Organizational Research Assignment
Small Group Research 42(5) 536 –561
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Creativity in Virtual Work: Effects of Demographic Differences
Luis L. Martins1 and Christina E. Shalley2
Abstract
Organizations are increasingly using virtual teams, in which individuals work with their teammates across distance and differences, using a variety of information and communication technologies. In this study, the authors examined how demographic differences (i.e., differences in race, sex, age, and nationality) between individuals working virtually affected their collective creativity. Specifically, the authors examined how demographic differences interacted with the nature of interaction processes (establishment of rapport, participation equality, and process conflict) and difference in technical experience, to affect creativity in short-term virtual work interactions. Differences in age interacted with the processes and with differences in technical experience to affect creativity. Differences in nationality had a strong negative direct effect and interacted with differences in technical experience to affect creativity. Differences in sex and race did not significantly affect creativity. Implications of findings for managing virtual teams are discussed.
Keywords
creativity, demographic differences, virtual teams
1The University of Texas at Austin, USA 2Georgia Institute of Technology, USA
Corresponding Author: Luis L. Martins, University of Texas at Austin, McCombs School of Business, 1 University Station, B6300, Austin, TX 78712-0210 Email: luis.martins@mccombs.utexas.edu
Martins and Shalley 537
With an increase in global competition, companies have been looking to cre- ativity and innovation to give them a competitive edge (Amabile, 1988; Magadley & Birdi, 2009). Thus, effectively utilizing knowledge resources wherever they may reside in the organization has become an important stra- tegic priority for organizations (e.g., Dew & Hearn, 2009). Until recently, much of organizational knowledge was locked within individuals and units separated by various boundaries. However, with advances in information and communication technologies, organizations are increasingly using virtual teams to break down boundaries and connect employees regardless of their geographic location and subunit affiliation, so that they can combine their knowledge and perspectives to produce creative solutions to various business problems (e.g., McDonough, Kahn, & Barczak, 2001; Townsend, DeMarie, & Hendrickson, 1998). Indeed, increased creativity and innovation have been touted as among the primary benefits of using virtual teams (e.g., Zakaria, Amelinckx, & Wilemon, 2004). Thus, for example, consulting firms such as Bain and Company and McKinsey and Company use information technology (e.g., e-mail, instant messaging, and databases with the contact information and areas of expertise of every consultant) and other communication tools to enable consultants to reach peers in the company’s globally distributed work- force to work collaboratively on client problems as the needs arise, which contributes to their organizations’ ability to provide innovative solutions to their clients (Hansen, Nohria, & Tierney, 1999).
A natural consequence of the increase in the prominence of global virtual teams is that individuals are increasingly working virtually with others who are demographically different from themselves (Griffith & Neale, 2001; Griffith, Sawyer, & Neale, 2003). The extant literature on creativity has gen- erally proposed that demographic differences have the potential to be benefi- cial for creativity because demographically different individuals working together are able to bring to the task different perspectives and approaches (e.g., Milliken & Martins, 1996). On the other hand, researchers have found that, in the short run, demographic differences make it harder for team mem- bers to work together, thus potentially reducing their creative performance (e.g., see van Knippenberg & Schippers, 2007, for a recent review). Whereas there is a growing literature on the effects of demographic differences on creativity in more traditional face-to-face teams (e.g., Hoffman & Maier, 1961; Nemeth, 1986), the effects have not been examined much in virtual teams. This study addresses this gap in the literature by examining how demographic differences interact with group process and input conditions to affect creativity in short-term virtual work interactions.
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Of the variety of approaches that can be used to study dynamics in virtual teams, we focused on dyadic interactions within virtual teams (i.e., on virtual collaborations between any two members of a team). Although there are groupware systems that enable whole groups to interact at the same time, much of the interaction in virtual teams involves two team members at any one time collaborating virtually on a component of their team’s task (e.g., Majchrzak, Malhotra, Stamps, & Lipnack, 2004). Thus, though meetings of whole virtual teams occur episodically, their members’ day-to-day interactions are typically dyadic. For example, it is typical for a knowledge worker based in the United States to work with a colleague in Spain, one in Ireland, and one in India, at different times on different parts of the same task or project. Indeed, Dew and Hearn (2009) found that aggregating the creativity of pairs of collaborators within teams produced similar results as for the whole team, leading them to suggest that “structuring innovation teams into networked, nominal pairs may be just as productive as purely nominal group structures” (p. 521). Further- more, the limited examination of the role played by demographic differences in dyadic interactions has been pointed out as a deficiency in the literature on diversity in teams in general (e.g., Tsui, Xin, & Egan, 1995) and in virtual teams in particular (Martins, Gilson, & Maynard, 2004).
Our examination of how the interactions of demographic differences with group process and input conditions affect creativity in virtual collaborations contributes to the literature in several ways. Whereas there is a strong interest among managers in using virtual teams to enhance creativity in their organi- zations, very few studies have empirically examined creativity in a virtual work context (e.g., Nemiro, 2002; Ocker, 2005), and fewer still have looked at how demographic differences affect creativity or innovation in virtual con- texts (e.g., Giambatista & Bhappu, 2010; Gibson & Gibbs, 2006). Recently, researchers have argued that most work in organizations is now virtual to a greater or lesser extent, depending on the amount of time the employees spend working together on a task, the extent to which they use technology to support their interactions, and their geographic and temporal separation (e.g., Griffith & Neale, 2001; Martins et al., 2004). Therefore, these researchers have suggested that we need to move beyond comparing virtual to face-to- face teams and, instead, empirically examine variation in behavioral phe- nomena within virtual teams. The moderated effects examined in this study advance understanding of the circumstances affecting the ability of virtual collaborators to leverage their knowledge resources, which prior researchers (e.g., Martins et al., 2004; Ocker, 2005) have suggested is an important area in need of further research. Specifically, the examination of moderated effects
Martins and Shalley 539
enriches theory on the relationships between demographic differences and creativity by investigating how various process conditions and an input fac- tor determine whether demographic differences benefit or hurt creativity in virtual work (van Knippenberg, De Dreu, & Homan, 2004; van Knippenberg & Schippers, 2007).
