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Abstract

Few research studies investigate the relationship between enterprise knowledge management (KM) and project management (PM). These studies have shown that today's PM approaches do not sufficiently include the required KM processes to derive the highest value from project investment. This research highlights the potential benefits of implementing an enterprise-wide KM approach to project management. The relationship between the four KM pillars, as defined by Stankosky, and the PM knowledge areas, as defined by the Project Management Institute (PMI), are investigated. A survey questionnaire reveals significant positive relationships between the KM pillars and PM knowledge areas. The significance and the nature of this association support the study's assertion that the management of projects improves as enterprise-wide KM processes and activities are increasingly used.

Headnote

Abstract: Few research studies investigate the relationship between enterprise knowledge management (KM) and project management (PM). These studies have shown that today's PM approaches do not sufficiently include the required KM processes to derive the highest value from project investment. This research highlights the potential benefits of implementing an enterprise-wide KM approach to project management. The relationship between the four KM pillars, as defined by Stankosky, and the PM knowledge areas, as defined by the Project Management Institute (PMI), are investigated. A survey questionnaire reveals significant positive relationships between the KM pillars and PM knowledge areas. The significance and the nature of this association support the study's assertion that the management of projects improves as enterprise-wide KM processes and activities are increasingly used.

Keywords: Knowledge Management, Performance Measures, Project Management, Project Management Knowledge Areas, Project Success

EMJ Focus Areas: Knowledge Management, Program & Project Management

Today's recognized project management (PM) tools and approaches do not sufficiently include the required knowledge management (KM) processes to derive the highest value from project investment. Although the body of knowledge includes research studies that focus on the role of KM to help organizations sustain their competitive advantage, few consider the use of KM to improve the management of projects (Haddad & Ribière, 2007; Lierni & Ribière, 2008). Project-based organizations are characterized by the time-limited nature of operations, complexity of tasks, and unpredictability of problems (Whitley, 2006). These characteristics imply an increased need for enhanced PM tools and processes to transform project-related knowledge and expertise into a collective capability and to control the achievement of project objectives (Metcalfe & James, 2000). In practice, managing knowledge is a challenge for every organization (Davenport, De Long, & Beers, 1998; Davenport & Prusak, 2000). In particular, managers in project-based organizations, regardless of their work environment or industry, agree on how important proper knowledge asset management is to project success (Lierni & Ribière, 2008). Researchers have indicated ways in which project knowledge can be protected and utilized to enable individuals to make good decisions using actionable information, experience, and insight (Lierni & Ribière, 2008). Recommendations have been made to enhance the mobilization of knowledge across projects using appropriate technology and KM tools (Ajmal, 2009; JavernickWill & Levitt, 2009). The suggested technologies and KM tools include asynchronous/synchronous communication tools, lessons learned, best practices, communities of practice, and repositories of artifacts (Lierni & Ribière, 2008). Researchers have also suggested creating a culture of knowledge sharing that depends on policies (Ajmal, 2009), managerial styles, organizational and project team structures, and business strategies (Lierni & Ribière, 2008; Newell, 2004; Srikantaiah, Srikantaiah, Koenig, & Al-Hawamdeh, 2010; Sutton, 2010; Turner & Müller, 2005; Waters & Beruvides, 2012).

These recommendations from the literature provide KM and PM professionals with tools and processes that may help in the formalization of an integrated KM/PM process. However, Stankosky (2005) realized that successful KM programs are typically comprehensive, well planned, and more complex than most organizations expect. He defined an enterprisewide KM conceptual framework based on four pillars- namely, Leadership, Organization, Technology, and Learning. The four KM pillars are viewed as essential for a successful enterprise-wide KM system. Failure to identify and manage the four KM pillars can result in them becoming major barriers to KM (Bixler, 2002; Calabrese, 2000; Stankosky & Baldanza, 2000).

From a PM perspective, the Project Management Institute (PMI) provides guidelines (A Guide to the Project Management Body of Knowledge, or PMBOK® Guide) to define PM processes and to manage individual projects. The PMBOK® Guide includes 10 knowledge areas that are representative of most projects: project integration, scope, time, cost, quality, human resources, communication, risk, procurement, and stakeholder management. In theory, each of the PM knowledge areas includes the processes that need to be completed to successfully manage projects. While the PMBOK presents the processes within each knowledge area as discrete components with well-defined boundaries, in practice, these processes are repeated, interactive, and can overlap among knowledge areas (PMI, 2013). In this work, the four KM pillars and the PM knowledge areas are used as a theoretical basis; more specifically, they were used to guide the creation of operational measures to investigate the relationship between enterprise KM and PM.

To provide background for this work, we begin with a review of the literature to highlight the importance of KM in a project environment, and we then identify elements related to each of the four KM pillars. In addition, using the PM knowledge areas, as defined in the PMBOK® Guide, we identify a set of performance measures that includes attributes by which the processes, inputs, tools and techniques, and outputs related to each PM knowledge area can be effectively managed. We then describe the research methodology used in this cross-sectional field study, which includes a survey questionnaire to measure variables relating to the KM pillars and the PM knowledge areas. By statistically testing these relationships, we expand our understanding of the role of KM in PM. Finally, we present and discuss the study results, implications, and conclusions, followed by recommendations for future research.

