Unpacking the Concept of Virtuality: The Effects of Geographic Dispersion, Electronic Dependence, Dynamic Structure, and National Diversity on Team Innovation

Abstract

To understand why the virtual design strategies that organizations create to foster innovation may in fact hinder it, we unpack four characteristics often associated with the term ‘virtuality’ (geographic dispersion, electronic dependence, structural dynamism, and national diversity) and argue that each hinders innovation through unique mechanisms, many of which can be overcome by creating a psychologically safe communication climate. We first tested the plausibility of our arguments using in-depth qualitative analysis of interviews with 177 members of 14 teams in a variety of industries. A second study constituted a more formal test of hypotheses using survey data collected from 266 members of 56 aerospace design teams. Results show that the four characteristics are not highly intercorrelated, that they have independent and differential effects on innovation, and that a psychologically safe communication climate helps mitigate the challenges they pose. We discuss the implications of these findings for theory and research.

Full text

To understand why the virtual design strategies that orga-
nizations create to foster innovation may in fact hinder it,
we unpack four characteristics often associated with the
term “virtuality” (geographic dispersion, electronic
dependence, structural dynamism, and national diversity)
and argue that each hinders innovation through unique
mechanisms, many of which can be overcome by creat-
ing a psychologically safe communication climate. We
first tested the plausibility of our arguments using in-
depth qualitative analysis of interviews with 177 mem-
bers of 14 teams in a variety of industries. A second
study constituted a more formal test of hypotheses using
survey data collected from 266 members of 56 aerospace
design teams. Results show that the four characteristics
are not highly intercorrelated, that they have independent
and differential effects on innovation, and that a psycho-
logically safe communication climate helps mitigate the
challenges they pose. We discuss the implications of
these findings for theory and research.
•
Virtual teams, variously defined as geographically dispersed,
electronically dependent, dynamic, or comprising diverse
members working remotely (Gibson and Cohen, 2003; Grif-
fith, Sawyer, and Neale, 2003; Martins, Gilson, and Maynard,
2004; Kirkman and Mathieu, 2005), are growing in number
and importance. A recent study by the Gartner Group indi-
cates that more than 60 percent of professional employees
work in teams characterized by virtuality (Kanawattanachai
and Yoo, 2002). Such teams potentially make it easier to
acquire and apply knowledge to critical tasks in global firms
(e.g., Madhaven and Grover, 1998; Sole and Edmondson,
2002). For example, geographic dispersion and electronic
dependence can provide access to relevant expertise even
when it is scattered around the globe (Kirkman et al., 2002)
and better understanding of global clients, operations, and
suppliers (Boutellier et al., 1998; Gluesing and Gibson, 2004).
A dynamic structure and diverse participants can enable cre-
ative and flexible responses to challenging development
needs through access to diverse expertise and perspectives
on an as-needed basis (Brown and Eisenhardt, 1995; Sole
and Edmondson, 2002). Such capabilities are central to inno-
vation, the collective process of making sense of new and
diverse information and incorporating this knowledge into
new methodologies, products, and services (Nohria and
Berkley, 1994; Nonaka and Takeuchi, 1995; Dougherty, 2001).
Innovation has become a critical means of competitive advan-
tage for firms in a variety of industries because it allows orga-
nizations to diversify, adapt, and even reinvent themselves to
match evolving market and technical conditions
(Schoonhoven, Eisenhardt, and Lyman, 1990). This has been
demonstrated in single industry studies, including technology
(Vessey, 1991; Eisenhardt and Tabrizi, 1995; Galunic and
Eisenhardt, 2001), pharmaceuticals (Zellmer-Bruhn and Gib-
son, 2006), and automotive settings (Clark and Fujimoto,
1991; Obstfeld, 2005), as well as in multi-industry studies,
which often control for industry effects, such as agriculture,
aerospace, retail, professional services, medical products,
chemicals, telecommunications, and consumer electronics
(Imai, Nonaka, and Takeuchi, 1985; Hargadon and Sutton,
© 2006 by Johnson Graduate School,
Cornell University.
0001-8392/06/5103-0451/$3.00.
•
This research was conducted with sup-
port from NSF grant #9975612. The
authors would like to thank Brad Kirkman,
Ed Lawler, Alec Levenson, Larry Mestad,
Sue Mohrman, Jude Olsen, Arjan Raven,
Ben Rosen, and Paul Tesluk for pushing
our thinking about virtual teaming, and
Taryn Stanko for help in the literature
search. Very special thanks to Susan
Cohen for expert assistance in the inter-
view data collection and for encouraging
us to pursue the ideas in this paper. She
was an inspiration.
Unpacking the
Concept of Virtuality:
The Effects of
Geographic Dispersion,
Electronic Dependence,
Dynamic Structure, and
National Diversity
on Team Innovation
Cristina B. Gibson
University of California, Irvine
Jennifer L. Gibbs
Rutgers University
451/Administrative Science Quarterly, 51 (2006): 451–495

1997; Gatignon et al., 2002). Innovation is especially critical in
subunits charged with new product development, design, or
research, but it is important even in those whose mandate is
implementation (Brown and Eisenhardt, 1995). For example,
Zellmer-Bruhn and Gibson (2006) documented how workers
on a pharmaceutical manufacturing assembly line were
rewarded for more efficient and effective work and had more
satisfying internal team processes when they developed new
mechanisms for coordinating their work across stages of
assembly, an essential form of innovation.
Teams—a set of interdependent parties, small in number,
who recognize themselves as a team and have some degree
of shared accountability (Cohen and Bailey, 1997)—are
increasingly recognized as a key mechanism for innovation in
complex firms, as they are pivotal in creating and acquiring
knowledge (Edmondson, 2002; Gibson and Vermeulen,
2003). Edmondson (2002) argued that innovation inherently
occurs at the team level because it requires learning behav-
ior, or transmission of knowledge bounded by tasks and
opportunities that takes place through conversations among a
limited number of interdependent people. These interactions
are necessary because they enable individuals to combine
different insights and institutionalize knowledge beyond that
held by a single member (Nonaka and Takeuchi, 1995). Simi-
larly, empirical research by Tagger (2002) suggested that
team-level innovation processes are needed to bring individ-
ual creativity into use. Without favorable group interactions,
individuals’ insights and efforts may be carried out in vain,
with no organizational benefits accrued.
Given the fundamental nature of innovation and the potential
for teams to contribute to it, organizations increasingly imple-
ment them for that purpose, using designs that include some
combination of geographic dispersion, electronic depen-
dence, dynamic structure, or national diversity. Yet, to date
the literature on such “virtual” teams has primarily docu-
mented the challenges involved, all of which have implica-
tions for the success of innovations (Cramton, 2001; Kirkman
et al., 2002, 2004; Sole and Edmondson, 2002). For example,
geographically separated team members lack “mutual knowl-
edge” of each other’s situations, increasing coordination
problems in acquiring knowledge and resources (Cramton,
2001). Electronic dependence creates logistical and techno-
logical constraints that limit informal spontaneous interaction,
hindering knowledge interpretation (DeSanctis and Monge,
1999). In structurally dynamic teams, full disclosure is often
hampered by inexperience with the other party and lack of a
shared history (Gibson and Cohen, 2003). When collaborators
represent different national backgrounds, each of which has
its own set of values, orientations, and priorities, this can
detract from effective internal communication (Watson,
Kumar, and Michaelson, 1993). Accessing, combining, and
applying knowledge relevant for innovation may be inherently
problematic in teams characterized by these features. As a
result, team members often struggle to understand each
other and must resolve misinterpretations before they can
truly innovate (Dougherty, 1992; Carlile, 2004). Just bringing
people with the required knowledge and skills together virtu-
452/ASQ, September 2006

ally provides no guarantee that they will be able to work
effectively and innovate across contexts.
Part of the problem in identifying the organizational perfor-
mance implications of this relatively new form of teaming is
that the term “virtual” has been applied imprecisely in the lit-
erature to represent very different types of teams: teams
that are geographically dispersed (consisting of members
spread across more than one location), mediated by technol-
ogy (communicating using electronic tools such as e-mail or
instant messaging), structurally dynamic (in which change
occurs frequently among members, their roles, and relation-
ships to each other), or nationally diverse (consisting of mem-
bers with more than one national background). Early research
on virtual work defined it as “work carried out in a location
remote from the central offices or production facilities, where
the worker has no personal contact with coworkers but is
able to communicate with them electronically” (Cascio, 2000:
85), while virtual teams were initially defined as groups of
geographically distributed coworkers that are assembled
using a combination of telecommunications and information
technologies to accomplish a variety of critical tasks
(Townsend, DeMarie, and Hendrickson, 1998). Definitions of
this type assumed that teams can be viewed as either com-
pletely virtual or face to face, leading researchers to treat vir-
tual teams as a single “ideal” type (Bell and Kozlowski, 2002:
16). A common research design in the early experimental
research was comparing manipulations of pure face-to-face
versus pure computer-mediated interactions (e.g., Kiesler,
Siegel, and McGuire, 1984; Spears and Lea, 1992; Straus and
McGrath, 1994; Walther, 1995; Huang et al., 2003).
Recently, scholars have shifted away from this dichotomy to
focus on the extent of virtualness, recognizing that most
teams can be described on a continuum of virtuality. There is
conceptual agreement that virtuality is multidimensional
(Cohen and Gibson, 2003; Griffith, Sawyer, and Neale, 2003;
Martins, Gilson, and Maynard, 2004; Kirkman and Mathieu,
2005), but the number and complexity of the dimensions
varies from one conceptual framework to another. Cohen and
Gibson (2003) included two dimensions, electronic depen-
dence and geographic dispersion. Griffith, Sawyer, and Neale
(2003) developed three—level of technology support, percent
of time apart on task, and degree of physical distance—as
did Kirkman and Mathieu (2005): the extent to which team
members use virtual tools, the amount of informational value
provided by such tools, and the synchronicity of team mem-
bers’ interaction. Martins, Gilson, and Maynard (2004) devel-
oped four dimensions: geographical dispersion, use of com-
puter-mediated communication, temporality, and diversity.
Further, previous research has tended to lump together vari-
ous features of “virtuality,” without examining the possible
independent, differential, perhaps even unintended effects of
each characteristic. A Web of Science search of articles pub-
lished since 2000 in the organizations, communication, psy-
chology, international management, and information systems
journals indicates that the majority of studies have included
at least three dimensions as defining characteristics of virtu-
ality. Of the 143 articles published between 2000 and 2006
453/ASQ, September 2006
Virtuality

that contained variations of the search terms virtual, distrib-
uted, or dispersed in a work setting, 80 included at least
three dimensions. For example, Nemiro (2002) defined virtual
teams as geographically dispersed, relying heavily on infor-
mation technology to accomplish work, with fluid member-
ship. Majchrzak et al. (2000) defined virtual teams as those
that are geographically distributed and reliant on technology,
with a more malleable structure than traditional teams.
Pauleen and Yoong (2001) defined virtual work as work per-
formed across time and distance, using information and com-
munication technology, by members from different countries,
cultures, and functions. Baba et al. (2004) defined virtual
teams as culturally diverse, involving two or more nations,
physical and temporal distance, interdependence, and
reliance on technology. Shin (2004) suggested that virtuality
is the degree to which a group has temporal, cultural, spatial,
and organizational dispersion and communicates through
electronic means. Chudoba et al. (2005) wrote that virtuality
depends on discontinuities in geography, time zone, organiza-
tion, national culture, work practices, and technology. Paul et
al. (2005) argued that virtual teams are those that cut across
national, functional, and organizational boundaries and are
connected by telecommunications and information technolo-
gy. Finally, Harvey, Novicevic, and Garrison (2005) defined vir-
tual teams as geographically and organizationally dispersed,
with members who work in different time zones, in different
nations around the world, with membership that is often
temporary and structure that is transitory, and who communi-
cate primarily via technology.
Summarizing across this growing literature, the most com-
mon characteristics investigated are geographic dispersion
and electronic dependence. The Web of Science search
uncovered 138 articles that included geographic dispersion,
122 that included electronic dependence, 82 that included
national diversity, and 23 that included a component repre-
senting fluid structure or membership. It is often assumed
that teams that are more geographically distributed are also
more electronically dependent, and thus more “virtual” (e.g.,
Baba et al., 2004; Kirkman et al., 2004; Majchrzak et al.,
2004; Martins, Gilson, and Maynard, 2004; Ocker, 2005).
Although electronic dependence sometimes coincides with
geographic dispersion, it doesn’t always. Teams whose mem-
bers are located in the same office may use e-mail to avoid
the trip up to another floor. Such teams are electronically
dependent but not geographically dispersed. Their reliance on
computer-mediated communication likely reduces informal
exchanges and social cues, yet they share the same geo-
graphic context and hence will be unlikely to experience the
challenges associated with linking numerous external net-
works.
The next most common defining characteristic is having
members from multiple countries, assuming that if a team is
“virtual,” it is nationally diverse (e.g., Maznevski and Chudo-
ba, 2000; Baba et al., 2004; Chudoba et al., 2005; Paul et al.,
2005; Janssens and Brett, 2006). In fact, many are of a single
nationality and are less likely to experience the cultural differ-
ences in communication preferences that often cause inter-
454/ASQ, September 2006

