the contingent effects of leadership on team collaboration in virtual teams

Computers in Human Behavior 26 (2010) 1098–1110

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Computers in Human Behavior

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The contingent effects of leadership on team collaboration in virtual teams

Rui Huang *, Surinder Kahai 1, Rebecca Jestice 2

State University of New York at Binghamton, USA

a r t i c l e i n f o

Article history:Available online 9 April 2010

Keywords:Leadership stylesMedia richnessVirtual teamsDecision-making

0747-5632/$ - see front matter � 2010 Elsevier Ltd. Adoi:10.1016/j.chb.2010.03.014

* Corresponding author at: State University of NeBox 6000, Binghamton, NY 13902, USA. Tel.: +1 6074422.

E-mail addresses: [email protected] (R. Hu(S. Kahai), [email protected] (R. Jestice).

1 Tel.: +1 607 777 2410; fax: +1 607 777 4422.2 Tel.: +1 607 777 6425; fax: +1 607 777 4422.

a b s t r a c t

Decision-making in virtual teams creates challenges for leaders to structure team processes and providetask support. To help advance our knowledge of leadership in virtual teams, we explore the interactioneffects between leadership styles and media richness on task cohesion and cooperative climate, whichin turn influence team performance in decision-making tasks. Results from a laboratory study suggestthat transactional leadership behaviors improve task cohesion of the team, whereas transformationalleadership behaviors improve cooperative climate within the team which, in turn, improves task cohe-sion. However, these effects of leadership depend on media richness. Specifically, they occur only whenmedia richness is low. Our results also suggest that task cohesion leads to group consensus and members’satisfaction with the discussion, whereas cooperative climate improves discussion satisfaction andreduces time spent on the task.

� 2010 Elsevier Ltd. All rights reserved.

1. Introduction

Today, the prevalence of technology enables work from anyplace at any time. To respond to increasing competition, organiza-tions are taking advantage of the flexibility of technology-enabledwork to create virtual teams and tap into globally dispersed,cross-functional expertise. Virtual teams are technology-enabledteams consisting of members who span different organizations,time zones, geographic locations, and cultures. They can improveproductivity by reducing operational costs and by employing themost appropriate human resources for a task (Townsend, DeMarie,& Hendrickson, 1998). However, productivity gains from virtualteams are not guaranteed because the lack of physical co-locationand the use of lean media create challenges for a virtual team tocoordinate its work, get and stay motivated, create commitment,and develop trusting relationships. Past research suggests thatthese challenges can be overcome with proper facilitation of avirtual team’s process by its leader (Avolio, Kahai, & Dodge, 2000;Joshi, Lazarova, & Liao, 2009; Kayworth & Leidner, 2000; Leenders,van Engelen, & Kratzer, 2003; Purvanova & Bono, 2009). However,despite the importance of virtual team leadership, its empiricalinvestigation remains an under explored research topic (Fjermestad

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w York at Binghamton, P.O.777 6863; fax: +1 607 777

ang), [email protected]

& Hiltz, 1998; Kahai, Fjermestad, Zhang, & Avolio, 2007; Powell,Piccoli, & Ives, 2004). Developing an understanding of how leader-ship behaviors affect virtual team interaction is clearly needed.

Situational perspectives of leadership (e.g., Howell, Dorfman, &Kerr, 1986; Podsakoff, MacKenzie, Ahearne, & Bommer, 1995) sug-gest that leadership effects are likely to vary across situations.Therefore, in order to develop a more accurate understanding ofthe effects of leadership in virtual teams, we also need to developan understanding of how the different situations in which virtualteams operate influence the effects of leadership behaviors. Suchknowledge can benefit virtual team leaders as they try todetermine what specific behaviors would be most beneficial forovercoming challenges and realizing the benefits of teamingvirtually. The situations in which virtual teams operate arise fromtheir characteristics, which include the level of technologicalsupport, physical distance of team members, and the amount oftime members spend apart while working on the team’s task(Griffith, Sawyer, & Neale, 2003). One way in which greaterreliance on technology, greater distance, and greater time spentapart give rise to different situations faced by virtual teams is byincreasing the level of mediation during communication amongteam members. Mediation during communication can be describedin terms of media richness or the ease with which team memberscan share their viewpoints and resolve their differences on theissues they face quickly (Daft & Lengel, 1986).

In light of the above arguments about the need to advance ourknowledge of the effects of virtual team leadership in differentsituations, we examine how transactional and transformationalleadership influence task cohesion and cooperative climate in avirtual team at different levels of media richness. Past research

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R. Huang et al. / Computers in Human Behavior 26 (2010) 1098–1110 1099

has identified transactional and transformational leadership as twoeffective leadership styles for facilitating a virtual team’s process(e.g., Avolio et al., 2000), where process facilitation is defined asproviding procedural and general support during a team’s process(Miranda & Bostrom, 1999). Transactional leaders facilitate ateam’s process by clarifying role and task expectations and rein-forcing them through contingent rewards, whereas transforma-tional leaders facilitate a team process by developing followersand inspiring them to rise above their self-interests and focus onhelping the group and its members (Bass, 1998).

Virtual team research based on situational perspectives of lead-ership suggests that facilitation enabled by transactional andtransformational leadership styles is likely to be more effective insituations in which a virtual team’s process faces more challenges(Avolio et al., 2000; Joshi et al., 2009; Purvanova & Bono, 2009). Forinstance, Joshi et al. (2009) found that inspirational leadership, acomponent of transformational leadership, had a more positiveeffect on trust among team members in teams that were moredispersed; more dispersed teams typically face a greater challengein the development of trust because forming interpersonal bonds ismore difficult. Like dispersion, the richness of the communicationmedium employed by a virtual team creates different situationsand influences the challenges faced during a virtual team’s process;the lower the richness, the more difficult it is for team members toquickly communicate, develop an understanding of each other’sviewpoints, and resolve differences (Daft & Lengel, 1986). Sincethese challenges can potentially influence the need for and, hence,the effectiveness of facilitation offered by a leader, we examinehow media richness moderates the effect of transactional andtransformational leadership on task cohesion and cooperative cli-mate in a virtual team.

In addition to the interaction effects between leadership behav-iors and media richness on task cohesion and cooperative climate,we also examine the effects of task cohesion and cooperativeclimate on team performance variables that include consensus,satisfaction with the discussion, and time spent on the task. Bystudying how transactional and transformational leadership affecttask cohesion and cooperative climate in different communicationsituations and how the latter two, in turn, influence consensus,discussion satisfaction, and task time, we are analyzing the medi-ating processes by which leadership affects team performance invarious virtual settings. Upon opening the ‘‘black box” of howleadership affects virtual team performance at varying degrees ofmedia richness, we will be in a position to (a) design appropriateprocess interventions to achieve certain outcomes when it is notpossible to alter team leadership and (b) build more advancedmodels of virtual team performance.

The rest of the paper is organized as follows. We first provide aconceptual background for the main variables in our study and dis-cuss possible theoretical relationships between them. We thenpresent the research method, followed by analyses and results.Finally, main findings, research implications, limitations and direc-tions for future research are discussed.

2. Conceptual framework and hypotheses

2.1. Virtual team leadership

The features of virtual teams tend to make leadership demandsof such teams different from the demands of traditional face-to-face teams. Virtual team collaboration requires facilitation(Susman & Majchrzak, 2003; Tullar & Kaiser, 2000), which can beenabled by effective leadership, as briefly described below.

In the absence of prior interaction among team members, theinitial level of cohesion and trust among members of a virtual team

is typically low (Jarvenpaa, Knoll, & Leidner, 1997). Additionally,because of different backgrounds, team members do not sharecommon norms and procedures for accomplishing work. Undersuch circumstances, leaders in virtual teams can facilitate learningabout different individuals and cultures represented in the teamand make deliberate efforts to build trust, cohesion, and a sharedunderstanding of norms and procedures. A virtual team leader,for instance, may create task deliverables that are due early in ateam’s life cycle and work with team members to ensure that theydeliver on time. This can help build knowledge among team mem-bers that others can be trusted to accomplish work that is assignedto them.