Literature Review

Due to globalization, international competition, and continued technological advances, knowledge has become one of the most valuable organizational assets (Anantatmula, 2005), and sharing knowledge is known to be vital in supporting firms in their pursuit of business success (Hall & Sapsed, 2005). As such, KM is important in relation to projects because knowledge transfer across projects affects project performance in terms of costs, schedule, and quality (Berteaux & Javernick-Will, 2015; Landaeta, 2008). KM is also important in relation to projects, because some projects depend on the generation of new knowledge that needs to be integrated and included in organizational practice, otherwise it becomes lost or worthless (Javernick-Will & Levitt, 2009). The transfer and integration of knowledge between projects are very important to avoid repetition of past mistakes at both the project and organizational levels (JavernickWill & Hartmann, 2011). Regardless of progress in the PM profession in general, project-based organizations face continued challenges to sustain their competitive advantage. In addition, organizations face challenges in improving project performance and addressing the risks of project failure, highlighting the significance of the role of knowledge management and its impact on project success (Anantatmula, 2010; Lierni & Ribière, 2008).

As codification and personalization are the two known approaches to KM strategies, project managers primarily rely on the codification approach, which is more often focused on the use of technology to manage explicit knowledge (Lierni & Ribière, 2008). However, project and project team performance are influenced by knowledge workers whose main capital is kept in their minds in the form of tacit knowledge (Davenport, 2013; Waters & Beruvides, 2012). Managing skills and tracking who knows what is necessary in order to utilize undocumented tacit knowledge. As such, Rus and Lindvall (2002) identify expert identification and managing expert knowledge as two of the most common problems that KM addresses. It is important to understand that project managers strongly benefit from codification as well as a personalization approach that is more focused on tacit knowledge by connecting people and individual expertise (Lierni & Ribière, 2008). The literature suggests that the balance between people and technology in KM is a key element to project success (Ho, 2009; Lierni & Ribière, 2008). Researchers have studied the potential benefits of KM implementation in projects, including investigating people- and technology-related KM tools and processes that may help to carry out successful projects (Hu & He, 2008; Lierni & Ribière, 2008; Waters & Beruvides, 2012). People-centric tools and processes include, but are not limited to, interviewing experts in the field, afteraction reviews, communities of practice, mentoring, education opportunities, and training programs (Lierni & Ribière, 2008). Alternatively, researchers, such as Hu and He (2008), have adopted a more technology-centric approach when studying the issue of project-related knowledge between projects. They suggest a web-based project KM system to capture, digitize, validate, share, and reuse project knowledge.

A Four-Pillar Approach to Knowledge Management

In his KM conceptual framework, Stankosky and Baldanza (2000) identifies the four pillars supporting an enterprise-wide KM framework (Leadership, Organization, Technology, and Learning), which, according to Bixler (2002), must be addressed in order to achieve a basic entry-level KM program. These pillars represent components considered important to implementing a successful KM framework (Stankosky & Baldanza, 2000). Calabrese (2000) grouped KM-related activities based on their role in supporting KM relating to leadership, organizational aspects, technology, and learning. Research by Bixler (2002), Stankosky (2005), Stankosky and Baldanza (2000), and Ternes (2011) describe the four pillars as they apply to the reality of KM implementation as follows:

* Leadership. Visionary leaders build business and operational strategies that align KM with business plans to increase the value of KM throughout the organization. Leadership support is required to ensure successful implementation of a knowledge management system. The leadership pillar addresses the objectives, policies, strategies, and priorities of organizational leadership involving requirements identification, resource allocation, prioritization, and application of organizational knowledge. The leadership pillar stresses the role of organizational leadership in using KM to achieve organizational goals.

* Organization. The Organization pillar addresses the operational aspect of KM in organizations, including progress and performance measurement, organizational strategy, process workflows, organizational structures, and organizational culture. Davenport, De Long, and Beers (1998) argues that organizational structure, processes, strategies, and culture are as important to successful KM as the tools and technologies supporting them. According to Stankosky (2005), a knowledge sharing culture is one of the most important attributes of an organization. Davenport, De Long, and Beers (1998) found that many workers consider knowledge as a source of personal power and they tend to resist sharing their knowledge with others to maintain their self-regard. Therefore, Davenport, De Long, and Beers (1998) considers that successful KM systems should assimilate, and adjust to organizational culture instead of change them, and attempt to improve processes instead of establishing them.

* Technology. This pillar provides all other parts of the KM framework with the technological platform and information technology support needed to achieve the organization's goals and objectives. It is necessary for organizations to define their KM strategy, scope, and requirements prior to selecting a solution or activity and before identifying the technology that effectively meets KM system requirements. The technological platform and information technologies include tools that promote knowledge sharing and collaboration. However, Zack (1999) asserts that over-reliance on technology to implement KM systems is a great weakness in many organizations. Bixler (2002) argues that the positive impact of technology alone rarely results in longterm success.

