Computers in Human Behavior 35 (2014) 565–577

Contents lists available at ScienceDirect

Computers in Human Behavior

journal homepage: www.elsevier .com/locate /comphumbeh

Institutional boundaries and trust of virtual teams in collaborativedesign: An experimental study in a virtual world environment

http://dx.doi.org/10.1016/j.chb.2014.02.0510747-5632/� 2014 Elsevier Ltd. All rights reserved.

⇑ Corresponding author. Tel.: +1 937 775 2230; fax: +1 937 775 3533.E-mail addresses: shu.schiller@wright.edu (S.Z. Schiller), mennecke@iastate.edu

(B.E. Mennecke), nahf@mst.edu (F.F.-H. Nah), andyluse@okstate.edu (A. Luse).1 Tel.: +1 515 294 8100.2 Tel.: +1 573 341 6996.3 Tel.: +1 405 744 4049.

Shu Z. Schiller a,⇑, Brian E. Mennecke b,1, Fiona Fui-Hoon Nah c,2, Andy Luse d,3

a Department of Information Systems & Supply Chain Management, Raj Soin College of Business, Wright State University, 3640 Colonel Glenn Hwy, Dayton, OH 45435, USAb Supply Chain & Information Systems, College of Business, Iowa State University, Ames, IA 50011, USAc Department of Business & Information Technology, Missouri University of Science and Technology, 101 Fulton Hall, Rolla, MO 65409, USAd Management Science & Information Systems, Spears School of Business, Oklahoma State University, 408 Business Building, Stillwater, OK 74078, USA

a r t i c l e i n f o

Article history:Available online 29 March 2014

Keywords:TrustSatisfactionVirtual teamBoundary spanningVirtual world

a b s t r a c t

Members of virtual teams often collaborate within and across institutional boundaries. This researchinvestigates the effects of boundary spanning conditions on the development of team trust and teamsatisfaction. Two hundred and eighty-two participants carried out a collaborative design task over severalweeks in a virtual world, Second Life. Multigroup structural equation modeling was used to examine ourresearch model, which compares individual level measurement between two boundary spanning teamconditions. The results indicate that trusting beliefs have a positive impact on team trust, which in turn,influences team satisfaction. Further, we found that, compared to cross-boundary teams, within-bound-ary teams exhibited not only higher trusting beliefs and higher satisfaction with the collaboration processbut also a stronger relationship between team trust and team satisfaction. These results suggest that trustand group theories need to be interpreted in light of institutional affiliation and contextual variables. Animportant practical implication is that trust can be fostered in a virtual world environment and collabo-ration on complex tasks can be carried out effectively in virtual worlds. However, within-boundary vir-tual teams are preferred over cross-boundary virtual teams if satisfaction with the collaboration processis of the highest priority.

� 2014 Elsevier Ltd. All rights reserved.

1. Introduction

Virtual teams can function effectively both within and acrossorganizational boundaries (Espinosa, Cummings, Wilson, & Pearce,2003; Levina & Vaast, 2008; Pauleen & Yoong, 2001; Romano, Pick,& Roztocki, 2010). Yet collaborations across organizational bound-aries have always posed challenges (Levina & Vaast, 2008).Whether it is with fellow employees or with partners or clients,the challenge faced by many is to find ways to effectively commu-nicate and collaborate when addressing complex tasks. As organi-zations increasingly spread their operations and partnershipsacross wider geographic distances, teams are challenged to effec-tively engage in various forms of virtual teamwork.

Virtual worlds have been shown to be a promising and powerfulenvironment for collaboration and communication (Schultze &

Orlikowski, 2010). This is due, in part, to the affordances of virtualworlds that allow users to share a common space, engage in inter-active design and development activities, and enjoy rich visual andauditory stimuli and feedback (Nah, Eschenbrenner, & DeWester,2011; Nah, Eschenbrenner, DeWester, & Park, 2010; Park, Nah,DeWester, Eschenbrenner, & Jeon, 2008). Because of these features,virtual worlds are able to bring people from different places andorganizations to work together, socialize, and engage in collabora-tive activities in ways that mimic the real world. While someresearch has examined cross-boundary teams in a variety of con-texts, ‘‘much team boundary-spanning research literature appearsto implicitly assume the context of a single organization . . . andresearchers need to be clear on the inter-organizational contextualconditions . . . and how they affect team activities and practices’’(Calvard, 2014, pp. 133–134).

In an effort to address the importance of boundary-spanningteams in virtual worlds, we examine cross-boundary versuswithin-boundary collaboration where members have formalmembership structures and existing institutional affiliations (e.g.,business organizations or educational institutions). In a virtualcontext, trust is critical to the functioning of a team (Kim, Lee, &

566 S.Z. Schiller et al. / Computers in Human Behavior 35 (2014) 565–577

Kang, 2012). Trust among members is important in facilitatinggroup productivity and influencing a team’s processes and out-comes, including team performance and satisfaction (Jarvenpaa &Leidner, 1999; Jarvenpaa, Shaw, & Staples, 2004; Kanawattanachai& Yoo, 2002). It is therefore particularly important to address trustin cross-boundary teams where members of such teams may notshare a common contextual and environmental foundation. Thus,the objective of our paper is to examine the role of institutionalboundaries on the development of trust and satisfaction whenvirtual team members are engaged in collaborative activities in avirtual world.

The remainder of this paper is organized as follows: The nextsection presents the literature, theoretical support, and hypothe-ses. This is followed by the presentation and explication of theresearch model. Next, we report on the research methodologyand procedures as well as data analysis and the findings. We thendiscuss the findings, limitations of our study, theoretical contribu-tions and practical implications, and, finally, our conclusions.

2. Theoretical development

We begin our review of prior research by discussing institu-tional boundaries, which is followed by a discussion of therelevance of trusting beliefs and trust in team relationships. Wethen integrate these literatures in the formulation of the researchmodel and generation of the hypotheses.

2.1. Institutional boundaries

The manner by which people interact and communicate acrossdistance, time, and organizational boundaries has been studied in avariety of contexts (Ilgen, 1999) such as in virtual organizations(e.g., Ahuja & Carley, 1999; Cummings, Espinosa, & Pickering,2009; DeSanctis & Monge, 1999; Shin, 2004) and in virtual teams(e.g., Jarvenpaa & Leidner, 1998; Levina & Vaast, 2008; Majchrzak,Rice, Malhotra, & King, 2000; Malhotra, Majchrzak, Carman, & Lott,2001; Maznevski & Chudoba, 2000; Nunamaker, Reinig, & Briggs,2009). A team boundary represents ‘‘a discontinuity, edge, or otherdividing characteristic present in the work context of a team’’(Espinosa et al., 2003, p. 158). Virtual team members often workacross boundaries, such as those imposed by proximity, time,space, and institutional affiliations (Panteli & Davison, 2005). Auseful framework for understanding the key factors in studyingvirtual teams was offered by DeSanctis, Staudenmayer, and Wong(1999) who identify four dimensions differentiating virtual organi-zations: space, time, culture, and boundary. Of these variables, thedimensions relating to space (i.e., geographical dispersion) andinstitutional boundary (i.e., institutional dispersion stretching tra-ditional institutional confines) seem most pertinent to understand-ing issues related to boundary-spanning virtual teams.

Different types of boundaries can affect task performance ofvirtual teams. For example, researchers have found that boundariessuch as corporate culture (Cramton & Hinds, 2007; Krishna, Sahay,& Walsham, 2004), formal organizational boundaries (Espinosaet al., 2003; Srikanth, 2007), spatial and temporal boundaries(O’Leary & Cummings, 2007; Van den Bulte & Moenaert, 1998),and functional boundaries (Birnholtz & Finholt, 2007; Denison,Hart, & Kahn, 1996; Espinosa et al., 2003) account for a team’s suc-cess as well as pose challenges to effective team outcomes. Previ-ous research has suggested that the most salient boundaries existfor task-focused distributed teams during collaboration (Cramton& Hinds, 2007; Levina & Vaast, 2008) and that virtual teams ofteninclude members representing different institutions or organiza-tions (Espinosa et al., 2003). Unfortunately, empirical studies ofteams have often overlooked the boundary spanning conditions

of team members and how they influence team processes andoutcomes.

Virtual worlds provide a rich environment that appears to sup-port ‘‘boundary spanning’’ by increasing group synergies resultingfrom the feeling of ‘‘nearness/togetherness’’ and ‘‘offers opportuni-ties for coincidental and planned interactions across the member-ship of two organizations’’ (Ives & Junglas, 2008, p. 155).Researchers have conducted studies to try to understand howteamwork is influenced by team member affiliations in virtualteams but unfortunately, boundary-spanning teams are oftenneglected in empirical studies and ‘‘very little research has focusedon organizational boundaries at the group level’’ (Espinosa et al.,2003, p. 181). To address this gap, we examine boundary-spanningteams in a virtual world by considering the institutional affiliationof team members. Specifically, we examine two types of workteams in a virtual world: (1) cross-boundary teams that includemembers from different institutions and (2) within-boundary teamsthat include members from the same institution (Lipnack &Stamps, 1997).

2.2. Trust

Trust is critical to the functioning of a work team (Larson &LaFasto, 1989; Lipnack & Stamps, 1997; Mayer, Davis, &Schoorman, 1995). Trust helps to reinforce interdependenceamong team members, which is necessary to accomplish personaland institutional goals. Trust is defined by Mayer et al. (1995) as‘‘the willingness of a party to be vulnerable to the actions ofanother party based on the expectation that the other [party] willperform a particular action important to the trustor, irrespective ofthe ability to monitor or control that other party’’ (p. 712).Rousseau, Sitkin, Burt, and Camerer (1998) define trust as ‘‘apsychological state comprising the intention to accept vulnerabil-ity based upon positive expectations of the intentions or behaviorof another’’ (p. 395). Trust reduces the need to project control overthe other party and it mitigates the expectation that the otherparty will engage in opportunistic behaviors (Hill, 1990; Lewis &Weigert, 1985).