Theory and Hypotheses It is important to specify the boundary conditions of the model we develop and test in this study, as has been recommended in both the diversity and the virtual team literatures (Martins et al., 2004; Webber & Donahue, 2001). Our unit of analysis was dyadic interactions, which comprise most of the day-to- day work dynamics within virtual teams. The demographic differences we focused on were differences in race, sex, age, and nationality, which are among the major dimensions of demographic difference examined in prior research (e.g., Tsui & O’Reilly, 1989). The virtual working technology we focused on was computer-mediated communication (CMC; specifically, electronic chat room), which forms a large component of virtual work, particularly among geographically and temporally distributed individuals (Griffith et al., 2003). In addition, we focused on short-term dyadic collaborations aimed at solving immediate managerial problems. This short-term time perspective was chosen for a few reasons. First, virtual interactions for short-term problem solving are prevalent in virtual teams (Martins et al., 2004). In addition, virtual teams have been found to have a shorter lifecycle than face-to-face teams, as they are brought together as needed to work on specific tasks (Jarvenpaa & Leidner, 1999). Finally, since membership in virtual work groups is often fluid, dyadic interactions with any one team member often are of a one-time or short-term nature (Zakaria et al., 2004). Finally, we looked at variation within virtual work, as has been recommended by researchers (e.g., Griffith et al., 2003), rather than comparing virtual to face-to-face work.
Effects of Demographic Differences on Creativity Creativity is defined as the production of novel, potentially useful ideas about work products, practices, services, or procedures (Amabile, 1996; Shalley, 1995). It is a major part of work quality and effectiveness and is increasingly valued for a variety of tasks, occupations, and industries (Amabile, 1988). In collaborative work, creativity requires the pooling together and effective integration of different perspectives, knowledge, skills, and abilities on a
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task (Hargadon & Bechky, 2006; Taggar, 2002; Woodman, Sawyer, & Griffin, 1993).
Research on diversity suggests two opposing expectations regarding the effects of demographic differences on creativity, based on the contrasting predictions of what have been termed the information/decision-making per- spective and the social categorization and similarity/attraction perspective (Williams & O’Reilly, 1998). Using the information/decision-making per- spective, researchers have proposed that differences in perspective and expe- riences underlying demographic differences should result in a greater range of information, ideas, and approaches to a problem being generated and, in turn, improved creative problem solving (e.g., Nemeth, 1986; Pelled, Eisenhardt, & Xin, 1999). Also, they have found that working with different others may stimulate consideration of nonobvious alternatives that could potentially lead to higher creativity (McLeod & Lobel, 1992). Similarly, the group brainstorming literature (see Paulus, 2000) has found that differences may be beneficial for the generation of more novel ideas. For example, McLeod and Lobel found that ethnically diverse groups produced higher- quality ideas. Also, culturally heterogeneous groups were found to generate more alternatives in the long run (Watson, Kumar, & Michaelsen, 1993). Thus, this perspective suggests that demographic differences have the potential to contribute to creativity by increasing the number of unique ideas brought to bear on a task (Milliken, Bartel, & Kurtzberg, 2003).
On the other hand, using the social categorization and similarity/attraction perspective, the literature suggests that demographic differences can lead to a variety of process losses leading to negative effects on team performance. This expectation is based on a variety of sociocognitive theories, especially the argument that individuals are attracted to those who they perceive to be demographically similar to themselves (Byrne, 1971) and those who they categorize as belonging to the same social category as themselves (Tajfel, 1981). Social categorization and stereotyping based on demographic charac- teristics are particularly prevalent when teams are first formed or in short- lived teams, since individuals tend to use these cognitive mechanisms to make sense of other team members until their stereotypes are invalidated through extended positive interactions (e.g., Allport, 1954). Thus, in the short term, demographic differences within teams have been found to result in greater conflict, communication difficulties, and other negative processes, as well as lower cohesion and social integration (e.g., Harrison, Price, & Bell, 1998; Pelled et al., 1999), and consequently, lower creative performance (Ancona & Caldwell, 1992).
Martins and Shalley 541
Depending on what theoretical and process foundations are used, demo- graphic differences can both help and hurt performance (for reviews, see Milliken & Martins, 1996; van Knippenberg & Schippers, 2007; Williams & O’Reilly, 1998). Commenting on this conundrum, van Knippenberg and Schippers noted that these competing predictions and findings are primarily caused by focusing on main effects instead of potential moderators. Consis- tent with some prior work (e.g., Jackson & Joshi, 2004; Milliken & Martins, 1996; Williams & O’Reilly, 1998), they argue for models that are more com- plex and that consider moderator variables in explaining the effects of diver- sity. We examine the moderating effects of processes and input factors that are likely to determine the extent of cognitive elaboration and combination of various perspectives on a collaborative task, which are critical to translating the potential benefits of demographic differences into actual performance benefits (e.g., van Knippenberg et al., 2004). We argue that in order for the potential creativity benefits due to differing perspectives to be realized, it is important that interactions among demographically different individuals enable the surfacing, pooling together, and integration of their differing perspectives (Gilson & Shalley, 2004; Milliken et al., 2003; Shalley & Perry-Smith, 2008).
Demographic Differences and Creativity: Moderation by Processes and Inputs The literature (e.g., Ocker, 2005) suggests that input and process factors that facilitate positive exchanges and the building of a positive relationship are important in determining the quality of interactions in virtual teams. This is particularly true for problem solving, which benefits from multiple perspec- tives but requires collaborators to work through their differences in attitudes and values to arrive at a consensus on solutions (Straus & McGrath, 1994). For instance, Taggar (2002) found that a team’s creativity-relevant process moderated the relationship between the average creativity of its members and the team’s creative output. Therefore, processes and inputs that facilitate information elaboration may be the key to reducing the negative effects and accentuating the benefits of demographic differences in virtual interactions (van Knippenberg, et al., 2004). For example, a team’s process skills have been found to be important to leveraging its members’ creative resources (e.g., Stroebe & Diehl, 1994). Also, Payne (1990) found that communication patterns in research teams had critical effects on their creative performance. The nature of the interaction process in virtual collaborations also may affect how members approach a task as well as affect their attention to the heuristic
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aspects of the task. The specific process factors we examined as moderators of the effects of demographic differences on creativity are the degree to which virtual collaborators spend time establishing rapport (i.e., bonding through informal conversation) before beginning work on the task, equality of participation in their task-related discussions, and degree of experienced process conflict. The input factor we examined as a moderator is the differ- ence in technical experience between collaborators, which prior research has found to be one of the most influential inputs affecting the quality of virtual interactions (Sarker & Sahay, 2002). Difference in technical experience refers to the difference between virtual collaborators in their experience with using the information and communication technologies required to collaborate virtually.
