in Globally Distributed Teams - IEEE Computer Society

Proceedings of the 40th Hawaii International Conference on System Sciences - 2007

Path to “Stardom” in Globally Distributed Teams: An Examination of a Knowledge-centered Perspective using Social Network Analysis Saonee Sarker Washington State University [email protected]

Sarah Kirkeby Copenhagen Business School [email protected]

Abstract In response to a call from the research community to identify individuals who become “stars” in globally-distributed teams, this study takes a knowledge-centered view in identifying the core factors leading to “stardom” in such teams. It adopts a structural/relational approach consistent with the core principles of social network analysis in developing and empirically validating the research model. Data from US-Norway and US-Denmark distributed teams engaged in systems development provide support for most of the predictions.

1. Introduction The widespread globalization of the workforce in many industries, especially in knowledge-intensive ones, and rapid advances in information and communication technologies have led to an increasing use of “globally distributed collaborations and virtual teams” in organizations [23 p. 37]. Consequently, researchers in the area of information systems and related fields have focused considerable attention on understanding the social and technological dynamics within such teams [e.g., 29]. Given that such teams, typically composed of members from across the globe who work towards the accomplishment of a certain task, are significantly different from traditional faceto-face teams, this new found attention is warranted. However, a review of the existing literature on globally-distributed teams reveal an unmistakable pattern: most of the studies have focused on examining the factors and processes that lead to the effectiveness and higher performance of distributed teams [e.g., 29; 47; 28; 23; 35]. While undoubtedly this set of studies has made an important contribution to the state of knowledge on these new forms of ITmediated social collectives, recent research suggests

Suranjan Chakraborty Suprateek Sarker Washington State University [email protected]. [email protected]

that there is a need to identify the “stars” of distributed teams [e.g., 1; 31], key individuals who make things happen, and understand why they come to be viewed as the “stars.” The recent call to focus on the individual member effectiveness may be attributed to two underlying reasons: 1) A resurgence of the “Cartesian view” of organizations that argues that the individual should always be the center of focus, given that groups/teams and organizations are mere derivatives (i.e., aggregates) of individuals [e.g., 11]; and 2) in many globally-distributed team contexts (such as those associated with “open source development”), the structure and composition is fluid, ad-hoc, and loosely coupled, making it increasingly difficult and less meaningful to assess the performance of the entire collaborative unit. In such contexts, it is more important to identify (and enable) star individuals, and leverage their abilities, behaviors, and status to enhance effectiveness of the entire collaborative unit. Unfortunately, with few exceptions such as the work of Piccoli, Powell, and Ives [31], which examined key factors leading to individual teammembers’ satisfaction, the mainstream virtual team literature has little to offer with respect to factors leading to the emergence of highly valuable individual members (whom we refer to as the “stars”) within a globally distributed teams. This current study attempts to fill that void, by focusing on the following research question: RQ: What are the factors that lead to the emergence of a “star” in a globally distributed team? In examining the research question, we focus on globally distributed teams involved in information systems development (ISD). We specifically chose the ISD context since it is a core function of the Information Systems disciplines. Also it remains one of the most common areas in which these new forms of collaborations are being consistently used in

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practice [6; 37]. Further, given that ISD is considered as a form of knowledge work, in examining the specific factors leading to the emergence of the star and in developing our model, we take a knowledgecentered view, drawing on concepts from the Intellectual Capital Theory. Finally, we take a structural/relational approach towards developing and empirically validating our research model (as opposed to a behavioral approach), as recent researchers have stated that “by bringing to bear measures and constructs of social structure, we can begin to understand how simple notions of .. autonomous individuals are incomplete” [36, p. 181]. Similarly, Ahuja et al. [1, p. 25] suggests that a networked approach is more appropriate in a distributed team, since the “salience … of individual characteristics” and behaviors “may be muted by distance.” We believe that this network approach, consistent with the concept of the “networked individualism” proposed by Wellman et al. [46], serves a dual purpose for our study: 1) It enables us to focus on the individual; and, 2) yet, it lets us view the individual, not in isolation, but in a network of relationships. This allows us to take the collaborative environment in which the individual is embedded into consideration.

2. Theoretical Framework Consistent with our network-based approach, in defining a “star” globally-distributed team member we draw on social network analysts, who refer to a “star” as an individual “who.. stands at the centre of attention” [40, p. 82]. Given our focus on globallydistributed teams engaged in information systems development (which are typically extremely-task focused and have a limited temporal scope), we believe there are two different indicators based on which one could ascertain that an individual is a star: 1) the individual makes significant contribution towards the completion of the ISD task, that is he/she is at the center of task accomplishment, and 2) he/she emerges as the leader of the distributed team, that is the individual serves as the center for coordinating activities across the various geographical locations and team members, overseeing member relations, and steering the team towards its task accomplishment. Below, we discuss the specific the specific theoretical concepts that we draw upon to understand the factors leading to an individual emerging as “star” within a globally-distributed team. The Intellectual Capital-based view (ICV) is a “mid-range theory” that focuses on factors affecting a firm’s effectiveness, specifically, the relationship between the knowledge that is created, shared, and