A trust relation is interpersonal and is established between oneindividual and another; therefore, many scholars examine trust inbasic dyadic structures, which forms the primary team structureupon which more complex group structures are built (Lusher,Kremer, & Robins, 2014). The dyadic trust relation is especiallyimportant in some activities such as knowledge sharing betweena sender and a recipient (Hasty, Massey, & Brown, 2006) because‘‘individuals diagnose trustworthiness primarily by referring toinformation about trustee behavior in the dyadic relationship’’(Ferrin, Dirks, & Shah, 2006, p. 870). In a dyadic relationship, trustinvolves two specific parties: a trusting party (trustor) and a partyto be trusted (trustee). Trusting beliefs (one agent’s beliefsof the other party’s trustworthiness) can be reflected by threeattributes — ability, benevolence, and integrity (Jarvenpaa & Leidner,1998; Mayer et al., 1995). Ability refers to the perception about thecompetencies, skills, and knowledge of the other party.Benevolence is the extent to which a trustee is believed to wantto do good to the trustor. Integrity refers to the trustor’s perceptionthat the trustee will adhere to a set of principles or rules ofexchange that is acceptable to the trustor.

Trust is critical to effective team processes and performance(Dirks, 1999; Dirks & Ferrin, 2001; Kanawattanachai & Yoo, 2002;Kiffin-Petersen, 2004; Kirkman, Jones, & Shapiro, 2000; LaFasto &Larson, 2001; Mennecke & Valacich, 1998; Nunamaker, Briggs,Mittleman, Vogel, & Balthazard, 1996). The lack of trust is one ofthe main reasons many individuals resist teamwork because whentrust is lacking it interferes with effective teamwork (Hyatt &Ruddy, 1997; Kirkman et al., 2000; LaFasto & Larson, 2001; Larson

S.Z. Schiller et al. / Computers in Human Behavior 35 (2014) 565–577 567

& LaFasto, 1989). Furthermore, Kim and colleagues (Kim et al.,2012) showed that user identification with their online communityin a virtual world had strong effects on user trust in their virtualcommunity as well as their performance efficacy. Despite thesignificant amount of literature on trust, it is a construct that isoften overlooked in team research (Kiffin-Petersen, 2004). As dem-onstrated by the studies above, trust is an important construct thatis related to team performance and is included as one of the keyvariables in our research model.

2.3. Hypothesis development

Our research model and hypotheses are developed based onsocial identity theory and self-categorization theory. Social identityrefers to one’s membership in certain social groups to which oneattaches emotional and value significance (Tajfel, 1972). Socialidentity thus represents the social category defined by characteris-tics that are part of the self-concept of the group member such asthe internalization of a group or collective identity resulting fromidentification with the institution to which one belongs (Hogg,Terry, & White, 1995). Hence, individuals in a group are deperson-alized and each person becomes an embodiment of the contextu-ally relevant in-group prototype (Hogg & Terry, 2000).

The transformation from a depersonalized individual to asocially categorized individual in the group produces ‘‘positive in-group attitudes and cohesion, emotional contagion and empathy,collective behavior, shared norms, and mutual influence’’ (Hogg& Terry, 2000, p. 123). Social identity thus describes and prescribesone’s attitude as a member of that group and how one shouldthink, feel or even behave (Hogg et al., 1995). Social identity is alsoevaluative; ‘‘group members are strongly motivated to adoptbehavioral strategies . . . that favor the in-group’’ (Hogg et al.,1995, p. 260). The in-group favoring distinctiveness suggests thatgroup members develop in-group positive feelings and social iden-tity effects, and therefore, trustworthiness of members emergesmore naturally for within-boundary teams compared to cross-boundary teams. As a result, group members identify morestrongly with one another in within-boundary teams than incross-boundary teams (Hogg & Terry, 2000; Hogg et al., 1995;Tajfel & Turner, 1979).

Self-Categorization Theory, developed by Turner and his col-leagues (Turner, Hogg, Oakes, Reicher, & Wetherell, 1987), extendsSocial Identity Theory with an emphasis on the social cognitiveprocesses (Hogg & Terry, 2000). The process of categorization‘‘accentuates both perceived similarities between stimuli (physicalobjects or people, including self) belonging to the same categoryand perceived differences between stimuli belonging to differentcategories’’ (Hogg et al., 1995, p. 260); therefore, the theory sug-gests that individuals define self and others into ‘in-group’ and‘out-group’ categories.

When category distinctions are salient, people perceptually en-hance similarities with those from the same category, such asmembers affiliated with the same institution. Memberships oraffiliations, for instance, institutions that people belong to, createself-categorization among individuals (i.e., in within boundaryteams) that is favored at the expense of those from other member-ships or affiliations (i.e., cross-boundary or boundary-spanningteams). This, in turn, is expected to give rise to higher trustingbeliefs for within-boundary teams compared to cross-boundaryteams. Because members of within-boundary teams have greateropportunities to engage in more frequent interactions, trust-devel-opment opportunities increase.

As compared to within-boundary teams, the degree of sharedgoals, experiences, and social norms is weaker in boundary-span-ning teams, leading to lower levels of trusting beliefs among teammembers (Lewis & Weigert, 1985; Mayer et al., 1995). Similarly,

individuals tend to categorize themselves based on attributes suchas physical similarity, proximity, or social and contextual environ-ment (institution/firm) (Gaertner, Dovidio, Nier, Ward, & Banker,1999a). Because social categorization ‘‘decreases psychological dis-tance and facilitates the arousal of promotive tension or empathy’’(Gaertner et al., 1999a, p. 177), it leads people to favor membersbelonging to the same category (e.g., institution/firm) in rewardallocations and evaluation (Tajfel, Billing, Bundy, & Flament,1971), resulting in higher trusting beliefs among members of with-in-boundary teams compared to members of cross-boundaryteams.

Closely related to the two theories reviewed above, previousstudies have also demonstrated that smooth communication pro-cesses allow users to experience social presence through rich media(such as virtual worlds) and develop a feeling of psychologicalcloseness, which is crucial to the development of interpersonaltrust (Zack, 1993). The presence of institutional boundaries can af-fect team communication and coordination through rich media inthree ways. First, barriers to shared understanding are potentiallyhigher in cross-boundary teams. Institutional and organizationalboundaries construct identities, interests, and practices internally,which are hard to transfer externally (Jarzabkowski, 2004; Kogut& Zander, 1993; Levina & Vaast, 2005). When teams cross institu-tional boundaries, they bring with them diverse cultures, beliefsand assumptions (Kayworth & Leidner, 2000; Pauleen & Yoong,2001), which can threaten psychological ties between team mem-bers (Wiesenfeld, Raghuram, & Garud, 1998). Team members work-ing within the same institutional boundary have common priorknowledge (Espinosa et al., 2003), which facilitates shared under-standing. In addition, their familiarity and shared understanding re-duce uncertainties about expectations for their team members,which increase their trusting beliefs (Komiak & Benbasat, 2006;Luhmann, 1979). Hence, we expect trusting beliefs to be higherfor within-boundary teams than cross-boundary teams.

Second, members in cross-boundary teams are more likely toexperience coordination problems because members may bringwith them strong and unique cultural histories and norms associ-ated with their institutions (Panteli & Davison, 2005), which couldresult in conflicts or confusion. Different characteristics of teammembers can contribute to intragroup conflict, which in turn candecrease team performance if the conflict is not well managed(Sawyer, 2001). Challenges such as different time zones anddifferent work processes can interfere with effective teamwork(Nunamaker et al., 2009). The lack of knowledge of the institutionalcontext of other team members could pose challenges in develop-ing a common ground for communication (Olson & Olson, 2007),which can lead to potential misunderstanding and conflict escala-tion (Carmel, 1999).

Third, team members who collaborate across institutionalboundaries may be less forthcoming with information in order tomanipulate impressions or jockey for control of time, resources,or other forms of power. For example, one team member maysay that he or she is occupied at his home institution to try to avoida meeting arranged at an unfavorable time or otherwise procrasti-nate. Such behaviors can make coordination more problematic andamplify the likelihood of collaboration inefficiency, mistrust, and,ultimately, team disputes or disruptions (Espinosa et al., 2003).

In all, theory and prior research suggests that cross-boundaryteams are more likely to have a lower degree of shared knowledge,goals, experiences, interests, and social norms compared towithin-boundary teams. This can contribute to a lack of perceivedpsychological closeness (Zack, 1993) or psychological ties betweenteam members (Wiesenfeld et al., 1998), which will likely impedetrusting beliefs. Similarly, in a virtual world environment, we ex-pect cross-boundary teams to exhibit lower trusting beliefs thanwithin-boundary teams. We therefore propose that:

568 S.Z. Schiller et al. / Computers in Human Behavior 35 (2014) 565–577

H1. For teams working in a virtual world, within-boundary teamsexhibit higher trusting beliefs (ability, benevolence, and integrity)than cross-boundary teams.

Trusting beliefs, which refer to perceived trustworthiness ofmembers in a team, contribute to team trust (Jarvenpaa, Knoll, &Leidner, 1998; Mayer et al., 1995). The more competent the mem-bers in a team, the higher the level of team trust. Similarly, thehigher the levels of benevolence and integrity of team members,the higher the level of team trust. We therefore propose that:

H2. For teams working in a virtual world, higher trusting beliefslead to higher team trust

Trust has been shown to have a direct positive effect on attitu-dinal and perceptual outcomes (Dirks & Ferrin, 2001). For instance,Driscoll (1978) demonstrates that trust is a key factor influencingsatisfaction in decision-making. Gamson (1968) views trust asthe belief that decision makers will produce outcomes favorableto a team member without any influence by that member. Thisidea is similar to the definition of trust provided by Mayer et al.(1995) where the trustor expects the trustee to perform actionsand achieve outcomes important to the trustor, irrespective ofthe trustor’s ability to monitor the trustee. In high-trust decision-making teams, team members show high support for one anotherand provide substantive feedback on each other’s work (Jarvenpaa& Leidner, 1999). The positive and supportive atmosphereobserved in high-trust teams contributes to greater satisfactionwith team processes and outcomes. In low-trust decision-makingteams, team members would be expected to have less optimism,excitement, initiative, and social elements, and major lapses incommunication are also more likely (Jarvenpaa & Leidner, 1999).Such an unsupportive atmosphere in low-trust teams can lead tolower satisfaction with team processes and outcomes.

