Communication and Trust in Global Virtual Teams

Journal of Management Information Systems / Summer 2011, Vol. 28, No. 1, pp. 273–309.
© 2011 M.E. Sharpe, Inc.
0742–1222 / 2011 $9.50 + 0.00.
DOI 10.2753/MIS0742-1222280109
The Role of Communication and Trust in
Global Virtual Teams: A Social Network
Perspective
Saonee Sarker, Manju Ahuja, Suprateek Sarker, and
Sarah Kirkeby
Saonee Sarker is an associate professor and interim chair of the Department of Entrepreneurship and Information Systems at Washington State University. She received
a Ph.D. in management information systems from Washington State University, an
MBA from the University of Cincinnati, and a B.A. (Honors) from Calcutta University.
Her research focuses on globally distributed software development teams and other
types of computer-mediated groups, technology adoption by groups, technologymediated learning, and information technology capability of global organizations. Her
publications have appeared in outlets such as Information Systems Research, Journal
of Management Information Systems, MIS Quarterly, Journal of the Association for
Information Systems, Decision Sciences, European Journal of Information Systems,
Decision Support Systems, Information Systems Journal, Journal of ComputerMediated Communication, and International Conference on Information Systems
proceedings, among others. She is also the principal investigator of a National Science
Foundation grant awarded to study work–life balance in globally distributed software
development teams.
Manju Ahuja is professor of computer information systems at the University of Louisville. She has previously held faculty positions at the Kelley School of Business,
Indiana University, Florida State University, and Pennsylvania State University. She
is involved in research related to virtual and outsourcing software development teams,
online communities, mobile technologies, and effect of IT on work–life balance. Her
publications have appeared in MIS Quarterly, Management Science, Information
Systems Research, Organization Science, Communications of the ACM, Journal of
Management, European Journal of Information Systems, Journal of the Association
of Information Systems, and Small Group Research, among other outlets. She is an
associate editor at MIS Quarterly and has recently served in this role at Information
Systems Research and other journals. She has received three National Science Foundation grants totaling $1,095,000 for her research on IT workforce issues. Her research
has been cited by publications such as the Wall Street Journal, Strategy & Business,
and Computerworld.
Suprateek (“Supra”) Sarker currently holds the Philip L. Kays Distinguished Professorship of Information Systems at Washington State University, Pullman. He received
a Ph.D. from the University of Cincinnati. One of his emerging areas of interest relates
to the network view of organizations, and he has explored actor-network theory and
social network analysis perspectives through empirical studies. Much of the work
related to this study was undertaken when he held the position of visiting associate
274 Sarker, Ahuja, Sarker, and Kirkeby
professor (2004), and thereafter of Professor and Microsoft Chair of Information
Systems (2009–2010), at the Copenhagen Business School. He is currently serving as
a senior editor of MIS Quarterly and editor-in-chief of Journal of Information Technology Case and Application Research, and on the editorial boards of the Journal of the
Association of Information Systems, IEEE Transactions of Engineering Management,
and IT & People.
Sarah Kirkeby is a Ph.D. candidate in the Department of Informatics, Copenhagen
Business School. She studies social networks, entrepreneurship, and innovation. She is
currently writing her Ph.D. dissertation on structure in action—linking entrepreneurs’
action to structure in social networks.
Abstract: The importance of communication and trust in the context of global virtual
teams has been noted and reiterated in the information systems (IS) literature. Yet
precisely how communication and trust influence certain outcomes within virtual
teams remains unresolved. In this study, we seek to contribute some clarity to the
understanding of the theoretical linkages among trust, communication, and member
performance in virtual teams. To this end, we identify and test three proposed models
(additive, interaction, and mediation) describing the role of trust in its relationship
with communication to explain performance. In testing the relationships, we note that
the concepts of communication and trust are inherently relational and not properties
of individuals. Thus, we argue that a social network approach is potentially more appropriate than attribute-based approaches that have been utilized in prior research. Our
results indicate that the “mediating” model best explains how communication and trust
work together to influence performance. Overall, the study contributes to the existing body of knowledge on virtual teams by empirically reconciling conflicting views
regarding the interrelationships between key constructs in the literature. Further, the
study, through its adoption of the social network analysis approach, provides awareness
within the IS research community of the strengths of applying network approaches in
examining new organizational forms.
Key words and phrases: communication, distributed teams, global virtual teams,
hybrid teams, individual performance, mediation, networked individualism, social
network analysis, trust.
Few would disagree that trust is one of the key behavioral themes of interest to
organizational and information systems (IS) scholars today. McEvily et al., for example,
contend that while “trust has long figured prominently in scholarly and lay discourse
alike” [67, p. 1], it is only recently that organizational researchers have started devoting substantial attention to understanding the significance of trust. They suggest that
this trend toward trust arises due to two primary developments: (1) an emphasis on
collaboration and (2) changes in technology “that have reconfigured exchange and
the coordination of work across distance and time” [67, p. 1]. Not surprisingly, an
ICIS (International Conference on Information Systems) 2005 panel highlighted that
trust has become a key topic of interest among IS researchers today, with 129 papers
The Role of Communication and Trust in Global Virtual Teams 275
being published in this area as of the end of 2005 [37]. Two recent issues in leading
journals of the discipline further highlight the continuing interest in this topic (e.g.,
[8, 9, 24, 53]).
Nowhere is trust more critical than in teams where members bring divergent goals,
values, and ideologies [18], and where trust has been viewed as an “efficacious
means” for ensuring a successful collaboration [16, p. 45]. The issue of trust is even
more problematic in the context of distributed teams where members (1) often do not
have a shared history, (2) are “geographically dispersed,” (3) are initially unknown to
each other and lack a “shared social context,” and (4) interact primarily through an
electronic media, with very limited “face-to-face encounters” [48, p. 792; see also 89,
99]. O’Hara-Devereaux and Johansen [77] view trust as a “glue” that helps in creating
virtual team relationships. Finally, McEvily et al. summarize the criticality of trust in
distributed teams by arguing that individuals in such teams “become more dependent
on, and more vulnerable to, the decisions and actions of others—both preconditions
and concomitants of trust” [67, p. 1 ].
In light of this importance of trust in distributed teams, IS researchers have unquestionably made immense contributions (e.g., [46, 48, 49, 81]). However, a review of this
literature, especially in the context of globally distributed teams, suggests that trust
has predominantly been treated as a dependent variable, with few studies examining
the effect of trust on outcomes in distributed teams (see Table 1 for a summary of this
body of research).
For the most part, studies have primarily examined the effect of trust on the performance of an entire group, following Handy’s suggestions [41]. While examination
of factors leading to group performance is important, in many globally distributed
team contexts, 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 [94]. We suggest that it is equally important, if not more so,
to examine the performance of individual members so that the abilities, behaviors, and
status of these individuals can be recognized and leveraged in distributed contexts to
develop a more effective collaborative unit. In fact, a recent case study highlighted
that even within a collaborative environment, organizations are increasingly focusing
on new initiatives that introduce “individual productivity measures” and emphasizing individual performance, in contrast with the earlier practice of implementing
team-based incentive systems [11, p. 197]. This recognition has led to recent calls for
investigating individual performance [2, 68, 84].
Further, research investigating trust in distributed teams has so far adopted a traitbased, or behavior-based, approach. This approach can often provide incomplete results
because teams are a collection of interacting individuals and taking into account the
effects of such interaction is important [68]. Individual team members tend to influence each other in a way that can affect their performance. Thus, we propose that it
is critical to incorporate this oft-missing element using the structural approach [101]
in virtual teams. Brass defines the structural approach as one where “the focus [is]
on relations rather than attributes, on structure rather than isolated individual actors”
to predict outcome [10, p. 284]. Along similar lines, Rice argues that “by bringing
276 Sarker, Ahuja, Sarker, and Kirkeby
Table 1. Sample
Prior Studies Examining
Trust in Distributed
Teams
Study Overall summary of the study
Focus on group-level
or individual-level
performance
Relational/
structural
approach?
Webster and
Wong [108]
In this study, the authors compare trust, communication, and satisfaction in traditional
teams, semi-virtual teams, and pure virtual teams. Results indicated that pure
virtual team members enjoyed greater satisfaction than traditional team members.
Further, semi-virtual team members demonstrated greater positive feelings toward
their local members than their remote members.
Trust treated as a dependent
variable as opposed to an
independent variable for
explaining performance.
No
Lu et al. [61] In this study, the authors use both qualitative and quantitative approaches to examine
how different components of virtuality influence specific aspects of virtual team
performance. Results indicated that different types of practice had several negative
effects on performance, especially on communication and trust in team members,
and on the ability of the team to meet project deadlines.
Focus on group level; trust
treated as a dependent
variable as opposed to an
independent variable for
explaining performance.
No
Wilson et al.
[110]
This study examined the development of trust in both computer-mediated and faceto-face teams. Results highlight that computer-mediated teams exhibit lower trust
in the initial phases, but that over time, the trust levels in such teams increase to
levels similar to face-to-face teams.
Trust treated as a dependent
variable as opposed to an
independent variable for
explaining performance.
No
Edwards and
Sridhar [32]
This study reports an exploratory research involving 24 virtual teams based in
Canada and India. The focus of the teams was to generate and define the business
requirements for software projects. Results indicate that trust and task structure
positively affect the effectiveness, satisfaction, and efficiency of such teams.
Focus on group level. No
Ocker [76] This study examined the creativity in and performance of asynchronous virtual teams.
Results indicated that factors such as domain knowledge, downward norm setting,
lack of shared understanding, and time pressure negatively affect the performance
of such teams. On the other hand, factors such as stimulating colleagues, social
influences, and a collaborative team climate were found to have the potential to
improve performance.
Focus on group level. No
The Role of Communication and Trust in Global Virtual Teams 277
Breu and
Hemingway
[11]
In this paper, boundary theory was used to examine the factors affecting
organizational virtual teams. Results suggest that low levels of trust among team
members and the technology used by the team affect performance and knowledge
sharing in such virtual teams.
Focus on group level. No
Brown et al.
[12]
This paper builds on the interpersonal circumplex model (ICM) and examines the
role of personal traits in virtual collaboration. The proposed model argues that
interpersonal traits affect individual team members’ disposition to trust, perceived
trustworthiness, communication, and thereby their willingness to collaborate.
Focus on individual
level; trust treated as
a dependent variable
as opposed to an
independent variable for
explaining performance.
No
Jarvenpaa et
al. [50]
This paper examines trust in global virtual teams during two different stages of team
development. Findings indicate that a member’s trusting beliefs have a positive
effect on his or her trust in the team and perceptions of team cohesiveness in
the early phases. However, in the later phases, a member’s trust in his or her
team operates as a moderator, indirectly affecting the relationship between
communication and perceptual outcomes.
Focus on group level; trust
treated as a dependent
variable as opposed to an
independent variable for
explaining performance.
No
Coppola et al.
[19]
This paper presents a model of trust development in online/virtual courses.
Specifically, it examines the development of swift trust in both highly rated and
poorly rated online courses. Results indicated that course success depended a
great deal on the early development of swift trust.
Focus on course (or group)
level.
No
Panteli and
Duncan [79]
This paper adopts a “dramaturgical’ perspective on trust relationships and examines
trust development in temporary virtual teams. The study’s results argue that trust
relationships in such teams are mutually negotiated and jointly constructed and
emerge from the scripted, prescripted, coscripted, rescripted, and unscripted
computer-mediated interactions of virtual players.
Focus on group level; trust
treated as a dependent
variable as opposed to an
independent variable for
explaining performance.
No
(continues)
278 Sarker, Ahuja, Sarker, and Kirkeby Study Overall summary of the study Focus on group-level or individual-level performance
Relational/
structural
approach?
Zolin et al.