The dispersion of team members in a virtual team can make theteam and its tasks less salient to team members (Kayworth &Leidner, 2000). A virtual team’s leader can counter this by communi-cating frequently with team members and acting as the boundaryspanner among team members. The dispersion of team membersin a virtual team also prevents impromptu meetings between theleader and other members. The leader can overcome such chal-lenges by fleshing out and clarifying early on the details that aretypically covered on an ad hoc basis via impromptu meetings intraditional teams.

Communication constraints in a virtual team tend to create con-fusion about the team’s status at any point in time. To alleviate thisconfusion, the leader of a virtual team can periodically facilitateintra-team communication to create a consolidated picture of theteam’s status (Kayworth & Leidner, 2001). The lack of face-to-facecontact in virtual teams severely restricts a leader’s ability to moni-tor members’ performance, implement solutions to problems, andperform typical mentoring and developmental functions (Bell &Kozlowski, 2002). To overcome this challenge, a leader can createa structure that allows team members to regulate their own perfor-mance as a team. Finally, since a virtual team relies on technology tocommunicate and carry out much of its work, a leader needs to makesure that team members are trained to properly use the technology.

In summary, virtual team leaders are expected to play a morerobust ‘‘process facilitation” role than established practices forface-to-face teams. Unfortunately, virtual team leadership hasbeen virtually ignored, and more studies are needed to increaseour understanding on how to lead virtual teams effectively(Fjermestad & Hiltz, 1998; Kahai et al., 2007).

2.2. Leadership styles

It should be clear from the above discussion that virtual teamsrequire active rather than passive leadership. Transactional andtransformational leadership are two leadership styles that are con-sidered to be active (Avolio, 1999; Bass, 1998), and a combination oftransactional and transformational leadership styles is often con-sidered as critical to follower success (Bass, Avolio, Jung, & Berson,2003). Transactional leaders primarily influence followers byexchanging rewards for performance through two behaviors.Specifically, a transactional leader clarifies roles and task expecta-tions, including the material or psychological rewards that afollower will receive when task expectations are met. Atransactional leader also provides followers with material orpsychological rewards that depend on how well task expectationshave been achieved.

Transformational leaders primarily influence followers byinspiring them to rise above their immediate self-interests andfocus on helping the group and its members. Behavioral compo-nents of transformational leadership include intellectual stimula-tion, individualized consideration, inspirational motivation, andidealized influence. Intellectual stimulation behaviors involveencouraging followers to challenge assumptions, approach oldissues in new ways, take risks, and be innovative and creative.

1100 R. Huang et al. / Computers in Human Behavior 26 (2010) 1098–1110

Individualized consideration behaviors focus on coaching and men-toring, and paying attention to followers’ perspectives and theirneeds for achievement and growth. Inspirational motivation behav-iors focus on motivating followers by providing an appealing visionthat followers find as meaningful and, therefore, identify with.Inspirational motivation behaviors also include encouraging team-work, setting high expectations for the team, and expressing confi-dence in the team’s ability to achieve those expectations. Idealizedinfluence, or charismatic, behaviors include displaying admirablecapabilities and being a role model that followers respect and trust.

Through the above behaviors, transactional and transformationalleaders are likely to play a vital role in facilitating the processes of avirtual team by providing structures, motivating and engaging teammembers, and attending to socio-emotional aspects of the team.Through process facilitation, such leaders introduce changes infollowers’ behaviors and in the way they interact with one another,thereby changing the capabilities of a team. Past research seems tosupport this view. For instance, work by Sosik, Avolio, and Kahai(1998a) suggests that transactional and transformational leadershipin virtual teams can overcome process losses to improve teamcreativity and other group outcomes. To further our understandingof how transactional and transformational leadership facilitate avirtual team’s process or the way in which team members interactwith one another, we explore later the connection between leader-ship and two emergent states of a team, and the connection betweenthese two emergent states and team performance.

2.3. Media richness

The richness of a communication medium refers to the ability ofthe medium to enable the development of a shared understanding inambiguous situations within a given time period (Daft & Lengel,1986). While richer media enable shared understanding to be devel-oped quickly, leaner media take considerable time. A medium’s rich-ness arises from its capacity for immediate feedback, multiple cues,language variety, and personalization (Daft & Lengel, 1986).

Rapid feedback facilitates faster development of a sharedunderstanding by enabling timely correction and acknowledge-ment of messages. The communication of multiple cues facilitatesmore accurate understanding of others’ messages by making theirperspectives and expectations transparent. By highlighting individ-ual perspectives and expectations, multiplicity of cues promotesthe understanding that one is working with others, which in turncan give rise to social facilitation and social behavior (Dennis &Kinney, 1998; Kahai, Avolio, & Sosik, 1998; Kahai & Cooper,2003). The availability of cues also enables better assessment ofconfidence one should place in somebody’s inputs (Kahai & Cooper,2003). Additionally, communication takes less time when multiplecues are possible because more information can be packed into amessage. The ability to personalize one’s message and the flexibil-ity to choose one’s language allow a communicator to use informa-tion that is best suited for the situation at hand and which wouldallow differences in understanding to be bridged quickly.

Earlier conceptualizations of richness saw capacities for immedi-ate feedback, multiple cues, language variety, and personalization asfixed for any communication medium. The current understanding,however, depicts them as dependent on a variety of experiential fac-tors, such as experience with the medium, topic, communicationpartners, and the organizational context, and on social factors, suchas norms and relationships among team members (Carlson & Zmud,1999; Chidambaram, 1996; Yoo & Alavi, 2001).

2.4. Emergent states of teams

Teams provide an organized mechanism in which individualswith different expertise and skills work collectively on a task

(Adler, 1995). How team members interact with one anotherdetermines their performance as a group (Gladstein, 1984;Mathieu, Heffner, Goodwin, Salas, & Cannon-Bowers, 2000). Akey indicator of how team members interact with one another is‘‘emergent states”, which are defined as dynamic properties anddescribe cognitive, motivational, and affective states of a team(Marks, Mathieu, & Zaccaro, 2001). Emergent states influence theexecution of teamwork and alter subsequent emergent states,and may consequently serve as inputs for team outcomes. In thisstudy, we focus on two emergent states of the team: task cohesionand cooperative climate.

Task cohesion is one of two types of group cohesion. Groupcohesion is a dynamic characteristic of the team and it refers to‘‘the resultant forces which are acting on the members to stay ina group” (Festinger, 1950, p. 274). Members may stay in a groupdue to two dominant reasons: interpersonal attraction (i.e., socialcohesion) and/or shared commitment to the group task (i.e., taskcohesion) (Hackman, 1987). Prior studies have suggested that taskand social cohesion usually have different effects on group perfor-mance, in the sense that a task cohesive group exerts a high level ofeffort on the task, which improves group performance, whereas asocially cohesive group tends to have interactions that directmember attention away from the task, thereby leading to processloss (Zaccaro & Lowe, 2001).

We suggest that both transactional and transformational lead-ership may improve task cohesion, though via different paths.Transactional leadership can directly stimulate task cohesion. Byclarifying goals and reward contingencies, transactional leadershipreduces team members’ uncertainty about what is expected fromthem and builds their effort-accomplishment expectancy, i.e., theexpectancy that a certain type and level of effort by team memberswill help the team accomplish certain goals and receive appropri-ate rewards (Kahai, Sosik, & Avolio, 2003). This effort-accomplish-ment expectancy is further reinforced by contingent rewardingbehavior. When team members have clarity on the expected effortand how it would lead to rewards, then in order to receive therewards, they will be motivated to commit themselves to theteam’s task and help the team reach its goals.

