of open innovation, an increasing share of value creation will be organized in the form of a project network.

Project management research does not yet reflect this increasing importance of value creation by project networks. An analysis of the unit of analysis used in project manage- ment research articles published in the years 2000 through 2011 in the International Journal of Project Management ® and Project Management Journal ®, by former PhD students at TU Berlin, Drs. Ekrot, Kock, and Kopmann, and myself show that there is a trend to analyze project networks more often. Compared with single projects or the project-oriented organization, however, the overall share is still very small (see Table 1). I searched the home pages of both journals for the years following 2011 and found only eight articles on project networks in the Project Management Journal ® and 16 in the International Journal of Project Management ®. This indicates a further increasing trend, but projects are still seldom con- ceptualized as networks—despite the fact that not only mega- projects are offered by large inter-organizational networks. Rather, the development of mass-customized products and services occurs also very often in globally cooperating inter- organizational networks.

These findings indicate a gap between theory and prac- tice, which leads to a call for a special issue on projects as

networks. This special issue is edited by Robert DeFillippi, Stephen Pryke, Jörg Sydow, and John Steen. I have also invited Robert DeFillippi and Jörg Sydow to write an invited article as a guiding contribution for this special issue.

The article from Robert DeFillippi and Jörg Sydow on “Project Networks: Governance Choices and Paradoxi- cal Tensions” is the first article in this issue. It examines project networks as either a single inter-organizational project or as a series of projects interconnected by inter- organizational relationships. In the first case, such a proj- ect is clearly a temporary organization. In the second case, which occurs very often in practice, the project is tempo- rary, but the context of the project is characterized by the long-term stable business relationships of partners, who repeatedly do business together, have invested in their relationships, and have adapted to each other. Therefore, the authors conceptualize project networks as more than temporary systems. In the following chapter, the authors elaborate on some core theoretic assumptions about proj- ect networks compared with the extant empirical research:

1. No single actor may act as a legitimate authority for the network as a whole.

2. There are no definite criteria by which the boundary of the network may be identified and controlled.

3. Each project is temporally limited and dynamically changing and (partially) reconstructed from one project to the next.

Next, the article presents four types (the four R’s) of mechanisms for governing not only projects but also proj- ect networks: responsibilities, routines, roles, and relation- ships. Finally, the article unearths five types of paradoxes (the distance paradox, the learning paradox, the identity paradox, the difference paradox, and the temporal para- dox) impacting project networks and offers insights into the governance-based choices available for coping with these paradoxical tensions.

Personally, I would add a fifth “R” to the governing mech- anisms and these are shared resources. Shared resources, which are only available to (core) members of a project network, generate a competitive advantage vis-à-vis all the actors not parts of this network, but they can also create path dependencies and lock-in effects. In addition, the partners may decide which resources they want to share, and which they want to keep exclusively for themselves or for partners

Project Management Journal, Vol. 44, No. 6, 2–5 © 2013 by the Project Management Institute Published online in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/pmj.21383

First, I want to share some very good news with the project management research community and all our readers inter- ested in project management research. The deadline for the submission of papers to the PMI research conference has been prolongated to 13 January 2014.

The PMI® Research and Education Conference, “Standing on the Shoulders of Giants: In Search of Theory and Evidence” will be held on 27–29 July 2014 in Portland, Oregon, USA.

We welcome conceptual, empirical, or theoretical work using project, program, or portfolio management as the subject or context of the research. PMI also solicits papers and sympo- sia on project management education; doctoral students are encouraged to submit their work to the pre-conference doctoral colloquium. For submission guidelines and instruc- tions, please contact PMI.org/REC2014submit. Conference registration is scheduled to open March 2014 and details can be found on PMI.org/REC2014.

The December issue of Project Management Journal® offers a rich variety of articles, each of which delivers a significant contribution to theory building in project organizing and new empirical findings with a high value of theory and practice. The first paper by Dietrich, Kujala, and Artto addresses a fun- damental organizational design question in project manage- ment: How should the interdependencies between different teams in a multi-team project be managed? There are many different coordination mechanisms, but each of them has its advantages and drawbacks and they can be combined in dif- ferent ways, which differ in terms of coherence and potential synergies. The organizational design reflections stated in this article can also be used for the management of programs con- sisting of an array of different projects or for the management of a project portfolio in which the management of interdepen- dencies between projects is also a critical challenge.

The management of interdependencies between proj- ects is an issue that has been neglected in multi-project management. Very often the interdependence is restricted to resource conflicts between projects and the solution is to identify the bottleneck resources and the projects that con- flict with one bottleneck resource. The solution to this prob- lem is often a muddling-through approach that delivers an immediate solution, yet doesn’t acknowledge that typically there are too many projects occurring at the same time, and

that an organization usually experiences a number of bottle- necks simultaneously. This bottleneck obstacle makes it dif- ficult to assess the consequences of measures that have been taken to repair an immediate problem—a problem that may only be a symptom of a much larger and obscure problem.

In addition, there are many kinds of different interdepen- dencies between projects that have not been addressed sys- tematically and simultaneously. Markowitz’s pioneering work showed that the risk of a portfolio of projects can be reduced if the project portfolio mixture combines projects, which in sum show a smaller covariance of cash-flow. Thus, managing risk interdependencies between financial invest- ments, which could have been projects, in such a way that the overall risk of a portfolio of financial investments, which could have been projects, is reduced, has been an essential element of designing portfolios since long.

Organizational design theory made the claim that the kinds of interdependencies matter; in other words, for pooled, sequential, or reciprocal interdependencies, differ- ent kinds of organizational coordination instruments—or more precisely, different kinds of coherent mixes of coordina- tion instruments—should be used. Regarding project portfo- lio management, pooled interdependencies among scarce (human) resources during the development stage of a new product, process, or service, have been the focus of interest. But pooled interdependencies are not restricted to human resources in the development process or to financial resources in a more aggregated view. If potential users of a project can only cope with a limited amount of new products or product releases that are delivered to them, this creates a new, thus far, often neglected type of bottleneck. The ability and willingness of users or intermediaries may also create bottlenecks and thus “pooled” interdependencies. Transfer prices or prioritization systems have been proposed to solve the internal resource coordination problem, but do they also apply to the customer acceptance bottleneck problem? Taking a marketing perspective or a purchasing perspective, additional interdependence aspects have to be considered. If two projects share the same customer as a recipient, or the same supplier as a source, then these two projects need to be coordinated. (This may pertain to the following questions: When should which project be done? What should it deliver to other projects serving the same client?) Or: Do resource

From the Editor Hans Georg Gemünden, Dr. rer. oec. habil., Chair for Technology and Innovation Management, Technische Universität Berlin, Berlin, Germany

Photo credit: Markus Bullick

2 December 2013 ■ Project Management Journal ■ DOI: 10.1002/pmj

Photo credit: Markus Bullick

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From the Editor: Project Networks—An Important—But Still Under-Researched Topic

4 October/November 2016 ■ Project Management Journal

in other inter-organizational (project) networks. In the context of open source development projects selective revealing of strategic information has become a very intensively discussed behavior—why not also in project networks in general?

However, this article is not the end of our discourse; rather it is intended to open it. I recommend this very insight- fully written article to all our readers and also to all authors writing on projects as networks. The editor of this article was Hans Georg Gemünden.

The next contribution is by Jan Christoph Albrecht and Konrad Spang: “Disassembling and Reassembling Project Management Maturity.” The concept of “maturity” is an important one for many organizations that wish to benchmark their use of project management approaches, which is often motivated by the search for continual improvement and for reassurance of internal progress through external verification. In order for academic research to be helpful in providing those in practice with reliable and helpful knowledge regarding this area of concern, it is critical to ensure that our understand- ing of the domain and its terminology are clear so that our accumulation of knowledge is also clear in terms of its appli- cation. In this article, Albrecht and Spang provide a detailed examination of two separate but interconnected dimensions of maturity as a broad concept. The authors focus on the relative scarcity of knowledge about one of these dimensions, strategic project management infrastructure, and stress the importance of the role of project management within the organization. This approach suggests that rather than being a universal standard, maturity may be regarded as having varied components or stages based on the varied approaches to project management within the firm. The implication is that industries or firms where project orientation is cen- tral—perhaps the film and construction industries—maturity may have a different look and feel relative to those in which projects are more incremental—perhaps healthcare and man- ufacturing. In any event, the article produces interesting observations regarding how we look at maturity and what the possibilities are for more thoroughly engaging with the con- cept. The editor of this article was Fred Niederman.

The third article by Jordy Batselier and Mario Vanhoucke: “Practical Application and Empirical Evaluation of Refer- ence Class Forecasting for Project Management,” addresses the estimation of project cost and duration, which is a persis- tent challenge in project management. It has been highlighted in the megaproject context by Flyvbjerg (2006, 2007, 2014), who also recommends reference class forecasting as a way of circumventing many of the decision biases in these large proj- ects. This article is a contribution that lends further support to the use of reference class forecasting (RCF) by comparing this technique with other cost and time estimation methods. RCF uses results from similar projects to estimate time and costs rather than the more optimistic assumptions that can be present in traditional project plans. The authors compare RCF with other estimation techniques, such as Monte Carlo simu- lation and earned value management, using real project data for the key project performance dimensions of time and costs. While earned value management (EVM) is different from RCF in its use for controlling the project during delivery rather than forecasting, the authors use RCF as a baseline and then the final outcome can be compared with EVM. A Monte Carlo simulation of various outcomes from different components of the project can also produce a forecast range if the risks of each component are also known. The context of the compari- son is a real-life construction project in an office building and the authors have compiled a reference class from previously completed projects. From the results comparing RCF with EVM and the Monte Carlo simulation, the authors conclude that RCF indeed provides the most accurate forecast of project performance. The editor of this article was John Steen.

The fourth article in this issue by Xiaoyan Huo, Liany- ing Zhang, and Haiyan Guo on “Antecedents of Relation- ship Conflict in Cross-Functional Project Teams” provides an ambitious theoretical framework and its empirical test. Relationship conflict is a pervasive phenomenon in cross- functional project teams. Although previous studies have demonstrated the dysfunctional effect of relationship conflict, the direct drivers of relationship conflict in cross-functional project teams remain unclear. To address this gap, a literature

Years 2000–2002 2003–2005 2006–2008 2009–2011

Unit of Analysis n % n % n % n % Single Project 168 78.50% 161 68.22% 226 72.44% 194 62.18%

Program 9 4.21% 11 4.66% 17 5.45% 23 7.37%

Portfolio 3 1.40% 7 2.97% 9 2.88% 7 2.24%

Project-Oriented Organization 32 14.95% 53 22.46% 54 17.31% 77 24.68%

Project-Network 2 0.93% 4 1.69% 6 1.92% 11 3.53%

Total 214 100% 236 100% 312 100% 312 100%

Table 1: Development of the units-of-analysis in articles published in the Project Management Journal® and International Journal of Project Management® in 2000–2011.

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October/November 2016 ■ Project Management Journal 5

review and an advisory group discussion were performed to identify the antecedents of the relationship conflict frame- work. Afterward, the structural equation model (SEM) was used to confirm the influence of such antecedents on rela- tionship conflict. Intrapersonal diversity, uncertain project tasks, organizational culture diversity, and inappropriate behavior positively influence relationship conflict. These findings help researchers better understand the drivers of relationship conflict.

This article is not only of interest for the project manage- ment research community, it is also highly relevant for the innovation management research community and the team research community. It has been expected that an increasing cultural and cognitive diversity of a team provides more oppor- tunities for higher creativity and global diffusion of a product or service. Increasing diversity, however, does not only lead to task conflicts, which may act as creative tensions, overcom- ing functional fixations, and opening new ways of thinking; task conflicts also pose higher challenges and require a more cooperative and resilient team. The typical observation is that task conflicts may drive relationship conflicts, which escalate and cannot be regulated anymore; therefore insights into the drivers of relationship conflicts are very important for finding remedies to prevent or stop such escalation spirals. The editor of this article was Hans Georg Gemünden.

Dirk Basten, Georgios Stavrou, and Oleg Pankratz inves- tigate the following theme: “Closing the Stakeholder Expec- tation Gap: Managing Customer Expectations Toward the Process of Developing Information Systems.” Whereas expectations concerning both process and product are essen- tial for information system development (ISD) project suc- cess, research has focused on end-user expectations toward the product. Based on semi-structured interviews, we shed light on the relevance of process expectations for customer satisfaction in ISD projects, concrete customer expectations toward the process, and approaches to managing these expec- tations. Our study provides means to managing customer expectations, thus increasing the likelihood of customer satis- faction. The editor of this article was Hans Georg Gemünden.

The sixth article in this issue by Chuck Millhollan and Michelle Kaarst-Brown provides “Lessons for IT Project Manager Efficacy: A Review of the Literature Associated with Project Success.” The authors state that overall proj- ect success requires that project managers develop not only project management skills, but also project success skills. The article points out that even if one manages the project successfully, the project can still fail, for example, because of misalignment with organizational strategy or poor stake- holder representation. Project managers need to develop a range of skills outside those proposed in the project manage- ment literature. Indeed, because we ignore these other skills, there is often no difference in performance between Project Management Professional (PMP)® and non-PMP® certified

managers. The article proposes a project manager needs seven classes of skills:

1. Project management skills (hard skills; temporal) 2. Business and management skills (hard skills; perhaps soft

skills; temporal) 3. Knowledge of the project technical disciplines (hard skills) 4. Interpersonal skills (soft skills; stakeholders’ influences;

temporal) 5. Managing the project sponsor (soft skills; stakeholder

influences) 6. Situational awareness (soft skills; stakeholder influences;

temporal) 7. Integration management, or integrating the previous skills

and knowledge (soft skills; temporal)

This is an insightful literature analysis, which deserves further discussion and reflection. The editor of this article was Cecil Eng Huang Chua.

The last article is by Monique Aubry and Maude Brunet on “Organizational Design in Public Administration: Cat- egorization of Project Management Offices.”

This article aims to enlighten the organizational process involved in managing multiple concurrent projects within the public sector and associated with organizational design. Public administration is not a monolithic entity; rather it com- prises a collection of multiple organizations that differ in sta- tus, some of them being departments, others being agencies. Following this, the article proposes an empirical categoriza- tion of PMOs based on four types of projects, based on a sur- vey of 114 entities belonging to 42 departments and agencies within a single public administration. This article contributes to the relevance of organizational design within the project management field. The editor of this article was Ralf Müller.

References Flyvbjerg, B. (2006). From Nobel Prize to project management: Getting risks right. Project Management Journal, 37(3), 5–15.

Flyvbjerg, B. (2007). Eliminating bias in early project development through reference class forecasting and good governance. In K. J. Sunnevåg (Ed.), Decisions based on weak information: Approaches and challenges in the early phase of projects (pp. 90–110). Trondheim, Norway: Concept Program, The Norwegian University of Science and Technology.

Flyvbjerg, B. (2014). What you should know about megaprojects and why: An overview. Project Management Journal, 45(2), 5–19.

Schoper, Y., Gemünden, H. G., & Nguyen, N. N. (2016): Fifteen future trends for project management in 2025. In: Hans Knoepfel and Jesus Martinez-Almela (Eds.). Future trends in project, programme and portfolio management 2016. Proceedings of the International IPMA Expert Seminar in Zurich, February 18–19, 2016, pp. 23–43.

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P A

P E

R S

6 October/November 2016 ■ Project Management Journal

IntroductIon

P rojects matter for organizations, even whole industries or regions. Not only are many products and services developed, produced, and marketed with the help of this form of “temporary organization” (Lundin & Söderholm, 1995), but processes within or across

organizations are generated or changed within projects. What is more, projects are central to quite a number of firms, in particular so-called “project-based organizations” (Hobday, 2000) and they are characteristic of those industries and/or regions in which “project business” (Artto & Wikström, 2005) dominates. Examples of project-based organizations include general contractors, television production firms, as well as advertising and event agencies; the industries and/or regions in which these organizations are embedded and operate are correctly conceived as “project ecologies” (Grabher, 2004).

Projects, however, are not only embedded in organizations, industries, and regions but also in networks of interorganizational relationships. If this is the case, the notions of “project coalitions” (Pryke, 2004) or “project alliances” (Abrahams & Cullen, 1998; Clegg, Pistis, Rura-Polley, & Marosszeky, 2002; Kwok & Hampson, 1996) have gained some prominence in the literature. This is, however, particularly true for the notion of “project networks,” although its meaning is often not quite clear. Even worse, at least two different, though legitimate, understandings can be distinguished: (1) a single interorganiza- tional project (Hellgren & Stjernberg, 1995), and (2) a series of projects that are interconnected by interorganizational relationships (Sydow & Windeler, 1999) that enable and constrain the management of projects.

In what follows, both types of project networks as forms of temporary organizing will be explained in more detail and illustrated using empirical insights from a variety of industries. Then, we will explore how projects and, in particular, these two types of project networks are governed. We will high- light four R’s as mechanisms for governing and coordinating not only projects but also project networks: responsibilities, routines, roles, and relationships. We will conclude by unearthing the tensions and contradictions and even the paradoxes that management has to deal with when project management implies also managing interorganizational relations and networks.

Project Networks as (More Than) Temporary Systems Hellgren and Stjernberg (1995) define project networks in terms of three com- ponent characteristics: (1) a set of relations, where no single actor may act as a legitimate authority for the network as a whole; (2) a situation where the net- work is open in the sense that there are no definite criteria by which the bound- ary of the network may be identified and controlled; and (3) an environment where the network is temporally limited, dynamically changing, and (partially) reconstructed from one project to the next. These authors, not unlike Jones and Lichtenstein (2008), focus mainly on a single inter organizational project

Project Networks: Governance Choices and Paradoxical Tensions Robert DeFillippi, Sawyer Business School, Suffolk University, Boston, Massachusetts, USA Jörg Sydow, School of Business & Economics, Freie Universität Berlin, Berlin, Germany

This article examines how project net-

works may be viewed as either a single

interorganizational project or as a series of

projects that are interconnected by interor-

ganizational relationships. The article then

discusses some core theoretic assumptions

about project networks as more than tempo-

rary systems in comparison with the extant

empirical research. Next, the article presents

four types of mechanisms for governing

and coordinating not only projects but also

project networks: responsibilities, routines,

roles, and relationships. Finally, the article

unearths five types of paradoxes (the dis-

tance paradox, the learning paradox, the

identity paradox, the difference paradox,

and the temporal paradox) impacting proj-

ect networks and offers insights into the

governance-based choices available for cop-

ing with these paradoxical tensions.

KEYWORDS: project; project-based organization; project network; governance;

tensions; paradox.

Project Management Journal, Vol. 47, No. 5, 6–17

© 2016 by the Project Management Institute

Published online at www.pmi.org/PMJ

ABStrAct ■

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October/November 2016 ■ Project Management Journal 7

actors (principal contractor or client as lead organization and subcontrac- tors) and how this disconnect can fil- ter down to the operational interactions impacting the relationships between the (temporary) project (often team) enti- ties created by each permanent prin- cipal in the project. These disconnects can become irreconcilable and result in project termination, as illustrated in Van Marrewijk and colleagues’ (2016) case study of the Panama Canal megaproject.

Characteristic 2: There are no definite criteria by which the boundary of the network may be identified and controlled.

The blurring of network boundaries in general and project network boundar- ies in particular is widely observed. One reason is that project membership may be less than definitive. What is more, membership is dynamic and may vary, as in sequential projects where a set of actors in rather stable constellations coordinates the production of a series of projects. Here, specific participants may vary from project to project while the project principals may be more per- manent. This ambiguity of boundaries is well documented in a series of studies by Sydow and colleagues on German television production (Manning, 2008; Manning & Sydow, 2011; Sydow, 2009; Sydow & Windeler, 1999; Windeler & Sydow, 2001). Their research suggests that the single project as a temporary organization is, in this industry at least, embedded in a more durable network of relations between permanent organiza- tions and institutions that work on mul- tiple projects over time. The more this is the case, however, the clearer the bound- aries of a project network, even over time. Even if one avoids hastily classify- ing some parts of these systems—prefer- ably at the periphery of the network—as “market-organized projects” (Lorenzen & Frederiksen, 2005), certainly not all rela- tionships should be tagged as having a collaborative, reciprocal quality—which is a defining characteristic of networks as a form of governance (Powell, 1990).

of authority and typically require the coordination of diverse sets of actors and relationships. Hence, it comes as no surprise that the governance of such net- works may vary profoundly. As for any network, authority to coordinate may be organized in at least three forms (Provan & Kenis, 2008): (1) Network governance can be shared among the participat- ing members, (2) a lead organization may govern the network, or (3) a net- work administrative organization (NAO) may govern the network (an NAO is a dedicated organization responsible for coordinating the network or at least sup- porting such processes). The effective- ness of each of these three governance modes hinges on particular boundary conditions that, according to Provan and Kenis (2008), include the level of trust, the number of participants, the degree of goal consensus, and to what extent there is a need for network-level competencies. To give just one example, shared network governance seems to require more goal consensus and trust and, thus, may be suitable only for networks of smaller size. All three forms of governance may also be used in project networks, including what Hellgren and Stjernberg (1995) exclude: a lead organization. This form, like the NAO, seems to be particularly appro- priate, perhaps even indispensable, for larger and dynamic (project) networks in which shared governance, not least because of a lack of goal consensus and trust, may be difficult to establish.

There is abundant evidence of not only the lead organization mode in proj- ect networks (e.g. Manning & Sydow, 2011; Sydow & Windeler, 1999) but also of governance failures in coordinat- ing project networks. For example, Van Marrewijk and his research colleagues (2016) observed the critical importance of agreements about responsibilities and roles made in the tender phase of an interorganizational project being clearly communicated to the project employees of both the contractor and client organi- zation during the execution phase. Their research documents the disconnect that can occur between the more permanent

as a temporary system—and the tempo- rary network of relationships that sup- ports project coordination.

This focus on single projects and internal relationships is perfectly legiti- mate, not least as a unit of analysis considered at one point in time. For that reason, researchers talk of “project governance” (Ahola, Russka, Artto, & Kujula, 2014; Müller, 2009) and highlight governance mechanisms at work on the level of single projects in order to ensure a predictable delivery of projects in time, quality, and cost. However, most proj- ects are embedded either in organiza- tions or in interorganizational networks so that they are surrounded by corpo- rate governance or network governance, respectively, and in many cases even by both. The project business perspective takes this into account by focusing on “the part of business that relates directly or indirectly to projects, with a purpose to achieve objectives of a firm or several firms” (Artto & Wikström, 2005, p. 351).

Many projects have a predecessor as well as a successor of some kind (Davies, Dodgson, & Gann, 2016). More than  a general sense that “history matters” (Engwall, 2003), this kind of temporal embeddedness is particularly obvious if projects are part of a series (Manning & Sydow, 2011), a lineage (Midler, 2013), or even an entire program (Artto, Mar- tinsuo, Gemünden, & Murtoaro, 2009). In consequence, project networks more often than not are likely to be more than mere temporary systems. In this respect, they emulate project-based organiza- tions (Hobday, 2000; Lundin et al., 2015). With regard to the three characteristics highlighted by Hellgren and Stjernberg (1995), empirical evidence seems some- what to contradict this characteristic of strict temporariness.

Characteristic 1: No single actor may act as a legitimate authority for the network as a whole.

Project networks are complicated as authority structures because they are not only dynamic but polycentric; in other words, they have several centers

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of project-based organizing in cre- ative industries (DeFillippi, 2015) but deemed to be useful also in other proj- ect ecologies, including construction and science-based industries. Although we do not claim that these four R’s cover all dimensions of project and project network governance, they are certainly the more important ones. What is more, these mechanisms interact and serve the purpose of governance to be practiced—governing (Pitsis, Sankaran, Gudergan, & Clegg, 2014).

Given the fact that many project networks, as laid out above, should be conceived as more than temporary systems, all four governance mecha- nisms are relevant on two levels: the focal project and the broader network in which this temporary form of organi- zation is embedded (see Table 1), con- tributing to outcomes on both levels in terms of project and network efficiency and effectiveness. Interestingly, proj- ect governance is often discussed with relation to corporate governance (Joslin & Müller, 2016; Müller, 2009), neglect- ing the fact that projects may not only

put in place to regulate exchange, mini- mize exposure to opportunism, protect transaction-specific investments, and promote the continuance of relation- ships (Jap & Ganesan, 2000; Jones, Hes- terly, & Borgatti, 1997). Olsen, Haugland, Karlsen, and Husøy (2005) described the use of contracts, relational norms, and administrative controls as governance mechanisms for handling complex pro- curements involving several actors. Their work highlighted the importance of the interplay among more than one gover- nance mechanisms.

Such mechanisms, potentially at work also in project networks as (more than) temporary systems, may be use- fully summarized under a “four R’s” classification: responsibilities, routines, roles, and relations. In terms of gover- nance, responsibilities represent more contract-based governance, whereas routines and roles reflect administrative controls, and relationships represent social modes of governance. Previously, the second through fourth R’s of this classification had been conceptual- ized and applied to the examination

Characteristic 3: Each project is temporally limited and dynamically changing and (partially) reconstructed from one project to the next.

If anything, temporariness is the defining feature of projects or temporary organi- zations (Bakker, 2010; Lundin & Söder- holm, 1995). At the same time, it has been observed that no project is an island (Burke & Morley, 2016; Engwall, 2003; Lundin et al., 2015). History matters and many single projects seem to be embed- ded in either project-based organizations or project networks, as outlined above. In addition, both such organizations and interorganizational networks are embedded within broader institutional fields (Windeler & Sydow, 2001) or proj- ect ecologies (Grabher, 2004). Finally, in the shadow of past project engage- ment and anticipation of future project opportunities, managing a focal project is very different from the idea of manag- ing an isolated project as an outcome of temporary organizing. To capture these particularities, we can only underline the value of Hellgren and Stjernberg’s (1995) third characteristic, that projects are “(partially) reconstructed from one project to the next (p. 381).”

A recent case study of Dutch film- making illustrated how a movie produc- er’s specific sponsorship of sequential projects affects the permanent and tem- porary organization’s connectedness and project outcomes. This research by Stjerne and Svejenova (2016) suggests that the shadows of the past and future experienced in earlier projects in the sequel sequence indeed impacted the tensions, boundary work, and bound- ary roles created in subsequent sequel projects to address these tensions.

Project and Project Network Governance—Governing by Four R’s Projects and project networks, depending among others on the above-differentiated governance modes, utilize a variety of mechanisms to coordinate their work. More generally, governance mecha- nisms are safeguards that organizations

Level of Analysis Focal Project Project Network Network emphasis Internal network of relationships External network of relationships in

which projects are embedded

Governance types Project governance: (1) shared, (2) project manager, (3) PMO

Network governance: (1) shared, (2) lead organization, (3) NAO

Governance mechanisms

Dominantly designed and formal, but increasingly reflexive with regard to unintended consequences

Dominantly emergent and informal, despite increased reflexivity

- Responsibilities Project responsibilities Network responsibilities

- Routines Project routines Interorganizational routines

- Roles Project roles, including project manager

Roles in the network, including lead organization

- Relations Within project, relations are temporary

Across project relations more than temporary

Governance outcome Project success, often measured in terms of efficiency and effectiveness (i.e., with regard to quality, time, and cost)

Project network success, to be measured in number of projects “successfully” completed, but also in terms of broader network effectiveness

Table 1: Project governance and project network governance.

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tion of outliers that require more urgent and closer attention by personnel whose roles mandate their attention to par- ticular areas of project performance and associated project activity. Like respon- sibilities, within-project routines can be distinguished from across-project rou- tines situated in the project network. Again, we argue that the former may be more deliberate, and the latter of a more emergent nature. However, coordination of project networks—not unlike supply chains or networks—may require more managerial attention to interorganiza- tional routines (Zollo, Reuer, & Singh, 2002) on the network level.

The importance of repeated collab- orative experiences on this level of anal- ysis upon choice of governance modes is borne out in several studies. Davies and Brady (2000) develop the concept of economies of repetition to show that project-based organizations can offer “repeatable solutions by recycling expe- rience from one project for others in the same line of business” (Davies & Brady, 2000, p. 932). Crucial to the achievement of economies of repetition is the very development of routines, which may— as interorganizational routines—also be in effect in project networks. Once hav- ing undertaken a one-off project, the same participants are involved in suc- cessive ones of the same type in order to consolidate routines, which they adapt according to the contingencies of each project (D’Andrea, 2014). García-Canal, Valdés-Llaneza, and Sánchez-Lorda (2014) argue that when developing new collaborative projects with the same partner, firms tend to repeat the same contractual form used in previous proj- ects to take advantage of the governance routines developed in the past. Support for their predictions is provided by an analysis of a sample of technology alli- ances carried out by European firms. We assume that the inclination toward rep- etition is not restricted to formal con- tracts but includes informal routines as well, in particular if project networks are composed of project participants from previous collaborative projects.

instance, of the production of a series or a portfolio of projects to be man- aged with regard to cross-project rela- tionships. From the music industry, typically clustered in major cities, it is well known that the “majors” use con- tracts to coordinate projects (Lorenzen & Frederiksen, 2005).

Though not focusing on projects, Huang, Cheng, and Tseng (2014) exam- ined the influence of formal, contract- based controls and social controls (e.g., relationship-based governance) upon the buyer–supplier cooperative perfor- mance in supply chains. Empirical evi- dence obtained via a mail survey from 106 firms participating in the Taiwanese “Center Satellite Production System” indicates that (1) there is an inverted U-shaped relationship between formal control and cooperative performance; (2) social control has a consistent posi- tive effect on cooperative performance; and (3) the joint use of formal control and social control could enhance coop- erative performance in supply chains, but only in cases with moderate usage of formal control. Otherwise, social control becomes a supportive factor that repairs cooperative performance damage from overwhelmingly applied formal control. This finding also makes sense for projects and project networks; in other words, we would expect that contractual governance is more effec- tive if used in moderation and com- plemented by more relationship-based modes of governance.

Routines are repetitive patterns of interdependent actions (Parmigiani & Howard-Grenville, 2011). In projects, routines are often supported by shared artifacts (including information sys- tems) and typically reflect established cycles of project work activities and their monitoring. These routines define the expected work flow and the milestones for evaluating project progress. Routines serve as a complement to responsibilities insofar as many of the project manage- ment routines facilitate the monitoring of project progress in meeting perfor- mance requirements and the identifica-

be embedded in corporations, but also in other types of organizations (project- based or not) and even in interorganiza- tional networks or fields.

On the level of the focal project, responsibilities refer to the requirements or deliverables expected of all project participants and their liability for failing to fulfill these responsibilities, which encompass the four T’s of temporary systems associated with project-based organizing: to manage a specific sets of tasks that are time-limited, and typically performed by a semi-temporary collec- tion or team of individuals with differ- ent expertise who collectively enable the sponsoring or host organization to transition from one state of perfor- mance and capability to a new state (Bakker, 2010; Lundin & Söderholm, 1995). More often than not, the respon- sibility for project outcome, in other words, predictable delivery of projects in time, quality, and cost, is allocated to a project manager, sometimes sup- ported by a project management office (PMO), typically installed in organiza- tions that run portfolios of projects in order to standardize project manage- ment and enhance across-project learn- ing (Hobbs, Aubry, & Thullier, 2008; Narayanan & DeFillippi, 2012). Only in rare cases is this responsibility shared among project members.

Though for any single project Lun- din and Söderholm’s (1995) four T’s result from more or less intentional decisions or design choices, the same four T’s can be identified on the net- work level as well for clarifying respon- sibilities. However, despite increasing attempts that Sydow and colleagues have observed in the television indus- try to coordinate activities more reflex- ively also on this level (Manning & Sydow, 2011; Sydow & Windeler, 1999), the whole network of relationships is—not least because of its complexity and dynamics—more of an emergent nature (Mintzberg & McHugh, 1985). Nevertheless, contracts regulating responsibilities are likely to play a role on the network level as well. Think, for

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previously codified skills. Coordination of tasks and skills is more complex in these kinds of project-based organiza- tions, but their greater organizational flexibility enables them to change work processes (routines) more readily. In consequence, we expect project net- works to employ standardized, separate, and stable roles on projects that require stable and standardized requirements for each project network engagement. By contrast, project networks are likely to employ more idiosyncratic roles on proj- ects that require unique requirements for each project network engagement.

Relations refer to specific qualities of the interactions among participants in project work. Powell (1990) argues that network forms of organizing are distinc- tive from both market (contractual) and hierarchical (administrative) forms of governing economic activity, as they are based on trust, reciprocity, and open- endedness. Further examination of net- work forms of organizing projects has emphasized the relational as well as the structural embeddedness of economic activity involving network participants (Jones et al., 1997; Jones & Lichtenstein, 2008). Relational embeddedness refers to how focal dyadic (interpersonal or interorganizational) relationships and their qualities, histories, and devel- opmental processes affect economic behaviors and outcomes. The structural aspect of embeddedness emphasizes the relevance and impact of the larger ongoing network of relationships—its density or centrality, for instance—in which economic action occurs and develops and the dyads are themselves embedded. Capaldo (2014) concludes that simultaneous consideration of structural and relational embedded- ness can enrich our understanding of network-based forms of organization and their impact on outcomes, specifi- cally of interorganizational cooperation.

Qualities of relationships often include trust and reciprocity, as illus- trated in Swärd’s (2016) longitudinal case study of a three-year construction project. Jones and Lichtenstein (2008)

organizations (Gemünden, Salomo, & Hölzle, 2007). Turner and Keegan (2001) suggest two specific interface roles, the broker and the steward, which should be of particular importance not only in projects but also in project networks. The broker is responsible for the rela- tionship with the external project cli- ent, whereas the steward focuses on the relationship between the parent orga- nization and the project team. These roles may be generically defined by project contracts, but the specific types of relationships enjoyed by various role participants are more likely to emerge during the course of the project, in other words, to result not simply from passive role-taking but also active role-making (Graen, 1976). In project networks, tak- ing and making the broker role seems particularly important, to connect the project not only to the project client but also to other external project partners. In television production, the client, the producer, and the director—all of whom are involved in brokering of some kind—are very likely to form the stable core that coordinates project networks (Manning & Sydow, 2011).

Whitley (2006) distinguishes between those project-based organizations that expect project participants to utilize standardized, separate, and stable roles and skills versus those that require project participants to utilize change- able roles and skills. Stable and sepa- rate roles are most likely to be found in craft-dominated sectors (e.g., filmmaking and video games) where roles are skill based, craft standardized, and remain stable over a succession of projects for various project participants, who can move quickly from project to project and join new project teams with their roles predefined by their skill specialization and function (Bechky, 2006). By contrast, in some project-based organizations, such as those in the Munich enterprise software industry (Grabher, 2004; Ibert, 2004), workers adopt different roles over the course of projects and in different project teams, and the division of labor is not so strongly structured around

Drawing primarily on the televi- sion industry, where the tendency to repeat past project collaborations is significant, Sydow (2009) argues that the repetitive patterns and practices surviving beyond single projects may become project network routines. These routines may even become industry practices when they are repeated in different project networks. One risk of such project network routines is that they may lead to path dependence or even “lock-in” where these systems find it difficult to depart from previously employed routines even when specific project circumstances might call for a more flexible modification of previous project network practices. The lock-in problem of project network routines has particular implications for the rela- tionship between project network rou- tines and innovation. We expect that project network routines are positively associated with performance on proj- ects that have similar requirements to projects that previously utilized these project network partners. By contrast, we expect project network routines to be negatively associated with perfor- mance on projects that have unique requirements not previously experi- enced by project network participants. Finally, the usefulness of routines may also depend on the innovativeness of the task. For very innovative tasks, more flexible, informal coordination (infor- mal roles and relationships) may be a better mechanism to support project progress than routines.

Roles refer to the various authority assignments for each party to a proj- ect contract, and these roles typically include hierarchical authority lines as well as expected lines of commu- nications among project participants occupying specified project roles. Pre- sumably there is an expectation that authority assignments are matched to responsibility assignments for each project participant. This expectation may even be valid for innovator roles that are not only particularly dynamic but characteristic of many project-based

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Sometimes project networks can anticipate the likelihood of needing new sources of expertise in a complex project by including representatives with diverse previous project experi- ences and expertise. This intentional injection of diversity and novelty into a project network comes with the risk that the new project partners may lack the past experience of working with its other partners who share common past project engagements. As a result, the challenge becomes one of integrat- ing new project partners into a project network where other project network partners have past project experi- ence that shapes their expectations for their respective project relationships. Such relationships, characterized by trust and reciprocity, must be earned, whereas project responsibilities, roles, and routines are more an artifact of industry- or region-wide project norms and historical practice.

Paradoxical Tensions in Project Networks Project networks are subject to a variety of tensions whose resolution or even mitigation poses problematic dilemmas for the participating indi- viduals or organizations (DeFillippi, Grabher, & Jones, 2007). These ten- sions are referred to as paradoxical insofar as they typically resist simple binary choices among alternatives for their management (Lewis, 2000). A recent review of the paradox literature describes paradoxes as persistent con- tradictions between interdependent elements (Schad, Lewis, Raisch, & Smith, 2016). Our discussion will exam- ine a set of tensions and contradictions between interdependent elements in project networks and note the implica- tions of these paradoxes for project net- work governance. In more detail, we will present the distance paradox, the learn- ing paradox, the identity paradox, the difference paradox, and the temporal paradox. All five paradoxes—and others (cf. with regard to interorganizational relationships more generally, Sydow,

in some fields or ecologies but not in others (cf. Lundin et al., 2015). In both cases, context enables and constrains project network organizing. Moreover, the complexity of working in project networks means that contract-based project responsibilities may need to be revisited during the course of project work, more often than not relating to past experiences and/or future expecta- tions. Previous research has indicated how project responsibilities in the con- tract may be contradicted by operational responsibilities assumed by the tempo- rary project network organization as a result of the legacy of responsibilities assumed in previous project engage- ments (Van Marewijk et al., 2016).

The uncertainties of complex proj- ect work may induce unexpected proj- ect changes and a search for innovative solutions to these uncertainties, which may be exogenous and/or endogenous to the project network. As a result, proj- ect participants may be compelled to make real-time adaptations to their original expectations for project respon- sibilities, roles, routines, and relation- ships for working with one another. It is during these periods of crisis, uncer- tainty, and innovative challenge that the quality of the contractual relationships among project participants is tested and becomes—or fails to become—the rela- tional governance mechanism for cop- ing with these uncertainties (Macneil, 1974). In these situations, the “required” trust must be swift (Meyerson, Weick, & Kramer, 1996), but must also be nur- tured during a complex project, or even a series of projects, by large and small actions that signal reciprocal commit- ment to the project and the basis for making larger trust-based actions that may not have been anticipated ex ante in the project contract (Swärd, 2016). Recent work has examined the multidi- mensionality of trust, and such concep- tual development should contribute to more nuanced applications in examin- ing trust-based governance of project network relations (Shazi, Gillespie, & Steen, 2015).

propose that trust evolves out of prior relations that reduce transactional uncertainty and increase the shared understanding needed for effective coordination. Ebers and Maurer (2016) have empirically tested and modeled how prior relationships by project part- ners and relationship-specific invest- ments by these partners can overcome recent project collaboration disappoint- ments and provide the trust for these partners to renew their collaborations on future projects. However, such proj- ect commitments are not absolute. The availability of alternative potential part- ners whose expertise better fits new collaborative project requirements can lead to such new partners joining col- laborations.

Network analysis of the structural embeddedness among project network participants offers additional insights into the importance of these charac- teristics on project performance. For example, Sedita and Apa (2015) inves- tigated how a contractor’s network position affects his or her success in winning public procurement proj- ects, measured as the average value of projects won. They examined the network positions of general contrac- tors involved in public procurement projects in the construction industry in the Veneto, Italy region from 2008 to 2012. They employed three measures of network position: breadth, reach, and brokerage. Only network breadth was found to be crucial in determining the success of firms in public procure- ment practices. Such studies promise to enrich our understanding of how the structural positions of key individual actors within project networks can sup- port their collaborative success.

Project networks, quite like single projects or project-based organizations, occur in a context that impacts the use and effectiveness of these governance mechanisms. In particular, the institu- tional or regulatory context may allow for some contract or work arrangements and not for others. Moreover, particular coordinative practices may be common

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2008; Grabher, 2004; Ibert, 2004). This dilemma between the ease of knowledge creation and the difficulty of knowl- edge transfer is com monly re ferred to as the learning paradox.

In their study of the learning paradox in knowledge transfers between interor- ganizational project ventures and their parent organizations, Bakker, Cambré, Korlaar, and Raab (2011) identified three relational governance factors (relational embeddedness, cognitive embedded- ness, and temporal embeddedness) that contribute to knowledge transfer from the interorganizational project venture to the parent organizations of the proj- ect network:

• Relational embeddedness refers to the strength of the tie between two or more organizational actors (Uzzi & Lancaster, 2003). In interorganizational collabo- rations, such as project networks, the relational embeddedness of the tie between the project and the parent organization(s) is commonly mani- fested in the frequency of interaction between the project and parent, and the level of resource commitment (Rowley, Behrens, & Krackhardt, 2000). Another important indicator of the relational embeddedness of the relation between the project venture and the partnering organizations concerns the level of trust (Moran, 2005), both between the proj- ect venture and its parents and among the parents themselves. Higher rela- tional embeddedness fosters knowl- edge transfer.

• Cognitive embeddedness refers to the extent to which the relation between the parent organization and the proj- ect venture is characterized by “shared representations, interpretations, and systems of meaning” (Van Wijk, Jansen, & Lyles, 2008, p. 835). In interorgani- zational collaborations, the degree of overlap between the knowledge bases of partner organizations is critical: Too low an overlap and partners cannot work together; too high an overlap and there is little for the partners to learn from one another.

new media industries, respectively. By allowing for local autonomy within specific core project teams, the over- all project organization can provide a more flexible organizing context for project work. However, there are challenges involved in getting differ- ent project stakeholders to agree about how they can align their contribu- tions within some coherent (inter-) organizational context.

Our conjectures suggest the follow- ing: These tensions between the tempo- rary and permanent organizations are likely to be intensified in project net- works where the responsibilities, rou- tines, roles, and relationships among interorganizational project teams depart from the constituent responsibilities, routines, roles, and relationships that exist between the participating project organizations and project stakeholders or sponsors. Hence, modes of collab- oration during the interorganizational project may depart from modes of collab- oration that more typically define rela- tions between the durable (permanent) organizations participating in the project network. Such risks seem heightened when unique project requirements and the participation of project managers and project staff with divergent project experiences and modes of collaboration come together.

The learning paradox: Tensions exist between knowledge creation and transfer.

A frequent paradox in the study of project learning refers to the following dilemma: On the one hand, through their transience and interdisciplinary nature, project ventures are likely to be very suitable for creating knowl- edge in the context of its application (Gann & Salter, 2000; Grabher, 2004; Hobday, 2000; Scarbrough et al., 2004). On the other hand, however, the tem- porary nature of projects seems to inhibit the circulation of knowledge. When the project dissolves and par- ticipants move on, the created knowl- edge is likely to disperse (Cacciatori,

Schüßler, & Müller-Seitz, 2016)—have to be managed, not only within single projects but also on the level of the whole project network. Although in some cases the network level will make managing paradoxes more difficult, in others it may remove some of the ten- sions involved.

The distance paradox: Tensions exists between the temporary and permanent organization.

The “attachment-detachment dilemma” (Sahlin-Andersson & Söderholm, 2002, p. 19) or “distance paradox” (Cohendet & Simon, 2007, p. 598) represents an ongoing debate regarding the extent to which a project organization should be decoupled from, or embedded in, a wider organizational context. Lun- din and Söderholm (1995) advocate the planned isolation of the project organi- zation once the task is defined in order to minimize disturbances from the envi- ronment and subsequent obstacles to implementation. However, Bresnen, Goussevakaia, and Swan (2004) point out that by encouraging project auton- omy, project sponsors increase the dif- ficulty of subsequently integrating the resulting project organization’s activi- ties within an overarching set of organi- zational processes.

A related dilemma or tension within project-based networks is between the autonomy requirements of project par- ticipants and their embeddedness within interorganizational settings that demand integration of project activities within interorganizational coordination efforts (Sydow, Lindqvist, & DeFillippi, 2004). Alliance or coalition participants seek to maintain some control over project performance by their partners. However, effective collaboration requires some degree of organizational autonomy so that different project partners can con- tribute their specialized expertise with- out undue constraints.

Grabher (2002a, 2002b) identifies networks of governance and control as defining features of project-based organizing within the advertising and

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(routines) and customized crafted solu- tions to the challenges of unexpected or innovative project work tasks and chal- lenges. Standardizing policies provide economies of repetition and repeatable solutions (Davies & Brady, 2000). How- ever, these standardizing policies can become dysfunctional when a project or a series of projects contains unique (innovative) requirements.

Several options have been rec- ognized for managing the difference paradox. One option is to create sep- arate routines for managing the familiar versus more innovative ele- ments of the project. Such a separa- tion strategy assumes that the overall project is decomposable into such com- ponents and can have dissimilar oper- ating routines for managing them. This simultaneous management of both standardized and customized operat- ing routines has been characterized as an ambidextrous strategy (Tushman & O’Reilly, 1996). Indeed, a study of Heathrow’s Terminal 5 project suggests that routines may be adapted ambi- dextrously in response to changing cir- cumstances as a dynamic capability in complex projects, although Davies et al. (2016) point to the continuing fragility of such a capability. A second option is to create sequential project organi- zations. A vanguard project organiza- tion will create customized solutions for managing initially the most innovative elements of a project assignment. A second project organization will then transfer those lessons learned back into the mainstream project organization so that it can standardize these routines for coping with similar project assign- ments in the future (Brady & Davies, 2004). However, neither the separa- tion nor the sequentialization strategy makes full use of the paradox theory and other dialectical approaches that prescribe accepting and managing rather than suppressing or circumvent- ing the underlying tensions (Farjoun, 2010; Lewis, 2000). These paradox man- agement approaches have received only limited attention in the project

certifications of competency, and their record of performance in previous proj- ect engagements.

As noted previously, some indus- tries and project circumstances may require project team members to play different roles from project to project and sometimes within a single project. Grabher (2004) observed in the Munich software industry that software profes- sionals in the course of their careers, and sometimes even in the course of a single project, switch roles, in part because of the lack of standardized role expectations for different categories of software workers, such as design- ers, coders, and testers. The practice of switching roles is also facilitated by the absence of explicit training cer- tification for competency in software development. Collaboration within a software project team more typically evolves from an interaction between strict professional roles into relation- ships among acquainted colleagues. This finding suggests that relationships can sometimes replace roles as the pri- mary governance mechanism for man- aging the tensions between individual and collective identity.

Another mechanism for reducing tensions between individual and collec- tive identity is the creation of swift trust among project participants who have not previously worked together (Mey- erson et al., 1996). Such trust may arise from team-building efforts necessitated by the high level of project work engage- ment among project participants, who subsequently form a team identity that complements rather than contradicts their individual role-based identities. This trust-building process thus reflects the utilization of relationship building as a governance mechanism for mitigat- ing the tensions between individual and collective identity.

The difference paradox: Tensions exist between crafting and standardizing practices.

Project networks experience tensions between standard operating procedures

• Temporal embeddedness refers to whether the parent organizations have worked with one another on previous project ventures in the past, and whether they expect—as is typi- cal of project networks—to do so again (Bakker, Cambré, & Provan, 2009; Brady & Davies, 2004). Other things being equal, one would expect higher levels of temporal embedded- ness of the project venture relation- ship to be associated with higher levels of knowledge transfer.

These three relational governance factors or forms of embeddedness are not exhaustive. Additionally, but some- what cross-cutting, issues of resource control, power, and domination need to be mentioned. Together with the others, they contribute to the learning paradox and how it may be managed.

The identity paradox: Tensions exist between individual and collective identity.

A challenge facing all project networks is that of creating a collective identity for project participants while respecting the individual identities that participat- ing individuals bring to the enterprise. A further complication of project net- works is that project participants bring a third identity—namely, their orga- nizational membership. These orga- nizational identities and associated loyalties can create tensions with the requirements for working on a project with participants from other organiza- tions with seemingly conflicting cultural norms and work expectations. A variety of governance mechanisms has been employed to address the tensions of individual and collective identity.

Role assignments are an organi- zational and industry mechanism for managing the identity paradox. Project participants in project networks bring with them a set of role-based identities that can be transferred from project to project (Bechky, 2006). These role- related identities are based upon partici- pants’ training, industry, or professional

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to be managed—or practiced—not only in projects but also in project networks.

Acknowledgments We thank John Steen and Arnold Windeler and, in his role as Project Management Journal® editor-in-chief, Hans Georg Gemünden, for very help- ful comments on an earlier draft of this article.

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experiences with the outcomes of prior collaboration influence expectations of future collaboration.

Conclusions Beyond the debate of whether project networks are a temporary or more than temporary organizational form, project networks are amorphous because they can be governed and coordinated in a variety of ways. Like other networks, project networks may be either gov- erned by a lead organization, in a shared form, or with the help of a network administrative organization. In addi- tion, responsibilities, routines, roles, and relations contribute to the coor- dination of project networks. Though in the focal project, these four R’s— not least in the face of well-established project management techniques—are more an outcome of intentional design, on the level of the whole network they are more likely to be dominantly of an emergent nature. The more the four R’s develop on this level behind the back of project managers, the more additional reflexivity may be needed with regard to the project network—for instance, in terms of sensing and seizing oppor- tunities and reconfiguring capacities (Teece, 2007).

In sharp contrast to other types of interorganizational networks (cf. Sydow et al., 2016), project networks unsur- prisingly dominate the business of proj- ects. In consequence, project networks, not unlike project-based organizations, marry temporary activities with more permanent forms of organizing (Sahlin- Anderson & Söderholm, 2002). How- ever, from this marriage tensions arise, some of which are of a paradoxical nature. In this article, we pointed to five such tensions that have to be managed: the distance paradox, the learning para- dox, the identity paradox, the difference paradox, and the temporal paradox. Although future research should devote more attention to unearthing these par- adoxes, project management should be more conscious about their indissolu- bility and acknowledge that they have

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The temporal paradox: Tensions exist between past, present, and future project work. Project networks experience tensions between their past affiliations with project participants, their present requirements of these participants, and their future expectations of working with these participants. In more gen- eral terms, the present unfolds in the light of the past as well as the future. In fact, previous research has demon- strated that project network member- ship often is based on a consideration of past project history (the shadow of the past) when selecting project partici- pants (Manning & Sydow, 2011; Schwab & Miner, 2008).

The degree to which a project relies not only on the shadow of the past but contains a credible shadow of the future (expectation of future project work with the same participants) can impact how a project network governs its interactions with project partici- pants. Some evidence exists that the flexibility shown by a project network coordinator toward performance by its project partners is in part deter- mined by positive experiences in pre- vious interactions and expectations of possible future collaboration (Ligthart, Oerlemans, & Nooderhaven, 2016).