nal process challenges. Finally, many of the published studies
also define virtual teams as structurally dynamic, assuming
that if a team is “virtual,” it has fluid membership (e.g.,
Majchrzak et al., 2000; Chudoba et al., 2005; Harvey, Novice-
vic, and Garrison, 2005; Shin, 2005). Yet teams that are geo-
graphically dispersed or electronically dependent are not
always dynamic—they are sometimes stable across several
years and hence do not experience the difficulties associated
with changing membership.
What is clear from our review is that we won’t understand
the problems virtual designs create for innovation without
examining individual elements of virtuality. To begin this task,
we conceptualize virtuality here as a multifaceted higher-
order construct comprising four independent defining charac-
teristics identified in previous literature: geographic disper-
sion, electronic dependence, dynamic structural
arrangements, and national diversity. Although they each con-
tribute to the virtuality of the team, they are likely to have
unique effects and should be considered independently.
Further, to understand how the problems associated with the
elements of virtuality might be mitigated, we examine the cli-
mate for communication, because communication is so criti-
cal to virtual work, drawing on Edmondson’s (1999, 2003)
concept of team psychological safety. A psychologically safe
communication climate, defined as an atmosphere within a
team characterized by open, supportive communication,
speaking up, and risk taking (Gibb, 1961; Baer and Frese,
2003; Edmondson, 1999, 2003) may help turn geographic dis-
persion, electronic dependence, dynamic structure, and
national diversity from liabilities into assets and promote
innovation. We test these ideas in two studies, the first
exploring the general plausibility of our arguments using qual-
itative analysis of interviews in complex teams representing a
variety of industries and crossing many geographic contexts;
the second constituting a formal test of our hypotheses using
survey data from a larger sample of aerospace teams formed
at the same point in time, allowing us to control for industry,
organization, team and task type, and team longevity.
VIRTUAL INNOVATION
The ability of teams to innovate depends on how well they
generate, import, share, interpret, and apply technological
and market knowledge, particularly of local markets,
economies, and customers. That knowledge is a combination
of information, experience, context, interpretation, and reflec-
tion (Davenport, De Long, and Beers, 1998). It must be open-
ly shared across contexts through relationships and net-
works, and there must be confidence in the value of that
knowledge for achieving the objectives of the collaboration
(Kanter, 1988). Once these requirements have been met,
innovation involves dissemination and application of the
knowledge, including combining and integrating it to develop
novel insights, solutions, processes, or products (Obstfeld,
2005). In a comprehensive review, Brown and Eisenhardt
(1995) chronicled several decades of innovation research pub-
lished in major journals, focusing on normative work in which
projects were the unit of analysis, many of which involved
455/ASQ, September 2006
Virtuality

teams developing new products or processes. They synthe-
sized the research into a model of factors affecting innova-
tion success that integrates common findings. Their theoreti-
cal framework is useful in understanding how the elements
most commonly included in conceptualizations of virtuality
each hinder the development of unique success factors for
innovation.
Geographic dispersion. Rather than being an “on-off”
switch (i.e., a team is distributed or not), geographic disper-
sion is a continuum (Cohen and Gibson, 2003; Griffith,
Sawyer, and Neale, 2003). A team that spans multiple conti-
nents is more dispersed than one whose participants are
located in the same city. A high level of geographic disper-
sion complicates and hinders three important innovation suc-
cess factors related to knowledge and resource acquisition
included in Brown and Eisenhardt’s (1995) framework: exter-
nal communication with different contexts, support from
those outside the project in the form of resources, and the
speed/productivity of the innovation process.
First, as Brown and Eisenhardt (1995) documented, a stream
of research in the innovation literature has focused on the
benefits of flows of information into teams (e.g., Allen, 1971,
1977; Katz and Tushman, 1981). These studies highlight the
importance of external communication outside the team for
innovation success. Projects are more innovative when infor-
mation is brought into the collaboration, translated, and dis-
persed to fellow team members. Second, support and
resources predict innovation success. Teams whose mem-
bers lobby for such support, buffer the team from outside
pressures, engage in impression management, and coordi-
nate the use of external information for technical or design
issues are more innovative (Ancona and Caldwell, 1990,
1992b). The Stanford Innovation Project asked senior execu-
tives in the electronics industry to compare pairs of product
successes (defined as profit contributors) and product failures
in their firms. Products that had top management support in
the form of resources and expressed commitment were
more likely to be successful (Maidique and Zirger, 1984,
1985; Zirger and Maidique, 1990). Finally, several studies
have focused on the importance of speed in innovation,
including lead time and productivity (Eisenhardt and Tabrizi,
1995). For example, Iansiti (1993, 1995) deductively exam-
ined all major products developed by the 12 chief competi-
tors in Europe, Japan, and the U.S. in the computer industry
and found that lead time and productivity were indicative of
product integrity and the performance of new product devel-
opment teams.
When team members are highly dispersed across different
geographic locations, this hinders external communication,
support, and speed of innovation. In geographically dispersed
teams, members are embedded in different external contexts
and thus have less shared contextual knowledge, with far
greater understanding of their own (geographical) context
than others’ context in the team (Gluesing et al., 2003). Par-
ticipants in a site usually take for granted common knowl-
edge and therefore often cannot readily describe that knowl-
edge nor articulate its relevance to colleagues from other
456/ASQ, September 2006

locations (Rennecker, 2001). Sole and Edmondson (2002)
called this “situated knowledge” and found evidence from
qualitative analysis of seven dispersed project development
teams that the majority of conceptual misunderstandings
resulted from lack of awareness of or failure to appropriate
such knowledge. In contrast, co-location facilitates interaction
and experience and generates greater understanding of local
context and other hard-to-communicate aspects of work that
help facilitate information exchange for innovation (Tyre and
Von Hippel, 1997; Carson et al., 2003).
Geographic dispersion also affects innovation by increasing
the coordination requirements for acquiring resources, mak-
ing the process less efficient and hindering productivity. Gar-
nering resources is far more challenging when there are
numerous, diverse, and remote “environments” from which
they are gathered, adding complexity that must be managed
(Kirkman et al., 2002). In highly geographically dispersed
teams, it is more difficult to coordinate resources, given that
there are shorter windows of time for synchronous meet-
ings, and many meetings take place at other than standard
hours. Certain team members with access to resources or
top management support may even be inadvertently left out
of decision processes because they are not physically pre-
sent (Cramton, 2001). Decreased proximity may also result in
less attention and effort by dispersed coworkers and more
free riding (Kiesler and Cummings, 2002). A certain amount
of focused, devoted attention, and mental energy is needed
to pursue innovation, and context-specific circumstances that
distract from this may not leave one with enough time devot-
ed to the team (Csikszentmihalyi, 1996). In contrast, co-loca-
tion affords greater efficiency in garnering knowledge and
resources, as well as top management support from a single
context (Carson et al., 2003). Thus, due to reduced contextu-
al knowledge and coordination costs when collaborators are
geographically dispersed across multiple locations, innovation
will be more difficult to establish:
Hypothesis 1 (H1): Geographic dispersion is negatively relat-
ed to team innovation.
Electronic dependence. Some teams depend heavily on
computer-mediated communication to stay in touch and get
their work done. Again, electronic dependence is a continu-
um and is a question of the relative extent of computer-medi-
ated versus face-to-face communication (Cohen and Gibson,
2003; Griffith, Sawyer, and Neale, 2003). A team that oper-
ates entirely through e-mail, text exchanges, and teleconfer-
ences, never meeting face to face, is more electronically
dependent than a team whose participants meet monthly
face to face. Two predictors of successful innovation Brown
and Eisenhardt (1995) documented, subtle control and impro-
visation, are limited when electronic dependence is high.
Across industries, researchers have observed that exercising
subtle control, such that resulting products or process
improvements fit with overall corporate competitive strategy,
is critical (Imai, Nonaka, and Takeuchi, 1985; Clark and Fuji-
moto, 1991). At the same time, Eisenhardt and Tabrizi (1995)
showed that innovation teams that engage in more experi-
mental or improvisational processes, through frequent itera-
457/ASQ, September 2006
Virtuality

tions, more testing of ideas, and creative problem solving,
develop more successful innovations.
Reliance on computer-mediated communication reduces
opportunities for monitoring that enable subtle control and
makes it more difficult to interpret knowledge during the
improvisation process. Directly observing participants is often
impossible (Carson et al., 2003), there is less informal feed-
back in computer-mediated communication (Hollingshead,
1996a, 1996b), and managers prefer to give feedback face to
face rather than electronically (Kirkman et al., 2002, 2006), so
there is often less knowledge of results, making corrective
behavior more difficult. Electronic groups have also been
found to have more difficulty interpreting feedback in discus-
sions (DeSanctis and Monge, 1999). Computer-mediated
communication reduces nonverbal cues about interpersonal
affections such as tone, warmth, and attentiveness, which
contribute to message clarity and communication richness,
and collaborators who use computer-mediated communica-
tion often use more direct styles of communication with
fewer social cues than those in face-to-face conditions (Tid-
well and Walther, 2002). Communicators use physical and lin-
guistic “co-presence” to make inferences about one anoth-
er’s knowledge. Difficulty in interpreting knowledge reduces
experimentation (Hollingshead, 1998). Hence, by reducing
understanding of a message, electronic dependence may
have an impact on improvisation processes during innovation.
As a result we propose:
Hypothesis 2 (H2): Electronic dependence is negatively relat-
ed to team innovation.
Dynamic structural arrangements. Work teams in organiza-
tions today are often structurally dynamic in that change
occurs frequently among participants, their roles, and their
relationships to each other (Brown and Eisenhardt, 1995).
Many firms partner with each other through informal, tempo-
rary, relatively unstructured arrangements, such as outsourc-
ing or consortia, or using slightly more formal but dynamic
partnerships such as licensing, networks, or project-limited
structural arrangements, especially on knowledge-intensive
tasks (Carson et al., 2003). Structural dynamism negatively
affects a third set of factors related to innovation that Brown
and Eisenhardt (1995) documented, pertaining to political
processes, team member tenure, and planning. First, several
researchers have noted the importance of managing the
political process in innovation (Allen, 1971, 1977; Katz and
Tushman, 1981; Katz and Allen, 1985). When key members
of a project team act as politicians to manage the power
dynamics both inside and outside the team, innovation is
enhanced. Second, innovation research has established that
teams with a short history together tend to lack effective pat-
terns of information sharing and working together (Katz,
1982), limiting the amount and variety of information that can
be communicated across team members. Third, extensive
research has shown that planning and coordinating phases of
development are critical to innovation. Examining product
successes and failures in terms of profitability, sales, and
market share in hundreds of industrial and manufacturing
firms, Cooper and colleagues (Cooper, 1979; Cooper and
458/ASQ, September 2006

Kleinschmidt, 1987) found that the internal organization of
the innovation effort was crucial for success. Other studies
(Hise et al., 1990; Zirger and Maidique, 1990; Dwyer and
Mellor, 1991) have also found smooth execution of all phases
of development by well-coordinated subgroups to be critical
for innovation.
A highly dynamic team structure hinders the development of
these success factors because it increases uncertainty and
perceived risk, which complicates political processes.
Turnover makes it nearly impossible to develop strong rela-
tionships and preserve organizational memory, and it makes
it more difficult to plan and structure the flow of develop-
ment. Dynamically structured collaborations typically include
some degree of uncertainty (Chiles and McMackin, 1996),
and so a complete contract specifying all relevant contingen-
cies is impossible to write or enforce legally. Thus it is
inevitable that the parties need to review, renegotiate, or
reinterpret their initial agreements (Das and Teng, 1998),
increasing the need to manage political processes but also
making it more difficult to do so. Uncertainty often spills over
into attributions and interpretations about the motives of the
parties involved, resulting in hesitancy to share information in
the innovation process (Dougherty, 1992, 2001). This exacer-
bates perceptions of risk, decreasing openness to new and
innovative ideas that may come from interaction with other
sites (Sole and Edmondson, 2002). Often, concerns about
confidentiality and proprietary knowledge prevent members
from sharing knowledge across sites, hindering the team’s
creative process and ability to innovate. A dynamic structure
also reduces the strength of social ties among members of
the team. The strength of a tie (or social relationship) is a
function of the amount of interaction, emotional intensity,
and reciprocity between any two individuals (Granovetter,
1973). Although it has been argued that weak ties potentially
lead to greater creativity (Granovetter, 1982) because new
participants bring fresh knowledge into the team, individuals
often feel more comfortable sharing information that requires
risk and candor across stronger ties (Perry-Smith and Shalley,
2003), such as those that have been built up over time in
teams with a consistent membership. Further, it is more diffi-
cult to implement knowledge when the structure is dynamic
(Burt, 2004; Granovetter, 2005). Obstfeld (2005) referred to
this distinction as the idea problem versus the action problem
and provided empirical evidence that the action problem may
often overwhelm whatever information advantage certain
structural arrangements such as networks of loosely connect-
ed actors may have. Given that innovation encompasses gen-
erating knowledge as well as making sense of it and incorpo-
rating that knowledge into new methodologies, products, and
services (Nonaka and Takeuchi, 1995), the overall effect of a
dynamic structure on innovation is likely to be negative. With-
out a shared history, the planning of development phases is
hampered. When members lack knowledge of what each can
contribute, it is more difficult to assign responsibilities and
coordinate, especially around novel ideas (Obstfeld, 2005).
Based on this argumentation, we propose:
459/ASQ, September 2006
Virtuality