* Learning. Organizations will not achieve their KM strategy by using only the best technology or having effective leadership. KM also requires the involvement of people who are responsible for using the right KM tools in performing their duties. In this context, learning is an important part of the overall KM process where knowledge is acquired through experience, instruction, or study. This pillar addresses the activities and tools involved in the collaboration and sharing of organizational knowledge among individuals, groups, and departments. Organizational learning is not just people learning as individuals but also includes learning within groups or teams (Bixler, 2002). Thus, this pillar also addresses practices involved in the development of individual and team skills and capabilities in order to achieve organizational objectives.

In order to validate the existence of the four pillars and their related key elements, and to determine the strength of the four pillars as foundational elements of a KM framework, Calabrese (2000) assessed the beliefs, practices, and preferences associated with each of the four pillars using a survey questionnaire with 240 respondents. The results of this study support the utility of the pillars for use in the assessment and implementation of effective enterprise-wide KM systems. Further, Ternes (2011) tested the strength of the four KM pillars as a foundation for a KM framework. Ternes developed and administered a 45question survey and concluded that, while practices associated with Leadership, Technology, and Learning satisfy current KM needs, improvement in organizational practice is needed related to the Organization pillar. Ternes's recommendations include identifying and recording key processes required for a successful KM system, integrating organizational structure as part of the KM system, and developing plans to manage changes in the KM system.

Because KM is dependent on the project environment in which it is implemented, there is no general procedure to know the unique contribution of each pillar on project success or on the various dimensions of project management success. However, it is generally accepted that all four KM pillars must be functioning to some degree in order for a KM system to be viewed as successful (Mohamed, Stankosky, & Murray, 2004). According to Stankosky (2005), failure to identify and manage the key elements of the four KM pillars can result in these elements becoming major barriers to successful KM. In this work, we investigate the presumption that the presence of an enterprise-wide KM system addressing all four KM pillars is associated with project management success. In order to outline the presumed association model to support the intended investigation, it is deemed necessary to understand the various dimensions of project success and project management success.

Understanding Project Success and Failure

Improving project performance and ensuring project success are challenges that project-based organizations face. The risk of project failure is generally due to deficiency in applying the necessary knowledge, skills, tools, and techniques to project activities (PMI, 2013). In 1994, the Standish Group began providing statistics from studying projects around the world. Based on a survey of executives, they found that only 16% of projects were considered successful. According to the Standish Group International (2001, 2013), the projects were divided into three distinct categories: Project Success (projects completed on time and budget, with all features and functions as specified), Project Challenged (projects completed, but over cost, over time, and/or lacking all of the features and functions originally specified), and Project Impaired/Failed (projects abandoned or canceled at some point, thus becoming total losses). In 2013, the report "The CHAOS Manifesto 2013" showed an increase in project success rates, with 39% of all projects being considered as successful, with 18% considered failures, and 43% challenged. Despite the noticeable improvement in the project success rate, there is still a significant proportion of unsuccessful projects, highlighting the need to improve project management processes and tools.

Although the project management literature describes project success and failure, there are differences of opinion on what represents project success and the means by which project success is measured. Hughes, Tippett, and Thomas (2004) and PMI (2013) make a very important distinction between project success measures and project success factors. Project success measures are the criteria by which success or failure is evaluated, whereas project success factors are considered as inputs leading to project success. Traditionally, a successful project is defined as one that delivers the desired results within an agreed-upon timeframe and using the chosen resources (Hughes et al., 2004; Kirsch, 2000; Murphy, Baker, & Fisher, 1974; PMI, 2013). Researchers like Aladwani (2002), Cates and Mollaghasemi (2007), Parsons (2006), and Rosenfeld (2013) describe success or failure of a project in terms of the classical objective outcome measures, such as project cost (below, on, or over budget), project duration (early, on time, or late), and outcome quality (with less or better than the required features and functions). According to Langston (2013), the connection between the three main constraints that reinforce successful project delivery was originally defined by Martin Barnes as the iron triangle: time, cost, and output. The three main project constraints continued to be illustrated throughout the literature in different sets of terms-"time, cost, and output" (Langston, 2013), "time, cost, and quality" (Ika, 2009), "budget, schedule, and scope" (Agarwal & Rathod, 2006), and "cheap, fast, and good" (Langston, 2013). However, with the progress in the PM profession, the triangle has increasingly lost favor due to the vast number of project constraints that have emerged in the PM literature (Langston, 2013).

