Modeling High-Quality Knowledge Sharing in cross-functional software development teams

Information Processing and Management 49 (2013) 138–157

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Information Processing and Management journal homepage: www.elsevier.com/locate/infoproman

Modeling High-Quality Knowledge Sharing in cross-functional software development teams Shahla Ghobadi ⇑, John D’Ambra Australian School of Business, University of New South Wales, Australia

a r t i c l e

i n f o

Article history: Received 5 January 2012 Received in revised form 28 February 2012 Accepted 5 July 2012 Available online 9 August 2012 Keywords: Knowledge management Knowledge transfer Knowledge sharing Cooperation Competition Coopetition Co-opetition Cross-functionality Software development teams Multi-party software development teams

a b s t r a c t We present and empirically validate a Coopetitive Model of Knowledge Sharing that helps understand the forces underlying High-Quality Knowledge Sharing in multiparty software development teams. More specifically, we integrate the Coopetitive Model of Knowledge Sharing and Social Interdependence Theory to explain the forces behind High-Quality Knowledge Sharing in cross-functional software development teams. Based on the analysis of data collected from 115 software development project managers, we explore the mechanisms through which simultaneous cooperative and competitive behaviors drive High-Quality Knowledge Sharing among cross-functional team members. We also show how multiple interdependencies that are simultaneously set in motion engender cooperative and competitive behaviors. This study is the first study that encompasses both the antecedents of simultaneous cooperative and behaviors and the mechanisms through which simultaneous cooperation and competition influence knowledge sharing behaviors. The model adds to the emerging contingency perspective pertaining to the study of cooperation and competition in system development teams. Ó 2012 Elsevier Ltd. All rights reserved.

1. Introduction Software development is a collaborative process where its success is dependent upon knowledge acquisition, information sharing, and the minimization of communication breakdown (Andres & Zmud, 2002; Fang & Neufeld, 2009; Joshi, Sarker, & Sarker, 2007). This is particularly true since the cross-functional nature of software development projects involves representatives with diverse expertise, who are usually drawn from various functional units (He, Butler, & King, 2007; Paul, Samarah, Seetharaman, & Mykytyn, 2004). For example, technical specialists, business stakeholders, strategists and brand specialists work together to design and implement new systems. As a consequence, to harness the expertise of team members and to make communication better tailored to the needs of stakeholders, software development teams must be capable of discovering effective ways of knowledge sharing (Sawyer, Guinan, & Cooprider, 2010; Tiwana & McLean, 2003). Nonetheless, achieving effective knowledge sharing in cross-functional software development practices may encounter several challenges (Espinosa, Slaughter, Kraut, & Herbsleb, 2007; Joshi et al., 2007). It has been widely acknowledged that software development projects become confronted with the challenges of managing knowledge-based teams (Faraj & Sproull, 2000). For example, the software development process could be plagued by the challenges of information quality such as incorrect and irrelevant information (Gorla, Somers, & Wong, 2010). Previous studies indicate the importance of knowledge as a competitive advantage for those who possess it at the right place and at the right time (Van der Bij, Song, ⇑ Corresponding author. Address: Australian School of Business, Quad Building, University of New South Wales, Sydney, NSW 2052, Australia. Tel.: +61 2 9385 7130. E-mail addresses: [email protected], [email protected] (S. Ghobadi), [email protected] (J. D’Ambra). 0306-4573/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.ipm.2012.07.001

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& Weggeman, 2003). The competitive advantage of knowledge can make team members hoard knowledge or offer incomplete transfer of knowledge (Jarvenpaa & Staples, 2001). This is particularly true in cross-functional teams where team members are likely to experience tension caused by diverse professional philosophies and competing goals (Witt, Hilton, & Hochwarter, 2001). For example, shared resource pools, different backgrounds and project roles might make the subgroups of business and IT professionals have different goals of their own, in addition to the project goals (Pee, Kankanhalli, & Kim, 2010). The positive interdependencies make them share their knowledge in order to promote mutual goal attainment; however, if the conflict becomes a dominating concern, they may behave uncooperatively in order to prevent others from achieving their goal, since one‘s success in some contexts comes at the expense of others. As an example, end users desire specific functions to serve individual business needs, programmers focus on overcoming technical constraints and enhancing competency of their careers, and project managers might be concerned with meeting budget expectations and milestones. IT professionals might experience similar challenges within their own teams too; for example, designers and coders may disagree on design methodology, programming language, database server and etc. (Tolfo & Wazlawick, 2008). There has been a recent research interest in collaboration and knowledge sharing within multiparty software development teams (e.g., (Levina, 2005; Pee et al., 2010)). However, little systematic efforts exists on discovering the antecedents of ‘knowledge sharing’ in these contexts, and in particular ‘High-Quality Knowledge Sharing’, which enables the internalization of the shared knowledge (Levina & Vaast, 2008). This has resulted in a somewhat limited comprehensive understanding of the mechanisms through which High-Quality Knowledge Sharing behaviors are produced in these contexts. To deepen our understanding in this area, this study is undertaken to answer the following question: What are the underlying mechanisms that generate High-Quality Knowledge Sharing in cross-functional software development teams?’’ We integrate Coopetitive Model of Knowledge Sharing (Loebecke, Van Fenema, & Powell, 1999) and Social Interdependence Theory (Deutsch, 1949) to construct and operationalize a model that explains a comprehensive set of factors that contribute to High-Quality Knowledge Sharing in cross-functional software development teams. The contribution of this study in terms of presenting an operationalizing a theoretical model helps both academics and practitioners gain insights into how to stimulate effective knowledge sharing in multi-party software development processes. More specifically, the model helps (a) understand, (b) measure, and (v) influence and manage different antecedents that affect High-Quality Knowledge Sharing in multi-party software development teams. The remainder of the paper proceeds as follows. First, the theoretical background of the research is presented, and the primary hypotheses of the study are postulated. This is followed by a discussion on the research methodology, data collection procedures, analysis techniques, and the results. The validated model stands as one of the first attempts in exploring the concept of coopetitive knowledge sharing in cross-functional software development teams in particular, and cross-functional project teams in general. Theoretical and practical implications are discussed. The paper concludes by highlighting the study’s limitations as well as pointing out venues for future research studies. 2. Theoretical background 2.1. Cross-functional software development teams There is consensus in the literature that defines cross-functional teams as composed of individual representatives drawn from various functional units, such as departments or organizations, who possess specialized knowledge and skills relevant to the completion of the project (Clark & Wheelwright, 1992; Holland, Gaston, & Gomes, 2000). Based on this definition, throughout this study, cross-functional software development teams refer to ‘temporary workgroups that are charged with the responsibility of completing a development project within a limited time frame, and they consist of member representatives drawn from various functional units (e.g., departments, organizations)’. The increasing global competition in software development industry has heightened the need to rely on cross-functional teams (e.g., either in the form of global software development teams or collocated traditional software development teams). Therefore, our focus on cross-functional teams provides a more realistic approach for studying modern software development teams. 2.2. High-Quality Knowledge Sharing The extant literature has questioned the simple notion that knowledge sharing is good and sufficient for organizations (Argote, Ingram, Levine, & Moreland, 2000; Fox, Levitin, & Redman, 1994). Whilst knowledge sharing is a necessary condition for achieving effective collaborative practices, it is more important that the shared knowledge be transformed to become an integrated part of a synergic solution, rather than being merely combined or even completely ignored (Carlile, 2004; Levina, 2005). Knowledge management literature draws attention to the importance of insightfulness, usefulness, and the quality of the shared knowledge (Haas & Hansen, 2007). Information Systems literature highlights the importance of the practical usefulness of the knowledge being shared in achieving innovative and collaborative solutions in multiparty software development teams (Levina, 2005). Similarly, Knowledge Transfer literature views successful knowledge sharing through the lens of the internalization approach (Kane, 2010). Accordingly, knowledge transfer requires both the transmission of the knowledge

