unit6_assignment

Journal of Purchasing & Supply Management 22 (2016) 141–153

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Journal of Purchasing & Supply Management

http://d 1478-40

n Corr Sociolog Jaén, Sp

E-m jmoyan

journal homepage: www.elsevier.com/locate/pursup

Supply chain integration through community cloud: Effects on operational performance

Sebastián Bruque-Cámara a,b,n, José Moyano-Fuentes a,b, Juan Manuel Maqueira-Marín b

a Department of Business Organization, Marketing and Sociology, University of Jaén, EPS Linares, C/ Alfonso X el Sabio, 28. Office: A-202, E-23700 Linares, Jaén, Spain b Department of Business Organization, Markleting and Sociology, University of Jaén, EPS Linares C/ Alfonso X el Sabio, 28. Office: A-228, E-23700 Linares, Jaén, Spain

a r t i c l e i n f o

Article history: Received 17 November 2014 Received in revised form 8 April 2016 Accepted 8 April 2016 Available online 23 April 2016

Keywords: Cloud computing Community cloud computing Supply chain integration Operational performance Informational and physical supply chain integration

x.doi.org/10.1016/j.pursup.2016.04.003 92/& 2016 Elsevier Ltd. All rights reserved.

esponding author at: Department of Business y, Universidad de Jaén, Campus Cientifico Te ain. Tel.: +34 953 648634; mobile: +3468663 ail addresses: [email protected] (S. Bruque-Cá [email protected] (J. Moyano-Fuentes), maqueira@uj

a b s t r a c t

In this paper we analyze the effects exerted by a specific type of cloud technology (community cloud computing) on a particular type of supply chain integration (integration of informational and physical flows). We also analyze the combined effect of community cloud computing and physical-informational supply chain integration on firms’ operational results. To test the hypotheses in this paper we use a sample of 394 companies randomly selected from a population of 2036 companies with a staff of at least 50 employees, taken from the DUNS 50,000 database for companies in Spain. We use factorial analyses and structural equation modeling to test our hypotheses. Our three hypotheses are confirmed, indicating that the community cloud exerts a positive and significant effect both on the informational-physical integration of the supply chain and on operational performance. The value of this study lies in the fact that it enables academics and practitioners to understand which parts of a relatively new technology (cloud technology and its sub-types) might contribute to integrating flows in the supply chain and, ul- timately, impacting operational performance.

& 2016 Elsevier Ltd. All rights reserved.

1. Introduction

The role played by information technology (IT) on company structure, behavior and performance has been a constant stimulus for research in recent decades. One of the factors affecting research into the organizational impact of information technology is the continuous change faced by companies having to deal with a shifting environment. These technologically-changing environ- ments provide firms with new capabilities and resources that can be quickly implemented in the organizational arena. However, these unceasing changes sometimes involve modifications to the business structure and targets that can result in challenging, hostile, managerial situations that have to be fully understood and addressed for business sustainability and, ultimately, a competitive advantage to be achieved.

In this paper we analyze the role played by a specific type of technological breakthrough that has been progressively

Organization, Marketing and cnológico, E-23700, Linares, 5741. mara), aen.es (J.M. Maqueira-Marín).

implemented by companies in recent years. We also take a closer look at cloud computing and, in particular, at community cloud computing. In cloud computing (Buyya et al., 2011, 2009; Fingar, 2009; Hayes, 2008) resources are not located in firms, but in vir- tualized, distributed environments that are geographically dis- perse, and can be accessed on an on-demand basis through web- based technologies. Community cloud computing can be defined as the cloud infrastructure provisioned for exclusive use by a specific community of users from organizations that have shared concerns (e.g., mission, security requirements, policy, and com- pliance considerations). It may be owned, managed, and operated by one or more of the organizations in the community, a third party or some combination of them, and it may exist on or off premises (Mell and Grance, 2011).

One important finding in the literature is that while IT is va- luable, it is dependent upon internal and external factors relating to chain partners. Viewed from the IT-enabled organizational capability perspective (Bharadwaj, 2000), some authors consider IT as a set of complementary resources that augments the value of other organizational resources and capabilities that will lead to further business performance improvement (Melville et al., 2004). The literature on IT-enabled organizational capabilities suggests that supply chain process integration (Rai et al., 2006) is a

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capability that can turn the value of IT into business performance. This paper investigates the role played by a specific type of cloud computing (community cloud computing) in supply chain in- tegration through the integration of the physical and informa- tional flows between companies. We have adopted an operational view because it is a field in which there is a research gap, with a significant lack of prior research addressing company cloud com- puting and Supply Chain Integration (SCI) and their relation to operational effectiveness. We have adopted an operational point- of-view rather than simply a financial approach due to the fact that this technology might be strictly connected, at least at the first stage of development, to the ways that operations and in- formation flows are organized and executed in the company (Dominy, 2012). Therefore, it is more likely during the initial stages of development that cloud computing -and, specifically, commu- nity cloud computing- which is designed to bind, share and con- nect the links in the firm's value chain, can also have a major impact on the components of the supply chain.

From a theoretical point-of-view, we have used the arguments of classic extended Resource Based View (RBV), Knowledge Man- agement and Social Capital Theory to build the theoretical fra- mework for this study. This theoretical framework highlights the roles of trust, identification and knowledge, while creating an appropriate atmosphere among the supply chain members that enables them (the supply chain members) to improve the com- pany's operational results by means of greater SCI.

The paper has been organized as follows: In the following section we briefly study the background and describe the argu- ments leading to the hypotheses in greater depth. The third sec- tion of the paper includes a description of the sample and methods used in the empirical analysis. The fourth section is devoted to the analysis and results drawn from the empirical findings of the pa- per, while the last section includes a discussion and the major conclusions of the paper. Additional research directions and lim- itations are also outlined in the final section of the present study.

2. Background and hypotheses

Cloud computing has emerged as a new technological option with huge potential for companies and is currently transforming IT infrastructure (Iyer and Henderson, 2010; Winans and Brown, 2009). With revolutionary effects on business (Abdulaziz, 2012; Marston et al., 2011), cloud computing is a term that figuratively refers to the bundle of virtualized and distributed resources con- figured in a diffuse, all-pervasive way. In cloud computing (Buyya et al., 2011, 2009; Fingar, 2009; Hayes, 2008), resources are not located within firms but in virtualized and distributed environ- ments that are geographically disperse and accessible on an on- demand basis using web-based technologies, combining Internet connectivity and pay-per-use systems as the foundation of the business model (Vaquero et al., 2009). There are several classifi- cations of cloud computing technologies. One of these (Mell and Grance, 2011; Ryan and Loeffler, 2010) organizes the cloud spec- trum into four deployment models: (1) Private cloud: an internal cloud infrastructure which covers a single organization; (2) Com- munity cloud: distributed infrastructure provisioned by a group of closely-linked business partners in order to share business re- sources; (3) Public cloud: infrastructure that can be openly used by the general public and that may be owned, managed, and operated by a business, academic, or government organization, or some combination of them, and (4) Hybrid cloud: the hybrid cloud in- frastructure is a composition of two or more distinct cloud infra- structures (private, community, or public) that remain unique entities, but are bound together by standardized or proprietary technology that enables data and application portability (e.g.,

cloud bursting for load balancing between clouds). In view of these definitions, community cloud computing is a type of cloud com- puting that could be especially applicable to the supply chain (Cheng et al., 2014).

Community cloud computing can be implemented in con- junction with other cloud-based service models (Mell and Grance, 2011; Ryan and Loeffler, 2010), such as: (1) Software as a Service (SaaS), where the capability provided to the consumer is to use the provider's applications running on a cloud infrastructure. The ap- plications are accessible from various client devices through either a thin client interface, such as a web browser (e.g., web-based email), or a program interface; (2) Platforms as a Service (PaaS), the capability provided to the consumer is to deploy onto the cloud infrastructure consumer-created or acquired applications created using programming languages, libraries, services, and tools supported by the provider, and (3) Infrastructure as a Service (IaaS), according to which the capability provided to the consumer is to provision processing, storage, networks, and other funda- mental computing resources where the consumer is able to deploy and run arbitrary software, which can include operating systems and applications.