Early researchers, such as Baker, Murphy, and Fisher (1974), considered a project successful if it satisfied objective as well as subjective factors. According to the American Heritage Dictionary, factors that are objective are those relating to actual events and verifiable data or information as opposed to thoughts, while subjective factors relate to personal feelings, interpretation, perception, attitudes, beliefs, or opinions, instead of reliance on actual events. Baker, Murphy, and Fisher (1974) studied 650 projects and determined that subjective factors characterized by perception have a significant influence on project success. Hughes et al. (2004) studied subjectively versus objectively-measured factors in assessing project performance, and while focusing on metrics beyond the traditional objective metrics of cost, time, and specifications, they acknowledged the existence of more subjective factors that, while not easy to quantify, can have a significant effect on projects. DeCotiis and Dyer (1979) and Pinto and Slevin (1997) identified three subjective measures of project success: project perceived value, project implementation process, and customer satisfaction with the project outcome. Kirsch (2000) recommended that measurement of project success include project team member and stakeholder satisfaction with the project team. Hughes et al. (2004) and PMI (2013) make a distinction between measuring project management performance and project performance. Project management performance is evaluated against objective factors (cost, time, quality, etc.), whereas project performance is evaluated against objective as well as subjective factors characterized by perception (customer satisfaction, project team satisfaction, etc.).

Project Management Knowledge Areas and Related Performance Measures

In this study, we acknowledge the importance of the PM knowledge areas as defined in the PMBOK® Guide, where all project management processes are described. According to PMI (2013), project success is a result of balancing competing project constraints while managing projects. The competing constraints include, but are not limited to scope, schedule, budget, quality, resources, and risk. Each constraint represents a PM knowledge area that includes a set of processes, inputs, tools and techniques, and outputs. These PM tools and techniques include superior capabilities that enable project managers to plan and execute projects with maximum chances of project success. PMI (2013) describes the relationship among the competing project constraints as overlapping such that, if any one changes, at least one other constraint is likely to be influenced. Based on a field study involving 783 project managers to investigate the impact of the PM knowledge areas on project success, Zwikael (2009) found that the knowledge areas Human Resources, Risk, Scope, and Time have the greatest impact on project success. However, Langston (2013) highlights the importance of all knowledge areas and specifically highlights the important role of project integration management as it provides an opportunity to unify and optimize all processes, inputs, tools and techniques, and outputs for the other knowledge areas. Exhibit 1 presents objective and subjective performance measures classified based on the relevant PM knowledge areas as provided in the PMBOK® Guide. The performance measures encompass attributes by which the processes, inputs, tools and techniques, and outputs related to each PM knowledge area can be evaluated and successfully managed.

This research extends the existing body of knowledge in knowledge management and project management through a quantitative investigation of the relationship between enterprise-wide KM pillars and PM knowledge areas.

Methodology

This study focused on how the four KM pillars (Leadership, Organization, Technology, and Learning) relate to the PM knowledge areas (project integration, scope, time, cost, quality, human resources, communication, risk, procurement, and stakeholder management). It has been determined by other researchers (Bixler, 2002; Stankosky, 2005; Stankosky & Baldanza, 2000; Ternes, 2011) that all four KM pillars must be addressed in order to achieve a basic KM system. In addition, since it has been suggested by PMI (2013) that project success is a result of balancing project constraints represented by the PM knowledge areas, the aim of this work is to determine whether a significant association exists between the four KM pillars and the PM knowledge areas.

Research Hypotheses

Exhibit 2 shows the conceptual model for this study depicting the relationship between KM pillars and PM knowledge areas. The model illustrates the conceptual definition of the KM pillars by identifying the elements relating to each pillar. The pillars and their corresponding elements are viewed collectively as defining an enterprise-wide KM system. Exhibit 2 also illustrates the performance measures by which the application of inputs, processes, tools and techniques, and outputs related to each PM knowledge area measured. Variables are framed this way to indicate the believed direction of causation (i.e., enterprisewide KM system, or KM pillars, relating to successful project management, or PM knowledge areas), although, as discussed later, statistical analyses performed in this work do not allow us to infer causality. Accordingly, the following main research hypothesis is proposed:

H^sub r^ There is a positive association between an enterprise-wide KM system and PM knowledge areas.

The association between an enterprise-wide KM system and PM knowledge areas is investigated from two aspects as defined by the following two sets of research sub-hypotheses:

* Set 1-The association between an enterprise-wide KM system and each PM knowledge area:

H^sub 1^a There is a positive association between an enterprise-wide KM system and project integration management.

H^sub 1^b There is a positive association between an enterprise-wide KM system and project scope management.

H^sub 1^c There is a positive association between an enterprise-wide KM system and project time management.

H^sub 1^d There is a positive association between an enterprise-wide KM system and project cost management.

H^sub 1^e There is a positive association between an enterprise-wide KM system and project quality management.

H^sub 1^f There is a positive association between an enterprisewide KM system and project human resource management.

H^sub 1^g There is a positive association between an enterprise-wide KM system and project communication management.

H^sub 1^h There is a positive association between an enterprise-wide KM system and project risk management.

H^sub 1^i There is a positive association between an enterprise-wide KM system and project procurement management.

H^sub 1^g There is a positive association between an enterprise-wide KM system and project stakeholder management.