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from the source agent, and its internalization/ learning by the recipient agent (Joshi et al., 2007). Therefore, the success of knowledge transfer lies in the changes that occur in the knowledge understanding of the receiver. For example, Li and Hsieh (2009) explained knowledge transfer success in terms of the extent to which recipients were satisfied with the usefulness and applicability of the knowledge being shared (Li & Hsieh, 2009). With regard to the above discussion, this study applies the concept of ’High-Quality Knowledge Sharing’ to refer to the degree to which people are satisfied with the quality of the shared knowledge & find it useful in accomplishing their activities. 2.3. Coopetitve Model of Knowledge Sharing The Coopetitive Model of Knowledge Sharing suggests that the phenomenon of knowledge sharing among individuals is influenced by the value of the knowledge being shared, and by the perception of individuals of the payoffs that they gain or lose by sharing knowledge (Loebecke et al., 1999). This model indicates that individuals are more likely to share their perceived valuable knowledge in cooperative contexts, whereas competitive situations make them more reserved in sharing what they perceive as valuable knowledge. In addition, it is often acknowledged that cooperative behaviors increase shared understanding among individuals, and this is a major prerequisite for sharing the knowledge that has the most innovative characteristics for others (De Dreu, 2007; Nelson & Cooprider, 1996). It can be expected, therefore, that cooperative contexts result in sharing high-quality knowledge, whereas conflicting interests and tension as the result of competition can impede this process. Hypothesis 1. Cooperation among cross-functional team members has a positive impact on the quality of the shared knowledge among them. Hypothesis 2. Competition among cross-functional team members has a negative impact on the quality of the shared knowledge among them. The choice of the Coopetitive Model of Knowledge Sharing in this study has a number of reasons. First, this model takes into account the mixed characteristics of knowledge, which are often ignored in other approaches toward knowledge sharing (Yang & Wu, 2008). More specifically, knowledge has two characteristics; it is both a ‘source of’ and a ‘barrier to’ innovation (Dougherty, 1992). In other words, knowledge can be effectively shared to facilitate innovation in collaborative contexts. At the same time, the perceived competitive value of knowledge in collaborative contexts makes individuals reserved in sharing the important knowledge, which is essential for innovation. Second, the Coopetitive Model of Knowledge Sharing has a general conceptual approach toward knowledge sharing, and thereby it provides the possibility of exploring the detailed mechanism through which knowledge sharing occurs. This is shown in detail in Section 3.2. 2.4. Cross-functional cooperation and competition The Coopetitive Model of Knowledge Sharing is an extension of the game theoretical model of knowledge sharing, and thereby it is limited in addressing the underlying nature of cooperative and competitive behaviors. To supplement this approach, the concepts of cross-functional cooperation and competition are borrowed from the cross-functional projects’ literature. Cross-functional cooperation is adopted from the work of Pinto, Pinto, and Prescott (1993) and Song et al. (1997), and is defined as ‘the degree, extent, and nature of relationships among cross-functional team members’. Following the work of Ghobadi and D’Ambra (2011), cross-functional cooperation is treated and formed by the three first-order constructs of: (i) Cooperative Task Orientation, (ii) Cooperative Communication, and (iii) Cooperative Interpersonal Relationships (Ghobadi & D’Ambra, 2011). Similarly, Cross-Functional Competition is defined as ‘the degree to which cross-functional team members have the tendency of rivalry with each other for limited resources and values’. It is treated as formed by the two first-order constructs including (i) Competition for Tangible Resources, and (ii) Competition for Intangible Resources. 2.5. Social interdependence theory Social Interdependence Theory indicates that the structure of interdependencies among individuals (positive, negative, or no interdependence) determines the degree of cooperative and competitive interactions (Deutsch, 1949; Johnson & Johnson, 1995). Specifically, positive interdependence results in a process of efficient promotive interaction, whereas negative interdependence results in oppositional interaction, and no interdependence results in the absence of interaction (De Dreu, 2007). The extant literature points to a broad range of interdependencies such as goal interdependence, task interdependence, Outcome Interdependence, and resource interdependence (e.g., (Parolia, Jiang, Klein, & Sheu, 2010)). Johnson and Johnson (2006) offered a comprehensive categorization for interdependencies including: (i) Outcome Interdependence (including: goal and reward interdependence), (ii) means interdependence (including: task, resource, and role interdependence), and (iii) Boundary Interdependence (including: sense of identity, friendship, and environmental closeness).

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Outcome Interdependence is defined as the degree to which individuals perceive that their outcomes (goals and rewards) are interdependent with each other (Johnson & Johnson, 2006). Means interdependence is defined as the degree to which individuals perceive that they depend on the means of others to achieve mutual outcomes, and can be explained by the degree to which individuals depend on the tasks, roles and resources of others to achieve their mutual outcomes. Boundary Interdependence is defined as the degree to which individuals perceive that they have continuous relations among themselves. The negative Boundary Interdependence would be abrupt discontinuities, which segregate individuals into separate groups. The discontinuity may be caused by factors such as environmental factors (e.g., functional units, environmental closeness) and similarity (e.g., backgrounds, groups). Boundary Interdependence includes three dimensions including friendship, sense of identity, and environmental closeness. Social interdependence theory provides an appropriate explanation for the emergence of cooperative and competitive behaviors in collaborative contexts, particularly in cross-functional contexts where simultaneous cooperative and competitive behaviors are highly evident and expected (Ghobadi & D’Ambra, 2011). In addition, Social Interdependence Theory is in congruence with the collaborative nature of software development processes (Pee et al., 2010). Therefore, this study employs this theory, and proposes that higher positive levels of interdependencies among cross-functional software development team members account for higher levels of cooperation. In other words, positive levels of interdependencies (outcome, means, and boundary interdependencies) result in cooperative behaviors among cross-functional team members. Hypothesis 3. Outcome Interdependence among cross-functional team members is positively related to the extent of cooperation among them. Hypothesis 5. Means interdependence among cross-functional team members is positively related to the extent of cooperation among them. Hypothesis 6. Boundary Interdependence among cross-functional members is positively related to the extent of cooperation among them. Based on the Social Interdependence Theory, this study argues that negative levels of interdependencies give rise to competitive behaviors. Both outcome and boundary interdependencies might have negative values. Negative Outcome Interdependence happens when individuals perceive that they can obtain their outcomes if and only if other individuals fail to obtain their outcomes. Negative Boundary Interdependence occurs when discontinuities have segregated individuals into separate groups. The definition of means interdependence (the degree to which individuals perceive that they depend on the means of others to achieve mutual outcomes) suggests that this concept may only have positive value (if they perceive they depend on the means of others), Or it may have no interdependence value (if they perceive they do not depend on the means of others). Therefore, means interdependence may not be associated with a negative value. Hypothesis 4. Outcome Interdependence among cross-functional team members is negatively related to the extent of competition among them. Hypothesis 7. Boundary Interdependence among cross-functional team members is negatively related to the extent of competition among them. Based on the previous discussions, Fig. 1 demonstrates the conceptual model of the study. 2.6. Instrument development Each of the constructs in Fig. 1 has multi-item scales derived from relevant prior studies. Two scales were adopted. First, a 7-point Likert scale was adopted with anchors ranging from strongly disagree (1) to strongly agree (7). Second, a semantic differential scale was adapted from very low (1) to very high (7). Five steps were taken to generate appropriate measures and questions for the constructs of the model. These steps include: (i) creating a pool of measures, (ii) consolidating measures and questions, (iii) developing reflective and formative measures and questions, (iv) refining measures and questions (face validity), and (v) final refinement of the measures and questions. First, the relevant literature on the constructs of the model was investigated to find appropriate measures for each construct. The findings were combined in a pool of measures. The items were adapted from the literature wherever possible. If no explicit definition of a measure was provided in the literature, the original manuscript was carefully investigated. Based on the sample questions or examples, a short description was provided. Second, measures that were repeated in different instruments were identified and removed from the pool. Measures that reflected the same concept were consolidated by reviewing their definition.