[111]
In this study, the antecedents of interpersonal trust in cross-functional globally
distributed teams were examined. The study adopts a longitudinal approach and
focuses on variables such as cultural diversity, perceived trustworthiness, trustor’s
propensity, and perceived follow-through as the antecedents of trust. In addition,
perceived risk and reward serving were found to be important moderators.
Trust treated as a dependent
variable as opposed to an
independent variable for
explaining performance.
No
Piccoli and
Ives [81]
This article reports on a longitudinal study exploring the effect of behavior control
on trust in the temporary virtual teams. Results indicated that the behavior control
mechanisms typically used in traditional teams have negative effects on trust in
virtual teams.
Focus on group level; trust
treated as a dependent
variable as opposed to an
independent variable for
explaining performance;
some hypotheses focused
on the individual level.
No
Sarker et al.
[92]
This study proposes and validates an instrument measuring the different bases
of trust in global virtual teams. Drawing on the literature, three bases of trust
applicable to virtual teams were identified: personality based, institutional based,
and cognitive trust, with cognitive trust being further subdivided into three
dimensions: stereotyping, unit grouping, and reputation categorization. In addition
to confirming the conceptual bases of trust, the study’s results also indicated
that stereotyping in virtual teams can be of three distinct types: message based,
physical appearance/behavior based, and technology based.
Focus on group level; trust
treated as a dependent
variable as opposed to an
independent variable for
explaining performance.
No
Table 1. Continued
The Role of Communication and Trust in Global Virtual Teams 279
Morris et al.
[74]
This study investigates the effect of information technology and trust on the job
satisfaction of virtual team members. Information technology was operationalized
using user’s satisfaction with the technology used. Results indicated that both user
satisfaction and trust positively affect job satisfaction of virtual team members.
Focus on individual level. No
Jarvenpaa
and Leidner
[48]
This study examined the challenges of creating and maintaining trust in global
virtual teams, through a series of descriptive case studies. The results of the study
suggest that global virtual teams experience “swift” trust, but such trust is typically
very fragile and temporal.
Focus on group level; trust
treated as a dependent
variable as opposed to an
independent variable for
explaining performance.
No
Coutu [20] This study examined the formation and development of trust in virtual teams.
Specifically, the study illustrated that trust does exist in virtual teams, but it
develops differently from that of traditional teams.
Focus on group level; trust
treated as a dependent
variable as opposed to an
independent variable for
explaining performance.
No
Jarvenpaa et
al. [49]
In this study, the antecedents of trust in global virtual teams were examined. In
the early stages of the team project, trust was predicted by perceptions of other
team members’ integrity. The effect of team members’ perceived ability on trust
decreased over time. The study’s results also indicated the formation of swift trust
in global virtual teams.
Focus on group level; trust
treated as a dependent
variable as opposed to an
independent variable for
explaining performance.
No
Iacono and
Weisband [46]
This study examined how virtual teams where members have never met each other
develop and maintain trusting relationships. Results indicate that high levels of trust
were maintained in teams that engaged in continuous and frequent interaction, and
that this level of trust positively affected their work effectiveness.
Focus on group level. No
280 Sarker, Ahuja, Sarker, and Kirkeby
to bear measures and constructs of social structure, we can begin to understand how
simple notions of . . . autonomous individuals are incomplete” [85, p. 181].
Finally, the research on mechanisms with which trust transmits itself (i.e., the nature
of its influence) has been inconclusive. Some researchers suggest that “effects of trust
[are] transmitted in a relatively straightforward manner,” implying direct effects [29,
p. 450, emphasis added], while others suggest that “trust facilitates the effect of other
determinants on desired outcomes” [29, p. 450, emphasis added] through moderation or mediation (e.g., [48, 64, 87]). One such critical determinant in the context
of distributed teams is communication, given the lack of shared understanding and
temporal/geographic dispersion (e.g., [2, 82, 100]). Kankanhalli et al. [52] highlight
that important role of communication on task conflict in global virtual teams. Massey
et al. [65] argue for the strong effect of communication and interaction on outcomes
within global virtual teams. Martins et al. [64] as well as Panteli and Davison [78] suggest that communication is an important virtual team process, while Ridings et al. [87]
empirically show the importance of communication in virtual communities. However,
despite the acknowledged importance of communication and trust, few distributed
team researchers have examined trust in conjunction with communication [48, 108].
In addition, the nature of their linkage and their effect on performance has remained
unclear. While some research suggests that trust interacts with communication to
affect performance [29, 50], others imply that trust plays a mediating role between
communication and performance [111], and yet others argue that it plays an additive
role along with communication [48, 49, 95].
Our primary objective in this paper is to understand the simultaneous effect of
communication and the closely related construct of trust on individual performance
within globally distributed teams. Consistent with the structural approach followed by
Mehra et al. [68], we adopt the paradigm of networked individualism [109] to empirically examine the validity of the three competing models (additive, interaction, and
mediating) relating to this effect. The networked individualism paradigm argues that
an individual acts within the context of a network of other individuals and artifacts,
rather than in isolation. Degenne and Forse state that within the networked/structural
approach, behaviors are seen to “arise from the structural position of individuals or
groups, because this position is sufficient to determine the opportunities and constraints
which influence the allocation of resources and to explain the behavioral regularities
observed” [26, p. 2]. It is worth mentioning that although researchers such as Cross
et al. [21, p. 7] have suggested that the network analysis perspective can provide beneficial information regarding an individual, and regarding the “effectiveness of one’s
personal network,” few IS studies [2, 58] have actually adopted this approach.
The remainder of the paper is organized as follows. First, we discuss the social network and structural approach adopted, followed by some boundary conditions of the
study. Next, we discuss the concepts of trust and communication and present, from a
network perspective, the three competing models, capturing their effect on individual
performance. This is followed by a discussion of our research methodology, including details of the sample, data collection procedures, and the analysis techniques.
Finally, we provide a discussion of our results and conclude with the contributions
The Role of Communication and Trust in Global Virtual Teams 281
of the paper, notably a clarification of the relationships among communication, trust,
and performance using the perspective of “networked individualism,” considered by
many scholars as more appropriate for examining these constructs within distributed
groups [44, 109].
Theoretical Foundations
As mentioned above, the networked individualism paradigm relies on the notion that
individuals do not act in isolation; rather, they act within the context of a network of
other individuals and artifacts [109]. This paradigm utilizes the social network approach and provides the theoretical foundation for the notions of structural position,
trust, and communication.
Social Network Approach and Structural Position
Cummings and Cross argue that “despite a tremendous increase in the use of . . .
groups in organizations over the past several decades” [23, p. 197], there has been
little research adopting the social network analysis (SNA) perspective, especially when
examining performance-related consequences. SNA “focuses on [the] relationships
among social entities and on the patterns and implications of these relationships” [34,
p. xii]. Through its focus on relationships, SNA captures the interactions and connections between different social entities (e.g., individuals, groups) and enables the
researcher to study individuals’ actions and behaviors “within the context of larger
structural configurations” [34, p. xiv]. Given that individuals are typically situated in
a context and do not act in a vacuum, the structure of the context and the individuals’
relationships with other elements within the context have a significant bearing on their
behaviors/actions, and vice versa. The strength of the SNA perspective lies in the fact
that it bridges the attributional and structural aspects of individual actions/behaviors, as
opposed to simply focusing on their behaviors as if they exist in isolation [34, 36].
Social network research related to individual performance in groups posits that one
reason certain team members may perform better than their peers is the networks to
which they belong, as networks often provide critical resources and social support
to the team members. Structural position within a network may be more beneficial
to a network member than the size of the network [13] because a specific position in
the network may allow an individual to gain informational and other resources. Also,
an individual’s structural position can enable him or her to exert more influence owing to his or her ability to control/mediate information and resource flows. Further,
individuals in advantageous structural positions are more likely to be connected with
other powerful actors in the network. Past research has corroborated performance implications of one’s structural position [2, 47]. For example, Ibarra [47] found evidence
for a relationship between an individual’s centrality in a network and involvement in
innovation, which in turn led to higher performance.
Noted researchers have observed that computer-mediated groups are slowly moving
toward “networked individualism” [109], where the “network of relationships . . . are
282 Sarker, Ahuja, Sarker, and Kirkeby
as much, or more, the causal forces as the attributes of the actors” [10, p. 284]. Based
on the recent literature, and consistent with our adoption of the relational/structural
approach, we conceptualize a distributed team as a network of linkages among its
members, with each team member holding a structural position (e.g., based on their
communication patterns with team members) within that network [44].
Boundary Conditions
This study focuses on distributed work teams, where members are geographically,
temporally, and often even organizationally dispersed, but where the members share
“mutual accountability” and “work interdependently to solve problems or carry out
work” [54, p. 700]. Consistent with the work of Kirkman and Mathieu [54], we assume
reciprocal interdependence among the distributed team members in our study.
We also suppose, consistent with the real world, that distributed groups tend to
be “fluid, dynamic, multiplex” wherein members communicate with others “on the
basis of tasks to be accomplished, and their levels of interests and commitment” [44,
p. 232]. Indeed, in small projects, each member is free to communicate with any/all
other team members, and often the teams are self-organizing [94].
A core focus of our study is on communication and how it interacts with trust to
affect performance of members in distributed teams. The “babble hypothesis” argues
that people who communicate the most are seen the most positively within a group
[102, p. 281]. Evidence consistent with the babble hypothesis may be found in situations wherein individuals who quietly do much of the work are often not considered
to be top performers or contributors. Instead, those who speak up in meetings are
frequently acknowledged as the performers. This effect may be even greater in a virtual context, where work processes are even less visible than in situations involving
collocated contributors [84, 91, 111]. However, we contend that communication alone
will not determine perceptions of performance and suggest that members’ perceived
performance will be high only when high communication is accompanied by their
earning the team members’ trust by creating the impression (deceptively or otherwise)
that they are adding value to the team project. Extending the research suggesting that
trust (from the point of view of other team members) plays a key role in determining
performance [48, 64, 108, 111], our investigation seeks to clarify the nature of this
role in conjunction with communication.
Trust and Trust Centrality
Trust has been defined as the “willingness of a party to be vulnerable to the actions of
another party, based on the expectation that the other will perform a particular action
important to the trustor, irrespective of the ability to monitor or control that other party”
[66, p. 712]. Knoll and Jarvenpaa [55] suggest that trust is based on the assumption
that others will behave as expected. Trust can be seen in relationships between two
or more people, or in relationships between two or more collectives, such as among
The Role of Communication and Trust in Global Virtual Teams 283
subteams or subgroups [55]. Cummings and Bromiley [22] view collective trust as
the common belief among group members that a particular member will behave in
accordance with the commitments, will be honest in the negotiations preceding those
commitments, and will refrain from taking undue advantage of another. Prior research,
as the above discussion highlights, indicates that a “statement about trust, therefore,
always concerns at least two parties”: the trustor, “who holds certain expectations
about another party, and, as a result, may or may not be willing to be vulnerable to
the actions of the other party,” and the trustee, “who is assessed by the trustor” [7,
p. 33]. Becerra and Gupta [7] argue that for any study involving trust, it is “critical” to
differentiate between these two parties and explicitly state the direction of the trust. In
this study, we focus on the trustee (or the trusted party) and examine how the trustee’s
trustworthiness (as assessed by the potential trustors) plays a role in affecting his or
her performance in globally distributed teams.
Trustworthiness is that quality of the trustee that makes the trustor willing to be
vulnerable [58]. Tsai and Ghoshal [103] have found that individuals who enjoy more
central positions within a network are likely to be perceived as more trustworthy.
Drawing on this, it may be argued that within a network, a member’s trustworthiness
(i.e., the extent to which a member enjoys the trust of each of the other members within
a team) is reflected in his or her trust centrality. Centrality is defined as the “extent
to which an actor is central [or core] to a network” [10, p. 288]. In the context of the
current study, trust centrality may thus be defined as “the extent to which an individual
enjoys a central position within a trust network in the globally distributed team.”