By contrast, projects where partici- pants have no shadow of the past face the challenge of determining whether their current project participants are likely to be considered for future proj- ect engagements. Drawing on detailed, quantitative data on 102 construction projects in Germany, Ebers and Maurer (2016) find that a successful outcome of prior collaboration motivates project partners to continue their partnership, and that an increasing frequency of prior collaboration accentuates this positive effect. In addition, the authors iden- tify two boundary conditions—namely, the degree of trust and relationship- specific investments—that affect how

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Management, Queensland University of Technology, Brisbane, Australia. He is the co-editor of the Business Innovation and Disruption in Creative Industries book series (Edward Elgar). He also serves on the editorial board of Journal of Media Business Studies and has published twelve books and over 70 academic papers and book chapters. Professor DeFillippi has published in a range of leading journals, including Academy of Management Review, California Management Review, Journal of Organizational Behavior, Organization Studies, Regional Studies, and Research Policy. His primary areas of project scholarship include project-based organizing, project networks, project learning, and project-based careers. Dr. DeFillippi received his MA, M.Phil., and PhD in Organization Studies from Yale University, New Haven., Connecticut, USA. He can be contacted at [email protected]

Jörg Sydow is a Professor of Management at the School of Business & Economics at Freie Universität Berlin. He is a founding co-editor of two lead- ing German journals, Managementforschung and Industrielle Beziehunge – The German Journal of Industrial Relations, and a member of the editorial review boards of Organization Studies, Academy of Management Journal, Academy of Management Review, Journal of Management Studies, and The Scandinavian Journal of Management. Two of his most recent books are: Managing and Working in Project Society (Cambridge University Press, co-authored by R.A. Lundin, N. Arvidsson, T. Brady, E. Ekstedt, & C. Midler) and Managing Inter-organizational Relations (Palgrave Macmillan, co-authored by E. Schüßler & G. Müller-Seitz). He is currently also the director of the Research Unit “Organized Creativity,” sponsored by the German Research Foundation (DFG). For more information visit: http://www.wiwiss.fu-berlin.de/en/ fachbereich/bwl/management/sydow/index.html. He can be contacted at [email protected]

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© 2016 by the Project Management Institute

Published online at www.pmi.org/PMJ

Disassembling and Reassembling Project Management Maturity Jan Christoph Albrecht, University of Kassel, Kassel, Germany Konrad Spang, University of Kassel, Kassel, Germany

INTRODUCTION

A s an instrument to assess and enhance organizational project management, project management maturity models (PMMMs) are well recognized in the professional field. They have also evoked remarkable scholarly interest (Besner & Hobbs, 2008; Wendler,

2012). Research in the context of PMMMs can be structured along three phases (Albrecht & Spang, 2014): Initially, the research focused on the development of PMMMs, dealt with assessment techniques, or described case studies on the application of a particular model within an organization (Grant & Pennypacker, 2006, p. 60, and the references therein). In the second phase, scholars compared average maturity levels among selected industries (e.g., Pennypacker & Grant, 2003; Cooke-Davies & Arzymanow, 2003). In the most recent phase, research is focused on studying the benefits of project management maturity (e.g., Ibbs & Reginato, 2002; Thomas & Mullaly, 2008; Besner & Hobbs, 2008; Brookes, Butler, Dey, & Clark, 2014).

Walker (2014) states that there is still no agreed-on definition of project man- agement maturity; therefore, it is difficult for scholars in this field to build on pre- vious work and discuss their results based on previous literature. This situation is further aggravated by the large number of PMMMs available, the fact that no single model has gained broad acceptance (Schlichter, 2000, in Jugdev & Thomas, 2002a; Mullaly, 2006), and the limited transparency of the documentation of several PMMMs (Mullaly, 2006; Becker, Knackstedt, & Pöppelbuß, 2009b, p. 3). As a consequence, research on the benefits of PMMMs might arrive at a dead end, and the concept of “project management maturity” might lose its relevance.

This article’s superordinated research question is: Which constituents form the construct of “project management maturity” and how are they inter- related? The results of a qualitative content analysis of a number of PMMMs are presented in order to arrive at a generic understanding of project manage- ment maturity. The underlying idea is that project management maturity—as a theoretical construct—is documented in existing PMMMs. This idea was conceived on the basis of definitional, theoretical, and empirical evidence. As a result of our research, we identify two dimensions of maturity, which we introduce to the project management literature. Furthermore, we investigate one of these dimensions to provide greater detail about its nature. Our work can help to align research in the area toward common understanding and comparability of results. We discuss our findings by contrasting our approach with other potential paths to understanding project management maturity.

The remainder of this article is organized as follows: We provide a brief overview of the research on PMMMs along three phases; emphasis is put on the latest phase, which deals with the benefits of project management maturity. The use of several different PMMMs and the lack of a common understand- ing of project management maturity as a theoretical construct are stressed as major shortcomings of this phase of research. We discuss qualitative content

ABSTRACT ■

The ability of single research efforts in the

field of project management maturity mod-

els (PMMMs) to build upon previous work

is strongly limited by the adoption of differ-

ent conceptions of maturity. A number of

PMMMs were subjected to a qualitative con-

tent analysis in order to arrive at a generic

understanding of maturity. Maturity was

“disassembled” to single phrases and “reas-

sembled” to two dimensions of maturity.

One dimension was operationalized for use

in a quantitative field survey. We perceive

our analysis as one path to understanding

project management maturity and conclude

with an outlook on other potential paths.

KEYWORDS: project management maturity; project management maturity

models; mixed research

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To date, the most prominent quan- titative studies on the effect of project management maturity on certain suc- cess criteria are those of the research group of Ibbs at the University of Cali- fornia in Berkeley, California. In studies with Kwak and Reginato, the Berkeley Project Management Process Maturity Model, (PM)2 (Kwak & Ibbs, 2000b), was used to analyze the effects of maturity on the adherence to the project schedule and the project costs. In the first study, Kwak and Ibbs (2000a) mention a posi- tive effect in both success dimensions by tendency on the one hand; on the other hand, however, they concede that the relationships are not statistically significant (Kwak & Ibbs, 2000a, p. 42), which later was also stressed by other authors (Mullaly, 2006). In the second study, Ibbs and Reginato (2002) claim positive effects on the above-mentioned success criteria, but do not talk about the statistical significance. Besner and Hobbs (2006) comment: “[..] attempts to find a simple and direct relationship between project management practice and ROI have failed to find a statistically significant link.” (p. 38)

In a conceptual article, Jugdev and Thomas (2002b) analyzed the rela- tionship between project management

level of maturity, bringing along cer- tain benefits for the organization. These improvements include time, cost, qual- ity, and customer satisfaction; mini- mization of project risk; alignment of projects with the overall organization’s strategy (Project Management Institute, 2013, p. 6); improvements regarding transparency of project management (Paulk, 2008, in Maier, Moultrie, & Clarkson, 2012) and regarding the com- munication between the project man- agement and upper management levels (Peterson, 2000); as well as increased motivation of the project personnel (Kwak & Ibbs, 2000b).

As mentioned above, the majority of the research on PMMMs can be struc- tured along three phases (Table 1).

While the first two phases appear to have concentrated on a certain period of time, the research on the benefits of project management maturity is ongo- ing, with most recent contributions by Jiménez Jiménez, Martinez Costa, and Martinez Lorente (2012), Brookes et al. (2014), and Spalek (2014, 2015). The studies of this phase will be out- lined in the following section, with particular attention being paid to their understanding of project management maturity.

analysis as a method for addressing these shortcomings of prior research. In the first part of the results section, we present the outcome of a qualita- tive content analysis of a number of PMMMs, in other words, clusters of project management maturity, which we “reassemble” into two dimensions of project management maturity. In the second part of the results section, this qualitative analysis is complemented by analyses of survey data, which allow us to show relationships between the clus- ters. These analyses help to understand what project management maturity consists of; the clusters and dimensions of maturity might be used as definitions in terms of future research. Still, we do not perceive project management matu- rity as a singular concept; therefore, the article concludes with an outlook on potential avenues in terms of under- standing project management maturity.

Research on Project Management Maturity While the first PMMMs were devel- oped in the mid-1990s (for example, by Levene, Bentley, & Jarvis, 1995; Fincher & Levin, 1997), the concept of assigning levels of maturity to (proj- ect) management structures is rooted in the disciplines of quality and pro- cess management (Crosby, 1979) and the management of software engineer- ing projects (Paulk, Weber, Curtis, & Chrissis, 1995). The Capability Maturity Model (CMM) for the assessment of software development processes served as a blueprint for a vast number of process-based maturity models (Bruin, Freeze, Kulkarni, & Rosemann, 2005, for example, mention more than 150) in various management disciplines (Bruin et al., 2005; Mullaly, 2006; see Becker et al., 2009b, p. 3, for an overview). The basic idea behind PMMMs is to apply them in a cyclical manner of maturity assessment, analysis of the results, defi- nitions and execution of improvement activities, and re-assessment (Project Management Institute, 2013). These activities should result in an increased

Research Phase Main Period Authors Development of PMMMs, considerations on assessment techniques, cases on the application of PMMMs

1997...2003 Rosenquist, 1997; Couture & Russett, 1998; Rosenstock, Johnston, & Anderson, 2000; Gareis, 2001; Burns & Crawford, 2002; all in Grant & Pennypacker, 2006; Levene et al., 1995; Fincher & Levin, 1997; Kwak & Ibbs, 2000b; Gareis, 2002; Bryde, 2003

Comparison of average project management maturity levels along various industries

1998...2006 Levene et al., 1995; Mullaly, 1998; Ibbs & Kwak, 2000; Pennypacker & Grant, 2003; Cooke-Davies & Arzymanow, 2003; Fuessinger, 2005; Mullaly, 2006

Analysis of benefits of project management maturity

2001...present Kwak & Ibbs, 2000a; Ibbs & Reginato, 2002; Jugdev & Thomas, 2002b; Thomas & Mullaly, 2008; Besner & Hobbs, 2008; Yazici, 2009; Jiménez Jiménez, Martínez Costa, & Martínez Lorente, 2012; Pasian, Williams, & Alameri, 2012; Brookes et al., 2014; Albrecht & Spang, 2014; Spalek, 2014 & 2015

Table 1: Phases of research in the context of project management maturity models.

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techniques of grounded theory. The results of her analysis are single terms or short phrases, which are listed based on their frequency of being mentioned in the docu- mentation of the models (Pasian, 2011, pp. 78 ff.). Table 3 lists the 15 most fre- quently mentioned terms.

Both Cooke-Davies et al. and Pasian took a bottom-up-approach to describing constituents of project management matu- rity, yet relying on different bases. Certain parallels between the two, for example, “People and their competence” (Cooke- Davies et al.) and “Training” (Pasian); “Standardization and integration of meth- ods and processes” (Cooke-Davies et al.) and “Process management/ development” (Pasian); as well as “Continuous improve- ment” (both) can be observed. There remain, however, sub stantial unresolved differences, for example, the topics “Per- formance and metrics,” “Business align- ment and prioritization,” “Success criteria,” “Allocating people to projects” and “Orga- nizational fit” from Cooke-Davies et al.’s analysis, which are not reflected in Pasian’s

maturity, respectively. Moreover, concep- tual articles and research that attempt to analyze project management maturity as a theoretical construct are rare. This pattern is also confirmed by a literature analysis by Wendler (2012), who found that of 237 articles on maturity models, only twelve were conceptual in nature (he uses the phrase “theoretical reflections”).

The above-mentioned three phases of research on PMMMs are flanked by single research efforts to identify the con- stituents of project management matu- rity: In the course of the development of Project Management Institute’s Orga- nizational Project Management Matu- rity Model (OPM3®), Cooke-Davies, Schlichter, and Bredillet (2001) con- ducted a Delphi-study with experts from the “OPM3 Guidance Team” in order to identify thematic elements of an organi- zation that is “mature” in project man- agement; these were then aggregated to ten clusters of maturity (Table 2).

Pasian (2010) took a different approach and analyzed ten PMMMs employing

maturity and competitive advantage, but concluded that maturity would rather lead to competitive parity. In the course of their research project on the value of project management, Thomas and Mullaly also presented findings on maturity and stated that there is a posi- tive relationship between maturity and an organization’s ability to generate intangible value (for instance, improve- ments regarding communication and transparency of management structures; Thomas & Mullaly, 2008, pp. 350 ff.). They didn’t however, use a maturity model, but rather have inferred the organizational maturity “as a result of the comprehensiveness of [their] data” (Thomas & Mullaly, 2008, p. 352). In Besner and Hobbs’ (2008) research on project management practices in inno- vation projects, maturity was among the variables that could be used to differ- entiate between low and high perform- ing organizations. In this regard, they claim their research not to be robust enough and stress the need for fur- ther enquiry. Yazici (2009), who applied the PMMMs by consultancy firm PM Solutions, and Jiménez Jiménez et al. (2012), who applied the model P3M3 (Sowden, Hinley, & Clarke, 2010), were able to demonstrate a positive influence of maturity on certain success criteria on the level of the organization (i.e., sav- ings for the organization, improvements regarding its competitiveness [Yazici]; growth, profitability, new product suc- cess [Jiménez Jiménez et al.]; and rise in market share [both]). Brookes et al. (2014) analyzed the use of PMMMs, with a particular focus on the repeatability of maturity assessments (Crawford, 2006) and arriving at consistent results, as well as on the way assessment results are used in order to identify potential paths of improvement. As part of their results, they challenged the repeatability of assessments as a property of PMMMs.

On the foreground of the above- described research, it can be concluded that different scholars have been using different PMMMs, or have had a different understanding of project management

No. of Cluster Cluster of Project Management Maturity 1 Standardization and integration of methods and processes

2 Performance and metrics

3 Commitment to the project management process

4 Business alignment and prioritization

5 Continuous improvement

6 Success criteria for continuation or culling

7 People and their competence

8 Allocating people to projects

9 Organizational “fit”

10 Teamwork

Table 2: Results of a Delphi-study on clusters of an organization that is mature in project management (Cooke-Davies et al., 2001).

(1) Management (6) Business case and benefits (11) Training

(2) Organization (7) Project specifications (12) Communications

(3) Process management (8) Formality (13) Quality management

(4) Process, tool development (9) Project management office (14) Data management

(5) Awareness (10) Risks and management (15) Continuous improvement

Table 3: Excerpt of results from a textual analysis of ten PMMMs (Pasian, 2011, pp. 79 f.).

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pp. 213 f.), we emphasized other clus- ters in addition to the PMBOK® Guide’s Knowledge Areas.

The fifteen PMMMs incorporated into the qualitative content analysis were either published in books, journal articles or conference papers, or edited by organizations including PMI and the British Cabinet Office. The analysis did not include models that are designed for maturity assessments on the level of multiproject management (e.g., Pennypacker, 2005), with a focus on a particular element or knowledge area of project management (e.g., Hillson, 1997), or for exclusive use in a cer- tain industry (e.g., Fengyong & Renhui, 2007). Moreover, due to their nature as process maturity models, CMMI and SPICE were not included, even though project management is an integral part of their scope. An overview of the mod- els is presented in the Appendix at the end of the article.

As stated earlier, the initial clus- ters were formed from a first inspec- tion of the models’ documentation (i.e., general descriptions of the models, descriptions of the maturity levels, or assessment tools). In a second, more detailed, consultation of the documen- tation, text elements were assigned to the clusters. This step was an iterative procedure of assignment and refine- ment of the clusters. Two examples are presented to illustrate this process: In describing different levels of organiza- tional project management maturity, various models refer to the existence and occurrence of a project manage- ment terminology (Fincher & Levin, 1997; Kerzner, 2005, p. 42; Office of Gov- ernment Commerce, 2010, p. 7), which was therefore adopted as one cluster of maturity. In the assessment tool of the model PjM3, with regard to level 1, the following statement is made:

“Project management terminol- ogy is used by some members of the organization but not consistently and possibly not understood by all stake- holders.” (Office of Government Com- merce, 2010, p. 9).

maturity, organizations did appear to be influenced by the PMMM framework. [...] This does indicate that PMMMs based on different frameworks will have the propensity to stimulate different suggestions for project management performance improvement.” (p. 243).

As stated earlier in this article, the central method of this research is qualitative content analysis (Mayring, 2010; Bryman & Bell, 2011) and was conducted with the goal of deriving a generic understanding of project man- agement maturity as a theoretical con- struct. The qualitative content analysis is complemented by a quantitative field survey. The added value of the field survey mainly arises from two aspects: First, relationships between the con- ceptual clusters of project management maturity can be analyzed. Second, the scales presented herein can be used in terms of future research, for instance, for the purpose of testing the benefits of project management maturity.

The qualitative content analysis is based on a procedure described by Mayring (2004) and consisted of the fol- lowing steps: On the basis of an initial sighting of the PMMMs’ documentation, conceptual clusters of project manage- ment maturity were formulated. Short phrases or sentences from the model documentation were then assigned to these clusters. Throughout this process, the initial clusters were also refined (i.e., to a certain degree assignment and refinement were done in an iterative procedure). With the previous steps, the PMMMs were “disassembled” in order to arrive at a generic understanding of project management maturity. In the last step, the final clusters were “reas- sembled” into two dimensions of proj- ect management maturity. Since it is well documented in the extant literature that several PMMMs use the Knowledge Areas defined in PMI’s A Guide to the Project Management Body of Knowl- edge (PMBOK® Guide) – Fifth Edition (Project Management Institute, 2013) as their conceptual basis (e.g., Jugdev & Thomas, 2002a; Cooke-Davies, 2004,

results; and for example, “Risks and man- agement,” “Quality management,” and “Data management” vice versa.

These research efforts have not led toward a consensus. Still, specifically the work of Pasian (2010, 2011) and her proposal to complement and extend the concept of project management matu- rity (Pasian, 2014) has fueled the discus- sion about the constituents of project management maturity.

Research Gap and Methodology In the previous sections of this arti- cle, evidence of a need for a stronger conceptual examination of maturity in order to confer usefulness to the research on the benefits of project man- agement maturity was presented. We believe one way of contributing to this examination is to proceed in the line of Pasian’s work and elaborate on a generic understanding of project man- agement maturity through an analy- sis of PMMMs. For this purpose, we employ a definition of project manage- ment maturity by the German Institute for Standardization (DIN), which states: “Appraisal of an organization by means of maturity models with respect to their project management capability.”1 (DIN, 2009, p. 14) Interestingly, this defini- tion doesn’t provide any information on what maturity actually is. Rather, according to the DIN, it depends on the single model what maturity is or what is measured, respectively. In an earlier statement, Cooke-Davies (2004) made a similar point: “There is no generally agreed on definition of what a mature project-based organization looks like. Different maturity models embody both different concepts and different suggestions as to the route to matu- rity” (p. 211); this approach to project management maturity is also substan- tiated by recent empirical findings by Brookes et al. (2014): “At higher levels of

1“Beurteilung einer Organisation anhand von Reifegradmodellen

hinsichtlich ihrer Leistungsfähigkeit im Projektmanagement.“ –

translated from German by the authors.

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project management. These clusters build the foundation of organizational project management. The cluster “Pro- cess management” is represented in all of the PMMMs that were analyzed, fol- lowed by the concept of “Continuous improvement,” for which evidence was found in 12 of 15 models. Process man- agement deals with whether the orga- nization recognizes the usefulness of establishing processes for main activities of project management, the standard- ization of these processes, the use of performance indicators, and the con- tinuous improvement of the processes. Continuous improvement is represented by a separate cluster, because it is not only mentioned in relation to the pro- cesses but also as a general organi- zational mindset (e.g., Sowden et al., 2010, p. 5). “Project management–related training” was likewise found in 12 of 15 models. In terms of a project man- agement maturity appraisal, it would be assessed whether an organization offers this kind of training at all; to which hier- archical levels it does offer training (e.g., only to project managers or to all staff working in project environments); and whether there is a documented train- ing program. Eleven models dealt with “Knowledge management and transfer,” which refers to the degree to which best practices—or perceived good practices— and lessons learned are gathered from single projects and how they are made available and transferred within a program or portfolio of projects (e.g., through databases). The “Role of project management within the organization” (found in 9 out of 15 models) refers to whether the organization recognizes the importance of project management and perceives it as a core competency. Fur- thermore, “Role of project management” comprises the establishment of a proj- ect management culture and the insti- tutionalization of project management, for example, through the implemen- tation of project management offices. Regarding the cluster, “Top management support,” it should be noted that there is a certain inconsistency considering

(typically a machine or a component of a machine, plant, building or infrastruc- ture facility) and/or software is being developed under participation of one or more industrial enterprises” (Albrecht, 2014, Appendix C). First, the question- naire was tested in terms of two cogni- tive interviews (Prüfer & Rexroth, 2005). Cognitive interviews are conducted in terms of questionnaire development in order to understand how respondents understand and interpret questions or single words; how they recall informa- tion or incidents from their minds; how they decide about how to answer; and how they match their “internal” answer with the answering format (ibid.). The questionnaire was then tested with another 14 test candidates from seven different companies. It consisted of three sections: organizational project management (section A; 36 items, 21 of which were optional); latest engineer- ing project executed by the participant in the role of a (sub-) project manager (B; 63 / 17); and demographic informa- tion about the participant and his or her company (C; 15 / 2). Both paper and web-based versions were provided. Collection of the data was conducted in the period between February 2012 and March 2013. Among other channels, the contact database of a biannual sympo- sium on project management, hosted by the Chair of Project Management at the University of Kassel (Germany) since 2003, was used.

Results Qualitative Content Analysis

Table 4 shows the results of the quali- tative content analysis. In order to represent these data in a manageable fashion, we included only whether a PMMM touches on a conceptual cluster but did not present the degree to which it does so.

It should be noted that the year of the first publication of the models is men- tioned in Table 4, but we analyzed the latest edition instead. The clusters listed in Table 4 together form the dimen- sion of the strategic infrastructure of

The above-mentioned sentence was assigned to the cluster “Project manage- ment terminology.”

In order to provide a second exam- ple, we explain a case in which the initial clusters have been refined. Initially we differentiated between the two clusters “Formalization of project management” and “Process management”; however, the implementation of project manage- ment processes plays a central role in the formalization of project manage- ment, in other words, formalization and the implementation and management of project management processes go hand in hand. The following excerpts from an explanation of the Project Man- agement Maturity Model (ProMMM) provide evidence for this.

“No structured approach to proj- ects.” And: “No formal processes.”

“[...] organization [...] has no for- mal or structured generic process in place.” And: “[...] organization has not effectively implemented project man- agement processes.” (Hillson, 2001, descriptions of the ProMMM levels 1 and 2, respectively)

Each of the first two phrases was originally clustered as “Formalization of project management,” whereas each of the last two phrases was clustered as “Process management.” Under the impression of the large parallels it was finally decided to merge the two clus- ters and label the remaining cluster: “Process management.”

The results of the qualitative con- tent analysis and from a multiple quali- tative case study (Albrecht & Spang, 2014) formed the input for a quanti- tative study. Based on the knowledge of the PMMMs’ documentation, ques- tionnaire items were formulated, which together were supposed to represent a number of maturity clusters. A stan- dardized questionnaire was to be filled out by people possessing experience as (sub-) project managers of engineer- ing projects (German: “Industriepro- jekte”). The term “engineering project” was defined as follows: “Project in terms of which a tangible project product

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Model / Cluster

Process Management

Continuous Improvement

Project Management–

Related Training

Knowledge Management and Transfer

Role of Project

Management Within the

Organization

Top Management

Support

Project Management Terminology

Project Management

Software

PMMM (Levene et al., 1995)

• •

PMMM (Fincher & Levin, 1997)

• • • • • • •

CMM/PM Maturity Model (Goldsmith, 1997)

• • • •

(PM)2

(Ibbs & Kwak, 1997)

• • • • •

PMMM (Jain, 1998)

• •

PMMM (Ward, 1998)

• • • • • •

POC Competence Model (Gareis & Huemann, 2000)

• •

KPM3

(Kerzner, 2001) • • • • • • • •

ProMMM (Hillson, 2001)

• • • • •

PMCMM (Voivedich & Jones, 2001)

• • • • •

PMPA (Bryde, 2003)

• • •

OPM3

(Project Management Institute, 2003)

• • • • • •

P2MM (Office of Government Commerce & Williams, 2004)

• • • • • • •

P3M3 (Office of Government Commerce, 2006)

• • • • • • •

PMMM (Crawford, 2006)

• • • • •

Table 4: Results from a qualitative content analysis of selected project management maturity models (Albrecht, 2014, pp. 90 f.).

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project they had executed in the role of either a project or sub-project manager; 90 respondents had the role of a project manager, whereas 17 were sub-project managers (difference to 121 stems from missing or unclear data). At the time they completed the questionnaire, 72 of 121 respondents were project managers (see Table 7); 13 were operating at man- agement positions in the project organi- zations of their enterprises, such as head of PMO and so forth; whereas 20 were holding upper management positions in the line organization of their enterprises, such as head of the division (11) or head of the business unit (9). The respondents had an average of almost 11 years of experience as project managers.

Table 8 presents the results of item analyses, which were executed in order to arrive at scales for the maturity clusters and could then be used for fur- ther statistical analyses, as for example, correlation or regression analyses. All items were formulated by the authors based on the results of the qualitative content analyses, except for KNOW4 and KNOW5, which were adapted from Pérez López, Montes Peón, and Vázquez Ordás (2004), and Griese (2011, p. 194), respectively, as well as TMS1 to 5, which were taken from Pinto (1990). The maturity cluster “Project management terminology” is not listed in this table, because it was represented by only one item (for single-item reliability, see Wanous & Hudy, 2001). All statistical analyses were performed with SPSS, Version 21. Considering the item anal- yses, factor analyses (principal com- ponent analysis [PCA]) and reliability analyses were conducted. Regarding the latter, we looked at the values for Cron- bach’s alpha and discriminability as well as at the inter-item correlation. The values in the columns PCA, Cronbach’s alpha, discriminability, and inter-item correlation are the ones for the final scales; in other words, in the case of the cluster “Process management,” the val- ues result from analyses that incorpo- rated the items PROC3 to PROC7. With regard to discriminability, Kelava and

them 25.6 minutes on average to com- plete. These participants came from 13 different enterprises; the remaining 62 respondents filled out the online ver- sion of the questionnaire in an average of 22.3 minutes. Twenty of these 62 par- ticipants were working in a large Ger- man industrial enterprise, which offers quite a broad portfolio of products and services and it is very likely that the remaining 42 participants were all working in different companies. Conse- quently, the 121 respondents represent a minimum of about 50 companies and a maximum of 56 companies. Consider- ing the distribution of the individuals throughout the companies, the sample is biased in some way. However, practi- cal experience gained throughout the process of data collection relativizes this bias to a certain degree: From conversa- tions with PMO staff members and other people responsible for their companies’ project management it became appar- ent that project management structures develop very differently in single depart- ments and business units of one and the same large enterprise. With respect to other aspects, the sample is quite homo- geneous: More than 85% of the par- ticipants are working in large companies with an annual turnover of more than €50 million (approximately US$56 mil- lion). Furthermore, throughout their careers, 86% of the participants have been working in projects worth at least €1 million (approximately US$1 million).

Tables 6 and 7 present the sector distribution and hierarchy levels the participants were working on at the time they filled out the questionnaire.

The majority of the participants worked in the plant and special machine engineering sector, in the chemical/ pharmaceutical industry and/or medi- cine technology, and in the automotive industry (see Table 6). Additional indus- tries, including energy, construction, and IT/telecommunications are equally represented.

In section B of the questionnaire the participants were asked to pro- vide specific information about the last

the management level that the models deal with, for example, Kwak and Ibbs (2000b, p. 5) talk about “senior manage- ment,” Ward (1998, p. 868) uses “execu- tive management,” and Kerzner (2005, p. 47) refers to “senior and middle man- agement.” The cluster relates to the awareness of upper management levels for the importance of project manage- ment and the extent to which they pro- vide support for the establishment and further development of organizational project management. “Project manage- ment terminology” and “Project manage- ment software” describe the availability of a project management terminology or software, respectively, as well as their consistent and organization-wide use.

The second dimension is labeled as the dimension of project man- agement elements and processes. It embraces the project management tasks in the course of the execution of projects and largely builds on the PMBOK® Guide’s Knowledge Areas. Yet, some additional elements, such as proj- ect change management (Voivedich & Jones, 2001; Bryde, 2003; Kerzner, 2005, p. 89; Office of Government Commerce, 2010, p. 7), conflict management (Ker- zner, 2005, p. 55) or contract manage- ment (Bryde, 2003; Kerzner, 2005, p. 56), are incorporated by certain PMMMs (cf. final column in Table 4).

Table 5 summarizes the results of the qualitative content analysis in list- ing the clusters of project management maturity based on their frequency of mentioning in the PMMMs.

Quantitative Field Survey

The clusters of the strategic project management infrastructure were then analyzed in further detail in terms of the quantitative field survey, which was conducted in Germany and Austria. In total, 121 analyzable filled-in question- naires were collected. The vast majority of the respondents are professional con- tacts of the University of Kassel’s Chair of Project Management. Fifty-nine of 121 participants filled out the paper ver- sion of the questionnaire, which took

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Branch

Number of Respondents to this Study

Plant engineering 27

Chemical/pharmaceutical industry and/or medicine technology

24

Automotive 15

Construction 10

IT/telecommunications 10

Energy 9

Other and combinations of the above

25

Table 6: Sector distribution of the sample.

Hierarchy Level

Number of Respondents to this Study

Head of division/ department

11

Head of PMO/portfolio/ program

13

Head of business unit 9

Project manager 72

Sub-project manager 8

Head of working group 4

Project team member 1

Other 2

Table 7: Hierarchy levels of the participants.

Clusters of Project Management Maturity

Frequency of Mentioning

[X out of 15 models] Dimension of Project Management Maturity Process management 15 Strategic project management infrastructure

Continuous improvement 12 Strategic project management infrastructure

Project management–related training and personnel development 12 Strategic project management infrastructure

Project risk management 12 Project management elements and processes

Knowledge management and transfer 11 Strategic project management infrastructure

Project time management 11 Project management elements and processes

Project cost management 11 Project management elements and processes

Project communications management 11 Project management elements and processes

Project scope management 10 Project management elements and processes

Project quality management 10 Project management elements and processes

Role of project management within the organization 9 Strategic project management infrastructure

Project human resource management 9 Project management elements and processes

Top management support 8 Strategic project management infrastructure

Project procurement management 8 Project management elements and processes

Roles and responsibilities in project management 7 Strategic project management infrastructure

Project integration management 7 Project management elements and processes

Project resource management 7 Project management elements and processes

Classification and selection of projects (according to the organization’s strategy)

6 Strategic project management infrastructure

Integration of the client 5 Strategic project management infrastructure

Project management terminology 4 Strategic project management infrastructure

Project stakeholders management 4 Project management elements and processes

Project change management 4 Project management elements and processes

Project management software 3 Strategic project management infrastructure

Table 5: Overview of clusters of project management maturity according to their frequency of mentioning in selected PMMMs (Albrecht, 2014, pp. 94 f.).

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Maturity Cluster

Number of Items

(initial/final)

PCA (total eigenvalue/

variance explained) Cronbach’s

a

Discriminability (pot. critical values only)

Inter-item Correlation

Items Excluded

Process management 8/5 3.519/70% 0.895 - no neg. values PROC2 PROC8 PROC9

Continuous improvement 5/5 2.839/56% 0.807 CI2 0.464 no neg. values -

Project management– related training

2/2 1.625/81% 0.768 - no neg. values -

Knowledge management and transfer

5/4 2.498/62% 0.796 - no neg. values KNOW5

Role of project management 3/3 2.313/77% 0.842 - no neg. values -

TMS 5/5 3.383/67% 0.877 - no neg. values -

PM software 2/2 1.610/80% 0.756 - no neg. values -

Table 8: Results of item analyses performed with items representing certain clusters of project management maturity.

Moosbrugger (2007, p. 84) consider val- ues above 0.4 as “good,” whereas Kopp and Lois (2012, p. 98) refer to values . 0.5 as “high.” We decided to report values , 0.5 as potentially critical. As shown in Table 8, the values for discrim- inability (only one item with a value , 0.5 and no item with a value , 0.4) and inter-item correlation (all positive values) all look good. Taking this into account, our decision criteria for the creation of the final scales were (1) that in terms of the PCA there is only one component with an eigenvalue . 1, and (2) that the value for Cronbach’s alpha is . 0.7 (Hair, Black, Babin, & Anderson, 2010, p. 125, and the references stated therein).

When the factor analysis was per- formed on all eight items of process man- agement, there were two components with an eigenvalue . 1. The eigenvalue for the first component was 4.556 and it explained 56% of the variance, whereas the eigenvalue for the second compo- nent was 1.009 and this component explained 12% of the variance. The items PROC8 and PROC9 were rather load- ing on the second component; hence it was decided to remove them. More- over, Cronbach’s alpha was increased by removing PROC2 from the scale.

Considering the cluster “Knowl- edge management and transfer,” the

initial eigenvalue was 2.770, the items explaining 55% of the variance. The item KNOW5 was removed from the final scale, because this increased Cron- bach’s alpha.

Correlation analyses with the coef- ficients Kendall’s Tau-b and Spearman’s Rho were conducted with the final scales. Table 9 presents the results from the analysis with Kendall’s Tau-b.

First, it can be concluded from Table 9 that there is a large number of significant correlations between the maturity clusters (correlations on the 0.05-level are marked with one star, those on the 0.01-level with two stars). The majority of the correlations are on a weak level (0.2...0.4) and some are on a medium level (0.4...0.6). The high- est correlations are the ones between “Role of project management within the organization” and “Project man- agement software” (0.409), “Process management” and “Role of project management,” “Process management” and “Project management terminology” (both 5 0.408), and between “Role of project management” and “Project man- agement terminology” (0.393), respec- tively. Moreover, the clusters “Process management,” “Project management software” and “Project management terminology” significantly correlate with all other clusters. If Spearman’s

Rho is used as a coefficient instead of Kendall’s Tau-b, the overall picture is quite the same, except the level of the majority of correlations is raised to a medium level, and the fourth highest correlation is then the one between “Process management” and “Project management-related training” (0.496). Since these are correlations, it should be worth noting that they shouldn’t be interpreted in terms of unidimensional influences. To provide a practical exam- ple: As a result of a correlation analysis it is not possible to answer a question as, for instance, whether or not more mature organizations establish PMOs or whether or not PMOs foster the devel- opment of maturity.

In interpreting these results, we see three key points: First, the large num- ber of correlations between the single clusters speaks for the homogeneity of the maturity dimension “Strategic infrastructure of project management.” Second, the fact that “Process man- agement” is both among the clusters that correlate with all other ones and is represented twice (if Spearman’s Rho is used as a coefficient: thrice) in the four pairs of clusters with the highest levels of correlation, stresses its central role in the concept of project management maturity, which has been underlined in the extant literature (Pasian, Williams,

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is “reassembled” into two dimensions of maturity (i.e., the strategic project management infrastructure and the dimension of project management ele- ments and processes). Although there is a high proximity of the latter dimension to extant project management bodies of knowledge (particularly the PMBOK® Guide; cf. Research Gap and Methodol- ogy section), the former dimension is less understood.

It can be observed from the qualita- tive content analysis that the manage- ment of project management processes (present in all the PMMMs analyzed) and the idea of continuous improve- ment (present in 12 of 15 PMMMs) are central constituents of the strategic proj- ect management infrastructure. Results from a recent field survey allowed for a deeper analysis of this dimen- sion, underpinning its homogeneity and showing relationships between single maturity clusters. They stress the importance of another cluster: “The role of project management within an organization” was represented three times among the four highest correla- tions of maturity clusters, being linked to “Process management,” “Project management terminology” and “Project management software.” The awareness of project management being important for organizational success is a central feature of this cluster. Further clus- ters are “Project management-related training,” “Knowledge management and transfer,” and “Top management support.”

In general, this study is a step toward describing the construct of proj- ect management maturity in a holistic manner. The dimensions and clusters as well as the scales described herein, may be used for future research in this field and allow for a better comparabil- ity of results.

Future Research

We perceive our approach as one way to describe and better understand project management maturity as a theoretical construct. The final paragraphs of this

approximately 50 different companies are represented. Unique circumstances in those companies may unduly influ- ence the representativeness of the responses of those individuals; how- ever, no company supplied enough respondents to enable separate statisti- cal analysis. Moreover, we perceive the fact that the study was conducted in Germany and Austria only as a strength rather than a potential source of biases: It seems advantageous to have respon- dents from one particular region only, compared with a scenario in which the majority of respondents come from one region and smaller percentages come from totally different regions. Further- more, having all respondents from Germany and Austria leaves open the possibility that findings will be differ- ent elsewhere due to institutional or cultural differences of these countries.

Further biases might arise from the two different modes of data collection, non-response, social desirability, and whitewashing of completed projects (rosy retrospection). Considering social desirability, it was stated in the intro- duction to the questionnaire that the data were collected for scientific pur- poses only and would be treated anony- mously. As for rosy retrospection bias, the respondents were asked about the last project they had completed in the role of a (sub-) project manager, for the purpose of mitigating this bias’s influence.

Conclusion

This article aimed at describing proj- ect management maturity as a theoreti- cal construct. Building on a definition by the German Institute for Standard- ization (DIN), 15 PMMMs were ana- lyzed in order to arrive at a generic understanding of project management maturity. This analysis goes beyond earlier analyses in that it is broader; the selection of models appears to be more consistent (only original PMMMs were incorporated, models with a, for example, branch focus were excluded); and that project management maturity

& Alameri, 2012). Third, the fact that “Role of project management within the organization” is represented three times among the four pairs mentioned above shows that companies who place importance on project management, are also willing to invest in software, training, and so forth. Moreover, one aspect within this cluster is the insti- tutionalization of project management (e.g., in the form of a project manage- ment office). The visibility and attention these kinds of institutions draw from members of an organization might play a key role in their willingness to accept and actively operate within the project management structures represented by the other clusters of maturity.

Limitations, Contribution, and Future Research Limitations

Certain limitations of this research should be noted. The documentation of many PMMMs is made publicly accessi- ble only to a limited degree. This aspect was subject to criticism (Becker et al., 2009b, p. 3) and, ultimately, the mod- els’ usefulness was called into question (Wendler, 2012). Jugdev (2004) states: “Few project management maturity models have been empirically tested and many are based on anecdotal mate- rial, case studies, or espoused best practices” (p. 20; cf. also Ahlemann, Schroeder, & Teuteberg, 2005, p. 20, on this point). Authors of future PMMMs may consult guidelines for the design process of maturity models and its doc- umentation (e.g., Becker, Knackstedt, & Pöppelbuß, 2009a; Pöppelbuß & Röglinger, 2011; Maier et  al., 2012). As a result of the current situation, the quality of the materials looked at in terms of the qualitative content analy- sis varied; results should therefore be seen as a starting point for discussion. Viewpoints from other scholars might enhance and validate our findings.

The analysis is based on individ- ual views among the 121 participants and does not adjust for any possible effects from the company level, where

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maturity, empirical ways are explained as follows. PMI’s OPM3 was developed on the basis of expert opinions. The idea was to develop a generic PMMM by asking: “What characterizes an organization that is mature in project management?” The experts’ challenge regarding this approach is obviously to look beyond one’s own professional background and recognize the charac- teristics of a mature organization that are independent of industry in terms of the expert discussion. On the other hand, it is also plausible to form an expert group with a very homogeneous background considering industry, orga- nization type/size, type of project, and so forth in order to arrive at contingent views of maturity.

Due to the proximity of many PMMMs to various project management bodies of knowledge, maturity assess- ments are associated with some sort of best practice thinking, which has been fertilized by the step-like struc- ture of the majority of PMMMs. One advantage of this kind of depiction of maturity is that it is very bold and sim- ple, and can thus easily be used in

all kinds of projects, pointing out that different types of projects are being realized in one and the same organiza- tion (Mullaly, 2014). Furthermore, he refers to external contingency variables (“context”), which influence the indi- vidual project management implemen- tation as well as the way value is being created.

Figure 1 takes this differentiation as a starting point and shows potential avenues to understanding project man- agement maturity.

The first path has already been described in terms of this article. When looking at PMMMs that are applicable to various kinds of organizations and industries, this path will lead to a generic understanding of project management maturity. However, and as indicated earlier in this article, industry-specific PMMMs have also emerged; thus it is also possible to arrive at a contingent understanding of maturity by analyz- ing PMMMs that apply to a particular sector.

While the analysis of PMMMs is a theoretical/literature-based way of understanding project management

article are dedicated to a brief outlook on potential other ways.

For this purpose, a general distinc- tion needs to be made between project management maturity as one concept that fits for all types of organizations/ industries/projects on the one side, and a contingent view of maturity—an idea that was recently coined by Mullaly (2014)—on the other. This distinction directly corresponds to earlier devel- opments in the project management research literature: Pinto and Covin (1989) criticized other scholars for implicitly treating all projects as funda- mentally similar (requiring a manage- ment that is again similar; “a project is a project is a project”). This point was later picked up by Shenhar and Dvir (1996), and the contingent perspective in project management research was then put into practice in terms of the research of Dvir and Lechler (2004), Geraldi (2008), and others.

With reference to the results of the research project, “Understanding the value of project management,” Mullaly criticizes many PMMMs for defining one approach to the management of

Figure 1: Paths to an understanding of project management maturity.

Generic understanding of Project Management Maturity

(a) (b)

(c)

(e) (d)

Analysis of PMMM

(a) General PMMM (b) Branch-specific PMMM

Expert opinion

(c) Experience across industries/types of projects, etc. (d) Homogeneus expert panel (same industries/types of projects, etc.)

Project-as-practice

Contingent view of Project Management Maturity

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Blomquist, T., Hällgren, M., Nilsson, A., & Söderholm, A. (2010). Project- as-practice: In search of project management research that matters. Project Management Journal, 41(1), 5–16.

Brookes, N. J., Butler, M., Dey, P., & Clark, R. (2014). The use of maturity models in improving project management performance: An empirical investigation. International Journal of Managing Projects in Business, 7(2), 231–246.

Bruin, T. de, Freeze, R., Kulkarni, U., & Rosemann, M. (2005). Understanding the main phases of developing a maturity assessment model. In Proceedings of 16th Australasian Conference on Information Systems, November 30th to December 2nd 2015, Australia, New South Wales, Sydney.

Bryde, D. J. (2003). Modelling project management performance. International Journal of Quality & Reliability Management, 20(2), 229–254.

Bryman, A., & Bell, E. (2011). Business research methods (3rd edition). Oxford, NY: Oxford University Press.

Burns, J., & Crawford, J. K. (2002). Organizational project management maturity at the New York Times: Using the project management maturity model. In Proceedings of 33rd PMI Annual Seminars & Symposium, San Antonio, Texas, USA.

Cooke-Davies, T. J. (2004). Measurement of organizational maturity: What are the relevant questions about maturity and metrics for a project-based organization to ask, and what do these imply for project management research? In D. P. Slevin, D. I. Cleland, & J. K. Pinto (Eds.), Innovations: Project management research 2004 (pp. 211–228). Newtown Square, PA: Project Management Institute

Cooke-Davies, T. J., & Arzymanow, A. (2003). The maturity of project management in different industries: An investigation into variations between project management models. International Journal of Project Management, 21(6), 471–478.

maturity is something dynamic, in other words, it changes over time. Research can then only provide snapshots of this understanding.

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Jan Christoph Albrecht earned his master’s degree in Industrial Engineering at South Westphalia University of Applied Sciences, at the University of Siegen and at Universitat Politècnica de València. Between 2009 and 2015, Christoph was a research assistant at the University of Kassel, Germany, Chair of Project Management, and obtained a doctoral degree in project management. He is now a develop- ment project manager working with a tier-1 supplier to the automotive industry. He can be contacted at [email protected]

Konrad Spang graduated as Diplom Ingenieur (Dipl.-Ing.) in Civil Engineering from the University of Stuttgart in Germany in 1980 and obtained his PhD (Dr.-Ing.) in Civil Engineering at the Federal Institute of Technology (ETH) in Lausanne, Switzerland in 1988. Professor Spang was a project engineer in a big con- struction company between 1980 and 1984 and a chief consultant engineer between 1988 and 1992. From 1993 to 2002, he was chief project manager of two large infrastructure projects in Eastern Germany and, since 2002, he has been Professor and Chair of Project Management at the University of Kassel in Germany. Professor Spang is an experienced project manage- ment expert and specializes in general project manage- ment, project organization, and project optimization as well as project risk management, especially in automo- tive and infrastructure projects. He is responsible for current research work in partnering systems, risk management, decision making in international projects, and project quality management. His major research endeavors and projects dealt with partnering models for infrastructure projects, best practice and lessons learned for highway projects in Germany and manage- ment and organization of large infrastructure projects in Europe, which are sponsored by German Ministries. He can be contacted at [email protected]

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Appendix

Name Author Year of the First

Publication Project Management Maturity Model Levene et al. 1995

Project Management Maturity Model Fincher and Levin 1997

CMM/PM Maturity Model Goldsmith 1997

Berkeley PM Process Maturity (PM)2 Kwak and Ibbs 1997

Project Management Maturity Model Jain 1998

Educational Service Institute’s Project Management Maturity Model Ward/Education Service Institute 1998

Project-Oriented Company Competence Model Gareis and Huemann 1998

Kerzner’s Project Management Maturity Model (KPM3) Kerzner/International Institute for Learning 2001

ProMMM Hillson 2001

Project Management Capability Maturity Model (PMCMM) Voivedich and Jones 2001

PM Performance Assessment Model (PMPA) Bryde 2003

Organizational Project Management Maturity Model (OPM3) Project Management Institute 2003

PRINCE2 Maturity Model (P2MM) Williams/Office of Government Commerce 2004

Portfolio, Program and Project Management Maturity Model (P3M3) Office of Government Commerce 2006

Project Management Maturity Model (PMMM) Crawford/PM Solutions 2007

Table 10: List of PMMMs incorporated into the qualitative content analysis.

Label Item Text Response Format ROLE1 In my organization there is a general awareness for project management being important

for the organization’s success. Five-point Likert-type item 1 “don’t know”

ROLE2 In my point of view project management is a core competency of my organization. Five-point Likert-type item 1 “don’t know”

ROLE3 Project management is explicitly included in terms of strategic considerations in my organization.

Five-point Likert-type item 1 “don’t know”

KNOW1 Best practices /lessons learned are generated from projects of my organizational unit and in principal are made available to other projects.

Five-point Likert-type item 1 “don’t know”

KNOW2 Evaluations of previous projects are consulted prior to or at the beginning of new projects of my organizational unit (for example, in order to identify risks).

Five-point Likert-type item 1 “don’t know”

KNOW3 My organization has installed an information system for project-related knowledge in order to make this knowledge available to projects.

Five-point Likert-type item 1 “don’t know”

KNOW4 The exchange of knowledge and experiences between project staff is organized in a formal manner in my organizational unit.

Five-point Likert-type item 1 “don’t know”

KNOW5 In my organizational unit it is formally described how project-related data should be recorded.

Five-point Likert-type item 1 “don’t know”

PROC1 There are project management processes or similar formal guidelines for the execution of projects in my organizational unit.

Yes/no-item

PROC2 These processes cover all phases of a project’s life cycle. Five-point Likert-type item 1 “don’t know”

PROC3 These processes cover all elements of project management (e.g., project time management, project risk management) that I need for a project execution that meets the requirements.

Five-point Likert-type item 1 “don’t know”

PROC4 These processes are standardized within my organizational unit. Five-point Likert-type item 1 “don’t know”

(Continued)

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Label Item Text Response Format PROC5 Process ownership, which incorporates among other things the revision and further

development of the processes, is assigned clearly. Five-point Likert-type item 1 “don’t know”

PROC6 In their entirety, the processes form a consistent system of processes, which for example, means that interfaces are defined and aligned.

Five-point Likert-type item 1 “don’t know”

PROC7 The project management processes are aligned with other procedures within the company.

Five-point Likert-type item 1 “don’t know”

PROC8 The efficacy and/or efficiency of the processes is analyzed quantitatively (using, e.g., performance indicators).

Five-point Likert-type item 1 “don’t know”

PROC9 The processes are applied on all projects in a consistent manner. Five-point Likert-type item 1 “don’t know”

CI1 There is a continuous improvement process in place in my organizational unit, which focuses on project management processes.

Yes/no-item

CI2 This continuous improvement process is actually put into practice by project staff. Five-point Likert-type item 1 “don’t know”

CI3 The ownership of this continuous improvement process is clearly defined. Five-point Likert-type item 1 “don’t know”

CI4 There are meetings held routinely on behalf of this continuous improvement process for the purpose of discussing potentials for optimization and according measures.

Five-point Likert-type item 1 “don’t know”

CI5 This continuous improvement process is documented. Five-point Likert-type item 1 “don’t know”

CI6 Changes which are initialized as a result of this continuous improvement process are documented.

Five-point Likert-type item 1 “don’t know”

TRAIN1 My organization’s investments in project management training are sufficient when looking at the requirements of our project business.

Five-point Likert-type item 1 “don’t know”

TRAIN2 My organization’s top management has supported the implementation of project management training.

Five-point Likert-type item 1 “don’t know”

TERM My organization’s project management terminology is understood and applied by the project staff in my organizational unit.

Five-point Likert-type item 1 “don’t know”

SW1 In principal, our project staff is competent considering the application of project management software.

Five-point Likert-type item 1 “don’t know”

SW2 Project staff is actively using project management software. Five-point Likert-type item 1 “don’t know”

Table 11: Questionnaire items for scales of project management maturity discussed in this article.2

2 Original items have been translated from the German language by the authors (cf. Albrecht, 2014).

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IntroductIon

B efore a project is started, project managers have to make an estimation of how long the project will take and how much it is going to cost. Traditionally, project managers focus on the specifics of the considered project (e.g., its particular activities) to produce these

estimations, as they attempt to forecast uncertain events that would influence the future course of the project. Such an “inside view” forecasting approach is obviously based on human judgment. In their studies, Kahneman and Tversky (1979a, 1979b) found that human judgment is biased, as it is generally too optimistic because of overconfidence and insufficient regard to actual previous experience (i.e., “optimism bias”). Moreover, project managers could deliberately and strategically underestimate costs (and durations) to give the impression that they would surpass the competition (i.e., “strategic misinterpretation”). In order to overcome this human bias and the inaccurate forecasts that result from it, Kahneman and Tversky (1979a) and later Lovallo and Kahneman (2003) introduced the method of reference class forecasting (RCF). RCF takes an “outside view” on planned actions rather than an inside view by cutting directly to outcomes through the use of distributional information from other projects similar to the one being forecasted. More specifically, the RCF method consists of a three-step procedure (Flyvbjerg, 2006, 2007):

1. Identifying a relevant reference class of past projects similar to the considered project

2. Establishing a probability distribution for the selected reference class 3. Determining the most likely outcome for the considered project by

comparing that project with the reference class distribution

Regarding the first step, Flyvbjerg (2006) states that the reference class must be broad enough to be meaningful, but narrow enough to be truly comparable with the considered project. We will approach this statement from a quanti- tative point of view by identifying reference classes with different degrees of similarity and evaluating their performance, which has not been done in earlier studies.