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Cross-functional (CF) Software Development (SD) Project Team

Cooperative Interpersonal Relationships

Cooperative Task Orientation

form

Outcome Interdependence

form

form

H3 + H4 _

Means Interdependence

Cooperative Communication

CF Cooperation

H1+

H5 +

High-Quality Knowledge Sharing

H2 -

CF Competition H6 + Boundary Interdependence

H7 Social Interdependency Theory

form

Competition for Tangible Resources

form

Coopetitive Model of Knowledge Sharing

Competition for Intangible Resources

Fig. 1. Conceptual model.

Third, based on the definition of the constructs, constructs with formative measures were identified. Except Outcome, Means and Boundary Interdependencies, other constructs were considered as having reflective measures. In terms of formative measures, it is worthwhile to draw our attention to the work of Jarvis, MacKenzie, and Podsakoff (2003) regarding the major characteristics of formative measures (Jarvis et al., 2003). Firstly, formative measures are fundamental to the definition of the construct. In addition, formative measures define, produce, or cause the construct rather than vice versa (Diamantopoulos & Winklhofer, 2001). They also do not share any common theme among themselves, and are not necessarily covarying. The recent emerging research on formative conceptualization has suggested a need for increasing the use of formative indicators (Diamantopoulos & Winklhofer, 2001). However, judgment regarding whether scales should be considered formative or reflective does not depend only on the form of the measurement items. The manner in which participants process information and respond to questions is also important. If participants retrieve material from cognitive structures and generate responses to the measures of a particular construct, measures can be regarded as being reflective (Chin, Peterson, & Brown, 2008). With this background, a number of reflective measures were developed for formative constructs to ensure the statistical results, and to provide the possibility of comparing formative and reflective measures. The inclusion of both reflective and formative measures helps future studies decide whether to base their evaluation on the overall perceptions of constructs or on their underlying causes (Mathieson, Peacock, & Chin, 2001). Having the list of measures for each construct, proper questions to capture the value of each measure were developed. Fourth, prior to the test of the conceptual basis and its instrument in the target sample, we decided to have an initial content validation by checking the model and its instrument in a similar pilot context. Three experienced engineers holding the position of developers in multi-party software development teams were invited to review the pilot of the questionnaire, and note any items that they perceived as ‘not necessary’ or ‘requiring changes’ from their software development experience perspective. No item was ranked as ‘‘not necessary’’. In addition, three IS professors and three PhD students were asked to assess the logical consistencies, ease of understanding, sequence of items, and contextual relevance. Revisions included minor changes in the wording of the questions and the graphical representation of the questionnaire (e.g., order of the questions, the introduction page of the survey that explains the objectives and practical implications of the study). We also controlled for a number of factors that our postulated model does not cover, but might be important in predicting High-Quality Knowledge Sharing. These items include: (i) communication medium satisfaction, (ii) organizational importance of the project, (iii) project complexity, (iv) project size, (v) project duration, and (vi) project cross-functionality.

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Table 1 Constructs and measures and sources. Constructs

Measures

Source

High-Quality Knowledge Sharing: The degree to which cross-functional team members were satisfied with the quality of the knowledge shared among them

1. Satisfaction with the quality of knowledge 2. Usefulness of the knowledge 3. Overall quality of knowledge

(Haas & Hansen, 2007; Joshi et al., 2007; Li & Hsieh, 2009)

1. Cohesive activities 2. Help each other 3. Integrated activities 4. Project commitment 1. Regular discussion 2. Regular communication 1. Gratifying relationships 2. Close ties

(Ghobadi & D’Ambra, 2011)

1. Competition for resources 2. Tension on the distribution of resources 1. Competition for strategic power 2. Competition for strategic attention 3. Departmental turf

(Ghobadi & D’Ambra, 2011)

Outcome Interdependence: The degree to which cross-functional team members perceived that their outcomes (goals and outcomes) are interdependent. This can be explained by the degree to which their goals and rewards will be achieved only when the goals and rewards of other cross-functional members are also met.

2 Formative Measures 1. Goal interdependence 2. Reward interdependence 2 Reflective Measures

(Johnson & Johnson, 2006)

Means Interdependence: The degree to which cross-functional team members perceived that they depended on the means of other cross-functional team members to achieve mutual team outcomes. It can be explained by the degree to which cross-functional team members depend on the tasks, roles, and resources of other cross-functional team members to achieve their mutual outcomes.

3 Formative Measures 1. Task interdependence 2. Role interdependence 3. Resource interdependence 2 Reflective Measures

(Johnson & Johnson, 2006)

Boundary Interdependence: The degree to which cross-functional team members perceived that they have continuous relations. Boundary Interdependence includes having friendship, having sense of identity, and having environmental closeness.

3 Formative Measures 1. Friendship 2. Sense of identity 3. Geographical closeness 2 Reflective Measures

(Johnson & Johnson, 2006)

CF Cooperation: The degree, the extent and the nature of relationships among cross-functional team members. It is treated as formed by three first-order constructs: (i) Cooperative Task Orientation, (ii) Cooperative Communication, and (iii) Cooperative Interpersonal Relationship

CF Competition: The degree to which cross-functional team members have the tendency of rivalry with each other for limited resources and values. It is treated as formed by two first-order constructs (i) Competition for Tangible Resources, and (ii) Competition for Intangible Resources across functional units

Cooperative Task Orientation

Cooperative Communication Cooperative Interpersonal Relationships Competition for Tangible Resources

Competition for Intangible Resources

Note:  CF stands for cross-functional.  The questions for the construct of ‘High-Quality Knowledge Sharing’ are consistent with the internalization approach, since they investigate individuals’ opinions (recipients) of the shared knowledge in helping them accomplish project activities (Kane, Argote, & Levine, 2005; Szulanski, 1996).

Table 1 summarizes the constructs of the model, their definition, measures, and sources. The questions relevant to each construct are shown in Appendix A. 3. Research methodology 3.1. Data collection The research model was examined using data from software development project managers in Australia. Data was collected through an online questionnaire. The key-informant approach to data collection suggests identifying and collecting data from a person who would be highly knowledgeable about team events (e.g., Liu, Chen, Chan, & Lie, 2008; Sethi, Smith, & Park, 2001). Following this approach, project managers were targeted as the key respondents in our data collection. While this approach has limitation, it allowed us examine the theoretical model across a wide range of projects. A comprehensive list of ethical issues was carefully considered before conducting the research. Responses were voluntary. Confidentiality and anonymity were guaranteed to respondents. Project managers did not represent their organizations; rather their responses represent their team’s experience. The criterion for participation was managing or being highly-involved in a crossfunctional project within the last 2 years. We asked project managers to answer the survey questions for the most recent