In the prior literature, the advantages and disadvantages of cloud computing have been analyzed in comparison with tradi- tional or conventional IT (Tuncay, 2010; Paquette et al., 2010; Marston et al., 2011; Dutta et al., 2013; Trigueros-Preciado et al., 2013; Shkurti and Muça, 2014). Cloud computing is able to provide firms with capabilities (Iyer and Henderson, 2010) and increase their business value (Abdulaziz, 2012). Cloud computing benefits can be also related to other advantages, such as instant global platforms, the elimination of hardware infrastructure and software licenses, reduced cost, simplified scalability and the elimination or reduction of disaster recovery risks and the associated high costs (Tuncay, 2010). In the case of small and medium-sized enterprises (SMEs), community cloud computing offers some remarkable ad- vantages (Trigueros-Preciado et al., 2013), such as cost reductions in software, hardware and IT staff; scalability and flexibility in IT use, and access to IT resources which companies would otherwise not be able to employ. Integrated cloud-based supply chain sys- tems reduce the labor cost of traditional communication means in SMEs and improve operating efficiency and the quality of man- agement decision making (Cheng et al., 2014). According to the literature, some cloud services may also have several benefits over traditional IT models, including flexibility, configurability, general cost effectiveness and low implementation costs (Durowoju et al., 2011). It has been found that the greatest benefits of cloud com- puting technology in the accounting and financial industry (Shkurti and Muça, 2014) are perceived to be cost savings in both hardware and software, while information security and reliability are mentioned as its main shortcomings. Cloud computing also has its risks. The major risks in the governmental use of cloud com- puting are security related (Paquette et al., 2010), while there are other more important risks in private use related to the current legal and technical complexity of cloud computing (Dutta et al., 2013). Cloud computing used in SMEs (Trigueros-Preciado et al., 2013) has been found to be associated with several drawbacks, such as: security issues; the distrust generated by releasing data to third parties; the availability and quality of service, and the pos- sible difficulties caused by changing suppliers and complying with legal data protection requirements. Other prior research has found that the impact of cloud services on security is directly related to supply chain performance (Durowoju et al., 2011). The overall security risks are lower in the specific case of community cloud computing, since this is cloud infrastructure provisioned for ex- clusive use by a specific community of users from organizations with shared concerns, such as mission, security requirements, policy, and compliance considerations. Community cloud

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computing is therefore particularly useful for application in supply chain management (Cheng et al., 2014).

However, even though there might be many advantages to cloud computing in general, and community cloud computing in particular, research focused on cloud-based supply chain man- agement is very scarce (Durowoju et al., 2011; Cegielski et al., 2012; Wu et al., 2013; Cheng et al., 2014). In fact, we have found no research that addresses community cloud-based supply chain management. In order to fill this gap, our analysis will focus on the effects that community cloud computing has on operational re- sults, and on the level of informational and physical integration in the supply chain.

Basing themselves on the Resource Based View of the firm, Miles and Snow (2007) recognized the gains in capability that occurred when firms created trusting, cross-firm relationships that they then used to share knowledge and expertise. IT can be viewed as an organizational capability (Bharadwaj, 2000) in the sense that IT users tend to pay greater attention to the intangible benefits of IT, such as improved customer service, increased market respon- siveness and better buyer-supplier coordination when evaluating IT systems (Brynjolfsson and Hitt, 1997). IT has a vast potential to facilitate collaborative planning among supply chain partners by sharing information on demand forecasts and production sche- dules (Chen and Paulraj, 2004). By embedding IT into its supply chain system, a firm is able to enhance channel specific assets through effective information exchange and better coordination with supply chain partners; in other words, IT can facilitate the development of supply chain capabilities (Wu et al., 2006). This network or extended view of shared IT resources that can be used by a set of allied companies is also at the core of the so-called Extended Resource Based View of the firm (Lavie, 2006; Son et al., 2014).

Using the knowledge management perspective, Miles and Snow (2007) showed that collaborative efforts based on trust, knowledge and norms of information sharing and equitable treatment can result in highly entrepreneurial, cross-industry network organizations. From this theoretical perspective, firms that invest in collaborative capability can improve their capacity for integration across the supply chain. Information technology uses the Internet to improve the collaborative capability that can be turned into supply chain efficiency by providing real-time in- formation about product availability, inventory levels, shipment status and production requirements (Lancioni et al., 2000; Chen and Paulraj, 2004).

Furthermore, the implementation of a community cloud would be an indicator of a higher level of inter-organizational identifi- cation between supply chain partners, since community cloud computing entails a higher level of supply chain member com- mitment. Community cloud computing links organizations that accept and promote key resource sharing through close bonds. This inter-organizational identification arises as an attachment that a firm has to a certain group based on their similar char- acteristics (Nahapiet and Ghoshal, 1998). Identification facilitates the strategic, operational and technological integration of partici- pating organizations (Ireland and Webb, 2007). Thus, community cloud computing can generate trusting and collaborative cross- firm relationships and also enable and promote SCI, particularly of the chain's physical and informational flows.

Physical and informational supply chain integration is defined as cooperation, interaction and collaboration between the links that form the supply chain (Ellinger et al., 2000; Pagell, 2004). Trust between organizations and their partners creates an atmo- sphere in which firms willingly exceed the minimal requirements of a relationship to increase the likelihood of success for all part- ners (Ireland and Webb, 2007). Social Capital Theory argues that trust in inter-organizational relationships is a relational lubricant

allowing greater benefits of knowledge transfer, joint learning and the sharing risks and costs associated with exploring and ex- ploiting opportunities (Nahapiet and Ghoshal, 1998). Thus, Ber- nardes and Zsidisin (2008) found that strategic supply manage- ment can be a very important source of social capital for a focal purchasing firm.

In this setting, community cloud computing would be an ef- fective means to integrate data throughout the supply chain (in- formation integration), to provide pervasive and reliable data on inventory management thanks to real-time access to inventory data (information integration), and to facilitate the real-time in- terconnection of raw materials, production and order processing (physical and informational integration). In recent years, a large number of community cloud-based applications have emerged to better manage the flows of SCM information. In particular, cloud computing-based software as a service (SaaS) spans all the major SCM processes: plan, source, make and deliver (Dominy, 2012). Two other areas in the community cloud computing domain may have a substantial impact on SCI: Platform as a Service (PaaS) and Infrastructure as a Service (IaaS). PaaS helps users extend and customize a cloud-based business application that is offered as a service, or, alternatively, business partners can build their own solutions that extend the functional impact of a vendor's SaaS applications (Dominy, 2012). Using PaaS for SCM has the potential to reduce the cost of development and integration. On the basis of these arguments, we propose the following hypothesis:

Hypothesis H1. There is a positive relationship between com- munity cloud computing implementation and informational-phy- sical supply chain integration.

SCM has emerged as a key element to explain general business success due to the increasing level of outsourcing and dynamism of the business environment (McCormack et al., 2008; Romano, 2003). Value creation in a firm context depends not only on the integration and alignment of the internal business process, but also on the integration and alignment of processes between companies (Cagliano et al., 2006; Fantazy et al., 2009).

In order to achieve all SCM's potential, it is necessary to in- terconnect the informational and physical flows between the companies that form the links in the supply chain (Troyer and Cooper, 1995). Indeed, Pagell (2004) stated that SCM's effective- ness largely relies on integration.

SCI requires a massive commitment from all members of the value chain (Tan, 2001). Prior empirical evidence shows, however, that actual integration practices between companies in a supply chain are, quite often, very limited (Edwards et al., 2001; Fawcett and Magnan, 2002; Holweg et al., 2005). This would help to ex- plain the conflicting results found in the prior literature regarding the impact that SCI (particularly of the informational and physical flows) exerts on business performance (Fabbe-Costes and Jahre, 2008; Van der Vaart and van Donk, 2008), which could be ex- plained by the way that SCI was defined and implemented (Fabbe- Costes and Jahre, 2008). The resource based view suggests that firms support their strategy and gain advantage through com- plementary resources (Barney, 1991; Miles and Snow, 2007). Tak- ing the idea that integration practices enable strategy execution as our basis, we view SCI practices as resources to facilitate the ex- ecution and performance of the supply chain strategy.

SCI practices can have a significant effect on business perfor- mance in certain conditions. Most of the theoretical literature that analyzes the SCI-performance relationship agrees that full in- tegration, including internal and external integration, is more ef- fective than partial integration (i.e., where only the internal or the external physical or informational links in the chain are in- tegrated) (Frohlich and Westbrook, 2001; Romano, 2003;

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Rosenzweig et al., 2003). Therefore, internal and external in- tegration are very closely related and should be the key to the company exacting the full effect on its performance (Giménez and Ventura, 2005; Stank et al., 2001). SCI can be an indicator of trust in customers and suppliers (Zhang and Huo, 2013). Trust en- genders a number of cooperative relationship behaviors, such as joint responsibility for problem solving, shared planning and a flexible arrangement to deal with unexpected situations. All these behaviors positively contribute to getting the best out of supply chain performance (Johnston et al., 2004). Likewise, Paulraj et al. (2006) stress that companies that boost their strategic involve- ment in purchasing are in fact seeking deeper SCI and subse- quently achieve a greater impact on performance.