At the team level, boundary-spanning characteristics of teamscan influence the effect of trust on team outcomes. Both socialidentity theory (Tajfel & Turner, 1979) and self-categorization the-ory (Hogg & Terry, 2000; Hogg et al., 1995; Turner et al., 1987) sug-gest that trustworthiness of members emerges more naturally forwithin-boundary teams and may become more apparent in with-in-boundary teams than cross-boundary teams. The psychologicalcloseness (Zack, 1993) or psychological ties between team mem-bers (Wiesenfeld et al., 1998) are more salient for within-boundaryteams than for cross-boundary teams, which enhances the role oftrust in within-boundary teams. Role identities are organized toform the basis for actions that influence behavioral and affectiveoutcomes (Hogg et al., 1995). Both social identity theory andself-categorization theory suggest that shared commonality of in-group members increases saliency in performing social roles suchas task collaboration (Hogg & Terry, 2000; Hogg et al., 1995).Therefore, trust is assessed with greater confidence by membersof within-boundary than cross-boundary teams, leading to trustbeing a stronger predictor of satisfaction with team process andoutcome in within-boundary teams. Thus, the effect of team truston satisfaction depends on the presence and type of boundary.Based on the above, we hypothesize the following:

H3a. For teams working in a virtual world, team trust will have agreater positive effect on team process satisfaction for within-boundary teams than for cross-boundary teams.

4 We used dyadic teams to facilitate control associated with the implementation ofthe study and to improve the validity of results. The definition of trust is ‘‘inherentlyrelational’’ (Zaheer, McEvily, & Perrone, 1998, p. 143). The role of trust could beconfounded in larger sized teams because of differences in perceptions held by eachmember about the other team members. For example, if a team member perceivedthat one team member was trustworthy but another was not, the trust measureswould be less likely to capture the complex nature of the trusting relationships in theteam. Using dyads greatly reduces the potential for confusion about the role that trustplays in the findings.

H3b. For teams working in a virtual world, team trust will have agreater positive effect on team outcome satisfaction for within-boundary teams than for cross-boundary teams.

Institutional boundaries create differences in identities,interests, resources, and practices when team members spanboundaries and such differences can impede collaboration (Hogg

& Terry, 2000; Levina & Vaast, 2005). In addition, teams workingacross institutional boundaries are more likely to experience diffi-culty in managerial practices and may create or perceive potentialsocial boundaries such as intellectual and symbolic differences,which, in turn, negatively affect collaboration (Levina & Vaast,2008). Empirical studies have shown that the effectiveness of collab-orative teamwork contributes to team satisfaction (Driscoll, 1978;Gladstein, 1984). As a team, members are more satisfied if their col-laboration process and outcome are more effective; however, teammembers who are separated by institutional boundaries experiencedifferent social boundaries and will likely have greater difficultydeveloping a shared identity and shared norms and practices(Levina, 2005; Levina & Vaast, 2005). As a result, members from dif-ferent organizations, although working on the same team, will likelyexperience pressures associated with cognitive dissonance, the psy-chologically uncomfortable tension that comes from holding differ-ent or conflicting thoughts (Festinger, 1962). They will also be lesslikely to develop an agreed-upon process for decision-making,which may, in turn, hamper the collaborative process and outcomes.

Furthermore, members from cross-boundary teams are oftenchallenged by coordination problems, communication barriers, orinadequate knowledge of the other institution’s social norms, cul-ture, and management philosophy. For example, institutionalboundaries increase coordination complexity and more time and ef-fort are needed to harmonize differences across these boundaries(Pauleen & Yoong, 2001). When the necessary resources needed toovercome these challenges are not readily available, teams are morelikely to experience coordination breakdown (Ren, Kiesler, & Fussell,2008). As a result, members from different institutions, when work-ing together, tend to be less committed to the joint collaborative taskand less willing to contribute individual inputs, all of which result innegative affective perceptions. Thus, we hypothesize as follows:

H4a. For teams working in a virtual world, within-boundary teamswill report higher team process satisfaction than cross-boundaryteams.

H4b. For teams working in a virtual world, within-boundary teamswill report higher team solution satisfaction than cross-boundaryteams.

In summary, when collaborating in a virtual environment,members of cross-boundary teams are likely to find it more diffi-cult to establish and sustain trust, and the effect of team trust onsatisfaction is less salient compared to within-boundary teams.Fig. 1 shows our research model and hypotheses.

3. Research method

3.1. Sample and task

Two hundred and eighty-two MBA students from two institu-tions (600 miles apart in physical distance) participated in a SecondLife collaborative design project. We randomly assigned partici-pants to teams of two (i.e., dyads) with partners from the sameinstitution (within-boundary teams) or a different institution(cross-boundary teams) thereby forming 141 teams.4 Team

Fig. 1. Research model.

S.Z. Schiller et al. / Computers in Human Behavior 35 (2014) 565–577 569

members had no history of working with each other prior to thedesign project. Fifty-nine percent of the participants were maleand the rest were female. The majority of the participants were35 years old or younger (81%). On average, participants had11.5 years of Internet experience. Most participants (85%) had noprior experience with Second Life.

All teams worked on a creative design task for five weeks wherethey planned, designed, and built virtual structures for a businessfunction in Second Life. Each team completed their creative designon a platform that is sized 10 * 10 m that resembled a room’s woodfloor. Teams were free to choose the design theme of their businessplatform. All teams had access to a large selection of free inventoryitems for their design or they could obtain items from othersources in Second Life. Participants were instructed to log into Sec-ond Life and work on their design projects together with theirteammate. The completed team designs featured specific themes,using objects such as computer and server rooms, offices, livingrooms, backyard scenes, flower gardens, libraries, red carpet walk-ways, dance clubs, and many had multiple-story structures andwere decorated with sophisticated artwork. Fig. 2 shows a fewselected team designs.

3.2. Measures

Measurement items were adopted and adapted from publishedand well-established instruments and all were measured using anine-point Likert scale. Items measuring the constructs of trustingbeliefs, namely ability, integrity, and benevolence, were carefullyadapted from Jarvenpaa et al. (1998). The items selected werethose relevant to the context of working in the virtual environ-ment. We selected the relevant and appropriate items for use inthis study, and included four of the original six items for ability,four of the original five items for integrity, and all four originalitems for benevolence. Team trust was measured using four itemsadopted from Jarvenpaa et al. (1998), which was derived from ear-lier work on the trust concept by Mayer et al. (1995) and Pearceet al. (1992, 1994). Specifically, we used two items (‘rely on part-ner’ and ‘being considerate’) from Jarvenpaa et al. (1998) andtwo items (‘confidence’ and ‘team spirit’) from Pearce et al.(1992, 1994). We assessed team satisfaction using two factors, sat-isfaction with the team process and satisfaction with the teamsolution. Four items from Green and Taber (1980) were adaptedfor measuring satisfaction with team process and three items from

Green and Taber (1980) were adapted for measuring satisfactionwith team solution.

3.3. Common method bias

We adopted two techniques used in Lindell and Whitney (2001)and Pavlou and Gefen (2005) to check for common method bias.First, following the suggestion by Lindell and Whitney (2001), weincluded two reverse coded items in our measurement to reduceacquiescence problems. Reliability tests showed no sign of anyproblematic issues. Second, Pavlou and Gefen (2005) used theHarman’s one-factor test by entering all constructs into a principalcomponents factor analysis (PCA). If ‘‘a single factor emerges fromthe analysis or when one general factor accounts for the majorityof the covariance in the interdependent and dependent variables’’(Pavlou & Gefen, 2005, p. 388), it means common method bias ex-ists. We conducted a PCA analysis with all constructs. Six factorswere generated and there was no single factor accounting for themajority of the covariance. Therefore, results from these tests sug-gest that common method bias is not of concern in our data.

4. Data analysis and results

4.1. Measurement model

To evaluate the psychometric properties of the latent variables,a confirmatory factor analysis (CFA) was performed. Multiple fitcriteria were used to evaluate the measurement model includingthe comparative fit index (CFI), the non-normed fit index (NNFI),the root mean square error of approximation (RMSEA), and thestandardized root mean square residual (SRMR). Recommendedacceptable levels for each criteria are CFI P 0.95, NNFI P 0.95,RMSEA 6 0.08 for reasonable fit (between 0.08 and 0.10 for medi-ocre fit), and SRMR 6 0.08 for good fit (Bearden, Netemeyer, &Mobley, 1993; Browne & Cudeck, 1993; Gefen, Straub, & Boudreau,2000; Hu & Bentler, 1999; Kim & Son, 2009; MacCallum, Browne, &Sugawara, 1996).

The measurement model consisted of all the latent factorsincluding the three sub-factors of components of trust (ability,benevolence, and integrity), team trust, and satisfaction with boththe process and solution. The second-order factor of components oftrust was not estimated directly in the measurement model, but

Fig. 2. Design platforms and selected designs.

570 S.Z. Schiller et al. / Computers in Human Behavior 35 (2014) 565–577

instead the three sub-factors were each correlated with the otherlatent variables in the model as performed by previous research(Bagozzi & Heatherton, 1994).5 The fit results from the measure-ment model showed moderate to good fit [v2(215) = 719.62,p < 0.001, CFI = 0.94, NNFI = 0.93, RMSEA = 0.091, SRMR = 0.046].The means, standard deviations, Cronbach’s alpha, composite reli-ability (CR), average variance extracted (AVE), and correlations ofthe measures are shown in Table 1.