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cross-functional software development project that they had managed or they had been heavily involved in it. The survey questions were represented in a random manner. The questionnaire was sent through an Email that included the link to the survey to a group of 500 members of five IT associations in Australia. These associations are considered as the professional Australian organizations that help experienced IT professionals develop and broaden their expertise. There are restricted membership criteria and the certified member status within these associations is upon to specified criteria, which have limited the memberships to those who carry a reasonable breadth of responsibilities, seniority, and abilities in IT. In order to increase professionals’ motivation in filling out the survey, the survey began by highlighting the practical implications of the study to software development teams. It was guaranteed to send them an executive summary of the study, upon their request. One hundred and fifty questionnaires were returned (30%); out of these, 35 were not completed, and therefore were excluded from the analysis. Thus, 115 usable responses were obtained from a population of 500 IT professionals in Australia. This response rate is consistent with Baruch and Holtom (2008), who stated that studies that are conducted at the organizational level, and seek responses from organizational representatives or top executives, are likely to experience lower response rates, approximately 35–40% (Baruch & Holtom, 2008). We received some feedback from the sample population stating that they had not been recently involved in a cross-functional project. Therefore, the eligibility criteria limited the number of responses (e.g., cross-functionality of projects). As a consequence, a number of professionals in the sample size might not have been eligible to take part in the survey, and this has resulted in the final response rate of 30%. Because we did not have access to the characteristics of the non-respondents, this study does not report a non-response test. In fact, the eligibility criteria seem to be the major sources of non-respondents. Therefore, we do not consider that the respondents are different from the non-respondents except on the selection criterion. Detailed demographics of the projects are given in Table 2. The sample demonstrated a relatively broad range of projects in terms of project duration, project members, project complexity, etc. The sample included both in-house (72%) and outsourced (28%) projects. 90% of the projects took fewer than 3 years to be completed. The projects were mainly moderate or high in complexity, which explain the reason behind involving cross-functional representatives during the project. In addition, a majority of the project managers (67%) had more than 5 years of experience in software development project management. This reinforces that representatives of the sample are mostly professionals whose technical contributions and cross-functional experience are highly valued. In summary, the demographics suggest that the selected projects are representative of a considerable population of software development projects. 3.2. Data analysis Structural Equation Modeling (SEM) was employed to examine the measurement model, and to investigate the causal relationships between the dependent and independent constructs. We applied PLS-Graph Version 3.00, which uses a principal component-based estimation approach to test the hypotheses. The choice of Partial Least Square (PLS) rather than covariance-based tools (e.g., Amos, LISREL) had several reasons. It was mainly because PLS has the ability of analyzing formative measures and modeling latent constructs under conditions of fewer statistical specifications and constraints on the Table 2 Sample characteristics. Project type

 In-house: 83 (72%)  Outsourced: 32 (28%)

Number of functional units

   

Between 1–5: 80 (70%) Between 6–10: 24 (21%) Between 11–20:7 (6%) More than 20:4 (3%)

Team Size

   

Between 1–5: 41 (36%) Between 6–10: 37 (32%) Between 11–20: 16 (14%) More than 20: 21 (18%)

Project manager experience

    

Less than 1 year: 12 (10%) Between 1–2 years: 9 (8%) Between 2–3 years: 10 (9%) Between 3–5 years: 17 (15%) More than 5 years: 67 (58%)

Project duration

   

Less than 1 year: 49 (43%) Between 1–3 years: 54 (47%) Between 3–5 years: 6 (5%) More than 5 years: 6 (5%)

Project complexity

 Low: 19 (16%)  Moderate: 26 (23%)  High: 70 (61%)

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data (e.g., assumptions of non-normality), compared to the covariance-based strategy of LISREL. PLS has also much more consistency, flexibility and robustness in small to moderate sample sizes (Chin & Newsted, 1999). In terms of the sample size, we followed a procedure for estimating the power (Chin, Marcolin, & Newsted, 2003). This procedure takes into account the fact that both sample size and the number of the model’s indicators affect the power and contribute to statistical estimates. In order to control statistical power, a priori power analyses was conducted to determine the desirable sample size before conducting the study (Cohen, 1988). Using Gpower3 software (Faul, Erdfelder, Lang, & Buchner, 2007), with alpha = 0.05, power = 0.8 and the effect size = 0.5 (suggested by the method), a sample size of 102 was recommended as sufficient. This indicates that the sample of 115 responses in this study is adequate. Data analysis utilized a two-step approach as recommended by Anderson and Gerbing (Anderson & Gerbing, 1988). The first step involved the analysis of the measurement model, and the second step tested the structural relationships among latent constructs. The following sections explain the analysis of the measurement and structure models. 4. Result 4.1. Measurement model The aim of the measurement model analysis was to assess the reliability and validity of the constructs and their measures before their use in the full model. The research model includes nine reflective constructs (‘High-Quality Knowledge Sharing, Outcome Interdependence, Means Interdependence, Boundary Interdependence, Cooperative Task Orientation, Cooperative Communication, Cooperative Interpersonal Relationships, Competition for Tangible Resources, and Competition for Intangible Resources). Based on the instrumentation section, we also included 4 formative constructs to represent Outcome, Means, and Boundary Interdependencies, as well as the formative construct representing control variables. In total, the measurement model included 36 items. Prior to the analysis of the measurement model, a redundancy analysis was conducted to check the relative importance of the formative measures when used only to predict their relevant reflective indicators (Van Den Wollenberg, 1977). This is explained in the following section. 4.1.1. Redundancy analysis In general, a structural path above 0.80 between formative and reflective constructs is indicative of a valid set of formative measures, and a path of 0.90 and above indicates an extremely strong relationship (Mathieson et al., 2001). Fig. 2 demonstrates the redundancy model of this study. The redundancy analysis showed that the paths between two models of assessing Outcome, Means, and Boundary Interdependencies were: 0.845, 0.704, and 0.829. This implies an adequate coverage of perceptions in the formative sets of Outcome Interdependence and Boundary Interdependence. However, the path of 0.704 between the two modes of assessing Means Interdependence does not indicate a significant convergence. Therefore, the following analysis on the measurement model will examine Outcome Interdependence and Boundary Interdependence with their formative measures, whereas Means Interdependence will be examined with its reflective measures. 4.1.2. Measurement analysis After incorporating the results of the redundancy analysis, 29 items were included in the measurement model (reflective measures of Outcome and Boundary Interdependencies and formative measures of Means Interdependence were removed).

Outcome Interdependence Formative

0.845

Outcome Interdependence (R 2= 0.709)

Means Interdependence Formative

0.704

Means Interdependence (R 2= 0.501 )

Boundary Interdependence Formative

0.829

Boundary Interdependence (R 2= 0.688 )

Fig. 2. Redundancy model.

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Table 3 Factor loadings and weights. Construct

High-quality knowledge sharing

Questions

Mean

Std. error

Loading



AVE Composite reliability



1. Overall, CF representatives were satisfied with the quality of knowledge being shared among themselves 2. Overall, the quality of shared knowledge among CF team members was (1: very low, 2,3,4,5,6, 7: very high) 3. The shared knowledge among CF representatives was found useful in accomplishing project activities

4.67

0.105

0.9

0.858

4.92

0.106

0.934

0.948

4.67

0.085

0.943

1. CF representatives integrated their activities to ensure better attainment of the project outcomes 2. CF representatives accomplished project tasks in a cohesive manner 3. CF representatives willingly helped each other in their project activities. 4. CF representatives were committed in accomplishing project tasks

4.57

0.115

0.807

0.676

4.38 4.75 5.05

0.116 0.116 0.12

0.836 0.834 0.819

0.893

Cooperative Communication

1. CF representatives regularly communicated together 2. CF representatives regularly discussed common problems

4.9 4.86

0.119 0.104

0.907 0.88

0.799 0.888

Cooperative Interpersonal Relationships

1. CF representatives had close ties with each other 2. The relationships between CF representatives were mutually gratifying

4.31 4.51

0.124 0.115

0.852 0.879

0.749 0.857

Competition for Tangible Resources

1. Overall, CF representatives regularly competed for resources 2. When CF representatives discussed distribution of resources among their departments, tensions frequently occurred

3.6 3.57

0.146 0.143

0.908 0.84

0.88 0.886

Competition for Intangible Resources

1. CF representatives tried to gain more strategic power during the project 2. CF representatives regularly competed with each other for more attention from top executives 3. Protecting one’s departmental turf seemed to be a way of life by CF representatives

3.97 3.59

0.156 0.155

0.883 0.891

0.77 0.91

3.87

0.168

0.857

1. Overall, CF representatives perceived that they were dependent on each other for achieving their outcomes 2. The degree that CF representatives believed that they were dependent on the means of each other for fulfilling mutual outcomes was (1: very Low, 2,3,4,5,6, 7: very High)

4.57

0.121

0.893

0.811

4.66

0.11

0.908

0.896

1. CF representatives’ perception was that their goals could be achieved only when the goals of other CF team members were also attained 2. CF representatives believed they could receive rewards only if other CF team members could receive their expected rewards.