Drawing upon the same rationale, Malhotra and Mackelprang (2012) found that developing SCI reduces uncertainty and enables a more flexible response to supply chain members’ needs. In the same vein, Devaraj et al. (2007) show that supplier integration and customer integration have an interaction effect that is more highly associated with operational performance than either of the two alone.

Once an organization's physical and informational flows are integrated both internally and externally, positive effects can be expected through improvements to order issuing and planning, more efficient inventory management, shorter lead times, a better fit between the orders in the supplier-client interaction, better organization of informational flows in production and logistics processes, greater supplier commitment in the production process as a whole, a decrease in production and ordering errors and, therefore, a decrease in non-quality costs in production and in logistics management (Gunasekaran and Ngai, 2004; Lee and Whang, 2000; Stank et al., 1999). However, these benefits may require some time to have an effect on the bottom line, since there are other competitive and financial constraints that may affect the company's overall financial performance. It is more likely that physical-informational supply chain integration can exert an im- mediate and more direct effect on operational performance. Likewise, Flynn et al. (2010) stress the importance of integration between manufacturers and both their downstream and upstream supply chain partners for improving operational performance. Moreover, physical and informational supply chain integration is a response to certain SC inefficiencies (e.g., a solution to the bull- whip effect) and improves the service level (Lee et al., 1997; Bo- wersox et al., 1999; Kanda and Deshmukh, 2008). Therefore, physical and informational integration allows a firm to adjust and adapt its strategies and implement them throughout the supply chain ahead of its competitors when opportunities arise, and in- dicates a fit between strategy and knowledge. Hult et al. (2006) point out that capitalizing on knowledge can create competitive priorities, including speed, quality, cost and flexibility, thus im- proving the total value added for the user, and not simply one of the priorities, such as cost or speed (Ketchen and Hult, 2007). Drawing upon the above arguments, we now propose the fol- lowing hypothesis:

Hypothesis H2. There is a positive relationship between physical- informational supply chain integration and a firm's operational performance.

Community cloud computing enables companies to combine and reorganize various hardware and/or software resources Thus, an organization might purchase specific software components from Oracle, SAP, Apple or any other software provider and link them together via the cloud to create a business solution that may ultimately improve the company's operational performance (Amir, 2009). Another benefit of community cloud computing is its scalability (Durowoju et al., 2011) in the sense that, as demand for

computing changes, the necessary computing power can be dy- namically increased or decreased to meet changes in demand. All these features make computing in the community cloud very at- tractive and facilitate flexibility in the way business is conducted. In this way, powerful distributed infrastructures using community cloud can be adapted to changes required in the production scheme or to launching different products (flexibility as a di- mension of operational performance) (Chen et al., 2014). Wu et al. (2013) suggest that there is a positive relationship between the degree of business process complexity and the use of cloud com- puting, where the number of product types (product flexibility) also reflects business process complexity. This concept has im- pacted manufacturing, where product design, ordering, produc- tion, testing, management, and all other stages of the operations system, can be encapsulated into cloud services and managed centrally, thus generating flexibility (Xu, 2012). Cloud computing in general, and community cloud computing in particular, possess other features that can be linked to overall business operational performance. These other positive cloud characteristics are migr- ability, availability and security. Migrability allows the company to migrate data and information content without incurring a sig- nificant cost increase. Availability refers to the quality of the IT service, and its readiness for use at any time and in any location, regardless of the access device. Finally, security (Arinze and Ana- ndarajan, 2010) refers to protecting the shared information and the primary information, and preserving the quality of the information.

Furthermore, community cloud computing provides both the company and its business partners with broad network access that makes capabilities available over the network and accessible through standard mechanisms that promote more efficient use (Mell and Grance, 2011) through heterogeneous thin or thick client or business partner platforms (e.g., mobile phones, tablets, laptops and workstations). Another advantage of community cloud com- puting for operational results draws on resource pooling. Resource pooling means that the provider pools computing resources using a multi-tenant model to serve multiple consumers, with different physical and virtual resources dynamically assigned and reas- signed according to consumer demand (Mell and Grance, 2011). The overall capacity of a focal company and its business partners may induce them to build capabilities based on this increased processing and storage power, which will ultimately positively affect operational results in terms of flexibility, effectiveness, punctuality, and delivery accurateness. Within the technological arena of Internet-enabled systems, advances in cloud computing technologies now seem to offer organizations the opportunity to make their technology infrastructure more flexible while reducing the total ownership cost (Subramanian et al., 2014).

Community cloud computing also has a direct link to rapid elasticity (Mell and Grance, 2011), particularly in settings in which companies do not act in isolation but in a cooperation network with company stakeholders. This rapid elasticity enables compa- nies to quickly provide and release capabilities commensurate with demand. To the client, the capabilities available for provi- sioning often appear to be unlimited and can be appropriated in any quantity at any time, which links to improved company op- erational performance in terms not only of flexibility, but also speed and delivery efficacy. From the perspective of the Resource Based View (RBV), flexible and powerful IT infrastructures like the community cloud, which connect an organization with its partners and stakeholders, can in fact be said to be a valuable, scarce and difficult to imitate resource. This resource would be much more powerful and flexible than other traditional IT due to the above- mentioned advantages, such as scalability and structural flexibility. This, in turn will link to the greater fulfillment of acquired com- mitments, such as on-time and accurate, error-free delivery.

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Community cloud computing therefore provides a flexible tool shared by a network of companies, which may ultimately result in a more flexible production scheme in terms of production amounts and range of items that might also entail better opera- tional performance for each particular segment of clients.

Furthermore, community cloud computing (Mell and Grance, 2011) can be defined as a measured service by which the cloud system automatically controls and optimizes resource (e.g., sto- rage, processing, bandwidth and active user accounts) use by leveraging a metering capability. These accounts will provide transparency for both the provider and the consumer of the uti- lized service that in turn could develop the focal company's ability to improve flexibility and delivery performance (Guo et al., 2014). Therefore, the use of cloud computing would have a direct effect on the internal configuration of the production resources that might improve the overall operational standards for a wider range of client segments. Community cloud computing (technology) would have an effect on business strategy through flexibility, di- versity and agility and this business strategy would eventually have an effect on operational outcomes for each group of targeted customers.

In other words, community cloud computing may affect inter- nal aspects of the organization's productive structure, as well as interconnections and workings within inter-organizational set- tings. In other words, the implementation of community cloud computing enhances the service quality to internal customers, which in turn could enhance the ability to deliver a higher quality service to a diverse range of external customers. Taking all these arguments together we can now propose the following hypothesis:

Hypothesis H3. There is a positive relationship between the im- plementation of community cloud computing and firms' opera- tional performance.

Fig. 1 and Fig. 2 presents the hypothesized baseline model and the measurement model respectively.

3. Method

3.1. Population, questionnaire and data gathering

We used a population of 2036 companies with a staff of at least 50 employees to test our hypotheses. The population framework was obtained from the DUNS 50,000 database, which includes the most relevant companies in Spain. Companies were classified in sectors according to the CNAE catalogue (Standard Industrial Classification in Spain), excluding any industrial sectors that do not occupy an intermediate position in their supply chain (industries purely related to extractive–mining activities and sectors strictly related to raw materials and their processing). The study focuses on medium-sized enterprises in a single country, which can be indeed an added value for this research. Some prior studies fo- cusing on IT decisions adoption, IT investment and planning

Community Cloud Implementation

H1

Fig. 1. Theoretical b

analyzed several sectors in 10 European countries (Spain being among them). Results showed that country-specific differences have an effect on results (Van Everdingen et al., 2000). This would imply that a one-single country research design would be more appropriate as a means to control for this national, country-spe- cific variability. The units of analysis were selected by simple random sampling. The fieldwork commenced on 16 May 2012 and ended on 30 September 2012. The final sample was 394 companies (19.36% response rate) and the same number of valid questionnaires.

The questionnaire was pretested with the help of 5 inter- nationally recognized researchers in the areas specifically related to this study: Supply Chain Management (2) and Information Technology use and adoption (3). As a result of the pretest, several items were reworded, some formal aspects of the questionnaire were modified and the wording was simplified and modified ac- cording to the experts' suggestions.

The data gathering method consisted of a telephone survey using a computerized system (Computer Aided Telephone Inter- viewing, CATI). In this type of process, the interviewers have ac- cess to an information system that randomly shows the contact details of potential interviewees. The CATI system allows ap- pointments to be arranged with interviewees and, if necessary, to interrupt a survey that has already been started and to resume it at a later date. The questionnaire is saved in real time, thus allowing data to be stored and subsequently managed more efficiently (García et al., 1999). Data gathering was done by a total of 10 in- terviewers who received specific training as to the purpose, ob- jectives and background of the investigation. In addition, during the first day of work the authors personally supervised the inter- viewers with direct on-site training and work shadowing. All 10 members of the interview team worked simultaneously for 4 h a day during the fieldwork period.