Notice that the RCF method, as described by the three-step proce- dure, does not involve any attempt to forecast specific events that would affect the particular project. Multiple experimental studies (Kahneman, 1994; Kahneman & Tversky, 1979a, 1979b; Lovallo & Kahneman, 2003)

Practical Application and Empirical Evaluation of Reference Class Forecasting for Project Management Jordy Batselier, Faculty of Economics and Business Administration, Ghent University, Ghent, Belgium Mario Vanhoucke, Ghent University and Vlerick Business School, Ghent, Belgium; UCL School of Management, University College London, London, United Kingdom

Traditionally, project managers produce

cost and time forecasts by predicting the

future course of specific events. In contrast,

reference class forecasting (RCF) bypasses

human judgment by basing forecasts on the

actual outcomes of past projects similar to

the project being forecasted. The RCF tech-

nique is compared with the most common

traditional project forecasting methods, such

as those based on Monte Carlo simulation

and earned value management (EVM). The

conducted evaluation is entirely based on

real-life project data and shows that RCF

indeed performs best, for both cost and time

forecasting, and therefore supports the prac-

tical relevance of the technique.

KEYWORDS: project management; project forecasting; reference class

forecasting; earned value management;

Monte Carlo simulation; empirical database

Project Management Journal, Vol. 47, No. 5, 36–51

© 2016 by the Project Management Institute

Published online at www.pmi.org/PMJ

ABStrAct ■

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• Identify other realistic causes for biased forecasts that occur in practice, different from the traditional defini- tions of optimism bias and strategic misinterpretation; and

• Further support the practical relevance of the RCF technique for real-life applications.

Regarding the first objective, the forecasts used for comparison are those from EVM, Monte Carlo simulation, and baseline estimates. Note that the two latter forecasts have not been explicitly considered in the study of Batselier and Vanhoucke (2015b). In order to achieve the last two objectives, we base our study on a real-life construction project that originates from the empirical proj- ect database of Batselier and Vanhoucke (2015a). Furthermore, all projects of the different reference classes are also part of this database. Given the extent of the employed data, it is not our goal to pursue generalizability. Rather, we aim at providing a clear view of the practical application of different project forecast- ing methods—in particular, RCF—and obtaining a reliable indication of the relevance of RCF in terms of workability and performance with respect to the traditional forecasting methods.

More information about the con- sidered project and the real-life project database is provided in the next section, followed by the presentation of the dif- ferent project forecasting methods con- sidered in this article. In a subsequent section, the results of these methods are compared and discussed. Conclu- sively, the most important outcomes of our study are summarized and sugges- tions for future research are made.

Methodology We will start this section with the pre- sentation of the real-life construction project that forms the basis for the cur- rent study. Then the empirical database from which the considered project orig- inates is described. Moreover, the refer- ence classes that are selected in this study all consist of projects that are part

and PD represent the baseline schedule (BLS) of the project (i.e., the planned course of the project) and are, there- fore, collectively termed baseline esti- mates here. The baseline estimates are used as inputs for the earned value man- agement (EVM) methodology. EVM is a widely accepted technique for perform- ing project control that integrates the three critical project management ele- ments of cost, schedule, and scope. The technique also implicitly incorporates the quality aspect by taking into account project progress (Willems & Vanhoucke, 2015). Through the application of EVM, the project manager can monitor the performance of the project during exe- cution and receive warning signals for taking corrective actions needed to get the project back on track. Furthermore, the EVM technique can also be used to produce project forecasts. However, because EVM is a technique for perform- ing project control, the forecasts are pro- duced at different tracking periods (TPs) (i.e., evaluation moments) during the project’s progress. RCF-based forecasts, on the other hand, are made before the project starts. Nevertheless, the pre- project forecasts from RCF and the inter- mediately revised forecasts from EVM will be compared. In this light, RCF is seen as a technique for obtaining constant— and, therefore, stable—project forecasts, like the method proposed by Warburton (2011). The results of the RCF method will also be compared with the forecasts obtained from a pre-project Monte Carlo (MC) simulation, the specifics of which will be presented in the next section.

To provide a clear overview of the con- tributions of this article, we now explicitly summarize the intended objectives:

• Perform a quantitative evaluation of the RCF technique by comparing it with the most common traditional project forecasting methods;

• Apply RCF for project duration forecast- ing and evaluating the performance;

• Assess the influence of different selec- tions of reference classes with respect to similarity levels;

have indicated that RCF is more accu- rate than traditional forecasting meth- ods. However, these studies were not situated in the field of project manage- ment. The first and only instance of RCF in project management was presented by Flyvbjerg (2006), who considered a project in the transportation sector, though no quantitative evaluation of the accuracy of the RCF technique was per- formed. Therefore, this article will com- pare the performance of RCF with that of the most common traditional meth- ods for project forecasting. Moreover, this performance evaluation is not based solely on the most important forecasting quality criterion, accuracy (Carbone & Armstrong, 1982), but also on the two other criteria—timeliness and stability (Covach, Haydon, & Reither, 1981)—the latter not being considered by Batselier and Vanhoucke (2015b). Furthermore, and in contrast to Flyvbjerg’s (2006) study, RCF will not only be applied for forecasting project cost but also for proj- ect duration. As might appear from the above discussion, the focus of this article is on the time and cost aspects of project management. Although these are per- haps the two most important objectives for the project manager, other factors such as safety, sustainability, and espe- cially quality are also of interest. How- ever, it is outside the scope of this article to further elaborate on the latter factors. An overview of studies that incorporate quality, safety, and/or sustainability in the traditional time–cost project control framework is provided by Willems and Vanhoucke (2015). Nevertheless, the expansion of control models through the integration of other performance- defining factors, in addition to time and cost, remains a research path that should receive more attention in the future.

RCF produces forecasts prior to the project start. This corresponds to the budget at completion (BAC) and planned duration (PD) that reflect the final cost and duration of the project, respectively, estimated by the project managers based on their expectations of the future course of the project (i.e., inside view). The BAC

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of the said database. After the selec- tion of the different reference classes, the RCF method will be applied. Sub- sequently, we present the traditional project forecasting approaches that are considered for comparison: first, those that produce pre-project forecasts, and then those that yield forecasts during project execution. More concretely, the consecutive subsections will be about baseline estimates, Monte Carlo simu- lation, and EVM, respectively. For each forecasting method, both cost and time forecasting are considered.

Project Description

The study in this article is based on a real-life construction project. More specifically, it concerns the execution of the finishing works inside an office building, comprising the interior join- ery and the placement of plaster walls, movable partition walls (also acoustic), raised floors, suspended ceilings, and furniture. The works are performed by a medium-sized finishing construction company with extensive experience in the field. Nevertheless, the consid- ered project comprises a few smaller activities that are rather uncommon for the company, such as the placement of carpets and special glass walls. The complete list of activities, together with their planned costs and durations (i.e., the BLS), is shown in Table 1. Note that the last column of this table contains information that will be considered and discussed later in this article.

The outwardly irregular activity IDs (identification numbers) were chosen by the project manager who was respon- sible for this project and are therefore retained here. Durations are expressed in standard eight-hour working days. The displayed costs and durations rep- resent the pre-project expectations of the project manager.

The precedence relations between the activities—which express techni- cal constraints—are also displayed in Table 1. When there are no parentheses behind the listed activity IDs, the pre- cedence relation is a finish-start (FS)

ID Activity Name Predecessors Successors Cost [€]

Duration [d]

Distr Prof

1 Fixed ceilings 4(SS);6(SS);17 2,129 89 symm

2 Metal ceilings 4(SS);6(SS);17 19,509 89 symm

4 Movable partition walls (1)

1(SS);2(SS) 37,641 151 right

6 Plaster walls 1(SS);2(SS) 9(FF);10 36,184 22 left

9 Full subcontracting (1) 6(FF) 1,079 1 no risk

10 Disassembling ceilings 6 12 2,509 7 symm

12 Adjusting raised floor 10 11;21;3 1,800 3 symm

11 Placing carpet 12 13 27,162 5 symm

21 Full subcontracting (2) 12 20,068 67 no risk

13 Placing furniture 11 14;16 36,023 3 symm

14 Placing glass walls 13 17 180 1 symm

3 Acoustic dams 12 20;5;7;8 1,674 2 left

20 Movable partition walls (2)

3 17;15;22 4,926 9 right

5 Movable partition walls (3)

3 17;15;22 619 9 right

7 Doors 3 17;15;22 6,259 3 symm

8 Joinery 3 17;15;22 1,964 3 symm

17 Painting works 1;2;14;20;5;7;8 19(SS) 8,538 41 symm

19 Ancillary works 17(SS) 16,619 3 symm

15 Finishings 20;5;7;8 18(SS) 13,132 71 symm

22 Miscellaneous 20;5;7;8 998 77 right

16 Adjusting furniture 13 312 61 symm

18 Moving reinforcing screens

15(SS) 4,879 3 symm

23 Additional work 0 0 no risk

Table 1: Activity information for the considered real-life construction project.

relation. An FS relation is the most com- mon type of precedence relation and indicates that an activity can only start after its predecessor(s) has (have) fin- ished. Start-start (SS) and finish-finish (FF) relations, on the other hand, signify that an activity can only start after its predecessor(s) has (have) started and that an activity can only finish after its predecessor(s) has (have) finished, respectively. The precedence relations between the activities can also be iden- tified from the Gantt chart in Figure 1.

Notice that, although all prece- dence relations have a zero time lag, the

activities almost never directly follow one another in the BLS. This indicates that the project manager has incorpo- rated buffers for activity durations in the project planning.

More extensive data on the con- sidered project can be found at www .or-as.be/research/database, because the project is part of the real-life project database of Batselier and Vanhoucke (2015a). In this database, which will be described in the following section, the considered project is identified by the code C2013-17 and the name Office Finishing Works (5).

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sector is very broad, consisting of the civil, industrial, and building subsec- tors. The building construction subsec- tor, in turn, can be further subdivided into commercial, institutional, and residential building. Because the con- sidered project comprises the finishing works for an office building, it can be situated within the commercial building construction sector. Thus, three broader reference classes can be identified: projects from construction, building construction, and commercial build- ing construction, in order of increasing specificity and similarity to the consid- ered project. Projects from all of these (sub)sectors can be extracted from the database of Batselier and Vanhoucke (2015a). Logically, the broader the sector, the larger the number of rel- evant projects; this is also illustrated in Table 2, which shows the project codes and names of the projects within the dif- ferent reference classes. A number in a column indicates that the project in this study is part of the corresponding refer- ence class (Constr is construction, Build is building construction, and Comm is commercial building construction) for cost and/or time forecasting (C-column and T-column, respectively). Observe that there are four projects that can be

originate from the presented empirical database. How these different reference classes are composed is described in the next section.

Reference Class Selection

In order to apply the RCF technique, a reference class of projects similar to the considered project has to be iden- tified. As mentioned in the introduc- tion, it is our objective to assess the influence of the chosen reference class similarity level on the performance of RCF. To this end, we consider four different reference class composi- tions, ranging from broad sector-based to company-specific. Notice that this approach somewhat resembles the k-nearest neighbors (k-NN) nonpara- metric method, in which forecasts are based on the k projects closest (i.e., most similar) to the considered project. Paral- leling our approach, the fixed number k would thus be decreased to reflect the increasing similarity level of the refer- ence class. The explicit implementation of the k-NN method is beyond the scope of this article, but can be considered a potential future research topic.

Recall that the project considered in this study is a construction project. Note, however, that the construction

Database Description

The real-life project database utilized in this article was constructed by Batselier and Vanhoucke (2015a). At the time of this study, the ever-expanding database consisted of 56 projects, which originate from many different companies from various sectors (mainly construction, but also event management, IT, produc- tion, education, etc.) and show wide ranges of project budgets and dura- tions. The quality and authenticity of the project data are guaranteed by the application of a construction and eval- uation framework based on so-called project cards, which summarize the most important properties of a certain project and enable its categorization and evaluation (Batselier & Vanhoucke, 2015a). The project card of the project considered here—and of every other project in the database—is available at www.or-as.be/research/database, as are the project data themselves. The data were originally formatted as files from the project management software tool ProTrack (www.protrack.be), but can now also be obtained in Microsoft Excel format, thanks to the novel soft- ware tool PMConverter. Furthermore, all projects that constitute the refer- ence classes for the considered project

Figure 1: Gantt chart for the considered real-life construction project.

1 2 4 6 9 10 12 11 21 13 14 3 20 5 7 8 17 19 15 22 16 18 23

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In order to assess the influence of the reference class similarity level on the performance of the RCF tech- nique, all four of the reference classes included in Table 2 will be considered. The resulting cost and time forecasts are presented in the following section.

Reference Class Forecasting

In terms of the three-step procedure for applying RCF presented in the introduc- tion, we already performed the first step in the previous section (i.e., identify- ing relevant reference classes of past,

projects are available in the utilized database. All of them consist of activities that are strongly similar to those of the considered project, with the placement of the movable partition walls as the core activity. Moreover, the four projects were all completed in 2011, in the order indicated by the project codes (C2013- 13 to 16) and names (Office Finishing Works [1] to [4]). The considered proj- ect C2013-17 Office Finishing Works (5) only started in May 2012, so the OFW reference class for this project is indeed constituted of past projects.

used in a reference class for time but not for cost forecasting. This is because authentic actual cost data for these proj- ects are absent. The numbers displayed in Table 2 will be explained and utilized in the next section.

The last two columns of Table 2 pres- ent the reference class “OFW” or “office finishing works.” This is the most specific reference class with the highest degree of similarity, as it only comprises the finishing construction projects executed by the same company as the one that did the considered project. Four of these

Reference Class

Constr Build Comm OFW

Code Name C [%] T [%] C [%] T [%] C [%] T [%] C [%] T [%] C2013-13 Office Finishing Works (1) 214.5 28.1 214.5 28.1 214.5 28.1 214.5 28.1

C2013-14 Office Finishing Works (2) 212.1 23.8 212.1 23.8 212.1 23.8 212.1 23.8

C2013-15 Office Finishing Works (3) 29.7 229.8 29.7 229.8 29.7 229.8 29.7 229.8

C2013-16 Office Finishing Works (4) 220.0 233.2 220.0 233.2 220.0 233.2 220.0 233.2

C2011-12 Claeys-Verhelst Premises — 2.7 — 2.7 — 2.7

C2013-09 Urban Development Project 10.4 23.7 10.4 23.7 10.4 23.7

C2013-03 Brussels Finance Tower 5.8 0.2 5.8 0.2

C2013-04 Kitchen Tower Anderlecht 18.9 36.0 18.9 36.0

C2013-06 Government Office Building 10.9 22.3 10.9 22.3

C2013-07 Family Residence 23.0 7.6 23.0 7.6

C2013-08 Timber House 15.1 8.8 15.1 8.8

C2013-12 Young Cattle Barn 7.5 63.5 7.5 63.5

C2014-01 Mixed-use Building 2.8 25.5 2.8 25.5

C2014-05 Apartment Building (1) — 20.2 — 20.2

C2014-06 Apartment Building (2) — 11.7 — 11.7

C2014-07 Apartment Building (3) — 14.4 — 14.4

C2014-08 Apartment Building (4) 19.5 18.0 19.5 18.0

C2011-13 Wind Farm 22.0 14.3

C2012-13 Pumping Station Jabbeke 4.2 12.0

C2013-01 Wiedauwkaai Fenders 22.9 0.0

C2013-02 Sewage Plant Hove 27.3 0.0

C2013-10 Town Square 33.2 20.1

C2013-11 Recreation Complex 20.5 211.4

C2014-04 Compressor Station Zelzate 5.0 62.5

Avg [%] 5.6 9.5 2.4 8.9 29.2 23.5 214.1 211.8

# projects 20 24 13 17 5 6 4 4

Table 2: Reference class selections and deviations between planned and actual outcomes.

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profiles for the individual activity dura- tions. In this study, we apply triangular distribution profiles, which can be sym- metrical, skewed to the left, or skewed to the right. These profiles have predefined shapes (see Figure 2), as also used in earlier research (Batselier & Vanhoucke, 2015b). Furthermore, there is also the possibility that an activity exhibits no risk of its duration deviating from the expected value. The distribution profile in such a case is rather obvious (i.e., one single peak) and is therefore not included in Figure 2.

The worst case/best case duration of an activity is always 20% larger/smaller than its expected duration, regardless of the specific distribution profile (risk- free profile disregarded). Moreover, the expected duration of an activity corre- sponds to the 100% duration in Figure 2 and represents the duration estimated by the project management prior to the project start. These expected (or planned) activity duration values were already presented in the second to last column of Table 1. Notice that, for an activity with a distribution profile that is skewed to the left, there is a greater chance that the activity will take longer than expected, whereas the opposite is true for an activity with a right skewed distribution profile. It is important to realize that the assignment of distribu- tion profiles to the different activities of the considered project was performed by the project manager on the project based on his experiences from earlier projects that showed similar activities. Because historical data are used for this process, one would be inclined to regard

US$323,059) and the PD 161 days. The BAC can quite easily be calculated as the sum of all the activity costs displayed in Table 1. The calculation of the PD, on the other hand, is not as straightfor- ward, because the precedence relations between the different activities have to be respected. Only the activities that are part of the critical path (CP) define the PD. The critical activities of the consid- ered project are indicated by a different shade of gray in Figure 1. In fact, only the very long activity 4 is intrinsically critical, but activities 17, 22, and 16 also become critical because of the as late as possible (ALAP) planning approach adopted by the project management in question. Also note that the start of activity 4 is only planned after 10 days, whereas there is no technical constraint (i.e., precedence relation) that would inhibit the activity from beginning at project launch. The choice for delaying the start of activity 4—and for introduc- ing all other buffers in the project—was made by the project manager, perhaps taking into account the unavailability of a particular team or subcontractor until a certain date. Moreover, all activity costs and durations in Table 1 reflect the project manager’s pre-project expecta- tions for the future course of the project. Therefore, the BAC and PD estimates are also completely pre-project.

Monte Carlo Simulation

An approach for obtaining somewhat more substantiated pre-project estimates of project cost and duration is to use Monte Carlo simulation, which is based on the definition of risk distribution

similar projects). Therefore, we can now proceed to the second step. We will not explicitly consider the probability distributions for the selected refer- ence classes as was done by Flyvbjerg (2006). In his research, the goal was to determine the required uplift (i.e., bud- get increase with respect to the initial estimate or BAC) corresponding to a certain acceptable chance of cost over- run. Because our intention is to com- pare RCF with traditional forecasting approaches—which are all aimed at pro- viding point estimates of the most likely project cost and duration—we are only interested in obtaining the most likely outcome for the considered project (i.e., similar to the uplift needed for the 50% percentile of the cost overrun chance in Flyvbjerg [2006]). This corresponds to the third step of the RCF procedure.

We now refer to Table 2, in which the numbers represent the observed deviations of the actual project cost (C-column) and duration (T-column) from their respective baseline estimates (see the next section). A negative per- centage deviation indicates that the actual outcome turned out to be lower (i.e., more beneficial) than expected, whereas a positive number obviously signifies the opposite. The most likely cost or time outcome according to a certain reference class can be calcu- lated from the average deviation over all projects in that reference class (see the second to last row of Table 2). More specifically, the desired RCF results are obtained by applying those average deviations to the baseline estimates, that is, to the BAC for cost forecast- ing and to the PD for time forecasting. Table 3 shows all RCF outcomes for the considered project.

The values of the baseline estimates will be further discussed in the follow- ing section.

Baseline Estimates

The baseline estimates for the consid- ered project could already be observed from Table 3. More specifically, the BAC appeared to be €244,205 (approximately

Reference Class

Constr Build Comm OFW

C [€] T [d] C [€] T [d] C [€] T [d] C [€] T [d] Baseline estimate

244,205 161 244,205 161 244,205 161 244,205 161

Avg deviation 15.6% 19.5% 12.4% 18.9% 29.2% 23.5% 214.1% 211.8%

RCF outcome 257,771 176.4 250,141 175.4 221,787 155.4 209,833 142.0

Table 3: RCF outcomes for the considered project.

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because no additional work—or the risk of it—was taken into account in the ini- tial plan. We also assume that the project only includes variable costs, which cor- respond well to the actual situation, and that all activity costs thus vary uniformly with the corresponding activity duration.

The Monte Carlo simulation is per- formed with the project management software tool ProTrack. More specifi- cally, 100 simulation runs are executed. The project costs and durations resulting from these simulation runs are shown in Figure 3 and Figure 4, respectively. For both graphs, the value intervals were chosen in such a way that an equal

these approaches are beyond the scope of this practice-oriented article.

The selected distribution profiles were already included in the last col- umn of Table 1, labeled “Distr prof.” In this column, “symm,” “left,” “right,” and “no risk” indicate symmetrical, left skewed, right skewed, and risk-free dis- tribution profiles, respectively. For the activities that were rather uncommon for the company (e.g., activities 10 to 12) and with which the project manager thus had little to no experience, a standard symmetrical distribution profile was assumed. Furthermore, activity 23 was assigned a risk-free distribution profile

Monte Carlo simulation as an outside view forecasting technique. However, in contrast to RCF, Monte Carlo simulation still requires distributional information for every activity (and not only for the total project), which will often neces- sitate (unsupported) assumptions from the project manager (e.g., for uncom- mon activities). Therefore, Monte Carlo simulation could better be identified as a “semi-outside view” on project forecast- ing. Note that the distribution profiles for activity durations could also be derived in a more analytical manner (Colin & Vanhoucke, 2016; Trietsch, Mazmanyan, Gevorgyan, & Baker, 2012). However,

Figure 2: Activity duration distribution profiles.

Symmetrical

80 100 100

[percentages of the expected durations]

110 120 80 90 100 120120 80

Left skewed Right skewed

Figure 3: Project costs from Monte Carlo simulation.

225–228 228–231 231–234 234–237 237–240 240–243 243–246 246–249 249–252 252–255 255–258

24

22

20

18

16

14

12

Fr eq

ue nc

y

Simulated project cost [x 1,000 €]

10

8

6

4

2

0

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Earned Value Management

In contrast to the forecasting method- ologies of RCF, baseline estimates and Monte Carlo simulation, EVM does not provide fixed pre-project predictions, but produces project cost and dura- tion forecasts that are updated every TP based on the actual project prog- ress. For the project considered here, tracking was performed on a monthly basis. For the first TP only, a larger time span of two months was chosen (because of the slow initial progress of the project). The final status date was on 31 October 2012, when the project had already ended. A total of four TPs occurred during the project’s execu- tion (i.e., on the last day of June, July, August, and September 2012, respec- tively). Nevertheless, although the first TP was postponed by one month, the progress made at that point was still not substantial enough to allow for correct calculation of project performance. To avoid the potential bias of EVM fore- casting results, the data from the first TP were omitted. Consequently, only the next three TPs (i.e., from July 2012 to September 2012) were considered for the calculation of EVM cost and duration forecasts. Before being able to present the EVM forecasting formulas,

In contradiction to the project cost, only the critical activities (i.e., the activ- ities on the critical path) define the total project duration. In the considered project, activities 4, 17, 22, and 16 are critical, as is indicated by the different shade in Figure 1. From Table 1, one can observe that activities 16 and 17 exhibit a symmetrical distribution profile, whereas the durations of activity 22 and the very significant (i.e., long) activity 4 are skewed to the right. This explains the right skewed distribution of the simulated project duration that can be observed from Figure 4. In correspon- dence with the cost situation, the PD— which is 161 days for the considered project—reflects the expected duration (100 percentage points in Figure 2) on the project level. Furthermore, the peak of the third-degree polynomial of the simulated project duration distribution occurs around 142 days, which is about 88% of the PD. This indeed corresponds nicely to the right skewed distribution profile as defined in Figure 2, where the peak is situated at 90%. More impor- tant, the average project duration from Monte Carlo simulation, which will be the basis for further evaluation, is 147 days. Logically, this value is consid- erably lower (by 8.7%) than the PD.

and sufficient number (11) of outcome categories could be defined.

For project cost, all activities con- tribute to the total cost value. Because the cost distribution over the different activities is very close to symmetrical (i.e., a strongly similar percentage of the distribution profiles is left skewed and right skewed—16% and 18%, respectively— while the rest of the pro- files are symmetrical or risk-free), we also expect a rather symmetrical distri- bution of the simulated project costs. The third-degree polynomial in Figure 3 confirms this expectation. On a project level, the BAC represents the expected cost and thus corresponds to the 100% point in the distribution profiles of Figure 2. Recall that for the considered project, the BAC is €244,205 (approxi- mately, US$323,059). On the other hand, the average project cost over the 100 simulation runs is €242,432 (approxi- mately, US$320,713). This is the Monte Carlo simulation outcome that will be retained for further evaluation. Notice that this result is modestly lower than the BAC (less than 1% difference), which can be explained by the slightly higher fraction of right skewed distribution pro- files (i.e., greater chance of shorter activ- ity duration and thus lower activity cost).

Figure 4: Project durations from MC simulation.

125–130 130–135 135–140 140–145 145–150 150–155 155–160 160–165 165–170 170–175 175–180

20

18

16

14

12

Fr eq

ue nc

y

Simulated project duration [days]

10

8

6

4

2

0

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to plan) and PF 5 SPI(t) (i.e., future time performance equal to current time performance). To clearly indicate the use of the ESM, these methods are represented by ESM-1 and ESM-SPI(t), respectively.

The relevant EVM metrics and all four of the applied cost and time fore- casts are presented in Table 4 for all five TPs of the considered project. Recall that only the three middle TPs (i.e., TP2 to TP4) are retained for the upcoming eval- uation. The results for TP1 and TP5 are also included in Table 4 for complete- ness, but are shown in a lighter font.

Note that PC stands for percentage complete and is calculated by EV/BAC. The PC represents the progress that has already been made on a certain TP. From Table 4, we can see that the PC for TP1 was indeed still very low (i.e., 8%) and does not reach the proposed minimum PC-value of 10% that war- rants reliable performance calculation and thus forecasting (Lipke, 2009). Fur- thermore, the results of TP5 are also not relevant for further evaluation, as this TP occurs after the project has ended. At that time, the actual project outcomes are, of course, already known and fore- casting becomes redundant.

article, we only consider the PFs that were shown to provide the most accu- rate cost forecasts for real-life projects (Batselier & Vanhoucke, 2015b), which are PF 5 1 (i.e., future cost perfor- mance according to plan) and PF 5 CPI (i.e., future cost performance equal to current cost performance). The cor- responding methods are indicated by EAC-1 and EAC-CPI, respectively.

The EVM time forecasts are based on Lipke’s (2003) earned schedule method (ESM). The dominance of this technique over Anbari’s (2003) planned value method (PVM), and the earned duration method (EDM) of Jacob and Kane (2004) has been proven in several studies ( Batselier & Vanhoucke, 2015b; Vanhoucke & Vandevoorde, 2007). There- fore, the project duration forecast at the AT, termed estimated time at completion (EAC(t)), follows from the generic ESM formula:

EAC(t) 5 AT 1 PD 2 ES

PF

Just as for cost forecasting, only the PFs with the best real-life performance according to Batselier and Vanhoucke (2015b) are retained. Those are PF 5 1 (i.e., future time performance according

we first need to provide a brief over- view of the basic EVM metrics and their definitions:

• actual time (AT): the current point in time

• planned value (PV): the value that was planned to be earned at the AT

• earned value (EV): the value that has actually been earned at the AT

• actual cost (AC): the costs that have actually been incurred at the AT

• earned schedule (ES): the time at which the EV should have been earned according to the plan, calculated by

ES 5 t 1 EV 2 PVt

PVt11 2 PVt where t is the

(integer) point in time for which EV $

PVt and EV , PVt11 (Lipke, 2003) • cost performance index (CPI): CPI 5

EV/AC; if the CPI is smaller than, equal to, or larger than 1, the project is respectively over, on, or under budget

• schedule performance index (SPI(t)): SPI(t) 5 ES/AT; if the SPI(t) is smaller than, equal to, or larger than 1, the project is respectively late, on time, or early

The above listing is certainly not comprehensive, as only the met- rics that are needed for further cal- culations are presented. For a more extensive overview of the EVM meth- odology, there are multiple works avail- able for consultation (Anbari, 2003; Fleming & Koppelman, 2010; PMI, 2008; Vanhoucke, 2010, 2014). In this article, the ES methodology proposed by Lipke (2003) is conceived as part of the global EVM technique. The EVM cost and duration forecasting formulas can now be introduced.

The project cost forecast at the AT is called the estimated cost at completion (EAC) and is calculated according to the following generic formula:

EAC 5 AC 1 BAC 2 EV

PF

Here, PF is the performance fac- tor that reflects the assumptions made for future project performance. In this

TP1 TP2 TP3 TP4 TP5 Start date 05/01/2012 07/01/2012 08/01/2012 09/01/2012 10/01/2012

Status date 06/30/2012 07/31/2012 08/31/2012 09/30/2012 10/31/2012

PC [%] 8 61 91 98 100

AT [d] 44 66 89 109 132

PV [€] 58,946 77,311 169,890 184,870 226,159

EV [€] 19,535 148,261 222,988 238,834 244,205

AC [€] 27,993 139,833 175,006 202,523 203,606

ES [d] 18.0 79.3 131.6 151.1 161.0

CPI [-] 0.70 1.06 1.27 1.18 1.20

SPI(t) [-] 0.41 1.20 1.48 1.39 1.22

EAC-1 [€] 252,663 235,778 196,223 207,894 203,606

EAC-CPI [€] 349,933 230,324 191,658 207,077 203,606

ESM-1 [d] 187.0 147.6 118.4 118.8 132.0

ESM-SPI(t) [d] 393.5 133.9 108.9 116.0 132.0

Table 4: EVM metrics and forecasts for the considered project.

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planned, whereas the complete project finished 29 days early. Moreover, most of the predecessors of activity 17 were completed right on time. This means that the buffers introduced in the plan- ning (see Figure 1) were reduced as well and appeared to be oversized. In other words, the start dates of the successor activities could be advanced, as no orga- nizational constraints (e.g., unavailabil- ity of a particular team or subcontractor until a certain date) occurred. Therefore, the project was executed even faster than what would result from shortening the activity durations alone. Because of the large fraction of variable costs in the project, the eventual cost is also reduced significantly, with a magnitude quite similar to the reduction in project dura- tion (i.e., 16.6% compared with 18%).

Now reconsider Table 4. This table shows that it could already be seen from TP2 that the project was going to be both under budget and early, as both the CPI and the SPI(t) were consistently higher than 1. Again, it becomes clear that the progress data of TP1 were not yet reliable, as a CPI of 0.70 and SPI(t) of 0.41 incorrectly indicated that the project—when the performance-based EVM forecasting methods EAC-CPI and ESM-SPI(t) are applied—was going to be well over budget, and even more so, overdue. Furthermore, the RC and RD values could already be observed from the last column of Table 4 (i.e., the post-project forecasts of TP5). Because the RC and RD represent the actual project outcomes and, therefore, the optimal forecast values, they form the basis for evaluating the accuracy of the presented cost and time forecasting methods. More specifically, the mean absolute percentage error (MAPE) mea- sure is used to this end. The generic MAPE formula is as follows:

MAPE 5 1 n 

n

t51

A2 Ft

A

In this formula, A is the actual (eventual) value and Ft is the forecasted value at time instance t. In our case, the time instances t 5 1,...,n represent

to as the real cost (RC) and real dura- tion (RD) of the project, respectively. Thus, the project came in more than €40,000 (approximately, US$52,916) or 16.6% under budget and was completed 29 days or 18% earlier compared to the baseline estimates. Indeed, one can observe from Table 5 that all critical activities (i.e., activities 4, 17, 22, and 16) were completed significantly faster than planned—especially activity 4, which took only 22 days instead of 151 days, and activity 16, which even appeared to be superfluous (i.e., no adjustments to the furniture were needed). The critical activity for which the duration was least reduced is activity 17. Notice that this activity was only 19 days shorter than

Results and Discussion The considered project was executed and exhibited the real activity outcomes presented in the two last columns of Table 5. The baseline costs and dura- tions (i.e., the as-planned values) in the second and third columns were already shown in Table 1, but are again included here to allow for easier comparison. Furthermore, the project totals for base- line costs and durations are, of course, the BAC and PD, respectively.

On the project level, an even- tual cost of €203,606 (approximately, US$269,350) and eventual duration of 132 days were reached (see the two last columns of project total in Table 5). From now on, these outcomes are referred

ID Activity Name Baseline Cost [€]

Baseline Duration [d]

Real Cost [€]

Real Duration [d]

1 Fixed ceilings 2,129 89 1,929 22

2 Metal ceilings 19,509 89 20,190 62

4 Movable partition walls (1) 37,641 151 33,605 22

6 Plaster walls 36,184 22 34,103 81

9 Full subcontracting (1) 1,079 1 847 1

10 Disassembling ceilings 2,509 7 2,277 7

12 Adjusting raised floor 1,800 3 1,459 3

11 Placing carpet 27,162 5 21,457 5

21 Full subcontracting (2) 20,068 67 15,694 22

13 Placing furniture 36,023 3 29,191 3

14 Placing glass walls 180 1 178 1

3 Acoustic dams 1,674 2 1,520 2

20 Movable partition walls (2) 4,926 9 3,245 9

5 Movable partition walls (3) 619 9 615 9

7 Doors 6,259 3 5,529 3

8 Joinery 1,964 3 1,783 3

17 Painting works 8,538 41 6,185 22

19 Ancillary works 16,619 3 1,374 3

15 Finishings 13,132 71 11,920 65

22 Miscellaneous 998 77 906 22

16 Adjusting furniture 312 61 0 0

18 Moving reinforcing screens 4,879 3 905 3

23 Additional work 0 0 8,695 85

Project total 244,205 161 203,606 132

Table 5: Baseline and real activity costs and durations for the considered project.

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information from past experiences. This also indicates that complete and cor- rect historical data—here, in the form of project manager experience—are crucial to the performance of Monte Carlo sim- ulation. However, for Monte Carlo simu- lation, one needs distributional data for each activity in the project, whereas for RCF, only the general outcomes of simi- lar projects are required. The latter are obviously much easier to obtain, which is an advantage of the RCF technique.

Moreover, Table 6 even shows that RCF with the most specific reference class of projects from the same com- pany (OFW) is the most accurate cost forecasting method of all those consid- ered. It also becomes apparent that the RCF approach needs a reference class consisting of projects that are highly similar to the considered project, as forecasting accuracy clearly dimin- ishes with decreasing similarity level (i.e., from OFW over Comm and Build to Constr). RCF with a reference class comprising all construction projects (Constr) from the database of Batselier and Vanhoucke (2015a) even proves to be the worst-performing method.

Because RCF with reference class OFW is the overall most accurate tech- nique, it also surpasses both EVM cost forecasting methods (which show very similar results). This is remarkable, as the EVM methodology allows forecasts to be updated during project prog- ress (based on actual progress data), whereas RCF only produces one fixed pre-project forecast that remains con- stant throughout the entire project.

Because RCF yields constant fore- casts, the approach logically exhibits greater forecasting stability than EVM. This is visualized by Figure 5. Since the horizontal line represents the eventual project cost (i.e., RC), the closeness of the markers to this line reflects the forecasting accuracy of the correspond- ing methods (i.e., the closer, the more accurate).

In terms of timeliness (i.e., the third forecasting quality evaluation crite- rion according to Covach, Haydon, and

project considered in this article, as do the presented fore casting results.

Cost Forecasting The accuracy results for the different cost forecasting methods are presented in Table 6. More specifically, the table shows the difference in MAPEs between the various techniques. A negative number indicates that the horizontal method (row) is more accurate than the vertical method (column), whereas a positive value obviously represents the opposite. All the abbreviations used in the table were already explained earlier in the text.

We will now discuss the results in the order the methods are displayed in Table 6. The BAC represents the pure inside view on project cost forecasting and is used as a first reference value. We see that Monte Carlo simulation yields a more accurate forecast than BAC, albeit modest. The reason that the improvement is only modest might be that symmetrical distribution pro- files were assumed for the uncommon activities, whereas the most important of them (i.e., activities 17, 15, and 16) were executed faster—and thus, more cheaply—than planned. This means that right skewed distribution profiles would have been a better option for those activities, although symmetrical profiles were the more logical choice, given the unavailability of distributional

the n TPs that were selected for the considered project. Furthermore, A is substituted by RC and RD for cost and time forecasting, respectively, and Ft reflects the forecasting outcomes of the different methods. Note, however, that the methodologies of RCF, baseline estimates and Monte Carlo simulation, all produce one fixed forecast prior to the project start that remains constant throughout the entire project. In other words, the forecasts Ft are the same for every TP t (i.e., Ft can be replaced by F). Nevertheless, the MAPE remains a valid accuracy measure in these situa- tions, although its formula is implicitly simplified to |A 2 F|/A (i.e., an absolute percentage error). For the EVM fore- casting methods, on the other hand, the original MAPE formula, of course, continues to apply, with n 5 3 and F1 to F3 reflecting the forecasts for TP2 to TP4. It is always true that the lower the MAPE, the more accurate the forecast- ing method.

In the next two subsections, the per- formance of the considered forecast- ing methods is evaluated—first for cost, and then for time. Thereafter, both fore- casting dimensions are compared more elaborately. Finally, a qualitative discus- sion on the underlying causes for the observed performance of the different forecasting approaches is conducted. Note that this discussion emanates from the specific outcomes of the construction

RCF EVM

[MAPE %] BAC MC Sim Constr Build Comm OFW EAC-1 EAC-CPI

BAC — 0.9 26.7 22.9 11.0 16.9 12.8 13.0

MC Sim 20.9 — 27.5 23.8 10.1 16.0 11.9 12.2

RCF

Constr 6.7 7.5 — 3.7 17.7 23.5 19.4 19.7

Build 2.9 3.8 23.7 — 13.9 19.8 15.7 16.0

Comm 211.0 210.1 217.7 213.9 — 5.9 1.8 2.0

OFW 216.9 216.0 223.5 219.8 25.9 — 24.1 23.8

EVM EAC-1 212.8 211.9 219.4 215.7 21.8 4.1 — 0.3

EAC-CPI 213.0 212.2 219.7 216.0 22.0 3.8 20.3 — Table 6: Difference in accuracy for the considered cost forecasting approaches.

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profiles for Monte Carlo simulation. Also note that these distribution profiles are implicitly based on historical data, as during the construction of these pro- files, the concerning project manager is encouraged to take into account experi- ences from past projects, which would improve forecasting accuracy according to Caron, Ruggeri, and Merli (2013). In other words, the application of Monte Carlo simulation already guides the project manager toward taking more of an outside view on project forecast- ing. Nevertheless, the technique still

be that two of the four critical activities have a right skewed distribution profile (the two others are symmetrical), which depicts activities that are more likely to be completed faster than planned. More- over, these two activities—activity 22 and especially activity 4—are the most important (i.e., longest) ones, and thus have the greatest influence on the even- tual project duration. Both activities were indeed finished far ahead of sched- ule, and therefore, so was the project. Again, this indicates the importance of correct activity duration distribution

Reither [1981], which expresses the abil- ity of a forecasting method to produce accurate forecasts in different stages of the project life cycle), RCF also clearly outperforms EVM. Logically, accurate early-stage forecasts are most impor- tant, as they allow adequate corrective actions to be taken in a timely manner (Teicholz, 1993). Following the defini- tions of Teicholz (1993) and Vanhoucke and Vandevoorde (2007), only TP1 (with a PC of 8%) can be situated in the early stage. For this TP, RCF indeed pro- duces a much more accurate forecast than EAC-1 and certainly more accurate than EAC-CPI, which is off the charts in Figure 5 (see the value in Table 4).

Time Forecasting Table 7 is very similar to Table 6 in the previous subsection, but now shows the accuracy differences for the considered time forecasting methods.

Again, the outcomes are discussed in the order the methods are presented in the table. When we once again apply the baseline estimate—here, the PD—as a first reference value, we see that Monte Carlo simulation now provides a much greater accuracy improvement than it does for cost forecasting (i.e., 10.6% compared with 0.9%). The reason could

Figure 5: Project cost forecasting results.

TP1 TP2 TP3 TP4

260

RCF EAC-1 EAC-CPI RC

250

Pr oj

ec t c

os t [

x 1,

00 0

€]

Tracking period

240

230

220

210

200

190

180

RCF EVM

[MAPE %] PD MC Sim Constr Build Comm OFW ESM-1

ESM- SPI(t)

PD — 10.6 211.6 210.9 4.2 14.4 11.2 11.6

MC Sim 210.6 — 222.2 221.5 26.4 3.8 0.6 1.0

RCF

Constr 11.6 22.2 — 0.8 15.9 26.1 22.9 23.3

Build 10.9 21.5 20.8 — 15.1 25.3 22.1 22.5

Comm 24.2 6.4 215.9 215.1 — 10.2 7.0 7.4

OFW 214.4 23.8 226.1 225.3 210.2 — 23.2 22.8

EVM ESM-1 211.2 20.6 222.9 222.1 27.0 3.2 — 0.4

ESM- SPI(t)

211.6 21.0 223.3 222.5 27.4 2.8 20.4 —

Table 7: Difference in accuracy for the considered time forecasting approaches.

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clearly showed the highest accuracy for both dimensions. Traditionally, RCF was introduced to improve the accuracy of cost forecasts and was only applied in this context (Flyvbjerg, 2006; Flyvbjerg & Cowi, 2004). Indeed, our study indi- cates that the technique succeeds in the cost objective, as the BAC (i.e., inside view) and even the periodically updat- ing EVM methods are surpassed in accuracy. On the other hand, RCF has not been applied to time forecasting up to now. Nevertheless, the technique surpasses all other methods for the time dimension in our study. Further- more, compared to cost forecasting, a strongly similar accuracy improvement of the RCF approach, with respect to both the baseline estimate (i.e., PD) and the EVM time forecasting meth- ods could be observed. Specifically, the baseline estimate improvement is only 2.5% smaller for time forecasting (MAPE reduction of 14.4% for PD with respect to 16.9% for BAC), and for the best EVM forecasting method (i.e., ESM- SPI(t) for time and EAC-CPI for cost in this case), the difference even remains limited to 1% (MAPE reduction of 2.8% for ESM-SPI(t) with respect to 3.8% for EAC-CPI). Therefore, our results sug- gest that the RCF approach could just as

The TP1 forecast value for ESM-SPI(t) (see Table 4) is not included in Figure 6 because of the excessive deviation from the eventual outcome, just as for EAC- CPI in Figure 5. The performance-based EVM forecasts (i.e., ESM-SPI(t) and EAC- CPI) thus show a far greater instability than their counterparts, with a PF 5 1.

Comparing Cost and Time Forecasting The baseline estimates for cost and time forecasting exhibit a very comparable precision, although the PD is slightly less accurate than the BAC (MAPE of 22% compared with 19.9%). On the other hand, Monte Carlo simulation improves the forecasting accuracy for time to a far greater extent than for cost (MAPE reduction of 10.6% compared with 0.9%). The reasons for this were already given in previous subsections. Of course, these reasons are project-specific, and there- fore, the supremacy of Monte Carlo simulation for time with respect to cost should not be generalized.

In this section, we mainly focus on the comparison of the RCF perfor- mance for cost and time forecasting. More specifically, we consider the RCF approach based on the reference class of in-company projects (OFW), which

requires the project manager to make some assumptions (e.g., for activities without precedents).

To completely eliminate human judgment and cut directly to the project outcomes, RCF should be applied. The only concern for this approach regards the selection of an adequate reference class. Our study indicates that, also for time forecasting, a reference class should comprise projects that are suffi- ciently similar to the considered project in order to guarantee accurate forecasts. Indeed, an increasing similarity level of the reference class (i.e., in the order Constr, Build, Comm, and OFW) results in increasing forecasting accuracy.

Moreover, when applying RCF with the most specific reference class of in- company projects (OFW), both EVM methods are outperformed in nearly equal measure. This outcome corresponds per- fectly to that for cost forecasting. This is also the case for the comparison between RCF and EVM in terms of stability and timeliness, as for time forecasting, RCF also surpasses EVM. This can be ascer- tained from Figure 6, which should be interpreted in the exact same way as Figure 5. The theoretical explanation was already provided in the previous subsec- tion and is therefore not repeated here.

TP1 TP2 TP3 TP4

190

180

170

Pr oj

ec t d

ur at

io n

[d ay

s]

Tracking period

160

150

140

130

120

110

100

RCF ESM-1 ESM-SPI(t) RD

Figure 6: Project duration forecasting results.

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of all these forecasting methods dem- onstrates that the RCF technique is the most user-friendly, as it does not require a great deal of detailed information (such as distributional data about activ- ity durations for Monte Carlo simula- tion) or extensive calculations (like the periodical forecast updates for EVM).

Moreover, although RCF produces pre-project forecasts that remain con- stant throughout project execution (just like baseline estimates and Monte Carlo simulation), it surpasses all the tradi- tional techniques in accuracy, stabil- ity, and timeliness. The dominance of RCF in accuracy is especially remark- able, as the competing EVM technique yields forecasts that are updated dur- ing project progress. Furthermore, the strong performance of RCF occurs for both cost and time forecasting, and in nearly equal measure. Therefore, our study suggests that RCF could have the same merits for time forecasting as for cost forecasting, for which the technique had already been applied (Flyvbjerg, 2006; Flyvbjerg & Cowi, 2004). However, RCF only outperforms the other techniques when the degree of similarity between the considered project and the projects in the refer- ence class is sufficiently high. More concretely, in our case, the reference class had to consist of projects from the same finishing construction com- pany. A clear decrease in forecasting accuracy could be observed with the gradually declining similarity level of the reference class.

In our specific case, the qualitative reason for the dominance of the outside view on project forecasting over the traditional inside view could be found in the occurrence of a newly identi- fied type of strategic misinterpretation, which suggests that project managers in the post-approval phase are inclined to overestimate the expected costs and durations so that their targets (and bonuses) could be achieved more easily.

This article supports the practi- cal relevance of applying RCF for real- life projects and also shows how the

been approved, and therefore, under- estimating costs and durations would not offer advantages. On the contrary, it would only force the project manag- ers to work faster and more cheaply in order to reach the set goals and the possible bonuses that go with them. Consequently, it is plausible and per- haps even natural that these project managers—with their projects already approved and assigned to them—would rather overestimate the foreseen costs and durations (and build in buffers) so that their targets—and the correspond- ing bonuses—could be achieved more easily. The exact nature and the effect of strategic misinterpretation thus appear to depend on the phase the project is in when preparing the plan (i.e., produc- ing the baseline estimates): The preap- proval phase leads to underestimations, whereas the post-approval phases causes overestimations. Furthermore, the fact that many activities that were planned behind a buffer could actually be started before their foreseen start date not only indicates the absence of organizational constraints (e.g., unavail- ability of a particular team or subcon- tractor until a certain date), but also supports the idea of post-approval stra- tegic misinterpretation having occurred for the considered project. In any event, the RCF technique (i.e., outside view) can bypass the biasing effects of this new type of strategic misinterpretation, as our study has shown.

Conclusions The main objective of this article was to support the practical relevance of RCF by applying the technique to a real-life project and quantitatively evaluating it through comparison with the most commonly used traditional forecasting methods. More specifically, the consid- ered real-life project is a finishing con- struction project that was selected from the database of Batselier and Vanhoucke (2015a). The forecasting techniques with which RCF was compared are baseline estimates, Monte Carlo simulation, and EVM. First, practical application

well have merit for forecasting project duration.

Qualitative Discussion Previous studies (Flyvbjerg, Holm, & Buhl, 2002; 2005; Kahneman & Tversky, 1979b; Lovallo & Kahneman, 2003; Wachs, 1989, 1990) have argued that people—and, therefore, project managers—generally tend to underestimate costs (and dura- tions) when applying an inside view to project forecasting. They identified two reasons: optimism bias (i.e., uninten- tionally seeing future events in a more positive light than warranted by actual experience) and strategic misinterpreta- tion (i.e., deliberately and strategically making more positive predictions so as to give the impression that the com- petition would be surpassed). However, when looking at the baseline estimates (i.e., inside view) for the considered project and for similar projects within the same finishing construction com- pany (reference class OFW), we observed exactly the opposite—namely, a struc- tural overestimation of costs and dura- tions. This cannot be explained by the existence of an unintended “negativism bias” (i.e., seeing future events in a more negative light than warranted by actual experience), as this would be in contradic- tion with the usual manifestations of the human psyche according to the research of Kahneman and Tversky (1979b) and Lovallo and Kahneman (2003). In other words, negativism bias cannot exist alongside positivism bias; they would, by definition, be mutually exclusive. There- fore, strategic misinterpretation must be the root of the structural overestimations within the considered company.

Note that strategic misinterpreta- tion, as also presented by Flyvbjerg (2006), is traditionally defined for the preapproval phase of a project. Project managers would benefit from underes- timating costs and durations by increas- ing the chance of their project—and not that of the competition—would be approved (and funded). However, all considered projects of the finish- ing construction company had already

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Fleming, Q., & Koppelman, J. (2010). Earned value project management (4th ed.). Newtown Square, PA: Project Management Institute.

Flyvbjerg, B. (2006). From Nobel Prize to project management: Getting risks right. Project Management Journal, 37(3), 5–15.

Flyvbjerg, B. (2007). Eliminating bias in early project development through reference class forecasting and good governance. In K. J. Sunnevåg (Ed.), Decisions based on weak information: Approaches and challenges in the early phase of projects (pp. 90–110). Trondheim, Norway: Concept Program, The Norwegian University of Science and Technology.

Flyvbjerg, B., & Cowi. (2004). Procedures for dealing with optimism bias in transport planning: Guidance document. London, England: UK Department for Transport.

Flyvbjerg, B., Holm, M., & Buhl, S. (2002). Underestimating costs in public works projects: Error or lie? Journal of the American Planning Association, 68(3), 279–295.

Flyvbjerg, B., Holm, M., & Buhl, S. (2005). How (in)accurate are demand forecasts in public works projects? The case of transportation. Journal of the American Planning Association, 71(2), 131–146.

Jacob, D., & Kane, M. (2004). Forecasting schedule completion using earned value metrics? Revisited. The Measurable News (Summer), 11–17.

Kahneman, D. (1994). New challenges to the rationality assuption. Journal of Institutional and Theoretical Economics, 150(1), 18–36.

Kahneman, D., & Tversky, A. (1979a). Intuitive prediction: Biases and corrective procedures. In S. Makridakis & S. Wheelwright (Eds.), Studies in the management sciences: Forecasting (p. 12). Amsterdam, Netherlands: North Holland.

Kahneman, D., & Tversky, A. (1979b). Prospect theory: An analysis of decisions under risk. Econometrica, 47 (2), 313–327.

practical applicability and utility of RCF, but also of many other project manage- ment techniques.