* Set 2-The association between each of the four KM pillars and overall project management:

H^sub 2^a There is a positive association between the KM leadership pillar and project management.

H^sub 2^b There is a positive association between the KM organization pillar and project management.

H^sub 2^c There is a positive association between the KM technology pillar and project management.

H^sub 2^d There is a positive association between the KM learning pillar and project management.

Using the sub-hypotheses, the association and concordance testing between enterprise-wide KM system and PM knowledge areas are determined according to the conceptual model shown in Exhibit 2. To test the research hypotheses, a survey questionnaire was used to obtain perceptions of KM pillars and PM knowledge areas.

Design of Survey Questionnaire

Using methods suggested by Alreck and Settle (2003), a set of 51 questions and sub-questions was defined in the survey questionnaire, which was comprised of three main parts. Part I consists of four demographic questions used to assess the nature of the population sample (Alreck & Settle, 2003). Part II, with 29 questions, relates to elements within the four KM pillars that were illustrated in Exhibit 2. Questions in this part assess the extent to which KM, as measured by the elements within each pillar, is practiced within the organization as perceived by respondents. Questions were defined relating to each KM pillar, as shown in Exhibit 2, such that each element within a pillar was represented by a single question. For analyses investigating each element, responses for the single question were used, while for analyses investigating each KM pillar, the group of questions representing each pillar was used. Part III consists of 8 main questions and 10 sub-questions to assess the extent to which PM is successful within the participants' organizations. Each PM knowledge area is represented by questions corresponding to the knowledge area's performance measures. As shown in Exhibit 1, several performance measures represent more than one PM knowledge area at the same time. Thus, responses to several sub questions are used for the analysis of more than one PM knowledge area. For example, how well an organization defines a scope of work is considered a performance measure for project integration management as well as for project scope management.

The survey instrument was refined based on expert feedback to assess content validity. Using an evaluation form, four experts in the field of KM and PM were asked to code the survey questions for a set of characteristics: Response Latency (question is easy to answer and does not require time for an answer to come to mind), Burden (question does not require heavy cognitive work to answer), Sensitivity (question does not require revealing sensitive or private information), and Inclusivity of PM and KM elements (element represented by the survey question merits inclusion as a component of a KM/PM model developed to study the association between KM and PM) (Olson, 2010; Presser et al., 2004). For each question, the experts were asked for a "yes" or "no" response to the characteristics questions and provide comments for improvements.

While the focus of the survey questions relating to KM pillars is at the organizational level (since our interest is in enterprise-wide KM systems), the focus of the questions relating to PM knowledge areas is at the project level. Because respondents likely have served on multiple projects in the past, in completing the survey, respondents were instructed to refer to the most recent project in which they had participated. Likertscale responses were used for the questions inquiring about a specific KM pillar or PM knowledge area. All responses were given a numeric value from (1) to (5), where, for example, 1 = Very unsuccessful, 2 = Unsuccessful, 3 = Average, 4 = Successful, and 5 = Very successful.

Prior to statistical analysis, the internal consistency across the survey items attempting to measure the same construct was evaluated by calculating Cronbach's alpha for the survey results for each of the 4 KM pillars and for each of the 10 PM knowledge areas (Alreck & Settle, 2003). According to George and Mallery (2003), a Cronbach's alpha coefficient of 0.7 or greater is required for the internal consistency to be acceptable. The measurement scales of the sets of questions representing each of the 10 PM knowledge areas resulted in acceptable levels of internal consistency, with values of 0.70 to 0.93. For the sets of questions representing each of the four KM pillars, the measurement scales resulted in high levels of internal consistency with values of 0.93 to 0.96.

Survey Distribution

An invitation to participate in the survey study was sent to 1,118 individuals with PM experience. Participants were randomly selected from a list made available by the PMI and were limited to individuals having responsibilities within project-based organizations. The survey was distributed using online software hosted by Qualtrics. Within 60 days of launching the survey, 152 responses were received. Using the responses to demographic questions in Part I of the survey, individuals with the majority of their work activities not within project-based organizations were excluded from this study. As such, 128 responses were considered for this study, for an overall response rate of 11.5%.

Data Analysis

In this study, a primary aim of data analysis was to evaluate the association between: (1) overall enterprise-wide KM system and each PM knowledge area, and (2) each of the four KM pillars and overall PM. In addition, based on the research conceptual model shown in Exhibit 2, the association between an enterprise-wide KM system and PM was investigated in further detail to include the association between: (3) the four KM pillars and the PM knowledge areas, and (4) elements of the four KM pillars and overall PM. To measure the association between factors measured in the survey, Kendall's tau-b correlation coefficient, T^sub b^, was determined to be the appropriate nonparametric measure of association (Agresti, 2010; Siegel & Castellan, 1988).