The questionnaire was divided into two different areas de- pending on who the key informant was. The first section was di- rected at the head of supply chain management, logistics or op- erations management. This section included the questions on SCI and the firm's operational performance. The second section was directed at the head of IS or IT and related to the use and im- plementation of information and communication technologies.

Since there were two different informants in each organization, it was necessary to make two separate calls at different times to complete both the questionnaire sections for each company. A web-based questionnaire was designed for firms that had com- pleted only one section by the mid-point of the expected fieldwork period to make it easier for the remaining interviewees to answer the questions (some stated that they could answer the questions outside their usual work timetable). In these cases, a personalized letter was sent to the managers who had still not answered the questionnaire informing them that a web questionnaire was available that they could answer in a more flexible way.

As previously stated, the sample was made up by 394 compa- nies (19.36% response rate). The average company age in the sample was 28.5 years (std. dev. 17.7 years), with an average

Informational and Physical Integration

Operational ResultsH2

H3

aseline model.

Operational Performance

flexibiltiy

Physicaland Informational Integrationin theSupply

Chain

deliveries

Flex_1

Flex_2

Flex_3

Flex_4

Flex_5

Del_1

Del_2

Del_3

Community Cloud Implementation

Phys_Int

Phys_Int_1

Phys_Int_2

Inf_Int

Inf_Int_1

Inf_Int_2

Inf_Int_3

Phys_Int_2

Fig. 2. Theoretical and measurement model.

Table 1 Sample, population distribution and response rate by industry.

Sector No. of companies in population

No. of companies in sample

Response rate

Food 717 35.22% 124 31.47% 17.29% Tobacco and Related Products

7 0.34% 1 0.25% 14.29%

Beverages 111 5.45% 18 4.57% 16.22% Fabrics and Textile 83 4.08% 17 4.31% 20.48% Shoes and Leather 48 2.36% 13 3.30% 27.08% Chemicals 298 14.64% 55 13.96% 18.46% Pharmaceuticals 132 6.48% 31 7.87% 23.48% Informatics, Electro- nics and Optics

168 8.25% 14 3.55% 8.33%

Electrical Machinery and Materials

179 8.79% 44 11.17% 24.58%

Motor Vehicles 160 7.86% 53 13.45% 33.13% Furniture 133 6.53% 24 6.09% 18.05% Total 2036 100.00% 394 100.00% 19.35%

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workforce size of 198 employees (std. dev. 278.6 employees). Ta- ble 1 shows sample and population distribution by industry.

3.1.1. Variables 3.1.1.1. Community cloud computing implementation. Our definition of community cloud computing is based on the definition given by The National Institute of Standards and Technology, US Depart- ment of Commerce (Mell and Grance, 2011), and is that the com- munity cloud is the cloud infrastructure provisioned for exclusive use by a specific community of clients from organizations (in this case the members of the supply chain) that have shared concerns (e.g., mission, security requirements, policy, and compliance con- siderations) that may be owned, managed and operated by one or more of the organizations in the community, a third party, or some combination of them, and may exist on or off premises. The community cloud variable was measured through a direct ques- tion: respondents were asked to measure “the degree of im- plementation in our company of cloud computing gathering a variety of heterogeneous resources with an operational aim. We

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share these resources with other companies we have strong ties to in such a way that these companies usually also provide resources to the network” (following Mell and Grance's definition, 2011). This question was asked to the chief IS or IT officer. Respondents answered using a 1–7 Likert scale (1¼not implemented at all; 7¼fully implemented).

3.1.1.2. Informational-physical supply chain integration. was mea- sured following the construct structure proposed and tested by Rai et al. (2006) and took into account two dimensions: informational flow integration and physical flow integration. Physical and in- formation flow integration were defined, respectively, as the de- gree to which a focal firm uses global optimization with its supply chain partners to manage the stocking and flow of materials and finished goods, and the extent of operational, tactical and strategic information sharing that occurs between a focal firm and its supply chain partners (Rai et al., 2006). As in other studies (An- geles, 2009; Malhotra et al., 2005), the instrument used in this study measures physical flow integration in terms of the multi- echelon optimization of costs, just-in-time deliveries, joint man- agement of inventory with suppliers and logistics partners, and distribution network configuration for optimal staging of in- ventory, while informational flow integration is measured by the sharing of production and delivery schedules, performance me- trics, demand forecasts, actual sales data, and inventory data. In the survey, informants were asked to indicate the degree to which they agreed with a series of statements relating to informational- physical supply chain integration on a scale of 1–7 (1¼totally disagree; 4¼neither agree nor disagree; 7¼totally agree).

3.1.1.3. Operational performance. As it is difficult to compare the performance of plants operating in different industries we decided to use perceptual measures, as in Flynn et al., (1995). We therefore asked respondents to indicate the degree to which they agreed with a series of statements relating to operational performance on a scale of 1–7 (1¼totally disagree; 4¼neither agree nor disagree; 7¼totally agree). Operational performance was measured with a second order factor formed of two components: flexibility and delivery performance. Some authors consider the simultaneous achievement of flexibility and delivery performance to be a proxy of operational performance (Hallgren and Olhager, 2009). The

Table 2 Survey items and primary factors.

Factor Variable

Physical and informational flow integration

Physical flow integration Inventory holdings are minimized across Supply chain-wide inventory is jointly ma Suppliers and logistics partners deliver p Distribution networks are configured to m Informational flow integration Production and delivery schedules are sh Performance metrics are shared across th Supply chain members collaborate in arri Our downstream partners (e.g., distributo us* Inventory data are visible at all steps acro

Flexibility Our company can quickly modify product Our company can quickly modify product (item added to original scale) Our company can quickly launch new pro Our company can quickly respond to cha Our company can quickly respond to cha

Deliveries Our company has an outstanding on-time The lead time for fulfilling customer orde tomer order and the delivery of the good Our company provides a high level of cus

* Items excluded after exploratory and reliability analyses.

original scales proposed and tested by Flynn et al. (2010) were slightly adapted in order to include the role of competitors, as suggested by Danese et al. (2013).

Table 2 lists all the items included in the original scales. Those marked with an asterisk (*) were dropped after a reliability analysis.

4. Analysis and results

4.1. Measurement model

Content was validated with an analysis of the questionnaire by 5 internationally recognized researchers in the areas included in the present study: SCM and information technology adoption (with a particular emphasis on cloud computing). Scale uni- dimensionality was assessed using exploratory factorial analysis, with eigenvalues higher than the unit, standardized factorial loads higher than 0.5, a significant explained variance for each extracted factor and high values for Chi-Squared/degrees of freedom in Barlett's sphericity test (po .05). Two second-order factors were used to measure SCI and operational performance. For SCI, the two second order factors were informational flow integration and physical flow integration (Rai et al., 2006), while for Operational Performance the two second order factors were flexibility-related performance and operational delivery performance (Flynn et al., 2010). The results of the exploratory factorial analysis are given in Table 3. Reliability was tested using Cronbach's alpha, with scores higher than 0.7 (Bagozzi and Yi, 1988). Divergent validity, or the ability of the scales to discriminate between the different con- structs being measured, was confirmed by two tests (Ahire and Devaraj, 2001; Amand and Ward, 2004; Flynn et al., 1999).

First, as presented in Tables 3 and 4, the Cronbach's alpha coefficients for the scales were greater than their correlations with other scales. Second, the average item-to-total correlations with items not in the scales were substantially lower than the average item-to-total correlations with items in the respective scales.

Finally, a Confirmatory Factor Analysis (CFA) was performed using EQS 6.1 in order to confirm the scales’ dimensionality and test convergent validity. As a prior step, a data exploration was carried out with normalized estimation using Mardia's test, which

Source

the supply chain naged with suppliers and logistics partners (e.g., UPS, FedEx)* roducts and materials just-in-time inimize total supply chain-wide inventory costs

Rai et al. (2006)

ared across the supply chain e supply chain ving at demand forecasts rs, wholesalers, retailers) share their actual sales data with

ss the supply chain* s to meet our major customer's requirements. s in response to the innovations of our main competitors

ducts into the market. nges in market demand. nges in competitors (item added to original scale)

Flynn et al. (2010)

delivery record to our major customer rs (the time which elapses between the receipt of a cus- s) is short tomer service to our major customer

Table 3 Exploratory factorial analysis.