Hypothesis 3 (H3): Dynamic structural arrangements are
negatively related to team innovation.
National diversity. A final feature often assumed to coincide
with virtuality is national diversity (e.g., Tan, Watson, and
Wei, 1995; Jarvenpaa and Leidner, 1999; Maznevski and Chu-
doba, 2000). Cultures, broadly defined as characteristic ways
of thinking, feeling, and behaving shared among members of
an identifiable group (Earley and Gibson, 2002), exist at many
different levels beyond national cultures, including organiza-
tional (e.g., General Electric’s culture as compared with John-
son and Johnson’s culture) and functional cultures (e.g., engi-
neering as compared with human resources culture). But
nationality is a superordinate determinant of identity that is
engrained from birth and is likely to be more salient than a
particular organizational or functional culture (Hofstede, 1991;
Earley and Mosakowski, 2000). Being a salient source of
identity, national diversity hinders a final set of innovation
success factors Brown and Eisenhardt (1995) discussed that
pertain to internal communication, conflict resolution, and the
development of a shared vision. In the innovation literature,
Ancona and Caldwell (1992a) found that teams with more
thorough internal communication (e.g., they defined goals
better, developed workable plans and prioritized work) had
superior innovation performance. Similarly, research examin-
ing new product development efforts in over 20 firms
(Dougherty, 1990, 1992; Dougherty and Corse, 1995) has
shown that when diverse members of project teams com-
bined their perspectives in a highly iterative way to improve
integrated information flow, they were more innovative.
Members often had distinct “thought worlds”—they under-
stood different aspects of product development in different
ways—which led to varying interpretations of the same infor-
mation, but strong internal communication bridged these dif-
ferences. Relatedly, in a series of studies, Clark and his col-
leagues (Clark, Chew, and Fujimoto, 1987; Clark and
Fujimoto, 1991) found that developing a shared, overall vision
contributed to innovation. Finally, Hayes, Wheelwright, and
Clark (1988) described how bringing conflicts to the surface
early in the development process was an important factor in
successful innovation. By resolving conflicts through mutual
accommodation, a clear project vision was established early
on.
Establishing effective internal communication and a shared
vision for innovation is challenging when team members rep-
resent different nations, because national diversity creates
different expectations for communication practices (Gibson
and Vermeulen, 2003) and reduces identification with the
team as a whole (Fiol, 1991; Hambrick et al., 1998; Gibbs,
2002). Thus, although collaborations that consist of members
from different nations may have access to more information
(Watson, Kumar, and Michaelson, 1993) as a result of differ-
ent worldviews (Choi, Nisbett, and Norenzayan, 1999), they
have been found to be fraught with difficulties that can hin-
der innovation through misunderstanding, stereotyping, and
the inability to reach agreement, make decisions, and take
action (Adler, 1997).
460/ASQ, September 2006

Many different orientations that vary across nations have
been linked to team communication (see Earley and Gibson,
2002, for a review), and nationally diverse teams often have
trouble communicating due to different expectations about
the communication process. By way of example, some
national cultures are “high context” and others are “low con-
text,” referring to the importance of nonverbal, contextual
cues in communicating or interpreting messages (Hall and
Hall, 1987; Gordon, 1991). Members of high-context cultures
tend to avoid negative or confrontational responses in com-
municating with members of their own work group in order
to save face and preserve a sense of harmony in the group
(Adler, Brahm, and Graham, 1992). Members of low-context
cultures use explicit language to convey exactly what is
meant in a much more direct manner, even if the message is
negative or confrontational. Beyond high- or low-context dif-
ferences, other pertinent differences may include individual-
ism-collectivism, uncertainty avoidance, power distance, or
time orientation (Gibson and Zellmer-Bruhn, 2001; Earley and
Gibson, 2002). During multicultural collaboration, differences
across these dimensions are likely to cause communication
breakdowns (Kirkman and Shapiro, 1997; Gibson and Zellmer-
Bruhn, 2001), making it difficult to aggregate and process
information, particularly for knowledge that is uncodified
(Nonaka and Takeuchi, 1995).
In addition, high national diversity and members’ identifica-
tion with their nationality are likely to lead to social catego-
rization, a process in which individuals from different groups
(e.g., nations) make “in-group/out-group” distinctions purely
on the basis of nationality, particularly when they have inade-
quate information about others involved (Whitener et al.,
1998). These distinctions can result in stereotyping, distrust,
and suspicion of out-group members (Brewer, 1981), reduc-
ing team identification and integration as well as the team’s
ability to leverage information (Adler, 1997; Hambrick et al.,
1998). Although they examined collocated teams, Gibson and
Vermeulen (2003) found a strong negative relationship in a
variety of team types between the team’s demographic het-
erogeneity (including nationality) and team learning behaviors,
a set of actions that teams are likely to engage in during
innovation. In particular, developing a shared vision is precari-
ous in nationally diverse teams because of strong identifica-
tion with subgroups (Fiol, 1991; Mathieu et al., 2000), which
may hamper innovation. As a result, we propose:
Hypothesis 4 (H4): National diversity is negatively related to
team innovation.
Mitigating Effects of a Psychologically Safe
Communication Climate
For teams characterized by a high degree of geographic dis-
persion, electronic dependence, dynamic structures, or
national diversity, a psychologically safe communication cli-
mate may act as a moderating variable that helps overcome
the negative effects of these features of virtuality to increase
innovation. The general concept of communication climate is
grounded in the organizational communication literature
(Gibb, 1961; Bastien, McPhee, and Bolton, 1995) and refers
461/ASQ, September 2006
Virtuality

to the environment in which communication occurs, including
communicative phenomena such as management’s receptivi-
ty to employees and the accuracy of information (Dillard,
Wigand, and Boster, 1986). Although it overlaps with the
notion of organizational climate, communication climate has
been established as a separate dimension (Welsch and
LaVan, 1981) and has been distinguished from other types of
organizational climate, such as motivational climate or
achievement climate (Poole, 1985). Further, communication
climate has been shown to differ across teams or subunits
within organizations (Falcione, Sussman, and Herden, 1987:
205), with a “group communication climate” defined as
“those molar factors .|.|. which affect the message sending
and receiving process of members within a given organiza-
tional group.” A supportive group communication climate has
been shown to predict satisfaction and commitment (Guzley,
1992) and includes variables such as participation in decision
making and communication openness (Trombetta and
Rogers, 1988).
We focus on a specific type of group communication climate,
a psychologically safe communication climate characterized
by support, openness, trust, mutual respect, and risk taking.
A psychologically safe communication climate facilitates inno-
vation because it involves speaking up, raising differences for
discussion, engaging in spontaneous and informal communi-
cation, providing unsolicited information, and bridging differ-
ences by suspending judgment, remaining open to other
ideas and perspectives, and engaging in active listening. Psy-
chologically safe communication climate draws on the con-
struct of team psychological safety, defined as a shared
belief that a team is safe for interpersonal risk taking
(Edmondson, 1999). Most of the research on psychological
safety represents it as a cognitive phenomenon comprising
an aggregated set of individual perceptions (Edmondson,
1999; Edmondson, Bohmer, and Pisano, 2001), while our
concept of a psychologically safe communication climate
focuses more specifically on communication behavior and
team members’ interactions, as constituted by messages
and message-related events. Yet more recent research has
acknowledged the importance of communication in creating
psychological safety (Detert, 2003; Lee et al., 2004), and
Edmondson (2003: 1447) indicated that “speaking up” is a
behavioral manifestation of the psychological safety belief.
Further, although a psychologically safe communication cli-
mate involves trust, the two are not synonymous. Trust is an
assumption that the actions of others will be beneficial to
one’s interests and a resulting willingness to be vulnerable to
such actions (Robinson, 1996). This assumption is likely in
place when a psychologically safe communication climate
exists, but a psychologically safe communication climate
involves a broader set of communication-related behavior.
Finally, although a psychologically safe communication cli-
mate may help strengthen social ties among team members
that are weakened through the effects of virtuality, it is not
synonymous with cohesiveness, which can produce effects
opposite to psychological safety, such as groupthink and
decreased risk taking (Edmondson, 1999).
462/ASQ, September 2006

Psychological safety has been found to play a critical role in
fostering team learning and innovation. Analyzing 51 work
teams in a manufacturing company, Edmondson (1999) found
that psychological safety helped teams learn more effectively
by mitigating the interpersonal risks involved and encouraging
members to admit mistakes, question current practices, ask
for help, and solicit feedback. More recently, Edmondson and
colleagues (Edmondson, Bohmer, and Pisano, 2001; Edmond-
son, 2003) drew on interviews with members of 16 cardiac
surgery teams to illustrate the processes through which psy-
chological safety leads to team learning and innovation: mini-
mizing functional and status differences promotes speaking
up across such boundaries, and designing preparatory prac-
tice sessions and early trials helps encourage new behaviors
in technology implementation. Other empirical studies have
found that innovation was inhibited by the lack of psychologi-
cal safety in dyadic teams, as fear of failure resulted in less
experimentation (Lee et al., 2004), and that psychological
safety moderates the relationship between process innova-
tion and firm performance (Baer and Frese, 2003).
A psychologically safe communication climate likely helps
overcome the challenges posed by elements of virtuality for
a number of reasons. First, the establishment of a psycholog-
ically safe communication climate can help overcome the bar-
riers to innovation associated with geographic dispersion
(Donnellon, 1996). Only if partners are able to share informa-
tion across contexts despite their contextual differences will
they be able to garner the resources and support they need
to innovate. A psychologically safe communication climate
can help in ironing out the potential kinks in daily operations
across geographic locales and make for a satisfactory work-
ing relationship, increasing the likelihood that team members
will efficiently accumulate the necessary external links to
acquire knowledge and resources. In support of this, Sole
and Edmondson’s (2002) analysis revealed that a team’s situ-
ated knowledge was more likely to be shared and appropriat-
ed across locations when members were familiar with ongo-
ing practices in the multiple sites across which the team was
dispersed, and this was more likely when there was open
information exchange, such as that found in a psychologically
safe communication climate.
Second, a psychologically safe communication climate helps
to increase informal communication and giving and receiving
feedback to overcome problems of subtle control, low mes-
sage clarity, and knowledge interpretation that result from
reduced face-to-face interaction (Short, Williams, and
Christie, 1976) and lack of social cues (Sproull and Kiesler,
1986) during electronic communication. Empirical evidence
points to the need for social and personal communication (in
addition to the exchange of business or technical information)
in electronically dependent teams and its role in balancing
control with learning and experimentation (Knoll and Jarven-
paa, 1998; Gibson and Birkinshaw, 2004). A psychologically
safe communication climate encourages frequent, sponta-
neous, informal, and direct communication in quick conversa-
tions or short e-mails (Monge, Cozzens, and Contractor,
1992). This type of communication has been found to be criti-
463/ASQ, September 2006
Virtuality

cal to the work of teams with innovative projects because it
creates the opportunities to evaluate knowledge and ideas
necessary for improvisation (Miner, Bassoff, and Moorman,
2001; Edmondson, 2003).
Third, a psychologically safe communication climate has been
identified as particularly important in structurally dynamic col-
laborations because it helps create trust (Jarvenpaa and Leid-
ner, 1999; Gibson and Cohen, 2003) and reduce perceptions
of risk (Handy, 1995; Dutton, 1999). When a psychologically
safe communication climate exists, collaborators are more
likely to provide unsolicited information to other members as
a way of showing both goodwill and intimacy, strengthening
relationships in the team and creating incentives for building
a shared history, which contributes to work flow (Das and
Teng, 1998; Zaheer, McEvily, and Perrone, 1998). As the reci-
procal process engenders credibility, a sustained information
flow can overcome the uncertainty and weakened relation-
ships caused by dynamic structural arrangements (Knoll and
Jarvenpaa, 1998).
Finally, a psychologically safe communication climate may
also help to bridge national differences and reduce in-
group/out-group bias (Gudykunst, 1991; Maznevski, 1994).
Larkey (1996) argued that the social categorization process
that occurs in diverse teams often results in “divergence,”
defined as adherence to culturally based communication pat-
terns, in contrast to convergence, defined as adjustment of
one’s communication style to match one’s partner. Conver-
gence is more common when there is a psychologically safe
communication climate, and it helps to counterbalance in-
group/out-group dynamics (Larkey, 1996), which can facilitate
innovation. Open and accommodating communication is an
important antecedent of shared cognition (Gibson, 2001); in
its absence, teams’ mental models have been found to
diverge over time (Levesque, Wilson, and Wholey, 2001).
Team members who communicate more supportively with
one another are more likely to develop a common frame of
reference and shared mental model (Klimoski and
Mohammed, 1994). Further, the innovation process requires
that the parties involved suspend judgment, remain open to
others’ ideas and perspectives, and put forth the effort
required to integrate new knowledge with existing knowl-
edge to produce the innovation. When this occurs, exposure
to new processes of working or a new approach to a prob-
lem may propel one to pursue previously unexplored direc-
tions or to integrate new ideas, leading to novel and innova-
tive solutions (Okhuysen and Eisenhardt, 2002; Perry-Smith
and Shalley, 2003). In support of this, Gibson and Vermeulen
(2003) found that the differences associated with national
demographic heterogeneity in teams could be bridged if mild
subgroups formed and created a psychologically safe environ-
ment. Through information exchange, members identified
and developed more commonalities, reducing in-group/out-
group barriers and increasing information processing capacity
(Gibson and Vermeulen, 2003). Based on these observations,
we propose:
Hypothesis 5 (H5): A psychologically safe communication cli-
mate reduces the negative effects of (a) geographic disper-
464/ASQ, September 2006