Establishment of rapport. Whether individuals spend time establishing rapport with each other before working on their task can be important for the development of a good working relationship in virtual collaborations (Coutu, 1998; Saunders, 2000). For example, by spending a few minutes with intro- ductions and discussing how they should approach working on the task, virtual collaborators can establish a bond of trust that may make it easier for them to work together effectively (Jarvenpaa & Leidner, 1999). Such an establishment of rapport may create a psychologically safe environment (Edmondson, 1999) in which demographically different virtual team members are comfortable raising and discussing their differing perspectives on a problem without feeling interpersonally threatened (Griffith & Neale, 2001). In keeping with this argument, prior research has found that virtual teams whose members spend time at the onset of their work getting to know each other experience greater trust among members down the road, which facilitates the overall effectiveness of their working together (Jarvenpaa & Leidner, 1999; Suchan & Hayzak, 2001). This also should help them to overcome the interaction difficulties in working virtually. Therefore, we expect that demographically different virtual collaborators who establish rapport to a greater degree will be better able to surface, discuss, and integrate differing perspectives, which in turn enhances creativity. In contrast, when demographically different virtual collaborators do not establish rapport, they may find that their differences in perspective lead to difficulties in working together, which in turn diminishes creativity.
Hypothesis 1: The relationship between demographic differences and creativity will be positive when there is greater establishment of rap- port in virtual collaborations and negative when there is less estab- lishment of rapport.
Martins and Shalley 543
Participation equality. Participation equality reflects the extent to which each member of a dyad engaged in virtual collaboration participates equally in task interactions. In a diverse group, participation equality may enable “cognitive elaboration and information exchange within work groups, draw- ing out the different knowledge and skills represented” (Webber & Donahue, 2001, p. 158). Thus, more equal participation enables better surfacing and discussion of different ideas, resulting in greater creativity (Taggar, 2002). For example, Kruempel (2000) found that, in order for effective knowledge production to occur in a virtual team, the perspectives of all team members needed to be raised and debated. Also, Gilson and Shalley (2004) found that teams that valued participation by all members were more creative.
Consideration of the variety of views and ideas represented by demo- graphically different collaborators should lead to an expanded source of knowledge to use in decision making. Also, the intellectual stimulation of considering others’ ideas should encourage exploratory thinking, which results in greater creativity. However, effective collaboration and participa- tion are necessary for virtual teams to successfully integrate various team members’ ideas and perspectives (Sarker, Lau, & Sahay, 2001). Thus, when demographically different virtual collaborators do achieve equality of par- ticipation they may be better able to leverage their diversity of perspectives to generate creative solutions. In contrast, when demographically different virtual collaborators have unequal inputs they will be less likely to be work- ing with a broad range of information and perspectives, which diminishes their collective creativity.
Hypothesis 2: The relationship between demographic differences and creativity will be positive when there is greater participation equal- ity in virtual collaborations and negative when there is less partici- pation equality.
Process conflict. Process conflict is defined as “controversies about aspects of how task accomplishment will proceed” (Jehn & Mannix, 2001, p. 239; italics in original). Greater process conflict increases uncertainty and reduces the ability of groups working on a task to pool together their ideas effectively to come up with collective solutions to problems (Jehn & Mannix, 2001). Thus, for demographically different virtual collaborators, a high level of process conflict between them may worsen the interaction difficulties caused by their demographic differences and virtual interaction, which leads to process losses (Montoya-Weiss, Massey, & Song, 2001). This argument is consistent with Griffith and Neale’s (2001) observation that “more virtual environments
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require more attention to procedural matters for success” (p. 401). The greater the process conflict, the greater the increase in process losses, which negatively affects the joint creativity of demographically different virtual collaborators. In contrast, based on the diversity literature (e.g., Pelled et al., 1999; Williams & O’Reilly, 1998), when virtual collaborators do not experience a great deal of process conflict they may be better able to reduce the interaction difficulties caused by their demographic differences and virtual interaction, and therefore, enhance effective discussion and integration of differing perspec tives to arrive at creative solutions. Consistent with this argument, creativity researchers have found that effective collaboration is a key determinant of creativity and innovation in teams (Pirola-Merlo & Mann, 2004). Thus, virtual collaborators who have effective processes for integrating their efforts for productive teamwork may be better able to overcome the low media richness of virtual work technologies and to integrate the differing perspectives on a task resulting from their demographic differences, in order to produce creative outcomes.
Hypothesis 3: The relationship between demographic differences and creativity will be negative when there is greater process conflict in virtual collaborations and positive when there is less process conflict.
Differences in technical experience. Individuals collaborating virtually may be expected to differ in their extent of experience in using the technologies needed to interact virtually. Prior research has found that teams whose members all have high levels of competence in using virtual work technologies perform better than those in which some members are more proficient in using the technologies than others (Kayworth & Leidner, 2000; Sarker & Sahay, 2002). Similarities in technical experience may thus create a positive context for demographically different collaborators to surface and discuss their differing perspectives. Differences in technical experience, on the other hand, may create communication and interaction problems, as individuals with stronger technical abilities may feel frustrated or limited in their virtual collaborations with others who are not as proficient in using virtual working technologies. These difficulties would exacerbate any interaction diffi- culties due to demographic differences and the low media richness of virtual communication technologies. Thus, differences in technical experience between virtual collaborators may create process barriers to effective virtual interaction, causing frustration and miscues that reduce creativity. Similar levels of technical experience, on the other hand, may provide a common platform that establishes the nature of the interaction between virtual collaborators
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(i.e., both individuals will face the same difficulties if they both have low technical experience or may have the same level of proficiency if they both have high technical experience). Therefore, when there are wide differences between virtual collaborators in their technical expertise, they may have greater difficulty in operating effectively in a virtual context. In such a circumstance, it may be expected that they will have difficulties in establishing interactions that surface and utilize differing perspectives, which diminishes their creativity.
Hypothesis 4: The relationship between demographic differences and creativity will be negative when there is greater difference in tech- nical experience between virtual collaborators and positive when there is less difference in technical experience.
Method Sample, Task, and Procedures
The sample consisted of 94 MBA students in an organizational behavior course at a medium-sized urban university in the United States. As part of their normal course curriculum, the class worked on a virtual work project. The students were asked to volunteer to participate in this research by filling out a survey; all did. The sample was demographically diverse: 33% were women, 45% international (representing 24 countries), and 36% nonwhite. The participants were in the age range of 23 to 42 years (M = 27.6 years, median = 27 years). All participants were proficient in spoken and written English; the average TOEFL score for international students was 637 (out of a possible 677).