stored in a firm’s people (or the human capital) and firm performance [34]. Further, ICV also focuses on the social relationships (or social capital) and its effect on the human capital. Given its focus on knowledge, the human capital component of ICV has also been referred to as the “knowledge capital” [48]. According to proponents of ICV, human or knowledge capital is a “critical resource for differentiating .. performance among firms” [34, p. 870]. While the ICV was originally developed to understand firm-level performance, many of its concepts have also been drawn upon to understand individual-level performance. For example, relational/network researchers such as Burt (2003) [5] argue that two of the most important components of “capital” leading to an individual’s success is human capital and social capital. Degenne and Forse (1999) [13] argue that each individual possess different amounts of human capital, and this in turn leads to differences in their productivity or performance. Similarly, social capital has also been seen to affect an individual’s productivity or profitable, not directly as human capital, but indirectly through human capital [e.g. 13; Bourdieu 1980]. Human capital, consistent with the ICV is viewed as a “property of the individual” [5, p. 14] or natural qualities such as charm, intelligence, or looks, while social capital refers to the communicative and social relationships surrounding an individual (i.e., different types of ties that link an individual to other individuals who are proximate to them). Based on the literature on intellectual capital reviewed above, in this study, we examine 1) the role of human capital on the individual’s emergence as a star in a globallydistributed team, and 2) the effect of the social capital on the enhancement of the human capital.

2.1. Definition of the Constructs Given our focus on knowledge work and our interest of developing and testing a knowledgecentered view, we take a more narrow view of human capital and refer to it as an individual’s knowledge capital. Further, drawing on existing literature, we view knowledge capital as being composed of two different components: 1) the stocks of knowledge that the individual possesses, and 2) the extent to which the individual is able to cause the “flow” and transfer of that knowledge [30]. In the context of our study which focuses on a globally-distributed team engaged systems development, the stocks of knowledge that the individual needs to possess refers to both the technical knowledge and the Information System Project Management knowledge [23].

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Social Capital refers to an “individual’s personal network” [13, p. 116] and depends on the “content and structure” of this network. Unlike human capital, social capital is not a “thing owned” by an individual, but jointly by the parties to a relationship” [5, p. 14], and thus, no one individual has the “absolute ownership rights to social capital.” Another way to view social capital is by recognizing it as a “social action” that “requires cooperation” from other actors [13, p. 116], and involves “expectations and reciprocal obligations between individuals.” Degenne and Forse (1999) [13] suggest that the set of reciprocal (or trusting) relationships and/or the set of relations of communication that exist between an individual and other individuals is his/her social capital base. In this study, we draw on this base of research to define social capital as being composed of the extent of trusting and communicative relationships that an individual has with his/her or globally-distributed team members. Next, we discuss the specific hypotheses surrounding the above constructs, especially, the relationship between knowledge capital and an individual’s emergence as a “star”, and the effect of the individual’s social capital on enhancing his/her knowledge capital.

2.2. Knowledge Capital and Stardom Drawing on the ICV and the theory of human capital, it may be argued that the extent of knowledge capital possessed by an individual will affect his/her effectiveness, and therefore, affect their ability to emerge as a star. As discussed earlier, we view knowledge capital as being composed of both “the inherent stocks of knowledge” possessed by an individual, and the “extent to which he/she transfers that knowledge.” For a globally-distributed team engaged in information systems development, the accomplishment of the software development project tasks is most critical. An individual who has superior stocks of both ISD process and technical knowledge will be able to contribute most towards the task accomplishment [10], and therefore emerge as the high performer. Specifically, based on their study of multiple ISD projects in organizations, Curtis et al. (1988) [10] concluded that in most ISD projects, one individual, owing to his/her technical knowledge, skills to coordinate relationships amongst the various stakeholders, and the ability to manage large projects, is able to steer the group towards its goal, and is therefore viewed as the star performer within the group.

