Globally Distributed Teams: The Effect of Diversity on Trust, Cohesion and Individual Performance

Abstract

Globally distributed teams are becoming more common among organizations that seek to maximize knowledge creation and innovation for competitive advantage. Although they are becoming widely used among global organizations, distributed teams are creating an environment replete in cultural and functional diversity. Whereas synergy among members is desired, diversity is likely to hinder team cohesion and individual performance. Our study models and empirically tests the effect of perceptions of diversity on trust, cohesion, and individual performance in actual globally distributed teams. The results indicate that individual productivity is negatively influenced by the extent of diversity within a team; however, this liability may be restrained if an environment of trust is encouraged and team cohesion develops.

Full text

Globally Distributed
Teams: The Effect
of Diversity
on Trust, Cohesion
and Individual
Performance
Gary Garrison
The Jack C. Massey Graduate School of
Business
Belmont University
Robin L. Wakefield
Hankamer School of Business
Baylor University
Xiaobo Xu
School of Business and Management
American University of Sharjah
Sang Hyun Kim
Kyungpook National University
Abstract
Globally distributed teams are becoming more
common among organizations that seek to maximize
knowledge creation and innovation for competitive
advantage. Although they are becoming widely used
among global organizations, distributed teams are
creating an environment replete in cultural and
functional diversity. Whereas synergy among
members is desired, diversity is likely to hinder team
cohesion and individual performance. Our study
models and empirically tests the effect of
perceptions of diversity on trust, cohesion, and
individual performance in actual globally distributed
teams. The results indicate that individual
productivity is negatively influenced by the extent of
diversity within a team; however, this liability may be
restrained if an environment of trust is encouraged
and team cohesion develops.
ACM Categories: D.2.9 Programming teams; H.0.
Information Systems.
Keywords: Globally distributed teams, Diversity,
Trust, Cohesion, Performance
Introduction
Competition in the global marketplace is driving the
use of distributed teams among organizations
seeking to leverage knowledge assets for
competitive advantage (McDonough, Kahn, and
Barczak, 2001). Vying to remain competitive,
organizations are infusing globally dispersed talent
into teams with the understanding that diversity may
enhance creativity and team performance. In fact, it
is suggested that maximizing knowledge creation in
multinational organizations may depend on
increasing the diversity of team members (Adler,
1997). However, infusing demographically diverse
individuals into a single team may create unique
challenges for individuals involved in the
collaborative effort (Powell, Piccoli and Ives, 2004).
These challenges are often due to diminished face-
to-face interactions and can include the
effectiveness of team leadership (Wakefield, Leidner
and Garrison 2008), the development of trust
(Kayworth and Leidner, 2001), and effective
communication and team cohesion (Kayworth and
Leidner, 2001; Lipnak and Stamps, 1997; Warkentin,
Sayeed and Hightower, 1997). Furthermore, the
removal of commonalities such as location,
language and culture often inhibits shared
understandings among distributed team members
(Cramton, 2001), and results in electronic
communications that may be misunderstood (Van
Ryssen and Godar, 2000). If the development of
The DATA BASE for Advances in Information Systems 27 Volume 41, Number 3, August 2010

trust is hindered and team cohesion is slack, it is
likely that diversity is detrimental to team
performance. Thus, an implicit tension exists such
that efforts to optimize the use of distributed teams
in a global strategy may actually reduce knowledge
sharing and hamper individual productivity and team
performance.
Research suggests that co-located teams comprised
of diverse members tend to outperform
homogeneous groups on creative tasks due to the
number of unique ideas generated by diverse group
members (Nemeth, 1992). However, research also
indicates that unique ideas are less likely to be
disseminated among diverse team members out of
the fear of ridicule (Chatman et al., 1998; Stasser
and Stewart, 1992). This apparent contradiction
implies that while creative synergy is experienced in
diverse groups such that heterogeneous teams
outperform homogenous teams, this creativity may
not be maximized at the individual level within the
heterogeneous team. Nemeth (1986) suggests that
members’ reluctance to share novel ideas is due to
a lack of trust among diverse team members,
although as trust develops the sharing of novel ideas
tends to increase (Kramer, Brewer, and Hanna,
1996).
In contrast to prior research investigating the
dynamics of distributed work (e.g. Jackson, May,
and Whitney, 1995; Gibson and Cohen 2003), our
study specifically examines the relationships
between team member diversity, trust, cohesion and
individual performance within globally distributed
teams. Incorporating self-categorization theory, we
address the research questions: Do perceived
differences among globally distributed team
members influence their individual performance? If
so, then through what mechanism(s) does this
occur? Rather than focus on team performance, we
take the discussion to a different level; one that
recognizes that team synergy and performance is
the compilation of individual contribution. Thus,
when individual performance is maximized it follows
that the team is operating optimally. We propose
that perceptions of differences (e.g., gender, age,
skill level) will negatively affect the development of
trust among team members, adversely affect team
cohesion, and ultimately hinder individual
performance. As individual performance suffers, the
team may experience a delay or disruption in
progress that may be considered a ‘liability’ of
diversity - the costs associated with teams
comprised of heterogeneous members. If team
composition is a factor in optimal individual output,
then understanding the impact of diversity on trust,
cohesion and individual performance will enable
organizations to implement successful global teams
as well as better manage those that are less
effective.
The paper proceeds as follows. First, we present a
brief overview of globally distributed teams prior to
discussing team member diversity and how it may
become an organizational liability. We construct a
model using self-categorization theory to explain
how perceptions of individual differences may
impede both the development of trust and team
cohesion as well as affect the relationships among
these variables. Data is collected from 78 individuals
participating in 18 global team projects to build a
database management system. The results indicate
that individual productivity is negatively influenced
by perceptions of diversity within a team; however,
this liability may be restrained if an environment of
trust is encouraged and team cohesion develops.
Background
Globally Distributed Teams
Our study defines the globally distributed team in
accord with prior distributed team literature. Globally
distributed teams are described as temporary teams
of people who are connected via communication
technologies across functional, organizational,
and/or geographic boundaries in order to combine
skills and resources to accomplish a goal (Hinds and
Bailey, 2003; Javenpaa, Knoll and Leidner, 1999;
Kayworth and Leidner, 2001; Lipnak and Stamps,
1997; Warkentin, Sayeed and Hightower, 1997).
Distributed teams have garnered significant attention
over the past decade from researchers in the fields
of management and information systems. Numerous
challenges to the successful coordination of virtual
teams are reported in the literature; the following
discussion focuses on those challenges (i.e.,
diversity and trust) most relevant to our study.
Powell, Piccoli, and Ives (2004) present a
comprehensive review of the virtual team literature in
which they cluster prior studies into issues pertaining
to cultural differences, socio-emotional processes,
task processes, and outputs (e.g., performance).
They also classify the research into global and non-
global categories with global virtual team studies
comprising the most recent additions to the
literature. In general, several factors are identified as
contributing to the successful performance in
distributed teams. These antecedents include team
cohesion (Maznevski and Chudoba, 2000), trust
(Jarvenpaa, Shaw, and Staples, 2004),
communication (Kayworth and Leidner, 2000),
The DATA BASE for Advances in Information Systems 28 Volume 41, Number 3, August 2010

training (Kaiser, Tullar, and McKowen, 2000),
leadership, and conflict resolution (Wakefield,
Leidner, and Garrison, 2008).
Across the virtual team literature, communication
challenges and trust issues appear to plague
distributed teams, but are of particular concern for
teams with greater diversity. For example, global
teams and/or teams characterized as having a
culturally diverse makeup are shown to have
coordination difficulties (Maznevski and Chudoba,
2001; Van Rysson and Godar, 2000), which may
impede effective communication (Kayworth and
Leidner, 2000; Van Rysson and Godar, 2000). While
communication is an ongoing challenge in virtual
teams, difficulties are intensified when the team
members are globally distributed (McDonough,
Kahn, and Barczak, 2001). Furthermore, mutual
understanding within the team diminishes and
overall understanding is hampered when a shared
language is lacking among members, and
communication becomes more strained when some
members are co-located while others are
geographically distributed (Crampton, 2001).
Another challenge for distributed teams involves the
slow development of relational bonds among
members. Compared with FtF teams, distributed
teams exhibit weaker relational links among team
members (Warkentin, Sayeed, and Hightower,
1997). Researchers attribute the weaker
relationships to the significant reliance on
communication technologies and the difficulties of
communicating with team members across time and
space (Powell, Piccoli, and Ives, 2004). The high
reliance on technology to communicate also
contributes to lack of cohesion among team
members (Warkentin, Sayeed, and Hightower,
1997). However, greater cohesiveness may be
achieved over time and as more social cues are
exchanged among team members (Chidambaram,
1996). Research also indicates that as teams
become more efficacious with the communication
technologies, higher levels of trust tend to develop
among members (Jarvenpaa and Leidner, 1999).
The development of trust has been an ongoing focus
of study by researchers trying to understand the
dynamics of distributed teamwork. Trust is found to
be a key variable related to team performance and
the successful completion of projects (Sarker, Lau
and Sahay, 2001), but is not without challenges in
the distributed environment. While the evaluation of
trustworthiness among virtual teammates is difficult
(McDonough, Kahn, and Barczak, 2001), the
development of trust is linked to increased
communication among members (Jarvenpaa, Shaw
and Staples, 2004), and this occurs as the
challenges of the communication media itself are
overcome (Jarvenpaa and Leidner, 1999).
Jarvenpaa and Leidner (1999) discuss the
importance of building trust early in a team project,
especially when the life of a project is relatively
short. This swift trust is then confirmed or
disconfirmed as the project runs its course
(Javenpaa and Leidner, 1999; Meyerson, Weick,
and Kramer, 1996). Higher levels of trust in virtual
teams results in positive outcomes such as greater
feedback, enthusiasm, positive leadership and
coping mechanisms (Jarvenpaa and Leidner, 1999).
Diversity in Globally Distributed Teams
Diversity is a concept that has been examined and
defined in numerous ways. The Shorter Oxford
English Dictionary (2007) defines diversity as "the
condition or quality of being diverse, different, or
varied; variety, unlikeness." Jackson, Stone, and
Alvarez (1992) use the term diversity to refer to
situations in which the parties of interest are
dissimilar with respect to some attributes. Lau and
Murnighan (1998) conceptualized diversity in terms
of the heterogeneity of individual attributes within a
group. These definitions imply that diversity is a
relative concept in that an individual is diverse only
in relation to other individuals (Austin, 1997). Along
these lines, researchers often use demographic
attributes as the means to evaluate individual
differences and similarities. One explanation is that
demographic characteristics serve as the basis for
how people spontaneously classify each other
(Stangor, et al., 1992), and these classifications
reinforce stereotyping, social cognitions, attributions
and expectancy outcomes (Jackson, Stone, and
Alvarez 1992). However, researchers also identify a
type of team diversity that is less overt and focused
on cognitive and skill-level differences (Jackson,
May, and Whitney, 1995). Pelled (1996) categorizes
this type of team diversity in terms of visibility level
and job-relatedness, or the perceptions of
‘unlikeness’ that arise in FtF evaluations and skill-
level assessments.
Following prior research, we define diversity as the
degree to which an individual is dissimilar to his or
her team members on individual-level attributes that
may be demographic or skill-related in nature. We
operationalize diversity as team member perceptions
of dissimilarity based on experiences with other
distributed team members during the course of a
team project. These individual dissimilarities may be
based on demographic variables such as visible
differences (e.g., age, race, country of origin,
gender), informational differences (e.g., college
The DATA BASE for Advances in Information Systems 29 Volume 41, Number 3, August 2010