We used a complex heuristic task for which responses were open ended, did not have correct answers, and required participants to “seek consensus on a preferred alternative” (Straus & McGrath, 1994) that has been used in a num- ber of prior creativity studies (e.g., Shalley, 1991; Shalley & Perry-Smith, 2001; Zhou, 1998). Participants were asked to generate solutions to various human resource problems (e.g., employee theft, motivating the sales force) that typically arise within organizations and that managers need to be able to effectively solve. The participants were told that we were particularly inter- ested in creative solutions, so they should try to think of unique ways to solve the problems that also would work well in the company.
Participants were randomly assigned a partner to collaborate with, which yielded 47 dyads engaged in virtual collaboration. Members of 59.57% of the
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dyads were of different races, 53.19% were of different sexes, and 63.83% were of different nationalities. The difference in age between virtual collabo- rators ranged from 0 to 16 years (M = 3.89 years). The participants were only allowed to communicate with their partner in an electronic chat room.
Participants were given instructions on how to log on to their assigned chat room to connect with their partner to work on the task, were briefly introduced to their partner face to face (they could see each other, but could not speak), and were given 60 min to work on the collaborative task. Before working on the task, they were asked to complete a brief survey that collected informa- tion on demographics and extent of prior technical experience with computer- based interaction (e.g., chat rooms, bulletin boards, and e-mail).
Measures Demographic differences. All participants were asked to indicate their race,
sex, age, and nationality. As has been done in previous research on demo- graphic differences (Tsui & O’Reilly, 1989), we used dichotomous mea- sures for differences in race, sex, and nationality (with 0 indicating no difference, and 1 indicating a difference in the respective characteristic) and computed difference in age as the squared difference between the ages of the two collaborators.
Creativity. According to Amabile (1996), a product is creative if observers independently agree that it is novel and appropriate. Two graduate research assistants independently rated the creativity of all solutions generated on a 7-point scale (1 = not at all creative to 7 = extremely creative). Interrater reli- ability was assessed using rWG. The mean rWG(j) for the creativity ratings was .96, which is well above the commonly used cutoff of .70. Thus, the overall creativity score for each pair of virtual collaborators was computed as the average of the two raters’ creativity ratings for the solutions generated by the collaborators. Since there may be an association between the number of solu- tions provided and the overall level of creativity, we tested the number of problems solved as a potential control variable. However, since it was not significant as a control, it was excluded from the analyses.
Virtual interaction process factors. We retained a transcript of the interac- tions within each chat room as the virtual collaborators worked, so that we could code aspects of their interaction process. Two raters independently coded the following factors on a 7-point scale: establishment of rapport, equality of participation in problem solving, and extent of process conflict. The factors were defined for both raters, and their understanding of the defi- nitions was ascertained. Establishment of rapport was defined as spending
Martins and Shalley 547
time at the start of the virtual collaboration discussing non-task-related issues that served to create a positive working relationship (e.g., spending time briefly getting to know each other, reassuring each other about the upcoming virtual collaboration). Equality of participation was coded as the extent to which each of the virtual collaborators had an equal amount of input and influence over the task. Process conflict was defined as the extent of disagreement between the virtual collaborators in how the task should be done (e.g., dis- agreement was high if one person wanted to read all the memos before com- mencing discussion and the other wanted to tackle them one by one). As was done with the creativity ratings, interrater agreement for the three factors was assessed using rWG. The mean rWG for the process ratings was .94 for estab- lishment of rapport, .91 for equality of participation, and .86 for extent of process conflict. Thus, for each of the three process factors, the ratings assigned by the two raters were averaged to obtain overall scores.
Virtual interaction input factor. For technical experience, respondents were asked to indicate on a 5-point scale (1 = none to 5 = a great deal) their degree of experience with using e-mail, chat rooms, bulletin boards, and any other electronic collaboration technologies (e.g., document sharing). Responses were averaged across the four items (alpha = .71). For each set of collabora- tors, difference in technical experience was computed as the variance between the scores of its members.
Results Correlations and descriptive statistics are reported in Table 1. The hypothe- ses were tested using hierarchical regression analysis (significant findings are reported in Table 2). Due to sample size constraints, we ran separate regressions for differences in age and nationality and for differences in race and sex. Also, we entered each moderator variable separately. The limita- tions of the separate tests for the dimensions of difference and moderator variables are noted in the discussion section below. The variables were entered into the hierarchical regression equation in four steps. In the first set of analyses, difference in age and difference in nationality were entered in the first step (Table 2, Model 1a-d), the respective moderator variable was entered in the second step (Table 2, Models 2a-d), the interaction term for difference in age and the respective moderator was entered in the third step (Table 2, Models 3a-d), and the interaction term for difference in nationality and the respective moderator was entered in the fourth step (Table 2, Models 4a-d). The second set of analyses repeated the process, but with differences in race and sex as the demographic difference variables. A centering procedure was
548
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Martins and Shalley 549
Table 2. Results of Hierarchical Regression Analyses
Model 1a 2a 3a 4a
Step 1: Independent variables Difference in age -.11 -.07 .03 .03 Difference in nationality -.42*** -.41*** -.40*** -.40*** Step 2: Moderator Establishment of rapport — .30** .39*** .39*** Steps 3 & 4: Interaction terms Establishment of rapport ×
Difference in age — — .32** .31**
Establishment of Rapport × Difference in Nationality
— — — -.03
R2 .17 .26 .34 .34 Adj. R2 .13 .20 .28 .26 F 4.26** 4.79*** 5.28*** 4.13*** R2 change — .09** .09** .00
Model 1b 2b 3b 4b
Step 1: Independent variables Difference in age -.11 -.07 -.02 -.01 Difference in nationality -.42*** -.36** -.34** -.35** Step 2: Moderator Participation equality — .27** .20 .17 Steps 3 & 4: Interaction terms Participation Equality ×
Difference in Age — — .29** .32**
Participation Equality × Difference in Nationality
— — — .14
R2 .17 .24 .31 .33 Adj. R2 .13 .18 .24 .24 F 4.26** 4.34*** 4.58*** 3.87*** R2 change — .07** .07** .02
Model 1c 2c 3c 4c
Step 1: Independent variables Difference in age -.11 -.09 -.18 -.18 Difference in nationality -.42*** -.40*** -.34** -.35** Step 2: Moderator Process conflict — -.15 -.08 -.08 Steps 3 & 4: Interaction terms Process Conflict ×
Difference in Age — — -.38*** -.38***
Process Conflict × Difference in Nationality
— — -.01
R2 .17 .19 .32 .32 Adj. R2 .13 .13 .25 .23 F 4.26** 3.21** 4.74*** 3.70*** R2 change — .02 .13*** .00
(continued)
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followed in the computation of interaction terms (Aiken, West, & Reno, 1991). All significant moderated effects found were examined further, using recommended procedures (Aiken et al., 1991).