Other researchers have drawn a linkage between the possession of stocks of knowledge and the emergence of a leader. Trice and Beyer [42] state that in group settings (such as in a globally distributed team) where there are no formally assigned leaders, individuals “who have the expertise that is highly valued by the group” (p. 281) will emerge as leaders. Similarly, Basselier et al. (2001)[2] suggest that technical and managerial ISD knowledge are extremely important ingredients for individuals who emerge as the project leader. Thus, we argue: H1a: Inherent stocks of ISD knowledge possessed by an individual member will positively affect his/her ability to emerge as a high performer within a globally-distributed software development team. H1b: Inherent stocks of ISD knowledge possessed by an individual member will positively affect his/her ability to emerge as a leader within a globallydistributed software development team. Further, prior researchers on knowledge also propose that the inherent stocks of knowledge possessed by an individual will affect his/her ability to cause the flow of that knowledge and transfer it to other individuals proximate to them [19]. Levin et al. [26] argue that an individual who has more competence in a given subject area is likely to transfer more knowledge. Similarly, Zander and Kogut [49] argue that a knowledgeable source, because of his/her understanding of the domain, is better equipped to facilitate the knowledge transfer process [49]. Davenport and Prusak [12] emphatically state that one of the most critical factors facilitating knowledge transfer is the presence of “smart” and knowledgeable people. Based on this, we argue: H1c: Inherent stocks of ISD knowledge possessed by an individual member will positively affect his/her ability to transfer that knowledge within a globallydistributed software development team. In addition to the “inherent stocks of ISD knowledge,” we believe that the second component of knowledge capital, “ability to cause the flow or transfer of knowledge” will also lead to stardom through high performance and/or emergence of the individual as a leader. Specifically, individuals who have the structural position enabling them to transfer more knowledge to other team members and therefore, contributing towards their learning will gain in reputation within the team [12]. This is even more true in a globally-distributed team composed of members coming from different social and professional backgrounds. Creation of a shared frame of reference is very critical to the success of such teams [38]. An individual who is able to transfer critical projectrelated knowledge to other members of the team will contribute towards a swift development of the shared

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frame of reference, and also enable other members’ efficient task accomplishment, thereby, being likely be considered as a very valued member of the team. Further, such individuals will also be able to guide, motivate, and manage the team and task dependencies among various members [10] -- in other words, they are likely to emerge as the leader. H2a: Individual member’s ability to transfer knowledge will positively affect his/her ability to emerge as a high performer within a globallydistributed software development team. H2b: Individual member’s ability to transfer knowledge will positively affect his/her ability to emerge as a leader within a globally-distributed software development team.

2.3. Social Capital and its Effect on Knowledge Capital As discussed earlier, in this study, we define social capital as being composed of the extent of trusting and communicative relationships that an individual has with his/her team members within a globally distributed team. We believe that social capital will play a significant role on an individual’s ability to transfer knowledge. On the other hand, inherent stocks of knowledge possessed by an individual is more of a stable quality, and is therefore, less likely to be affected by his/her network of social relationships in the short-run. The extent to which an individual has trusting relationships with other members of the team (and is thus viewed as credible), will significantly affect his/her ability to diffuse and transfer knowledge to those team members [7]. Szulanski et al. [41] suggest that “trustworthiness” of an individual enhances his/her ability to transfer more knowledge. Similarly, Levin et al. [26, p. 37] refer to trust as the “magic ingredient” leading to knowledge transfer. If an individual is involved in a number of trusting relationships with other team members, he/she is more likely to cause the internalization of that knowledge communicated by him/her to those other members [41]. Thus: H3a: The extent of trusting relationships that an individual is involved in will positively affect his/her ability to transfer knowledge within a globally distributed software development team. The linkage between the other component of social capital (i.e, extent of communicative relationships) and knowledge transfer has also been highlighted in the literature. Reagan and McEavily [33] argue that people would be more inclined to share knowledge with individuals with whom they

communicate frequently. Similarly, Davenport and Prusak [12, p. 88] state that a key factor enabling knowledge transfer is the presence of extensive communication between the source and the recipient. According to them, it is through communication, that an individual’s ideas, viewpoints, and beliefs are shared with, and made available to others. They further suggest that communication is the main mode by which workers share their knowledge with their colleagues. Venzin, von Krogh, and Roos [44] suggest that even in ICT-mediated distributed teams (such as the context of this study), computer-mediated communication forms the basis of all social action, including knowledge transfer [44]. From this it may be concluded that within a globally-distributed team, an individual who is involved in a large number of communicative relationships with other members is likely to transfer more knowledge to his/her team members. Thus, we have the following: H3b: The extent of communicative relationships that an individual is involved in will positively affect his/her ability to transfer knowledge within a globally distributed software development team.

3. Research Methodology 3.1. Social Network Analysis We attempted to test our research model using the social network analysis (SNA) perspective. Social network analysis is a shift from “the individualism common in social science towards a structural analysis” [17]. Galaskiewicz and Wasserman (1994, p. xii) [16] argue that “social network analysis focuses on relationships among social entities and on the patterns and implications of these relationships.” Through its focus on relationships, social network captures the interactions and connections between different social entities (e.g., individuals, groups), and enables the researcher to study “individual action or behavior within the context of larger structural configurations” [16, p. xiv], as opposed to simply examining “individual behaviors, attitudes, and beliefs,” in isolation (which has dominated much of prior research in social sciences).” In this regard, the social network perspective may be considered to be a much superior approach to analysis. Consistent with this perspective, we conceptualized a distributed team as a social network (or structure) with each individual member having a structural position within that network (with respect to knowledge, performance, leadership, trust and communication) within that network.