STUDY 1
Methods
Overview. Study 1 consisted of an exploratory interview-
based analysis of the plausibility of our arguments that geo-
graphic dispersion, electronic dependence, dynamic struc-
ture, and national diversity negatively influence innovation
and that a psychologically safe communication climate helps
to mitigate these effects. It was designed in accordance with
recommendations by qualitative researchers (Glaser and
Strauss, 1967; McCracken, 1988; Strauss and Corbin, 1990;
Wolfe, Gephart, and Johnson, 1993) and recent research on
related topics (e.g., Gibson and Zellmer-Bruhn, 2001;
Mohrman, Gibson, and Mohrman, 2001). Specifically, we (1)
verified the existence of the elements in our model, (2) elab-
orated on and defined them in terms of measurable variables,
465/ASQ, September 2006
Virtuality
sion, (b) electronic dependence, (c) dynamic structure, and (d)
national diversity on team innovation.
Table 1 summarizes the elements of virtuality, the innovation
success factors they each negatively affect, the mechanisms
by which the effect occurs, and the potential moderating
effect of a psychologically safe communication climate.
Table 1
Summary of Effects of Elements of Virtuality on Innovation
X
Element of virtuality
Geographic dispersion
Electronic dependence
Dynamic structure
National diversity
Innovation success factor
(Brown and Eisenhardt,
1995) affected by element
External communication
with contexts members
are embedded in
Support (garnering and
provision of resources)
Speed/productivity
Control
Improvisation/experimental
tactics
Power/political processes
Tenure
Planning/overlapping of
development phases
Internal communication
Vision
Specific effects on
innovation
Reduces contextual
knowledge of other sites.
Increases coordination
complexity in acquiring
knowledge and
resources.
Reduces opportunities for
monitoring.
Reduces message clarity
and communication
richness.
Increases uncertainty about
motives requiring political
risk management.
Difficult to preserve
organizational memory
due to turnover.
More difficult to develop
flow and development
phases.
Creates different
communication
preferences.
Reduces identification with
team as a whole (more
difficult to create shared
vision).
Mitigating effects of
psychologically safe
communication climate
Increases exchange of
contextual knowledge,
which aids in efficiently
garnering resources.
Increases willingness to
contribute and extend
effort on productive
learning.
Increases informal
communication and
giving and seeking
feedback.
Helps develop social cues
necessary for
improvisation.
Strengthens relationships
by increasing trust and
reducing perceptions of
risk.
Creates incentive for
building shared history,
which contributes to
workflow.
Bridges in-groups/out-
groups to resolve
conflicts.
Increases information
processing capacity by
aiding in integration while
allowing for cultural
differences to co-exist.

(3) examined interrelationships among the variables pro-
posed, and (4) verified the relationships in context. To provide
a deeper understanding, each author independently conduct-
ed a more holistic content analysis of the interview tran-
scripts, studying illustrative team interactions in the context
of the full interviews to corroborate and generate richer
insights.
Sample and contexts. The analysis includes a total of 177
interviews across 14 teams. Within these teams, 7 indus-
tries, 18 nations, 32 cities, 16 organizations, 45 organizational
subunits, and 11 different functional areas were represented.
Thirty percent of the sample was women. Team size ranged
from 4 to 23 members, and we were able to interview
83–100 percent of each team, with a mean of 92 percent of
each team represented in the sample. Team longevity varied
from two years to indefinite, and we followed each team for
six months to two years. Teams varied in the extent of their
geographic dispersion, electronic dependence, structural
dynamism, and national diversity. Table 2 provides descrip-
tions of the teams.
Procedure. Interviews were one to two hours in duration and
were conducted on site, including multiple sites for each
team. All were tape recorded, and about 50 percent were
videotaped, except when we were denied permission. Inter-
view questions were broad and pertained to the structure of
the team, members’ roles, the nature of work, communica-
tion processes, the technology used, interpersonal relation-
ships and processes, team characteristics, and team experi-
ences. The Appendix provides a summary of the interview
protocol. We also collected archival data, including back-
ground information about the teams, organizational charts, e-
mail or other electronic transcripts, evaluations of the team,
project plans, and written mission statements. We tran-
scribed all the interviews and entered them into a content
analysis text database. This text database consisted of over
1,000 pages, including 399,474 words, with an average of
2,257 words per interview. We used the Atlas.ti software
program for content analysis, which allowed us to use flexi-
ble non-hierarchical qualitative coding as well as to create fre-
quency distributions and take inventories of specific words or
categories in a text.
Measures. For geographic dispersion, following team hetero-
geneity research (Bantel and Jackson, 1989; Bunderson and
Sutcliffe, 2002), we used Blau’s (1977) formula to calculate a
measure of categorical dispersion across locations in each
team. This measure takes into consideration both the num-
ber of locations and the number of individuals in the team
residing in each location. The minimum value for this variable
was 0, indicating that all members had the same location,
and the maximum value was .85, indicating extreme geo-
graphic dispersion (e.g., 4 locations represented by approxi-
mately 2–3 members in each location), with a mean of .42
and a standard deviation of .35. To measure electronic depen-
dence, we employed two independent raters external to the
teams to rate each team. Raters were Ph.D.-level assistants
in organizational behavior, experienced at coding and knowl-
edgeable about the general domain. We measured electronic
466/ASQ, September 2006

467/ASQ, September 2006
Virtuality
Table 2
Characteristics of Teams in Study 1
X
Team
01. Competency
00. Center Team
02. Community
00. Team
03. Function Team
04. Design Team
05. Frame Team
06. Pilot Team
07. Europe Connect
08. Tool Team
09. South Market
00. Team
10. North Market
00. Team
11. Machine Team
12. Platform Team
13. European BU
00. Team
14. Canadian BU
00. Team
No.
of
members
20
17
16
11
23
21
09
16
10
12
13
17
05
04
No.
inter-
viewed
18
15
15
11
20
20
08
14
09
10
12
16
05
04
Industries
Professional
services
Automotive
Automotive
Retail/design
Aerospace
Aerospace
Information
technology (IT)
Agricultural &
machine tools
Travel
Travel
Agricultural &
machine tools
Agricultural &
machine tools
Agricultural &
machine tools
Agricultural &
machine tools
Nations
US, Netherlands,
Australia, Sweden
Argentina
US, Germany, Brazil
US, Germany
US
US, UK,
Netherlands,
Ireland
US, UK, Spain,
Mexico,
Netherlands,
India, Greece
Germany,
Netherlands,
India, Austria,
Finland
US
US, Mexico
US, Mexico
US
Canada, France, Italy
Ireland
France
No.
of
firms
1
2
2
1
4
4
6
1
1
1
1
1
1
1
No. of
loca-
tions
11
10
03
04
04
04
04
01
03
02
01
01
01
01
No. of
sub-
units
01
08
01
04
12
05
06
04
05
05
06
06
02
02
Team
longevity
Indefinite
Indefinite
Indefinite
2 yrs.
10–15 yrs.
2 yrs.
2 yrs.
Indefinite
3 yrs.
3 yrs.
Indefinite
Indefinite
Indefinite
Indefinite
Functions——
Professional services
Engineering, marketing, IT,
professional services
Management, accounting
Marketing, research, academic
Engineering, IT
Engineering, IT, management
Engineering, IT, academic,
professional services,
management
Engineering, sales, marketing,
management
Operations, IT, management,
accounting
Operations, IT, finance,
management, accounting
Engineering, operations,
marketing, management
Engineering, sales, IT,
marketing, management,
finance, accounting
Sales, management
Operations, sales
Team tasks——
Management consulting
Leadership development
and education
Develop internal
procurement methods
Identify future product
needs
Design/build jet fighters
Design/build jet fighters
Develop technology design
concepts for children
Product line focus
Technical service provider
Technical service provider
Product line focus
Cross-product
responsibility
Area-based product
management
Area-based product
management

dependence at the team level based on work by De Jonge et
al. (1999) and Karasek and Theorell (1990), who argued that
as reflections of the objective work environment, team-level
measures of job characteristics are less prone to bias than
individual-level self-reports. The two raters coded the teams
using a 3-point Likert scale based on overall subtext analysis
of the team’s interview transcripts, as well as records of e-
mail traffic, with “1” representing a low level of electronic
dependence, “2” representing a moderate level, and “3”
representing a high level. These categories were inductively
derived based on overall subtext analysis and comparisons
across teams. A subset of teams clearly used electronic com-
munication much more than all other teams; we considered
these highly dependent. Likewise, a subset clearly used elec-
tronic communication much less than all other teams; we
considered these low on electronic dependence. All other
teams were considered moderate. We computed an inter-
rater reliability of .87 (Cohen’s kappa) across the two raters,
and because it showed adequate internal consistency, we
averaged the two ratings to arrive at one score for each
team. A quote from a member of Design Team at Office Sys-
tems reflects low electronic dependence: “We often meet
face to face .|.|. there is a common visual. Work is supported
visually. Everyone is looking at it. We devote more time to it.
There is more of a sharing of information.” A quote from a
member of Function Team at Auto Unification Company
demonstrates high electronic dependence:
E-mail—I respond to probably 30–50 per day and receive 100 per
day. That has been an incredible benefit to us. I can’t imagine doing
the job that I have now before having it. Two and a half years ago, it
would have been a manual fax, or hard copy mail, and there would
have been a 3 to 5 or 10 day delay, with all the issues of proofing
and typing. You have responses within hours in some cases. You
can collect and complete an initiative.
To measure the dynamism of structural arrangements, we
asked the same two independent raters to rate each team,
instructing them to examine archival data about each team,
including organizational charts, team membership lists,
intranet sites, and historical documents to determine the
degree to which the members and structure had changed
during the life of the team. Raters used a 3-point scale, with
“1” representing a structure that had not changed, “2” rep-
resenting a moderately dynamic structure, and “3” repre-
senting a highly dynamic structure. Again, categories were
inductively derived based on overall subtext analysis and
comparisons across teams. We computed an interrater relia-
bility of .82 (Cohen’s kappa) across the two raters, which
showed adequate internal consistency, so we averaged the
two ratings to arrive at one score for each team. As an exam-
ple, the Community Team, charged with knowledge manage-
ment at Auto Unification Company, was highly dynamic, with
numerous instances of temporary outsourcing and subcon-
tracting and a constant flux in members, roles, and their rela-
tionships to one another. In contrast, Frame Team in the
aerospace industry had no changes in membership or struc-
ture during its history.
468/ASQ, September 2006