Communication and Communication Centrality
While several characteristics of individuals have been examined in connection with
trust, one trustee characteristic that has been identified as central is his or her communication with the trustor [7]. Communication has always been viewed as a key element
in any group [57], whether collocated or distributed. In distributed teams, the lack of
prior history, and thus an absence of shared understanding, and temporal/geographic
dispersions makes communication “critical” [82, 100]. Kankanhalli et al. [52] suggest
that communication affects the level of task conflict in virtual teams. Others also argue
that communication is an important process within virtual teamwork and has important
implications in terms of the outcomes [64, 87, 108]. Montoya et al. argue that communication helps distributed teams to “cope with the opportunities and challenges of
cross-boundary work” [72, p. 139]. Indeed, Ahuja et al. [2] have noted that the only
artifact of a distributed team’s existence is its communication; thus, development of a
trusting relationship and task performance necessarily involve communication.
Given many of the unique challenges faced by distributed team members, it is important examine the effects of communication (in conjunction with trust) on performance
in such contexts. In line with our reliance on the SNA perspective, we consider an
individual with high communication centrality as having communication linkages
with many members within a globally distributed team.
284 Sarker, Ahuja, Sarker, and Kirkeby
Communication and Trust in Distributed Teams:
The Inseparable Relationship
While trust and communication are two separate behavioral constructs, in distributed
teams, they often play out together. Much of a distributed team’s existence relies on
its interaction or communication through electronic spaces, where new behaviors are
developed, practices are co-constructed, and relationships are created and nurtured [91,
96]. Given that distributed teams are typically assembled for the duration of a project to achieve interdependent tasks, there are greater dependencies among the team
members. The temporary nature of these teams in conjunction with a high level of
interdependencies can increase the chances of exploitation among team members[12].
In other words, there is a possibility of individuals behaving in an untrustworthy manner, typically by engaging in freeloading, and by not contributing meaningfully to the
completion of project tasks.
Meyerson et al. [69] suggest that trust is formed based on behavioral evidence.
Some argue that a high level of communication enables the trustor to better assess
the characteristics of the trustee, thereby affecting “his/her evaluation of the trustee’s
trustworthiness” [7, p. 33]. Given that distributed teams utilize electronic media rather
than face-to-face interaction, the only behavioral evidence available to team members is
the communicative behaviors of other members [2, 111]. Thus, communication forms
the basis for expressing and inferring trusting behaviors in these contexts [50].
Some researchers suggest that communication exchanges among team members
through the electronic space over time leads to trust development. For example,
Jarvenpaa and Leidner [48] highlight how certain types of communicative behaviors
help in the creation or breaking of trust in globally distributed teams. Similarly,
Ridings et al. [87] show that communication in the form of responses to posts of remote members, sharing of personal information, and so forth can help to increase the
trustworthiness of the individual. Research has demonstrated that trust in distributed
teams is often affected by silence (or lack of communication) from remotely located
team members [48, 91]. The above discussion suggests that there is a close conceptual affinity between the constructs of trust and communication in the digital world.
However, as we highlighted earlier, few studies have examined the effect of both of
these variables on performance in one unifying study. Further, the exact nature of the
relationship and how they (i.e., communication and trust) interact to affect individual
performance has not been investigated formally. Our paper addresses this void.
Hypotheses Development
Drawing on the SNA tradition, we propose that an individual will be perceived as a
high performer if he or she has high trust and communication centralities. Our review
of prior research on trust and communication, with respect to individual performance,
in distributed teams or otherwise, suggests three different views regarding the role of
trust. Following Mehra et al. [68], we capture and label these views as the “additive”
model, the “interaction” or “moderation” model, and the “mediation” model. Below,
we discuss each of these models in further detail.1
The Role of Communication and Trust in Global Virtual Teams 285
The Additive Model
The additive model proposes “twin predictions”—that is, both trust and communication additively affect individual performance [48, 49, 95]. As Dirks and Ferrin [29]
highlight, a majority of research on trust points to its direct main effect on performance.
For example, a number of studies (e.g., [19, 45]) argue for a strong linkage between
trust and performance. Consistent with these studies, Jarvenpaa et al. observe that the
“prevailing view of trust in the IS literature contends that trust has direct positive effects on . . . performance” [50, p. 251]. Specifically, in distributed teams, given a lack
of transparency of the work process, individuals who are considered more trustworthy
tend to receive the benefit of the doubt with respect to performance more than those
who are considered less trustworthy [91].
Simultaneously, higher levels of communication by an individual have also been
linked positively to his or her level of performance. For example, Scarnati [95] suggests that inadequate communications may “hinder” performance. Further, Balthazard
et al. [5] argue that communication is a key determinant of performance in distributed
teams. Morgeson et al. [73, p. 588] argue that communicative individuals in the team
would be viewed as high performers within teams for several reasons. First, “talkative”
individuals are “likely to have a desire to work with others” and have higher confidence and ability to work in a team structure. Second, their communication is likely
to enhance “discussions of performance strategies and development of norms”; thus,
communicators are likely to be perceived as key contributors to their team’s success.
Finally, communicative individuals have been shown to exhibit “elements of positive affectivity,” which promotes “positive and cooperative interactions with others”
through a process of “emotional contagion.” Their contribution to creating this positive
environment within the team would also enable them to be recognized as superior
performers. Based on the above discussion on the importance of communication and
trust, we propose the following in SNA terms (see Figure 1):
Hypothesis 1: In globally distributed teams, trust centrality and communication
centrality of a team member will have an additive effect on his or her performance
as perceived by team members.
The Interaction (Moderation) Model
While a dominant body of literature suggests that trust has a direct effect on performance (additively with communication), another competing perspective is that trust
is beneficial because it “facilitates” the effect of other variables on performance outcomes [29, p. 450]. Specifically, Dirks and Ferrin [29] argue that “trust provides the
conditions under which certain outcomes, such as . . . higher performance, is likely
to occur” [29, p. 450]. Dirks and Ferrin [29] also assert that the concept of trust as a
moderator is not new, but it has received only “scant” attention from researchers. One
of the reasons trust might play a moderating role is because it “also affects how one
interprets the past or present actions of the other party” [29, p. 456]. Drawing on Dirks
and Ferrin [29], Jarvenpaa et al. [50, p. 255] examined the role of trust in distributed
teams and suggest that trust enables an individual to “interpret” the “communication
286 Sarker, Ahuja, Sarker, and Kirkeby
activity” of other individuals, which together affects their “judgments about the
work outputs” of other individuals. For example, the performance of a team member
with a high frequency of communication will be amplified if he or she is also highly
trusted [50]. In summary, researchers adhering to the moderating role of trust therefore
view trust as “a necessary, not a sufficient condition” [29, p. 456] that “facilitates”
performance, especially as perceived by distributed team members. Thus, adopting
the SNA perspective, we may capture the essence of the above discussion through the
following hypothesis (see Figure 2):
Hypothesis 2: In globally distributed teams, trust centrality of a team member
will play a moderating role on the relationship between his or her communication
centrality and his or her performance as perceived by team members.
The Mediation Model
In contrast to the additive model and the interaction model, the mediation model argues that in global distributed teams, trust mediates the effect of communication on
performance. In other words, a communicative individual will be more likely to be
trusted and will therefore be more likely to be a high performer [19]. Independently,
these conceptual linkages (i.e., communication → trust, trust → performance) have
been supported in the literature. For example, Becerra and Gupta [7] suggest that the
extent of communication that a trustee engages in will affect the perceptions of his or
her trustworthiness. Of course, in distributed teams, where electronic communication
can often be the only means of interaction, this effect is likely to be even more significant. Unlike traditional teams, in distributed teams it is difficult for team members to
directly observe whether an individual member is working (even if progress is being
made) or whether he or she is struggling with an issue (which might explain why
progress is not being made) unless the member communicates. Clegg and Hardy [18,
p. 434] also argue that trust develops and “exists as a result of frequent interaction”
between the trustor and the trustee. A greater frequency of communication will expose
the trustor to the trustee’s inner characteristics, and thereby enable him or her to better
judge the trustee’s trustworthiness.
However, researchers also argue that only when an individual is trusted will he or
she be viewed as being a high performer and contributor to the team’s success [111].
In fact, Zolin et al. assert that within globally distributed settings, only “if a worker is
Figure 1. An Additive Model
The Role of Communication and Trust in Global Virtual Teams 287
perceived as trustworthy, he or she will be perceived as delivering on work commitments” [111, p. 19]. We note that Dirks and Ferrin [29] also summarize a large body
of research investigating the role of trust on individual performance, finding trust to
play a significant positive effect on performance.
While the above discussion provides support for the independent linkages of communication → trust and trust → performance, thereby suggesting an indirect effect of
communication on performance through trust, other researchers allude more directly to
a full mediation of trust on many outcome variables. For example, Ridings et al. [87]
found from their empirical study on virtual communities that trust plays a perfect
mediating role within the relationship between communication-related variables such
as individuals’ responses to message posts and disclosure of personal information and
willingness to share information. While the outcome variable in the Ridings et al.
study [87] is not individual performance, its conclusions are helpful in understanding
the role of trust in globally distributed teams. Even Jarvenpaa and Leidner [48], in their
seminal work on global virtual teams, allude to the effect of different communicative
behaviors in elevating/deflating trust in virtual teams, finding that ultimately it was
the level of trust within the team that made a difference on the members’ ability to
deal with the uncertainties and to handle tasks, thereby suggesting a fully mediating
effect of trust. Martins et al., in their review of the virtual team literature, explicitly
state that it is only trust that is “a determining factor in the effectiveness” within such
teams, and that “several attributes of team communication . . . facilitate the formation of trust” [64, p. 816], again pointing to the complete mediation of trust within
this relationship. Drawing on the above discussion, and using SNA terminology, we
summarize the above discussion as follows (see Figure 3):
Hypothesis 3: In globally distributed teams, trust centrality of a team member
will play a key mediating role between his or her communication centrality and
his or her performance as perceived by team members.
Research Methodology
In this section, we discuss the data collection efforts, specific measures, data preparation, and our analysis techniques utilized to test the three competing propositions relating communication, trust, and performance—additive, interaction, and mediation.
Figure 2. An Interaction (Moderation) Model
288 Sarker, Ahuja, Sarker, and Kirkeby
Data Collection
Data for this study were collected from globally distributed student teams engaged in
systems analysis and development projects. Before proceeding, we clarify what we
mean by distributed/virtual teams. In the literature, Griffith et al. [40] and Saunders
and Ahuja [93] have provided typologies that show that virtual teams can have diverse
configurations. Specifically, Griffith et al. [40] propose three distinct team categories: traditional, hybrid, and pure virtual. This distinction is based on (1) the level
of technological mediation used, (2) the percentage of work that the team does with
its members distributed across time or space, and (3) the distribution of the physical
locations occupied by the team members. Griffith et al. note that few virtual teams
are purely virtual, and “most of today’s organizational teams are likely to fall into
the large hybrid category of teams composed of members who interact over time,
according to the needs of the moment, and through media and with the amount of
face-to-face contact determined by their own adaptation and structuration of the process” [40, p. 268]. Our work seeks to examine the linkages between communication,
trust, and performance in the hybrid category. Note that field studies on “real-world”
distributed software development teams show that such teams frequently have a hybrid configuration, with team members distributed in two locations (e.g., [15, 75]),
as in our empirical study. In addition, Armstrong and Cole [4] also suggest that in a
distributed collaborative setting, it is often the case that multiple team members are
located in each of the sites.