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Social network analysis enables researchers to take both a macro and a micro view. At the macro level, focus is on examining the configuration of the whole network. Measures such as density (the general level of cohesion within a network, calculated by the ratio of the number of actual links to the number of possible links in a network) and centralization (the extent to which the cohesion is organized around focal points, calculated by examining how tightly the graph is organized around its most central point) has often been used to understand the whole network configuration. On the other hand, the micro view (often termed as the “ego-centric network view) enables researchers to examine individual actors’ positions within the network [40]. Given our research question in this study (i.e., what leads to an individual’s stardom in a globally-distributed team), we take the ego-centric network view. The “sociometric concept of star” has led to the formulation of “centrality,” which till this date has remained the most commonly used indicator of an entity’s structural position within the network [40]. It has been defined as an entity’s “prominence” or “importance” within a network [45]. In other words, an individual who has high centrality in a certain set of dimensions may be considered as a star. Specifically, centrality is assessed by evaluating the number of relationships an actor is involved in. Applying the social network analysis concepts to our research model, we view an individual’s ability to emerge as a high performer and the ability to emerge as a leader (i.e., achieve stardom) as his/her contribution centrality and leadership centrality. On the other hand, an individual’s social capital is captured by his/her trust and communication centrality (i.e., the number of trust and communicative relationships an individual is involved in). Finally, we capture an individual members’ ability to transfer knowledge to other members as his/her knowledge centrality. Given that an individual’s inherent stock of knowledge is a natural quality, we do not capture it from a network perspective. Below, we discuss our data collection efforts, specific measures, data preparation, and our analysis techniques.

3.2. Data and Measures Data for this study was collected from two different sources: 1) US-Norway distributed student teams engaged in a semester-long systems development projects, where the teams worked on developing information systems applications for real clients located across the globe; and 2) US-Denmark

distributed teams engaged in systems analysis projects for real clients located in the US or Denmark. Given our individual level of analysis, the useable sample size was 91. Centrality in social network analysis may be measured using a variety of different indicators, with the three most common being degree, closeness, and betweenness. Degree centrality refers to the “number of connections to others” [13, p. 132]; closeness refers to the extent to which an individual is close to all other members in the network; finally, betweenness refers to the extent to which an individual “is in a position to act as a gatekeeper for information that flows through a network” [25, p. 90]. The ‘simplest” and the most “intuitive” measure of centrality is the degree centrality [13; 16]. In this study, we adopted degree centrality as the indicator of centrality. It is important to note that social network analysis requires “relational data.” Consistent with this requirement, and to calculate the degree centralities of individual members on the dimensions of trust, communication, knowledge transferred, performance and leadership, we asked each team-member to assess each other member in their team on their trustworthiness, extent of communication, extent of learning of both managerial and ISD knowledge, and extent of contribution to the project up until that point of time (assessment of performance) on a scale of 1 to 7. We chose to adopt this relational measure of performance as opposed to using supervisor or instructor ratings, in light of recent criticisms of using instructor/supervisors which has been argued to “contain political aspects” [3]. This relational data was held in an adjacency matrix where the columns consisted of each team member, and the rows consisted of the rating of that team member by each of the other team members. Given that the rating was done on a scale of 1 to 7, the data captured in the matrix was “valued.” Further, the data was directed. In other words, entity A rating entity B with a certain number did not mean that a reciprocal relationship exists (i.e., entity B gives the same rating to entity A). For convenience of analysis, valued data in the adjacency matrix was converted to binary data. For conversion to binary data, we followed past research on social network analysis which suggests selecting a cutoff (typically, the median), and using the median to “slice” the data and “dichotomize” the matrix [40, p. 48]. In a directed graph (such as our study), there are two ways to assess the degree centrality: indegree and outdegree. The indegree of an entity within a network refer to the “number of other people who choose that actor in the particular relationship” [25, p. 89], while an outdegree centrality refers to the “number of people

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chosen by the focal actor.” Thus, in our study which seeks to understand, for example, the effect of an individual actor’s knowledge centrality on his/her performance, etc., indegree centrality is more relevant. Furthermore, indegree centrality has been shown to be most stable even at a low sampling level [43]. For trust, communication, knowledge, and performance centralities, we calculated the Freeman’s (1979) [15] measure of relative in-degree centrality, which is the actual number of relational lines relative to the total number that it could sustain, thereby, normalizing centralities and making the measures comparable across groups. Further, the centralities were normalized within groups, therefore addressing the issue of group independence. UCINET 6.0 was used to calculate the centralities. For measuring the extent of leadership of an individual, we again asked each team member to name the individual who had emerged as the leader of the entire distributed team at that point of time. Based on this, we created directed and unvalued adjacency matrices. The matrices were then used to calculate the leadership-related normalized indegree centralities of each of the members. Next, we coded each individual member as 1 or 0 based on whether the indegree centrality was greater than equal to .5, or less than .5. We then used this binary measure as the dependent variable. We chose to measure leadership emergence by assessing the “presence or absence of a relation” [40, p. 48] as opposed to a continuum, since unlike other relational characteristics (e.g., knowledge transferred, trustworthiness), an individual either emerges as a leader or does not. Finally, we measured an individual members’ extent of knowledge (both technical and ISD) possessed by asking each participants to respond to a self-reported pre-questionnaire (validated in prior studies such as [39]) measuring their technical and IS project management knowledge. The items measured a variety of different abilities ranging from knowledge of procedural programming to ability to manage relationships between system development team members and users on a scale of 1 (low) to 7 (high). The mean of the relevant items were used as measures of technical knowledge and IS project management knowledge.