Hypothesis 1 stated that there would be a positive relationship between demographic differences and creativity when there was high establishment of rapport and a negative relationship when there was low establishment of rap- port. This hypothesis was supported (p < .05) for differences in age (Table 2, Model 3a; F = 5.28, p < .01) but not for differences in nationality, race, or sex. Difference in age was positively related to creativity when there was more establishment of rapport but was negatively related to creativity when there was less rapport established.
Hypothesis 2 stated that there would be a positive relationship between demographic differences and creativity when there was more equal participation by both virtual collaborators and a negative relationship when there was less equal participation. This hypothesis was supported (p < .05) for differences in age (Table 2, Model 3b; F = 4.58, p < .01) but not for differences in nation- ality, race, or sex. Difference in age was positively related to creativity when there was relatively equal participation in the virtual work interaction but was negatively related when there was less equal participation.
Table 2. (continued)
Model 1d 2d 3d 4d
Step 1: Independent variables Difference in age -.11 -.12 -.11 -.13 Difference in nationality -.42*** -.49*** -.47*** -.55**** Step 2: Moderator Difference in technical
experience — .25* .17 .22
Steps 3 & 4: Interaction terms Difference in Technical
Experience × Difference in Age
— — –.28** -.35***
Difference in Technical Experience × Difference in Nationality
— — — -.33**
R2 .17 .22 .29 .39 Adj. R2 .13 .17 .23 .32 F 4.26** 3.98** 4.26*** 5.14**** R2 change — .06* .07** .10**
Note: N = 47. *p < .10. **p < .05. ***p < .01. ****p < .001.
Martins and Shalley 551
Hypothesis 3 stated that there would be a negative relationship between demographic differences and creativity when there was high process conflict and a positive relationship when there was low process conflict. This hypothe- sis was supported (p < .01) for differences in age (Table 2, Model 3c; F = 4.74, p < .01), but not for differences in nationality, race, or sex. Difference in age was negatively related to creativity when there was high process conflict in the virtual interaction but was positively related to creativity when there was low process conflict.
Hypothesis 4 stated that there would be a negative relationship between demographic differences and creativity when there was a larger difference in technical experience between virtual collaborators and a positive relationship when there was a smaller difference in technical experience. This hypothesis was supported (p < .05) for differences in age (Table 2, Model 3d; F = 4.26, p < .01), and to some extent for differences in nationality (Table 2, Model 4d; F = 5.14, p < .01), but not for differences in race or sex. Difference in age was negatively related to creativity when there was a large difference in technical experience between virtual collaborators but was slightly positively related when there was a smaller difference in technical experience. Difference in nationality was more negatively related to creativity when there was a greater difference in technical experience than when there was a smaller difference.
Discussion The pattern of our findings is relatively consistent when looked at from the perspective of the various dimensions of difference we examined. We found that our hypotheses were consistently supported for differences in age. The effect of the difference in age between virtual collaborators on creativity was contingent on various aspects of their interaction and on their difference in technical experience. All the interaction process factors we examined moder- ated the effects of age difference on creativity. We found that a difference in age led to greater creativity when virtual collaborators had spent some time establishing rapport, when they had equal participation in the discussion, and when process conflict was low. These findings support the idea that when demographically different virtual collaborators are able to utilize effective processes, they are better able to deal with interaction difficulties that may arise from their differences and virtual interaction, and thus, benefit from the differences in perspectives associated with their age differences. In contrast, when such processes are not in place, the interaction difficulties caused by age differences and virtual interactions may lead to lower creative perfor- mance. We found that the difference in technical experience between virtual
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collaborators was an important moderator of the effects of difference in age on creativity, such that it exacerbated the negative effect of difference in age on creativity. Difference in technical experience likely created communica- tion problems that further increased the difficulty of interaction beyond that already caused by a difference in age and the virtual interaction medium, thereby reducing creativity. Overall, our findings for age differences are consis- tent with the suggestion in prior research that contextual conditions may be important in determining the effects of age differences on outcomes (Ferris, Judge, Chachere, & Liden, 1991; Shore, Cleveland, & Goldberg, 2003).
Given the strong effects for age differences in our model, we conducted post hoc analyses to examine alternative explanations for our findings. To determine whether the effects of differences in age were really due to differ- ences in experience between virtual collaborators, we reran the regression analyses using difference in work experience in place of difference in age. We found that whereas difference in work experience functioned similarly in the analysis to difference in age, the results were much stronger for differ- ence in age. This suggests that difference in work experience may explain the effects of difference in age to a large extent but that other factors associated with difference in age may also play a part in generating the effects obtained. Another argument that can be made to explain the effect of age difference is that the older of the virtual collaborators brought to the task greater life and work experience, thus increasing the creativity of the collaborative product. However, we found that creativity did not correlate significantly with the virtual collaborators’ average age (r = -.07, p = ns) or average amount of work experience (r = .02, p = ns). Taken together, our post hoc analyses sug- gest that the effects we found were more likely due to differences between the ages of the two collaborators (which partly reflects difference in work experience) rather than due to higher- or lower-average age of the two virtual collaborators.
For differences in nationality, we found only one significant interaction effect, and that was not entirely in the predicted direction. Essentially, for vir- tual collaborators from different nationalities, even relatively equal technical experience did not produce a positive effect of difference in nationality on creativity, though it did reduce the strength of the negative effect experienced by the virtual collaborators with unequal technical experience. In addition, though we did not predict a direct effect of a difference in nationality on creativity, we did find a relatively strong direct effect. The finding is consis- tent with prior research that has found that national differences create com- munication problems in cross-national teams (e.g., Kayworth & Leidner, 2000) and points out that these problems may be amplified by differences in
Martins and Shalley 553
technical experience between employees working virtually. Furthermore, the negative effect for difference in nationality on creativity was not moderated by the interaction processes we examined. This lack of moderation suggests that the interaction difficulties encountered in cross-national virtual teams may not be easily overcome in short-term virtual interactions, such as those examined in this study. Given that short-term problem-solving interactions among globally distributed employees are becoming increasingly common in organizations, our finding suggests that much more research needs to be con- ducted in order to understand and manage such interactions.