Acknowledgments We acknowledge the support provided by the “Nationale Bank van België” (NBB) and by the “Bijzonder Onderzoeksfonds” (BOF) for the project with contract number BOF12GOA021. Furthermore, we would also like to thank Gilles Bonne, Eveline Hoogstoel, and Gilles Vandewiele for their efforts in developing PMConverter.

References Anbari, F. (2003). Earned value project management method and extensions. Project Management Journal, 34 (4), 12–23.

Batselier, J., & Vanhoucke, M. (2015a). Construction and evaluation framework for a real-life project database. International Journal of Project Management, 33(3), 697–710.

Batselier, J., & Vanhoucke, M. (2015b). Empirical evaluation of earned value management forecasting accuracy for time and cost. Journal of Construction Engineering and Management, 141(11), 05015010.

Carbone, R., & Armstrong, J. (1982). Evaluation of extrapolative forecasting methods: Results of a survey of academicians and practitioners. Journal of Forecasting, 1(2), 215–217.

Caron, F., Ruggeri, F., & Merli, A. (2013). A Bayesian approach to improve estimate at completion in earned value management. Project Management Journal, 44(1), 3–16.

Colin, J., & Vanhoucke, M. (2016). Empirical perspective on activity durations for project management simulation studies. Journal of Construction Engineering and Management, 142(1), 04015047.

Covach, J., Haydon, J., & Reither, R. (1981). A study to determine indicators and methods to compute estimate at completion (EAC). Fairfax, VA: ManTech International Corporation.

technique can be evaluated on a quan- titative basis through comparison with other existing forecasting methods. Although this study provides interesting insights into the workings and perfor- mance of RCF and other forecasting methods, its results may not be readily generalized because of the restricted number of real-life projects from which the reference classes were selected. To increasingly substantiate the valid- ity of the RCF technique, it should be applied and tested on an ever-growing empirical project database.

Furthermore, following the con- cept of combining outside view with inside view for project forecasting (Kim & Reinschmidt, 2011), we identify the future research topic of integrating RCF in EVM. By replacing the baseline estimates with the forecasts from RCF, more accurate EVM performance met- rics could be obtained. In turn, this would lead to more reliable warning sig- nals and thus more adequate corrective actions. Therefore, it would ensure more effective project control in general. This assertion should, of course, be validated by extensive empirical research.

Although the RCF technique in itself is fairly straightforward, it is relatively difficult to correctly implement in prac- tice because of its strong dependence on the selected reference class. As this research has shown, a reference class of (highly) similar projects is needed to provide (highly) accurate forecasts. Such a collection of (highly) similar projects is not always readily available in practice. Even less often is the collec- tion of adequate size; indeed, forecast- ing bias increases as the reference class gets smaller, which can undermine the performance and applicability of RCF. That is why it is of great importance to have available many (and correct) real- life project data. Organizations should make a point of collecting their proj- ects’ progress and performance data in a structured way, as described, for example, in Batselier and Vanhoucke (2015a) or at www.or-as.be/research/ database. This would not only boost the

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evaluation, a real-life project database—freely available at www.or-as.be/research/database— was created under his guidance. He has presented his work at several international conferences on project management and operational research in cities that include Rome, Italy; Barcelona, Spain; Munich, Germany; and Ghent, Belgium. He can be contacted at [email protected]

Mario Vanhoucke is a full professor at Ghent University (Belgium), Vlerick Business School (Belgium, Russia, China), and UCL (University College London) School of Management (UK). He has a PhD in operations management (2001) and a master’s degree in business engineering from the University of Leuven (Belgium). He teaches Project Management, Business Statistics, and Decision Sciences for Business and Applied Operations Research, and is also a guest lecturer in the Beijing MBA program at Peking University (China). His main research interest lies in the integration of project scheduling, risk management, and project control using combinatorial optimization models. He is an advisor for several PhD projects, has published more than 60 papers in international journals, and is the author of four project management books published by Springer. He is a regular guest on international conferences as an invited speaker or chairman and a reviewer of numerous articles submitted for publication in international academic journals. He is a founding member and director of the EVM Europe Association (www.evm-europe.eu) and partner at the company OR-AS (www.or-as.be). His project management research has received multiple awards, including the 2008 International Project Management Association (IPMA) Research Award for his research project Measuring Time—A Project Performance Simulation Study, which was received at the IPMA world congress held in Rome, Italy. He also received the Notable Contributions to Management Accounting Literature Award from the American Accounting Association at their 2010 conference in Denver, Colorado, USA. He can be contacted at [email protected]

Vanhoucke, M., & Vandevoorde, S. (2007). A simulation and evaluation of earned value metrics to forecast the project duration. Journal of the Operational Research Society, 58(10), 1361–1374.

Wachs, M. (1989). When planners lie with numbers. Journal of the American Planning Association, 55(4), 476–479.

Wachs, M. (1990). Ethics and advocacy in forecasting for public policy. Business and Professional Ethics Journal, 9(1–2), 141–157.

Warburton, R. (2011). A time-dependent earned value model for software projects. International Journal of Project Management, 29(8), 1082–1090.

Willems, L., & Vanhoucke, M. (2015). Classification of articles and journals on project control and earned value management. International Journal of Project Management, 33(7), 1610–1634.

Jordy Batselier holds master’s degrees in civil engineering (2011) and business economics (2012) from Ghent University (Belgium). Since 2012 he has been working as a PhD researcher at the Operations Research & Scheduling research group of the Faculty of Economics and Business Administration of Ghent University. He is a teaching assistant for business games in project management courses and supervises multiple master’s students during the completion of their dissertations. Furthermore, he is co-developer of the project management software tool PMConverter (available at www. or-as.be/research/database). His research interest lies in project management, and more specifically, in performing project control by means of earned value management. His specific research actions are focused on the empirical evaluation and development of forecasting techniques for project duration and cost, on which he has published several papers in international journals. For the empirical

Kim, B., & Reinschmidt, K. (2011). Combination of project cost forecasts in earned value management. Journal of Construction Engineering and Management, 137(11), 958–966.

Lipke, W. (2003). Schedule is different. The Measurable News (Summer), 31–34.

Lipke, W. (2009). Project duration forecasting . . . A comparison of earned value management methods to earned schedule. The Measurable News, (2), 24-31.

Lovallo, D., & Kahneman, D. (2003, July). Delusions of success: How optimism undermines executives’ decisions. Harvard Business Review, 56–63.

Project Management Institute. (PMI). (2008). A guide to the project management body of knowledge (PMBOK® guide) – Third Edition. Newtown Square, PA: Author.

Teicholz, P. (1993). Forecasting final cost and budget of construction projects. Journal of Computing in Civil Engineering, 7(4), 511–529.

Trietsch, D., Mazmanyan, L., Gevorgyan, L., & Baker, K. (2012). Modeling activity times by the Parkinson distribution with a lognormal core: Theory and validation. European Journal of Operational Research, 216(2), 386–396.

Vanhoucke, M. (2010). Measuring time- improving project performance using earned value management (Vol. 136 of International Series in Operations Research and Management Science). New York, NY: Springer.

Vanhoucke, M. (2014). Integrated project management and control: First comes the theory, then the practice (Vol. Management for Professionals). New York, NY: Springer.

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Project Management Journal, Vol. 47, No. 5, 52–69

© 2016 by the Project Management Institute

Published online at www.pmi.org/PMJ

Antecedents of Relationship Conflict in Cross-Functional Project Teams Xiaoyan Huo, School of Management Science and Engineering, Hebei GEO University, Hebei, China; College of Management and Economics, Tianjin University, Tianjin, China Lianying Zhang, College of Management and Economics, Tianjin University, Tianjin, China Haiyan Guo, College of Management and Economics, Tianjin University, Tianjin, China

IntroductIon

T he cross-functional project team is one of the most dramatic organization forms. (Anthony, Green, & McComb, 2014; Bishop, 1999; Denison, Hart, & Kahn, 1996; Sarin & McDermott, 2003). This type of team is often defined as a collection of individuals who are interdependent for team

performance, who view themselves as embedded in political organizations, and who manage their relationships across organizational boundaries (Chiocchio, 2015; Liang, Jiang, Klein, & Lui, 2010; Sundstrom, De Meuse, & Futrell, 1990). The success of a cross-functional project team depends on a high degree of interaction and the minimization of relationship conflict (Anthony et al., 2014; Bishop, 1999; Dawes & Massey, 2005; Ghobadi & D’Ambra, 2012). However, relationship conflicts are common in cross-functional project teams (Anthony et al., 2014; Dawes & Massey, 2005; Menon, Bharadwaj, & Howell, 1996) because these teams are composed of team members who are drawn from various organization functional units (Chen, 2007; Luca & Atuahene-Gima, 2007). Different functional departments have distinct “thought worlds,” such as communication rules, values, and beliefs (Clercq, Thongpapanl, & Dimov, 2013; Griffin & Hauser, 1996). These diversities challenge the establishment of trust, cohesion, and group identity among team members (Chen, 2007). Relationship conflict stems from interpersonal incompatibilities and triggers negative feelings among team members, such as animosity and hatred (De Dreu & Weingart, 2003; Jehn & Mannix, 2001). When relationship conflict becomes a dominating concern, team members may become uncooperative and involve themselves in meaningless human struggles, which then influence their integrated goals and project performance (Deutsch, 1949; Song, Dyer, & Thieme, 2006). Some empirical studies have examined the dysfunctional effect of relationship conflict on decision making and team member satisfaction (Amason, 1996; Anthony et al., 2014; Bishop, 1999; De Dreu & Weingart, 2003; Jehn, 1995; Jehn & Mannix, 2001; Solansky, Singh, & Huang, 2014; Tepper, Moss, & Duffy, 2011). Obviously, relationship conflict hinders the effective delivery of the cross-functional project. Therefore, an increasing number of studies have focused on avoiding and reducing detrimental relationship conflict.

One research stream in the literature focuses on the influence of some contingent factors (moderator or mediation) on relationship conflict. These empirical studies confirm that relationship conflict caused a dysfunctional effect that is weakened by the influence of certain contingent factors. For example, Yiu and Cheung (2007) explored the moderating effect of behavioral flexibility on the tension-conflict relationship. Anthony et al. (2014) inves- tigated the mediation effect of the quality of cross-functional coordination on the relationship between project goal clarity and the level of boundary conflict in cross-functional project teams. Yang, Chen, and Wang (2014)

Relationship conflict is a pervasive phe-

nomenon in cross-functional project

teams. Although previous studies have

demonstrated the dysfunctional effect of

relationship conflict, the direct drivers of

relationship conflict in cross-functional proj-

ect teams remain unclear. To address this

gap, a literature review and an advisory

group discussion were performed to identify

the antecedents of the relationship conflict

framework. Afterward, a structural equation

model (SEM) was used to confirm the influ-

ence of such antecedents on relationship

conflict. Intrapersonal diversity, uncertain

project task, organizational culture diver-

sity, and inappropriate behavior positively

influence relationship conflict. These findings

help researchers better understand the driv-

ers of relationship conflict as well as open

a gateway for practitioners to control and

manage relationship conflict for a successful

cross-functional project.

KEYWORDS: conflict management; cross-functional project teams; relationship

conflict; team building

ABStrAct ■

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explored how requirement quality and stability mediated the relationship between the adoption of requirement definition and management (RDM) practice and relationship conflict in the new product development process. These studies highlight the importance of moderating or mediating the influ- ence of variables on relationship con- flict. However, further exploration of the underlying drivers (antecedents) of relationship conflict remains unclear. Therefore, given that researchers attach more importance to the antecedents (i.e., direct causes) of relationship con- flict these antecedents will contribute to researchers better understanding the drivers of relationship conflict and open a gateway for practitioners to control relationship conflict through direct interventions (Jehn, Chadwick, & Thatcher, 1997; Ren & Gray, 2009; Smith & Edmondson, 2006).

Given the importance of knowl- edge about the antecedents of rela- tionship conflict, several studies have attempted to investigate the anteced- ents of relationship conflict in vari- ous project contexts. On the one hand, researchers have attempted to establish a framework for identifying the ante- cedents of relationship conflict. For example, Camelo-Ordaz, García-Cruz, and Sousa- Ginel (2014) identified the antecedents of relationship conflict in top management teams. They con- sidered the input or latent conditions (i.e., team size, team tenure, intragroup trust, and value consensus) that evoked behavioral events (i.e., behavioral inte- gration) among team members, which in turn determined the detrimental relationship conflict. Dawes and Massey (2005) developed a conceptual frame- work to explain relationship conflict using three groups of variables, namely, structural, individual, and communica- tion. Ismail, Richard, and Taylor (2012) explained the causes and outcomes of relationship conflict and suggested the important roles of several factors, such as moods, values, and situational contexts. On the other hand, several

researchers have explored the influence of a specific factor on relationship con- flict. For instance, Liu, Chen, Chen, and Sheu (2011) examined the relationship between requirement uncertainty and relationship conflict. The study revealed that requirement diversity was directly associated with relationship conflict. Liang, Jiang, Klein, and Liu (2010) ana- lyzed the relationship between infor- mational diversity and relationship conflict, and then suggested that diver- sity increased relationship conflict. Pelled and Adler (1994) explained the challenge of intergroup conflict in multi- functional product development teams, and focused on conflict-inducing pro- cesses triggered by functional diversity. The above- mentioned direct-influence factors provide a reference for effec- tively controlling relationship conflict. However, the perspective is relatively unitary. In fact, cross-functional project teams are complex relationship project teams (Bishop, 1999). The intrapersonal diversity of team members (Cheung & Chuah, 1999; Mohammed & Angell, 2004; Ruuska & Teigland, 2009), organi- zational culture diversity (Iorio & Taylor, 2014; Ren & Gray, 2009), uncertain proj- ect task (Anthony et al., 2014; Bishop, 1999; Liu et al., 2011), and inappropri- ate behavior of team members (Koza & Dant, 2007; Tepper, 2007; Yiu & Cheung, 2007) are all potential drivers of rela- tionship conflict.

Therefore, this study aims to iden- tify the antecedents of the relationship conflict framework within the context of cross-functional project teams. The findings can help project managers rec- ognize the importance of specific fac- tors and employ appropriate conflict management strategies from multiple perspectives. As expected, although relationship conflict is common in cross-functional project teams, the identified antecedents of the relation- ship conflict framework will help proj- ect managers recognize, manage, and avoid detrimental relationship conflict.

The following steps were developed to achieve the research objective. First,

the researchers performed an exten- sive literature review to identify the pri- mary antecedents of the relationship conflict framework. Second, given the characteristics of cross-functional proj- ect teams, this study further refined and modified the primary antecedents of the relationship conflict framework and obtained the final antecedents of the relationship conflict framework by employing a pilot questionnaire and consulting an advisory group of experts who were all experienced and knowledgeable about cross-functional projects. Third, questionnaires based on the final antecedents of the rela- tionship conflict framework were dis- tributed to team members involved in cross-functional project teams. Fourth, a structural equation model (SEM) was employed to explore the correlation among the antecedents of the relation- ship conflict framework and to confirm the influence of the antecedents on rela- tionship conflict. The overall research framework is shown in Figure 1.

Literature Review The primary antecedents of the rela- tionship conflict framework were iden- tified through a literature review. Such antecedents denote a certain element that significantly contributes to the occurrence of relationship conflict. One must be able to determine the factors that induce relationship conflict to help project managers predict and govern detrimental relationship conflict. First, this research confirmed 14 relationship conflict factors from the literature in the cross-functional project context. The relationship conflict research was also investigated deeply in other organiza- tion areas. After reviewing this literature and comparing the research results, we found that some factors continued to appear, such as gender, team ten- ure, intergroup trust, value consensus, and communication. Hence, this study identified another seven factors that appeared more than three times in pre- vious organization literature. Finally, the primary antecedents of the relationship

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conflict framework with 21 factors were identified and used to determine the final antecedents of the relationship conflict framework. Table 1 presents the primary antecedents of relationship conflict. Based on previous research and the cross-functional project team con- text, these factors reflect four aspects: intrapersonal diversity, uncertain proj- ect task, organizational culture diver- sity, and inappropriate behavior.

Intrapersonal diversity is defined as the within-person differences in func- tional domains to reflect the breadth of functional diversity that an individual possesses (Horwitz, 2015). Intraper- sonal diversity factors can be divided into surface- and deep-level diversity (Harrison & Klein, 2007; Harrison, Price, Gavin, & Florey, 2002; Mohammed & Angell, 2004). Surface-level diversity refers to the extent to which a unit’s

member is different in terms of demo- graphics such as age, gender, ethnicity, and organizational tenure (Lawrence, 1997; Tsui, Egan, & Xin, 1995). Deep- level diversity of team members refers to differences in terms of personality (Chen, 2007; Cheung & Chuah, 1999; Earley & Gibson, 1998; Noordin, Wil- liams, & Zimmer, 2002), interest (Smith & Edmondson, 2006), perception (Lin- berg, 1999; McCann, Ostrom, Tyner, & Mitchell, 1985; Ruuska & Teigland, 2009; Taylor, Fiske, Etcoff, & Ruderman, 1978), and experience (Stephens, Heaphy, Carmeli, Spreitzer, & Dutton, 2013). Jehn et al. (1997) indicated that the more different an individual, the more likely he or she is to perpetrate relation- ship conflict. Similar team members can easily communicate and perceive one another as trustworthy (Grimme- likhuijsen & Meijer, 2014). When team

members are motivated to maintain their self- and social identities, they exhibit a favorable bias toward others who appear to have similar character- istics (Turner & Haslam, 2001) because they envision that their own values and beliefs will be reinforced. Most of the intrapersonal diversity studies on rela- tionship conflict have focused on both surface- and deep-level diversity (Bar- rick, Stewart, Neubert, & Mount, 1998; Harrison et al., 2002; Milliken & Mar- tins, 1996; Tekleab & Quigley, 2014; Tsui et al., 1995). These relationships have been examined at both individual and group levels of analyses (de Wit, Greer, & Jehn, 2012; Jehn et al., 1997; Kozlowski, 2015; O’Neill, Allen, & Hastings, 2013; Pelled, Eisenhardt, & Xin, 1999). All of the above-mentioned findings result in the general prediction that individuals with a high level of diversity tend to

Figure 1: The overall research framework of the research.

Literature Review • Draw on the antecedents of relationship conflict published in literature • Gain the primary antecedents of the relationship conflict framework

Questionnaire • Collect the data from cross-functional project team members

Structure Equation Model (SEM) • Data analysis and discussion

A Pilot Questionnaire (advisory group) • Comment on questionnaire readability • Comment on questionnaire comprehensiveness • Comment on questionnaire precision

Discussion (advisory group) • Delete some factors from the primary antecedents of the relationship conflict framework • Add some factors to the primary antecedents of the relationship conflict framework • Combine some factors in the primary antecedents of the relationship conflict framework • Gain the final antecedents of the relationship conflict framework

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Aspects Factors Descriptions Authors Intrapersonal diversity

1 Gender Males and females possess different tolerance levels to the rapidly changed social relationship. Different genders may result, to a certain extent, in different feelings of hostility or animosity toward other group members.

Jehn et al. (1997) Lawrence (1997) Tsui, Egan, & Xin (1995)

2 Ethnicity Different ethnicities present more visible diversity, strengthening its association with relationship conflict. Empirical research has found heterogeneity in ethnicity as a cause of relationship conflict.

Harrison et al. (2002) Pelled et al. (1999)

3 Age Age reflects one’s social experience to some extent; older people are able to deal with social change more reasonably, and this may decrease relationship conflict.

Jehn et al. (1997) Lawrence (1997) Tsui et al. (1995)

4 Organizational tenure

Organizational tenure reflects one’s organizational experience. In daily work, team members must face various organizational tasks, such as organizational decisions, negotiation, and information exchange. Members with rich experience could easily deal with complex working relationships.

Harrison et al. (2002) Lawrence (1997) Camelo-Ordaz et al. (2014) Bishop (1999)

5 Personality Personality consists of one’s intrinsic characteristics in emotional, attitudinal, or behavioral response patterns. A level of extraversion and conscientiousness in a pair of individuals is associated with reports of relationship conflict.

Chen (2007) Cheung & Chuah (1999) Barrick et al. (1998) Earley & Gibson (1998) Noordin et al. (2002)

6 Interest Interest is one’s individual deep-level diversity characteristics, which reflect individual preference or tendency toward something. Individuals with common interests are easier to reach a consensus with and avoid relationship conflict.

Smith & Edmondson (2006) Simons & Peterson (2000)

7 Perception Individuals attract similar others because they envision that their own perceptions and beliefs will be reinforced. High-perception diversity teams tend to have less positive attitudes toward one another, which may translate into relationship conflict among team members.

Ruuska & Teigland (2009) Mohammed & Angell (2004) Pelled (1996) McCann et al. (1985) Linberg (1999)

8 Experience Richly experienced team members understand their roles in the work. They could adjust their behavior appropriately when relationship conflict occurred.

Jehn et al. (1997) Lawrence (1997) Stephens et al. (2013) Bishop (1999)

9 Skill or ability Skill or ability reflects team members’ ways of dealing with daily work. A project team is a complex relationship project team. Some skills or abilities, such as team learning ability and social skills, help the individual work more smoothly.

Neuman & Wright (1999) Putnam & Folger (1988) Ferris, Davidson, & Perrewé (2005)

Uncertain project task

10 Requirement uncertainty

Requirement uncertainty is the absence of complete information about the project. Requirement uncertainty represents the extent of changes to the task at hand. Constant change in a project can generate stress in the organization, and dealing with stress inappropriately may result in negative emotions or relationship conflict in teams.

Cheung & Chuah (1999) Argote (1982) Robinson & Griffiths (2005) Liu et al. (2011)

11 Time urgency Time and cost pressures make team members tend to focus on their own benefits and neglect other parties’ objectives. This situation damages the collaborative team atmosphere and may even cause detrimental relationship conflict.

Waller, Conte, Gibson, & Carpenter (2001) Bishop (1999)

12 Risk allocation

Risk allocation is the process of determining how and to what extent risk should be shared. Uncertain risk and responsibilities may lead to dispute and thus cause relationship conflict.

Cheung & Chuah (1999) Al-Sobiei et al. (2005) Khazaeni et al. (2012) Hanna et al. (2013)

(continued)

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Aspects Factors Descriptions Authors Organizational culture diversity

13 Regulations or codes

Relevant institutional rules, norms, and values in which differences lead to significant added external work and conflict. Team members may be involved in human struggle.

Ren & Gray (2009) Mahalingam & Levitt (2007) Iorio & Taylor (2014)

14 Display rules Display rules are behavioral standards that regulate emotional expression. Emotional expression rituals vary across cultures, and cultural display rules regulate the latitude of emotional expressions within relationship conflict.

Ren & Gray (2009) Ekman (1973) Iorio & Taylor (2014)

15 Facework rules

Facework reflects team members’ communication tactics. Ren & Gray (2009) Barker (2010) Iorio & Taylor (2014)

Inappropriate behavior

16 Rebuff Rebuff reflects a situation in which one party fails to respond to the other as expected, which generates negative emotions between the team members and may even lead to relationship conflict.

Witteman (1992) Zhang & Lin (2009) Runde (2014)

17 Cumulative annoyance

In dealing with daily task work, team members must face frictions and disputes, which happen constantly. Cumulative annoyance will become the root of relationship conflict.

Tsui et al. (1992)

18 Criticism Criticism reflects team members’ attitude to the task. This negative attitude accordingly generates a negative relationship among team members.

Young et al. (2013) Zhang & Lin (2009)

19 Distrust Trust allows team members to recognize that, even though no one is perfect, everyone is trying their best to work through issues. A high level of trust among team members is a successful factor of a cross-functional project.

Smith & Edmondson (2006) Simons & Peterson (2000) Jehn & Mannix (2001)

20 Poor communication

Communication has a strategic sense to exchanging information and knowledge-sharing. Communication promotes cooperative conflict resolution behavior and has a positive effect on project performance.

Sinickas (2001) Cheung & Chuah (1999) Dawes & Massey (2005)

21 Abusive supervision

Abusive supervision, such as ignoring subordinates, yelling at them, and sabotaging them, has been linked to an array of negative emotions or displaced aggression, which increases the likelihood of relationship conflict.

Tepper et al. (2011) Harris et al. (2011)

Table 1: The primary antecedents of the relationship conflict framework.

have less positive attitudes toward one another, which may translate into rela- tionship conflict among team members. Therefore, the following hypothesis is proposed:

Hypothesis 1. The intrapersonal diversity of a team member is associ- ated positively and significantly with relationship conflict.

Uncertain project task refers to the absence of complete information about the project task under analysis (Argote, 1982). The nature of a cross-functional project lies in the change of internal and external environments (Anthony et al., 2014; Bishop, 1999; Liu et al., 2011), which results in unstable requirements. McFarlan (1981) identified requirement

uncertainty as a primary factor that could lead to relationship conflict because unstable requirements, such as changed resource requirements and project plans, would lead to stress and negative emotions among team mem- bers (Cheung & Chuah, 1999; Robinson & Griffiths, 2005). These negative emo- tions often trigger hostility, poor com- munication, and frustration, which form the root of relationship conflict (Chen, 2007; Yang, Chen, & Wang, 2014). In addition, cross-functional project teams are usually temporary task teams that involve various function units (Chioc- chio, 2015; de Wit, 2015). The alloca- tion of risk in cross-functional project teams is often a controversial process

(Al-Sobiei, Arditi, & Polat, 2005; Cheung & Chuah, 1999). Inappropriate risk allo- cation refers to the risk without sepa- rately considering the party that may be in the optimal position to evaluate, con- trol, bear the cost, or benefit from the assumption of the risk (Hanna, Thomas, & Swanson, 2013; Khazaeni, Khanzadi, & Afshar, 2012). It is a widely recognized reality that the more uncertain the proj- ect task, the more likely team members are to be uncooperative, antipathetic, and even hostile toward one another (Gladden, 2009). The following hypoth- esis is then proposed:

Hypothesis 2. Uncertain project task is associated positively and signifi- cantly with relationship conflict.

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Organizational culture diversity indicates that cross-functional project teams are composed of team mem- bers from various organization func- tional units, and team members have different organizational culture back- grounds. Organizational culture diver- sity describes distinct beliefs, values, and norms that are deeply embedded in the minds of team members and dem- onstrated in their behaviors (Trompe- naars & Hampden-Turner, 2004; Tsai & Chi, 2009). Ren and Gray (2009) argued that different cultural backgrounds were reflected in regulations or codes, dis- play rules, and facework rules. Different regulations or codes (Mahalingam & Levitt, 2007) are the various means of work that often cause extra duplicating effort and communication (Gladden, 2009). The more dissimilar the regu- lation, the greater the likelihood that a communication barrier may result in further relationship conflict among team members. Display rules are behav- ior standards that regulate emotional expression (Ekman, 1973; Ren & Gray, 2009). The negative emotion of one member is associated with the nega- tive emotion of another member and may instigate a cycle of negativity and dissatisfaction during the interaction (Ben-Naim, Hirschberger, Ein-Dor, & Mikulincer, 2013). Facework rules reflect the manner of conduct of team mem- bers, which represents either a positive or negative interpersonal relationship. A positive relationship conflict in teams will facilitate information transfer and trust building as well as promote coop- eration among conflicting parties (Chi- occhio, Forgues, Paradis, & Iordanova, 2011). Misunderstanding, inappropri- ate displays, or a frustrated manner will form the root of tension, hostility, and antagonism among team members. Therefore, the following hypothesis is proposed:

Hypothesis 3. Organizational cul- ture diversity is associated positively and significantly with relationship conflict.

Inappropriate behavior reflects unsuitable methods of communication

and interaction among team members. Communication has an important role in facilitating trust and promoting col- laboration (Anthony et al., 2014; Chen, 2007; Dawes & Massey, 2005; Sarker, Ahuja, Sarker, & Kirkeby, 2011; Yiu & Cheung, 2007). Conflict is rarely in the success of project teams. (Bishop, 1999; Cheung & Chuah, 1999; Fang & Neufeld, 2009; Joshi, Sarker, & Sarker, 2007) Cross-functional project team members have competing social identities. Team members are directed toward complet- ing the project objective, while shared resource pools and independent roles strongly encourage them to develop their own goals (Ghobadi & D’Ambra, 2013; Gladden, 2009; Pee, Kankanhalli, & Hee-Woong, 2010). Team members may engage in poor attitude and ill- mannered behavior, such as rebuff, criticism (Young, Struthers, Khoury, Muscat, Phills, & Mongrain, 2013; Zhang & Lin, 2009), and abusive supervision (Harris, Harvey, & Kacmar, 2011; Tepper et al., 2011), to protect their own inter- ests (Runde, 2014; Witteman, 1992). The cross-functional project team is a tem- porary task team whose members know that they can go their separate ways after completing the project. Therefore, these

members believe that building positive relationships within the team is unnec- essary, which prompts them to be rela- tively less inhibited in expressing their grievances and/or frustration to their fellow members (de Wit, 2015). In the long term, the collaborative atmosphere and trust within the team are broken. Subsequently, cumulative annoyance (Tsui, Egan, & O’Reilly, 1992) forms among the team members, which then triggers relationship conflict. The fol- lowing hypothesis is proposed:

Hypothesis 4. The inappropriate behavior of team members is associated positively and significantly with rela- tionship conflict.

The conceptual model is built from the hypotheses and presented in Figure 2.

Methodology The primary antecedents of the relation- ship conflict framework (Table 1) are summarized through a literature review. To ensure that the research endeavors could meet our cross-functional con- text need, a pilot questionnaire was employed and an advisory group discus- sion (based on the primary antecedents of relationship conflict) was conducted to modify the primary antecedents of

Intrapersonal diversity

Uncertain project task

Organizational culture diversity

Inappropriate behavior

H1

H2

H3

H4

Relationship conflict

Figure 2: The conceptual model.

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relationship conflict. We employed an advisory group that consisted of five experienced professionals and practitio- ners of cross-functional projects. Table 2 shows the background of the advisory group members, including their years of working experience, current position, and specialties. The research assistants arranged the discussion agenda and contacted every advisor in advance. To ensure that all the advisors were present at the discussion, the research assistants arranged the agenda during the week- end. First, the advisors separately com- pleted a pilot questionnaire to comment on its readability, comprehensiveness, and precision. Second, five advisors par- ticipated in a discussion and reviewed the primary antecedents of the relation- ship conflict framework. The advisors suggested deleting, adding, and com- bining some factors from which they obtained the final antecedents of the relationship conflict framework. The final antecedents of the relationship conflict framework removed gender and age, combined organizational ten- ure and experience within intrapersonal diversity, added equal power under the category of uncertain project task, and combined distrust and poor commu- nication within inappropriate behavior. The advisory group explained the main reasons for these changes as follows: First, cross-functional project managers have paid more attention to knowledge- able and skilled team members, rather than to gender and age, as the most important factors in team building. Sec- ond, project practitioners viewed equal

power as a symbol of acceptance and approval from collaborative task teams. When team members sense unfairness or feel that their interests are threatened, they may adopt an antagonistic action and refuse to cooperate in the project team. Third, the advisory group argued that the level of organizational tenure reflected the working experience of an individual to some extent and believed poor communication inspired distrust. As a result, the research put aside gen- der and age, added equal power, and combined organizational tenure and experience, as well as combined distrust and poor communication, in the final antecedents of the relationship conflict framework. The final antecedents of the relationship conflict framework (18 items) are presented in Table 3.

Sample and Data Collection

The sampling frame for the survey was based on Chinese industries, including manufacturing, electronics, design, and IT. These industries have created various cross-functional project teams to satisfy the needs of technology and innovation. For instance, manufacturing firms cre- ate cross-functional project teams for new product development, design firms create cross-functional project teams for integrated design tasks, and electronic and IT firms create cross-functional project teams to maintain their advan- tages in technology and innovation. Given the rapid development of the Chinese economy, a large number of cross-functional project teams has been created in various firms, thereby

providing this research with a poten- tial sample. This study identified the potential firms using participant lists for industry conferences and memberships of professional organizations. To begin with, the potential firms were contacted by directly mailing the senior manager. We explained the objectives, benefits for the respondents, and procedures of the study. Subsequently, 12 firms con- tacted us and expressed their intention to participate. After further communi- cation, we verified 21 random cross- functional project teams for our study from these 12 firms. Each of these firms was approached by one of the research

Aspects Items Intrapersonal diversity (ID)

ID1 Ethnicity ID2 Organizational tenure ID3 Personality ID4 Interest ID5 Perception ID6 Skill or ability

Uncertain project task (UPT)

UPT1 Requirement uncertainty UPT2 Time urgency UPT3 Risk allocation UPT4 Equal power

Organizational culture diversity (OCD)

OCD1 Regulations or codes OCD2 Display rules OCD3 Facework rules

Inappropriate behavior (IB)

IB1 Rebuff IB2 Cumulative annoyance IB3 Criticism IB4 Poor communication IB5 Abusive supervision

Table 3: The final antecedents of the relationship conflict framework.

Advisor Background Number Working Experience Positions Working Specialties

Experienced professional 1 32 years GM and professor Project implement management

Software development team 2 28 years GM and programmer Draw up project objective and management

3 15 years AM and designer Project implement management

Integrated design team 4 27 years TM and engineer Draw up project cost, schedule, quality objective

5 14 years Engineer and architect Design and coordination

Note: GM—General Manager; AM—Assistant Manager; TM—Top Manager

Table 2: Background of the advisory group members.

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assistants who assisted the authors dur- ing the data collection. The sample was relatively broad in terms of project type, project duration, number of team mem- bers, and project complexity. Table 4 shows the project sample characteristics.

Subsequently, 500 copies of the questionnaire were distributed among 21 cross-functional project teams. Our research assistants and human resource department members worked together for this survey. The participants satis- fied the following criteria: (1) They cur- rently performed their work directly in a cross-functional project, and (2) had more than three years of cross-functional project experience. Those questionnaires that were based on the final anteced- ents of relationship conflict were sent via mail. The respondents were asked to indicate to what extent they perceived the listed factors in Table 3 as the antecedents of relationship conflict (e.g., “I perceive ethnicity as the antecedent of relation- ship conflict in a cross-functional team”). Drawing from previous conflict research (Jehn, 1995) we measured relationship conflict using a four-item scale. The respondents indicated whether conflict between members was characterized by a relationship-related issue (e.g., “How much tension exists among the members in your work unit?”). The questionnaires used a five-point Likert scale (1 5 not at all, 2 5 just a little, 3 5 a moderate amount, 4 5 quite a lot, and 5 5 a great deal). We prepared a mailing packet that contained a cover letter, a questionnaire, and a written statement. The cover letter explained the purpose, significance, and concept of the investigation. The written

statement assured the subjects of the voluntary nature of the survey and the confidentiality of their personal informa- tion. Two weeks after the initial mailing, we conducted “thank-you” calls to those who had responded and reminder calls to those who had not. A total of 264 valid responses was received (52.8% response rate). Of these, 28 responses were not completed and were excluded from the analysis. A total of 236 complete question- naires were received. The basic demo- graphic information of the respondents is presented in Table 5. Generally, the respondents had a favorable education background (i.e., 77.6% hold a bachelor

degree or higher) and many of them have worked for more than five years (80.1%)

To test for nonresponse bias, we used Armstrong and Overton’s (1977) time- trend extrapolation procedure. When comparing early (first quartile) and late (fourth quartile) respondents, no signifi- cant differences emerged in the mean responses for any of the constructs. Sub- sequently, the existence of possible com- mon method bias was tested using Lindell and Whitney’s (2001) marker variable technique. In particular, we used the item “degree of satisfaction for the infrastruc- ture” as a marker variable, because it was not related to the variables in our article. Next, we correlated this question with the derived constructs. The results showed no significant correlation between the answers to this question and the impor- tant constructs and questions in the model. Thus, the results indicate that our data set is not severely contaminated by the common method bias.

Structural Equation Model

A structural equation model (SEM) is also called a latent variable model. A

Project Characteristics Number of Projects Project type Manufacturing — 4; Electronics — 4; Design — 7; IT — 6

Project duration Less than 1 year — 4; between 1 and 2 years — 5; between 2 and 3 years — 6; Between 3 and 5 years — 4; More than 5 years — 2

Number of team members Between 1 and 5 — 1; between 6 and 10 — 7; between 11 and 20 — 9; more than 20 — 4

Project complexity Low — 2; Slightly low — 2; Moderate — 9; Slightly high — 5; High — 3

Note: Number of projects 5 21

Table 4: The study project sample characteristics.

Variables Categories Numbers % Gender Male 101 42.8

Female 135 57.2

Age 20–29 77 32.6

30–39 75 31.8

40–49 45 19.1

50–59 27 11.4

. 60 12 5.1

Working experience 3–5 years 47 19.9

6–10 years 33 14.0

11–15 years 44 18.6

16–20 years 56 23.7

21–25 years 24 10.2

. 25 years 32 13.6

Education level College 53 22.5

University 97 41.1

Graduate or above 86 36.4

Note: Number of samples 5 236

Table 5: Summary of the basic demographic information of the respondents.

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complete SEM consists of a measure- ment model and a structural model (Byrne, 1994). The SEM is superior to the multiple regression method because it takes measure errors into consider- ation after a large number of variables are involved. An SEM can better test the causal relationship between variables by modeling measurement error (Chen & Lin, 2010). Moreover, the SEM is more helpful for users to visually recognize relationships among complex variables (Kim, Han, Kim, & Park, 2009). Because several studies have demonstrated the SEM’s ability to examine interrelation- ships (Chen, Chen, Sheng Lu, & Liu, 2012; Chen, Zhang, Liu, & Mo, 2011; Hon, Chan, & Yam, 2012; Ram, Corkin- dale, & Wu, 2014), we thought it was a perfect choice for exploring the ante- cedents of relationship conflict.

Assessment of the measurement model. Reliability and validity assess- ments were used first to analyze the 18 final antecedents of the relationship conflict framework. The reliability and validity assessments are generally rec- ommended as a first step to assess how well proposed constructs have been measured (Anderson & Gerbing, 1988). Cronbach’s alpha reliability test was employed to analyze the reliability of the data (Peter, 1979; Sharma, 1996). Cron- bach’s a values ranging from 0.6 to 0.7 are considered sufficient, and values more than 0.7 are considered good (Sharma, 1996). In addition, validity analyses incorporate convergent validity and dis- criminant validity. Convergent validity of the resulting measures is verified by the three criteria suggested by Fornell and Larcker (1981): (1) All variable load- ings (l) should exceed 0.7, (2) construct reliabilities should exceed 0.8, and (3) average variance extracted (AVE) should exceed 0.5. Furthermore, the square root of the AVE measure was used in the diag- onal elements of the correlation matrix in order to assess discriminant validity. For discriminant validity, these diagonal elements should be larger than any of the intercorrelations between the latent vari- ables (Barclay, Higgins, & Thompson,

1995). As presented in Table 6, all latent variable groups have Cronbach’s a value greater than 0.8, composite reliability value of each construct is greater than 0.8, and AVE ranges from 0.58 to 0.67, and all variables load higher than 0.70. Therefore, the measures satisfy the reli- ability and convergent validity. Addition- ally, the square roots of the AVE values in the diagonal elements of the correlation matrix (in bold) exceed the intercorre- lations between the latent variables in Table 6. Hence, the test of discriminant validity is acceptable.

Assessment of the structural model. The structural model could be verified by three criteria: the goodness-of-fit (GOF) indices, R2 values of endogenous ( dependent) latent variables, and path coefficient values. Several goodness-of-fit (GOF) indices were available to assess the structural model. If the GOF of the structural model was not good, an over- all fitness then needed to be revised. Various GOF indices could measure the appropriateness of a model from differ- ent aspects. Table 7 summarizes GOF indices of the structural model and the suggested levels of GOF. As presented in Table 7, the GOF indices (x2/df 5 2.19, GFI 5 0.87, RMSEA 5 0.07) failed to reach the suggested levels of the GOF. Therefore, the structural model needed revision. In this study, two methods were used to revise the model. The first method involved deleting low causal correlation paths and associated variables that had been tested and found to be statistically significant (Sarker, Aulakh, & Cavusgil, 1998). The second method added cova- riance error paths and revisited the interrelationship paths among the items (Molenaar, Washington, & Diekmann, 2000), after a few revised processes using AMOS 17, the structural model with the best performance for both the GOF and the theoretical expectations. The revised model deleted interest variables that had low causal correlation, and added covariance error paths. The initial model and the revised model are presented in Figures 3 and 4. Additionally, the R2 values of endogenous (dependent)

latent variables collectively explained 35% of the variance in relationship con- flict, which is adequate. R2 values of 0.33 can be interpreted as moderate for explaining dependent variables (Chin, 1998). Thus, we conclude that the R2 values, or the proportion of variance in relationship conflict explained in this study, are robustly acceptable. We also found that intrapersonal diversity (b 5 0.29, p , 0.01), uncertain project task (b 5 0.39, p , 0.01), organizational culture diversity (b 5 0.23, p , 0.01), and inappropriate behavior (b 5 0.37, p , 0.01) have significant positive effects on relationship conflict. These results are shown in Figure 5 and Table 8, and will be analyzed in detail in the next section.

Results and Discussion This article established the research framework and suggested that four endogenous latent variables (i.e., intra- personal diversity, uncertain project task, organizational culture diversity, and inappropriate behavior) positively influenced relationship conflict. The uncertain project task had the high- est path coefficient of 0.39 (p , 0.01), followed by inappropriate behavior (b  5 0.37, p , 0.01), intrapersonal diversity (b 5 0.29, p , 0.01), and orga- nizational culture diversity (b 5 0.23, p , 0.01). Therefore, the hypotheses are supported by the data.

The SEM measured the latent variable of intrapersonal diversity according to ethnicity, organizational tenure, person- ality, interest, perception, skill, or ability. Skill or ability had the greatest influence on intrapersonal diversity (l 5 0.82), followed by personality (l 5 0.81), orga- nizational tenure (l 5 0.79), ethnicity (l 5 0.73), perception (l 5 0.72), and interest (l 5 0.70). A cross-functional project team consists of members with varying skills, knowledge, and exper- tise. Therefore, such teams involve diverse individuals and are classified as complex-relationship project teams. From this perspective, the observed variables (i.e., ethnicity, organizational tenure, per- sonality, interest, perception, skill, and

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Number of Items AVE CR

Cronbach’s a

Correlations

ID UPT OCD IB RC Intrapersonal diversity (ID) 6 0.58 0.89 0.93 0.76

Uncertain project task (UPT) 4 0.59 0.85 0.87 0.18 0.76

Organization culture diversity (OCD)

3 0.67 0.86 0.85 0.20 0.16 0.82

Inappropriate behavior (IB) 5 0.64 0.89 0.92 0.35 0.19 0.40 0.80

Relationship conflict (RC) 4 0.61 0.86 0.89 0.24 0.33 0.29 0.40 0.78

Note: The diagonal elements in bold are the square root of average variance extracted (AVE). CR 5 composite reliability.

Items Mean St. dev

Standardized Factor Loadings (l) Cumulative Percentage of Variance p-ValueID UPT OCD IB RC

ID1 Ethnicity 3.38 0.66 0.73 35.24 , 0.01

ID2 Organizational tenure 3.14 0.89 0.79 , 0.01

ID3 Personality 3.27 0.92 0.81 , 0.01

ID4 Interest 3.45 1.01 0.70 , 0.01

ID5 Perception 3.33 0.75 0.72 , 0.01

ID6 Skill or ability 3.78 0.79 0.82 , 0.01

UPT1 Requirement uncertainty 3.96 0.92 0.83 41.79 , 0.01

UPT2 Time urgency 3.65 1.02 0.71 , 0.01

UPT3 Risk allocation 4.12 0.78 0.78 , 0.01

UPT4 Equal power 3.82 0.83 0.76 , 0.01

OCD1 Regulations or codes 3.11 0.69 0.76 55.33 , 0.01

OCD2 Display rules 3.31 0.77 0.90 , 0.01

OCD3 Facework rules 3.56 0.81 0.78 , 0.01

IB1 Rebuff 2.87 0.63 0.74 58.71 , 0.01

IB2 Cumulative annoyance 4.19 0.84 0.89 , 0.01

IB3 Criticism 3.27 0.76 0.86 , 0.01

IB4 Poor communication 3.58 0.58 0.77 , 0.01

IB5 Abusive supervision 3.20 0.74 0.72 , 0.01

RC1 How much friction is there among members in your work unit?

3.41 0.59 0.70 60.11 , 0.01

RC2 How much are personality conflicts evident in your work unit?

3.89 0.70 0.88 , 0.01

RC3 How much tension is there among members in your work unit?

4.01 0.84 0.73 , 0.01

RC4 How much emotional conflict is there among members in your work unit?

2.78 1.12 0.79 , 0.01

Note: All standardized factor loadings are significant at ** p , 0.01.

Table 6: Results of the analysis of the measurement model.

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Goodness-Of-Fit (GOF) Indices Suggested Level

The Structural Model

The Final Structural

Model x2/df , 2 (Bentler, 1985) 2.19 1.47

NNFI . 0.9 (Browne & Cudeck, 1993; Jöreskog & Sörbom, 1993) 0.90 0.91

CFI . 0.9 (Browne & Cudeck, 1993; Jöreskog & Sörbom, 1993) 0.94 0.98

GFI . 0.9 (Browne & Cudeck, 1993; Jöreskog & Sörbom, 1993) 0.87 0.92

RMSEA , 0.05 (Browne & Cudeck, 1993) 0.07 0.04

Notes: x2/df—chi-square normalized by degrees of freedom; NNFI—non-normed fit index; CFI—comparative fit index; GFI—goodness-of-fit index; RMSEA—root mean square error of approximation

Table 7: Goodness-of-fit (GOF) measurement of the SEM model.

Team member behavior

e22 1

Organizational culture diversity

e21 1

Uncertain project task

e20

0.68

0.83

0.57

0.75

0.69

0.52

1

Intrapersonal diversity

e18 CF18 Abusive supervision 1

e17 CF17 Communication 1

e16 CF16 Criticism 1

e15 CF15 Cumulative annoyance 1

e14 CF14 Rebuff 1

e13 CF13 Facework rules 1

e12 CF12 Display rules 1

e11 CF11 Regulations or codes 1

e10 CF10 Equal power 1

e9 CF9 Risk allocation 1

e8 CF8 Time urgency 1

e7 CF7 Requirement uncertainty 1

e6 CF6 Skill or ability 1

e5 CF5 Perception 1

e4 CF4 Interest 1

e3 CF3 Personality 1

e2 e19

CF2 Organizational tenure

CF1 Ethnicity

0.72

0.77

0.86

0.89

0.74

0.78

0.90

0.76

0.76

0.78

0.71

0.83

0.82

0.72

0.70

0.81

0.79

0.73 1

e1 1

1

Figure 3: The initial SEM model.

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ability) reflect three forms of intraper- sonal diversity. Each observed variable has its own potential effect on relation- ship conflict. Ethnicity and organiza- tional tenure represent social category diversity; skill or ability reflects informa- tional diversity; and personality, interest, and perception reflect value diversity. A higher degree of diversity increases the tendency for team members to perceive structures in dissimilar ways. When faced with task issues, team members often possess divergent interpretations that can manifest as relationship conflict.

The SEM measured the uncertain project task by referring to require- ment uncertainty, time urgency, risk

allocation, and equal power. Require- ment uncertainty had the stron- gest effect on uncertain project tasks (l 5 0.83), followed by risk allocation (l 5 0.78), equal power (l 5 0.76), and time urgency (l 5 0.71). Relationship conflict from uncertain project tasks is a prominent problem for cross-functional project teams in a rapidly changing, customer-driven environment. Uncer- tain project tasks consist of both diverse and unstable tasks. Requirement uncer- tainty and time urgency reflect the diverse task, whereas risk allocation and equal power represent the insta- bility task. Cross-functional project teams require team members with a

high degree of collaboration to ensure the effective delivery of a project. How- ever, stable collaborative relationships among team members often break down as a result of disagreements and a divergence of perspectives on uncertain project tasks. For example, upstream project members want the final prod- uct to adapt to environmental changes, whereas downstream project members prefer to lock in requirements to deliver the project within the budget and on time. Consequently, the improper dis- posal of these task issues will trigger detrimental relationship conflict.

The SEM showed organizational cul- ture diversity by referring to regulations

Team member behavior

e22 1

Organizational culture diversity

e21 1

Uncertain project task

e20

0.68

0.83

0.57

0.75

0.69

0.52

0.27

1

Intrapersonal diversity

e18 CF18 Abusive supervision 1

e17 CF17 Communication 1

e16 CF16 Criticism 1

e15 CF15 Cumulative annoyance 1

e14 CF14 Rebuff 1

e13 CF13 Facework rules 1

e12 CF12 Display rules 1

e11 CF11 Regulations or codes 1

e10 CF10 Equal power 1

e9 CF9 Risk allocation 1

e8 CF8 Time urgency 1

e7 CF7 Requirement uncertainty 1

e6 CF6 Skill or ability 1

e5 CF5 Perception 1

e3 CF3 Personality 1

e2

e19

CF2 Organizational tenure

CF1 Ethnicity

0.74

0.81

0.83

0.77

0.78

0.75

0.72

0.76

0.80

0.80

0.78

0.87

0.83

0.72

0.83

0.86

0.78

1

e1 1

1

Figure 4: The revised SEM model.

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P A

P E

R S

Intrapersonal diversity

Uncertain project task

Organizational culture diversity

Inappropriate behavior

H1:β = 0.29 t = 3.05**

Relationship conflict

**p < 0.01

H2:β = 0.39 t = 2.14**

H3:β = 0.23 t = 2.23**

H4:β = 0.37 t = 2.35**

Figure 5: The results of path coefficients.

or codes, display rules, and facework rules. Display rules had the strongest effect (l 5 0.90), followed by facework rules (l 5 0.78) and regulations or codes (l 5 0.76). Cross-functional project teams are temporary task teams; team members come from different orga- nizational functional units and from different cultural backgrounds. Organi- zational culture refers to the attitudes and values that determine the interactive behavior of individuals in organizations (Tsai, 2011). The SEM identified that the diversity of organizational culture was embodied in display rules, facework rules, and regulation codes. Display rules are behavioral standards that reg- ulate the latitude of emotional expres- sions within conflict situations. These rules indicate how emotions should

be conveyed or publicly expressed. Facework reflects the communication tactics of team members. Regulations or codes reflect different task codes or working procedures. In cross-functional project teams, every member must adapt to the new team culture. Particularly in the early stages of cross-functional projects, the more dissimilar the culture of team members, the greater the likeli- hood of dispute and divergence. All of these factors form the root of relation- ship conflict.