3.3. Analysis We used the partial least squares (PLS) approach for analyzing the data, especially given that our research model consisted of a number of mediating relationships and second-order factors. PLS has been

shown to be a superior technique when it comes to analyzing 1) process-oriented research models with a number of mediating relationships, and 2) when the model has second-order factors [e.g., 8; 27]. Further, PLS Graph, which analyzes the measurement model and the structural models concurrently, enabled us to assess the psychometric properties of our instruments and the strength of the hypothesized relationships within one unifying analytical framework. Following the guidelines of prior researchers [e.g., 4; 18; 21], we ensured the convergent validity of the items (especially those measuring technical and IS project management knowledge) by satisfying the following criteria: 1) all items loaded significantly on their respective constructs [18]. Further, most items had a high loading on their respective constructs (above .80), and none of the items had a loading below .40, which is often used as the cutoff [21] (see Table 1); 2) the composite reliabilities of each of the items were above .70 [21]; and finally, 3) the Average Variance Extracted (AVEs) of all of the constructs were over the threshold value of .50 (see table 2 which shows the square roots of the AVEs). In assessing the discriminant validity we ensured that the square root of the AVE of a construct exceeded all correlations between that factor and any other construct within the study [18; 14]. Please see Table 2 where the square root of the AVEs have been reported on the main diagonal, with the off-diagonal cells reflecting the correlations between that construct and other constructs. Next, we examined the significance and strength of our hypothesized relationships. We adopted a “molecular approach” in representing the role of the second-order factor in our model [9, p. 49-50]. This approach suggests that “an overall latent construct exists and is indicated by the first order constructs.” Specifically, we treated the second order factor of “inherent stocks of knowledge” as being indicated by the relevant first order constructs of technical knowledge and IS project management knowledge. To test the hypotheses, we created a hierarchical component model using repeated manifest variables (to address the issue of second order factors), following the guidelines of Chin et al. (2003) [8] and Lohmoller (1989) [27]. Specifically, we repeated the manifest variables (or measurement items) for technical knowledge and IS project management knowledge twice: once for each of the two dimensions, and once for the second order factor of “inherent stocks of knowledge.” The path coefficients from “inherent stocks of knowledge” to its two dimensions were high, with coefficients being over .40 and significant [8].

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Results supported most of the hypotheses of the model. Trust centrality significantly affected the extent of knowledge transferred by an individual (i.e., knowledge centrality). Similarly, an individual’s communication centrality also affected his/her knowledge centrality, but at p < .10. Contrary to the predictions, individual’s inherent stock of knowledge did not affect his/her extent of knowledge transferred. Overall, the variance explained by trust, communication, and inherent stocks of knowledge on knowledge centrality was 41.8%. Consistent with predictions, an individual’s knowledge centrality affected his/her performance centrality and leadership status. On the other hand, his/her extent of inherent stocks of ISD-related knowledge did not have any noticeable effect on the leadership status, and affected the performance centrality at p < .10. The variance explained by the knowledge-centric constructs on performance centrality and leadership status were 53.6% and 5.1% respectively. We summarize the results in Figure 1.

4. Discussion Our results indicated strong support for the knowledge-centered view of stardom in globallydistributed teams. Consistent with the predictions, social capital did play a significant role in enhancing an individual’s knowledge capital within globallydistributed teams. Further, one important component of knowledge capital (namely, an individual’s ability to transfer knowledge or his/her knowledge centrality) also contributed significantly to his/her ability to emerge as a star within a globally-distributed team. Contrary to predictions, the inherent stocks of ISDrelated knowledge possessed by individual members did not contribute toward an individual’s ability transfer knowledge nor emerge as a leader, and played only a marginal role on his/her ability to emerge as the high performer. While at first glance, this result may seem anomalous, a more thorough literature review suggests that this finding is consistent with a competing stream of research which argues that the more knowledgeable and skilled members tend to convey their knowledge in an incomprehensible manner, reducing the absorption of the knowledge, and thus, the overall extent of knowledge transferred to potential recipients. This inability of many knowledgeable individuals to codify and transfer the necessary knowledge also leads to lower overall contribution towards the group’s task performance, and hinders their ability to emerge as leaders. Hinds et al. (2001, p. 1232) [20] from their study of expert and novice behaviors in an electronic circuit wiring task,