pedigree, functional expertise, organizational
position) (Krebs, Hobman, and Bordia, 2006), and
value differences (e.g., work motivation) (Hobman,
Bordia, and Gallois, 2003).
Prior research examining group diversity is often
contradictory. For example, demographically diverse
individuals generally experience lower levels of trust
in team members (Chattopadhyay, 1999), increased
conflict (Jehn, Northcraft, and Neale, 1999),
decreased commitment (Tsui, Egan, and O’Reilly,
1992) and diminished communication (Zenger and
Lawrence, 1989). Chattopdayhyay (1999) found that
individuals who differed greatly by age reported less
trust toward their group members. Yet, globally
distributed teams are being utilized to tap the
complimentary yet diverse expertise of members
who may be nationally and organizationally
dispersed (Jarvenpaa, Knoll and Leidner, 1998).
Research suggests that demographic diversity in
teams can generate positive outcomes such as
increased creativity, decision quality and innovation
(Jehn, Northcraft, and Neale, 1999) and vital
benefits develop when diverse perspectives are
combined to accomplish work goals (Thomas and
Ely, 1996). Studies using graduate students show
that team diversity can enhance creativity and
learning given the right circumstances (Polzer et al.,
2006). Hence, the need to understand the
mechanism(s) through which diversity helps or
harms a team and its members is clear.
Interestingly, in practice, organizations attempting to
benefit from heterogeneous teams have
experienced mixed results (Watson, Kumar, and
Michaelsen, 1993). Williams and O’Reilly (1998)
found diversity to negatively affect team
effectiveness, offsetting the benefits of a
heterogeneous team. Team members who believed
they were demographically different from other team
members reported feeling uncomfortable and
detached from their team (Tsui, Egan, and O’Reilly,
1992). Hinds and Bailey (2003) posit that distributed
team members have difficulty establishing a shared
context since dispersion may result in varying
geographical and work environments as well as
culture. Consequently, members may adhere to
different behavioral norms and expectations (Kiesler
and Cummings 2002). Williams and O’Reilly (1998)
conclude that diverse groups communicate less, are
less integrated, and experience greater conflict.
Additionally, difficulties in communication, increased
miscommunication, decreased commitment,
heightened levels of conflict, and decreased
cohesion result in lower team performance (Austin,
1997; Williams and O’Reilly, 1998) if cultural
diversity sparks perceptions of differences among
members (Adler, 1997).
The similarity-attraction paradigm suggests that
homogeneous teams tend to be more productive
than heterogeneous teams based on higher levels of
mutual attraction between team members sharing
similar characteristics (Horwitz, 2005). This
proposition was empirically supported by Wiersema
and Bantel (1992) on tasks requiring coordination
among team members. In contrast, cognitive
resource diversity theory proposes that
heterogeneous teams are better than homogeneous
teams in terms of promoting innovation, creativity,
and problems solving (Horwitz, 2005; Nemeth,
1986). Research on age indicates that teams with
members of similar age outperform teams of
members with dissimilar ages (Tsui, Egan, and
O’Reilly, 1992) and homogeneously aged teams
were more socially integrated than heterogeneously
aged teams (O’Reilly, Caldwell, and Barnett, 1989).
Similar results were found with teams of diverse
gender (Tsui and O’Reilly, 1989). Related findings
have led small group researchers to conclude that
differences in demographic attributes result in less
cohesion and social integration and lead to the
formation of stereotypes (Horwitz and Horwitz,
2007). While the value of diversity is often touted,
that value may not be realized apart from
understanding how factors such as trust and
cohesion operate in relation to diversity within
groups.
The Liability of Diversity
In the management literature, the term ‘liability of
foreignness’ describes the disadvantages or costs
incurred by multinational organizations doing
business in unfamiliar or foreign environments.
These costs may include trade-related costs (e.g.,
increased taxes, tariffs) imposed on multinationals
and their products as well as other tangible and
intangible costs arising from the demands of local
politicians, consumers, and/or the foreign labor force
(Insch & Miller, 2005; Zaheer, 1995). In either case,
operating in an unfamiliar environment is
accompanied by additional costs related to distinct
differences in culture, government and/or political
forces, among others.
These costs may directly affect the success of the
multinational and are liabilities that are often
unknown and unanticipated. Similarly, joining
members with diverse attributes into a single work
group is akin to placing them in unfamiliar territory
with unspecified costs of assimilation. Whereas the
tangible costs of creating communication channels
The DATA BASE for Advances in Information Systems 30 Volume 41, Number 3, August 2010

to support the team are quantifiable and anticipated,
the costs of team members’ adjustments to the
heterogeneity of a group represents a distinct
liability. For example, adjusting to and/or
accommodating differences in language, gender,
time zones, work habits and attitudes may diminish
the cognitive resources that would otherwise be
focused on team goals. Thus, optimal member
performance may be limited by the extent of member
heterogeneity. We define this as the ‘liability of
diversity’, or the unexpected costs of combining
diverse individuals into a single team, where,
paradoxically, synergy is the expected outcome.
Theory
Self-Categorization Theory
Self-categorization theory is useful for explaining
how team members perceive themselves and
others, and why these perceptions may induce
negative performance-based outcomes. Self-
categorization theory implies that individuals
recognize differences among common
characteristics (e.g., age, gender, race, intelligence,
organizational membership) in order to define
psychological groups and to promote self-identity
(Tsui, Egan, and O’Reilly, 1992). The process of
self-categorization begins when an individual
classifies himself and others into social categories
using demographic characteristics (Turner, 1987).
The evaluation and categorization of demographic
characteristics is by definition subjective (McGrath
1976) since individuals’ perceptions determine
similarities and differences and also serve as the
primary basis for expectancies and attributions
(Stangor et al., 1992).
The categorization of others then prompts the
individual to formulate in-groups and out-groups with
the latter being less attractive to the individual who
defines the groups (Kramer, 1991), as the tendency
is to prefer others with similar characteristics
(Messick and Mackie, 1989). Self-categorization
theory asserts that individuals evaluate their self-
defined groups both positively and negatively. For
example, positive evaluations emanate from a
mutual attraction of team members having similar
characteristics. In contrast, negative attributions
originate from differences in team member
characteristics and are expressed by a decrease in
communication frequency and increased relational
conflicts among members.
Self-categorization theorists also posit that
demographic attributes serve as the foundation for
categorization since demographically similar people
tend to have common experiences and backgrounds
(Pfeffer, 1983). Therefore, demographic
characteristics are commonly used to predict future
behaviors of self-defined groups (Messick and
Mackie, 1989). For example, individuals in the out-
group are perceived as deviants and may be liked
less based on trait differences such as gender, race
and ethnicity (Marques, 1990).
Self-categorization theory is used by social
psychologists as the basis for describing how
individuals cognitively assimilate themselves into the
in-group or out-group, which serves as a foundation
for group behavior (Hogg and Terry, 2000). Despite
little FtF contact in a globally distributed team, social
categories tend to emerge quickly and in a polarizing
and exaggerated manner (Lea and Spears, 1992;
Walther 1995), and social categories tend to
become rigid since the assumed beliefs about the
team are difficult to (dis)confirm in the absence of
visible cues (Fiol and O’Connor, 2005). Group
members often rely on communication cues to
develop the social categories into which they sort
the in-group and out-group (Turner et al., 1994), and
when the categories are relatively stable, there is a
greater propensity to classify members (Turner
1987). The classification of team members using
demographic characteristics may result in hostility,
anxiety and stereotyping (Tsui, Egan, and O’Reilly,
1992) with detrimental effects on trust, individual
performance and team performance. Some
researchers have questioned whether or not trust
can develop among distributed group members in
the absence of FtF interaction (Krebs, Hobman, and
Bordia, 2006).
It is suggested that computer-mediated exchanges
are ineffective for sharing interpersonal information
and lack the necessary social cues for trust to
develop (Dubrivsky, Kiesler, and Sethna, 1991). This
may lead group members into misguided
classifications of other members that will sabotage
trust and team performance. Thus, trust may be
more difficult to develop in distributed teams that
face the challenges of computer-mediated
communication.
Walther’s (1996) social information processing (SIP)
theory suggests that the difference between
computer-mediated communication and FtF
communication is the rate of transfer, rather than the
ability to exchange social information. While there
may be fewer social cues per message in computer-
mediated exchanges, in time, distributed team
members tend to communicate as effectively as FtF
members (Krebs, Hobman, and Bordia, 2006). Since
fewer social cues are present in computer-mediated
The DATA BASE for Advances in Information Systems 31 Volume 41, Number 3, August 2010

exchanges, distributed teams may initially
experience lower levels of trust, individual
performance, and team performance.
Hypotheses
Model
Figure 1 represents our conceptual model based on
self-categorization theory. We posit that individual
perceptions of diversity have a detrimental effect on
trust, cohesion, and performance.
The Effect of Diversity on Team Trust
Trust is defined as “the willingness of a party to be
vulnerable to the actions of another party” (Mayer,
Davis, and Schoorman, 1995). Research shows that
trust can develop early based on first impressions,
available information, and a sense of urgency to act
(Meyerson, Weick, and Kramer, 1996). Trust elicits a
commitment between team members which supports
the belief that expectations will be met and team
members can be counted on to work in a timely
manner. Hence, trust between members reduces the
need to monitor and track other members’ work
progress (McCallister, 1995) and trust reduces
uncertainty and ambiguity so that members can work
in a cooperative and productive manner (Dirks and
Ferrin, 1998). However, Cohen and Mankin (1999)
note that trust among distributed group members
tends to develop slowly due to differences in culture
and functional expertise, and the limitations imposed
by communication technologies.
Trust is a critical aspect of successful teams and
may be hindered when members perceive
differences among themselves. Diversity drives
individuals to categorize each other and formulate
in-groups and out-groups, and the manner in which
a person categorizes another influences the level of
trust. For example, if the rater categorizes the rated
into a positive group category, higher levels of trust
are more likely. For the team members who fall into
the positive (i.e., in-group), this translates into group
cohesiveness and interpersonal attraction (Jackson,
Stone, and Alavarez, 1993) as individuals trust and
socialize with in-group members considerably more
than with out-group members (Brewer, 1995).
In contrast, out-group members experience lower
self-esteem and feelings of anxiety and stress,
which are likely to cripple productivity, satisfaction
and commitment (Tsui, Egan, and O'Reilly, 1992).
As social groups become salient over time, team
members tend to show favoritism toward the in-
group members and discriminate against out-group
members (Tajfel, 1978), which lessens trust and
inclusiveness among out-group members.
Categorization also leads individuals to perceive out-
group members as less cooperative as well as less
trustworthy than in-group members (Brewer, 1979).
Figure 1: Conceptual Model of Diversity and Individual Performance in
Globally Distributed Teams
Trust
Diversity
Individual
Performance
Cohesion
The DATA BASE for Advances in Information Systems 32 Volume 41, Number 3, August 2010