The SEM showed inappropriate team member behavior taking the form of rebuff, cumulative annoyance, criti- cism, poor communication and abusive supervision. Cumulative annoyance had the strongest effect (l 5 0.89), followed by criticism (l 5 0.86), poor

communication (l 5 0.77), rebuff (l 5 0.74), and abusive supervision (l 5 0.72). Cross-functional project team members come from various task units to ensure the final success of the project. The inappropriate behavior of team members when dealing with daily task issues may result in a misunder- standing or may drive team members to feel humiliated or offended by other members. These circumstances form the root of relationship conflict. As iden- tified in the SEM, rebuff and criticism behaviors will expand communication obstacles. Even in the management process, a poor relationship between managers and employees, abusive supervision, and cumulative annoyance will all result in relationship conflict.

Conclusion and Limitations This study identified four antecedents of relationship conflict (i.e., intraper- sonal diversity, uncertain project task, organizational culture diversity, and inappropriate behavior) through a liter- ature review and advisory group discus- sion, and then used the SEM to analyze the relationship among these identified antecedents of relationship conflict. The SEM had proven its reliability, validity, goodness-of-fit, and path coefficients, thereby indicating that the four ante- cedents significantly influenced rela- tionship conflict within cross-functional project teams. These factors could be employed in future research on organi- zational conflict mechanisms. Practitio- ners in cross-functional project teams could easily employ these factors to control and manage conflict for success- ful cross-functional projects. According to the findings, we propose two strat- egies regarding cross-functional team building and management to decrease detrimental relationship conflict.

Team-Building Strategy

To avoid and decrease the occurrence of detrimental relationship conflict, team-building strategy must consider intrapersonal diversity and inappropri- ate behavior factors. Managers must

Path in the Model Path

Coefficient t p-Value Hypothesis Intrapersonal diversity → Relationship conflict 0.29 3.05 , 0.01 H1 is supported

Uncertain project task → Relationship conflict 0.39 2.14 , 0.01 H2 is supported

Organizational culture diversity → Relationship conflict

0.23 2.23 , 0.01 H3 is supported

Inappropriate behavior → Relationship conflict 0.37 2.35 , 0.01 H4 is supported Table 8: The results of path analysis.

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be aware of the potential benefits in addressing intrapersonal diversity and team member behavior factors in the building and preparation of teams. On the one hand, managers must recon- sider intrapersonal diversity and focus on the information diversity of team members, such as the social skills men- tioned above. The information diversity of team members does not concern the attainment of social skills from training or education, but rather relates to their ability to use social skills in project teams to solve issues. Team members with a high level of social ability will know how to present themselves and influence oth- ers to act in ways that can help decrease the influence of relationship conflict. On the other hand, managers must focus on the quality of interactions among team members. Certain team activities before and during the course of the project can improve interactions among team mem- bers. These activities can range from small “happy hour” gatherings after work to weekend work retreats. Such activi- ties can rapidly improve understanding and build trust among team members as early as possible. Open and smooth com- munications among team members in their daily tasks will decrease the occur- rence of relationship conflict.

Team Management Strategy

To avoid and decrease the prevalence of detrimental relationship conflict, a team management strategy must con- sider uncertain project tasks and orga- nizational culture diversity factors. First, managers must specify the uncertainty as accurately as possible. On the one hand, uncertain project task analysis must be conducted as early as possible and must involve all team members of a cross-functional project. On the other hand, managers must deconstruct and achieve the designed objective layer by layer for an explicit risk allocation. Sec- ond, organizational culture diversity, such as codes, regulations, and display rules, must be given more attention. When organizing a cross-functional project team, managers must establish

mandatory regulations or rules that aim to reduce disagreements or duplica- tions of work. Other routine managerial actions, such as holding regular meet- ings with all team members, must be designed to promote communication and trust. A clear communication plan must also be established to assist team members in effectively communicating their requirements. In summary, effec- tively understanding the uncertainty requirements and organizational cul- ture diversity among team members can reduce the occurrence of relationship conflict and maximize cross-functional project success.

This study has several limitations. First, it is based upon cross-sectional data collected at one point in time. To further reinforce our findings, longitu- dinal studies must be performed using our methodology. Second, the sampling frame for the survey is only based on the manufacturing, electronics, design, and IT industries of China, which limits the potential to extend our findings to cross-functional project teams in other industries. Therefore, future studies must be conducted in other industries and in specific cross-functional project teams, such as software development teams and new product development teams, to shape a perfect theoretical framework. Researchers may also con- duct more intensive case studies to transform this preliminary model into grounded theory.

Acknowledgments The authors are grateful for the support of the National Science Foundation of China (NSFC, Project No. 71272146), Doctor Research Funds of Hebei GEO University (DQ201627), and College Science Research Project of Hebei Prov- ince (SQ161023).

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Yang, L. R., Chen, J. H., & Wang, X. L. (2014). Assessing the effect of requirement definition and management on performance outcomes: Role of interpersonal conflict, product advantage and project type. International Journal of Project Management, 33(1), 67–80.

Yiu, K. T. W., & Cheung, S. O. (2007). Behavioral transition: A framework for construction conflict-tension relationships. IEEE Transactions on Engineering Management, 54(3), 498–505.

Young, R. E., Struthers, C. W., Khoury, C., Muscat, S., Phills, C., & Mongrain, M. (2013). Forgiveness and revenge: The conflicting needs of dependents and self-critics in relationships. Journal of Social and Clinical Psychology, 32(10), 1095–1115.

Zhang, Y. B., & Lin, M. C. (2009). Conflict-initiating factors in intergenerational relationships. Journal of Language and Social Psychology, 28(4), 343–363.

Xiaoyan Huo has a PhD in management science and engineering from Tianjin University and an MS degree in management science and engineering from Shijiazhuang Tiedao University. She is currently a lecturer at Hebei GEO University. Her research focuses on project team conflict management and negotiation. She can be contacted at [email protected].

Lianying Zhang is a professor in the College of Management and Economics at Tianjin University. His current research interests include project governance and knowledge management in construction projects and multi-objective optimization in construction management. Professor Zhang headed three projects for the National Natural Science Foundation of China, and is a recipient of the 2002 Best Prize in International Symposium on Construction Management and Real Estate. He can be contacted at [email protected], zhanglianying@ tju.edu.cn.

Haiyan Guo is a PhD candidate in the College of Management and Economics at Tianjin University. She holds an MS degree in management science and engineering from Hebei University of Engineering. Her research focuses on project team management and knowledge management. She can be contacted at [email protected].

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IntroductIon

P roject success needs to reflect stakeholder benefits (Gemünden, 2015a), and successful projects require the management of stakeholders by the project manager (Eskerod, Huemann, & Ringhofer, 2015; Eskerod, Huemann, & Savage, 2015; Eskerod & Vaagaasar,

2014). Concerning information system development (ISD) projects, con­ temporary development approaches need to overcome a shortage of stakeholder involvement (Legris & Collerette, 2006). Otherwise, unsuccessful management of stakeholders’ often hidden and conflicting agendas (Bourne & Walker, 2005) is likely to result in project failure (Gemünden, 2015b).

Of particular importance is the research gap concerning ISD process expectations and strategies to meet them because both process success (i.e., project management pillars such as budget and schedule) and product suc­ cess (i.e., the developed information system, henceforth IS) are crucial for ISD project success (Baccarini, 1999; Basten, Joosten, & Mellis, 2012; Liu, Chen, Chen, & Sheu, 2011). Previous research has focused on users and the product component (Petter, 2008; Petter & Randolph, 2009). In the context of ISD pro­ cess expectations and the respective satisfaction of stakeholders, customers can be presumed to be highly important (Serrador & Turner, 2015; Turner & Zolin, 2012) because their satisfaction is crucial for contractor reputation and decisions about follow­up projects (Anderson & Sullivan, 1993). We address this gap by striving to answer the following research question: How can cus­ tomer expectations toward the process be managed in ISD projects?

In answering this research question, we focus on customer managers (i.e., project managers or other executives in charge of ISD projects on the side of the customer; cf. Diegmann, Basten, & Pankratz, 2015) and their expectations concerning the development process of the project. Our research study is inductive and focuses on generating theoretical insights from in­depth exami­ nations (Glaser & Strauss, 1967) of process expectations in practice. In devel­ oping these grounded findings, we use insights from expectation­confirmation theory (ECT) (Oliver, 1980; Santos & Boote, 2003) as a theoretical lens to pro­ pose a perspective for managing customer expectations in ISD projects.

This article proceeds as follows: In the next section, we review the cur­ rent state of research regarding satisfaction, expectations, and expectation

Closing the Stakeholder Expectation Gap: Managing Customer Expectations Toward the Process of Developing Information Systems Dirk Basten, Department of Information Systems and Systems Development, University of Cologne, Germany Georgios Stavrou, Department of Information Systems and Systems Development, University of Cologne, Germany Oleg Pankratz, Department of Information Systems and Systems Development, University of Cologne, Germany

ABStrAct ■

Whereas expectations concerning both pro-

cess and product are essential for infor-

mation system development (ISD) project

success, research has focused on end-user

expectations toward the product. Based on

semi-structured interviews, we shed light

on the relevance of process expectations

for customer satisfaction in ISD projects,

concrete customer expectations toward

the process, and approaches for manag-

ing these expectations. Our study provides

means to manage customer expectations

and thus increase the likelihood of customer

satisfaction.

Keywords: information systems; project success; customer satisfaction; expectation

management; project management; semi-

structured interviews

Project Management Journal, Vol. 47, No. 5, 70–88

© 2016 by the Project Management Institute

Published online at www.pmi.org/PMJ

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In the context of ISD projects, exem­ plary applications of ECT include analyz­ ing personnel skill discrepancies (Tesch, Jiang, & Klein, 2003) and managing user expectations (Petter, 2008).

Service Quality

The quality of a service is not easy to evaluate by objective criteria because of its intangible nature, the depen­ dence on customer and supplier, and the close link of service provision and usage (Parasuraman, Zeithaml, & Berry, 1985). Therefore, people heavily rely on expectations when evaluating a service. Parasuraman et al. (1985) define ser­ vice quality as the discrepancy between expectations regarding a service and the experienced quality. Expectations in service quality describe wishes about how a service should be (Parasuraman et al., 1988; Santos & Boote, 2003). Service quality is evaluated by measur­ ing the discrepancy between expected and experienced service (Parasuraman et al., 1988). Service quality has been mainly considered as user support from service providers as well as the qual­ ity of information or functions of an IS (Pitt, Watson, & Kavan, 1995).

To conclude, ECT’s concept of (dis) confirmation helps us understand the effects of expectations, while service quality highlights specific expectations, which can be tested with regard to the ISD process. The definition of expec­ tations as predictions is problematic because, according to ECT, users must be satisfied if a system does fulfill their negative expectations about its outcome (Santos & Boote, 2003).

For our study, ECT serves as a the­ oretical lens (Urquhart & Fernández, 2013), while expectations are seen as wishes in reference to service quality.

Managing Stakeholder Expectations Regarding the ISD Process

Expectation management is the process of confronting and forming expecta­ tions in order to generate advantages for principals and agents (Miller, 2000) and to increase the likelihood of project

extensive consequences, and the goal of which is to build long­term customer relationships (Ojasalo, 2001).

Expectations

Expectations are essential when evalu­ ating satisfaction (Zeithaml, Berry, & Parasuraman, 1993) and are standards for evaluating experiences. Differ­ ent stakeholders—we denote a project stakeholder in this article as “any indi­ vidual or group of individuals that is directly or indirectly impacted by a project” (Sutterfield, Friday­Stroud, & Shivers­Blackwell, 2006, p. 27)—have different expectations regarding ISD projects, which can overlap, influ­ ence, or even contradict one another (Nevo & Wade, 2007). As Parasuraman, Zeithaml, and Berry (1988) note, differ­ ent research streams define expecta­ tions in diverging ways. The following two perspectives regarding expectations will be considered in this study.

Expectation-Confirmation Theory

Following ECT (Oliver, 1980; Santos & Boote, 2003), relations between expecta­ tions and satisfaction have been shown for different domains, including infor­ mation systems (Bhattacherjee, 2001). Expectations are individuals’ predictions prior to usage of a product (Oliver, 1980) and a point of reference when evaluating a product. The confirmation of expecta­ tions through experience results in satis­ faction. If experience diverges from what is expected, it leads to disconfirmation. If experiences exceed expectations, it results in positive disconfirmation, which in turn leads to satisfaction. Negative dis­ confirmation (unfulfilled expectations) leads to dissatisfaction. Even though dis­ confirmation has mainly been addressed in the context of consumer expectations regarding products, ECT is not limited to physical products and can be transferred to ISD as a service (Bhattacherjee, 2001). ECT has been used in various contexts in IS research over the past decade (Hossain & Quaddus, 2011). This theory predomi­ nantly has been applied to explain IS user satisfaction and continuance intentions.

management. We then describe our research approach, that is, the design of the interviews and their analysis. Subse­ quently, we present our results, followed by a discussion that includes implica­ tions for researchers and practitioners as well as our study’s limitations. The article ends with a short conclusion.

Perspectives on Successful Information System Projects

Satisfaction in ISD Projects

Satisfied stakeholders evaluate a project as successful (Nevo & Wade, 2007). In this context, the fulfillment of project plans as a traditional success criterion is only one potential factor influencing stakeholder satisfaction in ISD projects. Projects exist that either fail to meet the traditional criteria but are still considered successful or that satisfy the traditional criteria but are perceived as failures (Nelson, 2005). Because IS users cannot be satisfied in the same way as project managers or devel­ opers (Nelson, 2005), comprehensive evaluations of ISD projects need to con­ sider all stakeholders’ satisfaction (Nevo & Wade, 2007). Furthermore, satisfaction with the process leads to satisfaction with the outcome, and dissatisfaction with the process can contribute to dissatisfaction with the final outcome (Ferreira & Cohen, 2008). Dissatisfaction with ISD projects usually does not result from technical deficiencies; rather, it is caused by too lit­ tle attention being given to psychological and organizational issues during devel­ opment, roll­out, and usage (Markus & Keil, 1994). Early studies in applied and social psychology show that satisfaction depends on expectations (Locke, 1969; Locker & Dunt, 1978). Besides technical aspects, expectations play a major role when evaluating satisfaction with ISD projects (Conrath & Mignen, 1990). The importance of expectations increases with the difficulty and ambiguity of satisfac­ tion assessments (Anderson & Sullivan, 1993). In particular, expectations play an important role when considering ISD as a service for which detailed information is required, the outcome of which can have

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success (Eskerod & Vaagaasar, 2014). For this purpose, expectations have to be continuously monitored, under­ stood, and formed. Expectation man­ agement aligns the views of different stakeholders, helping to minimize unre­ alistic expectations and increase overall project success (de Bakker, Boonstra, & Wortmann, 2012). However, the imma­ terial and complex nature of ISD proj­ ects makes expectation management a complicated endeavor.

Miller (2000) suggests that expecta­ tions in the ISD context focus mainly on interpersonal relationships and less on technical perfection or performance of the IS. He describes technical know­ how, problem­solving or consulting skills, and professionalism as poten­ tial expectations. Potter (2003) stresses the adherence to plans regarding time and budget. Boyd (2001) mentions feed­ back, customer involvement, and con­ flict solution as additional expectations regarding the ISD process.

Various factors influencing customer expectations can be found in literature (see Table 1), including both external (e.g., promises made by the contrac­ tor, word­of­mouth recommendations from experts) and internal influences (e.g., previous experiences of the cus­ tomer, personal requirements of the customer). Even though these causes have not been identified while focus­ ing on customer expectations regarding the ISD process, they can be seen as

hints for sources of unrealistic expecta­ tions in our interviews. Furthermore, Table 2 lists approaches given in lit­ erature to manage stakeholder expec­ tations in general. These approaches cover a set of practices that concern the professionalism of the contractor (e.g., knowledgeability, planning, leadership, referencing experiences and alterna­ tives) and those that aim to improve the relationship between contractor and customer (e.g., user involvement, trust and understanding, information exchange, realistic promises).

Researchers in both IS and project management domains emphasize the differentiation between process success and product success (Baccarini, 1999; Basten et al., 2012; Liu et al., 2011). While research in general is scarce concern­ ing strategies for meeting stakeholder expectations in ISD projects, previ­ ous studies have focused on manag­ ing product expectations (Petter, 2008) and a prevalent research gap concerns approaches for managing process expec­ tations. Because ISD project failure can be avoided by paying attention to prob­ lems that arise during the development process (Kappelman, McKeeman, & Zhang, 2006), and inadequate process expectations pose a severe risk within this process, effective management of such expectations is a crucial part of successful project management.

Recent studies highlight the role of the development process by focusing on

agile development approaches such as Scrum (Dingsøyr, Nerur, Balijepally, & Moe, 2012). In line with this emphasis, we observe both an increasing popu­ larity of such approaches in practice (Dingsøyr et al., 2012; Vlaanderen, Jansen, Brinkkemper, & Jasper, 2011) and a growing number of research studies (Hummel, Rosenkranz, & Holten, 2013). On the one hand, agile approaches are predominantly driven by practical considerations of how processes can be aligned to improve the interaction between customer and contractor in general and to account for customer expectations in particular. On the other hand, research strives for theo- retical explanations of such approaches (Dingsøyr et al., 2012; Lee & Xia, 2010). For an in­depth understanding of how to meet customer expectations, it is necessary to understand these expecta­ tions (cf. Petter, 2008). By focusing on the development process, we continue previous research on expectations in ISD projects, which has thus far empha­ sized product expectations. We explore approaches applied in the context of ISD projects to manage the expectations of customer managers pertaining to the development process.

Research Approach For the purpose of identifying ap­ proaches for managing customer expectations concerning the ISD pro­ cess, we collected data from multiple respondents and subsequently ana­ lyzed the data qualitatively through an iterative process of data examination, grounded coding, and comparison with literature. Our unit of analysis was the development process in IS projects. We refer to a process expectation as a belief that something will be the case with regard to the development process in IS projects. For data collection, we used semi­structured interviews (Myers & Newman, 2007), which are an effective means to uncover unobserved links (Rubin & Rubin, 2011). For data analysis, we aggregated the findings across the respondents and focused

Factor References Experiences of the customer from prior projects Lyytinen (1988), Zeithaml et al. (1993)

Mutual understanding (between customer and contractor of the abilities, difficulties, and issues of the other party)

Boehm (2000)

Word-of-mouth recommendations from personal contacts or experts (e.g., consumer reports or consultants)

Zeithaml et al. (1993)

Personal requirements and needs essential to the physical, psychological, and social well-being of the customer

Zeithaml et al. (1993)

Excessive enthusiasm by contractor’s developers and managers

Boehm (2000)

Promises made by the contractor to the customer (e.g., advertising, personal selling)

Boehm (2000), Zeithaml et al. (1993), Jørgensen & Sjøberg (2004)

Table 1: Factors influencing customer expectations.

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expectations and their management to influence customer satisfaction (see Appendix A). We asked each partici­ pant to recall situations in which he or she had managed process expec­ tations in ISD projects. Subsequently, we asked the respondents to think of further insights into managing cus­ tomer expectations of the ISD process. The interviews lasted between 45 and 70 minutes. They were audio­recorded and subsequently transcribed by one author for data analysis. We applied the seven guidelines of Myers and Newman (2007) for the design of the interviews (see Appendix B).

Data Analysis

The data analysis was performed in three steps. These steps and the respec­ tive outcomes are illustrated in Figure 1. First, the 12 recorded interviews were transcribed, resulting in 12 transcripts in the wording of our respondents.

organizations. We selected respondents with extensive experience in manag­ ing ISD projects. Although our respon­ dents do not exclusively communicate with a specific stakeholder group, the experiences reported in the interviews typically relate to the respondents’ inter­ action with customer representatives who are in charge of the project. The interviews were conducted via telephone during three months in the first half of 2013. In total, we interviewed 12 manag­ ers from 12 different software­developing companies. Table 3 lists the respondents (pseudonyms are used to ensure con­ fidentiality), their experience in project management, and their highest quali­ fication. Additionally, Table 3 provides key characteristics of the respondents’ companies.

Our interviews were organized in three parts: background of the respon­ dent, customer satisfaction concern­ ing the ISD process, and customer

on extracting approaches for managing customer expectations concerning the ISD process in the context described by the respondents. The processes of con­ ducting the interviews and analyzing the data obtained were both guided by ECT as theoretical lens. In other words, we did not aim to test ECT with our data. Rather, we used the fundamental idea of ECT to structure data collec­ tion and data analysis. Because this lens fits our data, its use is suitable in grounded theory research (Urquhart & Fernández, 2013).

Data Collection

For the acquisition of participants, we randomly contacted software­developing companies listed in the Hoppenstedt company database (www.hoppenstedt ­hochschuldatenbank.de). Because we were interested in approaches for manag­ ing customer expectations, our respon­ dents were project managers of contractor

Approaches to Expectation Management Definition References Objective references and models Using benchmarks and well-calibrated models for cost or

schedule estimation to frame customer expectations Boehm & Ross (1989), Sheth & Mittal (1996)

Trust and understanding Building a trustful relationship by being honest—sharing good as well as bad news openly throughout the project—as well as by providing specific times for deliverables

Petter (2008)

User involvement Working interactively with users. Includes letting users make decisions, listening to users and asking questions, and keeping users updated throughout the project

Petter (2008)

Knowledgeability Clarity regarding the goals and constraints of the other party Boehm (2000), Boehm & Ross (1989)

Planning Establishing a realistic plan considering objectives, milestones, responsibilities, approaches, and resources

Boehm (2000), Sheth & Mittal (1996)

Information exchange Regular and clear exchange of information among stakeholders Boehm (2000), Boehm & Ross (1989); Moynihan (2002), Petter (2008), Sheth & Mittal (1996)

Customer selection, training, and orientation

Targeting desirable groups of potential customers and educating customers on what they can realistically expect

Sheth & Mittal (1996)

Realistic promises by sales department

Prior to project initiation, keeping promises realistic rather than overly optimistic

Peters (1988)

Leadership Strong project manager/champion, social norms and enforcement mechanisms; articulating a clear project vision and motivating the project team

Petter (2008), Sheth & Mittal (1996)

Referencing experiences and alternatives

Often used to lower expectations, experiences from former projects can be referenced and alternatives suggested

Boehm & Ross (1989), Kopalle & Lehmann (2001)

Table 2: Approaches for managing expectations.

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Pseudonym

Experience as Project Manager

(# years) Experience (# projects) Qualification

Private/ Public Sector Industry Range of Services

# Employees in Company

Mark 17 65 Diploma Private System integration, IS development

Analysis, implementation, support, and operations

30

Paul 39 39 Diploma Public IT service provider

Standardized software, consulting, and order development for public administration

12

Thomas 15 6 PhD Private IT consultancy Training, development, and support

20

Robert 8 30 Diploma Private Software development (energy sector)

Standardized software for visualization of energy consumption data and automation of households

30

Kathy 23 20 Diploma Public IT service provider

Order development, service provision

350

James 10 8 Diploma Public IT service provider

Order development and customizing of standardized software

240

Emily 15 30 Practical Education

Both IT service provider (health- care)

Standardized middleware software, order development, support

40

David 7 120 Practical Education

Both IT service provider (facility management)

Standardized software and order development for extensions

25

Patrick 17 350 Practical Education

Both Web development

Full-service provider, order development, operations, and support

46

John 9 20 Practical Education

Both IS development System vendor, unified communications, infrastructure, VoIP

150

Michael 13 30 PhD Private IT service provider (chemical and pharmaceutical industry)

Standardized software and order development for extensions (laboratory software)

30

Ben 12 70 Diploma Private IT service provider (production industry)

Software development 70

 15  66  87

Table 3: Respondent and company characteristics.

Second, we used inductive, qualitative techniques (Eisenhardt, 1989; Glaser & Strauss, 1967) to analyze the tran­ scripts. Following the principle of dia­ logical reasoning (Klein & Myers, 1999), we note that our analysis was informed

by ECT. Accordingly, our analysis refers to customer expectations toward the development process in a client–vendor setting in ISD projects. We focused on expectations toward the develop­ ment process and customer satisfaction

(i.e., the importance of expectations being confirmed by the development process). Additionally, we analyzed the transcripts with regard to approaches for managing expectations. More con­ cretely, we assigned text passages to

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in the last five interviews in a row— interviews 8, 9, 10, 11, and 12—we are confident that we reached saturation.

Results Our results are threefold and thus orga­ nized in three subsections. First, we describe the impact of customer satis­ faction with the development process on overall project success, substantiat­ ing the relevance of this kind of satis­ faction. Second, we present customer expectations toward the development process. While we describe the expec­ tations in general, Table 4 provides an overview of the concrete expecta­ tions along with their definition and exemplary quotes from the interviews. Finally, we describe approaches for managing customer expectations, which can be applied to increase customer satisfaction and ultimately project suc­ cess. Beginning with factors that influ­ ence customer expectations, we present and describe in detail the eight iden­ tified approaches for managing those expectations, which are substantiated with numerous quotes from our inter­ views. In conclusion, Figure 2 schemati­ cally visualizes the results.

Customer Satisfaction

All respondents indicate a high relevance of customer satisfaction with the devel­ opment process. Thomas, for instance, attaches the “uppermost importance [to the development process]. The excel- lent cooperation with the customers is decisive for whether the customers get what they want.” As Ben explains, “sat- isfaction during the process is, of course, important to increase motivation and communication in the process as well as the priority of the process.” With higher process satisfaction, communication in the project increases and a cooperative climate is created in which customer and contractor collaborate to realize ideas. However, the relevance of this kind of satisfaction can be lower if the customer does not want a high degree of involvement: “For customers who want to get involved, it is highly important.

labels of the individual transcripts. In the process of this integrated coding (see Figure 1), redundancies were elim­ inated and the wordings of different respondents consolidated.

By following an inductive, quali­ tative coding, we ground our study on in­depth insights from interviews. What emerged was an understanding of process expectations in ISD projects and how project managers attempt to address these.

Quality Criteria

By sending the interview transcripts back to the respondents for verification, we ensured communicative va lidity (Flick, 2009). The coding and catego­ rization were performed by all three authors. While one author conducted data­driven coding based on insights from our interviews, the other two acted as “resident devil’s advocates” and tried to find rival explanations (Eisenhardt, 1989). We ensured plausibility (Guba, 1978; Patton, 2015) of the categoriza­ tion by independently verifying the con­ ceptual sense of the results. Finally, we analyzed our data with regard to the important goal of reaching theo­ retical saturation (Glaser & Strauss, 1967; Strauss & Corbin, 2015). Because no new approaches were identified

thematic labels, which were derived either from our interview questions (e.g., relating to the relevance of the customer satisfaction with the develop­ ment process; see Appendix A) or, in case of open­ended questions, from the answers of our respondents (e.g., relat­ ing to specific expectation management approaches such as transparency). To situate our research within its social and technical setting, we extracted both contextual information (i.e., direct quotes from the interviews) and abstraction from the contextual details (i.e., expectations and approaches) (Klein & Myers, 1999). Because our research is informed by a single group of respondents, our interpretations rely on a single perspective. However, we interviewed managers from several companies. We cycled among multiple readings of the interview transcripts of recurring themes to establish a compre­ hensive understanding of the respon­ dents’ elaborations and to take other potential explanations into account (Klein & Myers, 1999). Finally, the the­ matically structured individual tran­ scripts were integrated into one table with interviews as rows, thematic cat­ egories as columns, and respective text passages in the cells. Categories were derived by consolidating the thematic

12 interviews

1. Transcription

2. Individual coding

3. Integrated coding

12 transcripts

Thematically structured individual transcripts

Tabular categorization of all transcripts

Figure 1: Data analysis.

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Expectation Definition # Respondents Exemplary Quotes Customer involvement

Expected level of being involved in project management activities, especially in cases of critical and unanticipated situations

12 “For the development process, it is decisive that the customer is involved in making decisions to ensure customer satisfaction.” (Kathy)

“[The customer] expects to get involved. Depending on the agreement, by means of status reports, preliminary results, prototyping; hence, participation in the process. That is an expectation.” (James)

“Involvement by all means. The extent differs.” (Michael)

“Customer involvement is also important in the process. Since in the IT business, situations occur again and again that are not precisely defined . . . the involvement is also important in the ongoing process.” (Ben)

Responsiveness of the contractor

Contractor’s readiness to reply to questions from the customer as well as the contractor’s willingness to accept change requests

12 “There is an expectation to receive feedback in some form within a given time.” (Robert)

“In any case, preparedness is expected. Often the understanding prevails that we as service provider must make an effort.” (James)

“That’s when the customers contact us during the process and we try to help them as soon as possible, are available at any time, help them, and push the project forward.” (John)

Transparency Level of visibility of the development process to the customer (intermediate results, project progress, reaching milestones, arising problems or critical events, etc.)

11 “Transparency is important. The customer does not want to have the feeling that something is withheld.” (Michael)

“Basically, customers expect a transparent development process. . . . Here, transparency means that you reveal how much time you have actually spent. You should also offer customers the opportunity to quickly gain insights into the process, by tools or personal contact. The customer should not have the feeling that he deals with a black box.” (Ben)

Reliability Contractor’s adherence to agreed- upon plans

10 “The correct implementation of the requirements, of course. When the schedule is continuously exceeded, the customer is naturally dissatisfied.” (Michael)

“Reliability not only in the project, but also with regard to your statements. It is about meeting deadlines and clear communication.” (John)

Empathy Contractor’s ability to see customer’s requirements from the perspective of the latter

9 “Putting myself in the customer’s position is almost necessary to recognize his needs, what he wants or expects.” (John)

“Customers expect this because most customers are service providers like us. Thus, they expect us to relate to them, so that expectations can be aligned by quickly communicating with each other.” (Ben)

Expertise Contractor’s technical and functional competence (in order to be able to understand customer requirements and enable a climate of trust)

9 “If we do not proceed methodically and technically reasonable, nothing will work because the customers have no interest in trusting someone who ultimately does not bring anything to them.” (Paul)

“This is exactly what the customer wishes. He expects or says, ‘You are the expert, you have the expertise.’” (Robert)

Conjoint discussion of challenges

Exchanging information about recent and upcoming activities, deviations from project plans, as well as discussion of arising problems and conjoint decision of solutions

8 “This also means discussing problems conjointly, talking about things that were previously considered differently, and finding solutions together.” (Paul)

“The premise is that all people involved know what is at stake. As soon as possible, they need to meet and clarify this.” (Emily)

(continued)

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Expectation Definition # Respondents Exemplary Quotes Consulting and problem-solving skills

Contractors expressing their critical opinion regarding requirements and, ideally, suggesting alternatives to the customer (rather than just accepting and implementing customer’s wishes, which might be inappropriate)

7 “One part of the job as project manager is to make the customer feel good. He needs to feel that . . . you will handle it [the project].” (Mark)

“[Consulting and problem-solving skills] are an expectation. This is one aspect in our questionnaires and for which we receive good grades. This is exactly what the customer wishes. He expects or says: . . . ‘I expect you to give me advice.’ Then, we are expected to address problems proactively and to deliver solution proposals.” (Robert)

Process efficiency

Reduction of the effort to the required minimum, leading to a quick execution of the project

4 “The customer expected the process to be transparent and efficient. . . . As part of the project approach, meetings should be efficient.” (Kathy)

“You can make an appointment with a single email based on an invitation from the calendar or with ten emails to different people. . . . The more efficiently you work, the more professional [you are].” (Emily)

Establishing a personal relationship with the contractor

Getting to know the contractor during the development, possibly leading to a familiar personal relationship

4 “Personal contact facilitates collaboration. This again is a soft factor, but it just goes smoother when people have seen each other.” (Emily)

“However, particularly in development projects the personal touch is very important because then you just know with whom you are dealing.” (Ben)

Contractor’s professional appearance

Image, eloquence, smart, and well-groomed appearance of the contractor’s project team

2 “We call it cooperative relationship. If you want to work with your partner at this level, you need know-how and that comes along with professional appearance.” (Robert)

“Professionalism also involves the appearance. Our contact person for the customer is our external business card for the company. Thus, it must naturally be someone with a specific standing, who is able to speak a specific jargon . . . someone with a professional appearance.” (Emily)

Personal benefits

Customer representatives’ progress in business and personal regard, including making a good impression on internal colleagues

1 “Personal expectations play a very important role—whether the customers see progress for themselves in professional terms, whether they benefit personally.” (Paul)

Table 4: Identified customer expectations toward the ISD process.

For customers who simply expect a piece of software to be delivered, it is less important” (David).

Furthermore, all respondents see a connection between process and product satisfaction of the customer. The respondents, for example, refer to this connection as “strong correla- tion” (Kathy) and “major dependency” (Robert). There are two main reasons for this view. First, a satisfying process is said to lead to a product that meets customer expectations because close collaboration in such a process reduces the risk of failing to fulfill customer needs. Second, a customer who is satis­ fied with the process enters a coalition with the contractor on a psychological level: “If customers are satisfied with

the development process, they are more familiar or rather more satisfied with the overall situation. Then, if the product is not particularly good, the customers are more likely to overlook the shortcom- ings” (Michael). Regarding the impact of process and product satisfaction on the overall project success, the statements of our respondents differ. Although some respondents attach higher importance to product satisfaction, others consider process and product satisfaction equally important. How­ ever, there is general agreement that process satisfaction has an impact on product satisfaction, because “only by means of a professional process . . . the product will meet the customer’s expectations” (Robert). Furthermore,

the respondents agree that both kinds of satisfaction influence overall project success.

Customer Expectations Toward the Development Process

All but one respondent state that cus­ tomer expectations toward the devel­ opment process have a high impact on customer satisfaction with this process. One respondent reports on the idea of customers not having expectations at all. Others, however, doubt that a cus­ tomer having no expectations would be a meaningful scenario because a cus­ tomer without expectations would have no interest in the project. Moreover, expectations are often hidden rather than explicitly formulated.

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that establishing a personal relationship with the contractor implies the expecta­ tion of a designated project team, which ideally does not change during the proj­ ect. The relevance of the personal rela­ tionship expectation increases with a growing project size.

Expectations Management

All respondents state that customer expectations can be managed, and four even consider this an indispens­ able task in the early project stages. The goal is to obtain realistic expecta­ tions. In doing so, not only customer wishes but also the objectives of the contractor organization must be taken into account. All respondents further agree that expectation management can increase customer satisfaction with the development process by aligning expec­ tations and actual process perceptions.

Concrete expectations that emerged from our interviews are listed in Table 4 along with their definitions and exem­ plary quotes from the interviews. Regarding customer involvement, it is important to note that an expecta­ tion of extensive customer involvement is not always the case. Our respon­ dents pointed out that sometimes the opposite is expected. Customers who do not wish to be involved in the pro­ cess but want the contractor to imple­ ment and deliver the requested system autonomously rather expect a mini­ mum of involvement. Process efficiency refers, for instance, to efficiency of communication (e.g., talking directly to developers, leaving out intermedi­ ate project managers) and of meetings (e.g., a thorough preparation of meetings by communicating all relevant informa­ tion in advance). It is also noteworthy

Customer expectations can take dif­ ferent forms, and those forms can affect the impact of these expectations on cus­ tomer satisfaction. First, expectations can be realistic—that is, they reflect what is actually feasible. All respon­ dents consider realistic expectations the ideal state when it comes to satisfy­ ing customer needs. Second, expecta­ tions can be too high. Such expectations are considered a risk factor in satisfy­ ing the customer because meeting too high expectations is usually difficult or impossible. Customers with too high expectations need special care from the beginning, as it is particularly dif­ ficult to satisfy a customer who becomes unsatisfied early in the project. Finally, too low expectations can occur. Accord­ ing to our respondents, such expecta­ tions play a minor role; they are rather rare and can be easily met.

Figure 2: Results overview.

IS Project Success

Customer involvement, responsiveness of the

contractor, etc.

Planning of the development process, transparency, customer

involvement, etc.

Customer Satisfaction with the Development Process

Customer Satisfaction with the Product

(Dis)Confirmation

Customer Expectations Toward Development Process

Approaches to Manage Customer Expectations

Perception of ISD Process

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search feature. Then, he talked to Mr. X and Mr. X changed the search feature accordingly. The customer always knew what went on and who did what.” Emily explains, “Transparency enables under- standing, which leads to trust. The more transparent I am, the better can the customer understand why some things take longer, do not work, or cost more money.” Another specific example of creating transparency was granting cus­ tomers access to the quality assurance (QA) system of the contractor, including all work tasks, their progress, and bugs, thus giving the customer a constant overview of the current state of the project.

Another customer expectation described earlier that is also seen as an approach for managing expectations is customer involvement (seven respon­ dents). Beginning as early as possible, involved parties should get to know one another in regular meetings and align their expectations. System speci­ fications should be discussed with the customer step­by­step before imple­ mentation. Especially agile processes provide the opportunity for regular tests, preliminary results, and feedback. David describes how customer involve­ ment was realized by granting access to the quality assurance system, leading to success in a former project: “The customer was positively surprised about the involvement via the QA system as he was able to communicate to the devel- opers directly and discuss details with- out an intermediate project manager.” However, Emily raises the concern that such direct communication is prone to misunderstandings: “The customer is hoping to move forward more quickly by discussing something with the devel- oper directly. However, I have come to experience that the customer does not understand the developer. They talk at cross-purposes. Their thinking is differ- ent and needs to be translated. Thus, cus- tomers wanting to talk to developers can backfire.” Furthermore, the ideal degree of customer involvement depends on the customer and should thus be

the level of the contractor’s respon­ siveness that are desired and expected during the development process. As Thomas describes, “We discuss the development process with the customers in advance. . . . We explain how we work, how the interaction takes place, that we want to involve them, and that we are willing to react to their wishes immedi- ately. Thus, we influence their expecta- tions.” Several respondents stress that a detailed project plan needs to be pre­ sented to the customer as early as at the project kickoff. Customers who are familiar with the plans from the start will align their expectations accord­ ingly. Even if the process deviates from the plans, customers are more likely to be satisfied because they co­determined and approved those plans in the begin­ ning. Kathy recalls a one­year project in which a detailed plan that included development approach, responsibili­ ties, milestones, jour fixes, information handling, and escalation paths was presented to the customer at project kickoff. The customer accepted the sug­ gestions and was ultimately “grateful that there was a clear course of action.” By clearly communicating the plan, the project team aligned customer expecta­ tions with contractor capabilities and the project was completed successfully.

Next, transparency is not only a customer expectation as described above, but is also considered an effec­ tive approach for managing expecta­ tions (eight respondents). A contractor’s project managers should make the development process visible to cus­ tomers by providing information about current progress, mistakes, and plan deviations, as well as effects of the cus­ tomer’s requirements, expectations, and change requests. Disclosure of internal workflows is said to increase customer satisfaction by building understanding and trust. Thomas recalls in this regard, “The customer knew at all times what happened. . . . He knew which persons worked on what on our side. He called us and said, I want to talk to Mr. X; I have got an idea how to change the

Accordingly, considering the described influence of process satisfaction on project success, agreement exists on the positive impact of expectation manage­ ment on project success.

Several factors influencing customer expectations emerged in our interviews. All respondents mention the experi­ ence of the customer as an important factor in this regard. With little or no experience from former collaborations, unrealistic expectations become more likely. As Robert points out, “The cus- tomer does not know what is possible and what is not.” A similar influencing factor stressed by eight of our respon­ dents is the technical know­how of the customer. This does not mean that cus­ tomers must be able to implement the software; however, they should under­ stand what software is, how it is devel­ oped, and what technical terms mean. Lack of technical know­how often leads to too high expectations: “Working with people from the IT department is less problematic; they are relatively realistic. If you work with someone from market- ing, they only want to bring a product to the market fast. Their expectations are mostly too high and need to be brought down to earth first” (Mark). Further fac­ tors, which were mentioned by fewer than six respondents, include promises made by sales department (two respon­ dents), requirements resulting from a customer’s internal processes (two respondents), trust between customer and contractor (one respondent), the degree of the customer’s sympathy (one respondent), and the size of the cus­ tomer organization (one respondent).

Our interviews yielded various approaches for managing customer expectations. First, a detailed and early planning of the development process with the customer was mentioned (nine respondents). This includes defining milestones, work packages, responsi­ bilities, communication channels, esca­ lation ladders, and risks in cooperation with the customer. Furthermore, the contractor and customer should discuss the degree of customer involvement and

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that he is getting someone inexperienced and thus without competence, but costs an amount of X every day, he will not be particularly thrilled. Competence is indispensable in this regard” (Paul). James adds that trust helps solve con­ flicts and has long­term positive effects on the relationship with the customer: “A great amount of trust was estab- lished. . . . Of course, there were also conflicts, but with trust the obstacles were easier to overcome. The project was ultimately a success for the customer. We still sense it; as a result, the customer is now in general pleased with us.” The empathy (three respondents) of the contractor is his or her ability to put him or herself in the customer’s posi­ tion. Not only does this ability lead to better understanding and implemen­ tation of customer’s needs, but it also helps the contractor choose the most adequate solution by showing consid­ eration for the customer’s given con­ ditions (James). Emily suggests that empathy can be increased, for instance, by visiting the customer site and getting familiar with the respective customs and circumstances. Finally, realistic promises made by the sales depart­ ment (two respondents) address the management of expectations before project initialization. David points out that customer expectations begin to be formed by sales representatives; thus, expectations management needs to set in as soon as the first contact with the customer takes place. “An expectation raised by sales is difficult to redirect later in the project. This way, disappointment comes very quickly. Once it gets there, the project becomes more and more dif- ficult. If sales promises something that cannot be kept in the project, satisfac- tion of the customer decreases. Thus, the sales department needs to con- duct expectation management already” (David). Customer expectations should be steered in a realistic direction from the start by discussing feasibility and potentially required expenses.

In conclusion, seven factors that influence the process expectations of

requirements in this case. Similarly to customer involvement, our respondents also suggest that there is a possibility of too much communication. As Michael points out, “One should bear in mind, however, that the customer is busy, too. It can prove negative if you want to com- municate too much.” This should be avoided, especially when the customer expects the contractor to handle the project mainly autonomously.

The next approach mentioned by our interviewed project managers is referring to experience and alterna­ tives (six respondents). If customers formulate problematic requirements or are skeptical about certain ideas of the contractor, project managers can adjust customer expectations by referring to former projects in which certain alter­ natives proved inadequate: “Usually, customers first tell us what they expect us to do, verbally or in a specification document. We comment on it in a writ- ten form . . . and communicate clearly if we see risks, even before accepting the project. . . . Of course, many why- questions arise on the part of the cus- tomer. Then, we explain sensitively that we know this business area, we had this issue many times, and every time it was a critical one. Let’s not fall into that trap again” (Patrick). However, Kathy points out that it is also important to listen to customers: “You can ask the customer about his preferred course of action, and sometimes he has better ideas.”

The following approaches for man­ aging expectations were mentioned by fewer than six respondents. Building trust (four respondents) is enabled by the customer’s impression that the con­ tractor is interested in conducting the project successfully for the customer. Paul stresses that the customer should feel as though both parties (customer and contractor) are working toward the same goal. The competence of the con­ tractor is thereby a critical requirement for building trust: “This is a typical problem in big projects of large con- sulting companies, sending their young people over. If the customer realizes

chosen with care in specific situations. After all, our respondents pointed out that some customers expect rather little involvement. This is likely to be the case if the customer is being forced to carry out a project rather than choosing to do so. If the customer expects the con­ tractor to get the job done with as little effort as possible on the customer side, the contractor has a choice—to comply with this expectation or to convince the customer that a certain level of involve­ ment is necessary. As regards the latter type of case, Mark recalls: “They all were a little inexperienced. . . . [T]he company was forced to do it. Nobody was in this project because they wanted to. It was demanded. Their expectations were not particularly high. In fact, they did not want to be there. . . . That would have led to one meeting at the beginning and one meeting at the end. That just could not have gone well.”

Although it contributes to other approaches, communication itself is said to be an approach for managing expectations (seven respondents): “If you don’t talk to the people, everything runs aground” (Mark). By communicat­ ing early in the project, the involved parties learn what they can expect from one another. Several respondents advo­ cate forcing regular communication in the form of conference calls, emails, and personal meetings. The importance of the latter—getting to know one another in person—is emphasized. Response time should not exceed a single busi­ ness day. One means to enable effective communication is the use of prototypes, which allow the customer to provide feedback early in the development pro­ cess, facilitating the communication of expectations. Kathy described a less successful project, in which the require­ ments were not clearly defined in the beginning (e.g., must and can criteria were not distinguished). Additionally, the customer continually presented new requirements to be implemented. In Kathy’s retrospective view, a prototype would have been an effective commu­ nication basis for narrowing down the

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The focus of previous research on users led us to explore the expectations of customer managers. For most of our respondents, discussing the manage­ ment of manager expectations toward the process was a novel experience. This was reflected in the long time it sometimes took respondents to think of explicit situations concerning the man­ agement of process expectations. Often, they were tempted to think of expecta­ tions related to users and the prod­ uct, and needed to be reminded of our study’s focus. However, we suggest that the management of expectations toward the process in ISD projects is intui­ tively accomplished by project manag­ ers. Based on the insights gained from our interviews, we suppose that project managers implicitly use approaches to ensure customer satisfaction that rely on the basic idea of ECT, which can be seen as an implicit theory explain­ ing the behavior for handling process expectations in ISD projects. Implicit theories have a long research tradition in areas such as leadership (e.g., Dinh et al., 2014; Lord & Shondrick, 2011; Shondrick, Dinh, & Lord, 2010). Future research might pay increased attention to this phenomenon. One reason for the implicit character of ECT concern­ ing process expectations might be the intangible nature of the development process compared to the developed product. Although our explorative study answers the call for an empirical analy­ sis of stakeholder management prac­ tices (Eskerod & Huemann, 2013), our results are a first step only and need to be complemented with further inqui­ ries. Directly comparing expectations and approaches for managing expec­ tations might reveal causal relations. For instance, interesting insights might result from analyzing the approaches with regard to their applicability to spe­ cific expectations.

Our study provides initial insights into project managers’ perceptions on the relevant expectations on behalf of the customer. Most important, all of our respondents name customer

theory (Mishra & Dwivedi, 2012). Our respondents see satisfaction with the development process as predominantly having an indirect influence on project success (mediated by satisfaction toward the product). However, they confirm the general importance of managing expec­ tations toward the development process on behalf of customer managers, which has been widely neglected in research thus far. By using ECT to explore customer satisfaction in relation to expectations, our study is in line with research on the discrepancy theory model (Jiang, Klein, & Saunders, 2012).

The differentiation between process and product in general is relevant in various contexts. For instance, when it comes to striving for loyal, recurring customers, there is research suggest­ ing that delivering a perfect product in ISD projects is not enough (van Ekris, 2008, 2009). In fact, research sug­ gests that delivering a perfect prod­ uct on time and within budget (that is, meeting the traditional triple con­ straint criteria of project management) is insufficient. This is reported by our respondents; for instance, Thomas claimed that customer satisfaction toward the development process has the uppermost importance. Rather than the outcome of the project, the manner in which this outcome was achieved is found to be decisive. The management of the project involves factors such as transparency, communication, and cus­ tomer involvement, among others. This suggests that the development process is more important than the developed product when the goal is for customers to turn to the same supplier for upcom­ ing projects. Because stakeholder satisfaction is based on stakeholder expectations (Baccarini, 1999; Bourque & Fairley, 2014), the higher importance of the process in the context of customer loyalty suggests that managing process expectations has higher importance as well. In our view, more research is needed on the relative importance of process and product (expectations) in different contexts.

customer managers and eight ap­ proaches for managing those expecta­ tions emerged from our interviews, as described above. We discuss our find­ ings below.

Summary and Discussion To summarize, our study yields three major findings. First, our interviews reveal the importance of customer sat­ isfaction with the development process for overall ISD project success from the perspective of a contractor’s project managers. Second, our findings corrob­ orate the strong impact that customer expectations toward the ISD process have on customer satisfaction. While realistic expectations can typically be fulfilled, our respondents reported that they are often confronted with unrealistically high expectations. As a result of the disconfirmation of these expectations, customers are usually dis­ satisfied. Finally, our interviews yield multiple approaches project managers can use to successfully manage cus­ tomer expectations toward the ISD pro­ cess. By managing expectations, project managers can raise the likelihood of customer satisfaction and ultimately project success. In the following sec­ tions, we discuss the implications of these findings for researchers and practitioners, while noting the limita­ tions of our study.

Implications for Researchers

Using ECT as a theoretical lens and view­ ing expectations as wishes in reference to service quality, we continue previous research on stakeholder expectations in ISD projects. Stakeholder satisfaction (with outcome and process; Ferreira & Cohen, 2008) is deemed highly relevant for proj­ ect success (Nelson, 2005) and is based on stakeholder expectations (Baccarini, 1999; Bourque & Fairley, 2014). We shift the focus from user expectations, which are typically concerned with the product (i.e., the developed IS), to the expectations of customer managers concerning the devel­ opment process. In this way, we contrib­ ute to the literature stream on stakeholder

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between the identified approaches for expectation management.

It is apparent from our results (see the customer expectations in Table 4: customer involvement, conjoint dis­ cussion of challenges, responsiveness of the contractor, establishing a per­ sonal relationship with the contrac­ tor) that customers mostly expect to be involved in the development process, at least from the perspectives of our respondents on behalf of the contract­ ing organizations. Emily exemplified this by mentioning a customer “hoping to move forward more quickly by dis- cussing something with the developer directly.” This finding is in line with the increasing popularity of agile develop­ ment approaches (Dingsøyr et al., 2012; Vlaanderen et al., 2011). Consequently, our study suggests that such approaches are likely to increase customer satisfac­ tion in ISD projects. In this regard, the customer is not the only stakeholder to benefit from the increased involvement. A close collaboration with the client can lead to benefits for the contractor as well. First, a satisfied client is more likely to contract further projects to the same contractor. Second, knowledge that contractors can retrieve from close customer collaboration can be used to develop the contractor’s portfolio of products and services (e.g., strategic partnerships).

At the same time, generally valid recommendations on the level of cus­ tomer involvement, communication, and so forth cannot be given. The identified approaches should rather be carefully considered before being applied in specific projects. Although high customer involvement is in gen­ eral perceived as a success factor in ISD projects (McKeen, Guimaraes, & Weth­ erbe, 1994; Petter, 2008), customers may also be reluctant to closely collaborate with the contractor, for instance, because of other obligations in daily work. Our respondents point out that some of their customers expected less involve­ ment and just wanted the contractor to handle the project. Considering the

transparency of all relevant informa­ tion. The relevance of transparency in our context can be explained by the intangible character of the develop­ ment process, which—in contrast to the product—requires active commu­ nication on behalf of the contractor to manage expectations.

Implications for Practitioners

Our results show the criticality of man­ aging process expectations in ISD proj­ ects. As stated above, all respondents consider the management of customer expectations an important or even indispensable task. Considering our interviews, project managers tend to be primarily concerned with manag­ ing user expectations concerning the product. This is surprising because our respondents had no difficulty think­ ing of numerous expectations that cus­ tomer managers may have concerning the development process. As a conse­ quence, we recommend that project managers think explicitly about whether and how they have managed expec­ tations toward the development and to what extent their approaches have affected the success of ISD projects. They can use our study as a starting point to develop strategies for coping with this important project manage­ ment task.