argued that while “experts should have been wellpositioned to convey their superior knowledge skills to novices, the organization of that knowledge, and particularly its level of abstraction, may make it difficult for them to do so.” Further, Swap, Leonard, Shields, and Abrams (2004, p. 182) suggests that “the way in which experts exercise their knowledge is by calling on their long years and countless experiences in a great variety of contexts to recognize patterns.” This pattern recognition process tends to draw upon the “tacit dimensions” of the expert’s knowledge which is extremely contextualized and therefore challenging to transfer (Swap et al. 2004, p. 183). In this study, the effect of communicative relationships on knowledge transfer was found to be significant at p < .10, as opposed to the effect of trusting relationships, which was significant at p < .01. This differential effect could be a function of the fact that many researchers argue that in a computermediated setting (such as the context of the current study), communication alone may not lead to an individual’s ability to transfer knowledge [12]. Due to the impersonal nature of the computer-mediated environments, recipients tend to internalize the knowledge (resulting in successful knowledge transfer) from only those with whom they have a “personal acquaintance” and trusting relationship [12, p. 36]. Similarly, Ichijo et al .[22] also contend that communication alone may not be a significant predictor of knowledge transfer, and that both communicative interactions and mutual trust are required. In light of the above, we believe that future research should investigate the interactive effect of an individual’s communication and trust centrality on his/her ability to transfer knowledge (i.e., knowledge centrality).

5. Conclusion This study is one of the first to focus on identifying the “stars” of globally-distributed teams, and understanding the factors that enable them to emerge as “stars,” thereby responding to a recent call by researchers to focus on individual team members’ effectiveness, as opposed to overall team performance. We believe that this is an important contribution to the existing literature on globally-distributed teams. Further, in examining this issue, the study takes a structural/relational perspective (as opposed to an attribute-based or behavioral perspective). This is a significant extension of prior research in this area since it enabled us to focus on the individual, yet, let us view the individual, not in isolation, but in a network of relationships (consistent with the

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contemporary concept of “networked individualism”). Finally, owing to its adoption of a knowledge-centered view in identifying the factors affecting “stardom,” the study makes an added contribution to existing research and practice of knowledge management, as well as globally-distributed teams, since a majority of such teams is involved in knowledge work. We believe that understanding individual team members’ excellence in globally-distributed teams is important, and hope that this study provides a start to that journey.

6. References [1] M.K. Ahuja, D.F. Galletta and K.M. Carley, “Individual centrality and performance in virtual R&D groups: An empirical study”, Management Science, Linthicum, 2003, pp21-38. [2] G. Bassellier, B. H. Reich and I. Benbasat, “Information technology competence of business managers: A definition and research model”, Journal of Management Information Systems, Armonk, 2001, pp .159-182 [3] D. J. Brass, “ A Social Network Perspective to Human Resource Management” in Networks in the Knowledge Economy, R. Cross, A Parker and L. Sasson (Ed.), Oxford University Press, New York, NY, 2003, pp. 283-323 [4] S. A. Brown and V. Venkatesh, “Model Of Adoption Of Technology In Households: A Baseline Model Test And Extension Incorporating Household Life Cycle”, MIS Quarterly, Minneapolis, 2005, pp – 399-416 [5] R. Burt, “The Social Structure of Competition “, in Networks in the Knowledge Economy, R. Cross, A Parker and L. Sasson (Ed.), Oxford University Press, New York, NY, 2003, pp. 13-56 [6] E. Carmel and R. Agarwal, “The Maturity of Offshore Sourcing of IT Work”, MISQ Executive, 2002 [7] J. Coleman , “ Social Capital in the creation of Human Capital”, in Networks in the Knowledge Economy, R. Cross, A Parker and L. Sasson (Ed.), Oxford University Press, New York, NY, 2003, pp. 57-81 [8] W. W. Chin, B.L. Marcolin and P. R. Newsted, “A partial least squares latent variable modeling approach for measuring interaction effects: Results from a Monte Carlo simulation study and an electronic-mail emotion / adoption study”, Information Systems Research, Linthicum, 2003, pp 189 - 209 [9] W. W. Chin and A. Gopal, “Adoption Intentions in GSS: relative importance of beliefs”, ACM SIGMIS database, 1995 [10] B. Curtis,, H. Krasner and ,N. Iscoe, “A field study of software design processes for large systems”, Communications of ACM, New York,1988, pp. 1268 -1287