Krebs, Hobman, and Bordia (2006) examined the
consequences of demographic dissimilarity for group
trust in both distributed and FtF teams finding that
demographic dissimilarity is negatively related to
trust in the FtF teams, but not in the distributed
environment. However, in distributed teams diversity
may not be immediately recognized. Research
suggests that swift trust may develop in the early
stages of a team project before members have a
chance to interact (Jarvenpaa, Knoll, and Leidner,
1998), as a means to ‘jump-start’ collaborative work.
Also, team members tend to make assumptions
about other members’ initial trustworthiness when
information about members is limited (McKnight,
Cummings, and Chervany, 1998). Although Krebs et
al. (2006) contend that team members trusted each
other regardless of the level of dissimilarity, the
subjects were from a single university with half of the
members in the computer-mediated teams from
collectivist cultures; collectivists tend to put group
interests ahead of self-interests in work group
situations (Bond and Wang, 1983). In a similar study
of co-located versus distributed teams, Benoit and
Kelsey (2003) note that students reported higher
levels of trust with members from their own
university than for remote members.
Based on self-categorization theory, we believe that
globally distributed team members differentiate
among themselves based on social cues gained
from computer-mediated communications which
reveal demographic and functional differences. The
perceived differences further enable members to
categorize each other in ways that ultimately inhibit
a high degree of trust, leading to the first hypothesis:
Hypothesis 1: The more perceived diversity
within a distributed team, the less trust
among team members.
The Effect of Trust and Diversity on Team
Cohesion
Cohesion refers to a dynamic process that binds
individuals to each other causing them to remain
with their team to achieve team goals and objectives
(Festinger, Schachter, and Back, 1950; Guzzo and
Shea, 1992). A cohesive group is one in which the
members are attracted to the group and to its task
(Kozlowski and Bell, 2003). When members believe
they are an integral part of the team, cohesion
enables the group to remain intact and productive in
spite of difficulties. In the distributed team literature,
cohesion has been linked to team effectiveness
(Gonzalez et al., 2003), team satisfaction, and
effective communication (Chidambaram, 1996).
Trust has been identified as a key contributing factor
to the effectiveness of activities requiring member
coordination (i.e., cohesion) in distributed teams
(Martins, Gilslon, and Maynard, 2004). Greater
mutual trust among team members is likely to result
in unity and cooperation around common goals or
greater cohesiveness. Research suggests that trust
in virtual teams may lessen the adverse effects of
remote team work (Jarvenpaa, Knoll and Leidner,
1998), thereby enabling team members to
coordinate and focus on team objectives.
Trust may play an even more significant role in team
cohesion in the distributed environment since prior
research (e.g. Warkentin, Sayeed and Hightower,
1997) shows that FtF teams more often report higher
levels of cohesiveness than virtual teams. Due to the
self-directed nature of distributed teams and the lack
of social cues in computer-mediated communication
(Kasper-Fuehrer and Ashkanasy, 2001), it is likely
that a sense of commitment to the team is slow to
develop since more time and experience may be
required to reach a level of trust sufficient for greater
team and task commitment. In FtF teams,
cohesiveness may be more readily achieved through
direct observation and experience with other
members. Trust is also related to greater group
productivity and satisfaction (Staples and
Ramasingham, 1998) indicating that trust is a
determinant of an intact (i.e. cohesive) group that
functions efficiently and effectively. Thus, we posit
that team member trust is an important antecedent
of team cohesion.
Hypothesis 2: The more trust among
distributed team members, the greater the
team’s cohesion.
Identifying with team members is thought to be
especially desirable in a distributed environment
because it provides the glue that promotes group
cohesion despite the lack of FtF interaction (Fiol and
O’Connor, 2005). Although diversity is intended to
yield a variety of perspectives and solutions, these
perspectives are unlikely to emerge if team
members are reluctant to interact with individuals
who are different (Jackson, 1992). Research tends
to support this claim across various types of diversity
including cultural, work-unit and demographic
diversity. Studies suggest that culturally diverse
teams are less cohesive than culturally
homogeneous teams (Thomas, Ravlin, and Wallace
1994; Knouse and Dansby 1999), and thus less
likely to interact at a level that produces superior
outcomes. Tsui, Egan, and O’Reilly (1992), report an
inverse relationship between work-unit diversity and
psychological attachment among group members.
Without that attachment, it is probable that the
The DATA BASE for Advances in Information Systems 33 Volume 41, Number 3, August 2010

overall cohesiveness of the team suffers. Moreover,
teams whose members do share similar
characteristics are more cohesive, report higher
levels of satisfaction, have lower turnover (O’Reilly,
Caldwell, and Barnett, 1989; Tsui, Egan, and
O’Reilly, 1992), and are more likely to integrate
socially and remain cohesive than those who do not
(Ibarra, 1992). Hence, diversity is likely to inhibit the
interaction that is necessary for team members to be
fully committed to the team and each other.
Globally distributed teams tend to be more diverse
than FtF teams, leading some researchers to
propose that cohesion is more difficult to develop in
the distributed environment than in a co-located one
(e.g., Cramton 2001, Lipnack and Stamps 2000).
Research suggests that diversity may slow the
development of team cohesion by inflaming
perceptions of dissimilarity among members (Fiol
and O’Connor, 2005). For example, team members
who perceive they have been categorized into the
out-group experience decreased satisfaction,
cooperation, group cohesiveness and higher levels
of conflict (Williams and O’Reilly, 1998); all of which
work against a sense of team commitment. This
gives rise to the notion that cohesion develops out of
an attraction among individuals having similar
characteristics (Byrne, 1971). Hence, we expect that
perceptions of diversity among distributed team
members will generate similar results leading to the
following:
Hypothesis 3: The more perceived diversity
within a distributed team, the less the team’s
cohesion.
The Effect of Diversity, Trust and Cohesion on
Individual Performance
Researchers posit that performance is a
multidimensional construct that incorporates
measures including productivity, number of ideas
generated, quality of product, and the time it takes to
get a product to market, among other quantitative
and qualitative outcomes (Horwitz, Bravington, and
Silvis, 2006; Horwitz and Horwitz, 2007). Although
performance may be measured in numerous ways,
the primary unit of analysis for team performance is
in terms of effectiveness, efficiency, decision quality,
units produced, and number of novel ideas
generated. Additionally, researchers have measured
group performance primarily from an external
manager’s perspective that relates to overall
efficiency and project quality (Cohen and Bailey,
1997). In contrast to prior work, we explore the effect
of diversity, trust and cohesion on individual
performance since teams are ultimately compiled at
the individual level, and individual contribution is
necessary to maximize team success. The liability of
diversity includes the cost of carrying unproductive
members whose individual output may be masked
by overall team performance.
Research suggests that diversity brings many
challenges to individual and group productivity which
may be amplified in the distributed team context.
For example, difficulties associated with language
may promote communication barriers that increase
the complexity, confusion, and ambiguity of
communication (Adler, 1997). Globally distributed
team communication not only involves cross-cultural
communication, but includes the challenges of using
computer-mediated technologies (Jarvenpaa and
Leidner, 1999). Hence, cultural diversity in addition
to the difficulties posed by communication media
may amplify a project’s complexity and present team
members with challenges that ultimately weaken
team performance (Dube and Pare, 2001). Daily and
Steiner (1998) assert that cultural diversity alone is
sufficient to decrease team productivity since
heterogeneous teams exhibit lower levels of
integration, cohesion, and shared mental models.
The categorization that accompanies diversity leads
individuals to perceive out-group members as less
cooperative (Brewer, 1979), with detrimental effects
on team performance (Tsui, Egan, and O’Reilly,
1992). Consequently, if the team performs poorly, it
is rational to assume that individual performance is
below expectations.
We propose that diversity within a team is likely to
hinder the personal productivity of each member, as
perceptions of ‘unlikeness’ may inhibit members
from sharing knowledge and brainstorming.
Research indicates that culturally diverse co-located
teams display lower levels of integration that hamper
a shared understanding (Klimoski and Mohammed,
1994), and demographic diversity negatively affects
individual, team, and organizational outcomes
(Jackson, May, and Whitney, 1995). Without the
assimilation of others’ thoughts, ideas and
experiences into the formation of their own,
members are not likely to achieve the highest level
of creative, unique, or superior output. Thus, we
hypothesize the following:
Hypothesis 4: The more perceived diversity
within a distributed team, the lower
individual team member performance.
Within the management and IS community, the
prevailing view suggests a direct and positive
relationship between trust and performance (Benoit
and Kelsey, 2003; Jarvenpaa, Shaw, and Staples,
2004). The lack of trust within a team leads to
greater psychological distance among members
The DATA BASE for Advances in Information Systems 34 Volume 41, Number 3, August 2010