First, to measure the association between a KM pillar and a PM knowledge area, the responses for the survey questions were grouped such that each group included responses for questions representing a specific KM pillar or a specific PM knowledge area. For each respondent, the median of the responses for each group (i.e., each KM pillar or each PM knowledge area) was determined. These values were then used to calculate the association between each KM pillar and each PM knowledge area. Second, to investigate the relationship that overall KM has with each PM knowledge area, the median of the participant's responses to all 29 survey questions in Part II was determined as an aggregate measure of the presence of the four KM pillars. These values were then used to calculate the association between overall KM and each PM knowledge area. Third, to measure the association between each KM pillar and overall PM, the median of the participant's responses to all 18 survey questions and subquestions of Part III was determined as an aggregate measure for overall PM. Finally, to measure the association between elements of the four KM pillars and overall PM, the participant's responses to each survey question that corresponds to an element of the four KM pillars was tested against the median of the participant's responses to all 18 questions and sub-questions used to measure PM knowledge areas.

On a scale of -1.0 to 1.0, the degree and the nature of the association between variables were determined. A positive T^sub b^ value demonstrates a positive association between two variables such that, as the ranking of one variable increases, the ranking of the other one also increases. A negative T^sub b^ value demonstrates a negative association between two variables such that the ranking of one variable increases as the other decreases or vice versa (Siegel & Castellan, 1988). The following guidelines were used to determine the strength of absolute values of ordinal measures of associations (Le Roy & Corbett, 2008):

Less than 0.10 = very weak

0.10 to 0.19 = weak

0.20 to 0.29 = moderate

0.30 or above = strong

The level of statistical significance of Kendall's tau-b correlation coefficient, T^sub b^, is tested by the z-test, at a confidence level of 95% (Agresti, 2010; Everitt, 2001; Siegel & Castellan, 1988). The test statistic, Z, is computed using Equation (1) as follows, where t is Kendall's tau-b statistic and N is the number of observations:

(ProQuest: ... denotes formula omitted.) (1)

The p-value for significance, P^sub s^, is then computed using the standard normal distribution. The null and alternative hypotheses for significance are as follows:

* H^sub o^: T^sub b^ = 0 (there is no correlation between the two variables).

* H^sub a^: T^sub b^ # 0 (the two variables are correlated).

In addition, at a confidence level of 95%, the p-values for the test of concordance, P^sub c^, were calculated to describe the direction of the relationship between enterprise-wide KM and PM (Siegel & Castellan, 1988). The null hypothesis for the test of concordance is that the probability of concordance equals the probability of discordance. A significance level below 0.05 indicates that there is sufficient evidence to conclude that the variables move in the same direction and that the relationship is positive (Agresti, 2010). Exhibit 3 shows an example of the measures of association between the KM leadership pillar and the knowledge area of project cost management.

The value of T^sub b^ = 0.22 (P^sub s^ = 0.0002 and P^sub c^ = 0.002) suggests a moderate positive correlation between the Leadership pillar and project cost management. Hence, we can conclude that project cost management improves as organizations effectively utilize leadership-related KM processes and activities, such as management commitment to managing knowledge gained from projects, using performance measures to track project progress and performance, and rewarding the sharing of project knowledge.

Results

Demographic results from the survey are presented in Exhibit 4 and indicate that the largest number of survey participants (64, or 50%) reported that they had 5-15 years of experience, 35 (or 27%) reported that they had more than 25 years of experience, 28 (or 22%) reported that they had 16-25 years of experience, and 1 respondent reported less than 5 years of experience. Of those who participated in the research study, 78 (or 61%) are project managers, 22 (or 17%) are project engineers, 19 (or 15%) are department managers, and 8 (or 6%) are general managers.

Exhibit 4 also shows respondents' work field. The most frequently reported sector was information technology (32% of respondents), with the next most being energy (22%), construction (17%), telecommunication (12%), manufacturing (9%), and chemical and pharmaceutical (8%).

As shown in Exhibit 5a, the values for Kendall's tau-b (T^sub b^), p-value for test of significance (P^sub s^), and p-value for the test of concordance (P^sub c^) suggest strong, positive correlations, all above 0.30, between enterprise-wide KM and human resource management, stakeholder management, integration management, and communication management. For project procurement, quality, risk, and cost management, the results suggest a moderate, positive correlation with enterprise-wide KM, while there is a weak positive correlation between enterprise-wide KM and project scope and time management.

Considering the relationship between the aggregate measure of PM and the four KM pillars, results in Exhibit 5b suggest that the Technology and the Organization pillars had the strongest correlation with values of T^sub b^ = 0.32 and T^sub b^ = 0.30, respectively. For the Leadership and Learning pillars, the value of (T^sub b^ = 0.27) suggests a moderate, positive correlation with overall PM. It is interesting to note that the correlation between enterprise-wide KM and the overall measure of PM (T^sub b^ = 0.330), as shown in the last row of Exhibit 5b, is stronger than that between overall PM and any of the four enterprise KM pillars individually. This confirms a major premise of this study and indicates that, as enterprise-wide KM processes and activities increase, overall project management is increasingly successful.