Factor Variable Cronbach's Α

Standardized factor loading Barlett test % Explained variance

Physical flow integration Phys_Int_1 Phys_Int_2 Phys_Int_3

.564 .50 .50 .72

X2¼109.970 df ¼3 sig.¼ .00

53.655

Informational flow integration Inf_Int_1 Inf_Int_2 Inf_Int_3

.701 .65 .83 .58

X2¼247.946 df ¼1 sig.¼ .00

64.015

Flexibility Flex_1 Flex_2 Flex_3 Flex_4 Flex_5

.896 .81 .88 .82 .83 .85

X2¼1173.574 df ¼10 sig.¼ .00

71.176

Deliveries Del_1 Del_2 Del_3

.726 .80 .81 .81

X2¼260.960 df ¼3 sig.¼ .00

66.068

Table 4 Correlations between scale items.

Inf_Int_1 Inf_Int_2 Inf_Int_3 Phys_Int_1 Phys_Int_2 Phys_Int_3 Flex_1 Flex_2 Flex_3 Flex_4 Flex_5 Del_1 Del_2

Inf_Int_1 Inf_Int_2 .56**

Inf_Int_3 .36** .44**

Phys_Int_1 .32** .29** .26**

Phys_Int_2 .28** .32** .21** .18**

Phys_Int_3 .37** .44* .30** .32** .38**

Flex_1 .21** .23** .11* .13** .17** .17**

Flex_2 .19** .29** .10* .15** .18** .22** .62**

Flex_3 .15** .24** .11* .15** .22** .19** .51** .67**

Flex_4 .27** .27** .17** .18** .22** .25** .57** .57** .59**

Flex_5 .15** .26** .15** .17** .26** .24** .48** .68** .63** .66**

Del_1 .30** .33** .17** .24** .29** .23** .28** .20** .21** .29** .23**

Del_2 .16** .19** .08 .14** .23** .16** .21** .12** .21** .21** .18** .48**

Del_3 .26** .29** .19** .18** .24** .25** .29** .25** .24** .30** .26** .51** .49**

** po .01 * po .05

Table 5 Confirmatory factorial analysis.

Factor Variable/factor Standardized factor loading

R2

Supply chain integration Inform. integra- tion Physical integration

.52

.38

Operational performance Flexibility Deliveries

.31

.20 Informational flow integration

Inf_Int_1 Inf_Int_2 Inf_Int_3

.65

.83

.58

.241

.230

.517 Physical flow integration Phys_Int_1

Phys_Int_2 Phys_Int_3

.50

.50

.72

.421

.693

.332 Flexibility Flex_1

Flex_2 Flex_3 Flex_4 Flex_5

.76

.86

.76

.78

.82

.581

.734

.603

.615

.673 Deliveries Del_1

Del_2 Del_3

.68

.81

.73

.469

.473

.531

S. Bruque-Cámara et al. / Journal of Purchasing & Supply Management 22 (2016) 141–153148

confirmed multivariate non-normality of data. This meant that the Robust Maximum Likelihood method was applicable. Thus, a factor model was designed that included the 14 observed variables. The final fit of the Confirmatory Factorial Analysis was highly

satisfactory (Scaled, Satorra-Bentler, X2¼58.369, with 31 degrees of freedom, X2/df¼1.88; RMSEA ¼ .048; MFI¼ .919; NFI¼ .905; NNFI¼ .931; CFI¼ .955; IFI¼ .952). Standardized factorial loads and R2 are shown for each variable in Table 5.

4.2. Structural equation model

A structural equation model was developed (Fig. 3) to test the hypotheses. We used EQS and the Robust Maximum Likelihood Method. This is considered the most accurate model for non- normal settings (e.g., Bentler and Wu, 2002; Satorra, 1993) and yielded a good overall fit (Satorra-Bentler's scaled X2¼55.105; df ¼31; X2/df¼1.77, RMSEA¼ .044; NFI¼ .913; NNFI¼ .941; CFI¼ .960; IFI¼ .961; MFI¼ .933). All the hypothesized relationships presented significance levels below the po .05 threshold. Hypothesis H2 received the greatest support with a load of.64. H1 received sup- port with a significant relationship load of.56, whilst H3 also re- ceived significant support but with a slightly lower standardized parameter (.36). We ran a second model in which no direct effect between the community cloud and operational results was con- sidered to measure the potential mediating effects exerted by the integration of physical-informational flows in the supply chain. Although the parameter estimates are stronger for the indirect path (.56 and .81), there is no statistically significant change in the model fit, so it can be concluded that there is no significant mediating effect exerted on the relationship between community cloud implementation and operational results by the physical and informational integration of the supply chain. However, the high

CommunityCloud Implementation

Informational and Physical Integration

Operational Results

H1 .56

H2 .64

H3 .36

Fig. 3. Structural model.

S. Bruque-Cámara et al. / Journal of Purchasing & Supply Management 22 (2016) 141–153 149

load in the link between community cloud computing and op- erational performance through greater integration of the supply chain gives a high explanatory value to this indirect path.

The sample has a remarkable group of companies belonging to the food industry (31.47%). In order to control for potential in- dustrial-specific bias, we have run the model in the food industry subsample.. Results are practically the same when compared with the sample as a whole. However, for the food subsample, com- munity cloud influence on supply chain integration and on op- erational results is slightly weaker (although with non-significant differences with the whole sample). Therefore, the phenomenon under scrutiny in our analysis can be considered as non industry- specific.

We also ran alternative models to compare the effect exerted by community cloud computing on SCI (informational and physi- cal) and on operational results with other types of cloud com- puting (private and public cloud computing). These alternative models, which included private and public cloud computing re- spectively, provided a poorer fit to the data (X2/df41.8 in both cases).

In addition, we measured the consistency of our results com- pared to other types of supply chains. We performed an additional analysis which consisted of segmenting the sample into two groups according to the classification proposed by Holweg et al. (2005), which conceptualizes basic supply chain configurations for inter-organizational collaboration. The sample was split into two sub-samples, with the first made up of companies that intensively cooperate throughout the supply chain (synchronized SC), whilst the second was formed of companies that show weak cooperation behavior in the SC (non-synchronized SC). The results (available upon request) showed a non-significant difference (Δpo .05) compared to the results obtained for the overall sample or for the two sub-samples, which means that our outcomes are consistent, regardless of SC configuration. This effect could be explained if we take into account the intermediate position occupied by the ana- lyzed companies in the supply chain where the effects exerted by different degrees of cooperation might be abated (cooperation is relatively high in all the cases due to the close links companies

Table 6 Comparative partial structural models.

Partial model RMSEA X2/df

Community cloud-Physical integration-Operational results .049 1.93

Community cloud-Informational integration- Operational performance

.049 1.92

Community cloud-SC integration- Flexibility .053 2.10

Community cloud-SC integration-Delivery improvements .031 1.39

* po .05 ** po .01

have to nurture and maintain with other chain members down or upstream). Furthermore, our results also suggest that the benefits of community cloud implementation might compensate for the negative effects of a potential lower collaboration throughout the supply chain.

4.3. Comparative partial structural models

We ran several analyses to detect any potential differences between the effects exerted by community cloud computing on some specific SCI and operational performance dimensions. The results are presented in Table 6. We ran 4 different models with the respective underlying dimensions of the SCI and operational performance constructs. The results of these partial analyses are shown in the rows in Table 6. Overall, all the partial models fit the data with slightly poorer results in all cases except the community cloud-improvements in delivery performance relationship model. According to the analysis of the factor loadings, there is a stronger relationship between cloud computing and physical integration, followed by informational integration (both with similarly high significant loadings). There is also a positive and significant re- lationship between community cloud computing and the two di- mensions of operational performance. In both two cases the factor loading was significantly lower than for physical and informational integration. However, the partial model presents the best fit when only delivery performance is considered.

5. Discussion and conclusions

Our study stresses the importance of aligning the innovation process in the supply chain and in SCI to improve operational performance. The results specifically suggest that technological breakthroughs such as cloud computing, and a particular type of cloud computing -community cloud computing- enable supply chain process integration, which in turn yields better operational performance. Our results also highlight the importance of inter- firm coordination between supply chain partners for generating

Δ X2/df over the baseline model

Factor loading of focal dimension

þ .16 .88** (Community cloud-Physical integration)

þ .15 .75* (Community cloud-Informational integration)

þ .33 .41* (Community cloud- Flexibility)

� .37 .42* (Community cloud-Delivery improvements)

S. Bruque-Cámara et al. / Journal of Purchasing & Supply Management 22 (2016) 141–153150

business value, either by implementing community cloud com- puting or by integrating the supply chain.