For differences in race and sex we did not find any significant effects. In general, the effects of demographic differences hinge on the cognitive avail- ability of demographic characteristics that feed into social categorization processes (e.g., van Knippenberg et al., 2004). Since virtual communication technologies are low in the richness of information carried, this reduces the cognitive availability of demographic differences in virtual interactions (e.g., Nowak, 2003; Sproull & Kiesler, 1986). Thus, rather than react to surface- level characteristics using social category stereotypes, demographically dif- ferent collaborators may instead focus on differences in virtual interaction patterns that may accompany their demographic differences. Furthermore, since researchers have found that there are no readily detectable differences in the behavior of men and women in a virtual context, such as in the extent of interaction (Gefen & Straub, 1997), it is understandable that there were no significant effects for difference in sex.
In contrast, differences in age and nationality have been found to affect interaction patterns in virtual teams. Individuals from different nationalities may encounter difficulties in interacting virtually due to cross-national dif- ferences in communication styles and differences in word connotations even when communicating in the same language (e.g., Maznevski & Chudoba, 2000; Zakaria et al., 2004). Furthermore, difficulties may arise in virtual interactions between individuals from different nationalities due to differ- ences in reliance on body language, facial expressions, gestures, and physical distance in communication in their respective countries (Farmer & Hyatt, 1994). Also, national differences in usage of the English language may cause difficulties in communication in virtual teams (e.g., Zakaria et al., 2004). In a similar vein, prior research has found that an individual’s age affects his or her attitude toward, and comfort with the use of, information technologies such as e-mail (e.g., Agarwal & Prasad, 1999; Burton-Jones & Hubona, 2005). Also, individuals of different ages may communicate differently, in terms of the formalness of their communication style, their use of slang or
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certain terms, and norms regarding communication in general (e.g., Lancaster & Stillman, 2002).
Limitations, Directions for Future Research, and Contributions An obvious limitation of this study is the sample size, which was largely a consequence of the logistical and financial difficulty in setting up a large number of virtual collaborations with the type of students we used in the study—graduate business school students—and coding hour-long chat logs. As a consequence, we were unable to examine all demographic differences and moderator variables together in the same regression equation. Although the small sample may reduce confidence in the nonsignificant findings, it does make us more confident about the significant effects we found. In addi- tion, it is possible that the context in which our study was conducted (an MBA program) may have made differences in age salient. However, given that the participants on average had more than 5 years of work experience, this con- cern is mitigated to some extent. But the effects we found should be tested in other samples, in particular in field settings, to establish broader generaliz- ability of the findings.
Since the effects of demographic diversity can be different depending on time and the type of task, future research should examine these effects using different types of tasks that vary in complexity and need for consensus, as well as examine these effects over the entire lifecycle of a team. Also, we focused on short-lived virtual interactions, which can contribute to our under- standing of short-term virtual teams as well as early interactions in longer- term virtual teams. On average, virtual teams tend to have a shorter lifecycle than face-to-face teams (Jarvenpaa & Leidner, 1999); therefore, our findings should apply well to the average virtual team. However, future research should examine longer lifecycle virtual teams that will enable an examina- tion of different stages in a team’s lifecycle; such an examination is impor- tant because how demographic differences affect outcomes may vary by the amount of time that a team has spent working together (Harrison et al., 1998). In addition, we looked at only two-party virtual collaborations. If more peo- ple were involved, the need for effective processes would be expected to be even stronger, but this remains an empirical question. Finally, it should be pointed out that we examined the effect of demographic differences on cre- ativity for CMC exclusively. Although this medium represents a large part of virtual work, other ways of working virtually also need to be examined.
Martins and Shalley 555
This study contributes to research and practice in several ways. It is one of the first studies to examine how process and input factors influence the relationship between demographic differences and creativity in a virtual work context. As such, it contributes to research on creativity, virtual work, and diversity. Shalley, Zhou, and Oldham (2004), in their integration of the creativity literature, called for more research on team creativity since prior research on creativity had tended to focus on individual creativity, with only a few studies having examined team creativity (Gilson & Shalley, 2004; Taggar, 2002). Furthermore, since most employees are now working virtually, at least to some extent in their collaboration with coworkers on projects (Griffith & Neale, 2001), research is needed that explores what aspects are most important for creativity in virtual teams (Martins et al., 2004). In this study we start to address this topic by providing insights into how demographic differences may affect the performance of team members working together virtually on a problem-solving task. In addition, our study contributes to developing an understanding of the circumstances (i.e., mod- erators) that enable demographically different coworkers to overcome interaction difficulties resulting from their differences and from the limita- tions of virtual collaboration, and consequently, to improve the quality of their performance.
Our findings indicate that demographic differences can be effectively used to tease out creative contributions as long as organizations focus on important team input and process factors. Given that working virtually requires a certain level of technical expertise, attention should be paid on the front end to making sure that employees are comfortable with the technology and can easily use it to interact with others in their team. This is particularly important, as our findings indicate that for virtual teams that are working across national boundaries differences in technical abilities may cause inter- action difficulties that worsen the interaction difficulties teams face while working across nationalities. Also, developing routines that encourage the formation of rapport early on in virtual interactions may benefit performance when there are large age differences between individuals working together virtually. This could be done by encouraging employees to initially make time in a virtual interaction to chat and get to know each other. Finally, knowledge of our results can lead to the design of process interventions to improve creativity. For example, when individuals work virtually, managers should pay attention to facilitating the process by providing training up front in communication and process management skills. Overall, our findings also suggest that developing more elaborate research models is necessary in order to better understand the dynamics and outcomes of virtual teams.
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Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interests with respect to the authorship and/or publication of this article.
Financial Disclosure/Funding
The author(s) received no financial support for the research and/or authorship of this article.
References
Agarwal, R., & Prasad, J. (1999). Are individual differences germane to the acceptance of new information technologies? Decision Sciences, 30, 361-391. doi:10.1111/ j.1540-5915.1999.tb01614.x
Aiken, L., West, S., & Reno, R. (1991). Multiple regression: Testing and interpreting interactions. Newbury Park, CA: SAGE.