Three of the most frequently men­ tioned approaches (i.e., transparency, customer involvement, and communi­ cation) are closely related (i.e., they all concern direct contact with the customer) yet different from one another. For instance, although ensuring transparency throughout an ISD project requires the contractor to communicate with the customer, communication with the customer does not necessarily lead to transparency. Furthermore, involv­ ing the customer in the development process—for example, by letting the customer make suggestions—does not make the development process trans­ parent. When developing strategies to manage process expectations, it is thus important to consider dependencies

involvement and the responsiveness of the contractor as expectations toward the process in ISD projects (see Table 4). However, an extensive per­ spective on process expectations needs to be extended in at least the following two ways. First, to consider the stake­ holder dyad of contractor and customer (Eskerod & Vaagaasar, 2014), process expectations need to be retrieved directly from customer representa­ tives. Second, stakeholder networks comprising other stakeholders such as software developers need to be consid­ ered as well because they are likely to differ. Although project managers typ­ ically focus on traditional project suc­ cess criteria such as time, budget, and requirements, research indicates that software developers, for instance, might have a different perspective on proj­ ect success (Linberg, 1999). Because the involvement of the customer in the development process is empha­ sized in particular by agile develop­ ment approaches, the role models of such approaches should be explicitly considered.

Though most approaches for man­ aging expectations concerning the development process match approaches identified in other contexts (except for transparency, each approach identified in our study has been addressed in at least one previous publication), trans­ parency can be seen as particularly important in our context. As Thomas recalled from a positive experience: “The customer always knew what went on and who did what.” Transparency has been defined as the extent to which “team members incl. project manager are informed about project plan, sta­ tus and all events important to them” (Pankratz & Loebbecke, 2011, p. 6). When considering the management of user expectations (Petter, 2008), one of three main strategies is user involve­ ment, and it includes “keeping users involved and updated throughout the project” (p. 704). Although they point in the same direction, keeping users updated cannot be equated with the

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that they should follow to manage expectations. However, only one of the respondents even had such guidelines, and we were not given a copy.

Finally, the expectations presented in this study have been mentioned by project managers on behalf of con­ tractors. In our context, the choice of respondents was guided by our focus on identifying approaches for managing expectations. Future research needs to replicate our study and conduct inter­ views with customer managers. Com­ paring customer expectations with the contractor’s perception of those expec­ tations will help uncover discrepan­ cies between these two stakeholder groups and gain further insights into the development process in IS projects. For instance, contradictory findings will help better understand misled percep­ tions on behalf of project managers attempting to manage customer expec­ tations in ISD projects.

Conclusion We show the relevance of customer sat­ isfaction concerning the ISD process for the success of ISD projects from the perspectives of project managers. The identified approaches of managing cus­ tomer expectations toward the develop­ ment process can help project managers increase the likelihood of customer sat­ isfaction and thus project success. By revealing customer expectations toward the development process, we illustrate the diversity of aspects that project man­ agers need to address in order to pave the way for successful projects. Whereas we contribute to a deeper understand­ ing of the role of managing expectations in ISD projects, dependencies between expectations and approaches for man­ aging expectations are to be addressed by future research.

References Anderson, E. W., & Sullivan, M. W. (1993). The antecedents and consequences of customer satisfaction for firms. Marketing Science, 12(2), 125–143.

ing customer expectations during the development process. Robert explains in this regard, “Leadership . . . also means to me that I have the soft skills to direct and lead the customer. Not to enforce the own ideas, that is not what it is about; but to direct the customer to the better solution. . . . And at the end, he says: That is exactly where I wanted to go.” Similarly, Ben states: “Even though the first conversation was not like this to 100%, at the end the customer had the feeling that the decision was made together.” These quotes illustrate that the process of managing customer expectations needs to be cooperative rather than instructive in nature.

Limitations

As with any empirical study, ours is not free of limitations. First, though we randomly contacted companies to avoid a selection bias and the results in general converge to common themes, we cannot guarantee that interview­ ing further project managers would not lead to further insights. The generaliz­ ability is limited by the sample size of 12 respondents; however, our analy­ sis clearly shows theoretical saturation (see subsection Quality Criteria), which makes us presume the representative­ ness of our results.

Second, our interviewed project managers work for small and medium­ sized enterprises. Managing expec­ tations in larger companies may be subject to further factors, which influ­ ence expectations and approaches for managing these.

Third, the interviews have been conducted via phone. Consequently, we were unable to observe respon­ dents’ nonverbal communication. How­ ever, using telephone interviews, we were able to convince more project managers to participate in our study compared to conducting face­to­face interviews, which are typically more effort­intensive. Additionally, the inter­ views were our only data source. For the purpose of data triangulation, we asked respondents for company guidelines

growing popularity of agile develop­ ment, different customer expectations also pertain to the choice of the devel­ opment approach. Robert recalled a project in which “the customer was very experienced. . . . He stated clearly that he wanted the waterfall model as process model. . . . It was strictly prescribed by the customer.” Robert then went on to describe how the customer was con­ vinced to use a more agile approach in the face of the incomplete require­ ments at hand. Overall, a careful con­ sideration whether to comply with customer expectations or, for instance, to advise a higher level of involve­ ment is needed. As Mark stated, “This can barely be formalized. It strongly depends on the parties involved. The project manager needs to find his own connection to the customer.”

Moreover, approaches might not be applicable in some cases at all. Promises by sales departments might be made prior to initializing a project, when it is rather difficult to judge whether prom­ ises are realistic. Once promises are made, the customer might lose trust in the contractor if promises are adapted during the course of the project. The approaches’ applicability and thus the likeliness to increase project success might therefore be contingent on the project context. Moreover, differences might exist considering public and pri­ vate customers. The expectations toward the development process identified in our study mostly focus on the involve­ ment of the customer. However, public customers might have additional expec­ tations toward the development process (e.g., usage of the V­model is a typi­ cal requirement concerning the devel­ opment approach for projects in the German public sector to ensure quality). In such cases, contractors need to care­ fully consider whether to respond to such expectations (e.g., if relevant expe­ riences are missing, the fulfillment of such process expectations can be costly).

Finally, leadership and social skills are critical competencies of project managers when it comes to manag­

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project and program contexts, and organizational agility. His research has been published in journals such as Project Management Journal, Communications of the Association for Information Systems, IEEE Computer, Journal of Computer Information Systems, International Journal of Information Technology Project Management, and the proceedings of conferences, such as the European Conference on Information Systems and the International Conference on Information Systems. He can be contacted at [email protected]

Georgios Stavrou, MSc, is an IT business analyst in the automotive industry. He holds bachelor’s and mas- ter’s degrees in information systems from the University of Cologne, Germany. His professional and research interests cover the management of IT projects in gen- eral and the management of stakeholders in particular. He can be contacted at [email protected]

Oleg Pankratz, PhD, is a postdoctoral researcher in the Department of Information Systems and Systems Development at the University of Cologne, Germany. His research focuses on IS project success and has been published in the Project Management Journal, International Journal of Information Systems and Project Management, and the proceedings of conferences, such as the European Conference on Information Systems and the International Conference on Information Systems. He can be contacted at [email protected]

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Appendix A

Questions related to respondent’s experiences, current position, and tasks Part One What kind of training/education have you received?

What is your current position and range of duty?

What is your working experience in ISD?

Questions related to customer satisfaction with the development process in IS projects Part Two How relevant is customer satisfaction concerning the development process for the overall success of an IS project?

How relevant is customer satisfaction concerning the development process compared to customer satisfaction concerning the product?

Are there any dependencies between customer satisfaction concerning the development process and customer satisfaction concerning the product?

To what extent does your company measure customer satisfaction (in general, concerning process or product)?

Questions related to customer expectations toward the ISD process and their management Part Three What impact do customer expectations have on customer satisfaction?

What expectations do customers have concerning the ISD process?

To what extent can project managers specifically influence customer satisfaction?

Can you think of an IS project in which you positively influenced customer expectations concerning the development process?

How would you characterize the project and its context?

What expectations did the customer have?

How did you respond to these expectations?

What impact did your response have?

Can you think of an IS project in which you negatively influenced customer expectations concerning the development process?

How would you characterize the project and its context?

What expectations did the customer have?

How did you respond to these expectations?

What impact did your response have?

Can you think of other tactics that might have had a positive impact?

Do you have any further recommendations for dealing with customer expectations?

To which contexts do these recommendations apply?

Table 5: Extract from interview guide.

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Appendix B

Guideline Description of Our Interview Design 1. Situating the researcher The respondents did not know the authors in person, that is, we used cold calls to randomly selected companies.

The respondents’ openness toward the interviewer may thus depend on the respondents’ trust toward the authors’ institution and the confidentiality of disclosure (cf. guideline 7 below). We explained that the study is part of a PhD project at our university and that the interviewer has an IS background (MSc information systems).

2. Minimizing social dissonance To minimize social dissonance, we aimed to ensure that the respondents felt comfortable at any given time. Although the first contact was a cold call to the companies, we subsequently sent an email outlining the research project and the role of interviews in it. The interviewer himself contacted the potential respondents to ensure that any questions on behalf of the respondents could be directly answered. By emphasizing that the respondents are the experts and that no right or wrong answers existed in this context, we carefully sensitized the participants for our study. Moreover, we assured the participants of confidentiality (cf. guideline 7 below) and gave them full control over the audio-recording (i.e., the participants could decide to turn off the recording at any time).

3. Representing variety of voices All respondents are or have been working in the position of an IS project manager. Because we did not aim to assess the management of expectations within an intra-organizational context, we interviewed managers from a variety of organizations to enable subject triangulation. By randomly contacting companies and respondents, we are confident to have avoided biases related to the selection of respondents.

4. Everyone is an interpreter In order to reduce subjectivity, two authors independently analyzed the interviews and conjointly aggregated the results in a subsequent step. Diverging assessments were discussed until agreement was reached. For readers of this article, we provide several direct quotes from the interviews to enable a better understanding of the respondents’ views.

5. Using mirroring Beginning with general questions, we stepwise asked more specific questions about the respondents’ experiences. By assuring participants that no right or wrong answers existed in the context of the study, we encouraged the respondents to be as open as possible. We mostly used open-ended questions in our interviews (see Appendix A) to avoid imposing our wording on the respondents. By asking for concrete project situations, we aimed to focus on vivid stories that were revisited in follow-up questions.

6. Flexibility While following the interview guide in general, the interviewer paid special attention to the responses given by the respondents. In any occurrence of potentially relevant answers, the interviewer followed the emerging line of inquiry and adapted the structure of the interview accordingly.

7. Confidentiality of disclosures We guaranteed participants confidentiality and access to the aggregated results. In the beginning of the interviews, we explained the procedures taken to ensure confidentiality and adequate handling of the interviews. The interview transcripts were anonymized, that is, names related to individuals or companies were replaced by pseudonyms. Subsequently, the links between the transcripts and the respondents were removed and the audio files deleted.

Table 6: Consideration of the guidelines for qualitative interviews by Myers and Newman (2007).

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Project Management Journal, Vol. 47, No. 5, 89–106

© 2016 by the Project Management Institute

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A Review of the Literature Associated with Project Success Chuck Millhollan, Farm Credit Mid-America, Louisville, Kentucky, USA Michelle Kaarst-Brown, Syracuse University, Syracuse, New York, USA

INTRODUCTION

P roject management and IT project management have matured significantly over the past two decades; however, there remain conflicting findings on which factors result in successful projects. A trend in both the public and private sectors is an increase in demand

for practitioners with advanced certifications that demonstrate proficiency within a certain body of knowledge (Gabberty, 2013; Daniels, 2011). In addition to published research, the 2011 Project Management Salary Survey, Seventh Edition, of over 30,000 respondents from 29 countries supported the value placed on certification through an average 16% compensation variance in favor of certified practitioners when compared with non-certified practitioners (Project Management Institute, 2011). This growing demand for certified professionals is further evidenced by the increasing demand for certification preparation programs in higher education (Gale & Brown, 2003; Alam, Gale, Brown, & Khan, 2010; Daniels, 2011). This trend is diametrically opposed to Williams’ (2005) findings that project management methodologies have not provided the expected benefits. In fact, for this statement to be accurate in the context of this article, the expected benefits would need to be limited to factors influenced only by effective project management techniques.

Scholarly research provides clear evidence that there is much more to project success than mastering the project management body of knowledge, or continued study of that same body of knowledge. While there are demon- strated benefits related to earning a project management certification (Müller, 2013), empirical research provides evidence that the structured approach to learning the project management body of knowledge is only the foundation of a project manager’s professional journey.

We highlight the gaps between the research on success factors associated with project success, project management success, and project manager suc- cess and explore the question: What combination of skills contributes to IT project manager efficacy? After reading and synthesizing hundreds of articles, we report here on 59 relevant and influential articles about “success” in the project management context. We identified these articles through a rigorous citation chain and bibliometric analyses of over 200 articles, published over the past ten years in seven top IT journals, and the past three years in two top project management journals.1 There are few recent articles on success, sug- gesting that the focus has shifted to specific problems, or a complacent view that certification equates with success.

ABSTRACT ■

In the maturing IT project management

space, there are still many debates about the

skills needed to achieve success. This article

presents a review and synthesis of project

management literature that highlights the

potential conflict in goals and the measure-

ment of “success” from three perspectives:

project outcomes, project management pro-

cesses, and the project manager’s influence.

Our review indicates that each perspective

of success, defined by various stakeholders

at various points in time, shifts the focus

onto different skills and knowledge. Drawing

upon this tri-focal lens, we propose a shift

in focus on success to the intersection, or

“sweet spot of project manager efficacy.”

KEYWORDS: project success; project management success; project manager

success; efficacy; project outcomes

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1See the Appendix for additional details on search and selection criteria. Additional details on methodology can be

obtained from the authors.

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15 top paying certifications in 2013 based on high demand (Müller, 2013).

Hiring managers frequently seek certified IT and general project man- agement practitioners. A quick search on any job search engine will demon- strate that most job postings for project manager jobs indicate project manage- ment certification as either required or preferred. One of the leading reasons that project management certification is valued is that the effort required to earn a certification demonstrates a proj- ect management practitioner’s depth of understanding of the project manage- ment processes and tools. Simply, the implication of certification is a standard set of accredited skills and knowledge.

Seeking certified IT project manag- ers also influences a manager’s deci- sions related to his or her professional development budgets, because certifi- cation may be a condition of employ- ment. Once a practitioner earns a project management certification, he or she must maintain his or her certi- fication through continued education. Not all organizations fully sponsor the recertification requirements; however, whether the individual or the employer pays for recertification, professional development costs must be weighed against the benefits to both the indi- vidual and the organization.

Project management certifications also serve to set expectations on the parts of hiring managers and their orga- nizations about the more appropriate project management skills and knowl- edge. In addition to the pre-screening of potential job candidates after a hiring decision has been made, hiring manag- ers and IT leaders expect certified prac- titioners to join their teams prepared with the skills needed to effectively lead projects within their organizations. Earning a certification may provide evi- dence of experience and knowledge; however, holding a certification does not always provide evidence of an IT project manager’s skill or his or her efficacy. This is not a subtle difference when considering which skills should

provide the context in which these suc- cess debates are taking place in the gen- eral and IT-centric project management spaces. We close with revised defini- tions and implications for practice and directions for future research.

The Context of Skills of the IT Project Management Practitioner

The information technology (IT) indus- try spans the commercial, non-profit, educational, government, and military sectors, and is a ubiquitous and often strategic aspect of nearly all organi- zations. As such, project management is similarly a critical element in most organizations, be they large or small, because IT solutions must be in place to realize the benefit the project was undertaken to provide. Accordingly, certification in the project management discipline was one of the top certifica- tions in the IT arena for 2013 (The Top Five In-demand IT Certifications, 2012). Similarly, Global Knowledge, a world- wide IT and business skills training organization, lists project management certification first on their list of the

Despite the maturity of the disci- pline, published research and concep- tual articles reflect inconsistencies in definitions about what successful proj- ect management is all about, what skills are needed by project managers, and lit- tle focus on IT project managers (those working in IT-centric environments). In addition, our review identifies conflicts in goals and measurements of success (success metrics) from these three dif- ferent perspectives, as they focus on project outcomes, the process of project management, or the project manager. Each of these perspectives overlaps in importance and focuses on the skills and knowledge that are most relevant (see Figure 1)

Drawing upon this tri-focal lens, we offer a new definition of what we call “the sweet spot of project manager effi- cacy.” We are not proposing that the “sweet spot” is a constant. On the con- trary, the literature suggests the defini- tion of success is constantly shifting based on stakeholder perspectives and the project life cycle (see Ika, 2009; Pinto & Slevin, 1998a). The following sections

Project Success

A Project

Management Success

B

Project Manager Success

C

*

Sweet Spot

Figure 1: Tri-focal lens interrelationships (source: Millhollan, 2015).

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projects. The next three levels are anal- ysis, synthesis, and evaluation; these levels are aligned with the soft skills associated with critical thinking and decision making (see Table 1).

So then, what combination of skills, hard and soft, contribute to IT project manager efficacy, and how are those skills developed? This debate about skills for success in the IT project man- agement space returns us to what we feel is a central divergence in how “success” is defined by the project management community and related stakeholders. The understanding of IT project man- ager efficacy is enhanced by a review of these perspectives of success, spe- cifically understanding the differences among (1) project success, (2) project management success, and (3) project manager success; or outcome, process, and person, respectively. Project suc- cess and project management success appear to be well-researched topics, specifically in the project management publications; however, this does not imply a universal definition for, or agreement on, the primary factors that influence either project success or proj- ect management success.

The next section presents our review and lessons learned.

Factors Associated with Project-Related Success Experience, supported by scholarly research, indicates that perceptions

upon definition for the term “soft skills,” practitioners we spoke with from both senior IT leaders and project manage- ment groups regularly used that term. It is also important to note that project management standards and the schol- arly literature related to both project manager success and project manager skill sets uses the broader, more general term “soft skills.” One generic definition of soft skills is those qualities necessary for a practitioner that do not depend on acquired knowledge, or hard skills (Collins English Dictionary, 2013). We argue that this definition is incomplete and requires further refinement in the project management context. Drawing upon the project management litera- ture, we define soft skills as those abili- ties that a project manager must possess to apply effectively the project manage- ment tools and techniques within the organization, across stakeholder groups, and over the project life cycle (building on Pant & Baroudi, 2008; Gillard, 2009; Alam et al., 2010).

If we turn to traditional theory on skills acquisition, and apply the six lev- els of learning from Bloom’s Taxonomy of the Cognitive Domain (Asplund, 2006), we see a clear separation between hard and soft skills. The first three levels are knowledge, comprehension, and appli- cation; these levels are aligned with the project management hard skills dem- onstrated through certification or the other technical skills required for IT

be invested in; it speaks to the root issue of the differences between having proj- ect management skills/knowledge, hav- ing a successful project management outcome, or having an IT project man- ager who knows what to do and when.

Hard Versus Soft Skill Sets and Skill Acquisition

We are not questioning that earning a project management certification pro- vides evidence of baseline knowledge; however, holding a certification does not necessarily mean that the project man- agement practitioner is more efficient. Research indicates that there are indeed certified project management practitio- ners without the advanced or enhanced abilities needed to lead projects success- fully. The contrary is also true in that there are non-certified project management practitioners who do possess advanced skills and abilities that contribute to their efficacy throughout the project management process (Starkweather & Stevenson, 2011). In effect, organizations may experience no significant difference in project success rates between certi- fied project management practitioners and project management practitioners without a certification.

Earning a project management cer- tification demonstrates mastery of the hard skills or technical competencies related to project management that can be measured through testing. Similarly, traditional computer science or informa- tion technology training provides hard skills relevant to the IT professional and IT project manager. These hard skills are teachable abilities that practitioners can learn in a classroom setting. As has often been argued, however, the “soft” or interpersonal skills are also impor- tant.2 While there isn’t a clear, agreed

2An interesting observation from conversations with both

senior IT leaders and certified project management prac-

titioners when asked about the interpersonal skills most

important for IT project manager efficacy, they focused on

what they referred to as “social” skills. When we reworded

the question to elicit important “soft skills,” the list expanded

to include individual proficiencies and traits, such as critical

thinking skills and emotional intelligence.

Hard Skills Soft Skills Blooms Taxonomy of the Cognitive Domain

Knowledge Comprehension Application

Analysis Synthesis Evaluation

Adaptations for Project Management

Project management hard skills are those skills and knowledge outlined in the PMBOK® Guide or other technical training that are teachable and measureable through testing.

Soft skills are those abilities that a project manager must possess to apply effectively the project management tools and techniques within an organization, across stakeholder groups, and over the project life cycle

(This includes in-situ testing). (Building on Pant & Baroudi, 2008; Gillard, 2009; Alam et al., 2010)

Table 1: Adapting Asplund’s (2006) version of Bloom’s taxonomy for project management.

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about project-related success are poten- tially a moving target, or at the very least influenced by when, who, and what is being measured (Baker, Murphy, & Fisher, 1988; de Wit, 1988; Wateridge, 1995; Baccarini, 1999; Lipovetsky, Tishler, Dvir, & Shenhar, 2002; Jugdev & Müller, 2005; Müller & Turner, 2007; Ika, 2009). Our long list of references highlights that this phenomenon is confounded by the fact that reference to “project success” is often a comprehensive term that includes factors related to proj- ect outcomes, the project management methodology, and the project manager’s (person’s) proficiency in using project management techniques, and efficacy in managing across this entire range of issues. In addition, many of the arti- cles cited above attempt to address fac- tors that impact success as well as the outcomes. This is particularly relevant in the IT realm where there may be considerable differences between suc- cess of the project and success of the system (see Markus, Axline, Petrie, & Cornelis, 2000). As such, one of our key findings highlighted in the litera- ture is the repeated adage that the only agreement on definitions of success as related to projects and project manage- ment is that there is no agreement on the definitions (Wateridge, 1995; Shenhar & Levy, 1997; Cooke-Davies, 2002; Hyvari, 2006; Basten, Joosten, & Mellis, 2011; Mishra, Dangayach, & Mittal, 2011). An additional complicating factor is that different stakeholder groups may define success differently for the same projects (de Wit, 1988; Wateridge, 1988; Shenhar, Dvir, Levy, & Maltz, 2001; Hadaya, Cassivi, & Chalabi, 2012).

Among the first to argue that we should distinguish between project suc- cess and project management success, De Wit (1988) pointed out that the objectives for projects are not the same as objectives for the project management activities, and may even have a hierarchy of pri- orities. De Wit, along with several authors since then (see Ika, 2009), have argued that we need to distinguish between proj- ect success factors that impact success

and project success criteria that are an evaluation of the outcomes of the proj- ect. This raises the interesting question: Are the skills or knowledge needed by IT project managers based solely on those factors that might influence success (tra- ditional project management methodol- ogy), or should they include abilities to engage with the stakeholders evaluating the outcomes?

The following sections discuss the literature and the convergence or incon- sistencies in the literature associated with the skills and abilities needed for successful project management.

A Review of Project Success Literature

Meeting schedules, budget, and techni- cal performance measures—referred to as “the iron triangle” (de Wit, 1988) or “the triple measures” (Kloppenborg & Opfer, 2002)—are the traditional suc- cess metrics referred to in many stud- ies on project success. Yet, even some of the earliest research dedicated to project success identified that focusing on more than these three factors was an absolute necessity, often extending into the strategic goals of the project (Baker et al., 1988; de Wit, 1988; Pinto & Slevin, 1988a). For example, de Wit argued we should view “time” in mul- tiple dimensions—short, intermediate, and long term—as well as considering every phase of the project develop- ment life cycle (exploration, develop- ment, and production). Despite almost 30 years of lessons learned, his advice for post-implementation audits should be heeded “not so much to determine if absolute terms the success or failure but to identify what went right and what went wrong and why (p. 169).”

Others have argued that the three factors of time, cost, and quality relate more to the project management process than meeting the stakeholder expectations associated with true project outcome suc- cess (Munns & Bjeirmi, 1996; Atkinson, 1999; Shenhar & Dvir, 2007). Since understanding stakeholder perceptions and expectations is necessary for defin- ing project success, and projects are

by definition unique, it makes sense that there is not a single definition of project success or a universal set of criteria that one can use to predict proj- ect success or the associated skills that lead to it.

As shown in Table 2, this debate has been implicit (if not explicit), in most of the research on the many potential fac- tors that lead to project success. A weak- ness of many of these studies is that they present lists of success factors without sharing context-specific elements. We feel this lack of contextual focus is itself a crucial factor, as noted in several stud- ies (Wateridge, 1995; Belassi & Tukel, 1996; Jugdev & Müller, 2005; Ika, 2009; Ika, Diallo, & Thuillier, 2011); it is also worth comparing perspectives on proj- ect success from the way they look at time and the interaction of factors. The idea of interacting but divergent factors is reflected in other studies in which the authors suggest specific criteria but con- clude that success means different things to different people or that it applies to different groups, as highlighted by the diversity of factors that reflect on the per- sonal growth of team members versus their technical performance.

We observed that this subjective view has evolved, leading to categories of factors. Echoing parts of de Wit’s list, Freeman and Beale (1992) took an investment view of projects, argu- ing that these project ‘ventures’ should be evaluated based on seven criteria needed for project success:

1. Technical performance; 2. Efficiency of execution; 3. Stakeholder satisfaction; 4. Project team member personal growth; 5. Project termination completeness; 6. Identifying and overcoming technical

(includes procedural) problems; and 7. A combination of the project prod-

uct’s ease of use and performance. Belassi and Tukel (1996) clustered project success factors based on lev- els, for example the project, project manager and team, the organization, and the external environment.

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Taking a different view of roles, Shenhar and Levy (1997) presented project success factors in three gen- eral categories with different metrics based on design goals, the customer impact, and benefits to the organiza- tion. This apparently broadly based emphasis is actually more relevant in terms of addressing project outcomes, in that they address the management of organizational risks, costs, and attain- ment of benefits, but exclude specific focus on the project team members. For example:

1. Meeting design goals that tend to be objective and based on documented specifications and project constraints, such as budgetary limitations and schedules; factors measured through product verification, actual costs, and actual completion dates

2. The impact on the customer, such as meeting their needs and solving their problems; factors measured by satisfaction surveys or utilization rates

3. Benefits to the organization in the form of meeting a strategic objective such as increased market share and new product development

In a later study, Shenhar et al. (2001) grouped project success measures into four outcome dimensions; (1) proj- ect efficiency, (2) customer impact, (3) business impact, and (4) preparing for the future; each of these present different temporal influences—during the project, after implementation, and looking to the future. This is perhaps to be expected of IT projects that may be longer-term initiatives with signifi- cant infrastructure, software, and data investments. As such, this fourth cat- egory extends the assessment of proj- ect success into the longer project life cycles, continues bridging both the factors that influence success, and the eventual assessment of the success of the project.

Other valuable articles have com- pared project success factors across

several studies, including within IT- centric project environments. In partic- ular, Lally’s (2004) study on contributors to IT system project failure (the flip- side of project success) compared eight studies conducted between 1983 and 2002. Although there are commonalities in the lists for generic project success and IT project success (for example, top management support and clear require- ments or objectives), there is neither agreement on any one set of factors, nor is there a single factor that appears con- sistently in each set of findings. Again, this suggests that identifying project success factors is a moving target that may vary not only by stakeholder, but may also shift over time or with the nature of the project.

Considering time-based or tem- poral factors in a different way, focal aspects of project management have shifted over the decades, including defi- nitions and those skills or abilities that underpin them. Jugdev and Müller’s (2005) analysis of the literature pro- poses distinct trends over four specific eras. In the first era—from the 1960s through the early 1980s—they argue that project success literature focused on project delivery and transitioning the product or service into operations. They observed a shift in focus during the second era— the 1980s through the 1990s—to things that “must go right” or “critical success factors” for a project to be considered successful. Examples include understanding of the project management processes, executive com- mitment to those processes, and the project manager’s approach to lead- ing the project and project team. Their proposed third era—from the 1990s through the 2000s—shifts to developing frameworks to measure project success based on stakeholder expectations. This included interfaces between the inter- nal organization delivering the product or service and the external organiza- tions, such as vendor relationships and customer groups. They consider lit- erature from the fourth era—labeled the 21st century—to have expanded

research related to project success to include elements from ideation through product or service retirement. The lat- ter element implies a more end-to-end product life cycle view of the project undertaken to produce the product. While Jugdev and Müller’s (2005) analy- sis suggests an evolution in project suc- cess related research, they still highlight that project success has both an objec- tive and subjective component and different stakeholder groups interpret project success differently.

We summarize the above studies in Table 2 (including those covered in Lally’s 2004 review) and provide a brief comment on our interpretations. It is noteworthy that some of the included studies focus on the hard skills or pro- cess issues (such as involving end users), whereas others suggest the importance of keeping diverse stakeholders happy and avoiding politics.

Lessons from the Literature on Successful Projects

It is interesting to note that the most influential articles identified in our search for articles specific to project success haven’t been published in the past decade; this can be interpreted as having greater longevity and there- fore greater impact. We disagree with this view and ironically repeat de Wit’s (1988) caution that we need to separate the success of the project and the suc- cess of the project management activi- ties. The blended view evidenced in research in recent decades shows that we continue to make the mistake of ignoring the differences behind defini- tions; fail to parse out process criteria factors versus those that contribute to outcomes; and, therefore, creating con- fusion about the skills that project man- agers need to support both (see also Ika, 2009). It is evident from the evolving literature on project outcome success that success is dependent on balancing differing expectations and perceptions (Shenhar et al., 2001; Jugdev & Müller, 2005). Perceived IT project success is defined not only by meeting technical

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Lessons for IT Project Manager Efficacy

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October/November 2016 ■ Project Management Journal 95

requirements and providing a product, service, or result—as defined in the project objectives—but also by achiev- ing high levels of satisfaction from the stakeholder groups (Baker et al., 1988; Pinto & Slevin, 1988a; Wateridge, 1995). When considering project manager effi- cacy, three challenges relate to these dif- ferent sets of outcome success factors:

1. Some of the factors that contribute to project success are realized during a project, including meeting project- related constraints such as budgets and schedules and creating new prod- ucts or services.

2. Other project success factors might not be realized until long after project completion, such as customer satis- faction or commercial success, which can be outside the project manager’s control.

3. The factors influencing project suc- cess measurements and perceptions are often in conflict. For example, meeting a budgetary or schedule con- straint can have a negative impact on satisfying technical or functional requirements.

For success at both the individual project and portfolio levels (see Ika, 2009), it is important for the project manager to understand not only factors that influence project-related success, but also the varying stakeholder percep- tions of their most important project- related outcome success metrics. We noted that none of the project success factors or metrics addresses specific soft skills, but they strongly imply the need for skills in stakeholder analysis, decision-making, negotiation, conflict resolution, change management, and organizational politics.

A Review of Project Management Success Literature

The literature treats the project man- agement process as a contributing fac- tor to project success (Pinto & Slevin, 1988a; Pinto & Mantel, 1990; Freeman & Beale, 1992; Shenhar & Levy, 1997;

Baccarini, 1999; Cooke-Davies, 2002; Müller & Turner, 2007; Prabhakar, 2008; Nicholas & Hidding, 2010; Han, Yusof, Ismail, & Aun, 2012); however, there is less published research specifically dedicated to “project management suc- cess.” One exception mentioned earlier is an early article by de Wit (1988) that purposefully addressed the differences between project success and success- ful project management, holding that project management can contribute to project success; however, effective proj- ect management cannot prevent proj- ect failure. Where the literature does agree is that successful project manage- ment focuses on the process or what people do, rather than their interac- tions with others, and emphasizes the methodology by using the term project management. Pollack (2007) refers to this as emphasis on skills associated with delivery efficiencies, leadership by an expert in the application of proj- ect management tools and techniques, and control skills related to keeping the work on track to deliver on pre-existing, agreed upon goals. This implies agree- ment on and stability of these goals.

If project success and project man- agement success are assessed sepa- rately, the implication is that there must be more targeted factors related spe- cifically to project management suc- cess than those presented for the more holistic, but confounding, view of proj- ect success. Munns and Bjeirmi (1996) presented a list of project management success factors that focuses on the methodology and typical project con- straints, such as schedule, budget, and quality requirements. This narrowed list of factors relating specifically to project management success includes:

1. Project manager assignments, imply- ing that the assigned project manager must be versed in applying the proj- ect management processes, tools, and techniques;

2. Organizational support for the project management methodology, specifically executive leadership;

3. Effective task definition; a planning process; and

4. Reliance on established project man- agement methodology or project man- agement techniques

There is a quantifiable benefit in focusing on the delivery state of a proj- ect (Atkinson, 1999). Leveraging metrics such as schedule, budget, and quality requirements allows the project man- ager or other stakeholders to deter- mine if the project tasks have been completed according to plan. A project methodology alone cannot guarantee project success; however, identifying gaps in project identification, plan- ning, and execution processes, and dedicating effort to understanding how those procedural risks contributed to a project’s failure, can help identify enhanced project management pro- cesses that a project manager can apply to future initiatives (Sarantis, Smithson, Charalabidis, & Askounis, 2009). This claim is supported by both Azim, Gale, Lawlor-Wright, Kirkham, Khan, and Alam (2010) and Massis’ (2010) research, which revealed that “hard” project management skills can help with success factors related to planning and organizing effort, tracking, and managing throughout a project.

On the topic of IT project manage- ment success, Bannerman (2008) takes a different approach by focusing on risk management in software projects. He argues that the ability to react pro- actively to risks is an important factor, which requires more of project manag- ers than simply updating the project register that is part of project manage- ment methodology. His concluding argument, however, is that better risk management practices are important to project management hard skills.

The overarching argument across these articles is that project manage- ment methodology can support a proj- ect manager with a library of tools and provide a blueprint for project success when considered from a process per- spective; these tools are only loosely

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related to the short or long-term views of actual project success.

Lessons from the Literature on Project Management Success

It is not uncommon for stakeholders to place blame on project management practices when projects fail; however, for this perception to be true, the fail- ure would need to be rooted in the ineffective application of the project management methodology (see Munns & Bjeirmi, 1996); Azim et al., 2010; Lacerda, Ensslin, & Ensslin, 2011). As such, we conclude that:

1. Effective project management meth- odology can contribute to project success because it provides a struc- tured approach and standard tools or procedures.

2. Effective project management pro- vides a structured approach, but does not ensure success of the project.

3. The absence of effective project man- agement methodology contributes to project failure.

A second surprising conclusion from the literature on project manage- ment success is that determination of standards is rarely within the individ- ual project manager’s control. Rather, successful project management may require assessment by project managers of the best methodologies at the indi- vidual project and portfolio levels. What is noticeably missing from the litera- ture on successful project management, however, is explicit acknowledgment of the decision-making process associ- ated with the tools and techniques in a multi-stakeholder, IT-centric environ- ment. As indicated in Table 2, there are skills that tie back to various project success metrics; so, why do the project management literature and the most reputable project management stan- dard not focus on this? For example, A Guide to the Project Management Body of Knowledge (PMBOK® Guide) – Fifth Edition, identifies interpersonal skills as valuable assets for developing and

managing a project team and managing stakeholder engagement (Project Man- agement Institute, 2013). Interestingly, neither the research nor the project management standards provide guid- ance on how or when to acquire or develop these valuable skills. We might infer from this and the above stud- ies that project management tools and/ or techniques are needed throughout a project’s life cycle; however, there seems little consideration that some tools may be more relevant than others at different times.

A Review of the Project Manager Success Literature

Project manager success is a much more elusive topic, since perceptions related to project manager success are often intermingled with how the project man- agement methodology is applied and to the perceptions of overall project success (which may vary over time and stakeholders). Our review of the litera- ture revealed little research dedicated specifically to project manager success until the past decade or so. There is agreement that project manager com- petencies are an essential ingredient for project success (Müller & Turner, 2010), and a project’s success or failure is influenced by who manages that proj- ect (Patanakul, 2011). Pinto and Slevin (1988a) argued that a project’s success or failure is dependent upon who is selected to manage the project; this, along with other research, emphasizes the complex, multi-disciplinary teams that project managers often manage (Gillard, 2009).

Project managers have long been acknowledged as being “different” from regular managers and requiring differ- ent skills sets. Project managers are also often the scapegoats when projects fail, which may explain the emphasis on methods as contributing factors and success criteria or metrics. Despite this, the rare literature on project manag- ers or IT project manager success still focuses on project management skills, specifically the project manager’s depth

of knowledge about project manage- ment tools and techniques, despite findings that these do not necessar- ily make a project manager successful (Muzio, Fisher, Thomas, & Peters, 2007; Pant & Baroudi, 2008; Gillard, 2009; Fisher, 2011). In addition, a growing list of required abilities have emerged because of the overlap with project suc- cess and expanding project manage- ment tools.

We have adapted an early list from Gale and Brown (2003) and labeled these skills as specific to project man- agement “hard skills,” “soft skills,” skills dependent on stakeholder influences, or implying a temporal component over the project life cycle:

1. Project management skills (hard skills; temporal)

2. Business and management skills (hard skills; perhaps soft skills; temporal)

3. Knowledge of the project technical disciplines (hard skills)

4. Interpersonal skills (soft skills; stake- holders’ influences; temporal)

5. Managing the project sponsor (soft skills; stakeholder influences)

6. Situational awareness (soft skills; stake- holder influences; temporal)

7. Integration management, or integrat- ing the previous skills and knowledge (soft skills; temporal)

We also see later literature that high- lights the complementary relationship between a project manager’s mastery of project management tools and tech- niques, business and general manage- ment aptitude, and interpersonal skills (Muzio et al., 2007; McHenry, 2008; Pant & Baroudi, 2008; Gillard, 2009). Leader- ship skills, particularly, are emphasized for team, project, and stakeholder man- agement. For example, Geoghegan and Dulewicz (2008) measured leadership dimensions with a combination of the practitioner’s management, emotional, and intellectual competencies. They compared these measurements with project results using Pinto and Slevin’s (1988b) project success questionnaire,

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and found that leadership competen- cies correlated with ‘assessments of project success.’ While this is a some- what subjective concept, it suggests that good leaders may be less likely to be viewed as poor project managers.

Looking specifically at IT-centric environments, studies support that project managers require a combina- tion of project management acumen, disciplinary, and business management acumen, technical knowledge or famil- iarity, and interpersonal skills (includ- ing personal integrity) (Napier, Keil, & Tan, 2009). When contrasting across a variety of industries and types of proj- ects, however, the emphasis is still on a deeper understanding of the project management tools and techniques as enabling project management practi- tioners to leverage more effectively their soft skills to manage their project teams (Alam et al., 2010). This seems more consistent with practice, as noted in the opening discussion about the impor- tance of project manager certifications that emphasize hard skills.

Studies have also demonstrated that project managers tend to have certain personality traits compared with the rest of the population, and people with those

personality traits tend to function well in a project environment with partial data and higher ambiguity. For exam- ple, a survey of 280 project managers using the Myers-Briggs Type Indicator® (MBTI®) personality inventory revealed that there were significantly more NT (Intuitive, Thinking) project managers than represented in the general popu- lation (Cohen, Ornoy, & Keren, 2013). The authors postulated this is due to the fact project managers must make deci- sions in ambiguous situations and rely heavily on their intuition. These more recent observations have evolved since the older literature on project and proj- ect management success, which didn’t address project manager proficiencies, specific leadership styles or their impact on the project, or the softer skills and abilities required of a project manager.

Despite the growing research on soft skills, few studies have detailed the exact soft skills that project managers should possess, perhaps because of the ambiguity and shifting challenges man- agers face throughout the project life cycle, evolving contexts, and increas- ing technological complexity (Cohen et al., 2013). Through a combination of literature reviews, interviews, and

focus groups, Fisher (2011) identified an extended list of soft or interpersonal skills perceived as most important for project managers working with their project teams, including (1) managing emotions, (2) building trust, (3) commu- nication, (4) motivating others, (5) influ- encing others, (6) cultural awareness, (7) leading, and (8) team building. We believe these soft skills are equally important outside the project team, beyond the IT context, and regardless of industry but need to be sup plemented with industry-specific knowledge (see Chipulu, Neoh, Ojiako, & Williams, 2013). Table 3 provides our comparison of the few studies specifically focused on proj- ect manager success.

Lessons from the Literature on Project Manager Success

These studies highlight the complemen- tary relationship between a project man- ager’s soft skills as critical to enhancing their ability to apply their knowledge of project management tools and tech- niques. These interdependencies were highlighted in a recent discussion with a senior IT leader, who stated: “Inter- personal skills, without project manage- ment skills and knowledge, would be as

Categories Gale & Brown (2003) Napier, Keil, & Tan (2009) (IT) Fisher (2011)

Chipulu, Neoh, Ojiako & Williams (2013)

Project Management Acumen

Project management skills Integration management

Planning and control Budget management Time management Methodology experience

Business Acumen Business and management skills

General management Commercial awareness Industry knowledge

Technical Acumen Technical knowledge Systems development

Interpersonal Skills/ Traits

Interpersonal skills Managing the sponsor Situational awareness

Leadership Communication Team development Client management Problem solving Personal integrity

Managing emotions Building trust Communications Motivating others Influencing others Cultural awareness Leading Team building

Communication Team management Leadership Stakeholder management Teamwork

Interpretations Elaboration on soft skills and personal attributes

Focused on understanding soft skills in team leadership

More clustering of soft skills, but emphasis on the iron triangle of hard skills

Table 3: Project manager skill set comparison and our interpretations.

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ineffective as a project manager with advanced project management knowl- edge without interpersonal skills.”

The perceptions of success are heav- ily dependent upon project outcomes and how project management tools and techniques are leveraged to assist in pro- ducing expected outcomes; hence, many of the competencies outlined in the lit- erature related to project manager abili- ties are soft skills that require flexibility throughout the project life cycle (tem- poral aspects) and responsiveness to diverse stakeholders. Project managers in IT-centric environments increasingly must have the abilities to elicit, under- stand, and manage stakeholder expecta- tions throughout a project life cycle, and often even into the product or system life cycle. Similarly, the project manager’s opportunity to influence perceptions about project success lies in his or her ability to understand what stakehold- ers value; manage the real-world factors that influence how the project delivers value; and ensure that expectations and the reality delivered are aligned. At the same time, however, research needs to expand its focus beyond a single defini- tion of success, or even worse, the co- mingling of definitions.

The literature has evolved over time as the project management discipline has matured. It is clear that the sim- ple application of project management techniques will not make a project man- ager effective, nor ensure his or her overall efficacy in IT-centric environ- ments. Rather, an additional set of soft skills associated with technique appli- cation are the critical skills for project manager efficacy. The literature high- lights the following:

1. Skills associated with interpersonal interactions to elicit stakeholder expectations related to a specific project so that one can use this information to identify and priori- tize factors that will influence per- ceptions of success.

2. Ensuring alignment of expectations between different stakeholder groups

through communication, negotiation, and conflict resolution skills, because these expectations could not only be in conflict, but also evolve over time (temporal influences) as the proj- ect progresses from early planning through execution and delivery.

3. Decision-making and negotiating skills to develop strategies that actively manage not only the proj- ect, but also stakeholder expecta- tions about agreed upon end-state goals.

The main paradox here is that, if the research indicates that a specific set of skills, or range of skills, is neces- sary to being an effective project man- ager, why do the standards not provide descriptions that are more detailed or the guidance for procuring and devel- oping these skills?

Limitations in How We Know What We Know

Both researchers and practitioners need to understand “how we know what we know” about project success, project management success, and project man- ager success to make sense of why we find divergent and overlapping skills, and how this leads us to the importance of the overlapping sweet spot of IT proj- ect manager efficacy. Each method used to conduct research has its strengths and limitations. Other than the pub- lished literature review articles that summarized prior research (empirical and conceptual), the empirical studies included in our review were dominated by quantitative questionnaires and sur- veys, or qualitative interviews and focus groups. The majority of research on project success used positivist, quan- titative methods with questionnaires and surveys of larger samples, but less understanding of context. This is con- sistent with the fact that most projects are measured using quantifiable met- rics such as schedule, budget, and com- pliance to requirements. The majority of research on project manager success used constructivist, qualitative methods

with interviews and observation with smaller samples. This is consistent with seeking to understand life experi- ences in a practical project environment and factors that influence how people applied the project management meth- ods in context. To a much lesser degree, researchers used case studies with both qualitative and quantitative data (mixed method studies).

The challenge here is that the important distinctions between the def- initions of success or integration points haven’t been captured in these studies nor can easy comparisons be made. As noted in Figure 1, that “sweet spot” where project success, project manage- ment success, and project manager suc- cess overlap has been largely overlooked in terms of contextual issues such as temporal or stakeholder influences. While some researchers have tried to define or describe this intersection, we argue that it is a moving target, which is largely contextual. We conclude that rather than studying what constitutes project success, project management success, or even project manager suc- cess, research should focus on what contextual factors or attributes influ- ence project manager efficacy, consider- ing the importance of both the hard skills and soft skills over time as the make-up of the sweet spot shifts.

It is also important to note that many of the articles we reviewed have theoretical background sections; how- ever, the studies are largely atheoretical and not based on specific academic theories. Instead, scholars outline the seminal research related to proj- ect management, or combined project management theory and refer to con- tributing theories. Of the research with atheoretical basis, there are trends in using organizational theories, manage- ment theories, or leadership theories, which support the observation that project managers must be generalists in management and leadership and spe- cialists in project management appli- cation (McHenry, 2008). This is also consistent with claims that theory in

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project management is implied through the combined body of knowledge that outlines the multiple processes, tools, and techniques a project manager must apply in his or her profession (Williams, 2005; Pollack, 2007). For example, Cleland (2004) links manage- ment theory to the web of interper- sonal relationships a project manager must maintain in a matrix organization. Separate from discussions on success, Anantatmula (2010) highlights the dis- tinctions between classical manage- ment functions, situational leadership theory, and their application in proj- ect management, while claiming that a project manager’s role is more com- plex than most functional management roles. Supporting our findings across the literature, Williams (2005) argued that the project management profes- sion and related body of knowledge lack a comprehensive underlying the- ory, but does not suggest what a unify- ing theoretical framework might be. To make progress in our understanding, we may need to turn to other bodies of theory such as stakeholder theory and theories that include temporality (such as actor–network theory, design theory), or socio-technical theories that consider context and the shifting sweet spot presented by the overlap in per- spectives of success.

Discussion and Implications for Practice and Research We began our exploration of the lit- erature seeking insights for defining the skills associated with project-related success in the IT project management space. Our review of the literature highlights that it has both divergent views and explicit overlaps yet to be addressed. Nonetheless, this review leads us to that important “sweet spot” and a new focal area of research on proj- ect manager efficacy where these three areas converge and shift over time.

One message that has been con- sistent as research has evolved is that project managers and IT project man- agers must understand that project

success is a perception. Satisfying a single stakeholder group’s expectations may not address either process or out- come expectations. For example, we see many studies in which authors reveal that projects are delivered on time and within budget yet perceived as failures by the end-user if the product, service, or result hasn’t provided the desired outcomes. Similarly, a project can pro- duce the desired outcomes; however, stakeholders can perceive the project as a failure when the cost exceeds either the anticipated or the realized benefit.

The most effective project manag- ers are able to identify where the “sweet spot” resides in the Venn Diagram at any point in time; they understand the trade-offs between managing to proj- ect constraints and meeting stakeholder expectations; and they understand that the “sweet spot” moves based on the stakeholder group and where they are in the project life cycle. In short, the “sweet spot” is not a constant (see Figure 2). As such, the skills necessary to manage for success will shift with the

sweet spot. We argue this “sweet spot” is the crucial area of attention and pro- pose we focus on this new definition of project manager efficacy that explicitly acknowledges the overlap of factors that influence IT project outcomes, process, and the people best suited for a project.

In addition to accepting the dis- tinct definitions of success, we need to define and focus on the intersection of which skills and knowledge contribute to project manager efficacy in the IT- centric environment and beyond. Based on the literature, Table 4 provides our definitions of project success, project management success, and project man- ager success, but also reiterates our definition of project manager efficacy. Based on our review, we link the key skills suggested in the literature to these four definitions. It is important to note that “related soft skills and abilities” aren’t intended to be a comprehensive list but a representation of the reviewed literature.

The literature is clear that a focus on acquiring project management skills

Desired IT-Centric

Project Outcomes

A Project

Management Tools and

Techniques B

Project Manager

Soft Skills and Attributes

C

*

Shifting “Sweet Spot” of Project

Manager Efficacy

Figure 2: The sweet spot of project manager efficacy.

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means neither effective application of those skills nor successful outcomes. Possessing the necessary skills needed to effectively utilize project manage- ment techniques is a different issue— one that requires understanding of the stakeholder group, the ability to illicit expectations of stakeholders, and ver- satility in the ability to communicate with diverse groups of people. The literature also supports that project managers must possess advanced inter- personal skills to be effective, but we lack research that details how project managers’ interpersonal skills link with contextual issues of stakeholder percep- tions of project success or with proj- ect management success through the project life cycle (temporal influences). The lessons and gaps identified in the literature lead to our implications for

practice, for academic programs, and directions for future research.

As depicted in Figure 2 (our modified tri-focal lens), project manager efficacy is influenced by the project manager’s ability to work with stakeholders to identify the various perceptions of suc- cess, and then apply a combination of soft skills and project management tools and techniques to produce the desired outcomes. This does not mean that proj- ect success is an unattainable myth, or that it is so subjective or complex as to not be worth studying. Nor should we throw our hands in the air and claim that project management methodology does not matter. We know from the research that this is not the case. Rather, we argue that research has advanced enough that we can see the importance of successful synthesis of project objec- tives, good methodology, and under- standing of stakeholders. By shifting our research and practitioner focus from specific skills groups (such as what

factors influence project success), to a holistic view of project manager efficacy, we may be better able to not only match project managers to projects, but also guide our hiring and training.

Consistent with our discussion of the importance of stakeholders and temporal influences on project man- ager efficacy, we have organized our practice implications based on different stakeholder groups. Following, we pres- ent implications of our review for senior IT leaders/executives, project manage- ment practitioners, hiring and resource managers, those tasked with academic or project management curricula, and researchers seeking to address the para- doxes of success in the IT-centric proj- ect management space.