These findings are related to what Ireland and Webb (2007) suggested: better operational performance can be achieved in chains with members that are strategically, operationally and technologically integrated. When trust and knowledge are si- multaneously managed by the members of these supply chains, firms become more fully committed to supply chain effectiveness through better delivery outcomes and greater flexibility.

In our analyses, H1 receives sufficient support in the analyses described in the previous section: the use of community cloud computing improves the integration of the physical and informa- tional flows in the supply chain and enables them to be integrated more quickly and more effectively. As discussed in the arguments leading to Hypothesis H1, there are several possible ways for community cloud infrastructure to lead to the integration of data between internal and external functions, provide broader and more reliable data access for inventory management and facilitate the real-time interconnection of raw materials, production and ordering processes. Community cloud computing is also intimately linked to cloud service models such as SaaS, IaaS and PaaS, which can also be used to reduce SCI cost and time. This would point to a link between the adoption of technological breakthroughs and the progression from a silo-based activity to interdependent functions between suppliers, Original Equipment Manufacturers (OEMs) and customers in the supply chain.

As has been stated in several prior studies in the production and operations management field, integrating the links that form the informational and physical flows of the supply chain may deliver many benefits for the focal firm (and also its business partners): hypothesis H2. If a particular organization manages to integrate the physical and informational flows across the supply chain, benefits can be expected in the form of improvements in order issuing and planning, gains in storage management effi- ciency, shorter lead times, accurate supplier-client interaction, better organized informal flows and greater commitment from business partners, among others. The results presented by the structural model are clear and provide sound evidence of the ex- istence of these advantages, which confirms that more intense and stronger integration of the informational and physical supply chain will ultimately benefit operational performance. These findings build on the results found by Flynn et al. (2010), who found that customer integration (just one the dimensions of chain integration) was significantly related to operational performance.

Our study extends the scope of IT alignment across the entire supply chain. Thus our findings confirm the results of Hausman and Stock (2003) and Wu et al. (2006), who indicate that channel partners do not only need to adopt appropriate technology in the SCM process, but also have to work towards technology compat- ibility across the supply chain. In other words, all partners have to make a simultaneous investment in IT to achieve the full potential of their commitment. However, the multiple technology platforms often encountered in the supply chain and the high level of fi- nancial commitment required from all channel partners mean that community cloud computing (ubiquitous, easy to use and with a low implementation cost) provides valuable opportunities for physical and informational SCI.

Finally, we have found that there is a direct positive effect ex- erted by community cloud computing on operational results (hy- pothesis H3). Community cloud computing can be used not only to effectively increase flexibility in the value chain, but also to im- prove the delivery area. The community cloud can be used by supply chain members to boost several areas directly related to operational performance. For example, the community cloud can be easily used by the chain members to share information about product design and composition, provide the basis for a better

interconnection between chain members for modifying products in response to innovations by competitors, provide one single and reliable information platform so that members can store, share and elaborate the necessary knowledge required to quickly re- spond to changes in market demand and changes in the compe- tition. On the delivery side, community cloud computing can im- prove agility in the delivery of products and services. A community cloud platform possesses several features that may help to im- prove punctuality and service accuracy and reduce lead times. The use of community cloud computing is closely related to several core features, including: (1) on-demand self-service, which pro- vides agile access to the changing needs of data and information flows; (2) broad network access, with capabilities available within the organization and also across the supply chain network and accessible through standard mechanisms that promote the use of key delivery or process information by business partners, irre- spective of whether they use thin or thick client platforms (e.g., mobile phones, tablets, laptops or workstations) (Mell and Grance, 2011); (3) rapid elasticity, meaning that capabilities can be elas- tically provisioned and released, in some cases automatically, al- lowing the organization to scale rapidly outward and inward, thus improving the company's operational final outcome, and (4) the community cloud can automatically control and optimize resource use by leveraging and metering the actual use of resources both inside and outside organizational boundaries. Therefore, resource usage can be monitored, controlled and reported, providing transparency for the provider and the user of the community cloud platform, and thus enabling a more efficient use to be made of resources. This may in turn lead to better operational results.

We also ran the analysis in Table 5 to identify more accurate explanations of the relationships between community cloud computing, SCI and performance. These results indicate that community cloud computing exerts a greater effect on the physical and informational integration of the supply chain than on the two dimensions of operational performance. This finding supports our earlier results, according to which the effect of community cloud computing is primarily exerted on the integration of the physical and informational links of the supply chain. Thus, these integra- tion effects are stronger at this stage of development than the direct effects exerted by community cloud computing on flexibility and delivery management improvements. Interestingly, the pri- mary, stronger effect of community cloud computing is focused on enhanced inventory management, more agile logistics and im- proved just-in-time practices which are, post community cloud implementation, supported and boosted by the stability, broad access and overall enhanced information management capability provided by the community cloud. These advantages can ulti- mately result, somewhat indirectly, in gains in flexibility and im- provements in delivery management. We have therefore disen- tangled the intermediate paths followed by a specific type of community cloud in specific areas of SCI and operational performance.

5.1. Managerial implications, further research and limitations

The work done in this paper does not only shed light on the theoretical relations between a relatively new type of information technology (community cloud computing), the integration of in- formational and physical flows in the supply chain, and opera- tional performance. There are other conclusions and implications that may affect the way that managers regard and organize tech- nological and human resources in their companies to achieve better results. Managers should be aware of the importance of enhancing relationships between supply chain members at the technological, strategic and operational levels if they want to im- prove supply chain efficiency and effectiveness. In other words, it

S. Bruque-Cámara et al. / Journal of Purchasing & Supply Management 22 (2016) 141–153 151

is not enough to establish cooperation with other members of the supply chain and no more, it is necessary to cooperate with them on all the above-mentioned levels to achieve the specified objec- tives. That said, in order to achieve this collaboration scenario, first trust and inter-organizational identification have to be created, with the sharing of knowledge that might be considered useful by the participants in the supply chain. This trust and identification construction process can only be achieved on the basis of prior relationships among chain members that have proved to be useful and sustainable over time.

From a more specific perspective, managers should acknowl- edge the powerful tools that exist in the cloud computing universe and, particularly, the benefits of using community cloud platforms. The potential benefits will translate into better operational results (re: improvements in flexibility and delivery) when this type of cloud computing is used together with mechanisms that enhance and consolidate cooperation among supply chain members, such as the greater integration of the physical and informational flows in the supply chain.

Community cloud computing is a powerful infrastructure that enables third parties to provide applications and platforms on which new services can be developed involving a number of dif- ferent stakeholders, and a new environment in which fixed costs are turned into variable costs through a pay-per-use scheme. If managers are also prepared and able to develop new applications and services to strengthen integration in the supply chain, they will improve their operational results. This finding (the use of community cloud computing drives forward both SCI and opera- tional performance) might even give rise to new intermediaries in the cloud arena designed to facilitate the integration of the supply chain. There are some signs in the market that may indicate that this effect could already be occurring. Large, traditional technology providers specialized in supply chain management, such as SAP, are currently offering innovative community cloud-based solu- tions. The major e-commerce player, Amazon, also offers several integration tools to firms interested in promoting and selling their products through the Amazon platform as a pay-per-use service. Amazon has thus created a great cloud capable of providing its clients with the necessary means to integrate themselves into the Amazon platform and use it to manage their sales, as well as their own capability to link themselves to the Amazon storage system, and therefore use the same logistics and distribution systems as said American company.

The results also present some implications for managing SCI. In particular, managers need to recognize the role of Supply Chain Integration in realizing the value of cloud computing. As the Re- source Based View argues, IT resources offer benefits when they are embedded in specific organizational processes (Barney, 1991). Our findings suggest that community cloud computing not only exerts a direct effect on operational performance, but also an in- direct effect through the integration of the supply chain. In other words, the overall effect exerted by community cloud computing is greater when the technology (the community cloud) is embedded in the supply chain's physical and informational structure. This could improve the supply chain's competitiveness or, to put it another way, the general potential for detecting and filling any gaps between what markets require and what the firm currently offers.

This study has focused on industrial sectors in an intermediate position in the supply chain of the final products in which they participate. Likewise, the focal firms frequently interact with up- and downstream companies in the supply chain. The implications of the findings here should therefore be wide-ranging and robust. However, further research and longitudinal analyses should be carried out in a variety of industrial and geographical settings to confirm these findings.

In this study we do not analyze the use of Inter-organizational Information Systems related to supply chain integration, such as ERP systems. Therefore, a future research direction would consist of analyzing the impact on SCI of the interrelationships among the use of community cloud computing, the use of ERP systems and the incorporation of ERP systems into the SCI. Finally, this paper does not investigate the impact of the joint adoption of internal and external integration practices on operational performance. Consequently, future research could further analyze the results of this interrelationship.