To measure national diversity, following team heterogeneity
research (Bantel and Jackson, 1989; Bunderson and Sutcliffe,
2002), we used Blau’s (1977) formula to calculate a measure
of categorical dispersion across nationalities in each team.
The minimum value for this variable was 0, indicating that all
members had the same nationality, and the maximum value
was .78, indicating extreme national diversity (e.g., 7 national-
ities represented on the team with approximately 2–5 mem-
bers in each nationality), with a mean of .30 and a standard
deviation of .26. Nationality is not a reflection of where indi-
viduals were physically located but, rather, their national back-
ground, as indicated when asked about their nationality. In
addition, nationality is not confounded with company (mem-
bers often represented different firms but had the same
nationality and vice versa).
We used a different approach to measure psychologically
safe communication climate and innovation. Because these
variables are more complex and not easy to characterize
without intimate knowledge from the participants’ perspec-
tives, we captured evidence of these variables by examining
each individual team member’s experience as relayed in the
interviews. The first step was to identify interview excerpts
that contained this evidence. Following previous research
(e.g., Kabanoff, Waldersee, and Cohen, 1995; Gibson and
Zellmer-Bruhn, 2001), we compiled a list of key words per-
taining to each variable based on a comprehensive review of
survey instruments used to measure these variables,
research articles, and a snowball synonym using dictionaries
and thesauruses. For example, psychologically safe commu-
nication climate was captured by terms such as “empathy,”
“openness,” and “understanding”; innovation was captured
by terms such as “novelty,” improvement,” and “unprece-
dented” (the full set of terms is available from the authors).
We then instructed our content analysis program (Atlas.ti) to
search for any word in a category for each variable and gen-
erate output files for each variable containing excerpts that
included any word in the category list for that variable. Uniti-
zation was at the sentence level. During a process of “in-con-
text verification,” excerpts in the subtext databases were
then reviewed by two independent raters (with similar qualifi-
cations as those mentioned earlier) and coded as either (1)
reflecting the variable or (0) not reflecting the variable. An
interrater reliability coefficient (Cohen’s kappa) of .75 was
computed for psychologically safe communication climate
and .79 for innovation by comparing the codes of the inde-
pendent raters. Discrepancies between codes were dis-
cussed and reconciled, eliminating any excerpt that was not
coded as adequately reflecting each variable, to arrive at a
final set of excerpts demonstrating evidence of each variable.
For example, psychologically safe communication climate
was evidenced by excerpts such as “We’re really good at
asking questions, helping people make incremental improve-
ments in their understanding” (Office Systems, Design
Team), and “People seem to feel comfortable discussing
their problems and issues” (Auto Unification, Community
Team). Innovation was evidenced in excerpts such as “We
are strong on innovative ideas, and fairly flexible” (Machines
Inc., Machine Team), and “We also felt that it was important
469/ASQ, September 2006
Virtuality

that we churn out new stuff all the time. This is a world
where ideas are changing rapidly, and we are looking to build-
ing and churning out new things” (Professional Service, Com-
petency Center Team). We then computed a frequency count
for each interviewee for each variable of the number of times
he or she used a word representing the variable to express
evidence of that variable in his or her team. To control for dif-
ferences in the length of interviews and capture the empha-
sis on a variable relative to an interview’s length, we weight-
ed the number of occurrences by the total number of words
in an interview transcript.
Both a psychologically safe communication climate and inno-
vation are team-level concepts, but we used individual inter-
views to derive measures of those characteristics. In the par-
lance of multilevel theory (Klein and Koslowski, 2000: 41), we
considered these characteristics “shared unit-level con-
structs” and gathered data from individuals to assess team-
level characteristics that we presumed to be shared within a
team and capable of differentiating across teams. Conceptu-
ally, this makes sense, given that individual team members
are most familiar with the extent to which the team exhibits
these attributes. Yet it is critical with such aggregated vari-
ables that we statistically demonstrate within-unit agreement
and between-unit differences (Klein and Koslowski, 2000). To
do so, we computed intraclass correlation coefficients using
one-way analysis of variance on the individual-level data with
team as the independent variable and the scores on psycho-
logically safe communication climate and innovation as the
dependent variables. Others have suggested that an indica-
tion of convergence within teams is an ICC(1) value in the .05
to .20 range with a corresponding ANOVA F-statistic that is
statistically significant (Kenny and LaVoie, 1985; Bliese,
2000). For psychologically safe communication climate,
ICC(1) = .23, F = 5.11, p < .01. For innovation, ICC(1) = .20, F
= 4.57, p < .001. Finally, r
wg
was used to assess internal
agreement within a team for each variable, ranging from 0
(no agreement) to 1 (complete agreement) (James, Demaree,
and Wolf, 1993). Glick (1985) suggested a cut-off criterion of
.60. The mean r
wg
values were .80 for psychologically safe
communication climate and .78 for innovation, indicating ade-
quate internal agreement. Given these results, we aggregat-
ed this individual-level data to the team level by taking the
mean across individuals in a team for each variable.
Construct validity analysis. We conducted several tests of
convergent validity for our core concepts. To test that our
objective measure of geographic dispersion involving loca-
tions corresponded to subjective perceptions of geographic
dispersion, we constructed a perceptual measure based on
word counts in the interviews as described above and then
aggregated these to the team level. In our process of in-con-
text verification, in which excerpts in the subtext databases
were reviewed by two independent raters and coded as
either reflecting or not reflecting the variable, interrater relia-
bility was adequate (Cohen’s kappa = .70). Aggregation
indices were adequate [ICC(1) = .11; mean r
wg
= .87], and
this subjective measure was significantly correlated with our
objective measure (r = .59, p < .001). We also constructed a
470/ASQ, September 2006

perceptual measure of electronic dependence based on word
counts in the interviews, which captured each time a respon-
dent mentioned being reliant on electronic means of commu-
nication, aggregated to the team level. This variable also
showed adequate interrater reliability (Cohen’s kappa = .85),
and aggregation indices [ICC(1) = .19; mean r
wg
= .77] and
was significantly correlated (r = .57, p < .001) with our objec-
tive measure of electronic dependence.
Results
We first assessed direct relationships between the features
often associated with virtuality and innovation. We used non-
parametric statistics, specifically Spearman’s Rho, which is
an inferential test designed for small samples of ordinal or
interval measures that are potentially not normally distributed
(Saslow, 1982; Mohrman, Gibson, and Mohrman, 2001). The
interrelationships among the variables are presented in table
3. As argued, the four team characteristics demonstrated
varying degrees of association with each other, rather than
consistent positive interrelationships. National diversity was
closely associated with electronic dependence (rho = .65, p <
.01) as well as with geographic dispersion (rho = .71, p <
.01), but geographic dispersion was not significantly related
to electronic dependence. Likewise, dynamic structural
arrangements were not significantly associated with any of
the other characteristics, although previous research has typi-
cally assumed that these features coincide.
Next, we examined relationships between each element and
innovation. The relationship between geographic dispersion
and innovation was negative and statistically significant (rho
= –.55, p < .05), suggesting the plausibility of H1. The qualita-
tive analysis supported that being distributed across multiple
locations was often detrimental to innovation, as it impeded
sharing information and made it difficult to coordinate interac-
tion. For example, Platform Team in the agricultural product
development business had a core team that was co-located,
but many of its extended team members worked remotely.
In addition, the managers of most of the team members
were not on site but were based in different functional and
engineering discipline groups. Almost all decisions were
referred for approval through a complex maze of “hierarchical
superiors” who were distant physically and were not them-
selves aligned. As a result, members of this team felt they
were held hostage to slow processes of approval and micro-
management, which hindered the spontaneous innovation
471/ASQ, September 2006
Virtuality
Table 3
Correlation Matrix, Study 1 (N = 14 teams)
Variable Mean S.D. .1 .2 .3 .4 .5
1. Geographic dispersion 1.58 1.88
2. Electronic dependence 2.43 0.65 .44
3. Dynamic structure 2.43 0.76 .10 .17
4. National diversity 0.30 0.26 .71
••
.65
••
–.38
5. Psychologically safe communication climate 2.58 1.11 –.21 –.39 .30 –.60
•
6. Innovation 3.08 0.94 –.55
•
–.54
•
.01 –.66
••
.58
•
•
p < .05;
••
p < .01.

process. Requests for support to Europe from North Ameri-
ca, for example, were often seen as disappearing into a
“black hole,” unless there was a relationship with people at
the other end. People often did not know where to go for
information, and the interface points were not clearly speci-
fied. The challenges of geographical dispersion for innovation
are also illustrated through the following quote from a mem-
ber of the Competency Center Team in a professional service
firm:
I’m typically in a location where it’s very difficult to get to me, so I
don’t participate to the extent that I desire to and probably the rest
of the people who are working on it desire that I do. So I sort of
“fall into the cracks.” Any interaction with the team, I need to make
some extraordinary effort to do that, and the team is under no oblig-
ation to make any extraordinary efforts to accommodate me, so
they don’t.
The relationship between electronic dependence and innova-
tion was negative and statistically significant (rho = –.54, p <
.05), suggesting the plausibility of H2. Our qualitative inter-
view analysis corroborated this, providing numerous exam-
ples that electronic communication hindered message clarity
and the motivation for improvisation. Many informants indi-
cated that the creative synergy needed for innovation was
much more easily established face to face. The difficulty in
sparking a creative exchange of ideas over computer-mediat-
ed communication, rather than face to face, is illustrated in
the following quote from a member of Professional Service’s
Competency Center Team:
It is really tough working on knowledge creation over the phone and
via e-mail. A good example is this project. It is conceptual. We know
there is something there, but trying to kick-start a conversation on
that is really tough. The way I have done it is that Jack and I have
been in the same office with a white board, to at least kick-start it.
When you are introducing concepts, that is really hard to do over
the phone. How do you motivate people when they aren’t in the
same room? I think it is so valuable to be there in person.
There was no statistical evidence for the plausibility of the
negative relationship between dynamic structure and innova-
tion proposed in H3, although we did find evidence for a lack
of relationship building due to member turnover and tensions
and conflicts due to changes in reporting structures. For
example, turnover was very high among purchasing repre-
sentatives in Machines Inc.’s Platform Team, and members
reported that these representatives left just as they were
starting to understand the complex trade-offs involved in liv-
ing in a world of global purchasing and trying to meet the
needs of the development platform. Members who were
transferred to other teams were also perceived as not suffi-
ciently dedicated to the team’s efforts, and their experience
was often called into question. Several teams had a large
number of new hires, but training and mentoring for them
were limited, which created tension between the experi-
enced and new members. As a result, innovation was
inhibited.
Finally, suggesting the plausibility of H4 on the effect of
national diversity on innovation, the relationship between
472/ASQ, September 2006

these variables was negative and statistically significant
(rho = –.66, p < .01). Corroborating this, many informants
discussed how national differences in norms, expectations,
and behavior hindered innovation. These differences included
different definitions of concepts such as teamwork, as well
as different values placed on work and hierarchy. Norms
around knowledge sharing were culturally conditioned, as
were communication styles, resulting in in-group/out-group
dynamics that reduced information flow. Such differences are
evident in the following quote:
The major issue is probably cultural. In America, knowledge sharing
is a lot more promoted. People are very open about sharing knowl-
edge and work in these open cubicles.|.|.|. I’m from Europe, which
is a little more competitive in terms of what you know. You feel like
if you tell people what you know, then you are at a disadvantage.
People are then a little bit more reluctant to share knowledge. They
also think that if you share a lot of knowledge, then maybe your job
can be taken by somebody else. (Auto Unification, Community
Team)
We explored the plausibility of the idea that a psychologically
safe communication climate moderates the negative relation-
ship between the elements of virtuality and innovation, using
subgroup comparisons, the technique most appropriate for
an exploratory, qualitative analysis (Roberts, 1997; Fielding
and Lee, 1998), given that Study 1 was designed to explore
the plausibility of our arguments rather than to be a definitive
test of the relationships (Study 2 presents results of moderat-
ed regression analysis as a more formal test of hypotheses
5a–5d). First, we broke the sample into the most psychologi-
cally safe versus non-psychologically safe communication cli-
mates using a median split on the psychologically safe com-
munication climate scores. We then compared excerpts that
discussed the relationship between characteristics of virtuali-
ty and innovation in teams with high vs. low psychologically
safe communication climates. Table 4 provides examples of
these excerpts.
The strongest evidence for a moderating effect occurred for
the relationships between national diversity and innovation
and geographic dispersion and innovation. For teams without
a psychologically safe communication climate, national diver-
sity was perceived as very detrimental, and innovation was
dramatically low when diversity was high. For teams with a
psychologically safe communication climate, national diversity
was not nearly so much of a challenge and in some teams
was reported to be an asset. In terms of geographic disper-
sion, in teams without a psychologically safe communication
climate, geographic dispersion was perceived as a barrier to
overcome in order to achieve innovation, while in teams with
a psychologically safe communication climate, geographic
dispersion was seen as either “just a given” or a “plus.”
Less clear support was obtained for a moderating effect of a
psychologically safe communication climate on the relation-
ship between electronic dependence and innovation. For
teams with a psychologically safe communication climate,
electronic dependence was less of an issue in pursuing inno-
vation. In contrast, for teams without a psychologically safe
communication climate, innovation was higher when teams
473/ASQ, September 2006
Virtuality