[11] S. D. N. Cook and J. S. Brown, “Bridging Epistemologies: The Generative Dance between Organizational Knowledge and Organizational Knowing,” in Managing Knowledge: An Essential Reader, S Little, P. Quintas and T. Ray (Eds.), Sage Publications, Thousand Oaks, CA, 2002, pp. 68-101 [12] T. H. Davenport and L. Prusak, Working Knowledge, Harvard Business School Press, Boston, MA, 1998 [13] Degenne and M .Forse, Introducing Social Networks, Sage, London, 1999 [14] Fornell and D.F. Larcker, “Evaluating Structural Equations with Unobservable Variables and Measurement Error,” Journal of Marketing Research, Chicago, 1981, pp. 39-50. [15] L. C. Freeman, “ Centrality in Social Networks : Conceptual Clarifications”, Social Networks, Lausanne, 1979, 215- 235 [16] J. Galaskiewicz and S. Wasserman, Advances in Social Network Analysis: Research in Social and Behavioral Sciences, Sage, Thousand Oaks, CA 1994 [17] L. Garton, C. Haythornthwaite and B. Wellman, “ Studying online Social Networks”, Journal of Computer Mediated Communication,1997 [18] D. Gefen and D. Straub, “A Practical Guide To Factorial Validity Using PLS-Graph: Tutorial And Annotated Example”, Communications of the Association of Information Systems, Atlanta, 2005, pp 1 [19] Gupta and V. Govindrajan, "Knowledge Flows within Multinational Corporations " Strategic Management Journal, Chichester, 2000, pp.473496 [20] P.J Hinds, M. Patterson and J. Pfeffer, “Bothered by abstraction: The effect of expertise on knowledge transfer and subsequent novice performance”, Journal of Applied Psychology, 2001, pp. 1232-1243. [21] J. Hulland, “Use of partial least squares (PLS) in strategic management research: A review of four recent studies “, Strategic Management Journal, Chichester, 1999, pp 195 [22] Ichijo, K., von Krogh, G., and Nonaka, I., “Knowledge Enablers,” in Knowing In Firms: Understanding, Managing, and Measuring Knowledge, G. von Krogh, J. Roos, and D. Kleine (eds.), Sage Publications, Thousand Oaks, CA, 2000, pp. 173-203. [23] L.J. Kirsch, “Software Project Management: An Integrated Perspective for an Emerging Paradigm,” in Framing the Domain of IT Management: Projecting the Future Through the Past, R.W. Zmud (ed.), PinnFlex Education Resources, Inc, Cincinnati, OH, 2000, pp. 285304. [24] J. Kotlarski and I. Oshri, “Social ties, knowledge sharing and successful collaboration in globally distributed system development projects“,

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[25]

[26]

[27]

[28]

[29]

[30]

[31]

[32]

[33]

[34]

[35]

[36]

European Journal of Information systems, Basingstoke, 2005, pp. 37-49 D. Krackhardt “ The strength of Strong Ties: The importance of Philos in Organizations”, in Networks in the Knowledge Economy, R. Cross, A Parker and L. Sasson (Ed.), Oxford University Press, New York, NY, 2003, pp. 82-108 D.Z. Levin, R. Cross, L.C. Abrams, and E.L. Lesser, “Trust and knowledge sharing: A critical combination”. In Creating value with knowledge, E. Lesser and L. Prusak (Eds.), Oxford University Press, Oxford, 2004, pp. 36-41, J.B Lohmoller,. Latent variable Path Modeling with Partial Least Squares, Physica-Verlag, Heidelberg, 1989. M. Lu, M. B. Watson-Manheim, K. M. Chudoba and Eleanor Wynn, “Virtuality and Team Performance: Understanding the Impact of Variety of Practices”, Journal of Global Information Technology Management, Marietta, 2006, pp. 422 M. L. Manzevski and K. M. Chudoba, “Bridging space over time: Global virtual team dynamics and effectiveness”, Organization Science, Linthicum, 2000, pp 473-492 J. M. Pennings, K. Lee and A. van Witteloostuijn, “Human Capital, Social Capital and Firm dissolution”, Academy of Management Journal, Briarcliff Manor, 1998, pp. 425 – 440. G. Piccoli, A. Powell and B. Ives, “Virtual teams: team control structure, work processes, and team effectiveness”, Information Technology & People, West Lynn, 2004, pp. 359-379 Powell and S. Yager, “Exploring Reputation Differences In Information Systems Groups”, Journal of Information Technology Cases and Applications, Marietta, 2004, pp. 5-26. R. Reagans and B. McEavily, “Network Structure and Knowledge Transfer: The Effects of Cohesion and Range”, Administrative Science Quarterly, Ithaca, 2003, pp 240 K.K. Reed, M. Lubatkin and N .Srinivasan, “Proposing and Testing an Intellectual CapitalBased View of the Firm”,Journal of Management Studies, Oxford, 2006, pp. 867. R. Rico and S.G. Cohen, “Effects of task interdependence and type of communication on performance in virtual teams”, Journal of Managerial Psychology, Bradford, 2005, pp. 261274 R.E. Rice, “Network Analysis and ComputerMediated Communication Systems, Advances in Social Network Analysis: Research in Social and Behavioral Sciences, Sage, Thousand Oaks, CA 1994