To evaluate the quality of the measurement model, we usedseveral tests on reliability and validity (Bagozzi & Yi, 1988; Fornell& Larcker, 1981). First, reliability was examined using Cronbach’scoefficient alpha (Cronbach, 1951) and composite reliability. Val-ues of Cronbach’s alpha coefficients are 0.9 and above, much higherthan the recommended level of 0.7 (Cohen, 1988). All constructshave satisfactory composite reliability scores, above the recom-mended cutoff value of 0.7 (Bagozzi & Yi, 1988; Bearden et al.,1993; Fornell & Larcker, 1981). Second, construct validity, bothconvergent and discriminant, was tested and the results were sat-isfactory. Convergent validity is assessed using the average vari-ance extracted (AVE) and factor loadings of indicators. Table 1shows the AVE value for each construct, ranging from 0.72 to0.90, well above the recommended requirement of 0.50 (Chin,1998; Hu, Lin, Whinston, & Zhang, 2004). In addition, all loadingsof indicators were above 0.70, satisfying the requirement of 0.60and above (Chin, 1998; Hair, Black, Babin, Anderson, & Tatham,2006). Discriminant validity is assessed by cross loadings of con-structs and the square root of AVE compared to construct correla-tions. All indicators load more highly on their own constructs (ownloadings) than other constructs (cross loadings). The square root ofevery construct’s AVE was higher than its correlations with otherconstructs,6 indicating that each construct is more closely relatedto its own measure than to the measures of other constructs (Chin,

5 Given that the second-order construct is evaluated as a first-order construct, inevaluating the measurement model, the measurement model will not fit as well asthe full model because the true second-order nature of the components of trustconstruct are not adequately modeled.

6 The one exception to this was the correlation of benevolence and integrity at 0.87being higher than the square root of the AVE of Benevolence at 0.85. Given that theseitems are part of a second-order construct, we feel that this slight deviation isacceptable.

1998; Gefen & Straub, 2005; Majchrzak, Beath, Lim, & Chin, 2005).In summary, these results demonstrate high reliability as well asgood convergent and discriminate validity for all constructs in themodel.

4.2. Multigroup mean analysis

Multigroup structural equation modeling analysis (hereafterreferred to simply as multigroup analysis) was used to evaluatethe proposed hypotheses for a few reasons (see Hair et al., 2006;Luse, Mennecke, & Townsend, 2013). Traditional multigroupregression testing is not suitable given the latent variables in themodel as well as the mediational component of the structuralmodel. Multigroup analysis is a much more robust and thoroughmechanism for testing between groups by constraining the param-eters that are hypothesized to be different to be the same betweenthe two groups and then testing whether there truly is a differencebetween these constrained models. The process of multigroup test-ing involves testing models that impose increasing amounts ofconstraints and evaluating both the fit of these models and thesignificance of the change in fit between the less and more con-strained models (Hair et al., 2006). For this research, the measure-ment model is evaluated in a multigroup context to investigate theinfluence of institutional boundary on the mean differencesbetween within and cross-boundary groups for Trusting Beliefs(H1), Satisfaction with Process (H4a), and Satisfaction with Solu-tion (H4b). To evaluate the influence of Trusting Beliefs on Trust(H2) and the impact of institutional boundary on the influence ofTrust on Satisfaction with the Process (H3a) and Solution (H3b),the structural model is evaluated in a multigroup context (see nextsection).

First, to establish the necessary precondition of metric equiva-lence to allow analysis of a multigroup CFA, both a factor structureequivalent model and a factor loading equivalent model must beestimated (Hair et al., 2006). Table 2 shows that both the factorstructure equivalent model [v2(430) = 1078.87, p < 0.001,CFI = 0.92, NNFI = 0.91, RMSEA = 0.073, SRMR = 0.038] and the fac-tor loading equivalent model [v2(444) = 1094.55, p < 0.001,CFI = 0.92, NNFI = 0.91, RMSEA = 0.073, SRMR = 0.040] fit the datarelatively well for both groups. Also, the change in v2 analysis

Table 2Multigroup statistics for means hypotheses testing.

Model description v2 CFI NNFI RMSEA SRMR Dv2

Original combined model 719.62 (215) 0.94 0.93 0.091 0.046Factor structure equivalence 1078.87 (430) 0.92 0.91 0.073 0.038 –Factor loading equivalence 1094.55 (444) 0.92 0.91 0.073 0.040 15.69 (14), p = 0.33Intercept equivalence trusting beliefs 1106.56 (451) 0.92 0.91 0.072 0.040 12.01 (7), p = 0.10Mean equivalence trusting beliefs 1114.33 (454) 0.92 0.91 0.072 0.040 7.77 (3), p = 0.05Intercept equivalence satisfaction with process 1102.53 (446) 0.92 0.91 0.073 0.040 7.98 (2), p = 0.02Intercept equivalence satisfaction with solution 1096.10 (446) 0.92 0.91 0.072 0.040 1.55 (2), p = 0.46Mean equivalence satisfaction with solution 1096.81 (447) 0.92 0.91 0.072 0.040 0.71 (1), 0.40

Table 3Mean statistics for the measurement model.

Within Cross

Trusting beliefsAbility 7.4 7.1Benevolence 7.6 7.3Integrity 7.8 7.4

SatisfactionProcess 7.6 7.4Solution 7.8 7.7

Table 1Measurement model scale properties.

Mean Std. dev. Alpha CR AVE Correlations

1 2 3 4 5 6

1. Ability 7.10 1.65 0.91 [CI = 0.89,0.92] 0.91 0.72 0.852. Benevolence 7.63 1.42 0.91 [CI = 0.89,0.93] 0.91 0.72 0.75 0.853. Integrity 7.63 1.60 0.97 [CI = 0.97,0.98] 0.97 0.90 0.79 0.87 0.954. Trust 7.37 1.57 0.92 [CI = 0.91,0.94] 0.93 0.77 0.76 0.80 0.83 0.885. Process 7.48 1.44 0.95 [CI = 0.94,0.96] 0.95 0.82 0.66 0.74 0.75 0.74 0.916. Solution 7.87 1.05 0.90 [CI = 0.88,0.92] 0.80 0.75 0.47 0.60 0.52 0.60 0.66 0.87

CR: Composite Reliability. The bold numbers are the square root of AVE (average variance extracted). All correlations are significant at the 0.01 level.

S.Z. Schiller et al. / Computers in Human Behavior 35 (2014) 565–577 571

indicates that the factor loading equivalence model fit the data aswell as the factor structure equivalence model [Dv2(14) = 15.69,p = 0.33], thereby providing evidence for partial metric equivalencethat will allow further multigroup analyses.

To test the differences in means for Trusting Beliefs (H1) andSatisfaction between the two groups (H4a and H4b), the factorloading equivalence model was constrained between groups (with-in-boundary and cross-boundary) for each construct separately. Tothoroughly test for mean equivalence, both intercept equivalenceand mean equivalence must be satisfied. Therefore, to test forequal means between the two groups on Trusting Beliefs (H1),intercepts of each observed item for Ability, Benevolence, andIntegrity, respectively were constrained to be equal. As Table 2shows, the model testing for equal item intercepts across groupsfor Trusting Beliefs is not significantly different from the factorloading equivalent model [Dv2(7) = 12.01, p = 0.10].

Because the intercept equivalence model does not provideevidence for differences between groups, the mean equivalencemodel must be used to fully test for mean equivalence. The meanequivalence model constrains the intercept model by constrainingeach mean for Ability, Benevolence, and Integrity to be equal be-tween the two groups. Table 2 shows that the model testing forequal item means across groups for Trusting Beliefs is significantlydifferent from the intercept equivalent model [Dv2(3) = 7.77,p = 0.05], providing evidence that the means of Trusting Beliefsare significantly different between the two groups. Furthermore,for each construct of Ability, Benevolence, and Integrity, its meanis higher in the within-boundary group than that of the cross-boundary group (Table 3). Given the significant difference andthe direction of this significance, H1 is supported.

Next, we tested for differences in means for Satisfactionbetween the two groups (H4a and H4b). To test for equal meansbetween the two groups, intercepts of each observed item for Sat-isfaction with Process and Satisfaction with Solution, respectively,were constrained to be equal. The two Satisfaction constructs weretested separately to provide the most thorough test possible ofmean differences. As Table 2 shows, the model testing for equalitem intercepts across groups for Satisfaction with Process is signif-icantly different from the factor loading equivalent model[Dv2(2) = 7.98, p = 0.02]. Since the intercept equivalence model

provides evidence for differences between groups, the mean equiv-alence model is not necessary. Also, Table 3 shows that the mean ofSatisfaction with Process is higher in the within group than that ofthe cross group. Given the significant difference and the directionof this significance for Satisfaction with Process, H4a is supported.

Next, we tested for equal means between the two groups onSatisfaction with Solution. Table 2 shows that the model testingfor equal item intercepts across groups for Satisfaction with Solu-tion is not significantly different from the factor loading equivalentmodel [Dv2(2) = 1.55, p = 0.46]; therefore the mean equivalencemodel must be used to fully test for mean equivalence. Unfortu-nately, the model testing for equal item means across groups forSatisfaction with Solution is also not significantly different fromthe intercept equivalent model [Dv2(1) = 0.71, p = 0.40] (Table 2),leading to the conclusion that the mean of Satisfaction with Solu-tion is not significantly different between groups. Therefore, H4b isnot supported.

4.3. Multigroup structural relationship analysis

To test path differences between the two groups, the structuralmodel was also evaluated in a multigroup context. First, theoriginal combined structural model was estimated without anymultigroup effects estimated. Table 4 shows that this model hasmediocre fit [v2(224) = 806.96, p < 0.001, CFI = 0.93, NNFI = 0.92,RMSEA = 0.096, SRMR = 0.056]. Adding multigroup estimationshows both the factor structure equivalent model

Table 4Multigroup statistics for structural relationship hypotheses testing.