474/ASQ, September 2006
Table 4
Examples of Interview Evidence for Effects of Psychologically Safe Communication Climate on Innovation,
Study 1
X
Teams with
highly
psychologically
safe
communication
climates
Teams with non-
psychologically
safe
communication
climates
Relationship between
geographic dispersion
and innovation
“The fact that they are
virtual, spread out,
has introduced points
of view that we
wouldn’t have gotten
if they weren’t
virtual.|.|.|. If we made
everyone move to the
same place to do the
work, that would alter
their point of view and
wouldn’t be a very
effective solution for
this kind of work.”
(Office Systems, De-
sign Team)
“The biggest problem is
that 90% of the deci-
sions are made in the
bathroom or near the
coffee pot. They had to
get the people that
they needed to bring
information into the
meetings and discuss
it there. If 2 out of 3
people are in Fort
Worth and one of them
is in the UK and not
there, then that person
is out of the loop. Early
on it was a very signifi-
cant problem.” (Aero-
space, Frame Team)
“Everything about the
product was changing
very rapidly. We could-
n’t communicate all
those changes to dif-
ferent parts of the
world.” (Aerospace,
Frame Team)
“The geography is going
to prevent our team
and theirs from ever
becoming a cohesive
unit. Everything is
geared toward the
field. Our job function
is never incorporated
into it. For example,
we rolled out a new
tool, and nothing has
been communicated
out to the field. We’ve
received a lot of brick
walls.” (Travel Service,
South Market Team)
Relationship between
electronic depen-
dence and innovation
“It is important to get
to know each other
also personally. If you
are able to get on a
personal basis during
electronic meetings,
process improvements
can be realized very
fast. This makes all
the further telephon-
ing and videoconfer-
encing very simple.”
(Auto Unification,
Function Team)
“There is never that
connection with the
other team members.
When the CSRs [cus-
tomer service repre-
sentatives] were
here, they could visu-
alize the processes
and systems that the
CSRs use. Not having
this has created some
challenges for us.”
(Travel Service, South
Market Team)
“I am completely
locked into e-mail. But
I don’t think it
replaces the human
contact side of the
phone. It coordinates
things, but big things
are never sorted out
on e-mail.” (Aero-
space, Frame Team)
Relationship between
dynamic structure
and innovation
“I feel that we may
sometimes over-
team. We do things
by committee that
aren’t necessary. This
can stifle individual
innovation. It can get
frustrating. With ever
changing roles, we
tend to have some
overlap between jobs
that leads to blurring
of boundaries. This
can create frustra-
tions that we are
doing other jobs than
our own.” (Machines
Inc., Tool Team)
“If the group keeps
changing, if you don’t
have any communica-
tion in between meet-
ings, it’s very normal
that at the beginning
of each meeting you
have to update the
people that are new
or are not there any-
more what the meet-
ing is all about. This
wastes time.” (Auto
Unification, Commu-
nity Team)
Relationship between
national diversity
and innovation
“It has got to be that you
are bringing in different
points of views, differ-
ent practices, and as a
result of that coopera-
tion, you come up with
an improved product.”
(Aerospace, Pilot Team)
“I never felt that the cul-
tural differences were a
real problem. In most
every case, those differ-
ences are known by the
partners and accepted
by the partners. Those
things don’t make any
problems, they can be
an advantage for bring-
ing up new ideas.”
(Auto Unification, Func-
tion Team)
“Yes, there was discus-
sion. But most of the
time they were not
discussions in which
new ideas would
come up. It’s really
two sides and it stays
like that.” (Europe
Connect)
“I realized that in the
discussions about
motivation of people,
how to get people to
share their knowl-
edge, I had the feel-
ing that it is totally
natural for [Ameri-
cans] to share their
knowledge. For Ger-
mans I realized that it
is not natural.” (Auto
Unification, Commu-
nity Team)

relied less on electronic means of communication. Finally,
most teams reported a slight decline in innovation when their
structure was dynamic. Taken as a whole, these contrasts
provide preliminary support for the plausibility of the moder-
ating effect of a psychologically safe communication climate,
although this effect is not uniformly strong across character-
istics.
Study 1 thus indicated that the negative direct effects of geo-
graphic dispersion (H1), electronic dependence (H2), and
national diversity (H4) on innovation were readily apparent in
the contextual analysis of the interviews, while the effect of
dynamic structure (H3) on innovation was equivocal. Findings
also provide preliminary support for the argument that a psy-
chologically safe communication climate mitigates the nega-
tive effects of geographic dispersion (H5a) and national diver-
sity (H5d) on innovation, with less clear support for the other
proposed moderating relationships. Further, as we anticipat-
ed, the elements of virtuality were not all highly correlated.
This indicates that a team can be characterized as “high” on
one element, while being “low” on another. Perhaps most
importantly, geographic dispersion was not significantly relat-
ed to electronic dependence, as conventional wisdom has
often assumed. This coincides with our assumption that
although electronic dependence sometimes goes hand in
hand with geographic dispersion, this is not always the case.
We observed this in the automotive industry Community
Team and in the professional services Competency Center
Team. Although members were spread across the globe,
they found it more efficient to meet face-to-face once a
month rather than use electronic communication and in fact
rarely communicated using e-mail or other electronic tools
between meetings. Such teams are geographically dispersed
but not electronically dependent. These findings provide pre-
liminary support for the idea that the elements of virtuality
are independent and that the absence of one element does
not negate the effect of the other elements (e.g., having a
“0” value on electronic dependence does not mean that a
team should no longer be considered virtual). Extrapolating
from this evidence suggests that it is inappropriate to com-
bine the elements of virtuality in a multiplicative way (i.e.,
geographic dispersion  electronic dependence  dynamic
structure  national diversity), as has been suggested in
some of the conceptual literature (see Cohen and Gibson,
2003).
Our holistic analysis also confirmed that each element adds a
unique facet to the experience of working virtually, indicating
in a preliminary way that the elements are non-substitutable
and that having more of one element does not compensate
for having less of another. Combining the elements of virtuali-
ty additively to form a single index (i.e., geographic dispersion
 electronic dependence  dynamic structure  national
diversity) may result in a loss of explanatory power, because
the elements are differentially related to innovation. For
example, an additive index fails to discriminate between a
hypothetical “Team A” that scores equally on each of the
four elements (e.g., 3 on geographic dispersion  3 on elec-
tronic dependence  3 dynamic structure  3 national diver-
475/ASQ, September 2006
Virtuality

sity = 12) and “Team B” that scores differentially across ele-
ments (e.g., 0 on geographic dispersion  2 on electronic
dependence  10 on dynamic structure  0 on national
diversity = 12). But the results of this study suggest that geo-
graphic dispersion and national diversity are negatively relat-
ed to innovation, while dynamic structure is not related to
innovation. Thus, Team A is more likely to experience prob-
lems in innovation because of its higher score on geographic
dispersion and national diversity. This implies that a model
including each element independently may have more
explanatory power than one based on an additive combina-
tion. Based on these preliminary findings, in Study 2, in addi-
tion to formally testing H1–H5, we explored the idea that
independent effects of geographic dispersion, electronic
dependence, dynamic structure, and national diversity will
explain more variance in team innovation than a multiplicative
or additive combination of these elements.
STUDY 2
Methods
Overview. Study 2 was intended to complement Study 1 in
three ways. First, Study 2 consisted of a larger sample at the
team level (56 teams, as described below) and quantitative
survey-based measures and thus enabled us to use hierarchi-
cal moderated multiple regression techniques to formally test
H1–-H5. Second, the teams in Study 2 were all of the same
team type and function (engineering project teams), had
been existence for the same period of time (one year), and
were from the same firm in the aerospace industry. This
design controlled for many factors that may influence innova-
tion and enabled us to isolate characteristics of virtuality and
to examine effects of the communication climate. Finally,
innovation in Study 2 was rated by internal customers of the
focal teams, allowing for an independent assessment of the
independent and dependent variables.
Sample. The 56 engineering project teams included in the
study were working on a contract to design a state-of-the-art,
next-generation military aircraft. Worth over $200 billion, this
program was unique in that it represented a collaborative
team effort across numerous nations and sites. The pro-
gram’s managers had enlisted the assistance of the
researchers to measure, document, and provide feedback
about process and performance of the program, with the aim
of improving innovation, effectiveness, and the satisfaction of
team members. Viewing this as an opportunity to refine their
day-to-day interactions, team members were highly motivat-
ed to participate, as evidenced by a high response rate and
informal comments received, and eager to see results, which
were presented after the analyses were completed. The
measures for variables in this study were included as a part
of a larger research effort testing numerous theories and
hypotheses. A total of 266 individuals responded to the sur-
vey, with an average team size of 4.75, ranging from 4 to 10.
The average response rate within a team was 79 percent.
Discussions with project participants indicated that the sam-
ple was comparable to the population in terms of gender,
476/ASQ, September 2006

age, profession, and tenure, suggesting no evidence of non-
response bias.
Procedure. An on-line survey was administered to all teams
in the program and two to three internal customers of each
team over a period of four weeks. Customers were selected
by the program leader (who was two hierarchical levels
above the teams), were internal to the program but down-
stream in design and production, and had ample familiarity
with a given team’s work. For example, a team that worked
on airframe design was rated by a representative from
assembly and a representative from systems integration.
Teams varied in their degree of geographical dispersion, elec-
tronic dependence, structural dynamism, and national diversi-
ty, but they did not vary significantly in terms of task type. All
teams rated their design tasks as non-routine (mean of 3.63
on a 5-point scale), and ANOVA confirmed there were no sig-
nificant differences between teams on task routineness (F =
.90, n.s.).
Measures. We measured geographic dispersion as in Study
1, using Blau’s (1977) formula to calculate a measure of cate-
gorical dispersion across locations in each team. The mini-
mum value for this variable was 0, indicating that all mem-
bers had the same location, and the maximum value was .94,
indicating extreme geographic dispersion (e.g., 4 locations
represented on the team with approximately 1–2 members in
each location), with a mean of .13 and a standard deviation of
.29. Electronic dependence was measured by four items ask-
ing about the extent to which members relied on three forms
of electronic communication (e-mail, teleconferencing, and
collaborative software), as well as their overall reliance on
electronic communication, using a 5-point scale (1 = not at
all; 5 = to a very great extent). These four items loaded on a
single factor with an eigenvalue of 2.06, accounting for 51
percent of the variance, with loadings ranging from .60 to
.82. The reliability of this scale (alpha) was .72. We measured
the extent to which the team had a dynamic structure with
three items (“Members of this team change frequently”; “It
is difficult to know who is on this team and who is not”; and
“We lack a consistent operating structure in this team.”)
using a 5-point scale (1 = not at all; 5 = to a very great
extent). These items loaded on a single factor with an eigen-
value of 1.81, accounting for 60 percent of the variance, with
factor loadings ranging from .63 to .86. Reliability (alpha) was
.70. As in Study 1, national diversity was measured using
Blau’s (1977) formula to calculate a measure of categorical
dispersion across nationalities in each team. The minimum
value for this variable was 0, indicating that all members had
the same nationality, and the maximum value was .99, indi-
cating extreme national diversity (e.g., 5 nationalities repre-
sented on the team with approximately 1–2 members of
each nationality), with a mean of .26 and a standard deviation
of .34.
Discriminant validity of virtuality elements. To verify the
distinctiveness of our constructs, we established discriminant
validity through confirmatory factor analysis (Venkatraman
and Grant, 1986). The analysis clearly supported the four-vari-
able structure, with separate factors for each of the elements
477/ASQ, September 2006
Virtuality