Acknowledgement

This study was funded by the Spanish Ministry of Economy and Competitiveness' research project ECO2010-22105-C03-02.

References

Abdulaziz, A., 2012. Cloud computing for increased business value. Int. J. Bus. Soc. Sci. 3 (1), 234–239.

Ahire, S.L., Devaraj, S., 2001. An empirical comparison of statistical construct vali- dation approaches. IEEE Trans. Eng. Manag. 48 (3), 319–329.

Amand, G., Ward, P., 2004. Fit, flexibility and performance in manufacturing: coping with dynamic environments. Prod. Oper. Manag. 13 (4), 369–385.

Amir, M.S., 2009. It's written in the cloud: the hype and promise of cloud com- puting. J. Enterp. Inf. Manag. 23 (2), 131–134.

Angeles, R., 2009. Anticipated IT infrastructure and supply chain integration cap- abilities for RFID and their associated deployment outcomes. Int. J. Inf. Manag. 29 (3), 219–231.

Arinze, B., Anandarajan, M., 2010. Factors that determine the adoption of cloud computing: a global perspective. Int. J. Enterp. Inf. Syst. 6 (4), 55–68.

Bagozzi, R.P., Yi, Y., 1988. On the evaluation of structural equation models. J. Acad. Mark. Sci. 16 (1), 74–94.

Barney, J., 1991. Firm resources and sustained competitive advantage. J. Manag. 17 (1), 99–120.

Bharadwaj, A.S., 2000. A resource based perspective on information technology capability and firm performance: an empirical investigation. MIS Q. 24 (1), 169–196.

Bentler, P.M., Wu, E.J.C., 2002. EQS for Windows User's Guide. Multivariate Software Inc, Encino, California.

Bowersox, D.J., Closs, D.J., Stank, T.P., 1999. 21st century logistics: Making supply chain integration a reality. Michigan State University and Council of Logistics Management, East Lansing.

Bernardes, E.S., Zsidisin, G.A., 2008. An examination of strategic supply manage- ment benefits and performance implications. J. Purch. Supply Manag. 14 (2), 209–219.

Brynjolfsson, E., Hitt, L., 1997. Breaking boundaries. Inf. Week Spec. Issue 22, 34–365.

Buyya, R., Broberg, J., Goscinski, A. (Eds.), 2011. Cloud Computing: Principles and Paradigms. Wiley Press, New York.

Buyya, R., Yeo, C.H., Venugopal, S., Broberg, J., Brandic, I., 2009. Cloud computing and emerging IT platforms: vision, hype, and reality for delivering computing as the 5th utility. Future Gener. Comput. Syst. 25 (6), 599–616.

Cagliano, R., Caniato, F., Spina, G., 2006. The linkage between supply chain in- tegration and manufacturing improvement programmes. Int. J. Oper. Prod. Manag. 26 (3), 282–299.

Cegielski, C.G., Jones-Farmer, L.A., Wu, Y., Hazen, B.T., 2012. Adoption of cloud computing technologies in supply chains. An organizational information pro- cessing theory approach. Int. J. Logist. Manag. 23 (2), 184–211.

Chen, I., Paulraj, A., 2004. Towards a theory of supply chain management: the constructs and measurements. J. Oper. Manag. 22 (2), 119–150.

Cheng, J.S., Li, F.C., Ou, T.Y., Kung, C.C., 2014. The strategic research on integrating services model for SMEs cloud supply chain in Taiwan. Int. J. Electron. Bus. Manag. 12 (1), 33–40.

Danese, P., Romano, P., Formentini, M., 2013. The impact of supply chain integration on responsiveness: the moderating effect of using an international supplier network. Transp. Res. Part E: Logist. Transp. Rev. 49 (1), 125–140.

Devaraj, S., Krajewski, L., Wei, J.C., 2007. Impact of e-Business technologies on op- erational performance: the role of production information integration in the supply chain. J. Oper. Manag. 25 (6), 1199–1216.

Dominy, M., 2012. Impact of Cloud Computing on Supply Chain Management. In- formationWeek, Sept. 26 ⟨http://www.informationweek.in/⟩.

Durowoju, O.A., Chan, H.K., Wang, X., 2011. The impact of security and scalability of cloud service on supply chain performance. J. Electron. Commer. Res. 12 (4), 243–256.

Dutta, A., Peng, G.C.A., Choudhary, A., 2013. Risks in enterprise cloud computing: the perspective of IT experts. J. Comput. Inf. Syst. 53 (4), 39–48.

S. Bruque-Cámara et al. / Journal of Purchasing & Supply Management 22 (2016) 141–153152

Edwards, P., Peters, M., Sharman, G., 2001. The effectiveness of information systems in supporting the extended supply chain. J. Bus. Logist. 22 (1), 1–27.

Ellinger, A.E., Daugherty, P.J., Keller, S.B., 2000. The relationship between market- ing/logistics interdepartmental integration and performance in US manu- facturing firms: an empirical study. J. Bus. Logist. 21 (1), 1–21.

Fabbe-Costes, N., Jahre, M., 2008. Supply chain integration and performance: a review of the evidence. Int. J. Logist. Manag. 19 (2), 130–154.

Fantazy, K.A., Kumar, V., Kumar, U., 2009. An empirical study of the relationships among strategy, flexibility and performance in the supply chain context. Supply Chain Manag: Int. J. 14 (3), 177–188.

Fawcett, S.E., Magnan, G.M., 2002. The rhetoric and reality of supply chain in- tegration. Int. J. Phys. Distrib. Logist. Manag. 32 (5), 339–361.

Fingar, P., 2009. Dot. Cloud. The 21st Century Business Platform. Meghan-Kiffer Press, Tampa, Florida.

Flynn, B.B., Sakakibara, S., Schroeder, R.G., 1995. Relationship between JIT and TQM: practices and performance. Acad. Manag. J. 38 (5), 1325–1360.

Flynn, B.B., Schroeder, R.G., Flynn, E.J., 1999. World class manufacturing: an in- vestigation of Hayes and Wheelwright's foundation. J. Oper. Manag. 17 (3), 249–270.

Flynn, B.B., Huo, B., Zhao, X., 2010. The impact of supply chain integration on per- formance: a contingency and configuration approach. J. Oper. Manag. 28 (1–2), 58–71.

Frohlich, M.T., Westbrook, R., 2001. Arcs of integration: an international study of supply chain strategies. J. Oper. Manag. 19 (2), 185–200.

Giménez, C., Ventura, E., 2005. Logistics-production, logistics-marketing and ex- ternal integration: their impact on performance. Int. J. Oper. Prod. Manag. 25 (1), 20–38.

García, J., González, A., Maldonado, J.A., 1999. Problemas en el diseño y validación del cuestionario. Estad. Esp. 41, 19–46.

Gunasekaran, A., Ngai, E.W.T., 2004. Information systems in supply chain integra- tion and management. Eur. J. Oper. Res. 159 (2), 269–295.

Guo, Z.X., Wong, W.K., Guo, C., 2014. A cloud-based intelligent decision-making system for order tracking and allocation in apparel manufacturing. Int. J. Prod. Res. 52 (4), 1100–1115.

Hallgren, M., Olhager, J., 2009. Lean and agile manufacturing: external and internal drivers and performance outcomes. Int. J. Oper. Prod. Manag. 29 (10), 976–999.

Hausman, A., Stock, J.R., 2003. Adoption and implementation of technological in- novations within long-term relationships. J. Bus. Res. 56 (8), 681–686.

Hayes, B., 2008. Cloud computing. Commun. ACM 51 (7), 9–11. Holweg, M., Disney, S., Holmström, J., Småros, J., 2005. Supply chain collaboration:

making sense of the strategy continuum. Eur. Manag. J. Vol. 23 (2), 170–181. Hult, G.T.M., Ketchen, D.J., Cavusgil, S.T., Calantone, R.J., 2006. Knowledge as stra-

tegic resource in supply chains. J. Oper. Manag. 24, 458–475. Johnston, D.A., McCutcheon, D.M., Stuart, F.I., Kerwood, H., 2004. Effects of supplier trust

on performance of cooperative supplier relationships. J. Oper. Manag. 22, 23–38. Kanda, A.A., Deshmukh, S.G., 2008. Supply chain coordination: perspectives, em-

pirial studies and research directions. Int. J. Prod. Econ. 115 (2), 316–335. Ketchen, D.J., Hult, T.M., 2007. Bridging organization theory and supply chain

management: the case of best value supply chains. J. Oper. Manag. 25, 573–580. Ireland, R.D., Webb, J.W., 2007. A multi-theoretic perspective on trust and power in

strategic supply chains. J. Oper. Manag. 25, 482–497. Iyer, B., Henderson, J., 2010. Preparing for the Future: understanding the seven

capabilities of cloud computing. MIS Q. Exec. 9 (2), 117–131. Lancioni, R.A., Smith, M.F., Oliva, T.A., 2000. The role of the Internet in supply chain

management. Ind. Mark. Manag. 29, 45–56. Lavie, D., 2006. The competitive advantage of interconnected firms: an extension of

the resource based view. Acad. Manag. Rev. 31 (3), 638–658. Lee, H.L., Whang, S., 2000. Information sharing in a supply chain. Int. J. Technol.