[37] S. Sarker and S. Sahay, “Implications of space and time for distributed work: an interpretive study of US-Norwegian systems development teams”, European Journal of Information Systems, Basingstoke, 2004, pp. 3-20 [38] S. Sarker and S. Sahay, "Understanding Virtual Team Development: An Interpretive Study," Journal of the AIS, Atlanta, 2003 [39] S. Sarker, S. Sarker, D.B. Nicholson and K.D. Joshi, “Knowledge transfer in virtual systems development teams: An exploratory study of four key enablers,” IEEE Transactions on Professional Communication, (48:2), 2005, pp. 201-218 [40] J. Scott, Social Network Analysis: A Handbook, Sage Publications, London, 2000 [41] G. Szulanski, "Exploring internal stickiness: Impediments to the transfer of best practice within the firm. " Strategic Management Journal, Chichester, 1996, pp. 27-43 [42] H.M. Trice and J.M. Beyer, The Cultures of Work Organizations, Prentice Hall, Englewood Cliffs, NJ, 1993. [43] T.W. Valente and R. L. Davis, “Accelerating the diffusion of innovations using Opinion Leaders, The annals of the American Academy of Political and Social Science, 1999, pp. 55-67 [44] M Venzin, G von Krogh, J Roos , “Future research into knowledge management”, In Knowing in firms: Understanding, managing, and measuring knowledge, G. von Krogh, J. Roos, and D. Kleine (Eds.), Thousand Oaks, CA: Sage Publications, pp. 26-66. [45] M. E. Walker, S. Wasserman and B. Wellman “Statistical Models for support networks” in Advances in Social Netwrok Analysis, S. Wasserman and J Galaskiewicz (Ed.), Sage, Thousand Oaks, CA, 1994 [46] B Wellman, A Quan-Haase, J Boase, W Chen, K. Hampton, I. I. De Diaz and K. Miyata “ The Social Affordances of the Internet for Networked Individualism,” Journal of Computer Mediated Communication, , 2003 [47] S. Wong and R. M. Burton, “Virtual Teams: What are their Characteristics, and Impact on Team Performance?”, Computational and Mathematical Organization Theory, Boston, 2000, pp. 339-361. [48] M. .A. Youndt, M. Subramaniam, S. A. Snell , “ Intellectual Capital Profiles: An of investments and returns”, Journal of Management Studies, 2004, pp. 335-61 [49] Zander, U., and Kogut, B., “Knowledge and the speed of the transfer and imitation of organizational capabilities: An Empirical Test,” Organization Science, Vol. 6, No. 1, 1994, pp. 7692.

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Table 1: Descriptive Statistics and Item Loadings

Item Trust1 Comm1 TechKn1 TechKn2 TechKn3 TechKn4 TechKn5 ISPMKn1 ISPMKn2 ISPMKn3 ISPMKn4 ISPMKn5 TransfKn1 Perf1 Leader1

Item Mean 62.39 68.72 5.25 3.81 4.14 3.78 4.02 5.04 4.71 4.69 4.70 4.80 59.10 60.59 N.A.

Item S.D. 29.02 24.44 1.02 1.83 1.79 1.48 1.67 1.05 1.00 1.10 1.01 1.39 28.29 28.34 N.A.

Loading 1.000 1.000 .8048 .8706 .8574 .8480 .8822 .7492 .8716 .6358 .7923 .7665 1.000 1.000 1.000

Mean Loading 1.000 1.000 .8017 .8699 .8572 .8433 .8831 .7484 .8712 .6229 .7826 .7695 1.000 1.000 1.000

Table 2: Composite Reliabilities, Correlation between constructs, and Square root of AVEs

Construct 1 2 3 4 5 6 7

Trustworthiness Communication Technical Knowledge IS Project Management Knowledge Transferred Knowledge High Performer Emerged Leader

Composite Reliability 1.00 1.00 .930 .876

1

2

3

4

1.00 .730 -.048 -.026

1.00 -.052 .022

.728 .463

.588

1.00 1.00 1.00

.638 .790 .218

.537 .687 .258

-.026 .072 .123

-.031 .044 -.106

5

6

7

1.00 .726 .222

1.00 .214

1.00

Knowledge Capital (a component of human capital)

Social Capital Extent of Trusting Relationships

Inherent Stocks of ISD Knowledge .526***

- Trust Centrality Extent of Communicative Relationships

“Stardom”

-

Technical Knowledge IS Project Management Knowledge

.095* ns

.043

High Performer

- Contribution centrality R2= .536

ns

-.005

Emerged Leader .729***

- Leader Centrality

-Communication

Transferred Knowledge

Centrality

- Knowledge Centrality

.224***

R2= .051

.152*

R2= .418 ***- p < .01; **- p < .05; *- p < .10; ns- not significant Figure 1: Research Model and Results

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