4.44

0.117

0.662

–

3.77

0.134

0.47

1. The extent of friendship among representatives from different departments was: (1: very low, 2,3,4,5,6, 7: very high) 2. CF representatives had a sense of belonging to the group that attached them as a united group 3. The CF representatives had close working environments that allowed them have easy access to each other

4.78

0.124

0.437

4.58

0.125

0.684

4.58

0.15

0.223

1. The technological complexity of the project (e.g., the novelty and complexity of the language being used) was (1: very low – 7: very high) 2. How long did the project last (from beginning until the completion of implementation phase)? 3. How many departments had representatives in the project? (e.g., different user departments, different IT departments, etc.) 4. How many years of experience have you had in being engaged in software development project management? 5. Overall, team satisfaction of the communication mediums (e.g., meetings, Wiki, Email, Chat room discussion, etc.) were (1: very low – 7: very high) 6. The degree to which project was perceived to provide immediate tangible organizational benefits was (1: very low – 7: very high)

4.88

0.12

0.031

2

0.106

0.102

1.43

0.071

0.234

3.9

0.129

0.294

4.98

0.104

0.504

4.88

0.131

0.621

Cooperative Task Orientation

Means interdependence

Outcome interdependence – formative

Boundary Interdependence – formative

Project Characteristics

–

–

The weights and loadings for each measure in the validated model are given in Table 3. This table demonstrates the loadings for reflective measures and the weights for formative measures. As shown, all the constructs are modeled with reflective measures except Outcome Interdependence and Boundary Interdependence, as well as the Project Characteristics (the constructs being formed with control variables). Table 3 indicates that the loadings for all the reflective measures are significant and uniformly high (above 0.70) with a majority above 0.80. For formative measures, a number of issues were taken into account. Firstly, absolute value of item weight was examined to determine the relative contribution of items constituting each construct (Cenfetelli & Bassellier, 2009). Secondly, t-statistics for the formative measures were investigated. All the t-statistics were significant, except for Project Complexity, Project Duration, and Project Cross-Functionality, which had p-value of more than 0.1. Third, the collinearity of the formative

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Cooperative Communication (R 2= 0.431 )

Cooperative Task Orientation (R 2= 0.613 )

Cooperative Interpersonal Relationships (R 2= 0.684 )

*** 0.287 *** 0.269

*** 0.557

Outcome Interdependence Formative

0.83, - 0.049 , * 0.146

CF Cooperation *** 0. 592

High -Quality Knowledge Sharing (R 2= 0.646 )

* 0.144 , 0.152

** 0. 255 , 0.171,*** 0.245

Means Interdependence

0.00

*** 0. 300

*** 0.531, *** 0.537 , *** 0.533

Boundary Interdependence Formative

Project characteristics *** - 0.315 , *** - 0.301

CF Competition

* - 0.131 , ** - 0. 234 , - 0. 039 * P< 0.1 ** P< 0.5 ***P<0.01

0.024, * 0.168, - 0.105 *** 0.443

*** 0.644

Competition for Tangible Resource(R 2= 0.17 7)

Competition for Intangible Resource (R 2= 0.17)

Fig. 3. Tested model in PLS (1).

measures was checked to confirm that there are no high correlations among the formative measures of each construct. The VIF’s for the formative measures were almost less than 3.5 with high distance from 10 (Cenfetelli & Bassellier, 2009).

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Table 4 Correlations and square roots of average variance extracted. 1 High-quality knowledge sharing

2

3

4

5

6

Cooperative Task Orientation

0.926 0.739

Cooperative Communication

0.678

0.822 0.816

Cooperative Interpersonal Relationships

0.656

0.800

0.894 0.716

Competition for Tangible Resources

0.378

0.233

0.195

0.865 0.308

Competition for Intangible Resources

0.356

0.335

0.284

0.32

0.887 0.732

Means interdependence

0.617

0.726

0.671

0.705

0.207

7

0.877 0.276

0.901

Convergent validity was examined by generating 500 bootstrapping samples. Convergent validity is adequate if each of the constructs has the minimum average variance expected (AVE) of 0.50 (Fornell & Larker, 1981). Additionally, it is recommended that the factor loadings of all items should be above 0.60 for an adequate convergent validity (Hair, Anderson, Thatham, & Black, 1998). As shown in Table 3, measures of internal consistency and convergent validity of all constructs were much greater than 0.7 and 0.5, respectively. This finding ensured that measures of each seven reflective constructs (Means Interdependence, Cooperative Task Orientation, Cooperative Communication, Cooperative Interpersonal Relationships, Competition for Tangible Resources, Competition for Intangible Resources, and High-Quality Knowledge Sharing) are related to their specified construct as a whole. Discriminant validity was assessed by testing whether the correlation between pairs of constructs is below the threshold value of 0.90, and whether the square root of AVE is larger than the correlation coefficients (Fornell & Larker, 1981). Table 4 shows that the square root of the AVE for all first-order constructs was higher than their shared variances, and thus the variance shared between each construct and its indicators are distinct and unidimensional. This confirmed the discriminant validity of the constructs, and thus constructs’ measures are not simply a reflection of other constructs in the model. 4.2. Structural analysis Fig. 3 illustrates the examined model in PLS, including the R-square of the dependent constructs as well as the significant paths among the constructs. There are three values on the link between each Interdependence construct and CF Cooperation. These values point to the path between each Interdependence construct and the three facets of CF Cooperation including: Cooperative Task Orientation, Cooperative Communication, and Cooperative Interpersonal Relationships. Similarly, there are two values on the link between each Interdependence construct and CF Competition. These values point to the path between each Interdependence construct and the two facets of competition: Competition for Tangible and for Intangible Resources. The paths are estimated by a bootstrapping procedure using 500 samples. As shown, the R-square of the model (the main dependent construct of the model – High-Quality Knowledge Sharing) is 0.646. This indicates that the structural model explains 64.6% of the variance in the High-Quality Knowledge Sharing construct. This implies the satisfactory predictive power of the PLS model, compared to the minimum recommended R-square value of 0.1 (Falk & Miller, 1992). Table 5 presents the t-statistics and the p-value of each path. Most of the paths were fairly high compared to many found in related research (Cohen, 1988). The construct representing Project Characteristic had a significant impact on High-Quality Knowledge Sharing with the p-value of less than 0.01. The weights and the t-statistics of the formative measures of the Project Characteristics (shown in Table 3) suggested that the measures of ‘Project Importance’ and ‘Communication Medium Satisfaction’ might contribute the most to the High-Quality Knowledge Sharing construct. In order to find the most important indicator within the construct of Project Characteristics, all the formative measures except Project Importance and Communication Medium Satisfaction were deleted. The bootstrapping was again run. The path correlation (t-statistics) was still very significant at p < 0.01 with the t-statistics of 3.57. This signifies the importance of the two measures of Project Importance and Communication Medium Satisfaction. Then, Project Importance was removed and another formative measure, Project Complexity was added. The t-statistics dropped to 0.804. It was, therefore, concluded that Project Importance has the most influence on High-Quality Knowledge Sharing, compared to the other measures of the Project Characteristics’ construct. The path between CF Competition and High-Quality Knowledge Sharing was almost zero (p-value > 0.1). The extant literature suggests that competition has its unfavorable impacts on knowledge sharing through decreasing cooperation among individuals (Pinto et al., 1993). Therefore, the relationships between the 2 dimensions of CF Competition and the 3 dimensions of CF Cooperation were explored. The path coefficiencies indicated that Competition for Tangible and Intangible Resources have mixed impacts (increasing and decreasing) on Cooperative Communication (the second dimension of CF Cooperation). The mixed impacts of the Competition for Tangible Resources (+) and Intangible Resources ( ) on the dimensions of CF Cooperation suggest that the non-significant impact of CF Competition on High-Quality Knowledge Sharing can be due to the neutralized impacts of Competition for Tangible and Intangible Resources on High-Quality Knowledge Sharing. Therefore, the relationships between the two first order constructs of CF Competition (Competition for Tangible and Intangible Resources) and High-Quality