Two sets of hybrid virtual teams participated in the study: (1) distributed teams
with members from the United States and Norway engaged in systems development
projects, where the teams worked on developing IS applications for real clients located
across the globe; and (2) distributed teams with members from the United States and
Denmark engaged in systems analysis and design projects for real clients located in
the United States or Denmark. Note that while the tasks of both the U.S.–Norway
and U.S.–Denmark teams were related to information systems development (ISD),
the U.S.–Norway teams were required to develop and test the system in addition to
analyzing and designing it; the U.S.–Denmark teams were required to conduct the
systems analysis and design only. Also note that both Scandinavia and the United
States, for a long time, have significantly contributed to innovations in ISD and are
often looked up to for leadership with respect to ISD processes and methodologies.
Not surprisingly, many known U.S.-based technology companies (e.g., Microsoft) have
established development centers in Scandinavia (e.g., Denmark), such that employees
in the United States (e.g., Redmond, WA, and Fargo, ND) and Denmark (e.g., CopenFigure 3. A Mediation Model
The Role of Communication and Trust in Global Virtual Teams 289
hagen) collaborate on projects. Given such arrangements in Microsoft and in many
other companies in northern Europe (e.g., Nokia/Maemo, ABB, Telenor, Kvaerner),
we chose to concentrate on U.S.–Scandinavia teams.
Given our individual level of analysis, the usable sample size was 111, with approximately 3 to 5 members taken from each location. For example, each U.S.–Norway
team was typically composed of 3 to 5 members from the United States and 3 to 5
members from Norway. See Table 2 for a detailed summary of the sample.
Measures
Given our SNA approach in this study, and the fact that our research objective is
to assess the effect of an individual’s extent of communication and trustworthiness
on his or her performance, we take the ego-centric network view. One of the most
common measures used in this perspective is “centrality,” which is an indicator of an
entity’s structural position within the network [97]. It has been defined as an entity’s
“prominence” or “importance” within a network [106] and is assessed by evaluating
the number of relationships in which an actor is involved.
Centrality in SNA 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” [26, p. 132]. Closeness refers to the
extent of affinity of an individual with other members in the network. It is relatively
more global than degree centrality, since it focuses on the closeness to “all network
members, not just immediate neighbors” [26, p. 135]. Finally, betweenness refers to
the extent to which an individual “is in a position to act as a gatekeeper for information that flows through the network” [56, p. 90]. We adopted degree centrality as the
indicator of centrality because it is the “simplest” and the most “intuitive” measure
of centrality [26, p. 132; see also 34].
It is important to note that for calculation of the degree centrality, the SNA approach
requires “relational data,” unlike other types of behavioral studies that uses “attribute
Table 2. Description of Sample
Total sample
Sample size of each type of
distributed team
Gender
distribution Age
111 58 U.S.–Norway team members Majority in the
age range of
18–25
35 United States 6 females
29 males
23 Norway 6 females
17 males
53 U.S.–Denmark team members
22 United States 3 females Majority in the
age range of
18–25
19 males
31 Denmark 9 females
22 males
290 Sarker, Ahuja, Sarker, and Kirkeby
data.” Attribute data refer to data about attitudes, opinions, and behaviors of different
actors, which are “regarded as the properties, qualities, or characteristics” of different
individuals or groups. But “relational data” are the “contacts, ties, and connections, . . .
which relate one agent to another and so cannot be reduced to the properties of the
individual agents themselves” [97, pp. 2–3]. Typically, relational data are collected
by asking each participant to “rate a single characteristic . . . in numerous targets”
[27, p. 42]. Specifically, respondents “complete ratings of every network partner” on
a particular dimension using a single item [27, p. 42]. Consistent with the above, in
order to calculate the degree centralities of individual members on the dimensions of
trust and communication, we sought to collect relational data by asking each team
member to assess each other member in their team on their trustworthiness and extent
of communication on a scale of 1 to 7.
We acknowledge that there may be some concerns surrounding the use of single items
to measure key constructs, especially among scholars using the traditional attributebased approach. Researchers respond to such concerns by noting that a “single-item
measure eliminates item redundancy and therefore reduces the fatigue, frustration,
and boredom associated with answering highly similar questions repeatedly” [88,
p. 152]. Concerns about reliability surrounding the single-item measures have also
been addressed in the literature. For example, Robins et al. [88] have demonstrated
that single items have similar (or better) psychometric properties as multi-item scales,
and Dennisen et al. [27] showed the same to be true in the context of social network
designs. In fact, it is argued that in traditional questionnaires, “respondents rate a single
target (i.e., themselves or a peer) on a number of characteristics (i.e., items)” [27,
p. 42]. In the context of SNA, and relational data, “this logic is turned upside down,”
with each respondent rating a single characteristic for multiple targets,” therefore
making this approach not significantly different from traditional questionnaires. Rice
further argues that “the patterns of these matrices are stable across time and highly
correlated with a social communication network, . . . indicating test-retest reliability
and predictive validity” [86, p. 14].
The relational data that we captured were next 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 were “valued.” Further, the data were
directed. In other words, entity A rating entity B with a certain number did not mean
that a reciprocal relationship existed (i.e., entity B gives the same rating to entity A).
For convenience of analysis, valued data in the adjacency matrix were converted to
binary data. For conversion to binary data, we followed standard SNA guidelines,
which suggest selecting a cutoff (typically, the median) and using the cutoff to “slice”
the data and “dichotomize” the matrix [97, p. 48]. Within a binary adjacency matrix
capturing communication among team members, a 0 rating of entity A by entity B
on communication, for example, indicated that entity B did not perceive there to be a
communicative linkage between himself or herself and entity A.
Note that the conversion from a continuous 1–7 scale to a binary variable (using
median split) is a standard practice in SNA [42, 97]. Measurement of a phenomenon
The Role of Communication and Trust in Global Virtual Teams 291
on a binary scale (0 or 1) represents the “mere presence” or “absence” of a relation as
opposed to its strength [97, p. 52]. SNA researchers argue that by providing respondents
the option to signify only a presence/absence of relational constructs (e.g., trust, extent
of communication), researchers will tend to add undue “restrictions” to the respondents
(and draw “narrow boundaries” around their response options), thereby leading to the
“imperfect representation of the full network” [97, pp. 53–54]. Hanneman and Riddle
argue that “much of the development of graph theory in mathematics, and many of the
algorithms for measuring properties of actors and networks have been developed for
binary data, . . . [and thus] it is not unusual to see data that are measured at a ‘higher’
level transformed into binary scores before analysis proceeds” [42].
While some researchers argue that conversion of a continuous scale to binary
may lead to loss of information, Hanneman and Riddle argue that “very often, the
additional power and simplicity of analysis of binary data is ‘worth’ the cost in information lost” [42]. Thus, it is often recommended that responses be collected using
a continuous scale to assess the strength of a relation, followed by a conversion to a
binary scale (e.g., [97]).
In a directed graph, as in our study, “lines are directed to or from the various points”
[97, p. 68]. The simplest measure of degree centrality is the absolute degree, that is,
half the sum of all the incident relations of the node considered: d = En /2, where En
is the relation set of the node being considered. However, use of this measure in a
directed graph creates the risk of using both the connections to and from a node in
calculating the degree. Thus, in directed graphs, there are two additional ways to assess
absolute degree centrality: absolute in-degree (d in) and absolute out-degree (d out) [33].
The in-degree of an entity or a point is the “total number of points that have lines
directed towards it” [97, p. 69]. In other words, the in-degree of an entity within a
network refers to the “number of other people who choose that actor in the particular
relationship” [56, p. 89]. But the out-degree of an actor is the “total number of points
to which it directs lines” [97, p. 69] and reflects the “number of people chosen by the
focal actor” [56, p. 89]. Thus, in our study, which seeks to understand, for example, the
effect of an individual actor’s trust centrality (i.e., an individual member’s trustworthiness) and communication centrality on his or her performance, in-degree centrality is
more relevant. Furthermore, in-degree centrality has been shown to be the most stable
even at a low sampling level [104]. Thus, in this study, in line with prior research on
teams, we use in-degree centrality, which captures the number of incoming lines to a
particular node. UCINET 6.0 was used to calculate the centralities.
For measuring performance, we asked each team member to assess each other
member in their team on their extent of task performance on the project on a scale
of 1 to 7. The average of team members’ ratings of an individual team member was
used as a measure of that individual member’s performance. We chose to adopt this
relational measure of performance as opposed to using instructor ratings (or grade
point average) in light of recent criticisms of using instructors/supervisors, which
tends to “contain political aspects” [10]. Specifically, Brass [10, p. 309] argues that
since supervisors and subordinates often have a “multiplexity of relationships” (i.e.,
they are linked by more than one relationship, such as both a working and a friendship
292 Sarker, Ahuja, Sarker, and Kirkeby
relationship), performance evaluations made by supervisors are often tainted. Also,
in our study, the project coordinators (faculty at the two sites) had face-to-face (and,
generally speaking, closer) interactions with half the participants and virtual interactions with the rest, making fair ratings of participants’ ratings difficult.
Finally, adoption of a “network perspective on performance invites us to analyze
the pattern of relationships (from multiple perspectives) rather than view individuals’
performance in isolation” [10, p. 311]. This is particularly appropriate in a distributed
computer-mediated setting, where no one individual has a complete understanding of
another team member’s contributions (e.g., [111]), and each perspective has value.
Control Variables and Their Measurement
In this study we are interested in the effect of trust and communication on performance,
but other factors could be argued to have an effect on the performance of team members in a globally distributed context; thus, it is important to include them as control
variables. Ahuja et al. [2] suggest that certain individual characteristics can have an
effect on performance. One potentially important individual characteristic is gender,
given that gender can play an important role in both communication and performance.
It is argued that women, due to their nurturing and good social behaviors, are more
communicative and participative in contexts that require a high amount of social activity. However, in contexts where the group’s focus is on a complex task completion
(as in an ISD-related project similar to those used in this study), male members are
usually more active [31]. The other control variable that we included was the location where the individual team member was based. It is known that the United States
and Scandinavia, while Western nations, have differences in their work cultures [43].
Professionals in the United States tend to be more extroverted and communicative
in their work environments and more active in taking up roles and responsibilities
compared to professionals in Scandinavia [43]. Thus, it seemed to be an important
variable to control for. Both gender and location were measured using a binary variable where, in the case of gender, 1 referred to females and 2 referred to males, and
in the case of location, 1 referred to the United States and 2 referred to Scandinavia
(i.e., Norway and Denmark).
Whenever we are focusing on performance, the inherent ability of the individual can
be argued to play an important role [64]. Given our context of ISD, a key ability that
should be taken into consideration is the ISD ability, which refers to issues such as a
team member’s ability to communicate with users and others, manage projects, and
maintain relationships with users/clients. We thus used this as a control variable. ISD
ability was assessed using five self-reported items that tapped into the above issues.
Finally, Gallivan and Benbunan-Fich [35] argue that whenever one observes or examines behaviors of team members after (or during) their group interaction, it is important
to take the team that they belong to into consideration. Thus, we controlled for the team
as well by including information about the team an individual belonged to as a control
variable. We provide a list of the control variables and their measurement in Table 3.
The Role of Communication and Trust in Global Virtual Teams 293
Note, however, that in understanding the effects on individual performance within
distributed teams, we examined the role of “endogenous variables” (i.e., trust centrality
and communication centrality), which are “relational properties inherent in the focal
network” [71, p. 55]. Specifically, it has been argued by SNA researchers that such
endogenous network properties are “inherent in the network” itself, and “defined by
the node’s relations,” as opposed to psychosocial attributes such as age or gender,
which are “external to, and independent of, the network” [71, p. 57]. For example,
when an individual communicates with another team member, the specific action not
only changes the individual’s own position within the team structure (i.e., his or her
centrality) but also changes the others’ relative positions. Thus, by changing one’s own
position in the structure, an individual essentially changes the structure of the entire
network to some extent [14]. Yet a change in the individual’s age has no bearing or
effect on the ages of other team members. Thus, the use of the relational approach
itself controls considerably for the group environment, and lessens its possible confounding effect on the final result.