Model description v2 CFI NNFI RMSEA SRMR Dv2

Original combined model 806.96 (224) 0.93 0.92 0.096 0.056Factor structure equivalence 1179.71 (448) 0.91 0.90 0.076 0.053 –Factor loading equivalence 1196.37 (462) 0.91 0.91 0.075 0.054 16.66 (14), p = 0.28Structural equivalence trust ? process 1202.84 (463) 0.91 0.91 0.076 0.058 6.46 (l), p = 0.01Structural equivalence trust ? solution 1204.46 (463) 0.91 0.91 0.076 0.070 8.09 (l), p = 0.00

572 S.Z. Schiller et al. / Computers in Human Behavior 35 (2014) 565–577

[v2(448) = 1179.71, p < 0.001, CFI = 0.91, NNFI = 0.90,RMSEA = 0.076, SRMR = 0.053] and the factor loading equivalentmodel [v2(462) = 1196.37, p < 0.001, CFI = 0.91, NNFI = 0.91,RMSEA = 0.075, SRMR = 0.054] fit the data relatively well for bothgroups, providing credence to the use of a multigroup analysis.Also, the change in v2 analysis indicates that the factor loadingequivalence model fit the data as well as the factor structure equiv-alence model [Dv2(14) = 16.66, p = 0.28], thereby providingevidence of partial metric equivalence and allowing for furthermultigroup analysis.

Next, H2 is evaluated in the context of the overall effect ofTrusting Beliefs on Trust since no group effects are hypothesized.The model shows an overall positive significant relationship be-tween Trusting Beliefs and Trust (b = 0.91, p < 0.001), supportingH2.

To test for the influence of Institutional Boundary on the pathloadings between the two groups (H3a and H3b), the metric equiv-alent model was constrained between groups for each of the tworelationships separately; therefore, the Trust to Satisfaction withthe Process loading and the Trust to Satisfaction with Solutionloading were each constrained to be equal between the twogroups. As Table 3 shows, the model testing for equal beta loadingsacross groups for the Trust ? Process relationship is significantlydifferent from the factor loading equivalent model[Dv2(1) = 6.46, p = 0.01] providing evidence that this loading is sig-nificantly different across groups. Given that structural parameterequivalence for the Trust ? Process relationship has not been met,we can conclude that the influence of Trust on Satisfaction with

TrustingBeliefs

TrustR2 = 0.830.91***

Within

TrustingBeliefs

TrustR2 = 0.850.92***

Cross

Fig. 3. Blocked models for within-bou

Process is significantly different between the within and cross-boundary teams. The model testing for equal beta loadings acrossgroups for the Trust ? Solution relationship is significantly differ-ent from the factor loading equivalent model [Dv2(1) = 8.09,p = 0.00] providing evidence that this loading is significantly differ-ent across groups. Given that structural parameter equivalence forthe Trust ? Solution relationship has not been met, we canconclude that the influence of Trust on Satisfaction with Solutionis significantly different between the intra- and inter-universityteams.

Finally, to provide greater insight into the differences in thestructural relationships across models, blocking was utilized togenerate results for each group individually. These two modelswith their respective loadings are shown in Fig. 3. The relationshipbetween Trust and Satisfaction with Process for the within-bound-ary group (b = 0.88, p < 0.001) is more positively significant thanthat in the cross-boundary group (b = 0.64, p < 0.001), supportingH3a. Similarly, the relationship between Trust and Satisfactionwith Solution is more positively significant in the within group(b = 0.70, p < 0.001) than that in the cross group (b = 0.53,p < 0.001), supporting H3b. This separate analysis also showshow the difference in beta loadings affects the variance explainedin the two outcome variables between the two groups, with the R2

values for Satisfaction with Process and Satisfaction with Solutionbeing much higher in the within group (R2 = 0.77 and 0.49 respec-tively) as compared to the cross group (R2 = 0.41 and 0.28respectively).

Table 5 provides a summary of the hypotheses and the results.

Satisfaction with Process

R2 = 0.77

Satisfaction with SolutionR2 = 0.49

0.88***

0.70***

Satisfaction with Process

R2 = 0.41

Satisfaction with SolutionR2 = 0.28

0.64***

0.53***

ndary and cross-boundary teams.

Table 5Hypotheses and results.

Hypotheses Result

H1 For teams working in a virtual world, within-boundary teams exhibit higher trusting beliefs (ability, benevolence, and integrity) than cross-boundary teams

Supported

H2 For teams working in a virtual world, higher trusting beliefs lead to higher team trust SupportedH3a For teams working in a virtual world, team trust will have a greater positive effect on team process satisfaction for within-boundary teams than

for cross-boundary teamsSupported

H3b For teams working in a virtual world, team trust will have a greater positive effect on team outcome satisfaction for within-boundary teams thanfor cross-boundary teams

Supported

H4a For teams working in a virtual world, within-boundary teams will report higher team process satisfaction than cross-boundary teams SupportedH4b For teams working in a virtual world, within-boundary teams will report higher team solution satisfaction than cross-boundary teams Not

supported

S.Z. Schiller et al. / Computers in Human Behavior 35 (2014) 565–577 573

5. Discussion

5.1. Discussion of main findings

The objective of our paper is to examine the role of institutionalboundaries on the development of trust and satisfaction when vir-tual team members are engaged in collaborative activities in a vir-tual world. Our results support the notion that trust andinstitutional affiliation matter for teams working in a virtual world.Our results demonstrate that trusting beliefs of team membersinfluence team trust, which is significantly associated with teammembers’ satisfaction when collaborating on tasks in the virtualenvironment. We found that team trust was influenced by teammembers’ trusting beliefs in one another (i.e., ability, benevolence,and integrity), all of which are important contributors to teamtrust. In addition, the development of positive perceptions of trustin a team has a positive influence on team satisfaction with bothprocess and outcome. Our results are thus consistent with theliterature indicating that trusting beliefs influence team trust(Jarvenpaa et al., 1998, 2004; Mayer et al., 1995), which in turninfluences other attitudinal and perceptual variables (Dirks &Ferrin, 2001; Jarvenpaa et al., 2004; Kiffin-Petersen, 2004).

More importantly, our results show that institutional boundaryaffects teams working in a 3D virtual world. First, we found thatwithin-boundary teams exhibit higher trusting beliefs than cross-boundary teams, consistent with findings in the literature. For in-stance, a few studies have found high initial trust in new virtualwork relationships (e.g., Jarvenpaa & Leidner, 1999; Jarvenpaaet al., 1998; Kramer, 1994; Meyerson, Weick, & Kramer, 1996).McKnight, Cummings, and Chervany (1998) suggest that initialtrust in new work relationships is based on an individual’s preexist-ing disposition to trust, institutional-based trust, and cognitive pro-cesses (i.e., categorization and illusions of control). Meyerson et al.(1996) also attribute the occurrence of ‘‘swift’’ trust to institutional-based trust. Our findings provide additional evidence to this pool ofstudies indicating that an actor’s social categorization based onone’s team affiliation can result in high levels of trusting beliefs.

Second, when collaborating on a task in the virtual world, with-in-boundary teams reported higher satisfaction with team processthan cross-boundary teams. This finding, again, is consistent withother studies on boundary-spanning virtual teams. Organizationalboundaries define the edge of organizational entities (Gaertner,Dovidio, Anastasio, Bachman, & Rust, 1993; Gaertner et al.,1999a,b). Studies show that a common in-group identity can beachieved by increasing the salience of existing common superordi-nate memberships (e.g., working in a bounded environment) or byintroducing factors such as common goals or shared responsibili-ties (Gaertner et al., 1999a,b). Guided by the objectives of thecollaborative task, members of within-boundary teams ‘‘are likelyto have more positive thoughts and feelings, and to engage in morepositive behaviors toward these members’’ while working together

on the task (Gaertner et al., 1999a, p. 184). In turn, within-bound-ary teams develop an increased level of synergy and augmentedtendency of developing a sense of satisfaction with one’s partnerin the collaboration process.

Furthermore, we found that for within-boundary teams, thepositive relations between team trust and satisfaction (for bothprocess and outcome) are stronger than those for cross-boundaryteams. We believe that our study is the first to offer empirical evi-dence that team boundary affects the mechanisms of team trust onteam collaboration. So far, there have only been a few studiesinvestigating trust on virtual world team performance, althoughseveral of these studies demonstrate inconsistent results. Forexample, Chang, Chuang, and Chao (2011) found that in virtualteams with multiple cultures, trust has a positive effect on virtualteam performance. Nevertheless, others such as Goh and Wasko(2012) found that trust does not have any significant influenceon the performance of virtual teams in a gaming world. Further,Feldberg and colleagues (Van Der Land, Schouten, Feldberg, VanDen Hooff, & Huysman, 2013) found that while immersive virtualworlds facilitated individual understanding, users struggled withattaining a shared understanding in the team setting.

Research in embodiment in 3D virtual worlds is helpful in pro-viding an explanation of some of the empirical findings as well asour results. Mennecke and colleagues (Mennecke, Triplett, Hassall,Conde, & Heer, 2011) show that the embodied presentations ofindividuals have profound influences on the perceptions of self,others, and the shared task. Users in virtual worlds who engagein substantive collaborative tasks attain a high state of engagementwith their virtual personas as well as their collaborative partnerswhen they experience embodied social presence (Menneckeet al., 2011). Much like flow, embodied social presence is likelyto increase engagement in the team activities and thereby increaseperceptions of engagement and presence (Faiola, Newlon, Pfaff, &Smyslova, 2013) and creativity (Yan, Davison, & Mo, 2013). Teammembers who are affiliated with the same institution are likelyto experience lower coordination complexity and, as a result,perceive greater overall social presence with one another; thus,the level of team trust of within-boundary teams are expected tohave a more profound effect on satisfaction with team processand solution than that of cross-boundary teams.

In regard to satisfaction, we found that members of within-boundary and cross-boundary teams were both satisfied with thesolution of the collaboration but those from within-boundaryteams are more satisfied with the collaboration process than thosefrom cross-boundary teams. In other words, team boundary doesnot affect satisfaction with the collaboration outcome (i.e., the cre-ative design) in the virtual world but does affect satisfaction withthe collaboration process (i.e., the series of activities to constructthe design). This could be due to the competitive nature of the de-sign task in our study that could have directed the primary atten-tion of the team members toward being more outcome-oriented.