Furthermore, we expect media richness to moderate therelationship between transactional leadership and task cohesion.In situations with richer media, the effect of transactional leader-ship on task cohesion will be weakened, because with richer mediait is easier for team members to communicate quickly, understandeach other, and, therefore, work together in a coordinated fashiontowards the team’s goal irrespective of the facilitation by transac-tional leadership (Dennis, 1996; Warkentin, Sayeed, & Hightower,1997). On the other hand, with leaner media, it is more difficultfor team members to coordinate their inputs and work togethertowards the team’s goal, thereby requiring a transactional leader’sfacilitation to build task cohesion. Therefore, we propose thefollowing:

H1: Media richness will moderate the relationship betweentransactional leadership and task cohesion such that transac-tional leadership will be positively associated with task cohe-sion when media richness is low and it will not be associatedwith task cohesion when media richness is high.

While transactional leadership influences task cohesion directlyby focusing on individual performance (e.g., by clarifying whateach individual has to do and what s/he will get upon successfulperformance), transformational leadership influences task cohe-sion differently. Unlike transactional leadership, transformationalleadership does not focus on individual performance. Instead,transformational leadership helps followers transcend their self-interests and be connected with the group’s mission and vision


by emphasizing the power and the identity of the collective andthe higher-order valence of the outcomes it seeks (Bass & Avolio,1994). Under such circumstances, followers are more inclined tocooperate with one another (Jung & Sosik, 2002), creating a coop-erative climate within the team.

Cooperative climate refers to shared perceptions of supportive-ness and participation safety within the team as it works to accom-plish its goals (Chen & Huang, 2007). Prior research suggests thatwhen team members share mutual goals for a group task, theyare more inclined to cooperate with one another (Cox, Lobel, &McLeod, 1991; Tjosvold & Tjosvold, 1995). Cooperative climatemay also be facilitated by structures or mechanisms that coordi-nate the activities of team members to achieve a desired goal.Transformational leadership is likely to facilitate a cooperativeclimate by promoting shared goals as well as structures that teammembers use to work with each other.

As part of inspirational motivation behaviors, a transforma-tional leader ties the team’s work to a compelling vision, whichmembers can relate to and find intrinsically meaningful. The leaderchampions teamwork and helps members identify with the teamand its vision. Consequently, team members go beyond personalgoals and find mutual interests in the group’s task which, in turn,inclines them to support each other (Cox et al., 1991; Tjosvold &Tjosvold, 1995). As part of idealized influence behaviors, a transfor-mational leader displays admirable capabilities and values as wellas a strong conviction in the team’s vision. As a result of thesebehaviors, members idealize the leader as their role model andfollow the leader’s vision and her/his message of teamwork evenmore strongly.

A transformational leader also facilitates cooperative climate bycreating an environment in which team members feel that it issafe to offer intellectual input with their unique perspectives.Specifically, as part of intellectual stimulation behaviors, a trans-formational leader encourages the challenging of assumptions,reframing of problems, and approaching familiar situations innew ways. Such behaviors stimulate team members to challengeeach other and offer fresh perspectives that help the team becomemore creative. Team members are further encouraged to displaysuch behaviors by the leader’s individualized consideration.Specifically, a transformational leader pays attention to individualmembers’ needs for achievement and growth and creates an envi-ronment in which team members feel that there is support fortheir unique perspectives, thereby encouraging them to share theirperspectives as new ways to look at the issue at hand.

We expect media richness to play a moderating role in therelationship between transformational leadership and cooperativeclimate. When group members experience richer media, the effectof transformational leadership on cooperative climate will beweakened, because with richer media, it is easier for team mem-bers to quickly exchange and gain appreciation of individual view-points, resolve their differences, be social, and, thus, promotefeelings of supportiveness and participation safety (Suh, 1999),irrespective of the facilitation by transformational leadership. Onthe other hand, with leaner media, it is more difficult for teammembers to exchange and appreciate individual viewpoints,resolve their differences, and create feelings of supportivenessand participation safety, thereby requiring a transformationalleader’s facilitation to build a cooperative climate. Therefore, wepropose the following:

H2: Media richness will moderate the relationship betweentransformational leadership and cooperative climate such thattransformational leadership will be positively associated withcooperative climate when media richness is low and it willnot be associated with cooperative climate when media rich-ness is high.

When team members share an atmosphere of supportivenessand participation safety, they are likely to have a positive attitudetowards the team because such an atmosphere provides a basis forsatisfying members’ higher-order needs of self-expression, respect,and independence (Kahai, Sosik, & Avolio, 1997). Such a statebuilds team members’ commitment to the team and they areencouraged to contribute to the team and help it succeed. In otherwords, cooperative climate enables the creation of a force thatbinds the members of a team together as they work to accomplishthe team’s task. Therefore, we hypothesize:

H3: Cooperative climate will have a positive influence on taskcohesion.

In the preceding paragraphs, we argued that transactionalleadership will influence task cohesion directly whereastransformational leadership will influence task cohesion via itseffect on cooperative climate. We do not expect transactionalleadership to influence task cohesion via cooperative climate be-cause transactional leadership does not focus on the meansthrough which team members should accomplish their goals.Therefore, while team members are likely to be motivated toput in the individual effort required by transactional leadership,they are not likely to focus on creating a cooperative climate.

2.5. Decision-making performance

We use three constructs to evaluate decision-making perfor-mance: satisfaction with the discussion (Driscoll, 1978), time spenton the task (Biros, George, & Zmud, 2002; Speier, Vessey, &Valacich, 2003), and post-meeting consensus (Amason, 1996;Sambamurthy & Chin, 1994). Discussion satisfaction is an affectivecomponent of team performance, and it reflects the extent towhich team members are happy with the communication activitiesthey engage in to perform their task (Keyton, 1991). It predictswhether or not team members are likely to repeat the use of acommunication medium and engage in future decision-makingtasks with their fellow group members (Kahai & Cooper, 1999).Task time is a quantitative aspect of performance that describesthe temporal length involved with the discussion before teammembers are able to apply closure to their task. Consensus refersto the extent to which team members’ decision preferences aresimilar, and it determines how well team members are likely towork with each other to implement the team’s decision (Kahai &Cooper, 1999).

In a task cohesive team, members are focused on their task andcontribute to the team to help it succeed in accomplishing itsobjectives. Consequently, group meeting quality will improve andinteraction activities are more likely to be satisfying to individuals.Furthermore, when group members share commitment to the taskat hand, they are likely to stay focused on the task and enable it tobe completed more quickly (Baron, 1986). Therefore, we hypothe-size that the following:

H4: Task cohesion will have a positive influence on discussionsatisfaction.H5: Task cohesion will enable quicker task completion.

Similarly, when the group atmosphere encourages the team towork together towards a joint objective, group members tend tofeel included in the discussion. Consequently, they are more likelyto have positive experience and feel satisfied with the group activ-ity. Cooperative climate also encourages team members to activelyinteract with each other. The collective engagement of team mem-bers in the task clarifies the planning and decision processes for


them, which too enables the task to be completed more quickly(Stewart & Barrick, 2000). Therefore, we suggest that:

H6: Cooperative climate will have a positive influence on dis-cussion satisfaction.H7: Cooperative climate will enable quicker task completion.

Lastly, reaching an agreement among team members requirestheir active involvement in the task. As the details of the decisionare discussed, issues can arise and only in the presence of a com-mitment to the task, will team members be able to effectivelyresolve these issues (Guth & MacMillan, 1986). Without the com-mitment to the group and the task, reaching a consensus is likelyto be challenging. Thus, we propose that:

H8: Task cohesion will have a positive influence on consensuses.

3. Research method

3.1. Participants

Four hundred and eighty five undergraduate students from anintroductory MIS course signed up to participate in the study.Preliminary survey information on demographics and the use ofcomputer-mediated communication media was collected beforethe experimental task. Effort was made to keep participants whoknew each other well out of the same groups. Groups were con-structed in this way to more closely mimic many real-world,short-term virtual teams in which members who may not knoweach other or have not previously worked with each other meetto complete a task, after which they are disbanded. Ninety-sevenfive-member groups were created. Attendance and schedulingissues gave rise to a final number of 96 groups consisting of 3–7members each (N = 446, average group size = 4.28, number ofmales = 249, average age = 20.56). While members became awareof the total size of their group during task execution, they werenot told who else was in their group. The participants were mainlyfrom U.S.A. (88.8%), South Korea (5.1%), and China (1.3%).