(chi square = 27.69, d.f. = 18, p < .07; GFI = .98, CFI = .97,
root mean square residual = .04). We compared the four-fac-
tor model with a one-factor model which assumes that the
items represent a single construct. The results showed
reduced fit for the one-factor model (chi square = 32.51, d.f.
= 17, p < . 01; GFI = .75, CFI =.75; root mean square resid-
ual = .07). The four-factor model also had a significantly bet-
ter fit than alternative models. These tests demonstrate that
the items do not tap a single underlying construct.
As a final step, we created two additional variables to explore
the idea derived from Study 1 that virtuality is best captured
by considering the effects of the virtuality elements indepen-
dently (geographic dispersion, electronic dependence,
dynamic structure, and national diversity), rather than as an
additive combination (geographic dispersion  electronic
dependence  dynamic structure  national diversity), or a
multiplicative combination (geographic dispersion  electron-
ic dependence  dynamic structure  national diversity), as
has been traditionally assumed. The additive term was com-
puted as the sum of the four characteristics, and the multi-
plicative term was computed as their product.
We measured the extent to which a psychologically safe
communication climate existed by asking team members to
indicate the extent to which their team was characterized by
four items (“Members are able to say what they think”;
“When there’s a problem, members talk about it”; “People
use words that are considerate of others’ feelings”; and
“Members are free to be assertive about what they think and
feel.”) using a 5-point scale (1 = not at all; 5 = to a very great
extent). The minimum score was 3.0, the maximum score
was 5.0, the mean was 4.19, and the median and mode
were 4.25. Items loaded on a single factor with an eigenval-
ue of 2.49, accounting for 62 percent of the variance, factor
loadings ranged from .70 to .87, and reliability (alpha) was
.79.
Innovation was measured by a survey administered to two to
three internal customers of each team, as described above.
Customers were asked to respond to the following: “Com-
pared to what is possible (100%), estimate how effective has
this team been at innovation using a percentage. For exam-
ple, if Team X is 80% innovative compared to what is possi-
ble, enter 80% for innovation.” The minimum rating given by
customers was 60 percent innovative, the maximum was
100 percent innovative, the mode was 75 percent, and the
mean/median were 80 percent. Convergent validity for this
measure was demonstrated by the high correlation between
innovation and technical performance (r = .53, p < .001) and
between innovation and overall effectiveness (r = .42, p <
.001) as rated by customers. In addition, our measure for
innovation was positively correlated with a 4-item measure of
knowledge sharing that customers completed (“Did mem-
bers of this team share knowledge with non-team members
in your organization?”; “Were people outside the team able
to learn from the team?”; “Do you believe the success of
this team will spur others in your organization”; and “Will
processes and activities developed by this team provide a
478/ASQ, September 2006

road map for other teams in the organization?”) (r = .35, p <
.01).
Aggregation. As in Study 1, we computed intraclass correla-
tion coefficients using one-way analysis of variance on the
individual-level data with team as the independent variable
and the scores on electronic dependence, dynamic structure,
psychologically safe communication climate, and innovation
as the dependent variables. For electronic dependence,
ICC(1) = .09, F = 1.45, p < .05; for dynamic structure, ICC(1)
= .07, F = 1.41, p < .05; for psychologically safe communica-
tion climate, ICC(1) = .09, F = 1.47, p < .05; and for innova-
tion, ICC(1) = .08, F = 1.41, p < .05. Adequate internal team
agreement was demonstrated for electronic dependence
(mean r
wg
= .72), dynamic structure (mean r
wg
= .82), psycho-
logically safe communication climate (mean r
wg
= .85), and
innovation (mean r
wg
= .72). Given these results, as a final
step, we aggregated the individual-level data to the team
level by taking the mean across individuals in a team for each
variable; for innovation, we took the mean across customers
who rated a team.
Controls. We included four control variables: team size, task
interdependence, leadership style, and team training effec-
tiveness. These four variables have demonstrated impacts on
team outcomes in prior research, may vary across teams that
have different levels of the elements of virtuality, and may
represent alternative explanations for the variance in innova-
tion (e.g., Campion, Medsker, and Higgs, 1993; Brown and
Eisenhardt, 1995; Earley and Gibson, 2002; Gibson and Ver-
meulen, 2003). Team size is the number of people on the
team. Task interdependence was measured using Campion,
Medsker, and Higgs’ (1993) 3-item scale (e.g., “Members of
this team depend on each other for completion of their
work.”). Leadership style was measured using three items
that capture how proactive a leader is in taking initiative and
action (e.g., “Our leader fails to take necessary actions to
ensure team effectiveness,” reverse coded). Team training
effectiveness was measured using three items that assess
the extent to which team members perceived their team
training as effective (e.g., “The training I have received on
our computer systems helps me work effectively with others
in the team.”).
Results
Table 5 displays the means, standard deviations, and zero-
order correlations for the study variables. Corroborating the
results of Study 1, the four characteristics of virtuality
demonstrate varying degrees of association with each other,
rather than consistent positive interrelationships. As in Study
1, the relationship between national diversity and geographic
dispersion is positive and significant, while dynamic structural
arrangements is not significantly related to geographic disper-
sion. In contrast with Study 1, the relationship between
national diversity and electronic dependence is non-signifi-
cant, while the relationships between geographic dispersion
and electronic dependence and between dynamic structural
arrangements and electronic dependence are positive and
significant. The relationship between national diversity and
479/ASQ, September 2006
Virtuality

dynamic structure is also positive and significant. As expect-
ed, each of the elements of virtuality is significantly and neg-
atively correlated with innovation, while psychologically safe
communication climate is significantly and positively related
to innovation.
Hypothesis tests. We tested H1–H4, on the negative direct
effects of the four elements of virtuality, by regressing inno-
vation on the controls (team size, task interdependence, lead-
ership, and training) and geographic dispersion, electronic
dependence, dynamic structure, and national diversity. As
shown in table 6, none of the controls predicted a significant
portion of the variance in innovation in step 2. The relation-
ships between geographic dispersion and innovation,
between electronic dependence and innovation, between
dynamic structure and innovation, and between national
diversity and innovation in the regression model were consis-
tent with the correlational results, and the overall R
2
value for
the model was significant, providing support for H1–H4.
We tested H5a–5d using moderated regression. Table 6 dis-
plays the results. In step 1, we entered the control variables.
In step 2, we entered the main effects for geographic disper-
480/ASQ, September 2006
Table 5
Correlation Matrix, Study 2 (N = 56 teams)
Variable Mean S.D. 1 2 3 4 5
1. Geographic dispersion 00.13 0.29
2. Electronic dependence 04.18 0.75 .28
•
3. Dynamic structure 01.19 0.41 .24 .26
•
4. National diversity 00.26 0.34 .44
•••
.16 .31
•
5. Psychologically safe communication climate 04.19 0.45 –.53
•••
–.47
•••
–.41
••
–.32
•
6. Innovation 80.31 9.12 –.48
•••
–.41
••
–.46
•••
–.49
•••
.61
•••
•
p < .05;
••
p < .01;
•••
p < .001.
Table 6
Results of Moderated Regression Analysis, Study 2*
Predictor variable Step 1 Step 2 Step 3 Step 4
Team size –.26 –.01 .01 –.03
Task interdependence .03 –.04 –.08 –.05
Leadership –.28
•
–.07 –.03 .02
Training –.07 –.02 –.01 .03
Geographic dispersion –.24
•
–.11 –3.73
•••
Electronic dependence –.22
•
–.12 –5.34
•••
Dynamic structure –.24
•
–.17
•
–2.73
••
National diversity –.27
•
–.26
•
–1.89
•
Psychologically safe communication climate .34
••
2.60
•••
Geographic dispersion  Communication climate 3.18
•••
Electronic dependence  Communication climate –4.91
•••
Dynamic structure  Communication climate 2.29
••
National diversity  Communication climate –2.04
••
R
2
.31 .06 .17
F 6.77
•••
6.12
••
5.68
•••
d.f. 4.47 1,46 4,42
Total R
2
.16 .46 .53 .69
F 2.36 5.09
•••
5.70
•••
7.30
•••
D.f. 4,51 8,47 9,46 13,42
•
p < .05;
••
p < .01;
•••
p <.001.
* Reported values are standardized regression weights.

sion, electronic dependence, dynamic structure, and national
diversity. In step 3, we entered the main effect of a psycho-
logically safe communication climate. In step 4, we entered
interaction terms for each of the four virtuality variables and
the communication climate. As shown in table 6, adding the
interaction terms results in a significant increase in R
2
, indi-
cating moderating effects. In support of H5a–5d, the interac-
tion terms for all four interactions are significant. As recom-
mended by Aiken and West (1991: 12–13), for each
significant interaction term, we then plotted the relationship
between the element of virtuality of interest and innovation,
at values of a psychologically safe communication climate
one standard deviation above the mean and one standard
deviation below the mean. We examined the slopes of the
two regression lines to interpret the nature of the interaction.
These plots are shown in figures 1a–1d. As expected, the
relationships between each element of virtuality and innova-
tion are all less negative when a psychologically safe commu-
nication climate exists than when it does not.
We conducted a final set of analyses to test the idea derived
from Study 1 that virtuality is best captured by considering
481/ASQ, September 2006
Virtuality
Figure 1a. Effect of communication climate on the relationship between geographic dispersion and
innovation.
100
90
80
70
60
Innovation
 Psychologically safe
communication cli-
mate
 Non-psychologically
safe communication
climate
85.59
79.52
79.75
68.42




Low High
Geographic Dispersion
Figure 1b. Effect of communication climate on the relationship between
electronic dependence and innovation.
100
90
80
70
60
Innovation
88.59
76.17
81.15
63.07




Low High
Electronic Dependence

the effects of the virtuality elements independently, rather
than as an additive or multiplicative combination. Using the
approach recommended by Cohen and Cohen (1983), we
computed an F-test for the difference in total variance
explained by each model. Results supported our argument.
The total variance explained in model 1 (R
2
= .46, F
8,47
=
5.09, p < .01), which contained separate terms for each virtu-
ality characteristic, was significantly greater than the total
variance explained in model 2 (R
2
= .43, F
5,50
= 7.65, p < .01),
which contained the additive term (F-test for the difference in
R
2
values = 2.42, d.f. = 55, p < .05). The variance explained
in model 1 was also significantly greater than that explained
in model 3 (R
2
= .33, F
5,50
= 4.98, p < .01), which contained
the multiplicative term (F-test for the difference in R
2
values
= 8.93, d.f. = 55, p < .01).
DISCUSSION
We set out to capture the concept of virtuality more precisely
by unpacking the negative effects on innovation of character-
istics most often conceptualized as dimensions of virtuality
and showing how they can be mitigated by a psychologically
482/ASQ, September 2006
Figure 1d. Effect of communication climate on the relationship between
national diversity and innovation.
100
90
80
70
60
Innovation
85.15
78.57
80.59
68.11




Low High
National Diversity
Figure 1c. Effect of communication climate on the relationship between dynamic structure and innovation.
100
90
80
70
60
Innovation
86.12
83.11
78.93
70.85




Low High
Dynamic Structure
 Psychologically safe
communication
climate
 Non-psychologically
safe communication
climate

safe communication climate. The two studies we conducted
are among the first to examine comprehensively and simulta-
neously the features of relatively new work designs that have
proliferated rapidly. In doing so, we uncovered numerous
important findings that have implications for future theory
and research in several areas: conceptualization and theory
pertaining to virtuality, social network theory, and theories of
communication climate and psychological safety.
Theoretical Implications
First, this research represents a more nuanced conceptualiza-
tion of virtuality and one of the first comprehensive empirical
attempts to operationalize multiple components independent-
ly. As a result, we teased apart the effects of geographic dis-
persion, electronic dependence, dynamic structure, and
national diversity that have previously been either confound-
ed in one variable or studied in isolation. Our measures cap-
tured these specific elements with greater precision than
those in previous investigations. For example, some previous
research has contrasted teams that are “virtual” with teams
that are “not virtual” based on assignment to laboratory con-
ditions or a simple count of the number of face-to-face meet-
ings (e.g., Kirkman et al., 2004). Our results suggest that the
four team characteristics often associated with virtuality are
not as highly interrelated as previously assumed. For exam-
ple, national diversity was not associated with electronic
dependence in Study 2, contrary to research that has con-
founded these two distinct characteristics. Likewise, dynamic
structure was not related to geographic dispersion in either
study, suggesting that teams can be geographically dispersed
yet still have stable membership and structure. These find-
ings indicate the criticality of considering each team feature
in its own right. Researchers who lump them together are
missing important complexities in the realities of team work.
Importantly, in Study 2, our attempts to operationalize the
higher-order “virtual” construct by combining the four ele-
ments failed to produce a model with superior fit and predict-
ed less variance in innovation than did a model that consid-
ered each characteristic separately. This reiterates the
importance of considering the independent effects of each
characteristic.
Second, our results demonstrate the possible negative, often
unintended and unanticipated effects of the four team char-
acteristics on an important outcome, innovation. This has
implications for organization design theory and innovation,
which are of broad importance, given that features of virtuali-
ty are becoming commonplace in modern organizations and
innovation has become such a crucial source of competitive
advantage for organizations. Virtual teams have been pro-
claimed as a promising design for integrating firms and are
often established to take maximum advantage of innovation-
creating capabilities (Nonaka and Takeuchi, 1995). Yet our
findings suggest that these characteristics also pose chal-
lenges that can be detrimental to innovation. We contribute
to the extant literature on the success factors for innovation
(Brown and Eisenhardt, 1995) by extending this framework to
virtual teams, finding that national diversity, geographic dis-
persion, and electronic dependence had negative effects
483/ASQ, September 2006
Virtuality