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S. Ghobadi, J. D’Ambra / Information Processing and Management 49 (2013) 138–157 Table 5 Hypotheses testing. Hypothesis

p-Value

H1: (CF Cooperation) – (High-Quality Knowledge Sharing) H2: (CF Competition) – (High-Quality Knowledge Sharing) H3: (Outcome Interdependence) – (CF Cooperation): Supported (p < 0.05) H3a: (Outcome Interdependence-Formative) – (Cooperative Task Orientation) H3b: (Outcome Interdependence-Formative) – (Cooperative Communication) H3c: (Outcome Interdependence-Formative) – (Cooperative Interpersonal Relationships) H4: (Outcome Interdependence) – (CF Competition): Supported (p < 0.1) H4a: (Outcome Interdependence-Formative) – (Competition for Tangible Resources) H4b: (Outcome Interdependence-Formative) – (Competition for Intangible Resource) H5: (Means Interdependence) – (CF Cooperation): Supported (p < 0.01) H5a: (Means Interdependence) – (Cooperative Task Orientation) H5b: (Means Interdependence) – (Cooperative Communication) H5c: (Means Interdependence) – (Cooperative Interpersonal Relationships) H6: (Boundary Interdependence) – (CF Cooperation): Supported (p < 0.01) H6a: (Boundary Interdependence – Formative) – (Cooperative Task Orientation) H6b: (Boundary Interdependence – Formative) – (Cooperative Communication) H6c: (Boundary Interdependence – Formative) – (Cooperative Interpersonal Relationships) H7: (Boundary Interdependence) – (CF Competition): Supported (p < 0.01) H7a: (Boundary Interdependence – Formative) – (Competition for Tangible Resources) H7b: (Boundary Interdependence – Formative) – (Competition for Intangible Resources) (Project Characteristics) – (High-Quality Knowledge Sharing) Second Orders Modeling Path between Second-Order (CF Cooperation) and First Orders (Cooperative Task Orientation) – (CF Cooperation) Cooperative Communication – (CF Cooperation) (Cooperative Interpersonal Relationships) – (CF Cooperation) Path between Second-Order (CF Competition) and First Orders (Competition for Tangible Resources) – (CF Competition) (Competition for Intangible Resources) – (CF Competition)

<0.01 >0.1

t-Stat. 7.040 0.183

Outcome Supported (p < 0.01) Rejected (p > 0.1)

>0.1 >0.1 <0.05

1.006 0.442 2.075

Supported (p < 0.05)

<0.1 >0.1

1.683 1.262

Supported (p < 0.1)

<0.05 >0.1 <0.01

2.181 1.465 3.240

Supported (p < 0.01)

<0.01 <0.01 <0.01

6.447 6.795 6.671

Supported (p < 0.01)

<0.01 <0.01 <0.01

3.154 2.906 3.798

Supported (p < 0.01)

<0.01 <0.01 <0.01

23.259 19.170 12.255

–

<0.01 <0.01

21.454 31.658

–

Supported (p < 0.01)

Knowledge Sharing were investigated. Fig. 4 shows the examined model. The overall R-square value of the model increased to 0.657. Table 6 presents the t-statistics and p-values of each path. In summary, Fig. 5 demonstrates our final model. 5. Discussion 5.1. Theoretical contributions The final validated model addresses the research question of this study by identifying the underlying mechanisms that drive High-Quality Knowledge Sharing behaviors. Specifically, (i) two forces of cross-functional cooperation and competition are shown as the major drivers of High-Quality Knowledge Sharing behaviors, (ii) the interplay between cross-functional cooperation and competition, which affects knowledge sharing behaviors, is examined, and (iii) the antecedents of crossfunctional cooperation and competition are presented. The combination of the Coopetitive Model of Knowledge Sharing and Social Interdependence Theory allowed us postulate an integrated model, which encapsulates a comprehensive set of factors contributing to High-Quality Knowledge Sharing behaviors. Taking into account a more comprehensive model has resulted in a better understanding of the underlying mechanisms that produce High-Quality Knowledge Sharing (research question). In addition, the presented final model contributes to advancing the recent emphasis on understanding coopetitive structures at intra-organizational levels. The findings are discussed in detail in below: 5.1.1. CF Cooperation and CF Competition: antecedents of High-Quality Knowledge Sharing Our analysis illuminated how forces of CF Cooperation and CF Competition drive effective knowledge sharing in crossfunctional project teams. The significant impact of cooperation among cross-functional team members on increasing the quality of the knowledge being shared (H1, p < 0.01). This finding adds to the predominance of the literature on the beneficial aspects of inter-unit cooperation (Avital & Singh, 2007). Competition among team members had more complex influences. Competition contributes to High-Quality Knowledge Sharing both directly and indirectly, through the mediating impact of cooperative behaviors. Moreover, the two dimensions of CF Competition generated mixed positive and negative outcomes. The sections below further describe how the two dimensions of CF Competition contributed to High-Quality Knowledge Sharing. 5.1.1.1. Competition for Intangible Resources and High-Quality Knowledge Sharing. The model showed that Competition for Intangible Resources had a positive impact (+) on High-Quality Knowledge Sharing, while it had a destructive effect (–) through diminishing Cooperative Task Orientations and Cooperative Communication of individuals.

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Cooperative Communication (R 2= 0.431)

Cooperative Task Orientation (R 2= 0.613)

Cooperative Interpersonal Relationships (R 2= 0.684)

*** 0.287 *** 0.269

*** 0.557

Outcome Interdependence Formative

0.083, -0.049, ** 0.146

CF Cooperation *** 0.600 High -Quality Knowledge Sharing (R 2= 0.657)

*0.144 , 0.152

** 0. 255, 0.171,*** 0.245 Means Interdependence *** 0. 278 *-0.137 ,* 0.127 *** 0.531 , *** 0.537, *** 0.533

Boundary Interdependence Formative

Project Characteristics *** -0.315 , *** -0.301 CF Competition

* -0.131 , *** -0.234 , -0.039 * P< 0.1 ** P< 0.5 ***P<0.01

0.016, * 0.162 , -0.105 *** 0.443

*** 0.644

Competition for Tangible Resource (R 2= 0.177)

Competition for Intangible Resource (R 2= 0.17)

Fig. 4. Revised PLS model (2).

The extant literature on cross-functional project teams indicates that Competition for Intangible Resources is nearly always manifested in political behaviors (Witt et al., 2001).The positive and negative associations between Competition for

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S. Ghobadi, J. D’Ambra / Information Processing and Management 49 (2013) 138–157 Table 6 Hypotheses testing (revised model). Hypothesis

p-Value

H1: (CF Cooperation) – (High-Quality Knowledge Sharing) H2: (CF Competition) – (High-Quality Knowledge Sharing) 1: Competition for Tangible Resources – High-Quality Knowledge Sharing 2: Competition for Intangible Resources – High-Quality Knowledge Sharing H3: (Outcome Interdependence) – (CF Cooperation): Supported (p < 0.05) H3a: (Outcome Interdependence-Formative) – (Cooperative Task Orientation) H3b: (Outcome Interdependence-Formative) – (Cooperative Communication) H3c: (Outcome Interdependence-Formative) – (Cooperative Interpersonal Relationships) H4: (Outcome Interdependence) – (CF Competition): Supported (p < 0.1) H4a: (Outcome Interdependence-Formative) – (Competition for Tangible Resources) H4b: (Outcome Interdependence-Formative) – (Competition for Intangible Resource) H5: (Means Interdependence) – (CF Cooperation): Supported (p < 0.01) H5a: (Means Interdependence) – (Cooperative Task Orientation) H5b: (Means Interdependence) – (Cooperative Communication) H5c: (Means Interdependence) – (Cooperative Interpersonal Relationships) H6: (Boundary Interdependence) – (CF Cooperation): Supported (p < 0.01) H6a: (Boundary Interdependence – Formative) – (Cooperative Task Orientation) H6b: (Boundary Interdependence – Formative) – (Cooperative Communication) H6c: (Boundary Interdependence – Formative) – (Cooperative Interpersonal Relationships) H7: (Boundary Interdependence) – (CF Competition): Supported (p < 0.01) H7a: (Boundary Interdependence – Formative) – (Competition for Tangible Resources) H7b: (Boundary Interdependence – Formative) – (Competition for Intangible Resources) (Project Characteristics) – (High-Quality Knowledge Sharing) Path between Second-Order (CF Cooperation) and First Orders (Cooperative Task Orientation) – (CF Cooperation) Cooperative Communication – (CF Cooperation) (Cooperative Interpersonal Relationships) – (CF Cooperation) Path between Second-Order (Competition) and First Orders (Competition for Tangible Resources) – (CF Competition) (Competition for Intangible Resources) – (CF Competition) Path between (CF Competition) and (CF Cooperation) Competition for Tangible Resources – Cooperative Task Orientation Competition for Tangible Resources – Cooperative Communication Competition for Tangible Resources – Cooperative Interpersonal Relationships Competition for Intangible Resources – Cooperative Task Orientation Competition for Intangible Resources – Cooperative Communication Competition for Intangible Resources– Cooperative Interpersonal Relationships