3.2. Experimental design

This study is part of a larger laboratory study conducted overtwo academic semesters. Laboratory experimentation was chosenbecause it enables effective manipulation of different leadershipstyles. A 2 (instant messaging/virtual world communication condi-tion) � 2 (transactional/transformational leadership) design wasused.

Popular instant messaging and virtual world services, AIM andSecond Life, were used. Both AIM and Second Life allow forsynchronous, text-based communication among users. Users typetheir messages and send them to others in the group, who receivethem almost instantaneously on their own computer screen. Ahistory of the conversation can be accessed and scrolled throughin both programs. In AIM, private chat rooms were created fordifferent groups. In Second Life, users are represented by avatars,or digital representations of themselves, and they interact in a3-D virtual space. Second Life users met at a designated meetingplace and communicated using text communication features thatare similar to AIM. The meeting place in Second Life was createdusing rented space within the virtual world. It consisted of a confer-ence room with a rectangular meeting table around which thegroup member and leader avatars sat to conduct their discussion.With the appropriate software downloaded to their computers,participants joined their group from any location. The use of instantmessaging and virtual world enabled variation in media richnessacross virtual teams. This variation was likely not only due to the

virtual world’s visual channel, which included avatars in a 3-D vir-tual space, but also due to differences in participants’ prior experi-ence with instant messaging and virtual worlds. When participantswere recruited for the study, they indicated that they had usedinstant messaging more than virtual worlds. According to channelexpansion theory (Carlson & Zmud, 1999), prior experience with acommunication medium can increase perceptions of its richness.

Groups were facilitated by several trained confederate leaders ineither instant messaging (IM) or Second Life (SL) to complete a deci-sion-making task. To the extent possible, different confederate lead-ers were balanced across experimental conditions. Group leadersinteracted with the groups using the same text-based communica-tion within the IM and SL conditions. Equal numbers of male and fe-male leader identities were created in SL and IM in order to limitpossible gender biases in perceptions of the leader. In IM, the leaderidentities were male and female user names, and in SL male andfemale leader avatars were created using the same names as in theIM condition. Group leaders exhibited either transactional or trans-formational leadership behaviors during the group discussion.Leaders exhibited these behaviors through comments at specificintervals during the task discussion. Leaders were trained in leader-ship theory and given semi-scripted comments to use at thesetime intervals during the group discussion. Comments were semi-scripted so that leaders had some freedom to use natural languagewith the groups. Our leadership manipulation follows past studiesof both virtual and non-virtual teams that employed confederatesover a short period of time (e.g., 20 min) to manipulate leadershipfocused on process facilitation (e.g., Dickson, Partridge, & Robinson,1993; Ho & Raman, 1991; Jung & Avolio, 2000; Kahai et al., 1997,2003; Sosik et al., 1998a; Sosik, Avolio, Kahai, & Jung, 1998b).

Transactional leaders focused on exhibiting contingent rewardclarification and contingent rewarding behaviors. Transformationalleader’s comments exhibited individualized consideration, intellec-tual stimulation, and inspirational motivation. For example, at thesixth minute interval in the group discussion, transformationalleaders made comments with the goal of encouraging the consider-ation of each member’s perspective within the group, which is inline with individualized consideration. At the sixth minute interval,transactional leaders made comments about the group’s perfor-mance and their ability to complete the task satisfactorily, or a con-tingent rewarding statement. Because idealized influence is anattribution made by followers towards the leader rather than aset of specific behaviors the leader exhibits (Avolio, 1999), leadercomments were not generated for idealized influence. Instead, itwas expected that the individualized consideration, intellectualstimulation, and inspirational motivation comments made byleaders would be perceived as positive behaviors that followerswould want to emulate. This was expected to lead to idealizedinfluence.

The experimental task was an open-ended decision-makingtask. The task required the group to act as a management commit-tee in a small company tasked with allocating available bonusmoney to eligible employees. Basic information on each employeeand the company was provided. Before the group discussion, groupmembers filled out an individual decision form online with theirinitial bonus allocations. Group members could not see the alloca-tions of other members. After the individual allocations weremade, the group discussion began. Leaders instructed the groupthat they had 30 min to reach a group decision based on consensus.Leaders did not impose the expectation of consensus nor did theydirect the content of the discussion. The task ended either whenthe group reached consensus, or at the end of the 30 min if the con-sensus was not reached. At this time, group members again filledout an individual allocation decision form online. They were toldthat their final decisions could match or be different from thegroup’s decision.


As indicated earlier, this study is part of a larger laboratorystudy conducted over two academic semesters. The difference be-tween the first and second semester of the study was that in thesecond semester, participants completed an ice-breaker exerciseprior to the group discussion. The exercise, facilitated by theleader, lasted approximately 10 min. The ice-breaker allowedparticipants to answer questions about their likes and dislikesand discuss them with others in their group. The leader askedparticipants to respond to questions such as ‘‘What is your favoriterestaurant?” and, ‘‘Which is better, living on or off campus?” Whenthe conversation lagged, the leader moved onto the next question.In the SL condition, the leader also asked participants to stand andgroup their avatars based on similar answers. We controlled for theeffect of differences across semesters with a dummy variable asdescribed below. The individual allocation was still done the sameway as in the first study – they were done without discussing theproblem with others in the team.

3.3. Variable operationalization

Perceptual measures of leadership, media richness, task cohe-sion, cooperative climate, and satisfaction were collected at theindividual level using post-test questionnaire items. Demographicdata about participants too were collected at the individual levelbut via a preliminary questionnaire administered before the exper-imental task. Given that our research hypotheses of interest dealtwith groups, we conducted data analysis at the group level. Grouplevel scores for items were obtained by averaging individual levelscores across group members. The aggregation to the group levelwas supported by rwg analysis; the mean rwg statistic computedfor perceptual variable as per James, Demaree, and Wolf (1993)equaled or exceeded the customary cutoff of .7 (LeBreton, Burgess,Kaiser, Atchley, & James, 2003).

3.3.1. Leadership measuresLeadership perceptions were gathered using questionnaire

items. All items, except for those related to idealized influence,were adapted from Kahai et al.’s (2003) study of the effects of lead-ership styles in computer-mediated discussions. Idealized influ-ence items were adapted from Bass and Avolio’s (2000)Multifactor Leadership Questionnaire (Form 5X). Group levelscores on relevant items were averaged to create two scales fortransactional leadership (clarification behaviors and contingentrewarding) and four scales for transformational leadership (inspi-rational motivation, idealized influence, intellectual stimulation,and individualized consideration).

To create the above scales, four items were employed forclarification behaviors (sample item: during the bonus allocationdiscussion, the leader clarified what we had to do to succeed inour task; mean rwg = 0.72; a = .82), two items for contingentrewarding behaviors (sample item: during the bonus allocationdiscussion, the leader indicated how well we were meeting her/his expectations; mean rwg = 0.73; a = .75), six items for inspira-tional motivation (sample item: during the bonus allocation dis-cussion, the leader encouraged cooperation within the group;mean rwg = 0.77; a = .82), three items for idealized influence (sam-ple item: the leader acted in a way that earned my respect; meanrwg = 0.71; a = 93), four items for intellectual stimulation (sampleitem: during the bonus allocation discussion, the leader encour-aged us to be innovative in our views about the decision situation;mean rwg = 0.70; a = .89), and six items for individualized consider-ation (sample item: during the bonus allocation discussion, theleader encouraged us to include the views of everyone in thegroup; mean rwg = 0.83; a = .93).