Exhibit 6 shows the correlation using Kendall's tau-b (T^sub b^), as well as the p-value for the test of significance (P^sub s^) and test of concordance (P^sub c^) between each of the four KM pillars and each of the PM knowledge areas. The correlations suggest that, of all the PM knowledge areas, human resource management, integration management, and stakeholder management had the strongest positive correlation with the Learning pillar, with values all greater than 0.30. As shown in Exhibit 6, human resource management and stakeholder management had the strongest correlation with the Technology pillar, while human resource management, integration management, and stakeholder management had the strongest correlation with the Organization pillar. Lastly, the results suggest that human resource management and integration management had the strongest correlation with the Leadership pillar. Across all PM knowledge areas, time management had the weakest, although still positive, correlation found, which was with the Organization pillar (T^sub b^ = 0.13, P^sub s^ = 0.02, P^sub c^ = 0.061).

Exhibit 7 summarizes the correlations between elements of the four KM pillars and the aggregate measure of overall PM. The KM element most strongly correlated to PM is education opportunities and training programs provided by the organization in order to build project workers' competencies with (rb) = 0.426, with the next strongest being synchronous communication (instant messaging, application and screen sharing, video and audio conferencing, telephone) with Tb = 0. 414. With Tb = 0. 404, the third strongest correlation found was project teams understanding of what they need to do in order to achieve the project objectives (understanding organization strategy).

The relationships found emphasize the need for an enterprise-wide KM approach to managing projects. This approach should recognize and include all KM elements related to the four pillars of KM, as well as the PM knowledge areas acknowledged by PMI. Understanding the association between these elements and PM enables the estimation of how each of these elements contributes to project management success and project success. As an example, a negative association between a given element of KM and a PM performance measure indicates that the element may be an obstacle to project success, while a positive association indicates that it may be a facilitator, or enabler, of PM success. However, it is important to note that an association does not necessarily imply causation. As such, this study does not prove that effective KM causes successful PM, but the results can be used to justify further investigation into the relationship.

Implications for Engineering and Project Managers

The findings from this study can help practicing engineering managers and project managers to understand the relationship between KM on PM. It also contributes to the body of knowledge and provides practitioners with an overview of how project management and project success may be associated with effective management of project knowledge. The results of this study imply that increasing levels of PM success are associated with increasing use of KM processes and activities. The study did not find that every element in the KM pillars had equally strong correlations with PM knowledge areas. However, Exhibit 7 shows that every element in the KM pillars had a positive correlation and that every element is important to implement. The study results highlight the importance of an all-inclusive KM approach to PM that involves people who use the right KM tools and processes and perform project activities under visionary and KM-committed leadership. Thus, the value of this study to engineering and project managers is that success in projects may be influenced by the approach by which project knowledge is managed.

Limitations and Areas for Future Research

The primary limitation of this work is the scarcity of previous research regarding the association between actionable KM frameworks and PM. Several high-level studies have been conducted to investigate the effect of selected KM activities on projects based on researchers' perceived significance of various KM activities. However, other than limited research institutes and academic activities, a small number of in-depth quantitative studies were found during the literature review. Thus, the apparent lack of in-depth previous research is why we characterize this study as exploratory. Another potential limitation is the study's sample. The study surveyed 128 project management professionals from a wide range of projects and industries around the world. Moreover, based on the survey design, respondents were instructed to refer to the most recent project they took part in when completing the survey, thus, their responses do not reflect a broad range of project experiences participants may have had. Future research should use increased sample size, including the levels of KM implementation in order to identify relevant sub-groups such that the effect of KM on individual project types and industries can be studied.

Additionally, this study collected perceptual measures for both independent and dependent variables from the same survey participants. Thus, it is possible that common-method variance has jeopardized the validity of the study results, and the variance in the survey results is partially attributable to common-method variance effect (Spector, 2006). Future research could use objective measures of project performance or use different survey participants to collect perceptual measures for independent and dependent variables.

Based on the review of the literature and the findings reported here, suggestions for future research include research related to understanding the dynamics of an integrated KM/PM system, and studying the behavior of the integrated systems over time. It is also important to study the effect of implementing different KM systems and models on the management of projects. Another important future research avenue would be to study the effect of KM tools and processes on the management of projects in specific industries. We hope to continue with this research and to pursue the suggested avenues in the future to identify grounds for an effective knowledge management approach to project management.

Conclusions

This research study contributes to the body of knowledge by investigating the possible benefits of implementing an enterprise-wide KM approach to PM using quantitative research. In this work, the association between the four pillars of KM (Leadership, Organization, Technology, and Learning) and the PM knowledge areas was investigated. Results from measuring the association indicate that there is a significant relationship between the four pillars of KM and the PM knowledge areas. Specifically, results indicate that project human resource management and project stakeholder management have the strongest association with an enterprise-wide KM. Managing skills and tracking who knows what is necessary in order to utilize undocumented tacit knowledge. The association between the main areas of project human resource management (expert identification, developing project teams, project team performance, and conflict/problem solving) with knowledge management suggests that the role of knowledge management in project human resource management is significant. As such, the focus in project human resource management should be placed on enhancing KM tools and processes that highlight experts' quality, creativity, leadership, and problem-solving skills (Yahya & Goh, 2002). An effective project team performance and monitoring system should promote trust and collaboration between project team members, encourage experience and knowledge sharing, and reward employees for high-quality knowledge management practices.