Implications for Practice

Implications for Senior IT Leaders/ IT Executives

IT executives are often members of the senior project–leadership stakeholder

Terms Definitions Related Hard Skills or Knowledge Related Soft Skills or Abilities Project Success (Varies at individual project and portfolio levels)

Project success 5 Outcomes meet planned or desired business objectives

For Individual project success: Clearly defined requirements or project objectives

Change management Communication Conflict resolution Decision making Expectation management Negotiation Stakeholder analysis and management

Project portfolio success 5 A pattern of successful projects that meet strategic outcome objectives

For portfolio project success: standards for program management and standards for portfolio management3

Adaptability Strategic leadership Senior stakeholder management

Project Management Success

Accomplishment of cost, time, and quality objectives

Knowledge of project management methodology and tools; technical knowledge

Decision making Leadership

Project Manager Success

Successful application of project management methodology

Industry knowledge Organizational knowledge Disciplinary knowledge

Motivating others Leadership Negotiation Communications Conflict resolution Team development

Project Manager Efficacy

The sweet spot that integrates factors that influence IT project outcomes, process, and the best people to be assigned to a project

Matched to needs of the specific project (e.g., some projects need more technical knowledge than others)

Ability to draw upon skill and knowledge areas as needed based on the desired outcomes as they evolve over time, given the unique context and stakeholder combination

Table 4: Relationship between definitions of success and hard or soft skills.

3We refer to this generically, although PMI has published

standards in these areas.

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group as either the executive sponsor or IT sponsor. Given the literature, IT lead- ers need to acknowledge the variances in perceptions about project success, the lack of a clear definition of project success, and the multiple factors that influence project efforts over time. To contribute to positive project-related outcomes, we highlight three specific challenges that IT executives may need to address related to skills and project manager efficacy:

1. Articulating project-related expecta- tions and metrics for success at dif- ferent stages in the project life cycle so that context-specific skills can be identified

2. Understanding their executive role in project sponsorship to include ensuring the IT project manager has the necessary skills to lead projects within the specific organizational context

3. Selecting IT project managers who have good relationships with the specific set of project stakeholders or those who support the IT project managers in building such rapport. This may involve individuals who are not certified or pairing them with certified project managers.

Implications for Project Management Practitioners

Scholarly research provides clear evidence that there is much more to project manager efficacy than master- ing the project management body of knowledge, or continued study of that same body of knowledge. While there are demonstrated benefits related to earning a project management certifica- tion (Müller, 2013), empirical research provides evidence that the structured approach to learning the project man- agement body of knowledge is only the foundation for a project manager’s pro- fessional journey.

The key implication for project management practitioners is the need to clarify which skills will contribute to their overall efficacy over time in

different contexts of stakeholders, as noted in Table 4. The challenge is iden- tifying the necessary skills and related approaches to developing those capa- bilities so they can adapt to the shifting sweet spot of skill requirements. Recent research supports this observation as it relates to applying agile project man- agement processes and the related bar- riers to realizing the benefits of agile methodologies (Gandomani, Zulzalil, Ghani, Sultan, & Nafchi, 2013). Like- wise, practitioners may benefit from investing in the development of their interpersonal skills and understanding the impact of their personality traits, more so than seeking to enhance their project management acumen.

Implications for Hiring and Resource Managers

As such, our findings should influence thinking about hiring decisions, train- ing, or other developmental invest- ments in IT-centric project spaces. This requires a focus on understanding the candidate’s personality and interper- sonal skills and their fit within the con- text of the organization and types of projects. Our review supports that static checklists of skills or knowledge miss the point. The skills sweet spot for proj- ect manager efficacy may shift through- out the length of a project or changing stakeholders (including team members and vendors). As such, demonstrated project management skills that worked in one environment do not ensure the same skills will work in every organiza- tion. Identifying contextual consider- ations in relation to desired skill sets will increase the likelihood of success- ful hiring or project allocation deci- sions and encourages hiring those with greater skill diversity.

A second implication of our syn- thesized findings relates to continued investments in professional develop- ment. Our findings support that resource managers should assess the value of focusing professional development on softer interpersonal skills that will contribute the most to project success,

which may require seeking develop- ment opportunities outside of the tradi- tional project management skill-based training.

Implications for Academic and Project Management Curricula

Our review of the literature on “success” in the IT-centric project space leads us to conclude that project manage- ment development and training is still missing a key focus on the set of skills that contribute to IT project manager efficacy, and hence to project success. Project management curricula typically include courses on project finance; risk management; cost estimating and man- agement; schedule management; and project execution and control, blended with other management, leadership, and organizational theory courses. Cur- ricula that develop the skills necessary for students to apply project manage- ment tools and techniques in practice must include approaches for developing interpersonal skills, such as managing a diverse set of stakeholders, managing conflict, and leading an organization through change. Further study has the potential to provide valuable guidance in the development of project manage- ment certification standards and aca- demic curricula.

Implications for Future Research

Based on our analysis of the literature, we believe there are four areas that provide rich avenues for future research in the project management field: topic integration, methodological diversity, perceptions of necessary soft skills, and the politics of curricular metrics:

Topic Integration: We argue that despite implicit and even explicit over- lap between the factors associated with different definitions of success, research has not explicitly addressed the sweet spot of project manager efficacy in IT- centric project environments. There is clearly more room for richer, qualita- tive studies that seek to understand this “sweet spot” or complex interaction between metrics for project success; the

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process of project management; and the unique skills and experiences brought to projects by successful (and unsuc- cessful) project managers. Equally as important, we need to understand this complex interaction across multiple stakeholders over time.

Methodological and Theoretical Diversity: Studies that focus on quan- titative analysis add to our under- standing by providing large-scale, cross-project, or cross-firm compari- sons; however, we lose the nuances of the unique organizational contexts and the people involved; there is much to be learned from studies with greater depth (see, for example, Shenhar, Dvir, Levy, & Maltz, 2001). As noted earlier, there are also new theoretical lenses that can be brought into the IT-centric project space, which will contribute to the understanding of project manager efficacy.

Soft Skills: We have a wealth of research on hard skills or hard metrics, but a shortage of literature on soft skills and how business managers, IT man- agers, and project managers perceive them. Regardless of the methodologies used, simply repeating studies about project outcomes or project manage- ment techniques will add little to our understanding if we ignore the deci- sion-making, interpersonal abilities, and contextual challenges in applying various techniques that are often miss- ing in current research.

The Politics of Project Manage- ment Curricular Development and Standards: Separate from recommen- dations for increased focus on soft skills and decision making in profes- sional certification programs are the challenges, economics, and politics of change. Many business and other schools in the university environment can tell you that employers are always looking for employees with soft skills, yet their job advertisements ask for specific programming languages, such as C11 (a software programming language), and other technical abilities or project management certification. How do we

generate change in professional certi- fications to include the so-called softer skills that are critical to successful peo- ple, successful practices, and successful outcomes? Based on our review of the literature, we argue that we need to move beyond the artificial boundaries of different definitions of success to understand the sweet spot where these intersect in project manager efficacy in IT-centric project environments. To further advance the discipline, we also encourage exploration of this “sweet spot” in other contexts.

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Sarantis, D., Smithson, S., Charalabidis, Y., & Askounis, D. (2010). A critical assessment of project management methods with respect to electronic government implementation challenges. Systemic Practice and Action Research, 23(4), 301–321. doi: http://dx.doi.org/ 10.1007/s11213-009-9161-9.

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Chuck Millhollan, MBA, MSPM, DPS is the Vice President of Process Improvement and Execution for Farm Credit Mid-America; he was previously the Director of Program Management for Churchill Downs Incorporated, and has over 25 years of experience in personnel and project management. In addition to his work as a practitioner, Chuck develops curricula, teaches for several accredited universities, and has contributed to the following books by Dr. Harold Kerzner:

1. Project Management Best Practices: Achieving Global Excellence, 3rd Edition, John Wiley & Sons, New York (2014)

2. Project Management Metrics, KPIs and Dashboards, 2nd Edition, John Wiley & Sons, New York (2013)

3. Project Management Metrics, KPIs and Dashboards, John Wiley & Sons, New York, (2011)

4. Project Management Best Practices: Achieving Global Excellence, 2nd Edition, John Wiley & Sons, New York, (2010)

Chuck is a member of the International Institute of Business Analysis (IIBA), a senior member of the American Society for Quality (ASQ), and a member of the Project Management Institute (PMI), and Kentuckiana PMI Chapter.

Chuck earned a Doctorate of Professional Studies in Information Management from Syracuse University in 2015, a Master of Science in Project Management from the University of Wisconsin in 2003, a Master of Business Administration from the University of Florida in 2000, and a Bachelor of Science in Management in 1993 through Southern Illinois University.

Chuck’s holds the following PMI certifications: Project Management Professional (PMP)®, Program Management Professional (PgMP)®, and the PMI Agile Certified Professional (PMI-ACP)®. Chuck also holds the following certifications: Certified Business Analysis Professional (CBAP) through the International Institute for Business Analysis (IIBA); Certified Six Sigma Black Belt through the American Society for Quality (ASQ); Certified Manager of Quality/Organizational Excellence (CMQ/OE) through ASQ; Certified Software Quality Engineer (CSQE) through ASQ; and Certified Managed Healthcare Professional (MHP) through the Health Insurance Association of America

In October 2011, Chuck was the recipient of the 2011 Kerzner “International Project Manager of the Year” Award. You can find additional information about Chuck via his online business card at www .millhollan.net. He can also be contacted at chuck. [email protected]

Michelle Kaarst-Brown, FLMI, MBA, PhD, is tenured faculty at the School of Information Studies, Syracuse University, Syracuse, New York. She is a former Program Director for the Doctorate of Professional Studies in Information Management, and former Interim-Program Director for the Masters in Information Management program.

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Dr. Kaarst-Brown’s professional work with culture, symbolism, risk, and security has included compli- ance and financial risks associated with quality business practices in the financial services sector. Other experiences include developing and managing emerging marketing technologies, IT integration during a major merger and acquisition, organizational development initiatives for a national company, and consulting to smaller enterprises on market develop- ment and enterprise risk management.

Dr. Kaarst-Brown’s prior work experience became the foundation for her research interests in how the perceptions of risk and opportunity shape strategic

and individual action, including IT governance and IT workforce/STEM issues. After entering academia, her research into “IT culture” and perceptions of IT risk and opportunity have resulted in diverse projects with other scholars, doctoral students, and practitio- ners. An overarching theme in her research has been to look beyond traditional views to understand how to gain traction for organizational change associated with better risk and opportunity management. She has presented her work at international confer- ences and published in a number of top academic and practitioner-focused journals including MIS Quarterly, MIS Quarterly Executive, CIO Canada, Information Technology and People, the Journal of

Strategic Information Systems, and the Journal of Organizational Change Management.

Dr. Kaarst-Brown’s research has been widely adopted in practice and higher education. IBM’s West Coast Compliance Division adopted her early research (with Shirley Kelly) on Sarbanes Oxley’s potential impact on the CIO and IT function. Dr. Kaarst-Brown’s papers on her theory of “IT Cultures” have been reprinted in several IT textbooks, and are required reading in international undergraduate, masters, and doctoral programs; and have been integrated into train- ing materials in practice. She can be contacted at [email protected]

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Appendix: Review methodology. With the hundreds of articles available today on the various aspects of projects and project management, it was not pos- sible to cite each article that mentioned project management. Instead, we used a structured methodology to identify arti- cles most cited or reprinted that met our search criteria from within key journals in the project management and informa- tion technology (IT) space. We also did a separate search based on key terms that focused on research published within the last ten years in the project management journals and the last three years within selected IT journals. We acknowledge that we have not recognized every study that readers may feel should have been included, and apologize for any over- sights. We feel the articles represented here provide the coverage and evidence that supports the purpose of our review. We also encourage any researcher whose perspective wasn’t presented to bring it to our attention. An overview of our search methods and criteria for inclusion follow.

Citation Chain Analysis:

We began the review with a Citation Chain4 approach, starting with top project management journals, books, and educational texts viewed as expert subject matter. The goal for the cita- tion chain analysis was to identify the most enduring and influential articles that are used in practice and relevant to the topic of project management, project outcomes, project managers, and “suc- cess.” Books included textbooks used in project management undergraduate and graduate courses, books published by the Project Management Institute (PMI) and included as references for A Guide to the Project Management Body of Knowledge (PMBOK® Guide) – Fifth Edition (2013), and professional books written by expert practitioners. While not a traditional

approach (most would start with aca- demic literature), project management is a certified practice with an estab- lished reading list that includes scholarly research, and as such, we began there.

Traditional Literature Search and Bibliometric Analysis:

Our review then progressed to a traditional search of scholarly articles, leveraging ProQuest Central and EBSCO host data- bases, and supported by privileges associ- ated with PMI membership. To narrow the search and ensure a focus on current research, the search was limited to work published within the last three years, focus- ing on keywords within titles and abstracts that implied the central focus of articles were on project success, project manage- ment success, or project manager success. Given our focus on “IT-centric” project environments, and IT project managers, we further cross-checked by searching publication trends over the last ten years in seven IT-specific scholarly journals.

Selection Criteria:

In addition to seminal articles identi- fied from the citation chain analysis, we used similar search criteria for the bibliometric analysis, with the following search constraints:

1. ProQuest Central Database 2. Scholarly Publications only, Article

Type (Journal), and Peer Reviewed 3. Journals:

a. Journal of Information Technology (JIT)

b. MIS Quarterly (MISQ) c. Information Systems Research (ISR) d. Computer Information Systems e. Communications of the ACM

(CACM) f. Journal of Information Manage-

ment Systems g. Project Management Journal® (PMJ) h. International Journal of Project

Management 4. Years: The search was limited to the

previous three years (up to August 2015) in PMJ and IJPM, and ten years for IT journals (up to August 2015).

5. Abstracts to ensure articles were pri- marily focused on the research topic and more accurate than relying on titles.

Search Terms:

Using generic terms such as Project, Success, Management, or Information Systems (IS) and Information Technol- ogy (IT) proved too broad and produced results unrelated to the research topic. Research then focused on the following exact word combinations:

• Project management • IT project success or IS project success • Information technology/systems proj-

ect success • Project success (removing the IT/IS

constraint) • Successful IT projects or successful IS

projects • Successful projects (removing the IT/IS

constraint) • Project management success • Successful project management • Project manager success • Successful project manager

To ensure the exact word combina- tions weren’t too constraining, especially for the IT journals, the following word combinations were added to the search:

• Information technology or information systems 1 project management

• Project manager 1 success • Project management 1 success • Project 1 success

We removed duplicate articles rep- resented more than once if the abstract contained multiple search terms. After reading and analyzing 214 potential publi- cations, we identified 59 articles included as key references cited in our analysis. These articles focused specifically on fac- tors leading to success in the project man- agement space, with particular relevance to IT-centric success considerations. As noted above, it would be impossible to include very article written on the subject of project management success, but feel our structured approach is replicable and would yield the same seminal articles.

4Citation chain analysis follows references used in articles

back to the original source articles, seeking those articles that

were the foundation studies or supporting evidence. This is a

well-accepted method for identifying seminal works.

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Project Management Journal, Vol. 47, No. 5, 107–129

© 2016 by the Project Management Institute

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Administration: Categorization of Project Management Offices Monique Aubry, Université du Québec à Montréal (UQAM), Montreal, Canada Maude Brunet, Université du Québec à Montréal (UQAM), Montreal, Canada

INTRODUCTION

T his article aims to advance the scientific knowledge about orga­ nizational design in the specific context of public administration facing the management of multiple projects. It presents the results of research conducted within a public administration in which there is

a quest for implementing good practices in project management. Yet, poor project management results have been cited for several years by the auditor general (Vérificateur général du Québec, 2012; 2011; 2004). The media also plays a role in demanding a better return on public funds (e.g., Radio­ Canada, 2011; Radio­Canada, 2010), which isn’t something new for public administration. Countries including Norway, the United Kingdom, and Australia have been criticized for much the same situation (Samset & Volden, 2013; National Audit Office, 2012; Victorian Auditor­General, 2012). This is happening at a time when governments are facing strong pressure to adopt new, transparent public management policies, to open up to the public, and to interact with social media (Osborne, 2010).

In this particular situation various actions have been taken by the gov­ ernment under study over the last few years, which have failed to produce good results. The department responsible for government processes and performance began reflecting on the overall approach to project manage­ ment. The starting point for this reflection was to determine the current situ­ ation; therefore, a research team was formed and asked to participate in this effort. A public administration is a complex organization or, rather, a com­ posite of multiple, complex organizations subject to pressures in the national political system; therefore, generating a global (and central) understanding of the project management situation is a difficult task. Basic questions remain unanswered, such as: Who is involved in managing multiple projects through­ out the governmental apparatus? What are they doing? How sensitive are they to project management performance? It is in this context that this research was undertaken.

This article offers only a partial view of the overall reflection on the situation within the government under study at the time of this research. Accordingly, this research supports an overall evidence­based management approach (Rousseau, 2012) calling on academics and professionals to work together toward understanding crucial problems and envisioning contextual solutions. However, we strongly believe that the findings of this research could enlighten similar questions pertaining to any other government. It pro­ vides a unique survey of 114 entities in 42 departments and agencies manag­ ing multiple projects within the same government.

ABSTRACT ■

The article aims to renew the classification

system of PMOs within the context of orga-

nizational design and proposes an empiri-

cal categorization of PMOs based on types

of projects: engineering and construction,

information systems/information technol-

ogy, business processes, and new product

development. The research adopted a quan-

titative approach with a survey of 114 entities

belonging to 42 departments and agencies

within a single public administration. The

findings show that this categorization sys-

tem of PMOs has the potential to support

the organizational design of PMOs in orga-

nizational context, structural characteristics,

functions, and performance. This article con-

tributes to the relevance of organizational

design within the project management field.

KEYWORDS: public administration; organizational design; project management

office (PMO); PMO categorization

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the findings. The sixth section includes a discussion, followed by the conclud­ ing remarks.

Background Types of PMOs

A substantial effort has been deployed recently to find a PMO typology based on empirical data that would offer an alternative to the models found in an array of books written for professionals (Hobbs & Aubry, 2008; Müller, Glückler, & Aubry, 2013). This task is compli­ cated by the wide variety of PMOs. The approach by variables has not yet gen­ erated strong results. The most prom­ ising model is based on crossing the following variables (Hobbs & Aubry, 2008): the number of projects covered by the PMO’s mandate and the num­ ber of project managers. This suggests four types of PMOs, varying in terms of decision­making authority—whether they operate under a matrix type of organization or not—project manage­ ment maturity, and organizational cul­ ture supportiveness.

Using a completely different approach, Müller et al. (2013) suggested a model based upon the PMO’s relationship roles: serving, controlling, and partner­ ing. Following a qualitative methodology approach, they show the impact of these relationships on innovative performance: slack, innovativeness, and ambidexterity. Serving and controlling PMOs are more likely associated with less operational innovation; on the other hand, the most highly innovative PMOs are shown to include partnership relationships. Most PMOs played the controlling role and very few had an equilibrium role strong in ambidexterity (March, 1991; O’Reilly III & Tushman, 2008).

As shown above, the current effort to propose an empirically validated typology for PMOs has not yet pro­ duced any convincing results, which may come as no surprise, given that such coordination mechanisms are more likely to be considered an orga­ nizational innovation (Hobbs, Aubry, & Thuillier, 2008).

(Morris & Geraldi, 2011); and governance (Müller, 2011). Recently, Winch (2014) proposed an integrative view of organi­ zational design research in the project management literature. He proposed a three­domain framework to deal with the tension between the temporary nature of projects and the permanent nature of the parent organization.

Why this interest? One answer pro­ bably concerns the well­established proximity of project management to the professional world. Managing multiple concurrent projects poses major chal­ lenges in terms of understanding how to organize people and resources in order to collectively accomplish the desired ends.

Moreover, in project management research, these three obstacles men­ tioned by Greenwood and Miller (2010) seem to be of less importance. First, mainstream research still considers the organization and the problems inter­ nal to the organization. Second, project management scholars seem fascinated by the complexity of organizational forms. Third, the overall organization is not always the focus, but the net­ worked nature of projects makes the research on organizational design in project management more inclusive of multiple views.

Overall, project management research has something to offer to organizational theory on organizational design in over­ coming obstacles, as mentioned by Green­ wood and Miller (2010). This article, while highly quantitative and narrow in scope, aims to contribute to the goal of organiza­ tional design for project management.

This article is structured as follows. Following an introduction, the second section provides background informa­ tion on the current classification sys­ tems of PMOs and on categorization in the organization theory, followed by the conceptual framework used in this research. The third section describes the methodology used for data col­ lection and analysis. The fourth sec­ tion contains the results from factor analysis, and the fifth section presents

Clearly, this research contributes to a better understanding of project organization and the project organizing process (Van de Ven & Poole, 1995). The methodology adopted in this article is based on a survey involving statisti­ cal analysis. In this sense, the findings provide valuable knowledge about the number of entities dealing with mul­ tiple projects, along with findings useful to decision makers for organizational purposes, including the development of project management policies.

Some recognition exists that orga­ nization design has been neglected as a core concern of organizational theory (Greenwood & Miller, 2010). This situ­ ation is based on the assumption that “a focal interest of organization theory must inevitably be the understanding of how to organize people and resources in order to collectively accomplish desired ends” (Greenwood & Miller, 2010, p. 78). Over the past several decades, these authors have observed some neglect of organizational design among man­ agement and organization scholars and argue that three main reasons explain the lack of interest in organizational design:

1. A shift of empirical and theoretical focus within the field from the orga­ nization to the field, population, and community as the unit of analysis;

2. The sheer complexity of many of today’s organizations and the poten­ tial difficulty involved in data collec­ tion methodology requirements; and

3. The over­riding interest in parts of organizational design to the detri­ ment of the overall organization.

Conversely, over the last few de cades, a clear interest has emerged among scholars in the project management field in work on organizational design, includ­ ing project business (Artto & Wikstrom, 2005); project management offices (Aubry, Hobbs, Müller, & Blomquist, 2011; Hobbs & Aubry, 2010); project­based and project­oriented firms (Geraldi, 2009; Hobday, 2000); the institutional context

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to project categorization was then used to develop a multidimensional model for project success (Shenhar, Dvir, Levy, & Maltz, 2001). Two points must be mentioned regarding this framework: (1) the theoretical approach and (2) the particular study. First, contingency the­ ory has not been extremely useful in explaining the diversity of PMOs. The size of the organization, industries, and regions didn’t offer any insight into explaining diversity (Hobbs & Aubry, 2010); it seems that a rich context must be understood using a more integrative approach, such as one including histori­ cal perspective (Zeitlin, 2008). Second, the goal of this public administration research does not concern projects or project success; rather, it focuses on entities whose activities relate to mul­ tiple projects. This categorization might not be as relevant in the context of this specific research.

More research has been undertaken on a project categorization system (Crawford, Hobbs, & Turner, 2005) and it addresses two main objectives: to develop and assign appropriate com­ petencies to undertake projects suc­ cessfully and to form project portfolios. It also addresses the process of imple­ menting a categorization system within an organization. The authors found that such a system includes two main com­ ponents: the purpose of a classification and the attributes used for its classifi­ cation. Crawford, Hobbs, and Turner (2005) did not propose one particular model or framework of categorization. Rather, they “[. . .] offer a tool for organi­ zations to examine their existing project categorization systems, to better under­ stand how they work, and as a guide to redesign them” (Crawford et al., 2005, p. 45). While this research is of great interest in understanding the underly­ ing features of categorization systems, it is of no help in the specific case of a public context.

The goal of this research clearly deals with organizational design for project management. As such, the project categorization of Shenhar and

to move away from the “one­size­fits­ all” concept. Context became of prime importance in the study of organization. The configuration school (Mintzberg, 1979) has developed from contingency by adding the internal coherence to the identification of ideal types. The main critics of the contingency theory have addressed the difficulty in operational­ izing such types. Moreover, new social approaches to organizations tend to emphasize the complexity of organiza­ tions (Clegg, 2012) and their constant transformations (Weick, 2009).

Contingency Theory and Categorization Systems in the Project Management Field

The field of project management is still being criticized for being under­ theorized (Morris, 2013; Söderlund, 2011). Scholars are also encouraged to investigate the philosophical founda­ tions of their research (Konstantinou & Müller, 2016). Following Fenton and Pettigrew (2000), contingency theory has been part of the organization and management theory since the seminal work of Woodward (1965), Burns and Stalker (1961), and Lawrence and Lorsch (1967). Their main contribution to the development of contingency theory was to explain the diversity of forms of organizations in relation to contextual factors. As other theories, contingency theory was subsequently introduced to the field of project management, and more intensively, after 2000 (see Hanisch & Wald, 2012, for a good review in the project management literature).

Projects have also been the object of classification in an effort to achieve bet­ ter results, be they operational or strate­ gic. Shenhar and Dvir (2004) proposed a framework built on differences in proj­ ect types. Based on contingency theory and innovation literature, they argue that not all projects are the same and they determined project types based on the four dimensions of novelty, com­ plexity, technology, and pace and sug­ gest adapting the project management style to the project type. This approach

Alternatively, this article proposes a categorization for PMOs based on proj­ ect types. While a typology is usually conceptual before it is empirically tested (Borgès Da Silva, 2013), a categoriza­ tion system is established based on pur­ pose or attributes so that it becomes shared language for the people using it (Crawford, Hobbs, & Turner, 2006). While a categorization system may com­ prise few categories concurrently in use in organizations, we focus here on one single PMO attribute based on the nature of the work (the product of projects); we have found from data collection that this is fairly formal and well established within the government under study (ibid). Thus, we propose exploring PMO categorization based on project types.

PMO Categorization and Project Types

The role of categorization and classi­ fication in management is widely rec­ ognized. Classification systems rely on philosophy of science and help orga­ nize scientific knowledge (Parsons & Wand, 2013). Following these authors, there is a consensus on the outcome of such systems of classification that can bring order to the apparent chaos. They are important strategies that enable human beings to deal with complexi­ ties and numbers in a wide variety of situations. One excellent example is the early Linné plant classification sys­ tem (Linné, 1964 [1735]). In the theory of organization, typology takes place within the contingency theory in which the main questions revolve around the idea of developing generalized typology of organizations forms based on ideal types or variables for modeling orga­ nizations (Fenton & Pettigrew, 2000). Along this line, Burns and Stalker (1961) have strongly influenced the project management field by suggesting dif­ ferent forms of organizing based on the level of innovation. Typologies have also contributed to the establishment of some principles in terms of differ­ entiation and integration (Lawrence & Lorsch, 1967). The major contribution of the first contingency scholars was

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Dvir (2004) is inappropriate for study­ ing entities involving multiple projects within a public administration. The pro­ cess for developing and implementing categorization systems as suggested by Crawford et al. (2005) is not suitable for this particular research either.

This leads us to the following research question: “Is a PMO catego­ rization based on project types helpful in the organizational design of project management in public administration?”

Conceptual Framework

Clearly, the proposed conceptual frame­ work takes place within contingency theory; however, the intention is not to generate a narrow understanding of PMO types. Conversely, our approach is intended to be part of a larger process

of organizational design within a social environment in which tensions, politics, and power struggles take place. This research will only provide a partial and limited view of the process; however, it is a good starting point to contribut­ ing to the organizational design process and joining the project management scholars working under contingency studies (Hanisch & Wald, 2012). The proposed conceptual framework has been developed with the intention of capturing differences between entities dealing with multiple projects based on different types of projects. As illustrated in Figure 1, the global proposition is that these entities have specific characteris­ tics, depending on the types of projects they deal with: in terms of their orga­ nizational context, their structures, and

the functions they perform. Moreover, they may also have different performance results concerning embeddedness, proj­ ect management factors, and criteria. The conceptual framework that served as the basis for this article was mod­ eled after Hobbs and Aubry (2010). This framework was empirically validated by a worldwide database of more than 500 PMOs, which lends it significant strength. As mentioned recently by Pfeffer (2013), differences must clearly be specified by scholars in research and in teaching to avoid “[. . .] confusion between what is and what should be or what we would like to be [. . .]” (p. 277). This descriptive framework is not wishful thinking but a product of data. Although it may not rep­ resent the full truth of a situation, it none­ theless has some significance in reality.

Figure 1: Conceptual framework for PMO categorization.

PROJECT MANAGEMENT OFFICE

TYPES OF PROJECTS

• Characteristics of the organization • Characteristics of projects in the PMO mandate

• Structure • Profile of personnel • Practices/tools/methodology • Role of the entity

ORGANIZATIONAL CONTEXT

STRUCTURAL CHARACTERISTICS

• Importance of each function • Degree of realization of each function

Engineering and construction (EC) Information systems and information

technology (IS/IT) Business processes (BP)

New product/service development (NPD) FUNCTIONS (list of 27 functions)

• Project management performance • Most important criteria for project success • Embeddedness

PERFORMANCE

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Returning to the etymology of office, we find that the term has Latin roots and is defined as a major administrative unit in some government or a subdivision of some government department. The word has primarily been used in pub­ lic administration (Merriam­Webster, 2007), which is still the case in many governments, for example, post office or patent office. The 14th­century word bureau has roughly the same definition, for example, competition bureau. In this research, we adopted the following definition of a PMO:

An organizational body or entity assigned various responsibilities related to the cen­ tralized and coordinated management of those projects under its domain. The responsibilities of a PMO can range from providing project management support functions to actually being responsible for the direct management of a project (Proj­ ect Management Institute, 2008, p. 443).

This definition is preferred over the 2013 definition (Project Management Institute, 2013), which may limit some of the PMOs empirically included in this research.

Methodology This section describes how data was collected and analyzed based on the conceptual framework illustrated in Figure 1. In social science, a quantita­ tive approach is usually associated with a positivist paradigm—where reality is considered to be out there and ready to be observed in a unique and stable state. Following this approach, the typi­ cal methods are based on hypotheti­ cal–deductive methods. Con versely, scholars in management (includ­ ing project management) who have oriented their research in a practice perspective go away from this hypo­ thetical–deductive methods, in which context is not taken into account (e.g., Cicmil, Williams, Thomas, & Hodgson, 2006; Drouin, Müller, & Sankaran, 2013). This article presents the results of a questionnaire and, as such, involves hypothetical–deductive methods. This

research, however, is not intended to provide prescriptions for project man­ agement in a government; rather, it aims to describe a government reality considered as a very partial element of a much more complex reality. The value of this research is to generate new scientific knowledge to help understand this complexity by proposing a catego­ rization based on the types of projects in order to nurture reflection among decision makers on the organizational design for the management of multiple projects. In this sense, the methodology used here is more easily understood when situated within a multidimen­ sional continuum of research projects between the quantitative and qualita­ tive methods proposed by Teddlie and Tashakkori (2009). Using the three dimensions proposed by these authors, in the concept sphere, the qualitative component relates to the subjective purpose and the aims of this research to generate understanding (not confir­ mation). This research is more likely quantitative in the sphere of concrete processes with numeric data and statis­ tical analysis. In the areas of inference and explanation, this research leans more on the qualitative side, with more inductive and subjective inferences.

Data Collection Strategy

This research took place within a national government, where it had a unique opportunity to provide some input for reflective practitioners on how to significantly enhance the quality of the management of multiple projects undertaken in government departments and agencies. It was managed with the help of professionals and managers in an administrative unit responsible for the management and performance of the government apparatus as a whole.

To meet the needs of the govern­ ment, the questionnaire developed by Hobbs and Aubry (2007) has been used after being adapted to the specific con­ text of this government setting. This was conducted with the participation of members of GP­Québec, a community

of practices dedicated to public man­ agement and with managers and profes­ sionals in the government. For example, the question on the positions of respon­ dents was modified to adapt to cur­ rent classes in the government. Other questions were added to capture spe­ cific needs, for example, questions on portfolio management. A pretest was conducted with four respondents; their comments led to some minor adjust­ ments and these answers have not been included in the database. The question­ naire was in the French language only.

Many targeted respondents were not familiar with the project management vocabulary; various actions have been taken to account for this in our research and ensure confidence in the quality of answers. First, the term ‘project man­ agement office’ (or PMO) was avoided to facilitate a general understanding among a diversity of respondents in government departments and agen­ cies. The unit of analysis was defined as any entity responsible for managing multiple projects and performing func­ tions related to these projects, and thus considered a PMO. Second, a guide to answering the questionnaire was devel­ oped, in which definitions and explana­ tions were provided. Third, and lastly, information sessions were organized for all government departments and agen­ cies to which the basic concepts were explained and the main sections of the questionnaire were reviewed.

The initial goal of this research was not to create a sample of entities deal­ ing with multiple projects in this gov­ ernment but rather to represent them fully in order to create a census. The method used is not probabilistic, but based on judgment sampling (Govern­ ment of Canada, 2013). Participating entities were targeted with the specific aim of studying entities dealing with multiple projects. Invitations to answer the questionnaire were sent by email to the secretary­general of 50 depart­ ments and agencies. Many entities can coexist in each single department and agency, but only one respondent was

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allowed to answer for each PMO. The secretary­general was responsible for identifying respondents in his own organization and for following up. The questionnaire was available on a web­ site or in PDF format. Confidentiality and anonymity were ensured by having the completed questionnaires available only to the researchers, as agreed in an ethics certificate issued by the affiliated university.

The questionnaire was released in September 2012 and answers were col­ lected until the end of October 2012. A total of 119 questionnaires were com­ pleted, among which 98 valid question­ naires were entered in the database. An official return rate cannot be calculated, because the total number of entities dealing with multiple projects in the governmental apparatus is unknown. Exclusions were based on a level of completeness under 60%.

Another 16 questionnaires were added to the database. These question­ naires were completed during a pre­ liminary phase of the government study, using roughly the same questionnaire used in the fall of 2011. A comparison of both samples revealed no significant dif­ ferences, with a p­level , 0.05 and there is no historical bias. The total number of questionnaires (114) adequately repre­ sents the entities dealing with multiple projects in the governmental apparatus, although it is not a census. Sample demo­ graphics can be found in Table 1. Answers to questions represent the perception of respondents.

The diversity of the sources of ques­ tionnaires is presented in Table 2, which lists departments and agencies. Individ­ ual organizations were grouped by logical themes in order to shorten the list and pro­ tect the anonymity of respondents in which the number of answers was five or less.

Data Analysis Strategy

After having cleaned up the database as explained above, some answers were recoded when appropriate, usually based on the comment field.

Two main types of multivariate methods were used: the first, principle component analysis (PCA) and, the sec­ ond, nonparametric one­way ANOVA analysis along with multiple compar­ isons of means. PCA has been used to determine the underlying structure among three groups of variables; how­ ever, previous research has provided models for each of the three groups of variables. A discussion between the cur­ rent and previous models is provided in the next section on results.

PCA provides summarizations and reduces the number of variables, result­ ing in values on the dimension of fac­ tors, which are then included in the second multivariate method (Hair,

Project management maturity level

Initial level Repeatable level Defined level Managed level Optimizing level Total

21 50 34

7 2

114

18.4 43.9 29.8 6.1 1.8

100.0

Internal or external customers

Internal External Total

74 29

103

71.8 28.2

100.0

Number of people working on a typical project

10 or less 11 to 25 26 to 50 51 to 75 76 to 100 101 to 200 More than 200 Total

63 31 13

2 0 1 1

109

57.8 27.9 11.7 18 0

.9

.9 100.0

Positions of respondents

Senior executive Manager—higher levels Manager—lower levels Professional Total

4 57 33 18

112

3.6 50.9 29.5 16.1

100.0

Types of deliverables

Engineering or construction New product/service development Consultancy services IS or IT Business processes Events and cultural activities Total

28 11

1 44 26 3

113

24.8 9.7

.9 38.9 23.0

2.7 100.0

Number of employees in department and agency

1 to 100 101 to 500 501 to 1,000 1,001 to 5,000 More than 5,000 Total

5 44 29 20 16

114

4.4 38.6 25.4 17.5 14.0

100.0

Table 1: Sample demographics.

n Percentage n Percentage

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Black, Babin, & Anderson, 2010, p. 98). The three PCAs were conducted on Likert­type scales, ranging from 1 to 5. Two types of scales were used:

1. Not important to very important (PCA on functions)

2. Totally disagree to totally agree (PCA on embeddedness and on project performance)

The reliability of the factor data was tested using Cronbach’s alpha; values greater than 0.6 were considered reli­ able (Cronbach, 1951); factors loading greater than 0.5 were considered sig­ nificant (Hair et al., 2010). Those factors not meeting these two threshold levels were considered orphans and listed in the respective tables but excluded from further analyses. The Kaiser, Meyer, and Olkin method (KMO) was used to mea­ sure covariance. All KMO values are above 0.7 (Hair et al., 2010).

The second type of multivariate methods included two nonparametric methods:

1. Nonparametric analysis of variance: One­way analysis of variance using the Kruskal and Wallis test (Kerlinger & Lee, 2000). This method was per­ formed to see if differences between

the four types of projects are globally significant.

2. Mann–Whitney U Test: Two­by­two nonparametric comparisons between groups to locate where the significant differences are.

Results from Factor Analysis Three different factor analyses were performed for the purpose of summari­ zation and reduction: functions, embed­ dedness, and project performance.

Factor Functions

Functions constitute one component of the conceptual framework (See Figure 1) and it refers to what the PMO is per­ forming. Two different sets of questions were asked regarding the 27 functions: the importance and the degree of real­ ization. Several factor analysis strate­ gies were performed using these two sets of answers. In our judgment, the framework that better fits the context of this research is based on the question of the degree of realization (rather than importance). However, five functions had to be excluded from this analysis for three reasons: (1) some functions did not load into a factor; (2) some func­ tions loaded into more than one factor; and (3) some functions loaded into a factor making interpretation much more

difficult. Ultimately, 22 functions loaded into five factors, each comprising a group of functions (as shown in Table 3).

The first group labeled ‘Project per­ formance and portfolio management’ brings together the monitoring and con­ trolling of a single project within other functions associated with the manage­ ment of a portfolio of projects. Unger, Gemünden, and Aubry (2012) have related this function to a controller role in the PMO. In the public context, it seems that there is no locus for individual project monitoring and control outside portfolio management. This observation is complemented by the exclusion of two orphan functions: ‘Implement and oper­ ate a project information system’ and ‘Develop and maintain a project score­ card.’ Interpretation of this result puts into question the quality of the monitor­ ing and control of individual projects in public administration. Alternately, the inclusion of benefits management aligns with a view of portfolio management as a means to delivering expected benefits provided in a strategic process (Zwikael & Smyrk, 2012).

The second group of functions, ‘Methodologies and competencies,’ is quite coherent with previous models (e.g., Hobbs & Aubry, 2007) where two different types of activities are loaded together: activities related to standard­ ization and activities related to the development of project management competencies. The combination of both makes sense because training people in methodologies and standards will certainly lead to their better use. Note­ worthy is that networking and environ­ mental scanning were loaded in this group; it is quite interesting that the public administration includes open­ ness to external views that can incorpo­ rate innovation into more conventional approaches to projects. In this particu­ lar public setting, there is a strong com­ munity of practice dedicated to project management, where practices are con­ sistently discussed between practitio­ ners and where researchers are invited to present their results. One function

Groups of Departments and Agencies

Number of Departments or Agencies

Number of Respondents

Administration 4 19

Culture and museums 7 17

Infrastructures 4 14

Health and social services 2 8

Transportation 4 12

Solidarity, education, and research 4 7

Security and justice 4 11

Work and work security 4 10

Seniors and pensions 4 10

Resources, sustainability, and immigration

5 6

Total 42 114

Table 2: List of groups of departments and agencies.

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Group 1: Project Performance and Portfolio Management

Group 2: Methodologies

and Competencies

Group 3: Organizational

Learning

Group 4: Collaboration

and Communication

Group 5: Specialized

Tasks

Identify, select, and prioritize new projects 0.809 0.032 0.041 0.199 0.202

Manage one or more project portfolios 0.709 0.378 20.096 0.189 0.152

Coordinate between projects 0.724 0.134 0.090 0.322 0.168

Benefits management 0.711 20.014 0.437 0.171 20.038

Allocate resources between projects 0.726 0.242 0.055 0.308 0.020

Monitoring of project performance 0.658 0.288 0.327 20.001 0.352

Controlling of project performance 0.639 0.093 0.405 0.068 0419

Develop and implement a standard methodology 0.227 0.650 0.329 0.149 0.330

Provide a set of tools without an effort to standardize

0.173 0.791 0.001 0.017 0.251

Develop competency of personnel, including training

0.128 0.663 0.230 0.135 0.172

Promote project management within organization

0.087 0.724 0.125 0.406 20.025

Networking and environmental scanning 0.122 0.684 0.316 0.087 20.010

Conduct project audits 0.200 0.152 0.734 0.301 20.109

Conduct post-project reviews 0.462 0.089 0.574 20.028 0.358

Implement and manage database of lessons learned

0.080 0.456 0.680 0.111 0.090

Implement and manage risk database 20.007 0.304 0.724 0.038 0.197

Report project status to upper management 0.379 0.257 0.006 0.665 0.276

Provide advice to upper management 0.286 0.288 0.229 0.672 0.026

Manage stakeholders interfaces 0.272 0171 0.083 0.696 0.470

Monitoring and controlling performance of PMO 0.246 20.015 0.410 0.553 0.357

Execute specialized tasks for project managers 0.167 0.214 0.034 0.198 0.763

Manage archives of project documentation 0.293 0.214 0.195 0.318 0.699

KMO 0.868

Percentage variance 20.030 15.427 13.100 11.144 10.355

Percentage cumulative variance 20.030 35.458 48.557 59.701 70.056

Cronbach’s alpha (Q8) 0.901 0.846 0.802 0.842 0.716

Cronbach’s alpha (Q7) 0.874 0.816 0.794 0.798 0.661

Note: Factor loadings .0.50 appear in boldface type.

Table 3: Groups of PMO functions.

has been excluded and considered orphan: Provide mentoring for project managers. Mentoring might not be an active way of developing competences in this public setting.

The third group is ‘Organizational learning,’ where all functions related to knowledge were loaded with the excep­ tion of ‘Manage archives of project

documentation.’ This result is surpris­ ing because it does not contribute along with the other functions to organiza­ tional learning. Archives are associated with explicit knowledge and might not have any value once the project is fin­ ished; it might more likely be associ­ ated with very technical documentation work. Globally, this group of functions

includes lessons learned, which might be the most important reusable knowl­ edge for subsequent projects.

The fourth group, ‘Collaboration and communication,’ comprises rela­ tions with upper levels and stakehold­ ers, which is very interesting because it highlights the role these entities are playing outside the realm of project

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and portfolio management. This can be seen as strength to partnerships and to more active integration (Artto, Kulvik, Poskela, & Turkulainen, 2011) and has a positive impact on cooperation. Inter­ estingly, the function ‘Report project status to upper management’ is part of this group. This can be interpreted to indicate that reporting is more likely a means of communication than project control.

The fifth group is ‘Specialized tasks,’ which includes the ‘Manage archives of project documentation’ function. With this structure of five groups of func­ tions, we arrive at a refined proposition over three roles identified for PMOs and dedicated to portfolios (Unger et al., 2012). All five groups of functions can relate to one of the other PMO roles and at the same time adds more specificity to the activities performed by the PMO.

Globally, the factor analysis pro­ vides five groups from 22 functions. The five functions excluded from the factor analysis are considered orphans; they play important, but individual functions:

1. Develop and maintain a project score­ card;

2. Implement and operate a project infor­ mation system;

3. Provide mentoring for project managers;

4. Participate in strategic planning; and 5. Recruit, select, evaluate, and deter­

mine salaries for project managers.

Factor Embeddedness

The factor embeddedness is part of ‘Per­ formance,’ one component of the con­ ceptual framework for the study of types of entities (see Figure 1). The question­ naire asks about six different aspects of embeddedness. As a reminder, the initial research on this construct has produced interesting results for explain­ ing the role of a PMO in the perception of project performance (Hobbs & Aubry, 2007). In searching for a model using the factor analysis method, six variables were loaded together in a single factor

labeled ‘Embeddedness with a Cron­ bach alpha 5 0.753. The variables are:

1. PMO’s mission is well understood 2. PMO’s mission is aligned with the

organization’s mission 3. The PMO works in close collaboration

with others in projects 4. Expertise is recognized by others 5. The PMO is supported by executives 6. The PMO collaborates to achieve the

organization’s future vision

Together, these variable help gener­ ate an understanding of the different facets of what anchors the PMO within the organization.

Project Management Performance Factor

As for the previous factor, ‘embedded­ ness,’ project management performance is part of ‘Performance,’ one component of our conceptual framework (See Fig­ ure 1). Questions were asked on eight variables associated with the evaluation of project management performance. One variable had to be taken out in the process of factor analysis, referred to as ‘Upper level satisfaction.’ The other seven variables loaded in two factors. The first factor is labeled ‘Satisfaction with the project’ and includes four variables that relate to a large variety of actors in and around a project: team satisfaction, user satisfaction, client satisfaction, and stakeholder satisfaction. This first factor explains 42.957% of the variance. The second factor, labeled ‘Project manage­ ment performance’ includes three vari­ ables: respect of schedule, respect of the cost, and sponsor satisfaction. Respect­ ing schedule and cost refers to a project with no or limited variance between the planned and the actual results. Interpre­ tation for sponsor satisfaction loaded in the second factor suggests that the proj­ ect sponsor will be satisfied as long as the project management performance, expressed by the more typical indicators of time and cost, is achieved. Variance explained by this factor is 31.221%.

The results from these three fac­ tors analysis serve as input to the other

statistical analysis discussed in the fol­ lowing section.

Results from the Comment Fields In the questionnaire, three open questions were asked to capture more qualitative interpretations on the impact of the PMO:

1. What are the strengths associated with the entities? (142 comments)

2. What should be improved? (131 comments)

3. What is the most important obstacle in the implementation of the PMO? (82 comments)

An initial classification of all com­ ments was done using a common initial framework for all three questions as they refer—positively or negatively—to similar themes. Figure 2 illustrates the global results. Maturity in project man­ agement and in change management is the most cited theme in terms of obstacles and needs for improvement in entities. Respondents complained about the lack of maturity in these terms:

• Difficulty with the cultural change and the new philosophy of management

• Change in work organization and resis­ tance to this change

• Lack of common vision between departments

• Working in a silo

Competencies in project manage­ ment occupy the second place, with the particularity of having the highest num­ ber of strong points; this is consistent with the results obtained on embedded­ ness in which expertise is recognized by others.

Findings The aim of this article is to propose a categorization of entities based on the types of projects they deal with. To do so, requires differentiating among the entities based on their organiza­ tional characteristics, structural char­ acteristics, functions, and performance

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of the PMO, as shown in our concep­ tual framework (see Figure 1). For all variables, the non­parametric Kruskal– Wallis (K–W) method was used, fol­ lowed by two­by­two non­parametric comparisons between the types of proj­ ects using the Mann­Whitney (M–W) method. For each of these methods, the usual rules of the p value apply. Table 8 in Appendix A shows only the variables in which a significant differ­ ence was found with the K–W method. In the interpretation of the results, no difference is also a result and is dis­ cussed below when appropriate. Table 9 in Appendix B presents a synoptic view of the differences (more of; less of ) between the four categories of PMOs.

Variables with a stronger power of dif­ ferentiation appear in boldface type.

By counting the number of signifi­ cant differences in each of the six com­ binations of project types, the last row of Table 9 clearly shows that entities dealing with engineering and construction proj­ ects have many different structural char­ acteristics and perform different activities than those dealing with business pro­ cesses projects (1 versus 3). The lowest rate of differentiation was found among entities dealing with new product/service development (NPD) projects compared with information system/information technology (IS/IT) projects (2 versus 4) and to business process (BP) projects (3 versus 4).

Each of the following sub­sections emphasizes a component of the conceptual framework (see Figure 1): organizational context, structural characteristics, func­ tions, and performance. In these sub­ sections, a summary table (Tables 4 through 7) is provided, illustrating the major differences between the four types of projects using the different sets of variables specific to the component. For each couple, type of project x variable, the means is provided along with the num­ ber of significant differences in pair­wise comparisons (p  0.1) (Besner & Hobbs, 2013). This approach makes it simplest to visualize variables with the best potential to differentiate between types, given six pair­wise comparisons per variable.

Maturity in project management and change management

Competencies in project management

Organizational engagement toward project management

Standardization of project management methods

Availability of relevant information for decision-making

Performance of projects

Alignment of projects with strategy

Resources allocation between projects

Obstacles Strong points To improve

0 8070605040302010 90

Figure 2: Obstacles, strengths, and need for improvement.

Organizational Characteristics E & C IS/IT BP NPD

Organizational project management maturity 2.571 21 2.341 2.077 12 2.067 12

Percentage of external clients 43% 21 13% 22 48% 21 13% 22

Organizational culture support level of project management

3.524 11 3.667 21 2.579 32 3.333 11

Project Characteristics

Scope of project in terms of cost (thousands of CAD)

20,607 31 1,964 11/12 4,420 12 500 22

Scope in terms of duration (months) 20 31 14 12 14 12 13 12

Communication in post-delivery activities 25% 22 48% 11/12 42% 12 80% 31

Marketing in post-delivery activities 11% 12 2% 12 4% 33% 21

Organizational change in post-delivery activities 14% 32 50% 11 50% 11 47% 11

Table 4: Differences in organizational context between entities based on types of projects.

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Project Types and Organizational Context

Two classes of data are used to describe the organizational context in which enti­ ties exist: organizational characteristics and types of projects. For organizational characteristics, three variables display the significant differences between types of projects. First, organizational proj­ ect management maturity shows higher results in engineering and construction projects than the three other types. Significant differences happen with business process and new products and services development. Overall, however, project management maturity levels are almost the same as in the previous comparable studies of PMOs (Hobbs & Aubry, 2010). Second, a higher per­ centage of projects with external cli­ ents are based in entities dealing with

engineering and construction projects and business process projects. Con­ versely, a lower percentage of external clients is more likely in entities dealing with IS/IT projects and new product development projects. Differences are significant between these two groups regarding the percentage of external cli­ ents. Not surprisingly, IS/IT projects are often performed by internal resources as a service function of organization, contrary to engineering and construc­ tion in which projects are often carried out by contractors and sub­contractors. Public administrations are most likely to develop services (not products) through their mission of serving a population. Expertise in these services is internal to the departments participating in proj­ ects. Third, the level of organizational

cultural support for project manage­ ment is significantly lower for business process projects than other types of proj­ ects. This could be interpreted to show that business process projects happen in entities newer to the project manage­ ment approach than engineering and construction and IS/IT projects. Com­ bined, these three variables pertaining to the organizational characteristics ensure good anchorage for differentiat­ ing among entities dealing with different types of projects. Table 4 summarizes the results on organizational context.

When considering the characteris­ tics of projects conducted by the entities, the scope of projects—both in terms of costs and duration—significantly differ­ entiates engineering projects from the three other types, with projects having

Entity Structural Characteristics E & C IS/IT BP NPD

Percentage of entities having 5 years and more 48% 21 25% 12 13% 12 33%

Number of projects simultaneously 161 31 43 12 41 12 24 12

Percentage of the projects in the entity compared to the overall number of projects in the organization

76% 21 68% 21 53% 22 50% 22

Percentage of entities having more than 75% of project managers of the organization

50% 21 28% 11/12 4% 22 27%

Profiles of personnel

Percentage of project managers with master’s degree in project management in the entity

8.4 12 17.6 21 0.2 12 9.5

Experience in project management of the personnel in the entity (years)

7.8 31 5.9 12 4.6 12 5.1 12

Practices/tools/methodology

Percentage of home grown project management methodology

64% 21 42% 11/12 22% 22 52%

Role of the entity

Percentage of entities involved in project management

54% 32 95% 21 83% 11/12 87% 11

Percentage of entities involved in monitoring projects 96% 11 93% 11 78% 22 100%

Decision-making authority of the entity

Percentage of entities with no authority at all 21% 12 42% 67% 11 44%

Percentage of entities with strong authority 50% 11 38% 11 0% 32 22% 11

Variables NOT included for differentiation

With the exclusion of project managers, number of persons working in the entity

Percentage of entities involved in controlling projects

Table 5: Differences in entity structural characteristics based on project types.