3.3.2. Media richnessWe used four items to measure media richness. These items were

taken from Dennis and Kinney (1998) and represent the criteria forhigh media richness. Sample items included ‘‘the communicationconditions helped us communicate easily” and ‘‘the communicationcondition under which we communicated helped us to betterunderstand each other” (mean rwg = 0.70; a = .74).

3.3.3. Task cohesionTask cohesion was assessed using five items adapted from past

studies (Zaccaro, Gualtieri, & Minionis, 1995; Zaccaro & Lowe,2001). These items represent the shared commitment and task fo-cus displayed by team members to achieve the team’s objectives.Sample items were ‘‘during the bonus allocation discussion, mem-bers of my group pulled together to get the job done” and ‘‘thegroup was willing to do whatever it took to succeed during itstask”. The mean rwg was 0.87 and a was .92.

3.3.4. Cooperative climateFollowing the conceptualization of cooperative climate as con-

sisting of feelings of supportiveness (i.e., feelings of support fromothers) and participation safety (i.e., feelings that it is safe toparticipate in the discussion) (Chen & Huang, 2007), we operation-alized cooperative climate as consisting of supportiveness andparticipation safety scales. Both of these scales consisted of twoitems each (sample item for supportiveness: the atmosphere ofthe group during discussion was very supportive; mean rwg forsupportiveness = .75 and a = .82; sample item for participationsafety: I felt at ease while expressing my ideas during the discus-sion; mean rwg for participation safety = .74 and a = .89).

3.3.5. Discussion satisfactionSatisfaction with the discussion was measured using a six-item

scale that focused on group members’ satisfaction with various as-pects of the discussion (i.e., communication within the team, thedecision made, and the contributions of team members) as wellas with the overall discussion. Sample items were ‘‘I am very satis-fied with our discussion” and ‘‘I am very satisfied with the work wedid on our task”. The mean rwg was 0.83 and a was .93.

3.3.6. Task timeTask time was measured using the duration of the task, i.e.,

from the time the group discussion of the task began until the timea mutual decision was reached within the group. For groups thatcould not reach consensus within the scheduled experimental ses-sion, their task time was 30 min.

3.3.7. ConsensusConsensus was measured using a fuzzy logic computation

employed by Watson, DeSanctis, and Poole (1988). This methodemploys the pre- and post-discussion allocations made by individ-ual members for each eligible employee to compute pre- andpost-discussion consensus using a fuzzy logic algorithm proposedby Spillman, Spillman, and Bezdek (1980). Consensus thus pro-duced ranges from 0 to 1, where 1 implies complete agreement.

3.3.8. CovariatesWe included several variables as covariates in our analysis.

These included group size, the semester that groups participatedin (dummy variable: first semester = 0 and second semester = 1),pre-discussion consensus (measured the same way as post-discus-sion consensus), leadership condition (dummy variable: transac-tional leadership = 0 and transformation leadership = 1), andcommunication condition (dummy variable: IM = 0 and SL = 1).The leadership condition and communication condition variableswere employed to control for any effects of leadership and


communication conditions not accounted for by hypothesizedpaths in our model.

4. Analyses and results

We used PLS to test our hypotheses. Smart PLS version 2.0 wasused to analyze the data. Bootstrapping resampling (number ofiterations: 500) was used to test the significance of the regressioncoefficients in path modeling. Following Chin, Marcolin, andNewsted (1996), we centered the indicators for leadership and med-ia richness. We then multiplied each indicator from the transactionalleadership construct with each indicator for the media richnessconstruct to generate indicators for the construct representing theinteraction of transactional leadership and media richness.Similarly, we multiplied each indicator from the transformationalleadership construct with each indicator for the media richnessconstruct to generate indicators for the construct representing theinteraction of transformational leadership and media richness.Fig. 1 depicts the salient constructs and paths in our model.

In addition to the hypothesized paths, the PLS model also con-sisted of paths from various covariates to the dependent variablesin the study. Paths from group size, communication condition,leadership condition, and semester to all dependent variables inFig. 1 and a path from pre-discussion consensus to post-discussionconsensus were included for manipulation check or control pur-poses. For PLS analysis that includes reflective measures only (asis the case in our analysis, as described below), Chin (1997) sug-gests a sample size that equals or exceeds 10 times the largestnumber of structural paths leading to a latent variable. In our mod-el, the greatest number of paths leading to a variable were 8 (fortask cohesion), thereby requiring a sample size of at least 80. Witha sample size of 96, our PLS analysis exceeds the sample size re-quired according to the rule of thumb.

In PLS, indicators may be modeled as reflecting or forming theconstruct they represent; reflective indicators are determined bythe construct whereas formative indicators determine theconstruct (Sosik, Kahai, & Piovoso, 2009). The issue of modelingindicators as reflective or formative is relevant only for multi-indicator constructs because PLS is insensitive to how the indicators

Fig. 1. Model and PLS results. Note. **p < .01, *p < .05; paths for manipulatiopaths, there are paths from communication condition, leadership condition, semester of srichness, task cohesion, cooperative climate, discussion satisfaction, task time and consepre-discussion consensus to post-discussion consensus.

of single-indicator constructs are modeled. We expected the percep-tions of transactional and transformational leadership and mediarichness, and the feelings of cooperative climate, task cohesion,and satisfaction to determine or give rise to the questionnaire itemresponses employed to operationalize these constructs. Hence, theirindicators, which were based on questionnaire item responses, weremodeled as reflective.

Table 1 presents the descriptive statistics for the constructs, thecomposite reliability (CR), the average variance extracted (AVE),and the correlation matrix for constructs in our PLS model. Table 2depicts the indicators of the constructs and their factor andcross-factor loadings. For multi-item constructs, the reliability ofitems was assessed by the factor loadings of items, the compositereliability, and the average variance extracted (AVE). All of theseassessments indicate satisfactory reliability of questionnaire items.First, the items generally loaded well on their respective constructs,with all but one loading exceeding .7 (the single item that did notmeet this cutoff had a loading of .65), demonstrating adequatereliability (Bagozzi & Yi, 1988). Second, the composite reliabilities,which are akin to Cronbach’s a but recommended for PLS analysis,were well above 0.70, the general acceptable level for adequatereliability (Fornell & Larcker, 1981). Third, the AVE, which is theaverage variance extracted by a construct from its indicators, ex-ceeded the recommended cutoff of 0.50 (Fornell & Larcker, 1981).

In PLS, in a manner similar to the multitrait-multimethodanalysis (Carmines & Zeller, 1979), convergent and discriminatevalidity of items is assessed by comparing the loadings of indicatorson their respective constructs with cross-loadings, i.e., loadings ofindicators on other constructs. As shown in Table 2, the cross-load-ings in all cases were lower than the loadings of indicators on theirrespective constructs. Also, Table 1 shows that the square root ofaverage variance extracted by each construct from its indicatorswas greater than the magnitude of its correlation with otherconstructs, suggesting sufficient validity of our constructs.

4.1. Manipulation check

To check whether our leadership manipulation was successful,we followed the method used by Kahai et al. (1997, 2004) and

n checks; paths for hypothesis testing. In addition to the hypothesizedtudy, and group size to transactional leadership, transformational leadership, mediansus for manipulation check or control purposes. There is also a control path from

Table 1Descriptive statistics, reliabilities, and correlation matrix.