Analysis Technique
The three models presented earlier were tested using regression. Given our inclusion
of control variables, and following guidelines of prior researchers in virtual teams
(e.g., [17]), we used either a two-step or a three-step hierarchical regression to test
the models. In the first step for each of the models, we only included the control variables, followed by the control and independent variables in step 2. For the interaction
model, we used a three-step hierarchical regression, with the first step including the
control variables, the second step including communication and trust centralities
in addition to the control variables, and the third step including the two centralities
and the interaction term in addition to the control variables [68, 70]. For interpreting
the results with respect to the control variables, we drew on past research [17]. For
interpreting the results of the interaction model, we relied primarily on the research
of Miles and Shevlin [70]. For the mediation model, we followed the guidelines of
Baron and Kenny [6].
Table 3. List of Control Variables and Measurement
Control variable Measurement
Gender Binary variable; 1 referred to females and 2 to males
Location of the team member Binary variable; 1 referred to United States and 0 to
Scandinavia (both Norway and Denmark)
Inherent information systems
development ability
Five self-reported items capturing individual team
members’ ability to communicate with users and
others, management of the project, maintaining
relationships with users/clients
Team Team number
294 Sarker, Ahuja, Sarker, and Kirkeby
Results
We provide the descriptive statistics on our independent and control variables in
Table 4. In the test of the additive model, as the first step, we included the control
variables. As the results indicate, location had a significant effect on performance,
with team members located in Scandinavia having higher performance than the U.S.
members. Also, gender and the teams they belonged to had an effect on team members’
performance (though these results were significant at p < 0.10), with males performing
higher than females, and members of the U.S.–Norway teams seen as having higher
performance than U.S.–Denmark teams. In the second step for the additive model,
where all the control variables and the two independent variables were included, the
control variables failed to have any significant effect, and the effect of communication
centrality on performance was also not significant (b = –0.014, p > 0.10). However,
the effect of trust on performance was significant (b = 0.517, p < 0.01). The overall
R-square of the second model was 0.659 as opposed to a small R-square of 0.080 for
the first model including the control variables only. Further, the F-change from the
first to the second model was significant, suggesting that the second model (including
the independent variables) was a much better predictor of performance than the first
model (including only the control variables). Although trust had a significant effect,
the test did not satisfy the “twin predictions” of both communication and trust on
performance. Thus, the additive model was not supported. In the case of both models (step 1 and step 2), the variance inflation factors were all well below 3, and the
condition index was within the recommended range of 30, suggesting that there were
no significant problems of multicollinearity [70], despite the conceptual closeness
among the constructs.
Table 4. Descriptive Statistics
Variable Frequency Mean
Standard
deviation
Trust centrality 3.80 2.017
Communication centrality 4.27 1.887
Performance 4.37 1.303
Gender 29 females, 79 males,
4 missing information
Location 55 U.S. members,
51 Scandinavian
members, 5 missing
information
Information systems
development ability
4.80 0.866
Team 16 teams
The Role of Communication and Trust in Global Virtual Teams 295
The results of the hierarchical regression (for testing the moderation model) suggested that the model with the interaction term had a higher R-square (by 0.129) than the
model with just communication and trust and the control variables. Again, the control
variables did not have any significant effect in the second model (control variables and
independent variables), while only the team an individual belonged to had an effect
in the third model (control and independent variables, and interaction term). Further,
results indicated that the F-change from the second to the third model was significant,
and the effect of the interaction term on performance was also significant (though
trust continued to have a direct effect in the third model). However, surprisingly, the
direction of the effect of the interaction was opposite to the one hypothesized. Thus,
the hypothesized moderation model was not supported.
Results from the test of the mediation model provided strong support. In the first
equation, communication centrality (the independent variable) had a significant effect
on trust centrality (the mediating variable) (b = 0.792, p < 0.01). In the second equation, communication centrality had a significant effect on performance (the dependent
variable) (b = 0.396, p < 0.01). Finally, in the third equation, trust centrality had a
significant effect on performance (b = 0.507, p < 0.01). As per Baron and Kenny’s
guidelines [6], we found that all the effects were in the predicted directions, and the
effect of communication on performance disappeared (b = –0.014, p > 0.10) when trust
was introduced in the third equation. Thus, the mediation model was supported—in
fact, the results indicated a perfect mediation of trust centrality on the relationship
between communication centrality and performance. We summarize the results in
Tables 5 and 6.
Discussion of the Results
In this study, we identified three competing models capturing the conceptual linkages
among communication, trust, and individual performance that are in evidence in the
literature and subjected them to deductive empirical testing in the context of globally
distributed ISD teams.
Our results indicate that the additive model does not explain the role of trust and
communication on performance in distributed teams, given that both trust and communication do not have an effect on individual performance (i.e., only trust had a
significant effect). The results cast doubt not only on the “twin predictions” made in
the literature (regarding the effect of trust and communication on performance) but also
on the “babble hypothesis,” where “talkative” individuals are argued to be perceived
as key contributors to a team.
Indeed, our results indicate that the mediating model best explains the impact of
trust and communication on individual performance in distributed teams. The strong
support for the mediation model emphasizes the point that communication’s effect
on individual performance is through trust. We believe that this result highlights the
prominent role of trust in distributed teams, where it (i.e., trust) has been viewed as
facilitating “glue” by prior researchers (e.g., [12]). While much of the existing research
296 Sarker, Ahuja, Sarker, and Kirkeby
Table 5. Test of the Three Competing Models
Model/
variables
Step 1
Coefficient
(standard
error)
Step 2
Coefficient
(standard
error)
Step 3
Coefficient
(standard
error)
Additive model (dependent variable is Performance)
Gender –0.382
(0.278
0.034
(0.178)
Location 0.732***
(0.262)
0.161
(0.173)
Information systems
development ability
0.000
(0.149)
0.058
(0.094)
Team –0.038*
(0.027)
0.021
(0.017)
Trust 0.517***
(0.063)
Communication –0.014
(0.067)
R2 0.117 0.659
Interaction model (dependent variable is Performance)
Gender –0.382*
(0.278)
0.034
(0.178)
0.088
(0.142)
Location 0.732***
(0.262)
0.161
(0.173)
0.065
(0.138)
ISD ability 0.000
(0.149)
(0.058)
(0.094)
0.039
(0.074)
Team –0.038*
(0.027)
0.021
(0.017)
0.023**
(0.014)
Trust 0.517***
(0.063)
0.568***
(0.051)
Communication –0.014
(0.067)
–0.050
(0.050)
Trust × Communication –0.433***
(0.058)
R2 0.117 0.659 0.788
DR2 a 0.129
DFb 18.85
Mediation model (Equation 1—dependent variable is Trust)
Gender –0.825**
(0.430)
–0.226
(0.290)
Location 1.134***
(0.405)
0.194
(0.281)
ISD ability –0.112
(0.230)
–0.098
(0.152)
Team –0.118***
(0.041)
–0.060**
(0.028)
The Role of Communication and Trust in Global Virtual Teams 297
Model/
variables
Step 1
Coefficient
(standard
error)
Step 2
Coefficient
(standard
error)
Step 3
Coefficient
(standard
error)
Communication on Trust
(independent variable on
mediating variable)
0.792***
(0.071)
R2 0.177 0.643
Mediation model (Equation 2—dependent variable is Performance)
Gender –0.382*
(0.278)
–0.083
(0.232)
Location 0.732***
(0.262)
0.261
(0.225)
ISD ability 0.000
(0.149)
0.007
(0.122)
Team –0.038*
(0.027)
–0.090
(0.022)
Communication on
Performance (independent
variable on dependent
variable)
0.396**
(0.057)
R2 0.117 0.415
Mediation model (Equation 3—dependent variable is Performance)
Gender –0.382*
(0.278)
0.036
(0.117)
Location 0.732***
(0.262)
0.156
(0.170)
ISD ability 0.000
(0.149)
0.057
(0.093)
Team –0.038*
(0.027)
0.021
(0.017)
Trust (mediating variable on
dependent variable)
0.507***
(0.041)
R2 0.117 0.659
Mediation model (Equation 4—dependent variable is Performance)
Gender –0.382
(0.278)
0.034
(0.178)
Location 0.732***
(0.262)
0.161
(0.173)
ISD ability 0.000
(0.149)
0.058
(0.094)
Team –0.038*
(0.027)
0.021
(0.017)
Communication and Trust on
Performance (independent
variable and mediating
variable on dependent
variable)
0.517***
(effect of trust)
–0.014
(effect of
communication)
R2 0.117 0.659
Notes: a DR2
shows change from the first model (without the interaction term). b DF shows change
from the first model (without the interaction term). * p < 0.10; ** p < 0.05; *** p < 0.01.
298 Sarker, Ahuja, Sarker, and Kirkeby
has highlighted the importance of trust in ensuring a distributed team’s success and
performance, our study suggests that trust remains critical even when it comes to
explaining or predicting the performance of the individual team member. Jones and
George [51], for example, suggest that the trustworthiness of an individual may promote
certain positive characteristics regarding that individual. Drawing on their ideas, it may
be argued that an individual who is trusted in a distributed team will be viewed as one
who has a preference for communal relationships “characterized by helpfulness and
responsibility,” and an initiative to contribute to such relationships; further, he or she is
seen as one who engages in the “subjugation” of one’s own “personal needs and ego”
to “pursue a common goal” [51, pp. 541–542]. Such an individual is naturally a key
contributor to the distributed team, and therefore will be seen as the high performer by
peers. Given that distributed team members interact primarily through the electronic
media, this trust will be formed primarily based on the communication they engage
in with the other team members (e.g., [91]).
While the mediation model best described the relationships between communication
and trust and their effect on individual performance, a test of the moderation model
also revealed a significant effect of the interaction of trust and communication on
performance, albeit in a direction contrary to the one expected. We believe that this
opposite result leads to doubts regarding recent pronouncements suggesting that in
knowledge economies communication is “real work” [25, pp. 90–91] and indicates
that there are contexts in which more communication can lead to adverse results in
terms of performance. To verify this, we split our full data into two groups: one with
members having high trust centrality and the other with members having low centrality. In order to split the data, we calculated the median trust centrality and used
the median for coding an individual as high or low. We then conducted a regression
analysis to assess the effect of the interaction between trust and communication on
performance within each of the two split data sets. Our results indicated that in the
high trust centrality set, the interaction between communication and trust had a positive
Table 6. Nature of Hypothesis Support
Hypothesis
Type of model and
nature of prediction Nature of support
1 Additive; communication and
trust centrality will both affect
performance.
Trust had a significant effect, but
communication did not; thus, H1
was not supported.
2 Interaction/moderation; trust
centrality will play a moderating
role on the relationship
between communication
centrality and performance.
While trust centrality did
play a moderating role, its
direction was opposite to that
hypothesized; thus, H2 was not
supported.
3 Mediation model; trust centrality
will play a key mediating role
between communication
centrality and performance.
Trust centrality did play a
mediating role; thus, H3 was
supported.
The Role of Communication and Trust in Global Virtual Teams 299
effect on performance (b = 0.123, p < 0.05), whereas in the low trust centrality set, the
interaction between communication and trust had a negative influence on performance
(b = –0.815, p < 0.10). In testing this, we controlled for gender, location, ISD ability,
and team. We believe that this result provides us with a clearer picture about the role
of trust in globally distributed teams—when an individual has low trustworthiness
(indicated by low trust centrality), his or her communicativeness (indicated by high
communication centrality) is likely to be seen as unproductive or meaningless babbling,
thereby leading to the communicator being perceived as a poor performer. In contrast,
when an individual has high trustworthiness, communication and trust work in synergy
to positively affect performance as perceived by other team members. Further, the
high R-square in the low-trust model (0.483 as opposed to 0.094 in the case of high
trust) suggests that in low-trust situations, the negative effect of communication on
performance is extremely potent. To summarize, we believe that the results illustrate
the central role of trust played in globally distributed teams and provide support to
the not so commonly articulated view that more communication may not always be
better [56]. In terms of the test of the competing models, we can thus conclude that
the moderation model holds true when the level of trust is high.