The strong association between enterprise-wide KM and project stakeholder management highlight the significant role of KM in all processes required to identify and engage all people or organizations interested in, needed to support, and/or affected by the project in a way that ensures successful completion of the project to their satisfaction. The application of knowledge management to the main three areas of project stakeholder management (stakeholder identification, knowledge availability to stakeholders, and stakeholder satisfaction) could play a constructive role in achieving successful project outcomes (Hughes et al., 2004; PMI, 2013). KM could play a significant role in the processes of identifying experts by using expert directories. Stakeholders in projects include customers whose satisfaction is a necessary measure for successful projects. Stakeholders in projects also include project team members who are encouraged to acquire and share project knowledge by using enabling tools like communities of practice. It is necessary to provide all stakeholders with access to project knowledge and to keep them informed throughout the project management process by facilitating IT and communication tools and activities (e.g., synchronous and asynchronous communication tools). Management project communication is not just IT. It is also a matter of trust and collaboration between all stakeholders and includes all processes required to ensure timely and appropriate communication of project knowledge. Management of project communication could involve document control and data management systems to provide access to knowledge and could use virtual teams and communities of practice to build a sense of trust and collaboration within project teams. The strong association between enterprise-wide KM and project integration management highlights the significant role of KM in all processes required to identify, define, combine, unify, and coordinate the various project management processes and activities. Project integration management may well benefit from using PM software, as a KM tool, to schedule, manage, and control project information and activities to guarantee compliance with the project management plan and with the project scope of work. An effective performance and monitoring system could help coordinate and monitor various project processes and activities. The project team performance and monitoring system should promote trust and collaboration between project team members, encourage experience and knowledge sharing, and reward employees for high-quality knowledge management practices.

From the perspective of the four pillars of KM, results revealed that Technology-related and Organization-related KM elements have the strongest relationship with PM. However, the use of the four KM pillars tools and processes showed a balanced and a near uniform association across each PM knowledge area independently, as was shown in Exhibit 6. Thus, the results support the study's assertion that management of projects improves as enterprise-wide KM processes and activities are increasingly used. The results also highlight the importance of utilizing all four pillars in the organization's KM system to improve PM outcomes.

Acknowledgment

This research article is in partial fulfillment of the requirements for the Doctor of Philosophy degree at The George Washington University.

Sidebar

Refereed Research Manuscript. Accepted by Associate Editor Mazur.

References

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AuthorAffiliation

Tariq A. Oun, The George Washington University

Timothy D. Blackburn, The George Washington University

Bill A. Olson, The George Washington University

Paul Blessner, The George Washington University

About the Authors

Tariq A. Oun has more than 17 years of chemical engineering, engineering management, and project management experience within the energy industry. He worked as a senior process engineer and a senior operations engineer at ENI gas. He earned a Bachelor's degree in chemical engineering from the University of Al-Fatah and a Master's in technology management from the University of Bridgeport-Connecticut. Currently, he is a PhD candidate in the Engineering Management and Systems Engineering Department at The George Washington University.

Timothy D. Blackburn is a Summa Cum Laude graduate from UNC-Charlotte and also holds an MBA from UNC-Chapel Hill. He received a PhD in systems engineering from The George Washington University (GWU). He is a licensed professional engineer and holds a Black Belt in Six Sigma. Currently, he is a professorial lecturer in Engineering Management and Systems Engineering (EMSE) at GWU and is the North America Lead for Technical Learning and Capability at Pfizer.

Bill A. Olson is the Leading Risk Manager at Newport News Shipbuilding. He is a Certified Systems Engineering Professional and INCOSE past chapter president of the HRA chapter. He earned a Bachelor's from Chapman University, a Master's from Florida Institute of Technology, and a PhD in systems engineering from The George Washington University. Currently, he is a professorial lecturer in the Department of Engineering Management and Systems Engineering at GWU. Bill Olson retired from the U.S. Navy after 25 years' service.

Paul Blessner has more than 30 years of manufacturing/test/ quality/reliability engineering experience within the computer, aerospace, and defense industries. His education includes electrical engineering (BS, University of Nebraska), business administration (MBA, University of Colorado-Colorado Springs), applied statistics (MS, R.I.T.), and systems engineering (PhD, The George Washington University). He is certified as a Six Sigma Master Black Belt, a Quality Engineer, Reliability Engineer, and Quality Manager. He is a professorial lecturer of Engineering Management and Systems Engineering at The George Washington University.

Contact: Tariq Oun, The George Washington University, 950 North Glebe Road, Suite 118, Arlington, VA 22203; [email protected]

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