(Jarvenpaa, Knoll, and Leidner, 1998), indicating
that trust is important for the closeness required for
knowledge sharing and optimal team performance.
Since groups which disseminate knowledge perform
at a higher level than groups not sharing knowledge
(Cummings, 2004), it follows that distributed teams
with a greater level of trust that strengthens
knowledge sharing are likely to achieve at a higher
level both individually and cooperatively. Team
members that don’t trust are less likely to share the
knowledge and information which result in individual
creativity and effective solutions. In a virtual team
study at the Sabre, Inc., Kirkman et al. (2002)
concluded that establishing trust is one of five
important virtual team challenges for successful
teams. If miscommunication and mistrust escalate,
teams experience difficulties reaching consensus,
validating ideas, and reaching decisions which
ultimately diminishes performance (Adler, 1997).
Additionally, team trust is positively related to team
effectiveness in terms of perceived task
performance, team satisfaction, attitudinal
commitment, and continuance commitment (Costa,
2003). The overall importance of the relationship
between trust and performance is also emphasized
in various other studies (e.g., Goodbody, 2005;
Hertel, Konradt, and Voss, 2006; Peters and Manz,
2007). Since greater trust among team members
reduces the need for members to monitor each other
(McCallister, 1995), it is plausible that greater trust
frees individual members to perform tasks that
enhance individual achievements and contributes
positively to team performance. Dirks and Ferrin’s
(1998) trust model posits that high levels of trust
lead to increased perceptions of performance, and
Benoit and Kelsey (2003) found that in both FtF and
distributed teams trust had a significant impact on
team performance. Since research suggests that
high levels of trust yield positive attitudes and
perceptions of good performance (Jarvenpaa, Shaw,
and Staples, 2004), as well as greater knowledge
sharing (Staples and Webster, 2008), we
hypothesize the following:
Hypothesis 5: The more trust among
distributed team members, the higher
individual team member performance.
Traditionally, management theorists have concluded
that a connection exists between cohesion and team
performance (Mullen and Copper, 1995) since highly
cohesive teams tend to be more efficient and
successful at problem solving than less cohesive
teams. Numerous studies support the relationship
between cohesion and team performance. Cohesion
is found as an important determinant of distributed
team performance (Carron and Brawley, 2000;
Maznevski and Chudoba, 2001), cohesive teams
outperform non-cohesive teams on most tasks
(McGrath, 1984), and social cohesion and
relationship building are key factors contributing to
virtual team performance (Horwitz, Bravington, and
Silvis, 2006). Workman (2007) investigated the
moderating effect of virtualization on the relationship
finding that high levels of cohesion led to better team
performance, and Beeber and Schmitt (1986)
reported that higher levels of social integration and
cohesion were indicative of high-performing teams.
Chang and Prahsant (2001) reported that group
cohesion was the antecedent of group performance.
These studies taken together with the notion that
group performance is the synthesis of individual
performance supports the following hypothesis:
Hypothesis 6: The more cohesion among
distributed team members, the higher
individual team member performance.
Research Method
Sample
To test the hypotheses, we used 18 globally
distributed teams comprised of upper-division
undergraduate students in the United States, South
Korea, and the United Arab Emirates. Students,
enrolled in a database class at their respective
universities, were assigned to a project team with
the task of designing a database for an existing
company based on the company’s data needs. To
insure that collaboration was virtual, students were
randomly assigned to four to five person teams with
no two students belonging to the same database
class. Seventy-eight students participated from start
to finish with a majority of respondents in the 20-25
age range (89%) and male (58%). As shown in
Table 1, students were primarily juniors (30%) and
seniors (63%) with the majority majoring in a
computer-based discipline (30%) or Accounting
(36%).
Once the teams were assigned, students were
emailed the names of their group members and
contact information, which signaled the start of the
project. The students were then given one week to
complete a team charter. Some of key components
of the team charter included a team name, a mission
statement, a team leader, a communication plan, a
team resume, and the team’s success metrics. Upon
completion of the team charter, the next assignment
required each team to select an existing company
and identify a contact person at the company.
The DATA BASE for Advances in Information Systems 35 Volume 41, Number 3, August 2010

Table 1 Breakdown of Study Participants
Demographic
Categories Range Percentage
Age
20-25
26-30
31+
88.5%
10.4%
1.3%
Gender Male
Female
57.7%
42.3%
Country of Origin
(clustered by
continent)
North America
Middle East
Asia
Other
41.0%
33.3%
23.1%
2.6%
Major
Accounting
MIS/CS
Business
Other
35.9%
29.5%
20.5%
14.1%
Academic Rank
Freshman
Sophomore
Junior
Senior
0.0%
7.7%
29.5%
62.8%
Work Experience
0
1-5
6-10
46.2%
50.0%
3.8%
Managerial Experience
0
1-5
6-10
62.8%
29.5%
7.7%
Once the contact person was established and
he/she agreed to participate, then the students
developed an interview agenda. This assignment
involved interviewing the contact person to develop
a project charter, which set the stage for the five
phases of database development. It should be noted
that the students were not provided with formal
virtual team training prior to the start of the project.
However, they were provided templates and
guidelines on how to complete the team charter, the
interview agenda, and the project charter.
All teams worked through five phases of database
development according to the specifications
provided by the participating company. The first
phase of database design included a preliminary
investigation phase in which the teams developed
the project charter. In phase two, the analysis
phase, teams produced user requirements
describing workflows and business rules. Phase
three was the database design phase in which
teams produced a conceptual database and met
with key stakeholders for approval. In the next
phase, the teams used a database management
system of their choice to create the actual database
including table construction, forms and report
generation. Finally, the teams were tasked with
importing company data into the database and
testing for functionality, usability, and quality
assurance.
This project is particularly well suited for assessing
our research questions since completion of the
project required considerable interaction among
dispersed team members and involved a moderate
level of complexity with numerous tasks needing
deliberate coordination. Furthermore, this project
provided team members a realistic sense of the
distributed team environment in that computer-
mediated communication, planning, and
programming were necessary for successful
completion. Additionally, team members had to work
collaboratively to meet the requirements and
specifications set forth by an outside entity.
A variety of computer-mediated communication
technologies were used to complete the project.
These included Blackboard’s Discussion feature,
email, FaceBook, MySpace, Instant Messenger and
telephone (see Table 2). Respondents reported the
primary mode of communication was email (80%)
due to time zone differences and the ease in
exchanging documents.
Table 2 Reported Modes of Communication by
Group
Group
# Email Blackboard
Instant
Message
MySpace/
facebook
Phone
1 80 20 0 0 0
2 70 10 0 20 0
3 85 0 5 5 5
4 60 0 0 40 0
5 75 25 0 0 0
6 80 10 0 10 0
7 85 0 10 0 5
8 85 0 15 0 0
9 80 15 0 5 0
10 85 0 0 10 5
11 85 0 5 10 0
12 80 15 0 5 0
13 90 0 0 10 0
14 75 25 0 0 0
15 75 15 10 0 0
16 85 10 0 5 0
17 85 10 0 0 5
18 85 0 5 10 0
Aver
age 80.3 %
8.6 %
2.8 %
7.2 %
1.1 %
The DATA BASE for Advances in Information Systems 36 Volume 41, Number 3, August 2010

From start to finish, the distributed teams
communicated electronically and completed their
projects over three months and five days.
Measures: An online survey was used at the end of
the project to measure the model’s constructs. The
construct items were compiled from several sources
and adapted to the context of the present study (see
Appendix 1). With the exception of the Performance
variable, the focal unit of analysis was the team.
Following the completion of the database, team
members rated their perceptions of team diversity,
trust and cohesion using a 5-point Likert scale. The
Performance construct represents an independent
assessment of each team member’s performance as
indicated by the other members.
Diversity. Diversity was assessed using four items
adapted from Sirgy et al. (1997). It is operationalized
as the degree to which individuals perceive other
team members as consistent with their own self-
image. Items include measures that capture
perceptions of personal and physical self-image as
well as overall perceptions of commonness with
other team members.
Trust. Trust was measured using 5 items adapted
from Mayer, Davis, and Schoorman (1995) and
Pearce et al. (1992) and reflects the willingness of
an individual to be vulnerable to the actions of his or
her team members.
Cohesion. Cohesion was measured with 4 items
adapted from Bollen and Hoyle (1990) to reflect the
desire of an individual to remain with their team in
the pursuit of the team’s goals and objectives.
Performance. Each team member’s performance
was rated individually by the other team members
using five criteria: participation, quality of work,
initiative, ability to meet deadlines, and overall
performance. Similar outcome variables were used
in a study conducted by Chowdhury (2005). The
combined scores for each member served as a
proxy for individual performance. Additionally, the
final projects were graded by the instructors and
given an aggregate score. However, since the unit of
analysis for performance is the individual team
member rather than the team, we did not include
team performance in our model. Since this project
accounted for 80 percent of each participant’s
course grade, it was sufficient to motivate individual
member participation and contribution.
Data Analysis and Results
Measurement Model: Several tests were
conducted to evaluate the reliability of the survey
instrument. First confirmatory factor analysis was
performed in SPSS to assess each endogenous
construct as measured by the survey items (see
Table 3). Each of the items loaded highest on its
designated construct explaining 87.6 percent of total
variance. The item loadings ranged from 0.641 to
.953 surpassing the significance benchmark (±.30)
for a sample size less than 100 (Hair et al., 1998),
indicating reliability of the items. Partial least
squares (PLS) was used to analyze the relationships
between variables in our research model. This two
step data analysis technique was preferred to other
structural methods (e.g., LISREL) due to the small
sample size required by the PLS technique and our
objective to establish the predictive validity of the
specified paths, rather than the development of a
‘best fit’ causal model (Chin, 1998).
Step one of the analysis evaluated the reliability and
validity of the measurement instrument. PLS-Graph
3.0 was used to generate construct loadings and
weights for each item. Loadings are indicative of
individual item reliability (IIR) with loadings greater
than 0.70 considered acceptable, implying that the
item explains almost 50 percent of the variance in a
particular measure (Chin, 1998). As shown in Table
3, all items meet the minimum IIR requirement which
indicates the sufficiency of the survey items for
measuring each construct.
Table 3 Confirmatory Factor Analysis
Diversity Cohesion Trust Performance
DIV1
DIV2
DIV3
DIV4
.659
.679
.739
.709
COH1
COH2
COH3
COH4
.641
.813
.831
.840
TRST1
TRST2
TRST3
TRST4
TRST5
.854
.908
.846
.853
.874
PRF1
PRF2
PRF3
PRF4
PRF5
.919
.920
.854
.910
.953
* Principle Component Analysis with Oblimin Rotation.
The DATA BASE for Advances in Information Systems 37 Volume 41, Number 3, August 2010

In addition loadings and weights, Table 4 lists the
internal consistency scores that were generated for
each construct. Internal consistency uses estimated
item loadings within the causal model and is not
influenced by the number of items in the scale
(Fornell, 1981), and values of 0.70 or greater are
considered adequate (Nunnally, 1978). All
constructs significantly exceeded the 0.70
recommendation, with scores ranging between 0.84
and 0.97.
Table 4 Loadings, Weights, and Composite
Reliability of the Measurement Model
Original Model
Construct Item
Weight
Loading
(IIR)
Internal
Consistency
D1 0.317 0.729
D2 0.293 0.708
D3 0.403 0.876
Diversity
D4 0.374 0.820
0.840
C1 0.277 0.703
C2 0.283 0.931
C3 0.297 0.921
Cohesion
C4 0.304 0.920
0.922
T1 0.239 0.866
T2 0.253 0.918
T3 0.192 0.835
T4 0.216 0.817
Trust
T5 0.246 0.906
0.939
P1 0.366 0.822
P2 0.410 0.903
P3 0.337 0.917
P4 0.423 0.979
Individual
Performance
P5 0.454 0.970
0.965
Following the measurement model reliability
assessment, we used PLS-Graph 3.0 to assess
discriminant validity – the degree to which one
construct is different from all other constructs in the
instrument. The average variance extracted (AVE)
indicator represents the average variance shared
between a construct and its measures (Fornell,
1981), and provides an evaluation of discriminant
validity. Table 4 shows the AVE scores for all
constructs exceeded the 0.50 recommended
minimum score (Barclay, Higgins, and Thompson,
1995), suggesting at least 50 percent of
measurement variance was captured by the
construct (Chin, 1998). Additionally, the inter-item
correlations of the constructs are below the .90
threshold (Bagozzi, Yi, and Phillips 1991) indicating
the distinctness of each construct.
The square root of the AVE shown in bold on the
diagonal in Table 5 provides additional evidence of
discriminant validity in that the average variance
shared between each construct and its indicators is
larger than the variance shared between each
construct and other constructs.
Structural Model
Following successful reliability and validity
assessments of the research instrument, the
structural model was evaluated to test the
hypothesized relationships. A bootstrap procedure
using 500 re-samples was used in PLS Graph 3.0 to
calculate the path coefficients.
Table 5 AVE Scores and Correlation of Latent Variables
AVE 1. 2. 3. 4.
1. Diversity 0.574 0.757
2. Trust 0.756 -0.693 0.870
3. Cohesion 0.750 -0.567 0.789 0.866
4. Performance 0.846 -0.384 0.310 0.503 0.920
*Bolded items on the diagonal represent the square root of the AVE. For discriminant
validity, diagonal values should be larger than the off-diagonal correlations.
The DATA BASE for Advances in Information Systems 38 Volume 41, Number 3, August 2010