Limitations
The use of student subjects is a potential limitation of our research, given that findings based on student data have often been criticized for their artificiality and lack
of external validity [38]. However, Dipboye and Flanagan [28], among many others,
argue that student subjects represent a variety of backgrounds and goals, similar to
organizational members, and usually reflect a typical working professional. Locke [60]
also concluded from his study that results obtained from student samples are similar to
those obtained from managers in studies related to industrial organization psychology
and organizational behavior. Finally, we believe that the intense and longitudinal nature
of the projects with real clients, to whom the virtual teams were accountable, prompted
a majority of students to appropriate the roles of systems development professionals
engaged in distributed ISD rather than act as “typical” students. Of course, future work
should investigate these results in a variety of settings, including real-world distributed
project teams, to ensure the broad generalizability of the findings.
Another limitation of this study is the fact that it involved only distributed teams
with a hybrid configuration, where some members were collocated and others were
distributed. It may be argued that in a “pure” distributed team, wherein each member
is geographically separated from the others, the results may be different. Indeed,
past research highlights that, owing to differences in the social presence of the media
used for interaction and collaboration, face-to-face and distributed members interact
differently, leading to different outcomes (e.g., [98]). However, in recent times, the
premise of the social presence theory or media capacity theories has been contested
[44, 72]. For example, Hollingshead and Contractor [44] take the position that there
are no significant systematic differences between face-to-face and computer-mediated
groups in terms of basic interpersonal behaviors such as communication, or in terms
300 Sarker, Ahuja, Sarker, and Kirkeby
of performance, especially when groups are observed longitudinally (which is the
case in our study). Similarly, Walther and Burgoon [107] show that while members
of computer-mediated groups felt less connected to one another initially, over time
members of computer-mediated groups expressed more positive feelings about one
another that approximated those expressed by members of face-to-face groups. Finally,
Montoya et al. suggest that in “today’s advanced voice and data networks, increasingly
pervasive network access, integrated technologies, or integrated devices that facilitate
simultaneous multimedia use” [72, p. 143], oftentimes members’ behaviors toward
(or interactions with) remote and collocated members are not necessarily different.
Nevertheless, future research should test the relationships highlighted in this study
using pure virtual teams.
Another limitation of the study was the sometimes unequal team sizes in the two
dyadic locations (i.e., the United States and Norway, or the United States and Denmark)
due to practical issues related to creating teams (e.g., unequal number of students in
the two locations; students’ interests in certain projects due to which they may have
requested to be moved to a different team from the one they were initially assigned
to), as well as team member attrition. While the unequal team sizes (like numerous
other variables) could have affected the team dynamics, we believe that the variations
in teams do not necessarily weaken the study, but may actually strengthen it in terms
of its generalizability. We note that the study was not designed as a laboratory experiment, with emphasis on controlling all factors.
While the use of the SNA approach is undoubtedly a strength of this study, the fact
that key variables were assessed using a single item only may be viewed as a limitation of the study. However, given the acknowledgment among scholars that single
items are as reliable and stable as multiple items [88], and the fact that in the SNA
approach respondents rate a “single characteristic in numerous targets” [27], making
it not so inconsistent with traditional questionnaires where respondents rate a single
individual on multiple characteristics or items, we believe that the use of single items
is not a significant shortcoming of the study.
Another potential limitation of our study could be that in analyzing our three competing models we used our entire data set, which consisted of two types of distributed
teams: those that were engaged in systems analysis and design tasks only (U.S.–
Denmark teams), and those involved in systems development in addition to systems
analysis and design (U.S.–Norway teams). In order to assess whether this asymmetry
in the data set tended to taint our results, we split our data into two sections: U.S.–
Norway team members and U.S.–Denmark team members. We tested all three models
using the separate data sets. The results (in terms of the relationships between the
key constructs) were consistent with those using the full data set. We believe that this
highlights the stability of our results and indicates the generalizability of our study
across the two tasks.
A final limitation of the study could be an artifact of the specific data collection
approach employed. We used a cross-sectional survey technique to collect the data,
where the same respondent provided assessment of the predictor and the criterion
variables. While this is not an uncommon practice, common method variance
The Role of Communication and Trust in Global Virtual Teams 301
concerns regarding such studies have recently been raised [83]. There are several
known tests of common method variance. Initially, Harmon’s single-factor test was
suggested as the most suitable test for common method variance (e.g., [83]), but
recently researchers have criticized it by arguing that “negative bias in this procedure
is so large that . . . [it] would produce virtually meaningless results” [59, p. 114]. On
similar lines, Malhotra et al. also argue that “this technique does not offer an acceptable means to estimate and control for methods effects” [62, p. 1868]. An alternate
approach, using a “marker” variable was proposed by Lindell and Whitney [59], and
this approach has received much support from IS researchers [62, 80]. The marker
variable approach asks researchers to include a theoretically unrelated construct
in the regression equation and observe its correlation with the primary variables.
While it is recommended that this theoretically unrelated variable be measured during the original data collection, in cases where such a construct may not have been
measured, Pavlou et al. [80] suggest a modified test where any “weakly related”
construct that might have been measured can be used. Following the guidelines of
prior researchers, we used the country of origin of the respondents2
as the “marker
variable.” Our correlation results suggest that the average correlation between the
marker variable and all the primary constructs of the study was 0.12 (with the highest
correlation being 0.19), and none of the correlations were significant. This suggests
that there are minimal common method variance problems in the study. Further, the
correlations between the primary variables were well under 0.90, again indicating
that common method variance did not affect the results [80]. Finally, it is worth
mentioning that notable researchers have concluded from their empirical studies that
even if there are some common method biases in organizational research, “the detected bias in observed correlations . . . [is] not sufficient to challenge the theoretical
interpretation of the relationship” [30, pp. 399–400]. Further, Doty and Glick argue
that in “most organizational research, we are happy to predict the directionality of
the relationships,” thus “common methods bias . . . may be something to avoid . . .
but it is probably not sufficiently large enough to invalidate many of our theoretical
interpretations and research conclusions” [30, p. 400].
Contributions and Conclusion
Contributions to Research
To put our work in context, we submit that different types of contributions are seen
as valuable in different phases of knowledge on a research topic [39]. Consistent
with this general pattern, early virtual teamwork was largely definitional and descriptive. Thereafter, as the research community’s interest on virtual teams grew, studies
identified key constructs relevant to virtual teams (e.g., trust, leadership, communication, task performance) and examined relationships among them, with the goal of
systematically building and subsequently testing a set of propositions/theory. As a
significant and diverse body of literature on virtual teams developed, there was a need
for consolidation, in the form of in-depth reviews [93] that provide an understanding
302 Sarker, Ahuja, Sarker, and Kirkeby
of the trends and nuances regarding the phenomenon and in the form of empirical
tests of competing/unresolved relationships among key constructs, which, in the virtual/distributed team context, include constructs such as communication, trust, and
performance. Our study responds to this need. The current study specifically makes
an important contribution to the literature on trust in global virtual teams, particularly
in determining/predicting high-performing individuals, where communication forms
the underlying basis for all social action [2, 91]. Overall, our empirical examination
provides strong support for the mediation model, indicating that communication
leads to performance through trust. Although the additive and interaction models
were not supported, our study did add some nuances to the literature underlying the
interaction model. In particular, it suggests that for trustworthy individuals, communication can enhance their performance; however, for those who are perceived as
less trustworthy, high levels of communication can backfire. Our derivation of the
propositions reflected in the three competing models (i.e., additive, interaction, and
mediation) allowed us to proceed with Argyris’s notion of “good science,” wherein
we could subject these competing relationships embedded in the literature “to the
most rigorous tests available” [3, p. 250].
In a review of the literature on global virtual teams, Martins et al. recommend
that while “researchers have made considerable headway into understanding factors
contributing to the creation and destruction of trust within VTs [virtual teams], there
is room for future research that . . . [examines] their roles in VT” [64, p. 822]. We
believe that one of the important contributions of this study to the existing theory surrounding trust is that it clarifies the role trust plays within virtual teams, in particular
by highlighting the point that trust holds an important position in terms of enhancing
individuals’ performance within distributed teams.
The contribution of our study, we believe, also goes beyond the validation of a proposition (i.e., communication → trust → performance). Our empirical study, we submit,
advances our field’s understanding of the interrelationships among key constructs in
virtual team research by empirically reconciling conflicting views in the large body of
published work, where researchers were unable to judge the validity of the competing
posited models (involving the constructs of communication, trust, and performance).
Such consolidation studies involving the test of competing models have been conducted in a number of areas with matured/maturing bodies of knowledge, including
IS implementation [63], technology adoption [105], business process reengineering
[90], and individual work performance [68].
A related strength of this study is its adoption of a network approach to investigating
the issue at hand. As organizations are moving to the networked forms, it has been
argued that a paradigm of “networked individualism,” which enables the study of
an individual in the context of the individual’s existing relationships, facilitates the
understanding of key behavioral phenomena associated with these new organizational
forms. Indeed, we believe that one of the primary strengths of this study is its departure from the individual trait/behavior-based approach to trust and communication
used in much of the prior research and the use of the complementary network-based
perspective to study communication and trust—constructs that are inherently relational
The Role of Communication and Trust in Global Virtual Teams 303
in character. While network approaches in general, and SNA in particular, have much
to offer in our discipline, SNA remains underutilized, especially in examining virtual
organizations and teams [1, 2]. Gallivan and Benbunan-Fich [35] argue that one of
the main shortcomings of many past studies of computer-mediated groups is that they
tend to study team members’ behaviors/actions without taking the members’ context
into consideration. We believe that our study can contribute to the awareness within
the IS research community regarding the strengths of applying SNA. Within this approach, behaviors of individual team members can be examined, without isolating
them from the social context.
Contributions to Practice
While noting that the objective of this paper is primarily to provide a theoretical clarification of the interrelationships between the constructs of trust, communication, and
performance within globally distributed teams using an arguably more appropriate
methodological approach than the traditional attribute-based approach, we see some
practical implications of the study as well.
The study clarifies the role of communication. Recent research and practice seems
to promote the view that “in a knowledge-driven economy, talk is real work” [25,
pp. 90–91] and recommend that one discard the traditional management principle of
“stop talking and get to work” and begin the era of “start talking.” A similar belief,
the “babble hypothesis,” which highlights the importance of communication, has also
been popularized in the management literature. Our study urges caution on accepting
the above views unreflectively, at least in the context of distributed team members.
Members of virtual teams should understand that communication alone will not help
in their being viewed as contributing and high-performing individuals. Through their
communication, they need to secure the complete trust of their distributed members
before they can be acknowledged as contributing and performing members of the team.
Further, an awareness of the mediating role of trust will hopefully prompt virtual team
members to refrain from freeloading and other deceptive behaviors, thereby increasing
the effectiveness/productivity of the entire team.
The study’s results also highlight the importance of networks and network centrality within organizations. To be viewed as important contributors to the virtual team
and as high performers, it is important that individuals occupy a strong central position within their communication and trust networks. By occupying central positions
through high levels of communication and trust-inducing behaviors, an individual team
member is able to make him- or herself more visible to (and more relied upon by) the
other distributed team members. Such visibility also helps highlight the individual’s
contributions to the team.
Finally, the study’s results illustrate a suitable way to assess distributed team
members’ performance, not through objective supervisor ratings, but through ratings
of their contribution by their distributed team members. Owing to the geographical
distribution and differences in context [91], the “objective” performance evaluations
are likely to be more biased and potentially tainted [10, 111].