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a longer duration for higher costs. This result is quite obvious; engineering and construction projects usually involve large investments in materials and equipment. The costs of the three other types of projects largely relate to human resources. The scope of projects may include post­delivery activities. Three of these activities involve significant differences in project types. Eighty per­ cent of new product and service proj­ ects include communication activities, which significantly differentiate it from the three other types of projects. This result is quite natural, because a new product and service has to be promoted through the public administration and/ or the targeted population.

On the other hand, only 25% of engineering and construction projects

include such activities, which could be interpreted as an indication that com­ munication is undertaken by another entity or by the client when external. The same also applies to marketing activi­ ties, where new product and service development projects are differentiated from engineering and construction, and IS/IT projects for about the same reason. Very interestingly, engineering and con­ struction projects differentiate from the other three types for not including orga­ nizational change management within its scope. The reason for this could relate to the nature of deliverables in the public sector, where IS/IT, processes, and services projects are more likely to have an impact either on the distribu­ tion of services or internally, in the way the work is done (Crawford, Costello,

Pollack, & Bentley, 2003). Accordingly, change management is closely linked to project success ( Crawford et al., 2003). About half of these projects include management, which is consistent with the previous results in overall sectors (Hobbs & Aubry, 2010).

Project Types and PMO Structural Characteristics

This sub­section explores the differences between entities dealing with different types of projects and their structural char­ acteristics (see Table 5).

Considering the percentage of enti­ ties having five years or more, of interest in these results pertaining specifically to the public sector is that the entities do not change as frequently, with a mean of nearly 30% for all types among

Functions E & C IS/IT BP NPD

Group 1: Project performance and portfolio management

3.593 11 3.390 11 2.696 22 3.163

Group 2: Methodologies and competencies 3.438 21 3.037 12/11 2.500 22 3.071

Group 3: Organizational learning 2.723 21 2.248 12/11 1.667 22 2.054 12

Group 4: Collaboration and communication 4.134 31 3.576 12/211 3.167 22 3.143 22

Group 5: Specialized tasks 3.446 11 3.568 11 2.900 22 3.143

Table 6: Differences in functions.

Performance E & C IS/IT BP NPD

Factors of project management performance

Satisfaction with the project 3.545 12 3.921 12 3.764 12 4,286 31

Project management performance 4.188 11 4.095 11 3.663 32 4,232 11

Most important criteria for project success

Percentage of entities: Respect of budget 32.1% 11 17.1% 11 13.6% 0% 22

Percentage of entities: Satisfaction of stakeholders 10.7% 12 12.2% 12 36.4% 21 26.7%

Factor of embeddedness

Embeddedness of the entity 4.295 11 4.134 11 3.812 32 4,383 11

Variables NOT included for differentiation

Criteria for project performance—Respect of dates

Criteria for project performance—Respect of technical specifications

Criteria for project performance—Quality

Criteria for project performance—Final clients

Table 7: Summary of performance differences among entities.

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entities having a life span of five years or more. Previous research found 15% for PMOs with five years and more (Hobbs & Aubry, 2010). Recent research has provided some understanding of the reasons why PMOs are changing so frequently and of the change process (Aubry, Hobbs, Müller, & Blomquist, 2011). This result could be interpreted as an indication that entities in this research are focused on part of the public administration bureaucratic hierarchy. Political positions aside, this hierarchy is quite stable, even though the govern­ ing parties change (Kernaghan, 2005).

However, the variable of entities having five years and more contributes to the differentiation between entities. Entities dealing with engineering and construction projects have greater lon­ gevity than those dealing with IS/IT and business process projects with the highest percentage of entities having five years and more. The number of simultaneous projects significantly dif­ ferentiates engineering and construc­ tion projects from the three others with a high number.

The number of simultaneous proj­ ects within the PMO distinguishes entities dealing with engineering and construction projects by the high num­ ber of projects. This result is to be con­ sidered along with the scope of projects, in which engineering and construction projects have highest costs and longer durations.

The percentage of projects and proj­ ect managers in the PMO was used to propose a typology of PMO (Hobbs & Aubry, 2008). In the public sector, this typology was not validated; how­ ever, these two variables differentiate entities dealing with different types of projects. Entities with engineering and construction projects and IS/IT projects both differ significantly from entities involved with business processes and new product and service development projects. The former are more likely to have more projects than the latter. Having a large number of project man­ agers differentiates entities engaged in

engineering and construction projects, in which 50% have a large proportion of project managers. Conversely, very few entities with business process projects have such a large number of project managers.

The profiles of project managers have been assessed by a high level of education in project management and the extent of their experience in project management. The percentage of project managers with a master’s degree in project management significantly differentiates entities with IS/IT projects as those having the high­ est percentage. Project manager experi­ ence is significantly greater in entities conducting engineering and construction projects. Globally, however, all entities have experienced project management personnel.

In terms of methodology, developing a home­grown methodology differenti­ ates the entities, and those involved in engineering and construction projects are more likely to develop their own. Entities dealing with new product and services projects also post high levels, but do not significantly differ from the others.

Two different roles differentiate the entities: project management and proj­ ect monitoring. Entities engaged in engineering and construction projects perform the least direct project man­ agement compared with the three other types of entities. Conversely, IS/IT enti­ ties are more likely to engage in project management compared with engineer­ ing and construction projects and busi­ ness process projects. Interestingly, the role of controlling projects does not differ significantly among the entities. In terms of decision­making authority, entities with less authority are more likely to be engaged in business process projects, whereas those with the highest level of authority are found within engi­ neering and construction projects. It is also worth mentioning that the num­ ber of people working in the entities (with the exclusion of project managers) does not differ significantly among the entities.

Project Types and Functions

Conversely, based on the findings of a previous PMO typology study (Hobbs & Aubry, 2011), the functions here seem to differentiate among entities dealing with different sets of projects. All five groups of functions show a significant difference (p  0.1) for entities dealing with different types of projects.

Upon examining descriptive sta­ tistics on the functions, all functions follow approximately the same distri­ bution pattern: functions in group 4— Collaboration and communication—are the most frequently performed by all enti­ ties, whereas in group 3—Organizational learning—is the least frequent. The low­ est importance of ‘Organizational learn­ ing’ confirms previous results showing a lack of real motivation among organiza­ tions in all sectors to invest in knowledge and learning. But what is really astonish­ ing about these public sector results is the strength of the ‘Collaboration and communication’ function for all types of projects. As in previous studies (Hobbs & Aubry, 2007), we were expecting the function ‘Monitoring and controlling project management performance’ to be the most frequently performed func­ tion. This group of functions includes reporting to and advising upper man­ agement. In the public sector, it might be the rule of paying careful attention to political relationships because politi­ cians are major stakeholders. Following Unger et al. (2012), ‘Collaboration and communication’ have a positive impact on cooperation and resource allocation and are said to have a coordinator role. This finding challenges previous results in which ‘project performance monitor­ ing and control’ was typically the major function of a PMO; it also highlights the important coordinator role PMOs have in the public sector.

Now, turning to a statistical analy­ sis of the differences in what entities do differently in dealing with different types of projects, Table 6 shows that entities involved in new product and service development do not differentiate themselves significantly from the others.

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Yet, in our interpretation, we emphasize the three other types of projects. Table 6 presents a summary of the signifi­ cant differences between entities and the types of projects they deal with.

The strongest function differences (all five with p  0.07) occur between business process projects, on the one hand, and both engineering and construction and IS/IT projects on the other. These entities do not perform the functions with the same intensity. Entities dealing with business pro­ cesses perform significantly lower on all functions compared with engineer­ ing and construction projects and IS/IT projects. This is particularly evident in the ‘Collaboration and communication’ function.

‘Organizational knowledge manage­ ment’ is the least frequent function, but still seems to differentiate between project types; in fact, it differentiates between engineering and construction project types, which have the highest score, and the three other types. It also shows a significant difference between IS/IT and business process types, the latter having the lowest score.

Significant differences exist for the ‘Methodology and competencies’ function between the ‘Engineering and construction’ type and the two others, reflecting much more importance in this function. These results are consis­ tent with project management history, given that the engineering and con­ struction industries were among the first sectors to embark on project man­ agement practices back in the 1950s (Morris, 2011). At the opposite end of the spectrum, with little importance in this function, we find the ‘Business process type.’ This might be a serious issue as the current search for efficiency rests on process reengineering and this is particularly true in the public sector where the lean approach has been viewed as a solution to reaching efficiency. The IS/IT type lies in the middle, not adopting the strong meth­ odologies and competencies as those in engineering and construction, but

a little more than in the business pro­ cess. More recently, the IS/IT projects within different sectors have shown a strong tendency to adopt project man­ agement practices (Rivard & Dupré, 2009). Business process projects, such as IS/IT, cross different sectors. After the business process reengineering era and, more recently, in lean projects, it seems that neither methodologies nor competencies are very strong for these types of projects. New product devel­ opment projects showed no significant differences compared with other types.

Project Types and Performance

Three approaches have been taken to understanding the performance differ­ ences among entities dealing with dif­ ferent types of projects:

1 Project management performance factors

2 Project management success criteria 3 Factor of embeddedness

Table 7 summarizes performance results. Performance in this context is taken as a descriptive, not prescriptive concept.

All three perspectives reveal signifi­ cant differences in the analysis of the four types of projects. The first per­ formance perspective is based on the factors of project management per­ formance (See earlier). Differences in the means are not extremely high, but show significant differences among the types of projects. First, ‘Satisfaction with the project’ differentiates entities with new product/service development from the three others with a higher score on ‘Satisfaction with the project.’ However, developing new products/ services in a government setting cer­ tainly demands multiple perspectives in a complex environment (Hobday, 2000; Kujala, Putila, & Brady, 2013). Second, project management performance also shows a relatively high score. Significant differences exist among entities with business process projects and the three others, where the lower score applies to the project management performance

factor. Results show that these entities assign less importance to factors lead­ ing to better project management performance.

The second performance perspec­ tive comes from the importance placed by the entities on the project success criteria, with two variables differing sig­ nificantly among project types. First, respect for budget shows a relatively low percentage, with a zero score for new product/services development projects. This result is surprising, but indicates what is perceived as most valued for the success of projects. These entities reveal significant differences with engineer­ ing and construction projects and IS/ IT projects. Entities dealing with new product/service development are more likely to focus their attention on stake­ holders as a performance factor than budget. Second, stakeholder satisfac­ tion as the most important criterion for success differs significantly for busi­ ness process projects compared with engineering and construction projects and IS/IT projects. Overall, in these two latter project types, stakeholder satisfaction does not seem to be highly valued in project management perfor­ mance and project success.

The third and final perspective is the factor of embeddedness. Globally, this factor shows quite a high level of embed­ dedness for all types of projects: entities are well embedded in their organization. In this particular context, the PMO has a permanent status, being a functional unit not necessarily dedicated uniquely to projects. This context might partly explain why the PMO’s mandate is well understood and aligned with the overall mission, and why expertise is recognized. These entities also play a role in the close collaboration with others in projects as well as in the organization’s future vision; however, care should be taken to avoid cognitive bias in this interpretation, or the halo effect. In our study, this bias could have affected the interpretation of the PMO in a positive way. Most of the respondents of the questionnaire are in management positions within the PMO

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and might have overestimated their level of embeddedness. However, statistical analysis shows significant differences between PMOs; for example, PMOs deal­ ing with business process projects post a lower level of embeddedness than the three others.

Globally, the three perspectives on performance in relation to PMOs contribute to the specification of such entities based on the types of projects they deal with. Results for business pro­ cess projects and new product/service development projects show the most pronounced differences for project management performance and embed­ dedness factors.

Discussion: Proposed PMO Categorization Based on Project Types PMO Categorization within Organization and Management Theory

There have been several attempts by scholars (Hobbs & Aubry, 2008, 2011), professionals (i.e., Kendall & Rollins, 2003), or professional bodies in proj­ ect management (Office of Government Commerce, 2008; Project Management Institute, 2013) to categorize PMOs, yet none of the resulting classification sys­ tem seems to be fully satisfactory for any one group. This quest for classifica­ tion is understandable given the wide variety of PMOs and the difficulty for managers to decide on the mandate of one or multiple PMOs. Will this catego­ rization of PMOs in the public sector address some of the expectations?

From the theoretical aspect of this research, this article’s initial contribu­ tion is to relate the PMO with organi­ zation and management theory. First, we have positioned decisions regard­ ing the implementation and evolution of PMOs in the wider field of organiza­ tional design. This is an important point, because research on this phenomenon has adopted a narrow view of describ­ ing the PMO rather than focusing on the organizing process of creating a PMO. The categories of PMOs based on the final product they deliver should

contribute to making a PMO design decision. For example, PMOs dealing with engineering and construction proj­ ects may require less effort from the managers in governance and control­ ling projects because they already have strong project management practices in place. Conversely, PMOs with busi­ ness processes and new product devel­ opment will require, first, training in basic project management and coaching and implementation of new monitoring and control mechanisms. Second, we have situated our research in the lin­ eage of contingency theory. Contingency theory has been criticized to confine organizations in fixed and limited types or configurations not adapted to facing the current complexities and uncertain­ ties in an economic context (Fenton & Pettigrew, 2000; Hernes, 2014). Contin­ gency theory, however, has evolved and offers the potential to take care of the organization environment (Donaldson, 2001; Greenwood & Miller, 2010). Our findings show that different types of proj­ ects have an effect on the PMO context: As Tables 4 through 7 demonstrate, there are several different characteristics in each of the four components of the con­ ceptual framework, depending on the types of projects the PMO is dealing with.

Knowing more about the strengths and weaknesses associated with differ­ ent types of PMOs creates an opportu­ nity for learning between PMOs (Aubry, Müller, & Glücker, 2012). For example, PMOs that deal with business processes or new product development projects are strong in managing the satisfaction of stakeholders and the project, whereas this is lacking in engineering and IS/IT projects. Learning can be done in very dynamic ways, including communities of practices between PMOs (Williams, 2008). Moreover, mixing different types of groups may produce unexpected creativ­ ity through the recombination of differ­ ent routines (Cohendet & Simon, 2016).

PMOs and Organizational Design

This research provides empirical evi­ dence of the interest in organizational

design to counteract the three reasons provided by Greenwood and Miller (2010), which explain the lack of interest in organizational design. First, the unit of analysis of this research is at the organi­ zational level rather than the population or community level. Second, a govern­ ment organization is clearly a complex system that faces a number of difficulties in trying to put organizational project management into place. A multi­method methodology would be required to cap­ ture such complexity. In this particular case in public administration, a variety of actions have been undertaken, including this quantitative research, which made a partial contribution only. That said, this piece of research provides a picture of the situation by showing explicitly: (1) who is involved in managing multiple projects throughout the government apparatus; (2) what they are doing; and (3) how sensitive they are to project manage­ ment performance. Third, the focus of this research is on the overall entities in the government that deal with multiple projects; it avoids a narrow view cen­ tered on any one part in particular to the detriment of the overall organizational design. In proposing a categorization of PMOs in the public sector, this article’s main contribution to the academic field is to further the knowledge about orga­ nizational design. It also demonstrates, among other scholars, that project man­ agement research can contribute signifi­ cantly to the management field.

From a methodological point of view, the article also illustrates an aca­ demic contribution to elucidating a complex situation. Within the limit of a quantitative methodology, this article is very much aligned with the reflection undertaken by the project management scholars striving to introduce our meth­ odological approaches to translational and transformational approaches (e.g., Drouin et al., 2013).

Conclusion This article presents the results of researchers who have seized an oppor­ tunity to closely participate in a process

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of organizational design in a public administration. A very generic defini­ tion of a PMO—as an entity dealing with multiple projects—has been adopted in this research and had allowed for the capturing of multiple loci of proj­ ect management activities in a public administration through a survey. This article proposed a conceptual frame­ work for the categorization of PMOs based on four project types: engineering and construction, information systems and technology, business processes, and new product/service development. The framework also proposed four compo­ nents to describe the PMO categories: organizational characteristics, project characteristics, functions, and perfor­ mance. A quantitative methodology was adopted and based on a survey in one government.

The main findings suggest that this categorization discriminates quite well between PMOs. These findings have important implications for decision makers, particularly in public admin­ istrations where PMOs are often spe­ cialized in the delivery of a specific type of projects by department. Gover­ nance and coordination mechanisms might be adapted to the characteristics of PMOs—some being experienced in project management and, others, at a lower level. Training in project manage­ ment might be essential in some PMOs, while optional in others. Given the dif­ ferences in strengths and weaknesses between PMOs in the same govern­ ment, some learning opportunities may be developed between PMOs.

Second, and over and above the strict design of a PMO, the article shows how a government can use research as part of its process to answer the difficult question: How do we organize for proj­ ect management in public administra­ tion? Given that there are no models that can simply be copied and pasted in any context work has to be done to con- struct a local PMO solution; however, such a solution might be only tempo­ rary, as previous research has shown (Aubry et al., 2011). Being prepared for

change means being aware of the envi­ ronment in order to anticipate changing needs so that projects can be carried out successfully.

An important limitation of this research is the lack of context due to the quantitative approach. Although statisti­ cal analysis provides sound differences between project types, it misses the rich data needed to fully interpret the results and provide more generalization to other similar public administrations. Classification systems are criticized for this lack of cultural sensitivity (Glynn & Navis, 2013). In the same vein, having data from a single government imposes limitations in terms of generalization; however, we believe that making sense of these findings in any context will help inspire us to find a proper solu­ tion in terms of organizational design. Moreover, the method employed could be replicated in other governments or industries to complement the results. Furthermore, the confidentiality of the case study has the consequence of no possibility of situating the political and cultural dimensions in the discussion. We are convinced that these dimensions are important enough to be considered when addressing a governmental case study. This limitation refrains from developing a global understanding of the case.

This research opens up several opportunities for future research. First, as mentioned in the introduction, proj­ ect management scholars should diffuse more of their work on organizational design through the main stream of organization literature. There is a gap in the organization literature when it comes to understanding new forms of organizing—the project management lit­ erature should address some of these issues. Second, the focus of the research on organizational design should move to exploring the process of organizing for projects, in other words, move from the project organization to project orga­ nizing. Third, this research would ben­ efit from the practice theory, such as the toolkit proposed by Nicolini (2013).

Actually, not that much is known on the process of designing a PMO in a practice view.

Acknowledgments The researchers wish to warmly thank the participants who gave of their time to complete the questionnaire. We would also like to thank the govern­ ment for its financial support for this research. Our special thanks go to Carl Saint­Pierre who provided invaluable guidance for statistical analysis.

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Monique Aubry, PhD is Professor at the School of Business and Management, Université du Québec à Montréal (UQAM), Montreal, Canada. She teaches in the graduate program in project management

and the executive MBA program and her main research interest is in organizing for projects and organizational design, more specifically, project management offices (PMOs). The results of her work have been published in major academic journals in project management and presented at several research and professional conferences. Monique is a member of the Project Management Research Chair (www.pmchair.uqam.ca) and the UQAM’s Health and Society Institute. In 2012, she received the IPMA Research Award for her research on project management offices. Before joining UQAM, Monique was senior project manager in a major Canadian financial group for more than 20 years. Until recently, she was a member of the PMI’s Standards Advisory Group and the Research Inform Steering Committee. She is a senior editor for Project Management Journal® and is involved in the PMI Montreal Chapter community of practices on organizational project management, where she promotes engaged scholarship and

reflexivity among professionals and researchers. She can be contacted at [email protected]

Maude Brunet, MPM, PMP, is a doctoral student in management—with a specialization in project management—at the School of Business and Management, Université du Québec à Montréal (UQAM) under the supervision of Monique Aubry. Her research interests focus on the governance of major public infrastructure projects; more specifically, she is studying the Quebec (Canada) governance framework for major public infrastructure projects, how it has developed over time, and how it is applied for managing projects into practice. She has ten years of experience in project management and is currently a lecturer at UQAM for the master’s program in project management. Maude is also actively involved with the organization GP-Quebec, the association for public projects in Québec, and the PMI Montreal Chapter. Maude Brunet can be contacted at: maude.aumont. [email protected]

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Appendix A: Detailed table on PMO categorization based on project types.

E & C IS/IT BP NPD MORE OF: Organizational characteristics

Organizational project management maturity Percentage of external clients Cultural support

Cultural support Percentage of external clients

Project characteristics Scope of projects in terms of cost Scope of projects in terms of duration

Organizational change in post-delivery activities

Organizational change in post-delivery activities

Communication in post-delivery activities Marketing in post- delivery activities Organizational change in post-delivery activities

Structural characteristics of the entity

Percentage of PMOs having 15 years Number of projects simultaneously Percentage of projects in the entity More than 75% of project managers Experienced personnel in project management Percentage of homegrown methodology Percentage of entities with strong authority Percentage of PMOs involved in monitoring projects

Percentage of projects in the entity Percentage of entities with strong authority Percentage of project managers with a master’s degree Percentage of PMOs involved in project management Percentage of PMOs involved in monitoring projects

Percentage of entities with strong authority Percentage of PMOs involved in project management

Functions Group 1: Project performance and portfolio management Group 2: Methodologies and competencies Group 3: Organizational learning Group 4: Collaboration and communication Group 5: Specialized tasks

Group 1: Project performance and portfolio management Group 4: Collaboration and communication Group 5: Specialized tasks

Performance Project management performance Respect of budget Embeddedness of the entity

Project management performance Respect of budget Embeddedness of the entity

Legitimacy Satisfaction of stakeholders

Satisfaction with the project Project management performance Embeddedness of the entity

LESS OF Organizational characteristics

Organizational project management maturity Cultural support

Organizational project management maturity

Project characteristics Communication in post- delivery activities Marketing in post-delivery activities Organizational change in post-delivery activities

Scope of projects in terms of duration Marketing in post-delivery activities

Scope of projects in terms of cost Scope of projects in terms of duration Communication in post- delivery activities

Scope of projects in terms of cost Scope of projects in terms of duration

(continued)

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October/November 2016 ■ Project Management Journal 127

E & C IS/IT BP NPD Structural characteristics of PMO

Percentage of project managers with a master’s degree Percentage of PMOs involved in project management

Percentage of PMOs having 15 years Number of projects simultaneously Experienced personnel in project management

Percentage of PMOs having 15 years Number of projects simultaneously Percentage of projects in the entity More than 75% of project managers Percentage of entities with strong authority Percentage of project managers with a master’s degree Experienced personnel in project management Percentage of homegrown methodology Percentage of PMOs involved in monitoring projects

Number of projects simultaneously Percentage of projects in the entity Experienced personnel in project management

Functions Group 1: Project performance and portfolio management Group 2: Methodologies and competencies Group 3: Organizational learning Group 4: Collaboration and communication Group 5: Specialized tasks

Group 3: Organizational learning Group 4: Collaboration and communication

Performance Legitimacy Satisfaction with the project Satisfaction of stakeholders

Satisfaction with the project Satisfaction of stakeholders

Satisfaction with the project Project management performance Embeddedness of the entity

Respect of budget

Strengths Competencies in project management

Competencies in project management

Competencies in project management

Competencies in project management

Obstacles Maturity in project management and change management

Organizational engagement toward project management

Maturity in project management and change management

Maturity in project management and change management

To be improved Standardization of project management methods Maturity in project management and change management

Organizational engagement toward project management Maturity in project management and change management

Maturity in project management and change management

Maturity in project management and change management Competencies in project management

Note: In bold, the most important differences.

Table 8: PMO categorization based on project types.

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Organizational Design in Public Administration

128 October/November 2016 ■ Project Management Journal

P A

P E

R S

Variables

1. E & C N1  28 Mean

2. IS/IT N2  44 Mean

3. BP N3  26 Mean

4. NPD N4  15 Mean

K-W p-value two-tail

M-W p-value two-tail

M-W p-value two-tail

M-W p-value two-tail

M-W p-value two-tail

M-W p-value two-tail

M-W p-value two-tail

1 VS 2 1 VS 3 1 VS 4 2 VS 3 2 VS 4 3 VS 4

ORGANIZATIONAL CONTEXT

Characteristics of the organization Organizational project management maturity

2.571 2.341 2.077 2.067 0.0887 0.2337 0.0151 0.0720 0.1711 0.3861 0.7990

Percentage of entities dealing mostly with external project clients

43% 13% 48% 13% 0.0047 0.0057 0.7427 0.0516 0.0032 0.9603 0.0830

Organizational culture support level of project management (scale: 1 not at all to 5 very good)

3.524 3.667 2.579 3.333 0.0123 0.5749 0.0100 0.6570 0.0025 0.4190 0.0760

Characteristics of projects in the entity mandate Scope of project in terms of cost (thousands of CAD)

20,607 1,964 4,420 500 0.0000 0.0000 0.0001 0.0000 0.7159 0.0415 0.2370

Scope in terms of duration (months)

20 14 14 13 0.0001 0.0000 0.0004 0.0022 0.6414 0.4708 0.5310

Percentage of entities including communication within the scope of post-delivery activities

25% 48% 42% 80% 0.0072 0.0555 0.1817 0.0006 0.6624 0.0311 0.0460

Percentage of entities including marketing within the scope of post-delivery activities

11% 2% 4% 33% 0.0025 0.1301 0.3401 0.0726 0.7046 0.0007 0.1210

Percentage of entities including organizational change in post- delivery activities

14% 50% 50% 47% 0.0133 0.0023 0.0052 0.0219 1.0000 0.8250 0.8620

ENTITY STRUCTURAL CHARACTERISTICS

Percentage of entities having 5 years and more

48% 25% 13% 33% 0.0462 0.0469 0.0087 0.3584 0.2566 0.5342 0.3000

Number of projects simultaneously

161 43 41 24 0.0020 0.0008 0.0050 0.0041 0.9611 0.8319 0.8980

Percentage of the projects in the entity compared to the overall number of projects in the organization

76% 68% 53% 50% 0.0222 0.2207 0.0150 0.0330 0.0573 0.0702 0.9640

Percentage of entities having more than 75% of project managers of the organization

50% 28% 4% 27% 0.0040 0.0608 0.0003 0.1440 0.0194 0.9269 0.2460

Percentage of entities with strong authority

50% 38% 0% 22% 0.0048 0.4163 0.0005 0.1568 0.0031 0.3833 0.0414

Profile of personnel Percentage of project managers with master’s degree in project management in the entity

8.4 17.6 0.2 9.5 0.0150 0.0905 0.1234 0.7049 0.0021 0.3062 0.2770

Experience in project management of the personnel in the entity (years)

7.8 5.9 4.6 5.1 0.0102 0.0113 0.0054 0.0163 0.1841 0.4450 0.7270

Practices/tools/methodology Percentage of home grown project management methodology

64% 42% 22% 52% 0.0045 0.0472 0.0007 0.3495 0.0168 0.6061 0.0520

Appendix B: Detailed statistical table.

(continued)

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October/November 2016 ■ Project Management Journal 129

Variables

1. E & C N1  28 Mean

2. IS/IT N2  44 Mean

3. BP N3  26 Mean

4. NPD N4  15 Mean

K-W p-value two-tail

M-W p-value two-tail

M-W p-value two-tail

M-W p-value two-tail

M-W p-value two-tail

M-W p-value two-tail

M-W p-value two-tail

1 VS 2 1 VS 3 1 VS 4 2 VS 3 2 VS 4 3 VS 4

Role of the entity Percentage of entities involved in project management

54% 95% 83% 87% 0.0003 0.0000 0.0302 0.0320 0.0951 0.2688 0.8360

Percentage of entities involved in monitoring projects

96% 93% 78% 100% 0.0547 0.5312 0.0473 0.4642 0.0892 0.2918 0.2730

FUNCTIONS REALIZED IN THE ENTITY

Group 1 Project performance and portfolio management

3.593 3.390 2.696 3.163 0.0017 0.3485 0.0007 0.1814 0.0006 0.3921 0.1589

Group 2 Methodologies and competencies

3.438 3.037 2.500 3.071 0.0022 0.0529 0.0004 0.2446 0.0078 0.8015 0.0593

Group 3 Organizational knowledge

2.723 2.248 1.667 2.054 0.0010 0.0412 0.0003 0.0606 0.0032 0.4933 0.2347

Group 4 Collaboration and communication

4.134 3.576 3.167 3.143 0.0002 0.0025 0.0002 0.0013 0.0695 0.0689 0.8435

Group 5 Specialized tasks 3.446 3.568 2.900 3.143 0.0739 0.6391 0.0603 0.3870 0.0132 0.2102 0.5252

PERFORMANCE OF THE ENTITY

Factors of project management performance Stakeholders satisfaction 3.545 3.921 3.764 4.286 0.0760 0.1900 0.4723 0.0269 0.6915 0.0683 0.0239

Project management performance 4.188 4.095 3.663 4.232 0.0137 0.7179 0.0265 0.5793 0.0124 0.3036 0.0038

Most important criteria for project success Percentage of entities: Respect of the budget

32% 17% 14% 0% 0.0616 0.1482 0.1322 0.0147 0.7241 0.0900 0.1411

Percentage of entities: Satisfaction of stakeholders

11% 12% 36% 27% 0.0620 0.8514 0.0314 0.1820 0.0250 0.1956 0.5417

Factor of embeddedness Embeddedness of the entity 4.295 4.134 3.812 4.383 0.0147 0.2939 0.0052 0.5861 0.0817 0.1558 0.0030

Total number of variables with significant differences

15 22 15 17 7 9

Note: in bold, significant differences with p  0.1

Table 9: Non-parametric one-way ANOVA and NON-parametric comparisons.

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130 October/November ■ Project Management Journal

O c

to b

e r

20 16

Project Management Journal, Vol. 47, No. 5 © 2016 by the Project Management Institute Published online at www.pmi.org/PMJ

Calendar of Events

OCTOBER 3–4 October PMI Silicon Valley, CA Chapter PMI Silicon Valley Chapter Annual Sym- posium, Santa Clara, California, USA. “Managing Uncertainty in Modern Proj- ects: Risks in Your Project Are Closer Than They Appear” is the theme for project managers to share their knowl- edge with like-minded professionals and participate in an event where they will learn from others facing similar challenges. pmisv.org.

6–7 October PMI Rome Italy Chapter Excellence in Project Management, Rome, Italy. Come help the PMI Rome Italy Chap- ter celebrate its 20th anniversary. This international event (fully in English), themed “Excellence in Project Man- agement: Synchronized Actions to Improve Project Performance,” will help you move your organizational maturity to the next level. Hear speak- ers from HP, the PMI Board of Direc- tors, and Capgemini. 20thanniversary .pmi-rome.org/en.

7 October PMI Belgium Chapter National Con- gress 2016—Project Management (PM) Fair, Berchem, Belgium. The PM Fair is not another project man- agement conference—it’s an idea that leading projects is a calling. The prom- ise is that valuable knowledge is to be shared, offering familiar encounters that feel energizing. Our congress aims to explore “value” from multiple points of view. pmfair.org.

7–8 October PMI Savannah, GA Chapter Leader- ship Conference, Savannah, Georgia, USA. The featured speaker for the Meet Me in Savannah Leadership Confer- ence will be James R. Snyder, PMI Fel- low, PMI Founder, speaking on the first 50 years of PMI. Enjoy networking events and the opportunity to earn up to 7 PDUs. pmisavannah.org.

14 October PMI Eastern Iowa Chapter 9th Annual Professional Development Day, Cedar Rapids, Iowa, USA. The 2016 Annual Professional Development Day is a day-long event. The program includes workshops and exhibits from our local sponsors. It is the perfect forum for net- working. pmieasterniowa.org.

18–19 October PMI Indonesia Chapter SymEx 2016, Surabaya City, Indonesia. The PMI Indonesia Chapter would like to cor- dially invite you to attend a two-day symposium and exhibition that will fea- ture many speakers from a variety of industries and levels of expertise. This year’s theme is “Building Future Lead- ership in Project Management”—very appropriate, as leaders need to face many challenges and adapt to changes in the new ASEAN economic community. pmi-indonesia.org/symex.

21 October PMI Chicagoland Chapter NextGen PM: A Key to Manage Constant Changes Successfully, Rosemont, Illi- nois, USA. Join us for the PMI Chica- goland Chapter’s 6th Annual Project Management Symposium. Morning

keynote speaker: Harold Kerzner, PhD, “Planning for the Future of Project Management in a Constantly Changing World.” Afternoon keynote speaker: Jeff Tobe, “Coloring Outside the Lines: Creativity Thriving from Change.” More than 20 speakers in multiple tracks. pmi-chicagoland.org.

21 October PMI Southwest Virginia Chapter Sweeten Your PM Skills, Roanoke, Virginia, USA. Come to our annual fall symposium—The Project Manage- ment Candy Jar: “How to Sweeten Your PM Skills.” We will again have an agile track, but new this year is a track focus- ing on PMO tools. Our keynote speaker is Kari Maribal. pmi-swva.org.

22–23 October PMI China Congress 2016, Beijing, China. PMI China’s fifth annual con- gress is one of the largest project man- agement events in the Asia Pacific region. Initiatives such as the Silk Road, urbanization, Asian Infrastruc- ture Investment Bank, and Internet Plus are creating new opportunities for China and the world. Project managers and international talent will become key resources to support China’s growth. Speakers will include leading enterprise executives as well as subject matter experts from home and abroad. pmi.yoopay.cn.

22–25 October Dubai International Project Man- agement Forum, Dubai, United Arab Emirates. PMI is a co-sponsor of this event, which will focus on “Shaping the Future” and will explore 12 key

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October/November 2016 ■ Project Management Journal 131

Calendar of Events themes including green project manage- ment, agile and scrum, benefits real- ization and public–private partnerships. Stay ahead of the curve and hone your project management skills with five mas- ter classes designed to put theory into practice. Earn up to 26 PDUs over the four days. dipmf.ae.

24–26 October PMI Malaysia Chapter Third Inter- national Symposium, Kuala Lumpur, Malaysia. The PMI Malaysia Chapter is proud to bring to you its Third Interna- tional Symposium. This year’s theme is “Project Management Leadership in a Challenging Economic Environment.” The two-day conference is followed by a separately bookable one-day, two-track workshop. symposium. pmi.org.my.

NOVEMBER 14 November PMI Southern Germany Chapter PM Summit 2016—Climbing the Peak with Project Management, Munich, Germany. “Climbing the Peak with Project Manage- ment” is an analogy that compares the chal- lenges of a complex project with those of a difficult mountaineering campaign. From a content perspective, the presentations and workshops of the PM Summit 2016 are guided by the recently introduced PMI Talent Triangle™: technical project manage- ment, leadership, and strategic and busi- ness management. pm-summit2016.de.

17–19 November PMI India Project Management National Conference, India 2016, Mumbai, India. This three-day conference, organized by the PMI Mumbai Chapter and cohosted by the PMI Pune-Deccan India Chapter, is themed “Project Management—Indispensable for Vision India.” It builds on enhancing project management skills to realize India’s vision

to become a developed nation with world- class institutions, infrastructure, products, and services. Case studies, invited speeches, and technical papers focus on how project managers play a key role in redefining the way India builds its capabilities and creates the climate for growth. pmi.org.in/events/ conference2016.

22–23 November PMI Southern Alberta Chapter Profes- sional Development Conference, Cal- gary, Alberta, Canada. Theme: “Peak Performance.” Our clients, stakehold- ers, and organizations expect us to take on more than ever before, oftentimes with less resources amidst a climate of constant uncertainty. Achieving peak performance requires vision, focus, determination, and the relentless pur- suit of excellence. Join us as our speak- ers share their highlights, setbacks, and lessons learned. pmisac.com.

24 November PMI Madrid, Spain Chapter 13th Proj- ect Management Congress, Madrid, Spain. This event will congregate more than 400 delegates for a full day of conferences and workshops on sepa- rate tracks. Representatives from sev- eral industries (banking, construction, legal, interim management, informa- tion technology) are expected to be present, as well as project management practitioners sharing their experience, best practices, and latest developments on project management. pmi-mad.org.

Live Webinars from ProjectManagement.com As a valued member of the Project- Management.com community, you can access webinars that provide insight from the industry’s most respected voices on the most relevant and impor- tant topics today—and earn PDUs.

20 September 3:00 p.m. EDT (UTC –4) Project HEADWAY: Strategy, Projects and You, Projects are how strategy is executed, at least on paper. And yet stra- tegic initiatives all too often fail, all too often in an entirely spectacular manner. What does it really mean to implement strategy? And how do we do it suc- cessfully? Presented by Mark Mullaly, PhD, PMP.

For more information and to register, visit ProjectManagement.com/Webinars.

Upcoming PMI® Global Congresses and Events PMO Symposium® 2016 San Diego, California, USA 6–9 November 2016 PMOSymposium.org

SeminarsWorld® Events Leading subject matter experts share their experience and deep knowledge on a variety of emerging topics. Whether you are looking to build your leadership skills, work on soft skills such as communica- tions and collaboration, or delve deeper into agile, these events provide unique opportunities to learn and connect with the project management community.

Date Location 10–13 October Chicago,

Illinois, USA 7–10 November Cincinnati, Ohio,

USA 14–15 November London,

England 12–15 December Las Vegas,

Nevada, USA

Learn more about SeminarsWorld courses being held in these locations and through- out the world. Use PMI’s search tool for project management training matched to your specific needs. Visit learning.PMI.org.

101278_PMJ_08_130-131.indd 131 9/7/16 10:50 PM

132 October/November 2016 ■ Project Management Journal

Project Management Journal ® Author Guidelines

Project Management Journal® publishes research relevant to researchers, reflective practitioners, and organizations from the project, program, and portfolio management fields. Project Management Journal® seeks papers that are of interest to a broad audience.

Due to the integrative and interdisciplinary nature of these fields, Project Management Journal® publishes the best papers from a number of other disciplines, including, but not limited to, organizational behavior and theory, strategic management, marketing, accounting, finance, operations research, technology and innovation manage- ment, entrepreneurship, economics, political science, his- tory, sociology, psychology, information science, decision science, systems theory, and communication theory.

Project Management Journal® publishes qualitative papers as well as quantitative works and purely concep- tual or theoretical papers, including diverse research methods and approaches. Our aim is to integrate the vari- ous types of project, program, and portfolio management research.

Project Management Journal® neither approves nor disapproves, nor does it guarantee the validity or accu- racy of any data, claim, opinion, or conclusion presented in either editorial content, articles, From the Editor, or advertisements.

Project Management Journal® is a journal to dissemi- nate and discuss project management research. It is not a platform to discuss the content or quality of PMI stan- dards, credentials, or certifications, and those of other standard-setting organizations.

Authors’ Guidelines Papers published in Project Management Journal® must relate to research and provide new contributions to project management theory and/or project management prac- tices. Each paper should contain clear research questions, which the author should be able to state in one para- graph. Authors are expected to describe the knowledge and foundations underlying their research approach, and theoretical concepts that give meaning to data or to pro- posed decision support methods, and to demonstrate how they are relevant to organizations in the realm of project management. Papers that speculate beyond current think- ing are more desirable than papers that use tried-and-true methods to study routine problems, or papers motivated strictly by data collection and analysis.

Authors should strive to be original, insightful, and theoretically bold; demonstration of a significant value- added advance to the understanding of an issue or topic

is crucial to acceptance for publication. Multiple-study papers that feature diverse methodological approaches may be more likely to make such contributions.

Authors should make contributions of specialized research to project, program, and portfolio management theory and to the theory of the project-oriented organization or project network. They should define any specialized terms and analytic techniques used. Papers should be well argued and well written, avoiding jargon at all times. Project Management Journal® does not prefer subjects of study, as long as they are in the project, pro- gram, or portfolio management field, or in the field of the project-oriented organization or project network, nor do we attach a greater significance to one methodological style than another does.

Avoid Use of Commercialism Papers should be balanced, objective assessments that contribute to the project management profession or pro- vide a constructive review of the methodology. Papers that are commercial in nature (e.g., those that endorse or disparage specific products) will not be published.

Editing the Paper Make sure papers adhere to the theme or question to be answered. Write in clear and concise English, using active rather than passive voice. Manuscripts should not exceed 12,000 words, inclusive of figures, tables, and references. Count each figure and table as 300 words.

Manuscript Format/Style All manuscripts submitted for consideration should meet the following guidelines:

• All papers must be written in the English language (Ameri- can spelling).

• Title page of the manuscript should only include the title of the paper.

• To permit objective double blind reviews by two ref- erees, the abstract, first page and text must not reveal the author(s) and/or affiliation(s). When authors cite their own work, they should refer to themselves in the third person. Any papers not adhering to this will be returned.

Formatting the Paper

Papers must be formatted in an electronic format using a current version Microsoft Word. For Mac users, convert the file to a Windows format. If the conversion does not work, Mac users should save files as Word (.doc) files.

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October/November 2016 ■ Project Management Journal 133

• Helvetica or Arial font should be used for text within the graphics and tables.

• Figure numbers and titles are centered and appear in boldface type below the figure.

• Table numbers and titles are centered and appear in boldface type above the table.

• Figures and tables should be cited and numbered consecutively in the order in which they appear in the text.

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Use an appendix to provide more detailed information, when necessary.

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https://mc.manuscriptcentral.com/pmj Manuscript Central is a web-based peer review system

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Manuscripts should include the following in the order listed:

• Title page. Include only the title of the manuscript (do not include authors’ names).

• Abstract. Outline the purpose, scope, and conclusions of the manuscript in 100 words or less.

• Keywords. Select 4 to 8 keywords. • Headings. Use 1st, 2nd, and 3rd-level, unnumbered headings. • Text. To permit objective reviews by two referees, the abstract,

first page and the rest of the text should not reveal the authors and/or affiliations.

• References. Use author-date format. • Illustrations and tables. These should be titled, numbered (in

Arabic numerals), and placed on a separate sheet, with the preferred location indicated within the body of the text.

• Biographical details for each author. Upon manuscript acceptance, authors must also provide a signed copyright agreement.

By submitting a manuscript, the author certifies that it is not under consideration by any other publication; that nei- ther the manuscript nor any portion of it is copyrighted; and that it has not been published elsewhere. Exceptions must be noted at the time of submission.

Authors using their own previously published or submit- ted material as the basis for a new submission are required to cite the previous work and explain how the new submis- sion differs from the previously published work. Any poten- tial data overlap with previous studies should be noted and

Fonts

Use a 12-point Times or Times New Roman font for the text. You may use bold and italics in the text, but do not underline. Use 10-point Helvetica or Arial font for text within tables and graphics.

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Papers should be double-spaced and in a single-column for- mat. All margins should be 1 inch.

Headings

Use 1st, 2nd, and 3rd-level headings only. Do not number headings.

References, Footnotes, Tables, Figures, and Appendices Always acknowledge the work of others used to advance a point in your paper. For questions regarding reference for- mat, refer to the current edition of Publication Manual of the American Psychological Association. Identify text citations with the author name and publication date in parentheses, (e.g., Cleland & King, 1983), and listed in alphabetical order as references at the end of the manuscript. Include page num- bers for all quotations (page numbers should be separated by an en dash, not a hyphen).

Follow the formats in the examples shown below:

Baker, B. (1993). The project manager and the media: Some lessons from the stealth bomber program. Project Manage­ ment Journal, 24(3), 11–14. Cleland, D. I., & King, W. R. (1983). Systems analysis and project management. New York, NY: McGraw-Hill. Hartley, J. R. (1992). Concurrent engineering. Cambridge, MA: Productivity Press.

It is the author´s responsibility to obtain permission to include (or quote) copyrighted material, unless the author owns the copyright. Use the permission form, which is avail- able at the Manuscript Central site.

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Tips for creating graphics:

• Provide only the essential details (too much information can be difficult to display).

• Color graphics are acceptable for submission, although Project Management Journal® is published in grayscale.

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Author Guidelines

134 October/November 2016 ■ Project Management Journal

of the writing, which may be fixable, and the quality of the ideas that the writing conveys.

Respectful Reviews

PMI recognizes that authors have spent a great deal of time and effort on every submission. Reviewers will always treat an author’s work with respect, even when the reviewer disagrees or finds fault with what has been written.

Double-Blind Reviews

Submissions are subjected to a double-blind review, whereby the identity of the reviewer and the author are not disclosed. In the event that a reviewer is unable to be objective about a specific paper, another reviewer will be selected for that paper. Reviewers will not discuss any manuscript with any- one (other than the Project Management Journal® Editor) at any time.

Pointers on the Substance of the Review Theory

• Does the paper have a well-articulated theory that provides conceptual insight and guides hypotheses formulation?

• Does the study inform or improve our understanding of that theory?

• Are the concepts clearly defined? • Does the paper cite appropriate literature and provide proper

credit to existing work on the topic? Has the author offered critical references? Does the paper contain an appropriate number of references?

• Do the sample, measures, methods, observations, procedures, and statistical analyses ensure internal and external validity? Are the statistical procedures used correctly and appropri- ately? Are the author’s major assumptions reasonable?

• Does the empirical study provide a good test of the theory and hypotheses? Is the method chosen appropriate for the research question and theory?

• Does the paper make a new and meaningful contribution to the management literature in terms of theory, empirical knowledge, and management practice?

• Has the author given proper citation to the original source of all information given in the work or in others’ work that was cited?

Adherence to the Spirit of the Guidelines

Papers that severely violate the spirit of the guidelines (e.g., papers that are single-spaced, papers that use footnotes rather than conventional referencing formats, papers that greatly exceed 40 pages), or which do not clearly fit the mis- sion of the Journal will be returned to authors without being reviewed.

described in the letter to the Editor. The editorial team makes software-supported checks for identifying plagiarism and self-plagiarism.

Accepted manuscripts become the property of PMI, which holds the copyright for materials that it publishes. Mate- rial published in Project Management Journal® may not be reprinted or published elsewhere, in whole or part, without the written permission of PMI.

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Review Process The reputation of Project Management Journal® and contribu- tion to the field depend upon our attracting and publishing the best research. Project Management Journal® competes for the best available manuscripts by having the largest and widest readership among all project management journals. Equally important, we also compete by offering high-quality feedback. The timeliness and quality of our review process reflect well upon all who participate in it.

Developmental Reviews

It is important that authors learn from the reviews and feel that they have benefited from the Project Management Jour­ nal® review process. Therefore, reviewers will strive to:

• Be Specific. Reviewers point out the positives about the paper, possible problems, and how any problems can be addressed. Specific comments, reactions, and suggestions are required.

• Be Constructive. In the event that problems cannot be fixed in the current study, suggestions are made to authors on how to improve the paper on their next attempt. Reviewers docu- ment as to whether the issue is with the underlying research, the research conclusions, or the way the information is being communicated in the submission.

• Identify Strengths. One of the most important tasks for a reviewer is to identify the portions of the paper that can be improved in a revision. Reviewers strive to help an author shape a mediocre manuscript into an insightful contribution.

• Consider the Contribution of the Manuscript. Technical cor- rectness and theoretical coherence are obvious issues for a review, but the overall contribution that the paper offers is also considered. Papers will not be accepted if the contribu- tion it offers is not meaningful or interesting. Reviewers will address uncertainties in the paper by checking facts; there- fore, review comments will be as accurate as possible.

• Consider Submissions from Authors Whose Native Language Is Not English. Reviewers will distinguish between the quality

101278_PMJ_09_132-134.indd 134 9/7/16 10:56 PM

PM Curriculum · Flyer

© 2016 Project Management Institute, Inc. All Rights Reserved. “PMI” and the PMI logo are registered marks of Project Management Institute, Inc.

Download the materials at no charge at PMITeach.org

Give your students real job potential!

Give your graduates the skills that employers demand More and more employers are seeking candidates

with project management knowledge and skills.

By offering coursework in this area, colleges and

universities can equip students for success.

Project Management Institute (PMI) has

collaborated with faculty around the globe to

develop project management curriculum

guidelines along with a foundational course and

supplementary teaching materials. Qualified

faculty members are now able to introduce a

project management course more expeditiously

at their institution by drawing on this newly

available body of information.

What do the guidelines offer?

Materials are available to university educators who register through PMITeach.org. Email [email protected] for more information.

Instructional materials for one comprehensive entry-level course in project management, including a syllabus, mini-case studies, course projects, and activities 30 essential knowledge modules Instructional outlines for additional courses in project management with specific learning outcomes Guidance for enhancing existing courses An online open-source forum for faculty to share relevant content

How can faculty use the guidelines? To create a new foundational project management course at your academic institution To enhance an existing course in an allied discipline To create a sequence of project management courses To augment or benchmark an existing project management course

Project Management Curriculum and Resources

By faculty, for faculty

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CALL FOR PROPOSALS PROJECT MANAGEMENT JOURNAL® SPECIAL ISSUE

Projects and Networks SPECIAL ISSUE EDITORS: Robert DeFillippi, Strategy and International Business Department, Suffolk University, USA, [email protected] Stephen Pryke, Centre for Organisational Network Analysis, University College London, UK, [email protected] John Steen, Australian Institute of Business and Economics, University of Queensland, Australia, [email protected] Jörg Sydow, Department of Management, Freie Universität Berlin, Germany, [email protected]

Two basically different perspectives have brought projects and networks together. We welcome contributions from both viewpoints and integrative views. To this end, all papers should be based on theoretically informed and empirically rigorous research using qualitative or quantitative designs and methods.

PROJECTS FROM A SOCIAL NETWORK PERSPECTIVE Social or organizational network analysis not only provides a means to analyze project networks and develop theories of the flow of information and other resources through projects, it also provides a theoretical lens on control and coordination. There is scope here to extend beyond network analysis and apply network theory (Borgatti & Halgin, 2011) to generate broader theories of project-based organization.

PROJECTS FROM A NETWORK GOVERNANCE PERSPECTIVE Project networks as a specific form of governance are characterized by latent as well as activated ties with project entrepreneurs and/or organizations. In its purest form, project networks embed projects as a form of temporary organization (Lundin & Söderlund, 1995) into longer-term, open-ended networks (Sydow et al., 2016). As a consequence, their analysis requires not only investigations of the particular modes of organizing but also their specific contexts (DeFillippi, 2015).

Submissions: Full papers must be submitted by 31 October 2016 via the journal submission site. Papers accepted for publication but not included in the special issue will be published later in a regular issue of the journal. If you have any additional questions, please consult any of the guest editors.

For additional details about this call for proposals, please visit PMI.org/learning/publications-project-management-journal.aspx

®2015 Project Management Institute, Inc. All rights reserved. PUB-016-2015 (12/15)

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