574 S.Z. Schiller et al. / Computers in Human Behavior 35 (2014) 565–577

Hence, when members become highly focused on the completedproject (i.e., team members are goal- and outcome-oriented), theinstitutional boundary condition can become less influential.

5.2. Limitations and future research

We address a number of limitations in our study that should betaken into consideration when generalizing our findings to othercontexts and to practice. First, our study was conducted in an edu-cation institutional setting rather than a business organizationalsetting. Participants were MBA students studying at two NorthAmerican universities. While the use of student subjects has beencriticized because results from student-based studies are thoughtto lack external validity, the use of graduate student subjectsshould not present any major issues in this case because of the nat-ure of our subject pool. Specifically, our subjects consist of MBAand other graduate students with significant work and life experi-ences. Prior research has demonstrated that MBA students,because of their prior work experience and education level, sharesimilar patterns of values and beliefs with individuals in a typicalworkplace setting (Galletta, Henry, McCoy, & Polak, 2006;Jarvenpaa et al., 2004; Voich, 1995). Additionally, the task wasrelevant to the context in which these students operated in thecourses in which the project was assigned. MBA students, there-fore, are likely to exhibit similar behaviors to professionals andother populations to which we might want to generalize ourfindings. Although member affiliation with institutions is differentfrom business affiliations, we believe the concept of socialcategorization is well represented in our study. We acknowledge,however, that the use of institutional context in the study maylimit the generalizability of the findings to other contexts. Thus,although it might be argued that our students’ perceptions oftheir affiliations with their institution might not have been thatstrong or salient; nevertheless, even a weak association has pro-duced results that are statistically significant. Given this, weexpect that in institutional settings where affiliation is strongand salient, the effect of institutional boundary will be evenmore pronounced.

Second, team size should also be considered when interpretingour findings. Researchers agree that dyads are the most basic groupform and they share common characteristics with groups of othersizes (Zhou & Zhang, 2006) but also acknowledge that when teamsize grows, factors such as team dynamics, trust, leadership style,communication quality, process, and outcomes may intensify anddiffer (Cogliser & Schriesheim, 2000; Dennis & Wixom, 2002). Nev-ertheless, research has also shown few differences due to teamsize. For example, Stewart and Gosain (2006) studied open sourcesoftware development teams and the results showed that ‘‘groupsize did not have a significant effect on either effort or task comple-tion’’ (p. 309). Similarly, Egerbladh and Sjodin (1981) found no sig-nificant relationship between team size and individual learningoutcomes. In addition, dyadic interaction is the basic unit for com-munication and dyadic work is common in organizations where,for example, a consultant or sales representative from one firmworks with a contact in a client organization. Furthermore, thedyadic structure of the teams in this study was helpful in control-ling for spurious and inconsistent perceptions associated withinstitutional affiliation. In other words, if we had used larger teams,the effect of the affiliation manipulation might have been reduceddue to the mixed association of members within the team (e.g.,coalitions might form and reduce or, alternatively, exacerbate theeffects of boundaries). Therefore, the dyadic structure in our studyhelped to sharpen and make salient the member affiliation forcross-boundary teams and insured that work activities for thedesign project were completed with the cross-boundary partner.

In spite of this, caution should be taken in generalizing the resultsto larger team sizes.

In addition, while a five-week project is a moderate amount oftime for team interaction, we were not able to study the longitudi-nal effects of trust development in our teams over a longer dura-tion (see Luse, Mennecke, & Triplett, 2013). To date, severalresearchers (Jarvenpaa & Leidner, 1999; Jarvenpaa et al., 1998,2004; Kanawattanachai & Yoo, 2002) have studied trust from adynamic or longitudinal perspective, but such phenomenon hasnot been studied in 3D virtual environments. In future research,we suggest that the length of time subjects spend using Second Lifeas well as the complexity of the project requirements be increasedto better examine the longitudinal effects of trust maintenance in3D virtual worlds.

Finally, we examined the effects of boundary-spanning teamson trust and team-related perceptions in a 3D virtual environment,or more specifically, Second Life. Our findings may not be general-izable to other collaborative media due to the unique nature of thisparticular environment. Further research is needed to test thegeneralizability of our findings to other media and virtualenvironments.

5.3. Theoretical contributions

The results of our study have important theoretical contribu-tions that can be explained by social identity and self-categoriza-tion theories. Consistent with these theories, our results favorwithin-boundary teams over cross-boundary teams when collabo-rating in virtual worlds because the former developed higher trust-ing beliefs, were more satisfied with the collaboration process, anddemonstrated a stronger trust-satisfaction relationship. Whenexamining working teams in virtual worlds, it is thus importantto define the factors that are likely to influence trust and collabo-ration in any given context such as their institutional affiliations.Many cross-boundary relationships studied by organizationalresearchers have, for example, been examined in a context wherethe organizations and their members were in partnerships thatwere more tenuous or where external market conditions makethe status of the team’s relationship uncertain. Thus, it is importantto identify the nature of the institutions, the structure of the cross-boundary relationship, and the broader context for interaction andteamwork when investigating such issues.

Another theoretical contribution of our study is that it showsthat 3D virtual environments can offer opportunities to build trustin a manner similar to the way trust building has been observed inother environments. Similar to teams working in other types ofenvironments, the nature of boundary spanning teams in virtualworlds requires integrative approaches to deploy IT practices suc-cessfully (Lindgren, Andersson, & Henfridsson, 2008). We believethat social identity and categorization stay as salient to partici-pants in virtual worlds as they would in other environments be-cause of the highly interactive and engaging nature of virtualenvironments such as Second Life. We suggest that other research-ers examine team trust and collaboration in a number of task andenvironmental contexts.

5.4. Practical implications

Our results also have important implications for practice.Specifically, our results suggest that reaching out to team membersin one’s own organization as well as across boundaries can both beeffectively supported in virtual environments. After all, while themembers of within-boundary teams have higher trusting beliefsand satisfaction with process, team members in both conditionshave favorable scores for team trust (i.e., means between 7 and 8out of 10 for both types of groups) that positively contribute to

S.Z. Schiller et al. / Computers in Human Behavior 35 (2014) 565–577 575

team satisfaction. Thus, by extension, these results suggest thatvirtual environments offer promise as tools for fosteringcross-boundary relationships. As noted by Ives and Junglas(2008), proximity can have a profound influence on the casual,often serendipitous interactions that lead to positive outcomesfor teams. Teams generally need to invest their efforts not onlyin task activities, but also in member support and team well-beingfunctions.

Environments like Second Life offer the opportunity to let teams‘‘play’’ as well as work. Prior research shows that cross boundaryteams often benefit from the availability of flexible communicationaffordances (Akoumianakis, 2014); therefore, the multitude offlexible communication affordances in Second Life likely facilitatedan overall positive team experience for both within and cross-boundary teams. The ability of team members to use a virtual envi-ronment like Second Life for team interactions, and particularlycross-boundary team interactions, is important for business func-tions as organizations evolve to become more virtual (i.e., wheremembers routinely cross boundaries). An example where organiza-tional boundaries were routinely crossed using virtual worlds wasIBM’s use of their Virtual Business Center in Second Life. Ganiset al. (2008) describe how IBM used Second Life’s unique resourcesto work with clients and prospects. For example, they note, ‘‘IBMsubject matter experts (SMEs) and prospects or customers couldcollaborate and explore meaningful scenarios and simulationstogether’’ (Ganis et al., 2008, p. 21). The interactive nature of Sec-ond Life offered IBM and other business users the opportunity tocollaborate on design ‘‘simulations’’ in real time and our resultssupport the premise that cross-boundary teams can effectively besupported in developing trusting relationships in these contexts.

6. Conclusion

Our study examined trust in boundary-spanning teams in avirtual environment and found that trust formation can take placein virtual environments. Handy (1995) argues that ‘‘trust needstouch’’ (p. 46) and that teams cannot function effectively withoutface-to-face interaction. Our study shows that this is not necessar-ily the case as cross-boundary teams are able to establish trust in avirtual world. Given that team trust contributed to satisfactionwith process and solution in both within- and cross-boundaryteams but the relationships were stronger for within- than cross-boundary teams, we conclude that team trust and satisfactionachieved in these teams are attributable not only to social identityand categorization, but also to the interactive and vivid nature ofthe virtual environment that has great potential to foster the trustneeded for successful collaborative work. Hence, trust can befostered in virtual worlds to facilitate collaborative work but with-in-boundary teams may have advantages over cross-boundaryteams in a virtual-world environment.

References

Ahuja, M., & Carley, K. (1999). Network structure in virtual organizations.Organization Science, 10(6), 741–757.

Akoumianakis, D. (2014). Ambient affiliates in virtual cross-organizational tourismalliances: A case study of collaborative new product development. Computers inHuman Behavior, 30, 773–786.

Bagozzi, R., & Heatherton, T. F. (1994). A general approach to representingmultifaceted personality constructs: Application to state self-esteem.Structural Equation Modeling: A Multidisciplinary Journal, 1(1), 35–67.

Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models.Journal of the Academy of Marketing Science, 16(1), 74–94.

Bearden, W. O., Netemeyer, R. G., & Mobley, M. F. (1993). Handbook of marketingscales: Multi-item measures for marketing and consumer behavior research.Newbury Park, CA: Sage Publications.

Birnholtz, J., & Finholt, T. (2007). Cultural challenges to leadership incyberinfrastructure development. In S. Weisband (Ed.), Leadership at a

distance: Research in technology-supported work (pp. 195–207). New York:Lawrence Erlbaum Associates.

Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A.Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 445–455).Newbury Park, CA: Sage.

Calvard, T. S. (2014). Difficulties in organizing boundary-spanning activities ofinter-organizational teams. In C. L. C. Janice Langan-Fox (Ed.), Boundary-spanning in organizations: Network, influence, and conflict. New York: Routledge.

Carmel, E. (1999). Software teams: Collaborating across borders and time zones. UpperSaddle River, NJ: Prentice Hall.