Figure 2 Research Model of Diversity and Individual Performance in Globally Distributed Teams
The results provide support for H1 and H4 indicating
a significant negative relationship between diversity
and trust (β = -.643; p < .01), and diversity and
individual performance (β = -.198; p < .05),
respectively. A significant positive relationship was
indicated between trust and cohesion (H2; β = .760;
p < .01), trust and individual performance (H5; β =
.208; p < .05), and cohesion and individual
performance (H6; β = .406; p < .01). The
relationship between diversity and cohesion (H3)
was not supported. Additionally, the model explains
41 percent of the variance in trust, 69 percent in
cohesion, and 42 percent in individual performance.
Discussion
Group diversity is not a new phenomenon, but it is
understudied in relation to distributed teams,
especially globally distributed teams. As
organizations pursue global strategies, they begin to
leverage knowledge assets and human resources
that are increasingly heterogeneous. In the globally
distributed team, diversity perceptions may be more
pronounced as time and distance barriers are
lowered, and the integration of individuals with
varying abilities, beliefs, and values creates a
natural environment for individuals to feel isolated
and to withdraw (Heames, Harvey, and Treadway,
2006).
Our study shows that diversity may be a liability in
globally distributed teams as it appears to deliver a
one-two punch. First, diversity has a direct negative
influence on individual performance, and it also
contributes to a break-down in trust. Trust is a critical
component of performance, especially in distributed
teams. Trust has been shown as a key factor for
successful teamwork and the negative effects from a
lack of trust are well-documented in the virtual team
literature (e.g., Piccoli and Ives, 2003; Jarvenpaa et
al., 1998; Jarvenpaa and Leidner, 1999). If
members harbor doubts about other team members’
and/or their abilities, they are more likely to become
distracted from the task and unable to develop a
high level of commitment to the project. Trust
creates an environment in which the members have
freedom to openly contribute ideas and take
advantage of the synergistic effects of creativity that
occur. Without the openness provided by trust,
avoidance behaviors may escalate creating greater
anxiety and animosity that decreases the ability to
work collaboratively. Negative attitudes and
emotions are also likely to slow team momentum
and individual productivity if energy is diverted away
from achieving team objectives. As the ability of the
team to make quality decisions deteriorates, team
performance suffers (Amason 1996; Amason and
Schweiger 1997) and satisfaction declines (Jehn
1995, 1997). As a result, members are less
receptive to the input of others, are less willing to
tolerate opposition, engage in less effective
communication and are given to hostile attributions
about other’s motives and behaviors (see Janssen
et al., 1999).
Trust
R2 = 41.3%
Diversity
Individual
Performance
R2 = 41.8%
Cohesion
R2 = 69.3%
The DATA BASE for Advances in Information Systems 39 Volume 41, Number 3, August 2010

A deficiency in trust is detrimental to the morale and
productivity of any team (Williams and O’Reilly,
1998), but may be more pronounced in globally
distributed teams that have an increased
dependence on communication technologies. As
previously discussed, communication media create
additional challenges such as delayed interactions
that hinder and complicate the exchange of ideas
among team members. This may have the effect of
reducing efficiency and retarding both individual and
group productivity. Team member diversity may also
become over-emphasized with the use of
communication media. For example, electronic
communications may draw undue attention to
differences in language, time zones, geography, and
culture as team members try to establish work
routines and patterns. Without effective
management, these differences may become a daily
impediment to the completion of collaborative tasks.
Trust is perhaps one of the greatest challenges of
globally distributed teams because it is a significant
determinant of team cohesion and individual
performance. Although diversity does not show a
direct effect on cohesion, it operates via team trust
to influence the extent to which team members feel
connected as a group and committed to common
goals. Moreover, respondents holding greater
perceptions of dissimilarity between themselves and
other members did not perform as well as team
members perceiving fewer differences. The
composition of global teams and the management of
diversity within teams are factors that can be
directed and managed to optimize individual
performance.
The challenge for organizations is to effectively
manage diversity in ways that augment trust and
individual performance rather than detract from
them. Some organizations (e.g., IBM, General
Electric) manage diversity by creating centers of
excellence in which global team members are
clustered into groups that are highly specialized
(Siebdrat, Hoegl, and Ernst, 2009). These global
teams exhibit a high degree of cultural diversity, but
relatively weak functional diversity which enables
them to leverage their expertise in spite of
demographic or cultural differences. In effect, these
organizations have created a global employee
mindset in which synergy is derived from the degree
of expertise among team members and trumps the
disadvantages of diversity.
Research has also identified two types of team
processes, task-related and socio-emotional
processes, as key drivers of performance in
distributed teams (see Powell et al., 2004; Siebdrat
et al., 2009). Task-related processes include those
that facilitate communication and coordinate work
among members, as well as those that ensure team
members are fully contributing. Implemented
appropriately, these processes are likely to have a
positive effect on team trust, which in turn, benefits
performance. For example, managers that
implement the communication media that best
serves team members will help overcome some of
the challenges associated with asynchronous and
dispersed communication. Video conferencing tools
and synchronous electronic white boards that
display data and text from any location enable
dispersed members to work concurrently and
collaboratively in real time. Moreover, managers that
assist in coordinating and scheduling work tasks
among members provide assurances about the
contribution of all members, which may also support
trust and facilitate cohesion and performance.
Distributed teams with poor task-related processes
or ineffective management of these processes are
likely to experience a performance disadvantage.
Socio-emotional processes describe those that
create team commitment, team spirit, and
identification with a team (Siebdrat, 2009); elements
of cohesion. Cohesive teams tend to positively
benefit the performance of individual members, as
our study indicates, as well as the team as a whole.
The effective management of social processes is
likely to enable teams to stay on task and
successfully cope with conflict. Specific efforts to
encourage informal communication among members
and create team spirit are likely to support task
processes and positively influence individual
performance. For example, programs that address
team member differences (e.g., cultural awareness
training, acceptance training) may mitigate some of
the negative effects of cultural diversity (Robey et
al., 2000; Sarker and Sahay, 2002), and contribute
to overall communication and coordination among
members (i.e., task processes), that leads to more
effective individual performance.
Trust may also be facilitated in a distributed team
through the actions of the team leader. Virtual team
leadership is a complex task due to the distributed
nature of the team, and is more about relationships
and trust (Hunsaker and Hunsaker, 2008; Shriberg,
2009), than technology. Prior research discusses the
many roles and skills that may be undertaken by the
virtual team leader (see Wakefield et al., 2008;
Malhotra, Majchrzak, and Rosen, 2007), which
encourage both trust and performance. Team
leaders may undertake specific activities to build
trust such as creating face time, setting goals and
expectations, providing feedback, show-casing
The DATA BASE for Advances in Information Systems 40 Volume 41, Number 3, August 2010

member’s competence, and fostering cultural
understanding (Hunsaker and Hunsaker, 2008).
Leaders of successful virtual teams also ensure that
diversity is understood and appreciated (Malhotra et
al., 2007). These activities appear to have a socio-
emotional element that will encourage team
members to self-monitor and self-manage to
reinforce team cohesion. Moreover, team leaders
can manage information and communications tools
in ways that create trust and facilitate relationships
(Malhotra et al. 2007), which ultimately benefit team
cooperation (Thomas and Bostrom, 2008). The
implications for leadership to create a trusting,
cohesive environment for globally dispersed teams
is apparent, and the success of global teams may
rest on the ability to fashion such an atmosphere.
The competitive edge gained by forming diverse
work groups may be dulled by a lack of trust and
cohesion that renders individuals and hence the
group unproductive. As suggested by Luzio-Lockett
(1995), problems at the individual and team level are
not only harmful to the team, but are also
detrimental to the organization as a whole. Thus,
organizations with global strategies that are unaware
of the liabilities of distributed team heterogeneity
may unknowingly create rich environments that
impede organizational performance. The challenge
remains and research is needed to guide the
formation and management of global teams so that
the advantages of knowledge sharing are realized
despite the liabilities that often accompany diversity.
Managerial Implications
There is little doubt that the formation and use of
distributed teams have become increasingly
common practice for many organizations. However,
assembling a team of diverse individuals based on
their unique skills and knowledge does not
guarantee team success. In fact, the dispersed and
culturally heterogeneous nature of globally
distributed teams creates unique challenges for
managers. Assuming team cohesion and trust are
key characteristics of high performing teams, how
managers foster cohesion and trust amongst
culturally diverse team members is of great
importance to the success of the team.
The development of a high performing, globally
distributed team starts with team member selection.
Managers should identify those individuals who are
equipped with the social skills necessary to work in a
team made up of functionally and demographically
diverse individuals. This may be accomplished by
establishing a global culture, in which individuals
view their environment with a global mindset – one
that allows individuals to become aware and/or
sensitive to the subtleties of other cultures.. Once
the team members have been selected, managers
should get their team members to buy into the
project. As is the case with this study, participants
said they were energized by the complexity of the
project and the novelty of the virtual environment.
Third, cohesion and trust can be strengthened when
team members develop a team identity, establish
goals and modes and frequency of communication,
and develop processes for completing and sharing
tasks. Fourth, managers need to demonstrate a high
level of personal commitment to the team and
project by getting involved in regular team
communication exchanges that include informal
conversation and time for sharing personal
information.
For some high performing teams, functional diversity
appeared to be the most effective type of diversity
for developing cohesion and trust between
members. Therefore, managers should highlight the
unique functional skills of each member rather than
focus on the demographic differences between
members. Finally, building cohesion and trust
among members may be accomplished through task
cohesion rather than developing a strong social
interaction among team members. Therefore, it is
important for managers to build a team that supports
the particular type of project at hand rather than
taking a one size fits all approach to member
selection.
Conclusion
The advantages of globally distributed teams include
access to expertise and knowledge that is
geographically dispersed with the potential for the
organization to leverage knowledge in a strategic
manner. Our study suggests that these benefits are
diminished when trust and cohesion within a team
languish. We demonstrate the negative implications
of diversity on individual performance and argue that
trust is a critical requirement for member and team
success. Despite geographic dispersion, global
teams are not likely immune to members’ subjective
categorizations that plague traditional work groups.
Since competitive advantage relies on organizational
cohesiveness and cooperation to enable knowledge
creation and knowledge sharing, it is important to
develop strategies that generate trust and cohesion
in order to maximize individual and team
performance.
In view of this dynamic and the increased use of
global virtual teams, the implications for the
composition and management of virtual teams are
The DATA BASE for Advances in Information Systems 41 Volume 41, Number 3, August 2010