across the two studies, while dynamic structure had a nega-
tive relationship with innovation in the aerospace industry
teams surveyed in Study 2. Our conceptual arguments sug-
gest that these effects occur though unique mechanisms,
which should each be considered to predict the ramifications
for designing teams with a high degree of any one of these
features.
These findings provide further support for network theory,
which has shown that certain structural arrangements, such
as those that result in contextual complexity, weak ties, and
structural holes can be problematic for innovation by prevent-
ing the implementation of ideas (Burt, 2004; Obstfeld, 2005).
In prior work, scholars have suggested that it is important to
consider the potential benefits of teams with moderate levels
of features of virtuality, such as geographic dispersion (Burke
et al., 1999). This implies a possible curvilinear effect. To test
this alternative relationship, we re-ran our models in Study 2,
entering a squared term for geographic dispersion after the
direct effects. But we found no evidence for a curvilinear
relationship between geographic dispersion and innovation
(i.e., the addition of the squared term did not result in a sig-
nificant change in R
2
, and the coefficient for the term was
not statistically significant), indicating that the relationship in
our data is best described as a direct linear negative effect.
Likewise, we explored the possibility that our contradictory
evidence regarding dynamic structure (no relationship with
innovation in Study 1; a negative relationship with innovation
in Study 2) could be masking a potential curvilinear effect,
such that moderate levels of structural dynamism are most
beneficial. But again, we found no evidence for a curvilinear
relationship: adding a squared term for dynamic structure
after the direct effects did not account for significant variation
in innovation in Study 2. Hence, even though some benefits
may be realized in idea generation, our findings indicate that
on balance, contextual complexity and weak ties created by
geographic dispersion and dynamic structure may be prob-
lematic for innovation because of challenges in implementing
ideas.
Finally, our findings in Study 2 revealed significant interaction
effects, such that a psychologically safe communication cli-
mate reduced the negative effects of all four elements of vir-
tuality on innovation. Although it is certainly not a panacea, a
psychologically safe communication climate appears to be an
important facilitator of innovation in teams by helping them
overcome the challenges posed by virtuality. Our interviews
revealed that the negative effects of geographic dispersion
were often mitigated by a psychologically safe communica-
tion climate because it helped to raise and clarify contextual
differences, helping teams coordinate and garner resources
for innovation across contexts. The difficulty in sparking a
creative exchange of ideas over computer-mediated commu-
nication was mitigated in some cases by a psychologically
safe communication climate developed through expert use of
the technology. The disadvantages of a dynamic structure for
innovation, such as the lack of relationship building due to
member turnover and tensions and conflicts due to different
reporting structures, were lessened by a psychologically safe
484/ASQ, September 2006

communication climate that helped overcome mistrust and
turn the team’s fluid membership into a source of new ideas
and expertise. Finally, mitigating the negative effect of nation-
al diversity, a psychologically safe communication climate
helped raise and clarify differences in national orientations
and norms, resolve conflict, and foster an open environment
in which team members felt comfortable to ask questions,
admit to a lack of understanding, and voice opinions. This
increased innovation by allowing different perspectives and
viewpoints to be heard, enabling the merging of ideas and
helping to establish a middle ground and bridge differences.
These findings make important contributions to the research
on communication climates and psychological safety. First,
we extend theory on communication climates (Dillard,
Wigand, and Boster, 1986; Trombetta and Rogers, 1988; Guz-
ley, 1992) by being the first to demonstrate group-level
effects on the important outcome of team innovation. Previ-
ous research has focused on effects on attitudes such as sat-
isfaction and commitment. Second, we contribute to the the-
oretical refinement of psychological safety by conceptually
developing the communication component of the construct
and demonstrating new evidence of a direct relationship
between this component and innovation, as well as moderat-
ing effects. This supports conceptualizing the construct as
multidimensional and highlights important antecedents and
outcomes. Further, our findings extend related prior research
by testing relationships when teams varied on the four ele-
ments of virtuality and by indicating the critical role of psy-
chologically safe communication in such settings. Important-
ly, it was not just the creation of trust that mitigated the
effects of virtuality, as has been suggested in previous
research (Jarvenpaa and Leidner, 1999). To help rule out trust
as an alternative explanation, we included a nine-item mea-
sure of trust in our survey in Study 2. Replacing team scores
on trust for the psychologically safe communication climate
variable in our analyses produced an entirely different pattern
of results. There was no significant main effect of trust on
innovation, and there were no significant interaction effects
between the elements of virtuality and trust. This provides
evidence that it is a psychologically safe communication cli-
mate, and not trust, that is operating here to mitigate the
negative effects of the elements of virtuality.
Limitations and Future Research
Our contributions must be understood alongside the limita-
tions of our research stemming from the research design.
First, although we were able to unpack certain features
assumed to be associated with virtuality, there may be other
factors that contribute to innovation that we did not examine
that are in some ways confounded by the features we includ-
ed. For example, the underlying rationale for why national
diversity is a challenge in virtual teams pertains to the com-
plex differences that arise when members represent differ-
ent nations, such as different cultural values, legal and eco-
nomic systems, and religions (Earley and Gibson, 2002). We
did not directly measure these more specific manifestations
of national diversity, so we cannot be certain which mecha-
nisms are driving the impacts on innovation. Harrison and col-
485/ASQ, September 2006
Virtuality

leagues (Harrison, Price, and Bell, 1998; Harrison et al., 2002)
suggested that the longer a team works together, the lower
the importance of surface-level characteristics of diversity
and the greater the importance of deep-level diversity, such
as personality, values, and attitudes, become. The teams in
Study 2 were all formed at the same time, so there was no
variance in teams’ longevity, and we could not test this issue
directly; however, our qualitative analysis in Study 1, in which
teams did vary in longevity, still showed consistent negative
effects of national diversity on innovation regardless of team
longevity. That said, an important next step in research on
teams is to examine the variety of factors underlying national
diversity to determine their effects on innovation over time.
An additional step is to further examine the role of team type
or work type. We addressed this concern to some extent
through the design of our two studies. In Study 1, the type of
team and work varied dramatically. Some of the teams were
design teams charged specifically with innovation, while oth-
ers were more standard work teams that executed services
or other organizational processes. Yet the negative relation-
ship between elements of virtuality and innovation was evi-
dent in all types of teams. In Study 2, we controlled for team
type and work type by sampling teams that were all involved
in non-routine design tasks, and we ensured this by confirm-
ing that there was no significant variance in task routineness
across teams. Yet we saw different levels of the elements of
virtuality and innovation across these teams. Hence, we can
be fairly confident that the relationships we uncovered occur
even after controlling for the type of team and work. At the
same time, we acknowledge that the contexts we selected
may have restricted the range and variety of responses con-
cerning innovation and a psychologically safe communication
climate. But this restriction in range means that our results
are fairly conservative, and additional research in even more
varied contexts should provide that much more precision.
We also recognize the limitations of capturing innovation
based on a relatively simplistic measure (in Study 1, percep-
tions of members; in Study 2, the percentage of innovation
achieved as rated by customers downstream in the process).
The ideal design would include more objective measures of
the incorporation of new and diverse information into new
methodologies, products, and services, as well as stronger
indicators of subsequent organizational performance implica-
tions. We provided evidence for the construct validity of our
innovation measure in Study 2 by correlating it with overall
effectiveness as rated by customers. Scholars in the innova-
tion literature argue that although there may be some overlap
in predictors of innovation and predictors of overall effective-
ness, the set of success factors for each criterion is not
entirely the same (e.g., Brown and Eisenhardt, 1995). But an
alternative explanation for our findings is that our models
would hold if we were to replace our innovation dependent
variable with an overall effectiveness dependent variable. We
conducted this analysis and found that this was not the case.
Replacing innovation with overall effectiveness as the depen-
dent variable in Study 2 produced a different pattern of
results. There was no main effect for national diversity, elec-
tronic dependence, geographic dispersion, or a psychological-
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492/ASQ, September 2006

APPENDIX: Summary Interview Protocol
I. Interviewee’s background
a. Job in company, function, profession
b. Have you moved across functions/disciplines in your career?
c. Where located
d. Country of birth, residence, identity
II. Interviewee’s Role on the Team
a. What is your role on this team?
b. How long have you been with this team?
c. How was this role determined?
d. Percentage of time dedicated to team?
e. How were you selected for the team?
f. Do you have any other responsibilities outside of the team? What are
they?
g. To whom do you report? Where do you feel you owe your primary alle-
giance? To the team or to your function/unit? Why?
h. Do you belong to other teams? Are any of them virtual?
i. How would you prioritize your work on this team as compared to your
other responsibilities?
j. Have you worked with anyone on this team before? In what context?
III. Objectives
a. What is the mission of your team? How does it fit into the business’s
overall strategy?
b. Why is this team virtual as opposed to non-virtual?
c. From your perspective, what are the specific objectives of the team?
d. Are these objectives well understood and shared by the team members?
e. How were these objectives determined? By whom?
f. Is the team start and finish point clear? (i.e., scope and time?) What is it?
IV. Task and Team Structure
a. Describe how work gets done in your team. What are the steps
involved?
b. Do you share responsibility and accountability for outcomes (or do some
persons have this responsibility and others do not)?
c. How much does the work or work requirements for your team change
over time?
d. Are the boundaries between your team and other teams clear? If so,
how were they developed?
e. Who does the team report to? Do team members share the same report-
ing relationships? If not, what is the impact of this?
f. Are certain members core to the project and others more peripheral?
Please describe.
g. How much of the team’s work gets done face to face?
V. Information Technology and Applications
a. How frequently does your team use e-mail, phone calls, phone confer-
encing, voice mail, faxes, video conferencing, chat rooms, electronic
meeting systems or bulletin boards, intranet, file sharing, workflow appli-
cations, or other technologies?
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b. Which of the technologies are most helpful in working/communicating
with other members of your team? Why?
c. Which of the technologies are least helpful in working/communicating
with other members of the team? Why?
d. Could anything be done to increase the usefulness of these technolo-
gies?
e. Did the team receive any training to help members be able to use the
available technologies? If so, how effective was it?
f. Are these technologies used to communicate with other teams or with
key stakeholders? If so, for what purposes and how effectively?
VI. Benefits and Costs of Virtuality
a. What do you view as the benefits and costs of working across functions,
sites and countries? Why?
b. How would the team operate differently if it were less virtual?
c. What are the measures used to evaluate team effectiveness?
d. Compared to what is possible with a virtual team (100% is all that is pos-
sible), how effective is the team at achieving its overall mission?
e. Would it be easier to achieve the mission if everyone was co-located? If
yes, would it take more time, less time, or the same amount of time to
accomplish the mission?
f. How critical is the team’s mission to the success of your business?
g. Are team members excited about the work the team is doing? Why? Or
why not?
h. Would you want to work with the other members of your team in anoth-
er project? Why? Or why not?
VII. Team Leadership and Team Processes
Given what you have said about effectiveness, which of the following do
you believe impacts the team’s level of effectiveness the most: leader-
ship, goal-setting processes, coordination of work, communication, or
decision-making?
Leadership
a. Is there an officially designated leader for your team? Do other members
play leadership roles?
b. Give me an example of when leadership of the team occurred very well.
Not so well?
c. How effective is team leadership? Why do you say this?
Goal-Setting
d. Describe how the team sets goals.
e. Give me an example of when the goal-setting process went very well.
Not so well?
f. How effective is team goal-setting?
Coordination of Work
g. How does the team make sure that work is coordinated?
h. Describe a time when work was coordinated extremely well. Extremely
poorly?
i. In general, how effectively does the team coordinate its work?
Decision-Making
j. Describe how the team makes decisions.
k. Describe a time when you felt a key decision was made extremely well.
Not so well?
l. In general, how effectively does the team make decisions?
Communication
m. Describe how team members communicate with each other. With key
stakeholders?
n. Give me an example of when communication was handled really well. Not
so well?
o. I n general, how effectively do team members communicate with one
another? With stakeholders?
Information Sharing and Conflict Resolution
p. Do you find that you frequently use certain acronyms, expressions, or jar-
gon in this team? Which ones?
q. What kinds of misunderstandings arise on the team (probe for goals,
work processes, use of technology, resources, performance, and
494/ASQ, September 2006

rewards)? How often do misunderstandings occur? How are they han-
dled?
r. Do you encounter conflicting priorities? Explain.
s. Where does the team get the information/knowledge it requires for its
work? Are these information sources adequate for your needs?
t. What percentage of the team’s critical communications are virtual versus
face to face? (Probe).
VIII. Trust
a. How much trust is there on the team? How do you know?
b. How much do you trust others?
c. How much do others trust you?
d. If there are any discrepancies, why?
e. How much trust is there that time and money will be used in the best
interest of the team? That they will be used in a fair and equitable way?
f. Do people trust each other to contribute worthwhile ideas?
g. Do people trust each other to do what they say they will do?
h. How did the team develop trust among its members? What factors hin-
der trust?
IX. Characteristics of Members
a. Do members have all the skills that are needed on the team? Team and
interpersonal skills? Technical skills? Please describe.
b. If the team needs skills or knowledge that reside outside the team, how
is that knowledge obtained? What role do members play in obtaining it?
c. How do differences in culture, discipline, home organization, or other dif-
ferences influence the way members work together?
d. Do problems occur because of these differences? If so, how do you
resolve them?
e. Do individuals act as “interpreters” or “translators” between different
functional areas? Between different units? Between different cultures?
How do they do this?
f. Do members of the team have similar work values?
X. Performance Management and Human Resources
a. How are the contribution and performance on the team and individual
members evaluated?
b. What kinds of behaviors and performance are rewarded?
c. Is there a team performance reward and recognition system? If so, what
is it?
d. How aligned is the team reward and recognition system with the goals
of the business?
e. Are there any other organizational or human resource practices that we
haven’t discussed so far that have an impact on team effectiveness?
Describe positive or negative impact.
XI. Lessons Learned
a. What lessons has the team learned since it began operating on how to
make a virtual team effective?
495/ASQ, September 2006
Virtuality