304 Sarker, Ahuja, Sarker, and Kirkeby
Future Research
We believe that the study points to several avenues for future research. Here, we have
used one specific measure of an individual team member’s structural position (i.e., the
degree centrality) on his or her perceived performance, which was appropriate given
that our objective was to test the three competing models. Apart from using degree
centrality, structural roles (e.g., “gatekeeper,” “liaison,” “bridge”) of an individual
within a virtual team could have implications for individual-level and team-level outcomes [71, p. 32]. Thus, we invite future research that qualitatively or quantitatively
examines the effect of individuals’ structural positions in their respective teams on
their performance.
Another point worth mentioning is that we adopted a relational approach to measuring performance in this study, especially given that supervisory ratings have been
criticized for being too political [10, 62] and supervisory ratings may not be appropriate
in distributed teams that are relatively flat, with emergent roles and responsibilities[94].
However, having said that, it must be acknowledged that some distributed teams do
have assigned project managers, with some of these managers participating in the daily
activities of the team, and with others serving in the role of external coordinators and
evaluators. The evaluations by the project managers could be seen as more distant
and objective (not relational), though not necessarily more accurate. Future research
should thus investigate whether trust and communication centrality measures have a
similar effect on individual performance when the performance ratings are assigned by
the project managers rather than by peers in the team who are intimately aware of the
individual’s communicative behaviors and quality of contributions to deliverables.
In closing, we echo Jarvenpaa and Leidner’s view [48] of trust and communication
being two fundamental concepts associated with globally distributed teams undertaking knowledge work. Our hope is that this paper is able to provide clarification on
how these two frequently used concepts come together to contribute to individual
performance, a critical dependent variable for organizations operating in today’s
networked global economy.
Notes
1. It is worth noting that each of these three models is in evidence in the literature, sometimes with the same authors’ arguments shifting from one model to the other. In our opinion, a
fundamental step to furthering knowledge in this area is to empirically determine which model,
invoked overtly or implicitly in the discussions, can be considered valid.
2. There were 47 participants who were born in the United States, 23 in Norway, and 14 in
Denmark; 22 participants were born in other countries; and 5 respondents did not indicate the
country they originated from. We note that this variable is different from the location variable,
where each respondent was associated with either the United States or Scandinavia.
References
1. Ahuja, M.K., and Carley, K.M. Network structure in virtual organizations. Organization
Science, 10, 6 (1999), 741–747.
The Role of Communication and Trust in Global Virtual Teams 305
2. Ahuja, M.K.; Galletta, D.F.; and Carley, K.M. Individual centrality and performance in
virtual R&D groups: An empirical study. Management Science, 49, 1 (2003), 21–38.
3. Argyris, C. Knowledge for Action: A Guide to Overcoming Barriers to Organizational
Change. San Francisco: Jossey-Bass, 1993.
4. Armstrong, D., and Cole, P. Managing distances and differences in geographically distributed work groups. In S.E. Jackson and M.E. Ruderman (eds.), Diversity in Work Teams:
Research Paradigms for a Changing Workplace. Washington, DC: American Psychological
Association, 1995, pp. 187–215.
5. Balthazard, P.; Potter, R.E.; and Warren, J. Expertise, extraversion and group interaction styles as performance indicators in virtual teams. Data Base for Advances in Information
Systems, 35, 1 (2004), 41–64.
6. Baron, R.M., and Kenny, D.A. The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality
and Social Psychology, 51, 6 (1986), 1173–1182.
7. Becerra, M., and Gupta, A.K. Perceived trustworthiness within the organization: The
moderating impact of communication frequency on trustor and trustee effects. Organization
Science, 14, 1 (2003), 32–44.
8. Benbasat, I.; Gefen, D.; and Pavlou, P.A. Trust in online environments. Journal of Management Information Systems, 24, 4 (Spring 2008), 5–11.
9. Benbasat, I.; Gefen, D.; and Pavlou, P.A. Introduction to special issue on novel perspectives on trust in information systems. MIS Quarterly, 34, 2 (2010), 367–371.
10. Brass, D.J. A social network perspective to human resource management. In R. Cross,
A. Parker, and L. Sasson (eds.), Networks in the Knowledge Economy. New York: Oxford
University Press, 2003, pp. 283–323.
11. Breu, K., and Hemingway, C.J. Making organizations virtual: The hidden cost of distributed teams. Journal of Information Technology, 19, 3 (2004), 191–202.
12. Brown, H.G.; Poole, M.S.; and Rodgers, T.L. Interpersonal traits, complementarity,
and trust in virtual collaboration. Journal of Management Information Systems, 20, 4 (Spring
2004), 115–137.
13. Burt, R. The social structure of competition. In R. Cross, A. Parker, and L. Sasson (eds.),
Networks in the Knowledge Economy. New York: Oxford University Press, 2003, pp. 13–56.
14. Carley, K. A theory of group stability. American Sociological Review, 56, 3 (1991),
331–354.
15. Carmel, E. Global Software Teams: Collaborating Across Borders and Time Zones. Upper
Saddle River, NJ: Prentice Hall, 1999.
16. Carson, S.J.; Madhok, A.; Varman, R.; and John, G. Information processing moderators
of the effectiveness of trust-based governance in interfirm R&D collaboration. Organization
Science, 14, 1 (2003), 45–56.
17. Chudoba, K.M.; Wynn, E.; Lu, M.; and Watson-Manheim, M.B. How virtual are we?
Measuring virtuality and understanding its impact in a global organization. Information Systems
Journal, 15, 4 (2005), 279–306.
18. Clegg, S.R., and Hardy, C. Studying Organizations: Theory and Method. London: Sage,
1999.
19. Coppola, N.W.; Hiltz, S.R.; and Rotter, N.G. Becoming a virtual professor: Pedagogical
roles and asynchronous learning networks. Journal of Management Information Systems, 18,
4 (Spring 2002), 169–189.
20. Coutu, D.L. Organization: Trust in virtual teams. Harvard Business Review, 76, 3 (1998),
20–21.
21. Cross, R.; Parker, A.; and Sasson, L. (eds.). Networks in the Knowledge Economy. New
York: Oxford University Press, 2003.
22. Cummings, L.L., and Bromiley, P. The organizational trust inventory (OTI): Development
and validation. In R. Kramer and T. Tyler (eds.), Trust in Organizations. Thousand Oaks, CA:
Sage, 1996, pp. 302–330.
23. Cummings, J.N., and Cross, R. Structural properties of work groups and their consequences
for performance. Social Networks, 25, 3 (2003), 197–210.
24. Cyr, D. Modeling Web site design across cultures: Relationships to trust, satisfaction, and
e-loyalty. Journal of Management Information Systems, 24, 4 (Spring 2008), 47–72.
306 Sarker, Ahuja, Sarker, and Kirkeby
25. Davenport, T.H., and Prusak, L. Working Knowledge: How Organizations Manage What
They Know. Boston: Harvard Business School Press, 1998.
26. Degenne, A., and Forse, M. Introducing Social Networks. London: Sage, 1999.
27. Denissen, J.J.A.; Geenen, R.; Selfhout, M.; and Van Aken, M.A.G. Single-item big five
ratings in a social network design. European Journal of Personality, 22 (2007) (available at
http://homepage.psy.utexas.edu/homepage/faculty/Gosling/reprints/EJPDenissen2008.pdf).
28. Dipboye, R.L., and Flanagan, M.F. Research settings in industrial and organizational
psychology: Are findings in the field more generalizable than in the laboratory? American
Psychologist, 32, 2 (1979), 141–150.
29. Dirks, K.T., and Ferrin, D.L. The role of trust in organizational settings. Organization
Science, 12, 4 (2001), 450–467.
30. Doty, D.H., and Click, W.H. Common methods bias: Does common methods variance
really bias results? Organizational Research Methods, 1, 4 (1998), 374–406.
31. Eagly, A.H., and Karau, S.J. Gender and emergence of leaders: A meta-analysis. Journal
of Personality and Social Psychology, 60, 5 (1991), 685–710.
32. Edwards, H.K., and Sridhar, V. Analysis of software requirements engineering exercises in a
global virtual team setup. Journal of Global Information Management, 13, 2 (2005), 21–41.
33. Freeman, L.C. Centrality in social networks: Conceptual clarifications. Social Networks,
1, 3 (1979), 215–235.
34. Galaskiewicz, J., and Wasserman, S. Introduction advances in the social and behavioral
sciences from social network analysis. In S. Wasserman and J. Galaskiewicz (eds.), Advances
in Social Network Analysis. Thousand Oaks, CA: Sage, 1994, pp. xi–xvi.
35. Gallivan, M., and Benbunan-Fich, R. A framework for analyzing levels of analysis issues in studies of e-collaboration. IEEE Transactions on Professional Communication, 48, 1
(2005), 87–104.
36. Garton, L; Haythornthwaite, C.; and Wellman, B. Studying online social networks.
Journal of Computer-Mediated Communication, 3, 1 (1997) (available at http://jcmc.indiana
.edu/vol3/issue1/garton.html).
37. Gefen, D.; Pavlou, P.; Benbasat, I.; McKnight, H.; Stewart, K.; and Straub, D.W. ICIS
panel summary: Should institutional trust matter in information systems research? Communications of the AIS, 17, 1 (2006), 205–222.
38. Gordon, M.E.; Slade, L.A.; and Schmitt, N. The “science of sophomore” revisited: From
conjecture to empiricism. Academy of Management Review, 11 (1986), 191–207.
39. Gregor, S. The nature of theory in information systems. MIS Quarterly, 30, 3 (2006),
611–642.
40. Griffith, T.L.; Sawyer, J.E.; and Neale, M.A. Virtualness and knowledge in teams: Managing the love triangle of organizations, individuals, and information technology. MIS Quarterly,
27, 2 (2003), 265–287.
41. Handy, C. Trust and the virtual organization. Harvard Business Review, 73, 3, (1995)
40–50.
42. Hanneman, R., and Riddle, M. Introduction to Social Network Methods. Riverside: University of California, Riverside, 2005 (available at http://faculty.ucr.edu/~hanneman/nettext/).
43. Hofstede, G. Cultures Consequences: Comparing Values, Behaviors, Institutions, and
Organizations Across Nations, 2d ed. Thousand Oaks, CA: Sage, 2001.
44. Hollingshead, A.B., and Contractor, N.S. New media and organizing at the group level.
In L. Lievrouw and S. Livingstone (eds.), The Handbook of New Media. Thousand Oaks, CA:
Sage, 2002, pp. 221–253.
45. Hossain, L., and Wigand, R.T. ICT enabled virtual collaboration through trust. Journal of
Computer-Mediated Communication, 10, 1 (2004) (available at http://jcmc.indiana.edu/vol10/
issue1/hossain_wigand.html).
46. Iacono, S., and Weisband, S. Developing trust in virtual teams. In R.H. Sprague (ed.),
Proceedings of the 30th Annual Hawaii International Conference on System Sciences. Los
Alamitos, CA: IEEE Computer Society, 1997.
47. Ibarra, H. Network centrality, power, and innovation involvement: Determinants of technical and administrative roles. Academy of Management Journal, 36, 3 (1993), 471–502.
48. Jarvenpaa, S.L., and Leidner, D. Communication and trust in global virtual teams. Organization Science, 10, 6 (1999), 791–815.
The Role of Communication and Trust in Global Virtual Teams 307
49. Jarvenpaa, S.L.; Knoll, K.; and Leidner, D.E. Is anybody out there? Antecedents of
trust in global virtual teams. Journal of Management Information Systems, 14, 4 (Spring
1998), 29–64.
50. Jarvenpaa, S.L.; Shaw, T.R.; and Staples, D.S. Toward contextualized theories of
trust: The role of trust in global virtual teams. Information Systems Research, 15, 3 (2004),
250–267.
51. Jones, G.R., and George, J.M. The experience and evolution of trust: Implications for
cooperation and teamwork. Academy of Management Review, 23, 3 (1998), 531–546.