substantial. As firms deploy teams of increasingly
diverse members, the unintended consequences
related to varied beliefs, preferences, expectations
and behaviors are significant to team success. The
increased use of distributed teams has been fueled
by a heightened demand for a global workforce
(Townsend, DeMarie and Hendrickson, 1998) and
organizations must recognize diversity as a
potentially negative factor in order to retain the most
coveted employees and reap the benefits of virtual
collaboration. The success of a distributed workforce
rests on understanding the challenges and
effectively managing diversity to control the costs
and better predict team outcomes.
Limitations and Future Research
As with any empirical research, limitations to the
study exist. Since the data were self-reported with
most construct data collected concurrently, the
threat of common method bias exists. However, a
particular strength of our method is that individual
performance measures were not self-report, but
team report measures. Hence, this dependent
variable was captured independently of the other
model constructs and reduces the likelihood that the
model results suffer the effects of systematic bias
among the respondents (Podsakoff et al., 2003).
Since all the measures were obtained at the same
time, it is possible that the observed performance by
team members had an impact on their perceptions
of diversity, trust and cohesion. This raises issues
related to causality in our model. However, the
model’s hypotheses are well-supported by prior
group and virtual team research that lend a degree
of credibility to the findings, although additional
studies are encouraged to further strengthen the
relationships in our model.
Peer assessments of the performance measure
could be viewed as a limitation since it is not known
upon what basis each member’s work quality was
actually evaluated. For example, a student might
assess quality of work based on a different aspect of
the project such as a technical component, a design
component, and/or a written component. The
instrument specified a general assessment of overall
work quality and it is not known what the students
actually used to evaluate work quality. However,
students were able to assess whether the database
worked, was easy to navigate, looked appealing,
and whether the written components were of good
quality. The performance measure also included
items on member participation, initiative, and ability
to meet deadlines that are likely to be assessed
consistently.
The study is also limited in generalizability since the
participants were students. Thus, the degree to
which the findings are applicable to other globally
distributed teams is unknown. However, a strength
of this study is that each distributed team was
comprised of actual global members working on
projects that required communication technologies
and that produced a measurable outcome. Thus,
each respondent actually experienced a dispersed
global environment where each member was
expected to function synergistically with other
members over a period of time to bring a database
project to completion.
Future research is needed to examine the intensity
of trust and cohesion in the global context where
certain aspects of diversity (e.g., experience) are
acutely manifest. In our study the level of experience
among the team members and the time period over
which the teams operated may not be typical of
global teams. Furthermore, a longitudinal
investigation of global teams and how trust and
cohesion may vary over time would be an important
contribution. For example, a team with great
potential may not work optimally over a short time
period in contrast to a longer period if trust and
cohesion require time to solidify. Also, identifying
managerial tactics and interventions to create ‘swift’
trust may help new teams experience synergy and
success more quickly. Research that provides
additional guidelines for the composition and
management of distributed teams is especially
needed given the current demand for a globally
diverse workforce.
Acknowledgment
We thank the editor and the anonymous reviewers
for their valuable suggestions and comments further
improving this manuscript.
References
Adler, N. J. (1997). International Dimensions of
Organizational Behavior 3
rd ed. Cincinnati:
South-Western College Publishing.
Amason, A. C. (1996). Distinguishing the Effects of
Functional and Dysfunctional Conflict on
Strategic Decision Making: Resolving a Paradox
for Top Management Teams. Academy of
Management Journal, 39, 123-148.
Amason, A. C., & Schweiger, D. (1997). The Effect
of Conflict on Strategic DecisionMaking
Effectiveness and Organizational Performance.
In C.K. w. De Dreu & E. Van De Vliert (Ed.),
Using Conflict in Organizations (pp. 101-115).
London: Sage.
The DATA BASE for Advances in Information Systems 42 Volume 41, Number 3, August 2010

Austin, J. R. (1997). A cognitive framework for
understanding demographic influences in
groups. International Journal of Organizational
Analysis, 5(4), 342-360.
Bagozzi, R. Yi, Y., & Phillips, L. (1991). Assessing
construct validity in organizational
Research. Administrative Science Quarterly
36(3), 421-458.
Barclay, D., Higgins, C., & Thompson, R. (1995).
The partial least squares (PLS) approach to
causal modeling: Personal computer adoption
and use as an illustration. Technology Studies,
2(2), 285-309.
Beeber, L. S., & Schmitt, M. H. (1986).
Cohesiveness in Groups: A Concept in Search
of a Definition. Advances in Nursing Science, 8,
1-11.
Benoit A. A, & Kelsey, B. L. (2003). Further
Understanding Of Trust and Performance in
Virtual Teams. Small Group Research, 34(5),
575-618.
Bollen, K. A., & Hoyle, R. H. (1990). Perceived
Cohesion: a conceptual and Empirical
Examination. Social forces, 69(2), 479-504.
Bond, M.H., & Wnag, S.H. (1983). China:
Aggressive Behaviour and the Problem of
Maintaining Order and Harmony, in A.P.
Goldstein and M.H. Segall (eds), Global
Perspectives on Aggression, New York:
Pergamon Press.
Brewer, M. B. (1979). In-group bias in the minimal
intergroup situation: A cognitive-motivational
analysis. Psychological Bulletin, 86, 307-324.
Brewer, M. (1995). Managing Diversity: The Role of
Social Identities. In S. E. Jackson & M. N.
Ruderman (Ed.), Diversity in Work Teams (pp.
47-68). Washington, DC: American
Psychological Association.
Byrne, D. E. (1971). The attraction paradigm. New
York: Academic Press.
Carron, A. V., & Brawley, L. R. (2000). Cohesion:
Conceptual and Measurement Issues. Small
Group Research, 31(1), 89-106.
Chang, A., & Prashant, B. (2001). A
Multidimensional Approach to the Group
Cohesion-Group Performance Relationship.
Small Group Research, 32(4), 379-405.
Chatman, J. A., Polzer, J. T., Barsade, S. G., &
Neale, M. A. (1998). Being Different Yet Feeling
Similar: The Influence of Demographic
Composition and Organizational Culture on
Work Processes and Outcomes. Administrative
Science Quarterly, 43(4), 749-780.
Chattopadhyay, P. (1999). Beyond Direct and
Symmetrical Effects: The Influence of
Demographic Dissimilarity on Organizational
Citizenship Behavior. Academy of Management
Journal, 42, 273-287.
Chidambaram, L. (1996). Relational Development in
Computer-Supported Groups. MISQuarterly,
20(2), 143-165.
Chin, W. (1998). The partial least squares approach
for structural equation modeling. In G. A.
Marcoulides (Ed.), Modern Methods for
Business Research (pp. 295-336). Hillsdale, NJ:
Lawrence Erlbaum Associates.
Chowdhury, S. (2005). Demographic diversity for
building an effective entrepreneurial team: is it
important? Journal of Business Venturing, 20(6),
727-746.
Cohen, S. G., & Bailey, D. E. (1997). What Makes
Teams Work: Group Effectiveness Research
from the Shop Floor to the Executive Suite.
Journal of Management, 23(3), 239-290.
Cohen, S. G., & Mankin, D. (1999). Collaboration in
the Virtual Organization. In C. L. Cooper & D. M.
Rousseau (Ed.), The Virtual Organization:
Trends in Organizational Behavior (pp. 105-
120). New York, NY: John Wiley & Sons.
Costa, A. C. (2003). Work team trust and
effectiveness. Personal Review, 32(5), 605-622.
Cramton, C. D. (2001). The mutual knowledge
problem and its consequences in geographically
dispersed teams. Organization Science, 12(3),
346-371.
Cummings, J.N. (2004), Work Groups, Structural
Diversity, and Knowledge Sharing in a Global
Organization. Management Science, 50(3), 352-
364.
Daily, B. F., & Steiner, R. L. (1998).The Influence of
Group Decision Support Systems on
Contribution and Commitment Levels in
Multicultural and Culturally Homogeneous
Decision-making Groups. Computers in Human
Behavior, 14(1), 147-162.
Davis, J. (1982). Group Performance. Reading, MA:
Addison-Wesley.
Dirks, K.T., & Ferrin, D.L. (2001). The Role of Trust
in Organizational Settings. Organization
Science, 12(4), 450-467.
Dube, L., & Pare, G. (2001). Global Virtual Teams.
Communications of the ACM, 44(12), 71-73.
Dubrivsky, V. J., Kiesler, S., & Sethna, B. N. (1991).
The Equalization Phenomenon: Status Effects in
Computer-mediated and Face-to-face Decision-
making Groups. Human Computer Interaction, 6,
119-146.
Festinger, L., Schachter, S., & Back, K. (1950).
Social Pressure in Informal Groups. New York:
Harper and Row.
Fiol, C.M., & O’Connor, E.J. (2005). Identification in
Face-to-Face, Hybrid, and Pure Virtual Teams:
The DATA BASE for Advances in Information Systems 43 Volume 41, Number 3, August 2010