Allport, G. (1954). The nature of prejudice. Reading, MA: Addison-Wesley. Amabile, T. M. (1988). A model of creativity and innovation in organizations. In
B. M. Staw & L. L. Cummings (Eds.), Research in organizational behavior (Vol. 10, pp. 123-167). Greenwich, CT: JAI Press.
Amabile, T. M. (1996). Creativity in context. Boulder, CO: Westview. Ancona, D. G., & Caldwell, D. F. (1992). Demography and design: Predictors of
new product team performance. Organization Science, 3, 321-341. doi:10.1287/ orsc.3.3.321
Burton-Jones, A., & Hubona, G. (2005). Individual differences and usage behavior: Revisiting a technology acceptance model assumption. ACM SIGMIS Database, 36(2), 58-77. doi:10.1145/1066149.1066155
Byrne, D. E. (1971). The attraction paradigm. San Diego, CA: Academic Press. Coutu, D. L. (1998). Trust in virtual teams. Harvard Business Review, 76, 20-21. Dew, R., & Hearn, G. (2009). A new model of the learning process for innovation
teams: Networked nominal pairs. International Journal of Innovation Manage- ment, 13, 521-535. doi:10.1142/S136391960900239X
Edmondson, A. (1999). Psychological safety and learning behavior in work teams. Administrative Science Quarterly, 44, 350-383. doi:10.2307/2666999
Farmer, S. M., & Hyatt, C. W. (1994). Effects of task language demands and task complexity on computer-mediated work groups. Small Group Research, 25, 331-366. doi:10.1177/1046496494253001
Ferris, G. R., Judge, T. A., Chachere, J. G., & Liden, R. C. (1991). The age context of performance-evaluation decisions. Journal of Applied Psychology, 6, 616-622.
Gefen, D., & Straub, D. W. (1997). Gender differences in the perception and use of e-mail: An extension to the technology acceptance model. MIS Quarterly, 21, 389-400. doi:10.2307/249720
Martins and Shalley 557
Giambatista, R. C., & Bhappu, A. D. (2010). Diversity’s harvest: Interactions of diver- sity sources and communication technology on creative group performance. Orga- nizational Behavior and Human Decision Processes, 111, 116-126. doi:10.1016/ j.obhdp.2009.11.003
Gilson, L. L., & Shalley, C. E. (2004). A little creativity goes a long way: An exami- nation of teams’ engagement in creative processes. Journal of Management, 30, 453-470. doi:10.1016/j.jm.2003.07.001
Griffith, T. L., & Neale, M. A. (2001). Information processing in traditional, hybrid, and virtual teams: From nascent knowledge to transactive memory. Research in Organizational Behavior, 23, 379-421.
Griffith, T. L., Sawyer, J. E., & Neale, M. A. (2003). Virtualness and knowledge in teams: Managing the love triangle of organizations, individuals, and information technology. MIS Quarterly, 27, 265-287. doi:10.2307/30036531
Hansen, M. T., Nohria, N., & Tierney, T. (1999). What’s your strategy for managing knowledge? Harvard Business Review, 77(2), 106-116.
Hargadon, A. B., & Bechky, B. A. (2006). When collections of creatives become creative collectives: A field study of problem solving at work. Organization Science, 17, 484-500. doi:10.1287/orsc.1060.0200
Harrison, D. A., Price, K. H., & Bell, M. P. (1998). Beyond relational demography: Time and the effects of surface- and deep-level diversity on work group cohesion. Academy of Management Journal, 41, 96-107.
Hoffman, L. R., & Maier, N. R. F. (1961). Quality and acceptance of problem solu- tions by members of homogeneous and heterogeneous groups. Journal of Abnor- mal and Social Psychology, 62, 401-407. doi:10.1037/h0044025
Jackson, S. E., & Joshi, A. (2004). Diversity in social context: A multi-attribute, mul- tilevel analysis of team diversity and sales performance. Journal of Organiza- tional Behavior, 25, 675-702. doi:10.1002/job.265
Jarvenpaa, S. L., & Leidner, D. E. (1999). Communication and trust in global virtual teams. Organization Science, 10, 791-815. doi:10.1287/orsc.10.6.791
Jehn, K. A., & Mannix, E. A. (2001). The dynamic nature of conflict: A longitudi- nal study of intragroup conflict and group performance. Academy of Management Journal, 44, 238-251. doi:10.2307/3069453
Kayworth, T., & Leidner, D. (2000). The global virtual manager: A prescription for success. European Management Journal, 18, 183-194. doi:10.1016/S0263- 2373(99)00090-0
Kruempel, K. (2000). Making the right (interactive) moves for knowledge-producing tasks in computer-mediated groups. IEEE Transactions on Professional Commu- nication, 43, 185-195. doi:10.1109/47.843645
Lancaster, L. C., & Stillman, D. (2002). When generations collide. San Francisco, CA: Jossey-Bass.
558 Small Group Research 42(5)
Magadley, W., & Birdi, K. (2009). Innovation labs: An examination into the use of physical spaces to enhance organizational creativity. Creativity and Innovation Management, 18, 315-325. doi:10.1111/j.1467-8691.2009.00540.x
Majchrzak, A., Malhotra, A., Stamps, J., & Lipnack, J. (2004). Can absence make a team grow stronger? Harvard Business Review, 82(5), 131-137.
Martins, L. L., Gilson, L. L., & Maynard, M. T. (2004). Virtual teams: What do we know and where do we go from here? Journal of Management, 30, 805-835. doi:10.1016/j.jm.2004.05.002
Maznevski, M. L., & Chudoba, K. M. (2000). Bridging space over time: Global virtual team dynamics and effectiveness. Organization Science, 11, 473-492. doi:10.1287/orsc.11.5.473.15200
McDonough, E. F., Kahn, K. B., & Barczak, G. (2001). An investigation of the use of global, virtual, and colocated new product development teams. Journal of Product Innovation Management, 18, 110-120. doi:10.1111/1540-5885.1820110
McLeod, P. L., & Lobel, S. A. (1992). The effects of ethnic diversity on idea generation in small groups. Academy of Management Best Paper Proceedings, pp. 227-231.
Milliken, F. J., Bartel, C. A., & Kurtzberg, T. (2003). Diversity and creativity in work groups: A dynamic perspective on the affective and cognitive processes that link diversity and performance. In P. Paulus & B. Nijstad (Eds.), Group creativity (pp. 32-62). New York, NY: Oxford University Press.