<0.01

t-Stat. 7.025

Supported (p < 0.01)

<0.1 <0.1

1.792 1.677

Supported (p < 0.1) Supported (p < 0.1)

>0.1 >0.1 <0.05

1.009 0.433 2.006

Supported (p < 0.05)

<0.1 >0.1

1.691 1.265

<0.05 >0.1 <0.01

2.179 1.466 3.238

Supported (p < 0.01)

<0.01 <0.01 <0.01

6.814 6.432 6.653

Supported (p < 0.01)

<0.01 <0.01 <0.01

3.272 2.912 3.637

<0.01 <0.01 <0.01

23.29 19.14 12.26

<0.01 <0.01

21.45 31.66

>0.1 <0.1 >0.1 <0.1 <0.01 >0.1

0.197 1.723 1.525 1.767 2.681 0.477

Outcome

Supported (p < 0.1)

Supported (p < 0.01) Supported (p < 0.01)

–

–

– Supported (p < 0.1) – Supported (p < 0.1) Supported (p < 0.01) –

Intangible Resources and High-Quality Knowledge Sharing can be explained by two research perspectives on organisational politics. The first perspective emphasizes workplace politics to be positive for organizations (Egan, 1994). In the context of crossfunctional projects, political attitudes provoke the need to enhance power and position in individuals and functional units. Therefore, cross-functional representatives would be more likely to be active in the process of selling their ideas and advancing their roles in collaborative tasks. This explains the direct positive impact of Competition for Intangible Resources and High-Quality Knowledge Sharing. The second perspective, which is the more predominant view in the extant literature, acknowledges that politics in the workplace may lead to detrimental individual-based outcomes such as higher levels of stress, anxiety, job dissatisfaction and lower levels of organizational commitment and job performance. For example, individuals in political situations tend to immerse themselves in their work and be unavailable to help others. Other behavioral examples of workplace politics include withholding important information from others in order to weaken the ability of others to compete for scarce resources and to accomplish goals outside of the charter of the group. Therefore, this category suggests that political behaviors produce negative outcomes through reducing team cohesion and team member cooperation (Pinto et al., 1993; Witt et al., 2001). The findings of this study about the negative association between Competition for Intangible Resources and CF Cooperation tend to agree with the second category of research on organizational politics. Fig. 4 shows the direct path between Competition for Intangible Resources and High-Quality Knowledge Sharing is only significant at p < 0.1, while the path between the indirect path (between Competition for Intangible Resources and CF Cooperation) is significant at p < 0.05 (Cooperative Communication: p < 0.05 and Cooperative Task Orientation: p < 0.1). This is consistent with the predominant literature on organizational politics (Witt et al., 2001), implying that Competition for Intangible Resources has a stronger negative impact on cooperative behaviors of individuals, compared to its positive impact on the quality of the knowledge being shared. 5.1.1.2. Competition for Tangible Resources and High-Quality Knowledge Sharing. The model showed that Competition for Tangible Resources had a destructive negative impact ( ) on the quality of the shared knowledge, whereas it positively (+) contributed to Cooperative Communication and discussions among individuals.

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Positive Boundary Interdependence

Positive Means Interdependence

Positive Outcome Interdependence

+**

+***

+*** +***

+**

Cooperative Task Orientation

+***

Cooperative Communication

(R 2= 0.613)

(R 2= 0.431)

+*

Cooperative Interpersonal Relationships

(R 2= 0.684)

-***

-* +***

CF Cooperation

High-Quality Knowledge Sharing (Knowledge Quality ) (R 2= 0.657)

-* +* +***

Project Importance CF Competition

Tangible Resource

Intangible Resource

(R 2= 0.177)

(R 2= 0.17)

-* +***

+*** Negative Boundary Interdependence

Negative Outcome Interdependence

* P< 0.1 ** P< 0.5 ***P<0.01

The connecting path between first-order and the second order construct Path between constructs

Fig. 5. Final model.

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153

The extant literature draws attention to the potential positive impact of competition for resources on knowledge sharing behaviors. For example, Tsai (2002) argued that units that compete for internal resources and market share often have a strong tendency to communicate and share their knowledge. Tsai argued that this is because functional units have a strong incentive to understand their competitors and discover what their competitors think and know so that they can benchmark themselves. Therefore, it can be expected that Competition for Tangible Resources encourages individuals communicate with each other and discuss the work-related problems. However, when functional units are involved in a collaborative project, they may depend upon a common pool of scarce organizational resources (e.g., physical space, equipment, manpower, capital funds). In these situations, they tend not to share their perceived important information in order to weaken the ability of other units in competing for scarce resources (Shih, Tsai, & Wu, 2006). The findings of this study draw attention to the fact that different types of competition (e.g., Competition for Tangible Resources, competition for strategic power) generate mixed organizational outcomes. The ignorance of the fact that different dimensions of competition must be studied separately has resulted in inconsistent results regarding the organizational outcomes of competition. Therefore, investigation on different dimensions of competition and cooperation sheds light on explaining inconsistent results regarding the impact of simultaneous cooperation and competition on knowledge sharing behaviors. The results are consistent with the proposed models of prior research, suggesting that cross-functionality has its potential positive and negative outcomes through the collaborative patterns that occur among team members (Lovelace, Shapiro, & Weingart, 2001). 5.1.2. Interdependencies: Antecedents of CF Cooperation and CF Competition Our results explain how multiple interdependencies may incur simultaneous cooperative and competitive behaviors in cross-functional teams. Consistent with the proposed hypotheses (H3, H4, H5, H6, and H7), positive and negative interdependencies gave rise to cooperative and competitive behaviors among cross-functional members. The second-order conceptualization of CF Cooperation and CF Competition suggested by Ghobadi and D’Ambra provided a better understanding of the details of the relation between interdependencies and different dimensions of cooperation and competition. Positive Outcome Interdependence was found to enhance only Cooperative Interpersonal Relationships (p < 0.05). The linkage between negative Outcome Interdependence and Competition for Tangible Resources was also found significant but only at p < 0.1. Positive Means Interdependence was proved to significantly give rise to the two dimensions of CF Cooperation including: Cooperative Interpersonal Relations and Cooperative Task Orientation of individuals (p < 0.01). Positive Boundary Interdependence (e.g., friendship, sense of identity to team, easy access to other team members) was shown to have significant positive effects on the three dimensions of CF Cooperation (p < 0.01) and significant negative impacts on the two dimensions of CF Competition (p < 0.01). The significant R-squares of the constructs of the model emphasized the satisfactory predictive power of the PLS model. Yet, the predictability of the two dimensions of CF Competition (0.177 and 0.170) was not as strong as the other constructs. The extant literature on interdependencies is evolving quite rapidly and there are numerous studies, which have empirically studied different types of interdependencies such as task interdependence, Outcome Interdependence, resource interdependence, etc. In addition, this study is not the first work, which has used Social Interdependence Theory in software development teams. However, these studies are few in number and tend to draw more narrowly on investigating the impacts of a few interdependencies such as goal and task interdependencies. Therefore, none of the previous studies has investigated a comprehensive set of interdependencies in an integrative model. This study contributes to the discussion on interdependencies in software development teams, and examines a comprehensive set of interdependencies in order to have a fuller understanding of the complex and diverse routes through which cooperative and competitive behaviors may be developed. In addition, this research extends Social Interdependence Theory through developing and comparing formative and reflective measures for the three constructs of Outcome, Means, and Boundary Interdependencies. Developing and comparing formative and reflective measures are strongly recommended in the IS literature (Diamantopoulos & Winklhofer, 2001). 5.2. Practical implications Organizations are increasingly employing cross-functional project teams for releasing software in less time and with less friction than in previous product releases. IT managers are required to engage in coordinating the efforts among business users, IT developers, and corporate stakeholders. This need is multiplied by the growth of offshoring, outsourcing, and global software development practices (Espinosa et al., 2007). Whilst executives may recognize that achieving effective knowledge sharing in multi-party teams is a must, they should understand that the underlying nature of cross-functional relationships is the source of serious challenges. The results of this study direct project managers to understand the major factors that foster or inhibit High-Quality Knowledge Sharing in cross-functional relationships. Our validated model draws attention into the importance of managing simultaneous cooperative and competitive behaviors in achieving ideal levels of sharing high-quality knowledge. Our findings suggest the methods of fostering or enhancing cooperative and competitive behaviors and High-Quality Knowledge Sharing.