Untangling the Contradictions. Organization
Science, 16(1), 19-32.
Fornell, C. L. D. (1981). Evaluating structural
equation models with unobservable variables
and measurement error. Journal of Marketing
Research, 18(1), 39-50.
Gibson, C. B., & Cohen, S. G. eds. (2003). Virtual
Teams That Work. Jossey-Bass, San Francisco,
CA.
Goodbody, J. (2005). Critical success factors for
global virtual teams. Strategic Communication
Management, 9(2), 18-21.
Gonzalez, M. G., Burke, M. J., Santuzzi, A. M., &
Bradley, J. (2003). The Impact of Group Process
Variables on the Effectiveness of Distance
Collaboration Groups. Computers in Human
Behavior, 19, 629-648.
Guzzo, R. A., & Shea, D. (1992). Group
performance and intergroup relations in
organizations. In M. D. Dunnette & L. M. Hough
(Ed.), Handbook of industrial and organizational
psychology (Vol. 3, pp. 269-313). Palo Alto, CA:
Consulting Psychologists Press.
Hair, J., Anderson, R., Tatham, R., and Black, W.
(1998). Multivariate Data Analysis, 5th Edition,
Upper Saddle River, NJ: Prentice Hall.
Hunsaker, P., and Hunsaker, J. (2008). Virtual
Teams: A Leader’s Guide. Team Performance
Management, 14(1/2), 86-101.
Heames, J.T., Harvey, M., & Treadway, D. (2006).
Status Inconsistency: An Antecedent to Bullying
in Groups. International Journal of Human
Resource Management, 17, 348-361.
Hertel, G., Konradt, U., & Voss, K. (2006).
Competencies for virtual teamwork:
Development and validation of a web-based
selection tool for members of distributed teams.
European Journal of Work and Organizational
Psychology, 15(4), 477-504.
Hinds, P. J., & Bailey, D. E. (2003). Out of Sight, Out
of Sync: Understanding Conflict in Distributed
Teams. Organization Science, 14(6), 615-632.
Hobman, E. V., Bordia, P., & Gallios, C. (2003)
Consequences of Feeling Dissimilar from Others
in a Work Team. Journal of Business and
Psychology, 17(3), 301-324.
Hogg, M.A., & Terry, D.J. (2000), Social Identity and
Self-categorization Processes in Organizational
Contexts. Academy of Management Review,
25(1), 121-40.
Horwitz, S. K. (2005). The Compositional Impact of
Team Diversity on Performance: Theoretical
Considerations. Human Resource Development
Review, 4(2), 219-245.
Horwitz, F. M., Bravington, D., & Silvis, U. (2006).
The promise of virtual teams: identifying key
factors in effectiveness and failure. Journal of
European Industrial Training, 30(6), 472-494.
Horwitz, S. K., & Horwitz, I. B. (2007). The Effects of
Team Diversity on Team Outcomes: A Meta-
Analytic Review of Team Demography. Journal
of Management, 33(6), 987-1015.
Ibarra, H. (1992). Homophily and Differential
Returns: Sex Differences in Network Structure
and Access in an Advertising firm.
Administrative Science Quarterly, 37, 422-447.
Insch, G. S., & Miller, S. R. (2005). Perception of
foreignness: Benefit or liability? Journal of
Managerial Issues, 17(4), 423-438.
Jackson, S. (1992). Team Composition in
Organizations. In S. Worchel, W. Wood, & J.
Simpson (Ed.), Group Process and Productivity
(pp. 138-173). London: Sage.
Jackson, S. E., May, K. E., & Whitney, K. (1995).
Understanding the dynamics of diversity in
decision-making teams. In R. A. Guzzo & E.
Salas (Ed.), Team effectiveness and decision
making in organizations (pp. 204-261). San
Francisco: Jossey-Bass.
Jackson, S. E., Stone, V. K., & Alvarez, E. B. (1992).
Socialization amidst diversity: The impact of
demographics on work team oldtimers and
newcomers. Research in Organizational
Behavior, 15, 45-109.
Jannsen, O., Van de Vliert, E., & Veenstra, C.
(1999). How Task and Person Conflict Shape
the Role of Positive Interdependence in
Management Groups. Journal of Management,
25, 117-141.
Jarvenpaa, S., Knoll, K., & Leidner, D. (1998). Is
Anybody Out There? Antecedents of Trust in
global Virtual Teams. Journal of Management
Information Systems, 14(4), 29-64.
Jarvenpaa, S., & Leidner, D. (1999). Communication
and Trust in Global Virtual Teams. Organization
Science, 10(6), 791-815.
Jarvenpaa, S. L., Shaw, T. R., & Staples, D. S.
(2004). Toward Contextualized Theories of
Trust: The Role of Trust in Global Virtual Teams.
Information Systems Research, 15(3), 250-267.
Jehn, K. A. (1995). A Multimethod Examination of
the Benefits and Detriments of Intragroup
Conflict. Administrative Science Quarterly, 40(2),
256-282.
Jehn, K. A. (1997). A Qualitative Analysis of Conflict
Types and Dimensions in Organizational
Groups. Administrative Science Quarterly, 42,
530-557.
Jehn, K. A., Northcraft, G. B., & Neale, M. A. (1999).
Why Differences Make a Difference: A Field
Study of Diversity, Conflict and Performance in
The DATA BASE for Advances in Information Systems 44 Volume 41, Number 3, August 2010

Workgroups. Administrative Science Quarterly,
44, 741-763.
Kaiser, P., Tullar, W., and McKowen, D. (2000).
Student Team Projects by Internet. Business
Communication Quarterly, 63(4), 75-82.
Kasper-Fuehrer, E. C., & Ashkanasy, N. M. (2001).
Communicating Trustworthiness and Building
Trust in Interorganizational Virtual
Organizations. Journal of Management, 27(3),
235-254.
Kayworth, T.R., Leidner, D.E. (2001). Leadership
Effectiveness in Global Virtual Teams. Journal of
Management Information Systems, 18(3), 7-41.
Kayworth, T., & Leidner, D. E. (2001). Leadership
Effectiveness in Global Virtual Teams. Journal of
Management Information Systems, 18(3), 7-41.
Kiesler, S., & Cummings, J. (2002). What Do We
Know About Proximity in Work Groups? A
Legacy of Research on Physical Distance. In P.
Hinds & S. Kiesler (Ed.), Distributed Work
(pp.76-109). Cambridge: MIT Press.
Kirkman, B. L., Rosen, B., Gibson, C. B., Tesluk, P.
E., & McPherson, S. O. (2002). Five
challengess to virtual team success: Lessons
from Sabre, Inc. Academy of Management
Executive, 18(3), 67-79.
Klimoski, R., & Mohammed, S. (1994). Team Mental
Model: Construct or Metaphor? Journal of
Management, 20(2), 403-437.
Knouse, S. B., & Dansby, M. R. (1999). Percentage
of Work Group Diversity and Work Group
Effectiveness. Journal of Psychology, 133, 486-
494.
Kozlowski, S. W. J., & Bell, B. S. (2003). Work
Groups and Teams in Organizations. In W. C.
Borman, D. R. Ilgen, & R. J. Klimoski (Eds.),
Handbook of Psychology: Industrial and
Organizational Psychology (vol. 12, pp. 333-
375). Hoboken, NJ: John Wiley & Sons.
Kramer, R. (1991). Intergroup relations and
organizational dilemmas: The role of
categorization processes. In B. M. Staw & L. L.
Cummings (Ed.), Research in Organizational
Behavior (pp. 191-228). Greenwich, CT: JAI
Press.
Kramer, R. M., Brewer, M. B., and Hanna, B. A.
(1996). Collective Trust and Collective Action:
The Decision to Trust as a Social Decision. In
R.M. Kramer & T.R. Tyler (Ed.), Trust in
Organizations: Frontiers of Theory and
Research (pp. 357-389). Thousand Oaks, CA:
Sage.
Krebs, S. A., Hobman, E. V., Bordia, P. (2006).
Virtual Teams and Group Member Dissimilarity.
Small Group Research, 37(6), 721-741.
Lau, D. C., and Murnighan, J. K. (1998).
Demographic diversity and faultlines: The
compositional dynamics of organizational
groups. The Academy of Management Review,
23(2), 325-341.
Lea, M., & Spears, R. (1992). Paralanguage and
Social Perception in Computer-mediated
Communication. Journal of Organizational
Computing, 2, 321-341.
Lipnak, J., & Stamps, J. (1997). Virtual Teams:
Reaching Across Space, Time, and
Organizations with Technology. New York: John
Wiley & Sons, Inc.
Luzio-Lockett, A. (1995). Enhancing Relationships
within Organizations: An Examination of a
Proactive Approach to ‘Bullying at Work.
Employee Counseling Today, 7(1), 12-22.
Lu, M., Watson-Manheim, M. B., Chudoba, K. M., &
Wynn, E. (2006). Virtuality and Team
Performance: Understanding the Impact of
Variety of Practices. Journal of Global
Information Technology Management, 9(1), 4-
23.
Malhotra, A., Majchrazak, A., and Rosen, B. (2007).
Leading Virtual Teams. The Academy of
Management Perspectives, 21(1), 60-70.
Marques, J.M. (1990). The Black Sheep Effect: Out-
group Homogeneity in Social Comparison
Settings, in D Abrams and MA Hogg (eds)
Social Identity Theory: Constructive and Critical
Advances New York: Springer-Verlag 131.
Martins, L.L., Gilson, L.L., & Maynard, M.T. (2004).
Virtual Teams: What Do We Know and Where
Do We Go From Here? Journal of Management,
30(6), 805-835.
Mayer, R., Davis, J., & Schoorman, F. (1995). An
integrative model of organizational trust.
Academy of Management Review, 20(3), 709-
734.
Maznevski, M. L., & Chudoba, K. M. (2000). Bridging
Space Over Time: Global Virtual Team
Dynamics and Effectiveness. Organization
Science, 11(5), 473-492.
McAllister, D. J. (1995). Affect- and Cognition-based
Trust as Foundations for Interpersonal
Cooperation in Organizations. Academy of
Management Journal, 38(1), 24-59.
McDonough III, E.F., Kahn, K.B. & Barczak, G.
(2001). An investigation of the use of global,
virtual, and co-located new product development
teams. The Journal of New Product Innovation
Management, 18(2), 110-120.
McGrath, J. (1976). Stress and behavior in
organizations. In M. Dunnette (Ed.), Handbook
of Industrial and Organizational Psychology (pp.
1351-1395). Chicago: Rand McNally.
The DATA BASE for Advances in Information Systems 45 Volume 41, Number 3, August 2010