M SD CR AVE 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

1. Communication condition 0.52 0.50 1.00 1.00(1.00)

1.00

2. Leadership condition 0.49 0.50 1.00 1.00(1.00)

0.02 1.00

3. Media richness (MR) 4.72 0.63 0.86 .61(.78)

�0.17 �0.06 1.00

4. Semester of study 0.36 0.48 1.00 1.00(1.00)

0.12 �0.05 0.15 1.00

5. Pre-discussion consensus 0.51 0.12 1.00 1.00(1.00)

�0.01 0.05 0.12 �0.22 1.00

6. Transactional leadership (TRL) 5.60 0.61 0.83 0.71(0.84)

�0.04 �0.39 0.43 0.16 0.08 1.00

7. Transformational leadership (TFL) 5.34 0.66 0.96 0.86(0.93)

�0.02 0.40 0.44 0.10 0.18 0.48 1.00

8. Task cohesion 5.81 0.63 0.92 0.70(0.84)

�0.06 �0.03 0.59 0.01 0.12 0.52 0.49 1.00

9. Cooperative climate 5.87 0.53 0.86 0.76(0.87)

0.00 �0.06 0.59 0.15 0.05 0.35 0.40 0.64 1.00

10. Discussion satisfaction 5.70 0.62 0.95 0.75(0.87)

0.00 �0.12 0.71 0.18 0.07 0.59 0.51 0.80 0.80 1.00

11. Task time 24.70 6.09 1.00 1.00(1.00)

�0.24 0.45 �0.14 �0.22 0.01 �0.11 0.19 �0.03 �0.22 �0.14 1.00

12. Consensus 0.88 0.16 1.00 1.00(1.00)

0.08 �0.22 0.31 0.22 0.18 0.34 0.24 0.35 0.27 0.43 �0.19 1.00

13. Group size 4.65 0.68 1.00 1.00(1.00)

�0.13 0.05 �0.01 0.08 �0.03 �0.10 �0.07 �0.13 �0.28 �0.19 �0.10 0.03 1.00

14. MR and TRL interaction – – – – 0.16 0.02 �0.29 �0.07 �0.09 �0.23 �0.27 �0.43 �0.27 �0.37 0.08 �0.25 �0.15 1.0015. MR and TFL interaction – – – – �0.02 �0.12 �0.33 0.02 �0.16 �0.17 �0.42 �0.35 �0.33 �0.34 �0.09 0.20 �0.15 0.67 1.00

Note. Italicized correlations are significant at p < .05. The average variance extracted (AVE) by a construct from its indicators is shown with the square roots of AVE shown in parentheses. For adequate convergent and discriminantvalidity, the square root of AVE by a construct should be greater than the magnitude of its correlations with other constructs (represented as off-diagonal elements). M, mean; SD, standard deviation; CR, composite reliability. Themeans and standard deviations of multi-indicator constructs are for scales that were computed by averaging the indicator values. For the interaction variables (rows 14 and 15), CR and AVE are not relevant since these areconstructed from media richness, transactional leadership, and transformational leadership, whose reliabilities and validities are assessed separately.

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Table 2Factor loadings and cross-loadings.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

1. Communication condition 1.00 0.02 �0.17 0.12 �0.01 �0.04 �0.02 �0.06 �0.00 �0.00 �0.24 0.08 �0.13 0.16 0.21

2. Leadership condition 0.02 1.00 �0.06 �0.05 0.05 �0.38 0.41 �0.03 �0.06 �0.12 0.45 �0.22 0.05 0.02 �0.12

3. Media richnessIndicator 1 �0.24 �0.16 0.83 0.26 0.09 0.39 0.27 0.50 0.47 0.55 �0.18 0.29 0.08 �0.32 �0.28Indicator 2 �0.13 �0.05 0.83 0.07 0.05 0.42 0.43 0.58 0.53 0.65 �0.05 0.34 �0.07 �0.43 �0.40Indicator 3 0.03 0.05 0.75 0.06 0.14 0.30 0.46 0.40 0.34 0.55 �0.00 0.17 �0.00 �0.16 �0.22Indicator 4 �0.14 0.01 0.70 0.06 0.13 0.19 0.22 0.29 0.48 0.43 �0.20 0.09 �0.04 0.10 �0.05

4. Semester of study 0.12 �0.05 0.15 1.00 �0.22 0.16 0.10 0.01 0.15 0.18 �0.22 0.22 0.08 �0.07 0.02

5. Pre-discussion consensus �0.01 0.05 0.12 �0.22 1.00 0.08 0.18 0.12 0.05 0.07 0.01 0.18 �0.03 �0.09 �0.16

6. Transactional leadershipIndicator 1 (clarifying rewardsscale)

0.10 �0.53 0.38 0.17 0.09 0.92 0.31 0.43 0.23 0.50 �0.24 0.33 �0.12 �0.18 �0.06

Indicator 2 (contingentrewarding scale)

�0.25 �0.01 0.35 0.09 0.03 0.76 0.59 0.48 0.41 0.51 0.16 0.22 �0.02 �0.24 0.59

7. Transformational leadershipIndicator 1 (idealized influencescale)

�0.04 0.20 0.42 0.10 0.14 0.50 0.86 0.50 0.48 0.55 0.05 0.29 �0.07 �0.33 �0.41

Indicator 2 (individualizedconsideration scale)

0.05 0.48 0.38 0.14 0.21 0.37 0.95 0.39 0.32 0.42 0.18 0.19 �0.06 �0.16 �0.38

Indicator 3 (inspirationalmotivation scale)

�0.08 0.36 0.44 0.05 0.13 0.54 0.96 0.54 0.40 0.55 0.21 0.24 �0.08 �0.27 �0.42

Indicator 4 (intellectualstimulation scale)

�0.01 0.45 0.40 0.07 0.17 0.40 0.95 0.39 0.31 0.41 0.24 0.17 �0.05 �0.24 �0.36

8. Task cohesionIndicator 1 �0.16 �0.05 0.52 0.01 0.10 0.47 0.38 0.88 0.56 0.69 �0.03 0.29 �0.15 �0.40 �0.25Indicator 2 �0.02 0.13 0.36 0.01 �0.07 0.23 0.39 0.65 0.44 0.51 0.17 0.09 �0.07 0.08 �0.07Indicator 3 �0.06 �0.04 0.52 0.08 0.14 0.53 0.50 0.91 0.57 0.73 �0.05 0.36 �0.06 �0.59 �0.46Indicator 4 �0.04 �0.12 0.50 0.03 0.07 0.50 0.39 0.90 0.46 0.70 �0.08 0.37 0.00 �0.44 �0.28Indicator 5 0.04 0.00 0.52 �0.10 0.18 0.37 0.39 0.79 0.62 0.67 �0.06 0.30 �0.27 �0.27 �0.29

�0.16 �0.05 0.52 0.01 0.10 0.47 0.38 0.88 0.56 0.69 �0.03 0.29 �0.15 �0.40 �0.25

9. Cooperative climateIndicator 1 (participation safetyscale)

0.00 �0.06 0.35 0.11 �0.10 0.11 0.11 0.37 0.79 0.47 �0.13 0.08 �0.28 0.07 �0.05

Indicator 2 (supportivenessscale)

�0.01 �0.04 0.62 0.14 0.12 0.41 0.49 0.67 0.95 0.84 �0.23 0.32 �0.24 �0.40 �0.41

10. Discussion satisfactionIndicator 1 �0.08 �0.06 0.70 0.10 0.18 0.50 0.51 0.68 0.68 0.85 �0.05 0.38 �0.16 �0.42 �0.37Indicator 2 0.01 �0.01 0.66 0.12 0.14 0.37 0.44 0.65 0.67 0.78 0.00 0.35 �0.17 �0.15 �0.17Indicator 3 �0.02 �0.14 0.67 0.13 0.04 0.51 0.40 0.75 0.76 0.92 �0.18 0.35 �0.13 �0.36 �0.33Indicator 4 �0.02 �0.09 0.59 0.10 �0.02 0.61 0.47 0.74 0.70 0.91 �0.10 0.33 �0.20 �0.31 �0.29Indicator 5 0.05 �0.23 0.55 0.25 0.03 0.55 0.38 0.67 0.63 0.86 �0.30 0.47 �0.13 �0.42 �0.29Indicator 6 0.05 �0.07 0.53 0.25 �0.02 0.53 0.48 0.67 0.73 0.88 �0.11 0.36 �0.23 �0.28 �0.31