52. Kankanhalli, A.; Tan, B.C.Y.; and Wei, K.K. Conflict and performance in global virtual
teams. Journal of Management Information Systems, 23, 3 (Winter 2006–7), 237–274.
53. Kim, D.J. Self-perception-based versus transference-based trust determinants in
computer-mediated transactions: A cross-cultural comparison study. Journal of Management
Information Systems, 24, 4 (Spring 2008), 13–45
54. Kirkman, B.L., and Mathieu, J.E. The dimensions and antecedents of team virtuality.
Journal of Management, 31, 5 (2005), 700–718.
55. Knoll, K., and S.L. Jarvenpaa. Learning virtual team collaboration. In R.H. Sprague
(ed.), Proceedings of the 28th Annual Hawaii International Conference on System Sciences.
Los Alamitos, CA: IEEE Computer Society Press, 1995, pp. 92–101.
56. Krackhardt, D. The strength of strong ties: The importance of philos in organizations.
In R. Cross, A. Parker, and L. Sasson (eds.), Networks in the Knowledge Economy. New York:
Oxford University Press, 2003, pp. 82–105.
57. Krackhardt, D., and Hanson, J. Informal networks: The company behind the chart. In
R. Cross, A. Parker, and L. Sasson (eds.), Networks in the Knowledge Economy. New York:
Oxford University Press, 2003, pp. 235–247.
58. Levin, D.Z., and Cross, R. The strength of weak ties you can trust: The mediating role
of trust in effective knowledge transfer. Management Science, 50, 11 (2004), 1477–1491.
59. Lindell, M.K., and Whitney, D.J. Accounting for common method variance in crosssectional research designs. Journal of Applied Psychology, 86, 1 (2001), 114–121.
60. Locke, E.A. Generalizing from Laboratory to Field Setting: Research Findings from
Industrial Organization, Organizational Behavior, and Human Resource Management. Lexington, MA: Lexington Books, 1986.
61. Lu, M.; Watson-Manheim, M.B.; Chudoba, K.M.; and Wynn, E. Virtuality and team
performance: Understanding the impact of variety of practices. Journal of Global Information
Technology Management, 9, 1 (2006), 4–23.
62. Malhotra, N.K.; Kim, S.S.; and Patil, A. Common method variance in IS research: A
comparison of alternative approaches and a reanalysis of past research. Management Science,
52, 12 (2006), 1865–1883.
63. Markus, M.L. Power, politics, and MIS implementation. Communications of the ACM,
26, 6 (1983), 430–444.
64. Martins, L.L.; Gilson, L.L.; and Maynard, M.T. Virtual teams: What do we know and
where do we go from here? Journal of Management, 30, 6 (2004), 805–835.
65. Massey, A.P.; Montoya-Weiss, M.M.; and Hung, Y.T. Because time matters: Temporal
coordination in global virtual project teams. Journal of Management Information Systems,
19, 4 (Spring 2003), 129–155.
66. Mayer, R.C.; Davis, J.H.; and Schoorman, D.F. An integrative model of organizational
trust. Academy of Management Review, 2, 3 (1995), 709–730.
67. McEvily, B.; Perrone, V.; and Zaheer, A. Introduction to special issue on trust in an
organizational context. Organization Science, 14, 1 (2003), 1–4.
68. Mehra, A.; Kilduff, M.; and Brass, D.J. The social networks of high and low selfmonitors: Implications for workplace performance. Administrative Science Quarterly, 46,
1 (2001), 121–146.
69. Meyerson, D.; Weick, K.E.; and Kramer, R.M. Trust in Organizations: Frontiers of
Theory and Research. Thousand Oaks, CA: Sage, 1996.
70. Miles, J.N.V., and Shevlin, M.E. Applying Regression and Correlation. London: Sage,
2001.
71. Monge, P.R., and Contractor, N. Theories of Communication Network. Oxford: Oxford
University Press, 2003.
308 Sarker, Ahuja, Sarker, and Kirkeby
72. Montoya, M.M.; Massey, A.P.; Hung, Y.C.; and Crisp, C.B. Can you hear me now? Communication in virtual product development teams. Journal of Product Innovation Management,
26, 2 (2009), 139–155.
73. Morgeson, F.P.; Reider, M.H., and Campion, M.A. Selecting individuals in team settings:
The importance of social skills, personality characteristics, and teamwork. Personnel Psychology, 58, 3 (2005), 583–611.
74. Morris, S.A.; Marshall, T.E.; and Rainer, R.K., Jr. Impact of user satisfaction and trust on
virtual team members. Information Resources Management Journal, 15, 2 (2002), 22–30.
75. Nicholson, B., and Sahay, S. Some political and cultural issues in the globalization of
software development: Case experience from Britain and India. Information and Organization,
11, 1 (2001), 25–44.
76. Ocker, R.J. Influences on creativity in asynchronous virtual teams: A qualitative analysis
of experimental teams. IEEE Transactions on Professional Communication, 48, 1 (2005),
22–39.
77. O’Hara-Devereaux, M., and Johansen, R. Global Work: Bridging Distance, Culture and
Time. San Francisco: Jossey-Bass, 1994.
78. Panteli, N., and Davison, R.M. The role of subgroups in the communication patterns
of global virtual teams. IEEE Transactions on Professional Communication, 48, 2 (2005),
191–200.
79. Panteli, N., and Duncan, E. Trust and temporary virtual teams: Alternative explanations
and dramaturgical relationships. Information Technology & People, 17, 4 (2004), 423–441.
80. Pavlou, P.A.; Liang, H.; and Xue, Y. Understanding and mitigating uncertainty in online exchange relationships: A principal–agent perspective. MIS Quarterly, 31, 1 (2007), 105–136.
81. Piccoli, G., and Ives, B. Trust and the unintended effects of behavior control in virtual
teams. MIS Quarterly, 27, 3 (2003), 365–395.
82. Piccoli, G.; Powell, A.; and Ives, B. Virtual teams: Team control structure, work processes,
and team effectiveness. Information Technology & People, 17, 4 (2004), 359–372.
83. Podsakoff, P.M.; MacKenzie, S.B.; Lee, J.-Y.; and Podsakoff, N.P. Common method
biases in behavioral research: A critical review of the literature and recommended remedies.
Journal of Applied Psychology, 88, 5 (2003), 879–903.
84. Powell, A.; Piccoli, G.; and Ives, B. Virtual teams: A review of current literature and
directions for future research. Data Base for Advances in Information Systems, 35, 1 (2004),
6–36.
85. Rice, R.E. Network analysis and computer-mediated communication systems. In S. Wasserman and J. Galaskiewicz (eds.), Advances in Social Network Analysis: Research in Social
and Behavioral Sciences. Thousand Oaks, CA: Sage, 1994, pp. 167–203.
86. Rice, R.E. Relating electronic mail use and network structure to R&D work networks and
performance. Journal of Management Information Systems, 11, 1 (Summer 1994), 9–30.
87. Ridings, C.M.; Gefen, D.; and Arinze, B. Some antecedents and effects of trust in virtual
communities. Journal of Strategic Information Systems, 11, 3–4 (2002), 271–295.
88. Robins, R.W.; Hendin, H.M.; and Trzesniewski, K.H. Measuring global self-esteem:
Construct validation of a single-item measure and the Rosenberg self-esteem scale. Personality
and Social Psychology Bulletin, 27, 2 (2001), 151–161.
89. Rogers, E.M., and Albritton, M.M. Interactive communication technologies in business
organizations. Journal of Business Communication, 32, 2 (1995), 177–185.
90. Sarker, S., and Lee, A.S. Using a positivist case research methodology to test three competing practitioner theories-in-use of business process redesign. Journal of the Association for
Information Systems, 2, 7 (2002), 1–72.
91. Sarker, S., and Sahay, S. Implications of space and time for distributed work: An interpretive study of U.S.–Norwegian systems development teams. European Journal of Information
Systems, 13, 1 (2004), 3–20.
92. Sarker, S.; Valacich, J.S.; and Sarker, S. Virtual team trust: Instrument development and
validation in an IS educational environment. Information Resources Management Journal, 16,
2 (2003), 35–55.
93. Saunders, C., and Ahuja, M. Are all distributed teams the same? Differentiating between
temporary and ongoing distributed teams. Small Group Research, 37, 6 (2006), 662–700.
The Role of Communication and Trust in Global Virtual Teams 309
94. Scacchi, W. Socio-technical interaction networks in free/open source software development processes. In S.T. Acuña and N. Juristo (eds.), Software Process Modeling. New York:
Springer Science and Business Media, 2005, pp. 1–27.
95. Scarnati, J.T. Beyond technical competence: Developing mental toughness. Career
Development International, 5, 3 (2000), 171–176.
96. Schultze, U., and Boland, R.J. Knowledge management technology and the reproduction of knowledge work practices. Journal of Strategic Information Systems, 9, 2–3 (2000),
193–212.
97. Scott, J. Social Network Analysis: A Handbook. London: Sage, 2000.
98. Short, J.W.E., and Christie, B. The Social Psychology of Telecommunications. London:
Wiley, 1976.
99. Stohl, C., and Walker; K. A bona fide perspective for the future of groups: Understanding
collaborating groups. In L.R. Frey (ed.), New Directions in Group Communication. Thousand
Oaks, CA: Sage, 2002, pp. 237–252.
100. Tan, B.C.; Wei, K.; Huang, W.W.; and Ng, G. A dialogue technique to enhance electronic communication in virtual teams. IEEE Transactions on Professional Communication,
43, 2 (2000), 153–165.
101. Tichy, N.M. Networks in organizations. In P.C. Nystrom and W.H. Starbuck (eds.),
Handbook of Organizational Design, Vol. 2: Remodeling Organization and Their Environments.
New York: Oxford University Press, 1981, pp. 225–249.
102. Trice, H.M., and Beyer, J.M. The Cultures of Work Organizations. Upper Saddle River,
NJ: Prentice Hall, 1993.
103. Tsai, W., and Ghoshal, S. Social capital and value creation: The role of intrafirm networks.
Academy of Management Journal, 41, 4 (1998), 464–476.
104. Valente, T.W., and Davis, R.L. Accelerating the diffusion of innovations using opinion
leaders. Annals of the American Academy of Political and Social Science, 566 (November
1999), pp. 55–67.
105. Venkatesh, V.; Speier, C.; and Morris, M.G. User acceptance enablers in individual
decision-making about technology: Toward an integrated model. Decision Sciences, 33, 2
(2002), 297–316.
106. Walker, M.E.; Wasserman, S.; and Wellman, B. Statistical models for support networks.
In S. Wasserman and J. Galaskiewicz (eds.), Advances in Social Network Analysis. Thousand
Oaks, CA: Sage, 1994, pp. 53–78.
107. Walther, J.B., and Burgoon, J.K. Relational communication in computer-mediated
interaction. Human Communication Research, 19, 1 (1992), 50–88.
108. Webster, J., and Wong, W.K.P. Comparing traditional and virtual group forms: Identity,
communication, and trust in naturally occurring project teams. International Journal of Human
Resource Management, 19, 1 (2008), 41–62.
109. Wellman, B.; Quan-Haase, A.; Boase, J.; Chen, W.; Hampton, K.; de Diaz, I.D.; and
Miyata, K. The social affordances of the Internet for networked individualism. Journal of
Computer-Mediated Communication, 8, 3 (2003) (available at http://jcmc.indiana.edu/vol8/
issue3/wellman.html).
110. Wilson, J.M.; Strauss, S.G.; and McEvily, B. All in due time: The development of trust
in computer-mediated and face-to-face teams. Organizational Behavior and Human Decision
Processes, 99, 1 (2006), 16–33.
111. Zolin, R.; Hinds, P.J.; Fruchter, R.; and Levitt, R.E. Interpersonal trust in cross-functional
geographically distributed work: A longitudinal study. Information and Organization, 14, 1
(2004), 1–26.
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