McGrath, J. E. (1984). Groups: Interaction and
processes. Englewood Cliffs, NJ: Prentice-Hall.
McKnight, H., Cummings, L., & Chervany, N. (1998).
Initial Trust Formation in New Organizational
Relationships. Academy of Management
Review, 23(3), 473-490.
Messick, D. M., & Mackie, D. (1989). Intergroup
Relations. In M. R. Rosenzweig & L.W. Porter
(Ed.), Annual Review of Psychology (pp. 45-81).
Palo Alto, CA: Annual Reviews.
Meyerson, D., Weick, K., & Kramer, R. (1996). Swift
trust and temporary groups. In R. M. Kramer &
T. R. Tyler (Ed.), Trust in organizations:
Frontiers of theory and research (pp. 166-195).
Thousand Oaks, CA: Sage.
Mullen, B., & Copper, C. (1994).The Relation
between Group Cohesiveness and
Performance: An Integration. Psychological
Bulletin, 115(2), 210-227.
Nemeth, C. (1986). Differential Contributions of
Majority and Minority Influence. Psychological
Review, 93, 23-32.
Nemeth, C. (1992). Minority Dissent as a Stimulant
to Group Performance. In S. Worchel, W. Wood,
& J. Simpson (Ed.), Group Process and
Productivity (pp. 95-111). London: Sage.
Nunnally, J. C. (1978). Psychometric Theory. New
York, NY: McGraw-Hill.
O’Reilly, C. A., Caldwell, D. F., & Barnett, W. P.
(1989). Work Group Demography, Social
Integration, and Turnover. Administrative
Science Quarterly, 34(1), 21-37.
Pearce, J. L., Sommer, S. M., Morris, A., & Frideger,
M. (1992). A configurational approach to
interpersonal relations: Profiles of workplace
social relations and task interdependence
(Working paper). Irvine, CA: University of
California, Graduate School of Management.
Pelled, L.H. (1996). Demographic Diversity, Conflict,
and Work Group Outcomes: An Intervening
Process Theory. Organization Science, 7, 615-
631.
Peters, L. M., & Manz, C. C. (2007). Identifying
antecedents of virtual team collaboration. Team
Performance Management, Bradford, 13(3/4),
117-129.
Pfeffer, J. (1983). Organizational Demography. In L.
L. Cummings & B. M. Staw (Ed.), Research in
Organizational Behavior (pp. 299-257).
Greenwich, CT: JAI Press.
Piccoli, G. and Ives, B. (2003). Trust and the
Unintended Effects of Behavior Control in Virtual
Teams. MIS Quarterly, 27(3), 365-395.
Podsakoff, P. M., MacKenzie, S. B., Lee, J-Y, and
Podsakoff, N. P. (2003). Common method
biases in behavioral research: A critical review
of the literature and recommended remedies.
Journal of Applied Psychology 88(5), 879-903.
Polzer, J. T., Crisp, B., Jarvenpaa, S. L., & Kim, J.
W. (2006). Extending the Faultline Concept to
Geographically Dispersed Teams: How
Colocated Subgroups Can Impair Group
Functioning. Academy of Management Journal,
49(4), 679-692.
Powell, A., Piccoli, G., & Ives, B. (2004). Virtual
Teams: A Review of Current Literature and
Directions for Future Research. The DATA
BASE for Advances in Information Systems,
35(1), 6-36.
Sarker, S., Lau, F., and Sahay, S. (2001). Using an
Asapted Grounded Theory Approach for
Inductive Theory Building About Virtual Team
Development, Database for Advances in
Information Systems, 32(1), 38-56.
Sharda, R., Barr, S.H., and McDonnell, J.C. (1988).
Decision Support System Effectiveness: A
Review and an Empirical Test. Management
Science, 34(2),139-159.
Shriberg, A. Effectively Leading and Managing
Virtual Teams. The Business Review,
Cambridge, 12(2), I-II.
Siebdrat, F., Hoegl, M., and Ernst, H. (2009). How to
Manage Virtual Teams.MIT Sloan Management
Review, 50(4), 63-68.
Sirgy, M. J., Grewal, D., Mangleburg, T. F., Park, J.,
Chon, K. S., Claiborne, C. B., Johar, J. S., &
Berkman, H. (1997). Assessing the predictive
validity of two methods of measuring self-image
congruence. Journal of Academy of Marketing
Science, 25, 229-241.
Stangor, C., Lynch, L., Duan, C., & Glass, B. (1992).
Categorization of individuals on the basis of
multiple social features. Journal of Personality
and Social Psychology, 62, 207-218.
Staples, S., and Ratnasingham, P. (1998). Trust:
The Panacea of Virtual Management.
Proceedings of the International Conference on
Information Systems, 128-144.
Staples, S., and Webster, J. (2008). Exploring the
Effects of Trust, Task Interdependence and
Virtualness on Knowledge Sharing in Teams.
Information Systems Journal, 18 (6), 617- 632.
Stasser, G., & Stewart, D. (1992). Discovery of
Hidden Profiles by Decision-Making Groups:
Solving a Problem versus Making a Judgment.
Journal of Personality and Social Psychology,
63, 426-434.
Tajfel, H. (1978). Differentiation between social
groups: Studies in the social psychology of
intergroup relations. San Diego, CA: Academic
Press.
The DATA BASE for Advances in Information Systems 46 Volume 41, Number 3, August 2010

Thomas, D. and Bostrom, R. (2008). Building Trust
and Cooperation through Technology Adaptation
in Virtual Teams: Empirical Field Evidence.
Information Systems Management, 25(1), 45-57.
Thomas, D. A., & Ely, R. J. (1996). Making
Differences Matter: A New Paradigm for
Managing Diversity. Harvard Business Review,
74, 79-90.
Thomas, D. C., Ravlin, E. C., & Wallace, A. W.
(1994). Effect of Cultural Diversity in
Management Training Groups. Paper presented
at the Symposium of the Academy of
Management Meeting.
Townsend, A. M., DeMarie, S. M., & Hendrickson, A.
R. (1998). Virtual teams: Technology and the
workplace of the future. The Academy of
Management Executive, 12(3), 17-29.
Tsui, A. S., Egan, T. D., & O’Reilly, C. A. (1992).
Being different: Relational demography and
organizational attachment. Administrative
Science Quarterly, 37, 547-579.
Tsui, A. S., & O’Reilly, C. A. (1989). Beyond Simple
Demographic Effects: the Importance of
Relational Demography in Superior-Subordinate
Dyads. Academy of Management Journal, 32,
402-423.
Turner, J. C. (1987). A self-categorization theory. In
J. C. Turner, M. A. Hogg, P. J. Oakes, S. D.
Reicher, & M. S. Wetherell (Ed.), Rediscovering
the social group: A self-categorization theory
(pp. 42–67). Oxford, England: Blackwell.
Turner, J. C., Oakes, P. J., Haslam, S. A., &
McGarty, C. (1994). Self and Collective:
Cognition and Social Context. Personality and
Social Psychology Bulletin, 20, 454-463.
Van Ryssen, S. & Hayes Godar, S. (2000). Going
International Without Going International:
Multinational Virtual Teams, Journal of
International Management, 6, 49-60.
Wakefield, R., Leidner, D., & Garrison, G. (2008). A
Model of Conflict, Leadership and Performance
in Virtual Teams. Information Systems
Research, 19(4), 434-455.
Walther, J. (1995). Relational Aspects of Computer-
mediated Communication: Experimental
Observations over Time. Organization Science,
6(2), 186-203.
Walther, J. B. (1996). Computer-mediated
communication: Impersonal, interpersonal, and
Hyperpersonal Interaction. Communication
Research, 23, 3-43.
Warkentin, M. E., Sayeed, L., & Hightower, R.
(1997). Virtual Teams versus Face-to-Face
Teams: An Exploratory Study of Web-based
Conference System. Decision Sciences, 28(4),
975-996.
Watson, W. E., Kumar, K., & Michaelsen, L. K.
(1993). Cultural diversity's impact on group
process and performance: Comparing culturally
homogeneous and culturally diverse task
groups. The Academy of Management Journal,
36(3), 590-602.
Wiersema, M. F., & Bantel, K. A. (1992). Top
Management Team Demography and Corporate
Strategic Change. Academy of Management
Journal, 35(1), 91-121.
Williams, K.Y., & O’Reilly, C. A. (1998).
Demography and Diversity in Organizations: A
Review of 40 Years of Research. In B. M. Staw
& L. L. Cummings (Ed.), Research in
Organizational Behavior (pp. 77-140).
Greenwich, CT: JAI Press.
Workman, M. (2007). The proximal-virtual team
continuum: A study of performance. Journal of
the American Society for Information Science
and Technology, 58(6), 794-801.
Zaheer, S. (1995). Overcoming the liability of
foreignness. Academy of Management Journal,
38, 348-363.
Zenger, T. R., & Lawrence, B. S. (1989).
Organizational Demography: The Differential
Effects of Age and Tenure Distributions on
Technical Communication. Academy of
Management Journal, 32, 353-376.
About the Authors
Gary Garrison is an Assistant Professor of
Information Systems Management at Belmont
University. He received his MBA and Ph.D. from The
University of Mississippi. His research interests
include virtual team collaboration and technology
adoption. His published articles appear in
Information Systems Research and Information
Systems Frontiers among others.
Robin Wakefield is an Associate Professor of MIS
in the Hankamer School of Business at Baylor
University. Her published articles appear in
Information Systems Research, European Journal of
Information Systems, Information & Management,
and Journal of Strategic Information Systems among
others. Her primary research interests include
Information Security, Virtual Teams, and E-
commerce.
Xiaobo (Bob) Xu is an Assistant Professor of
Management Information Systems at American
University of Sharjah, United Arab Emirates. He
received his PhD in Management Information
Systems from the University of Mississippi. He
currently conducts research in the areas of
The DATA BASE for Advances in Information Systems 47 Volume 41, Number 3, August 2010

information systems project management, e-
commerce adoption and global virtual team project
management. His research papers have been
published in refereed journals such as Enterprise
Information Systems and Project Management
Journal among others.
Sanghyun Kim is an Assistant Professor at School
of Business Administration, Kyungpook National
University. He received his BA and MBA from
Washington State University, and Ph.D. from The
University of Mississippi. His research interests are
focused on ubiquitous computing, e-Business, Open
Source Software, and Web 2.0. His published
articles appear in Information & Management and
Information Systems Frontiers among others.
The DATA BASE for Advances in Information Systems 48 Volume 41, Number 3, August 2010

A
B
S
T
R
A
C
T
S
A Design Science Based Evaluation
Framework for Patterns
Stacie Petter
College of Information Science & Technology
University of Nebraska, Omaha
Deepak Khazanchi
College of Information Science & Technology
University of Nebraska, Omaha
John D. Murphy
College of Information Science & Technology
University of Nebraska, Omaha
Abstract
Patterns were originally developed in the field of archi-
tecture as a mechanism for communicating good solu-
tions to recurring classes of problems. Since then,
many researchers and practitioners have created pat-
terns to describe effective solutions to problems asso-
ciated with disparate areas such as virtual project
management, human-computer interaction, software
development and engineering, and design science
research. We believe that the development of patterns
is a design science activity in which an artifact (i.e., a
pattern) is created to communicate about and improve
upon the current state-of-practice. Design science
research has two critical components, creation and
evaluation of an artifact. While many patterns have
been created, few, if any, have been evaluated in this
sense. In this paper, we propose a framework to eval-
uate patterns in any domain and provide examples of
how to use the evaluation framework. This process of
evaluation could help researchers refine extant pat-
terns and improve the possibility of creating sustain-
able best practices for a given domain. We believe this
evaluation framework begins an important dialogue
related to the evaluation of patterns as artifacts of
design science research. We draw upon the literature
associated with patterns, evaluation of design science
research, and research methods to develop this
framework for evaluating patterns in a more consistent
and rigorous manner.
Globally Distributed Teams:
The Effect of Diversity on Trust,
Cohesion and Individual
Performance
Gary Garrison
The Jack C. Massey Graduate School of
Business
Belmont University
Robin L. Wakefield
Hankamer School of Business
Baylor University
Xiaobo Xu
School of Business and Management
American University of Sharjah
Sang Hyun Kim
Kyungpook National University
Abstract
Globally distributed teams are becoming more com-
mon among organizations that seek to maximize
knowledge creation and innovation for competitive
advantage. Although they are becoming widely used
among global organizations, distributed teams are cre-
ating an environment replete in cultural and functional
diversity. Whereas synergy among members is
desired, diversity is likely to hinder team cohesion and
individual performance. Our study models and empiri-
cally tests the effect of perceptions of diversity on trust,
cohesion, and individual performance in actual global-
ly distributed teams. The results indicate that individual
productivity is negatively influenced by the extent of
diversity within a team; however, this liability may be
restrained if an environment of trust is encouraged and
team cohesion develops.

Reproducedwithpermissionof thecopyrightowner.Furtherreproduction prohibitedwithoutpermission.