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The final model emphasizes the importance of keeping three interdependencies (outcome, means, and Boundary Interdependence) at high positive levels, in order to facilitate cooperative behaviors and foster High-Quality Knowledge Sharing. Besides, managers should be aware of the positive relation between Competition for Tangible Resources and Cooperative Communication. Mechanisms such as informal communication may be employed when there are low levels of Competition for Tangible Resources. However, attention should be paid to the employment of informal means of communication among those who compete for intangible resources. Notably, social interactions do not always have positive impacts. For example, informal interactions might amplify the beneficial effect of task conflict as well as the harmful effect of relationship conflict (De Clercq, Thongpapanl, & Dimov, 2009). Furthermore, project managers should consider the constructive consequences of organizational politics in cross-functional relationships. The results indicated the negative impact of Competition for Intangible Resources on cooperative behaviors. However, political behaviors had a somewhat positive impact on sharing high-quality knowledge. Organizations and team leaders are encouraged to establish healthy methods of creating political environments. For example, visibility of ideas, sharing best-practices, roles, responsibilities, and project-related achievements can be incorporated in organizational knowledge management systems. The findings can be also useful for those organizations that seek safe and fast ways of switching from cooperative to competitive structures or vice versa across their functional units (Beersma et al., 2009). For example, functional units with the history of being competitors might be required to work in a collaborative project, yet these units will often experience difficulties in cooperating with each other. The results of our study help project managers focus on the elements that foster cooperative behaviors in such contexts. 5.3. Limitations and concluding points This study offers promise for understanding cross-functional relationships in software development teams. However, it does have a number of limitations that must be addressed. First, the span of data collection was limited to one single country, Australia, partly because it helped in controlling for national culture. It would be insightful to check the appropriateness of the model in varying cultural contexts. As an example, how cultural factors such as individualistic/collectivist cultures impact on the relationship between interdependencies and coopetitive behaviors. Second, it is believed that interdependencies change during different stages of software development life cycle (Andres & Zmud, 2002). The current study, however, targeted completed software development projects, and asked the general levels of interdependencies during specified software development projects. Specifically, we asked project managers to rate levels of interdependencies for the most recent software development project that that they have managed, and not for different stages of that project. Longitudinal studies are encouraged to discover how interdependencies change at different phases of software development (e.g., requirement gathering, design, development, and implementation). The third area for further investigation would be linking the present study’s perspective of cooperation and competition to their more specific manifestations in different phases of development projects. Subsequently, it would be insightful to explore when cooperation and competition are more appropriate to occur. For example, different stages of project life cycle may require different levels of cooperation and competition (e.g., cooperation may be more effective during initial phases of project life cycle with competition following in the later phases). This study targeted project managers as the key respondents. Although key respondent research has been well-respected in the extant literature, it is recommended to examine the model with the data received from other team members as well (e.g., developers, IT business analysts, users). In fact, there might be different opinions on the levels of interdependencies or other constructs of the model, which taking them into account can result in bringing into attention new areas for examination. Finally, the R-squares of the major construct of the model (High-Quality Knowledge Sharing) and three dimensions of cooperation are significantly high. However, the two first orders of competition are not being predicted as strongly as are the other dependent constructs. Future research should extend the validated model of this study by investigating the other variables that help predict Cross-Functional Competition. Acknowledgements The authors appreciate the help and advice that we received from Prof Wynne Chin, particularly in the quantitative analysis and PLS modeling. The authors are grateful of the comments of Prof. Ronald Rice on the latest drafts of the paper. We appreciate the constructive comments of the annonymous reviewers that have helped in improving the paper. Appendix A. (survey questions) A.1. High-Quality Knowledge Sharing 1. Overall, CF representatives were satisfied with the quality of knowledge being shared among themselves. 2. The shared knowledge among CF representatives was found useful in accomplishing project activities.

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Overall, the quality of shared knowledge among CF team members was (1: very low, 2–7: very high). A.2. CF Cooperation A.2.1. Cooperative Task Orientation 1. CF representatives accomplished project tasks in a cohesive manner. 2. CF representatives willingly helped each other in their project activities. 3. CF representatives integrated their activities to ensure better attainment of the project outcomes. CF representatives were committed in accomplishing project tasks. A.2.2. Cooperative Communication 1. CF representatives regularly discussed common problems. 2. CF representatives regularly communicated together. A.2.3. Cooperative Interpersonal Relationships 1. The relationships between CF representatives were mutually gratifying. 2. CF representatives had close ties with each other. A.3. CF Competition A.3.1. Competition for Tangible Resources 1. Overall, CF representatives regularly competed for resources. 2. When CF representatives discussed distribution of resources among their departments, tensions frequently occurred. A.3.2. Competition for Intangible Resources 1. CF representatives tried to gain more strategic power during the project. 2. CF representatives regularly competed with each other for more attention from top executives. 3. Protecting one’s departmental turf seemed to be a way of life by CF representatives. A.4. Outcome Interdependence A.4.1. Reflective Questions 1. CF representatives believed that achievement of their outcomes was reliant upon outcome accomplishment of other CF members. 2. CF representatives perceived that they could attain their expected outcomes only if other CF team members could attain their outcomes. A.4.2. Formative Questions 1. CF representatives’ perception was that their goals could be achieved only when the goals of other CF team members were also attained. 2. CF representatives believed they could receive rewards only if other CF team members could receive their expected rewards. A.5. Means interdependence A.5.1. Reflective Questions 1. Overall, CF representatives perceived that they were dependent on each other for achieving their outcomes. 2. The degree that CF representatives believed that they were dependent on the means of each other for fulfilling mutual outcomes was (1: very low, 2–7: very high).

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A.5.2. Formative Questions 1. CF representatives perceived that they depended on the resources of each other to ensure accomplishment of project outcomes. 2. CF representatives believed they depended on the collaborative action with each other to be able to attain mutual project outcomes. 3. CF representatives’ perception was that they depended on the role of each other to be able to carry out mutual project outcomes. A.6. Boundary Interdependence A.6.1. Reflective Questions 1. Overall, CF representatives were attached together as a united team. 2. Overall, the degree that CF representatives seemed to be attached together as a team was (1: Very low, 2–7: very high) A.6.2. Formative Questions 1. The extent of friendship among representatives from different departments was: (1: very low, 2–7: very high) 2. CF representatives had a sense of belonging to the group that attached them as a united group. 3. 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