Manag. 20 (3/4), 373–387. Lee, V., Pachmanabhan, V., Whang, S., 1997. Information distortion in supply chain:

the bullwhip effect. Manag. Sci. 43 (4), 546–558. Malhotra, A., Gosain, S., El Sawy, O.A., 2005. Absorptive capacity configurations in

supply chains: gearing for partner-enabled market knowledge creation. MIS Q. 29 (1), 145–187.

Malhotra, M.K., Mackelprang, A.W., 2012. Are internal manufacturing and external supply chain flexibilities complementary capabilities? J. Oper. Manag. 30 (3), 180–200.

McCormack, K., Ladeira, M.B., Oliveira, M.P.V., 2008. Supply chain maturity and performance in Brazil. Supply Chain Manga: Int. J. 13 (4), 272–282.

Marston, S., Li, Z., Bandyopadhyay, S., Zhang, J., Ghalsasi, A., 2011. Cloud computing. The business perspective. Decis. Support Syst. 51 (1), 176–189.

Mell, P., Grance, T., 2011. The NIST definition of Cloud Computing. Recommenda- tions of the National Institute of Standards and Technology. National Institute of Standards and Technology, Gaithersburg, MD.

Melville, N., Kraemer, L., Gorbaxani, V., 2004. Information technology and organi- sational performance: an integrative model of it business value. MIS Q. 28 (2), 283–322.

Miles, R.E., Snow, C.C., 2007. Organization theory and supply chain management: an evolving research perspective. J. Oper. Manag. 25 (2), 459–463.

Nahapiet, J., Ghoshal, S., 1998. Social capital, intellectual capital, and the organi- zational advantage. Acad. Manag. Rev. 23, 242–266.

Pagell, M., 2004. Understanding the factors that enable and inhibit the integration of operations, purchasing and logistics. J. Oper. Manag. 22 (5), 459–487.

Paquette, S., Jaeger, P.T., Wilson, S.C., 2010. Identifying the security risks associated with governmental use of cloud computing. Gov. Inf. Q. 27, 245–253.

Paulraj, A., Chen, I.J., Flynn, J., 2006. Levels of strategic purchasing: Impact on supply

integration and performance. J. Purch. Supply Manag. 12 (3), 107–122. Rai, A., Patnayakuni, R., Seth, N., 2006. Firm performance impacts of digitally en-

abled supply chain integration capabilities. MIS Q. 30 (2), 225–246. Romano, P., 2003. Co-ordination and integration mechanisms to manage logistics

processes across supply markets. J. Purch. Supply Manag. 9 (3), 119–134. Rosenzweig, E.D., Roth, A.V., Dean, J.W., 2003. The influence of an integration

strategy on competitive capabilities and business performance: an exploratory study of consumer products manufacturers. J. Oper. Manag. 21 (4), 437–456.

Ryan, W.M., Loeffler, C.M., 2010. Insights into cloud computing. Intellect. Prop. Technol. Law J. 22 (11), 22–27.

Satorra, A., 1993. Multi-sample analysis of moment-structures: asymptotic validity of inferences based on second order moments. In: Haagen, K., Bartholomeusz, A., Deistler, M. (Eds.), Statistical modeling and latent variables. Elsevier, North Holland, Amsterdam.

Shkurti, R., Muça, E., 2014. An analysis of cloud computing and its role in accounting industry in Albania. J. Inf. Syst. Oper. Manag. 8 (2), 219–229.

Son, I., Lee, D., Lee, J.M., Chang, Y.B., 2014. Market perception on cloud computing initiatives in organizations: an extended resource based view. Inf. Manag. 51 (6), 653–669.

Stank, T.P., Crum, M., Arango, M., 1999. Benefits of inter-firm co-ordination in food industry supply chains. J. Bus. Logist. 20 (2), 21–41.

Stank, T.P., Keller, S.B., Daugherty, P.J., 2001. Supply chain collaboration and logis- tical service performance. J. Bus. Logist. 22 (1), 29–48.

Subramanian, N., Abdulrahman, M.D., Zhou, X., 2014. Integration of logistics and cloud computing service providers: cost and green benefits in the Chinese context. J. Netw. Syst. Manag. 22 (4), 517–558.

Tan, K.C., 2001. A framework of supply chain management literature. Eur. J. Purch. Supply Manag. 7 (1), 39–48.

Trigueros-Preciado, S., Pérez-González, D., Solana-González, P., 2013. Cloud com- puting in industrial SMEs: identification of the barriers to its adoption and effects of its application. Electron. Mark. Vol. 23, 105–114.

Troyer, C., Cooper, R., 1995. Smart moves in supply chain integration. Transp. Dis- trib. 36 (9), 55–62.

Tuncay, E., 2010. Effective use of cloud computing in education institutions. Pro- cedia Soc. Behav. Sci. 2 (2), 938–942.

Van der Vaart, T., van Donk, D.P., 2008. A critical review of survey-based research in supply chain integration. Int. J. Prod. Econ. 111, 42–55.

Van Everdingen, Y., van Hillegersberg, J., Waarts, E., 2000. Enterprise resource planning: ERP adoption by European midsize companies. Commun. ACM 43 (4), 27–31.

Vaquero, L.M., Rodero-Merino, L., Caceres, J., Lindner, M., 2009. A break in the clouds: towards a cloud definition. Comput. Commun. Rev. 39 (1), 50–55.

Winans, T.B., Brown, J.S., 2009. Moving information technology platforms to the clouds: insights into IT platform architecture transformation. J. Serv. Sci. 2 (2), 23–33.

Wu, F., Yeniyurt, S., Kim, D., Cavusgil, S.T., 2006. The impact of information tech- nology on supply chain capabilities and firm performance: a resource based view. Ind. Mark. Manag. 35 (4), 493–504.

Wu, Y., Cegielski, C.G., Hazen, B.T., Hall, D.J., 2013. Cloud computing in support of supply chain information system infrastructure: understanding when to go to the cloud. J. Supply Chain Manag. 49 (3), 25–41.

Xu, X., 2012. From cloud computing to cloud manufacturing. Robot. Comput. Integr. Manuf. 28 (1), 75–86.

Zhang, M., Huo, B., 2013. The impact of dependence and trust on supply chain in- tegration. Int. J. Phys. Distrib. Logist. Manag. 43, 544–556.

Sebastián Bruque is a lecturer in Business Adminis- tration and Management of Technology at the Uni- versity of Jaén (Spain). His research interests are related to Information Technology Adoption as well as the drivers of Technology Performance in Organizations. Sebastián Bruque has authored or co-authored several books and published articles in Journals such as the Journal of Management Information Systems, European Journal of Information Systems, Technology Analysis and Strategic Management Journal, the Journal of High Tech- nology Management Research and the Journal of Total Quality Management and Business Excellence.

José Moyano-Fuentes is Professor of Management at the Department of Business Organization, Market- ing and Sociology at the University of Jaén (Spain). He currently conducts research on the lean produc- tion, supply chain management and firm performance in the automotive and aeronautical industries. His research has appeared in the Administrative Science Quarterly, Journal of Management of Information Sys- tems, Journal of Management Studies, International Journal of Management Reviews, International Journal of Operations and Production Management, Interna- tional Journal of Production Research, Production Plan-

ning and Control, International Journal of Tech-

nology Management, Technology Analysis and Strategic Management, and Technovation.

S. Bruque-Cámara et al. / Journal of Purchasing & Supply Management 22 (2016) 141–153 153

Dr. Juan Manuel Maqueira is a lecturer in Business Administration and Management of Technology at the University of Jaén (Spain). He is currently conducting research on factors leading to Cloud Computing tech- nology adoption. He has a wide experience as a prac- titioner in some technological firms such us Fujitsu Technology Solutions Inc. His research has appeared in Technology Analysis and Strategic Management, Interna- tional Journal of Technology Management, International Journal of Advanced Manufacturing Technology, Interna- tional Journal of Management Concepts and Philosophy, Journal of Technology Management and Innovation and

International Journal of Biotechnology.