Chang, H. H., Chuang, S. S., & Chao, S. H. (2011). Determinants of cultural adaptation,communication quality, and trust in virtual teams’ performance. Total QualityManagement, 22(3), 305–329.

Chin, W. W. (1998). Issues and opinion on structural equation modeling. MISQuarterly, 22(1), vii–xvi.

Cogliser, C., & Schriesheim, C. (2000). Exploring work unit context and leader–member exchange: A multi-level perspective. Journal of Organizational Behavior,21(5), 487–511.

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.).Hillsdale, NJ: Erlbaum.

Cramton, C. D., & Hinds, P. J. (2007, August 3–8). Intercultural Interaction inDistributed Teams: Salience of and Adaptations to Cultural Differences.Proceedings of the academy of management annual meeting, Best Papers, (pp.1–6). Philadelphia, PA.

Cronbach, L. J. (1951). Coefficient alpha and the internal structure of a test.Psychometrika, 16(3), 297–334.

Cummings, J. N., Espinosa, J. A., & Pickering, C. K. (2009). Crossing spatial andtemporal boundaries in globally distributed projects: A relational model ofcoordination delay. Information Systems Research, 20(3), 420–439.

Denison, D., Hart, S., & Kahn, J. (1996). From chimneys to cross-functional teams:Developing and validating a diagnostic model. Academy of Management Journal,39(4), 1005–1023.

Dennis, A. R., & Wixom, B. H. (2002). Investigating the moderators of the groupsupport systems use with meta-analysis. Journal of Management InformationSystems, 18(3), 235–257.

DeSanctis, G. D., & Monge, P. R. (1999). Communication processes for virtualorganizations. Organization Science, 10(6), 693–703.

DeSanctis, G., Staudenmayer, N., & Wong, S. S. (1999). Interdependence in virtualorganizations. In C. L. Cooper & D. M. Rousseau (Eds.). Trends in organizationalbehavior (Vol. 6, pp. 81–103). New York: Wiley.

Dirks, K. T. (1999). The effects of interpersonal trust on work group performance.Journal of Applied Psychology, 84(3), 445–455.

Dirks, K. T., & Ferrin, D. L. (2001). The role of trust in organizational settings.Organization Science, 12(4), 450–467.

Driscoll, J. W. (1978). Trust and participation in organizational decision making aspredictors of satisfaction. Academy of Management Journal, 21(1), 44–56.

Egerbladh, T., & Sjodin, S. (1981). Group problem solving: Ability level, group size,and subsequent individual performance. Journal of General Psychology, 105(1),3–12.

Espinosa, J., Cummings, J., Wilson, J., & Pearce, B. (2003). Team boundary issuesacross multiple global firms. Journal of Management Information Systems, 19(4),157–190.

Faiola, A., Newlon, C., Pfaff, M., & Smyslova, O. (2013). Correlating the effects of flowand telepresence in virtual worlds: Enhancing our understanding of userbehavior in game-based learning. Computers in Human Behavior, 29(3),1113–1121.

Ferrin, D. L., Dirks, K. T., & Shah, P. P. (2006). Direct and indirect effects of third-partyrelationships on interpersonal trust. Journal of Applied Psychology, 91(4),870–883.

Festinger, L. (1962). A theory of cognitive dissonance. Stanford, CA: StanfordUniversity Press.

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models withunobservable variables and measurement error. Journal of Marketing Research,18(1), 39–50.

Gaertner, S. L., Dovidio, J. F., Anastasio, P. A., Bachman, B. A., & Rust, M. C. (1993). Thecommon ingroup identity model: Recategorization and the reduction ofintergroup bias. European Review of Social Psychology, 4, 1–26.

Gaertner, S. L., Dovidio, J. F., Nier, J. A., Ward, C. M., & Banker, B. S. (1999a). Acrosscultural divides: The value of a superordinate identity. In D. A. Prentice & D. T.Miller (Eds.), Cultural divides: Understanding and overcoming group conflict. NewYork: Russell Sage Foundation.

Gaertner, S. L., Rust, M. C., Mottola, G. R., Dovidio, J. F., Nier, J. A., Banker, B. S., et al.(1999b). Reducing intergroup bias: Elements of intergroup cooperation. Journalof Personality & Social Psychology, 76(3), 388–402.

Galletta, D., Henry, R., McCoy, S., & Polak, P. (2006). When the wait isn’t so bad: Theinteracting effects of website delay, familiarity, and breadth. InformationSystems Research, 17(1), 20–37.

Gamson, W. A. (1968). Power and discontent. Homewood, IL: Dorsey.Ganis, M., Hall, G., & McNeill, D. (2008). Virtual worlds as real-world sales tools.

Cutter IT Journal, 21(9), 19–25.Gefen, D., & Straub, D. (2005). A practical guide to factorial validity using PLS-graph:

Tutorial and annotated example. Communications of the Associations forInformation Systems, 16, 91–109.

Gefen, D., Straub, D. W., & Boudreau, M. C. (2000). Structural equation modeling andregression: Guidelines for research practice. Communications of the AIS, 4(1),1–70.

576 S.Z. Schiller et al. / Computers in Human Behavior 35 (2014) 565–577

Gladstein, D. L. (1984). Groups in context: A model of task group effectiveness.Administrative Science Quarterly, 29(4), 499–517.

Goh, S., & Wasko, M. (2012). The effects of leader–member exchange on memberperformance in virtual world teams. Journal of the Association for InformationSystems, 13(10), 861–885.

Green, S. G., & Taber, T. D. (1980). The effects of three social decision schemes ondecision group process. Organizational Behavior and Human Decision Processes,25, 97–106.

Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariatedata analysis (6th ed.). Upper Saddle River, NJ: Pearson Education Inc..

Handy, C. (1995). Trust and the virtual organization. Harvard Business Review, 73(3),40–50.

Hasty, B., Massey, A., & Brown, S. (2006). Role-based experiences, mediaperceptions, and knowledge transfer success in virtual dyads. Group Decision& Negotiation, 15(4), 367–387.

Hill, C. L. (1990). Cooperation, opportunism, and the invisible hand: Implications fortransaction cost theory. Academy of Management Review, 15(3), 500–514.

Hogg, M. A., & Terry, D. I. (2000). Social identity and self-categorizationprocesses in organizational contexts. Academy of Management Review, 25(1),121–140.

Hogg, M. A., Terry, D. J., & White, K. M. (1995). A tale of two theories: A criticalcomparison of identity theory with social identity theory. Social PsychologyQuarterly, 255–269.

Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structureanalysis: Conventional criteria versus new alternatives. Structural EquationModeling, 6(1), 1–55.

Hu, X., Lin, Z., Whinston, A. B., & Zhang, H. (2004). Hope or hype: On the viability ofescrow services as trusted third parties in online auction environments.Information Systems Research, 15(3), 236–249.

Hyatt, D. E., & Ruddy, T. M. (1997). An examination of the relationship betweenwork group characteristics and performance: Once more into the breech.Personnel Psychology, 50(3), 553–585.

Ilgen, D. (1999). Teams embedded in organizations: Some implications. AmericanPsychologist, 54(2), 129–139.

Ives, B., & Junglas, I. (2008). APC forum: Business implications of virtual worlds andserious gaming. MIS Quarterly Executive, 7(3), 151–156.

Jarvenpaa, S. L., Knoll, K., & Leidner, D. E. (1998). Is anybody out there? Antecedentsof trust in global virtual teams. Journal of Management Information Systems,14(4), 29–64.

Jarvenpaa, S. L., & Leidner, D. E. (1998). Communication and trust in global virtualteams. Journal of Computer Mediated Communication, 3(4).

Jarvenpaa, S. L., & Leidner, D. E. (1999). Communication and trust in global virtualteams. Organization Science, 10(6), 791–815.

Jarvenpaa, S. L., Shaw, T. R., & Staples, D. S. (2004). Toward contextualized theoriesof trust: The role of trust in global virtual teams. Information Systems Research,15(3), 250–267.

Jarzabkowski, P. (2004). Strategy as practice: Recursiveness, adaptation, andpractice-in-use. Organization Studies, 25(4), 529–560.

Kanawattanachai, P., & Yoo, Y. (2002). Dynamic nature of trust in virtual teams.Journal of Strategic Information Systems, 11(3–4), 187–213.

Kayworth, T., & Leidner, D. E. (2000). The global virtual manager: A prescription forsuccess. European Management Journal, 18(2), 183–194.

Kiffin-Petersen, S. (2004). Trust: A neglected variable in team effectivenessresearch. Journal of the Australian and New Zealand Academy of Management,10(1), 38–53.

Kim, C., Lee, S.-G., & Kang, M. (2012). I became an attractive person in the virtualworld: Users’ identification with virtual communities and avatars. Computers inHuman Behavior, 28(5), 1663–1669.

Kim, S. S., & Son, J. Y. (2009). Out of dedication or constraint? A dual model of post-adoption phenomena and its empirical test in the context of online services. MISQuarterly, 33(1), 49–70.

Kirkman, B. L., Jones, R. G., & Shapiro, D. L. (2000). Why do employees resist teams?Examining the ‘Resistance Barrier’ to weak team effectiveness. InternationalJournal of Conflict Management, 11, 74–92.

Kogut, B., & Zander, U. (1993). Knowledge of the firm and the evolutionary-theory ofthe multinational-corporation. Journal of International Business Studies, 24(4),625–645.

Komiak, S. Y. X., & Benbasat, I. (2006). The effects of personalization and familiarityon trust and adoption of recommendation agents. MIS Quarterly, 30(4),941–960.

Kramer, B. M. (1994). The sinister attribution error: Paranoid cognition andcollective distrust in organizations. Motivation and Emotion, 18, 199–230.

Krishna, S., Sahay, S., & Walsham, G. (2004). Cross-cultural issues in global softwareoutsourcing. Communications of the ACM, 47(4), 62–66.

LaFasto, F. M., & Larson, C. E. (2001). When teams work best. Newberry Park, CA:Sage.