11. Task time �0.24 0.45 �0.14 �0.22 0.01 �0.11 0.19 �0.03 �0.22 �0.14 1.00 �0.19 �0.10 0.08 �0.09

12. Consensus 0.08 �0.22 0.31 0.22 0.18 0.34 0.24 0.35 0.27 0.43 �0.19 1.00 0.03 �0.25 �0.19

13. Group size �0.13 0.05 �0.01 0.08 �0.03 �0.10 �0.07 �0.13 �0.28 �0.19 �0.10 0.03 1.00 �0.15 �0.15

Note. The construction of scales that were used as indicators of transactional leadership, transformation leadership, and cooperative climate is described in Section 3.3. Theindicators for media richness, task cohesion, and discussion satisfaction are questionnaire items that were aggregated to the group level. Communication condition,leadership condition, semester of study, task time, consensus, and group size employed single indicators described in Section 3.3. Columns 14 and 15 represent the interactionvariables between transactional leadership and media richness and transformational leadership and media richness, respectively. Due to the large number of items for thesevariables, they are not listed individually. Loadings and cross-loadings for the individual items can be obtained from the authors.


examined the paths from leadership condition to the perceptions ofleadership behaviors depicted in Fig. 1. Leadership condition (0 fortransactional condition and 1 for transformational condition) waspositively associated with the perceptions of transformationalbehaviors (b = 0.44; t = 4.45; p < .01). This indicates that theperceptions of transformational leadership behaviors were higherin the transformational versus the transactional condition. Leader-ship condition was negatively associated with the perceptions oftransactional leadership behaviors (b = �0.37; t = 3.52; p < .01),indicating that the perceptions of transactional leadership behaviorswere lower in the transformational groups versus the transactionalgroups. These results demonstrate that transformational andtransactional leadership behaviors were manipulated successfully.

4.2. Tests of hypotheses

Path coefficients and significance levels corresponding toresearch hypotheses are presented in Fig. 1. Hypothesis 1 proposeda moderated relationship in that transactional leadership wasexpected to be more positively related to task cohesion whenmedia richness is low as opposed to when media richness is high.Based on two-tailed t-statistics, results reveal that there was asignificant negative interaction effect between transactional lead-ership and media richness on task cohesion (b = �0.22; t = 2.12;p < .05). To further explore this interaction effect, we conductedsub-sample analysis using two samples created by median splitof data on the basis of media richness scale. We found that when


media richness was low, transactional leadership had a positiveand significant effect on task cohesion (b = 0.34; t = 2.14; p < .05),whereas when media richness was high, the effect of transactionalleadership on task cohesion became lower in magnitude and insig-nificant (b = 0.22; t = 1.39; n.s.). These findings provided support toHypothesis 1.

Similarly, Hypothesis 2 proposed a moderated relationship inthat transformational leadership was expected to be more posi-tively related to cooperative climate when media richness is lowas opposed to when media richness is high. Results show that therewas a significant negative interaction effect between transforma-tional leadership and media richness on cooperative climate(b = �0.19; t = 2.03; p < .05). Through the sub-sample analysis, itwas found that when media richness was low, transformationalleadership had a positive and significant effect on cooperativeclimate (b = 0.37; t = 2.41; p < .05), whereas this effect disappearedwhen media richness was high (b = �0.01; t = 0.06; n.s.). Therefore,Hypothesis 2 also received support.

Furthermore, cooperative climate was positively related to taskcohesion (b = 0.37; t = 3.67; p < .01), yielding support for Hypothe-sis 3. Both task cohesion (b = 0.50; t = 7.97; p < .01) and cooperativeclimate (b = 0.47; t = 11.50; p < .01) were positively related todiscussion satisfaction, supporting Hypotheses 4 and 6. Coopera-tive climate was also negatively related to task time (b = �0.34;t = 2.97; p < .01), thereby supporting Hypothesis 7. Nevertheless,task cohesion was not related to task time (b = 0.16; t = 1.78;n.s.). Therefore, Hypothesis 5 was not supported.

Lastly, task cohesion was positively related to post-discussionconsensus (b = 0.34; t = 2.91; p < .01), providing support toHypothesis 8.

4.3. Additional tests and results

In addition to testing hypotheses, we conducted additionalanalysis by adding a direct path from transformational leadershipto task cohesion, and a path from transactional leadership to coop-erative climate as a way to confirm that transactional leadershipand transformational leadership have different effects on taskcohesion and cooperative climate. Results indicated that neitherthe path from transactional leadership to cooperative climate(b = 0.00; t = 0.00; n.s.) nor the path from transformational leader-ship to task cohesion (b = 0.02; t = 0.12; n.s.) was significant,providing support for our argument that transactional leadershipdirectly impacts task cohesion, whereas the effect of transforma-tional leadership on task cohesion is fully mediated throughcooperative climate.

An idea that is implicit in the development of moderatingeffects of media richness (Hypotheses 1 and 2) is that when mediarichness goes up, both task cohesion and cooperative climatewould go up as well. Examination of the correlations matrix inTable 1 indicates that both task cohesion and cooperative climateare positively and significantly correlated with media richness(r = .59, p = .00 for both relationships), thereby suggesting thatboth task cohesion and cooperative climate increase when mediarichness increases.

4.4. Addressing common method bias

Except for consensus and task time, all other criteria and predic-tor variables were measured using group members’ perceptions,resulting in a concern about common method variance (Podsakoff,MacKenzie, Lee, & Podsakoff, 2003). Common method bias can beaddressed by a number of approaches, including Harman’s singlefactor test and the incorporation of a latent method factor in thehypothesized model. Harman’s test checks for the possibility ofcommon method variance whereas the latter approach partials

out the potential common method variance. We used bothapproaches to address the common method bias issue. First, asper Harman’s (1967) one factor test, we performed a factor analysisto see if a single factor or one general factor accounts for the major-ity of covariance among the measures. The presence of such afactor would suggest a high potential for a common method bias.Our factor analysis did not detect such a single factor.

Second, we also re-tested our research model after including aconstruct representing common method variance as describedbelow. This construct was created by using questionnaire itemsthat were unrelated to the constructs in the model. Sample itemsincluded in this latent variable are ‘‘I felt I was visiting anotherplace” and ‘‘During the bonus allocation discussion I was able toidentify who else was in my group”. These items were not expectedto share any meaningful variance with questionnaire-based indica-tors employed in the model beyond common method variance. Theidea of constructing a common method construct in this way is toget distant measures that are less likely to share variance withvariables represented in relationships that could be influenced bycommon method bias in our study. By using these measures to rep-resent a common method latent variable, we are ensuring that thevariance due to the common method is extracted while that due totheoretical relationships is not. After including the common meth-od factor, there is no change in support for our hypotheses. Further-more, the common method variable only accounted for smallincreases in the variances explained for most perceptual variables.More specifically, the common method variable accounted for anextra 8.2% of variance in transactional leadership, 8.2% of variancein transformational leadership, 1.3% of variance in task cohesion,0.8% of variance in cooperative climate, 6.5% of variance in discus-sion satisfaction, and 31.5% of variance in media richness.These common method variances are mostly lower than the levels(16–42%) observed by Williams, Cote, and Buckley (1989).

5. Discussion and conclusions

In order to increase our understanding of the facilitation effectsof leadership behaviors on team interaction, and subsequently ondecision-making performance in virtual teams, we conducted astudy to examine (a) how transactional and transformational lead-ership behaviors influence task cohesion and cooperative climatein situations with varying degrees of media richness and (b) howtask cohesion and cooperative climate, in turn, influence discussionsatisfaction, decision consensus, and task time.