Larson, C. E., & LaFasto, F. M. J. (1989). Teamwork: What must go right, what can gowrong. Newberry Park, CA: Sage.

Levina, N. (2005). Collaborating on multiparty information systems, developmentprojects: A collective reflection-in-action view. Information Systems Research,16(2), 109–130.

Levina, N., & Vaast, E. (2005). The emergence of boundary spanning competence inpractice: Implications for implementation and use of information systems. MISQuarterly, 29(2), 335–363.

Levina, N., & Vaast, E. (2008). Innovating or doing as told? Status differences andoverlapping boundaries in offshore collaboration. MIS Quarterly, 32(2), 307–332.

Lewis, J. D., & Weigert, A. (1985). Trust as a social reality. Social Forces, 63(4),967–985.

Lindell, M. K., & Whitney, D. J. (2001). Accounting for common method variancein cross-sectional research designs. Journal of Applied Psychology, 86(1),114–121.

Lindgren, R., Andersson, M., & Henfridsson, O. (2008). Multi-contextuality inboundary-spanning practices. Information Systems Journal, 18(6), 641–661.

Lipnack, J., & Stamps, J. (1997). Virtual teams: Reaching across space, time andorganizations with technology. New York: John Wiley & Sons.

Luhmann, N. (1979). Trust and power. Chichester, UK: Wiley.Luse, A., Mennecke, B. E., & Townsend, A. (2013, January 7–10). Experience richness:

Effects of training method on individual technology acceptance. Proceedings ofthe 46th Hawaii international conference on the system sciences (HICSS), (pp.853–862).

Luse,, A., Mennecke, B., & Triplett, J. (2013). The changing nature of user attitudestoward virtual world technology: A longitudinal study. Computers in HumanBehavior, 29(3), 1122–1132.

Lusher, D., Kremer, P., & Robins, G. (2014). Cooperative and competitive structuresof trust relations in teams. Small Group Research, 45(1), 3–36.

MacCallum, R. C., Browne, M. W., & Sugawara, H. M. (1996). Power analysis anddetermination of sample size for covariance structure modeling. PsychologicalMethods, 1, 130–149.

Majchrzak, A., Beath, C., Lim, R., & Chin, W. (2005). Managing client dialogues duringinformation systems design to facilitate client learning. MIS Quarterly, 29(4),653–672.

Majchrzak, A., Rice, E. R., Malhotra, A., & King, N. (2000). Technology adaptation: Thecase of a computer-supported inter-organizational virtual teams. MIS Quarterly,24(4), 569–599.

Malhotra, A., Majchrzak, A., Carman, R., & Lott, V. (2001). Radical innovationwithout collocation: A case study at Boeing-Rocketdyne. MIS Quarterly, 25(2),229–249.

Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995). An integrative model oforganizational trust. Academy of Management Review, 20(3), 709–734.

Maznevski, M. L., & Chudoba, K. M. (2000). Bridging space over time: Global virtualteam dynamics and effectiveness. Organization Science, 11(5), 473–492.

McKnight, D. H., Cummings, L. L., & Chervany, N. L. (1998). Initial trust formation innew organizational relationships. Academy of Management Review, 23(3),473–490.

Mennecke, B. E., Triplett, J. L., Hassall, L. M., Conde, Z. J., & Heer, R. (2011). Anexamination of a theory of embodied social presence in virtual worlds. DecisionSciences, 42(2), 413–450.

Mennecke, B. E., & Valacich, J. S. (1998). Information is what you make of it: Theinfluence of group history and computer support on information sharing,decision quality, and member perceptions. Journal of Management InformationSystems, 15(2), 173–197.

Meyerson, D., Weick, K. E., & Kramer, R. M. (1996). Swift trust and temporarygroups. In R. M. Kramer & T. R. Tyler (Eds.), Trust in organizations: Frontiers oftheory and research (pp. 166–195). Thousand Oaks, CA: Sage.

Nah, F. F. H., Eschenbrenner, B., & DeWester, D. (2011). Enhancing brand equitythrough flow and telepresence: A comparison of 2D and 3D virtual worlds. MISQuarterly, 35(3), 731–747.

Nah, F. F. H., Eschenbrenner, B., DeWester, D., & Park, S. (2010). Impact of flow andbrand equity in 3D virtual worlds. Journal of Database Management, 21(3),69–89.

Nunamaker, J. F., Briggs, R. O., Mittleman, D. D., Vogel, D. R., & Balthazard, P. A.(1996). Lessons from a dozen years of group support systems research: Adiscussion of lab and field findings. Journal of Management Information Systems,13(3), 163–207.

Nunamaker, J. F., Reinig, B. A., & Briggs, R. O. (2009). Principles for effective virtualteamwork. Communications of the ACM, 52(4), 113–117.

O’Leary, M., & Cummings, J. (2007). The spatial, temporal, and configurationalcharacteristics of geographic dispersion in teams. MIS Quarterly, 31(3), 433–452.

Olson, G. M., & Olson, J. S. (2007). Distance matters. Human–Computer Interaction,15(1), 139–179.

Panteli, N., & Davison, R. M. (2005). The role of subgroups in the communicationpatterns of global virtual teams. IEEE Transactions on ProfessionalCommunication, 48(2), 191–200.

Park, S., Nah, F. F. H., DeWester, D., Eschenbrenner, B., & Jeon, S. (2008). Virtualworld affordances: Enhancing brand value. Journal of Virtual Worlds Research,1(2), 1–18.

Pauleen, D. J., & Yoong, P. (2001). Relationship building and the use of ICT inboundary crossing virtual teams: A facilitator’s perspective. Journal ofInformation Technology, 16(4), 205–220.

Pavlou, P., & Gefen, D. (2005). Psychological contract violation in onlinemarketplaces: Antecedents, consequences, and moderating role. InformationSystems Research, 16(4), 372–399.

Pearce, J. L., Branyiczki, I., & Bakacsi, G. (1994). Person-based reward systems: Atheory of organizational reward practices in reform-communist organizations.Journal of Organizational Behavior, 15(3), 261–282.

Pearce, J. L., Sommer, S. M., Morris, A., & Frideger, M. (1992). A configurationalapproach to interpersonal relations: Profiles of workplace social relations and taskinterdependence. Irvine: University of California.

Ren, Y., Kiesler, S., & Fussell, S. R. (2008). Multiple group coordination in complexand dynamic task environments: Interruptions, coping mechanisms, andtechnology recommendations. Journal of Management Information Systems,25(1), 105–130.

S.Z. Schiller et al. / Computers in Human Behavior 35 (2014) 565–577 577

Romano, N. C., Jr., Pick, J. B., & Roztocki, N. (2010). A motivational model fortechnology-supported cross-organizational and cross-border collaboration.European Journal of Information Systems, 19(2), 117–133.

Rousseau, D. J., Sitkin, S. B., Burt, R. S., & Camerer, C. (1998). Not so different after all:A cross-discipline view of trust. Academy of Management Review, 23(3),393–404.

Sawyer, S. (2001). Effects of intra-group conflict on packaged software developmentteam performance. Information Systems Journal, 11(2), 155–178.

Schultze, U., & Orlikowski, W. J. (2010). Research commentary – Virtual worlds: Aperformative perspective on globally distributed, immersive work. InformationSystems Research, 21(4), 810–821.

Shin, Y. (2004). A person-environment fit model for virtual organizations. Journal ofManagement, 30(5), 725–743.

Srikanth, K. (2007). Coordination in the remote delivery of Services. In Paperpresented at the proceedings of the academy of management annual meeting, BestPapers, Philadelphia, PA.

Stewart, K., & Gosain, S. (2006). The impact of ideology on effectiveness in opensource software development teams. MIS Quarterly, 30(2), 291–314.

Tajfel, H. (1972). Social categorization (English translation of ‘‘La categorisationsociale’’). In S. Moscovici (Ed.). Introduction d la psychologie sociale (Vol. 1,pp. 272–302). Paris: Larousse.

Tajfel, H., Billing, M. G., Bundy, R. F., & Flament, C. (1971). Social categorization andintergroup behavior. European Journal of Social Psychology, 1, 149–177.

Tajfel, H., & Turner, J. (1979). An integrative theory of intergroup conflict. In W. G.Austin & S. Worchel (Eds.), The Social Psychology of Intergroup Relations(pp. 94–109). Monterey, CA: Brooks-Cole.

Turner, J. C., Hogg, M. A., Oakes, P. J., Reicher, S. D., & Wetherell, M. S. (1987).Rediscovering the social group: A self-categorization theory. Oxford: BasilBlackwell.

Van den Bulte, C., & Moenaert, R. (1998). The effects of R&D team co-location oncommunication patterns among R&D, marketing, and manufacturing.Management Science, 44(11), 1–18.

Van Der Land, S., Schouten, A. P., Feldberg, F., Van Den Hooff, B., & Huysman, M.(2013). Lost in space? Cognitive fit and cognitive load in 3D virtualenvironments. Computers in Human Behavior, 29(3), 1054–1064.

Voich, D. (1995). Comparative empirical analysis of cultural values and perceptions ofpolitical economy issues. Westport, CT: Praeger.

Wiesenfeld, B. M., Raghuram, S., & Garud, R. (1998). Communication patterns asdeterminants of organizational identification in a virtual organization. Journal ofComputer Mediated Communication, 3(4).

Yan, Y., Davison, R. M., & Mo, C. (2013). Employee creativity formation: Theroles of knowledge seeking, knowledge contributing and flow experience inWeb 2.0 virtual communities. Computers in Human Behavior, 29(5),1923–1932.

Zack, M. (1993). Interactivity and communication mode choice in ongoingmanagement groups. Information Systems Research, 4(3), 207–239.

Zaheer, A., McEvily, B., & Perrone, V. (1998). Does trust matter? Exploring the effectsof interorganizational and interpersonal trust on performance. OrganizationScience, 9(2), 141–159.

Zhou, L., & Zhang, D. (2006). A comparison of deception behavior in dyad and triadicgroup decision making in synchronous computer-mediated communication.Small Group Research, 37(2), 140–164.