Our results indicate that media richness moderates the effectsof leadership such that when media richness is low, transactionalleadership behaviors improve task cohesion and transformationalleadership behaviors improve cooperative climate. However, whenmedia richness is high, these effects dissipate. These results extendthe situational perspective of leadership by indicating that differ-ent levels of media richness create different situations and thatleadership effects depend on the level of media richness thatdescribes a situation. Past research on leadership in virtual teamshas suggested that transformational leadership is more helpful insituations that are more challenging for coordinating work andbuilding relationships (e.g., Joshi et al., 2009; Purvanova & Bono,2009). A key idea behind this suggestion is that there is more roomfor a transformational leader to have an impact when the situationfaced by a virtual team is more challenging. Our study both rein-forces and extends this idea. It reinforces this idea by indicatingthat the lack of media richness creates a challenge for a virtualteam; when media richness is lacking, the facilitation offered bya transformational leader is helpful for building a cooperativeclimate whereas when media richness is high, cooperative climatemay result without facilitation due to which transformational


leadership is less relevant. Furthermore, our study shows that inaddition to transformational leadership, transactional leadershipis also more helpful when the situation is more challenging forbuilding task cohesion. Specifically, when media richness is lack-ing, transactional leadership promotes task cohesion whereaswhen media richness is high, task cohesion may result withoutfacilitation due to which the relevance of transactional leadershipdecreases. To our knowledge, past research on virtual team leader-ship has not examined whether the facilitation by a transactionalleader is more or less relevant in a virtual team depending onthe level of challenge it faces.

Our results also suggest that task cohesion and cooperative cli-mate are two key factors in decision-making performance. Both ofthese are important for improving members’ satisfaction with thediscussion. In addition, task cohesion helps group members reachagreement, whereas cooperative climate within the team improvesgroup productivity by speeding up task execution. Cooperativeclimate also promotes task cohesion. Nevertheless, we did not findany association between task cohesion and task time, indicatingthat a task cohesive group does not simply translate to a group thatcan finish the task quickly.

Findings of our study support the suggestion made by leadershipresearchers that a combination of transformational and transac-tional leadership styles is vital for team performance (e.g., Basset al., 2003). While both transactional and transformationalleadership influence task cohesion (directly or indirectly), transfor-mational leadership provides an additional dimension to teaminteractions as compared to transactional leadership. Specifically,through an immediate and direct effect on task cohesion, transac-tional leadership draws individual attention to the decision-makingtask at hand, which promotes consensus and satisfactions with thediscussion. Nevertheless, it is insufficient for team members tosimply focus on the task as individuals, because team decision-making performance also depends on cooperation among teammembers. In this regard, transformational leadership highlightsthe team as a whole and stimulates a cooperative climate, whichconsequently leads members to focus on the task as a group ratherthan as individuals. Through cooperative climate, transformationalleadership also helps accomplish the task quickly, thus improvingteam productivity. Therefore, transformational leadership supple-ments transactional leadership by making the team and teamworksalient for team members. This is very vital for virtual teamsbecause the dispersion of team members tends to make the teamand the need for teamwork less salient to them.

Our study also contributes to the literature on virtual teamleadership. Specifically, we add to Sosik et al.’s (1998a) work onthe effects of components of leadership on creativity incomputer-mediated teams by examining how transactional andtransformational leadership influence the emergent states of teaminteraction, such as cohesion and cooperative climate, and howthese effects interact with communication characteristics, such asmedia richness. We also add to past studies that focused on therole of transformational leaders alone. For instance, Purvanovaand Bono (2009) suggested that transformational leadershipbehaviors were particularly relevant to project satisfaction invirtual teams. Our study complements theirs by showing that inlean media used by virtual teams, this effect may be occurringvia transformational leadership’s positive influence on cooperativeclimate and task cohesion, both of which, in turn, have a positiveeffect on satisfaction. In addition, Jung and Sosik (2002) suggestedthat transformational leaders highlighted the importance ofcooperation and created an environment of empowerment forperforming collective tasks. Similarly, Joshi et al. (2009) also indi-cated that transformational leaders developed socialized relation-ships with team members and fostered attitudes that werecritical for team effectiveness in virtual settings. Our paper adds

to these studies by including objective team performancemeasures (e.g., task time and consensus) and examining the effectsof both transactional and transformational leadership on teaminteractions under various communication conditions.

An obvious managerial implication that follows from our studyis that virtual team leaders should pay attention to the level ofmedia richness in their team and display greater levels of transac-tional and transformational behaviors to the extent that mediarichness is lacking. However, leaders should note that media rich-ness is not simply a property of the technology that the team maybe using to communicate. A variety of factors such as experiencewith the technology, the presence of a shared understandingrelated to the topic being discussed, the experience that teammembers have of working with each other, and the extent to whichteam members work in the same organizational context mayincrease media richness independent of technology (Carlson &Zmud, 1999; Chidambaram, 1996; Yoo & Alavi, 2001).

On another note, because all variables were measured at thesame time, it is reasonable to question whether teams that hadcooperative climate rated their leader as higher in transforma-tional behaviors and teams that had task cohesion rated their lea-der as higher in transactional behaviors. Since the leadershipbehaviors were manipulated prior to their measurement and theirmanipulation was successful, as indicated by the manipulationchecks, we can be confident that the perceptions of leadershipwere caused by our manipulations rather than by the levels ofcooperative climate and cohesion perceived by team members.

In summary, our findings help open the black box of leadership invirtual teams and shed light on how leadership behaviors can affectteam outcomes by facilitating the cooperative climate and taskcohesion within groups and how these effects can change acrossteams that vary in terms of media richness. This study also contrib-utes to the decision-making literature by suggesting that effectiveleadership behaviors can help establish structures within the teamespecially when teams communicate via lean media and providesupport for a decision-making task. It also adds to the decision-making literature by highlighting the importance of task cohesionand cooperative climate for decision-making performance.

5.1. Limitations

The findings of this study, however, should be tempered with anunderstanding of the conditions in which the research was con-ducted. Laboratory experiments enable control over key variables,thereby increasing our confidence in the validity of our results. Theuse of laboratory experiments, however, limits the generalizabilityof our results to zero-history, purely virtual teams consisting ofmembers who are not well acquainted with one another, have noopportunities for face-to-face contact, and are made to interactsynchronously over electronic media for a short-term decision-making task. Generalizability is also limited by the use of studentswho lacked an organizational context defined by workplace poli-tics, culture, and pressures. Such studies conducted in the labmay not adequately represent organizational virtual teams, whichmay be characterized by different degrees of virtuality and work ontasks of longer durations. Future research conducted using actualorganizational data and contexts will enhance our understanding.

Furthermore, our study did not take full advantage of the avail-able features in SL. Instead, we had participants communicateusing only the text features and not the voice capabilities for groupcommunication. Though the use of text in SL and IM programs isvery common, it is also possible to communicate via voice in SLand several IM programs. Future research should examine theeffects of use of the full range of technological features and theirimpacts on team outcomes. Other potential features include the


ability to share work documents and the use of mixed realityenvironments.

This study also used a one-time decision-making task, whichmay constrain the processes of leadership and communication.As suggested by a number of theories – e.g., Carlson and Zmud’s(1999) channel expansion theory and McGrath’s (1991) time-inter-action-and-performance theory – time plays an important role ingroup collaboration. Also, team interactions, to a great extent, de-pend on the fit between the technology and the task (Zigurs &Buckland, 1998). As a result, longitudinal studies with differenttypes of tasks should be conducted as future research to accountfor the effects of time and the interactions between the task natureand technological features in virtual world studies.

Lastly, we used trained confederates to act as leaders with alimited scope of leadership behaviors and messages. Specifically,the confederates displayed behaviors that would place themclearly on either end of the transactional/transformational contin-uum. A combination of these styles is often appropriate and opti-mal for performance (Bass & Avolio, 1994). Future research canlook at combinations of these leadership styles. Leadership’s rolein team collaboration in virtual words could further be extendedby not using confederates, but training team members in effectiveleadership and allowing leadership to emerge in virtual teams.

Despite these limitations, we believe that this research eluci-dates how leadership styles influence group interaction in variouscomputer-mediated contexts for decision-making tasks. Due to theincreasing interest in leadership and the use of electronic commu-nication media in organizations around the world, continued re-search in this area seems timely and warranted.

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