Milliken, F. J., & Martins, L. L. (1996). Searching for common threads: Understand- ing the multiple effects of diversity in organizational groups. Academy of Manage- ment Review, 21, 402-433. doi:10.2307/258667
Montoya-Weiss, M. M., Massey, A. P., & Song, M. (2001). Getting it together: Tem- poral coordination and conflict management in global virtual teams. Academy of Management Journal, 44, 1251-1262. doi:10.2307/3069399
Nemeth, C. J. (1986). Differential contributions of majority and minority influence. Psychological Review, 93(1), 23-32. doi:10.1037/0033-295x.93.1.23
Nemiro, J. E. (2002). The creative process in virtual teams. Creativity Research Journal, 14(1), 69-83. doi:10.1207/S15326934CRJ1401_6
Nowak, K. L. (2003). Sex categorization in computer mediated communication (CMC): Exploring the utopian promise. Media Psychology, 5, 83-103. doi:10.1207/ S1532785XMEP0501_4
Ocker, R. J. (2005). Influences on creativity in asynchronous virtual teams: A qualita- tive analysis of experimental teams. IEEE Transactions on Professional Commu- nication, 48(1), 22-39. doi:10.1109/TPC.2004.843294
Paulus, P. (2000). Groups, teams, and creativity: The creative potential of idea- generating groups. Applied Psychology, 49, 237-262. doi:10.1111/1464-0597.00013
Payne, R. (1990). The effectiveness of research teams: A review. In M. West & J. Farr (Eds.), Innovation and creativity at work: Psychological and organiza- tional strategies (pp. 101-122). New York, NY: John Wiley.
Martins and Shalley 559
Pelled, L. H., Eisenhardt, K. M., & Xin, K. R. (1999). Exploring the black box: An analysis of work group diversity, conflict, and performance. Administrative Sci- ence Quarterly, 44, 1-28. doi:10.2307/2667029
Pirola-Merlo, A., & Mann, L. (2004). The relationship between individual creativity and team creativity: Aggregating across people and time. Journal of Organiza- tional Behavior, 25, 235-257. doi:10.1002/job.240
Sarker, S., Lau, F., & Sahay, S. (2001). Using an adapted grounded theory approach for inductive theory building about virtual team development. Database for Advances in Information Systems, 32(1), 38-56. doi:10.1145/506740.506745
Sarker, S., & Sahay, S. (2002). Information systems development by US-Norwegian virtual teams: Implications of time and space. Proceedings of the thirty-fifth annual Hawaii international conference on system sciences (January), pp. 1-10.
Saunders, C. S. (2000). Virtual teams: Piecing together the puzzle. In R. W. Zmud (Ed.) Framing the domain of IT management: Projecting the future through the past, pp.29-50. Cincinnati, OH: Pinnaflex.
Shalley, C. E. (1991). Effects of productivity goals, creativity goals, and personal discretion on individual creativity. Journal of Applied Psychology, 76, 179-185. doi:10.1037/0021-9010.76.2.179
Shalley, C. E. (1995). Effects of coaction, expected evaluation, and goal setting on creativity and productivity. Academy of Management Journal, 38, 483-503. doi:10.2307/256689
Shalley, C. E., & Perry-Smith, J. E. (2001). Effects of social-psychological factors on creative performance: The role of informational and controlling expected evalu- ation and modeling experience. Organizational Behavior and Human Decision Processes, 84, 1-22. doi:10.1006/obhd.2000.2918
Shalley, C. E., & Perry-Smith, J. E. (2008). The emergence of team creative cogni- tion: The role of diverse outside ties, socio-cognitive network centrality, and team evolution. Strategic Entrepreneurship Journal, 2, 23-41. doi: 10.1002/sej
Shalley, C. E., Zhou, J., & Oldham, G. R. (2004). The effects of personal and con- textual characteristics on creativity: Where should we go from here? Journal of Management, 30, 933-958. doi:10.1016/j.jm.2004.06.007
Shore, L. M., Cleveland, J. N., & Goldberg, C. B. (2003). Work attitudes and deci- sions as a function of manager age and employee age. Journal of Applied Psychol- ogy, 88, 529-537. doi:10.1037/0021-9010.88.3.529
Sproull, L., & Kiesler, S. (1986). Reducing social context cues: Electronic mail in organizational communication. Management Science, 32, 1492-1512. doi:10.1287/ mnsc.32.11.1492
Straus, S. G., & McGrath, J. E. (1994). Does the medium matter? The interaction of task type and technology on group performance and member reactions. Journal of Applied Psychology, 79, 87-97. doi:10.1037/0021-9010.79.1.87
560 Small Group Research 42(5)
Stroebe, W., & Diehl, M. (1994). Why groups are less effective than their members: On productivity losses in idea-generating groups. European Review of Social Psy- chology, 5, 271-303. doi:10.1080/14792779543000084
Suchan, J., & Hayzak, G. (2001). The communication characteristics of virtual teams: A case study. IEEE Transactions on Professional Communication, 44, 174-186. doi:10.1109/47.946463
Taggar, S. (2002). Individual creativity and group ability to utilize individual creative resources: A multilevel model. Academy of Management Journal, 45, 315-330. doi:10.2307/3069349
Tajfel, H. (1981). Human groups and social categories: Studies in social psychology. Cambridge, UK: Cambridge University Press.
Townsend, A. M., DeMarie, S. M., & Hendrickson, A. R. (1998). Virtual teams: Technology and the workplace of the future. Academy of Management Executive, 12(3), 17-29.
Tsui, A. S., & O’Reilly, C. A., III. (1989). Beyond simple demographic effects: The importance of relational demography in superior-subordinate dyads. Academy of Management Journal, 32, 402-423. doi:10.2307/256368
Tsui, A. S., Xin, K. R., & Egan, T. D. (1995). Relational demography: The missing link in vertical dyad linkage. In S. E. Jackson & M. N. Ruderman (Eds.), Diver- sity in work teams: Research paradigms for a changing workplace (pp. 97-129). Washington, DC: American Psychological Association.
van Knippenberg, D., De Dreu, C. K. W., & Homan, A. C. (2004). Work group diver- sity and group performance: An integrative model and research agenda. Journal of Applied Psychology, 89, 1008-1022. doi:10.1037/0021-9010.89.6.1008
van Knippenberg, D., & Schippers, M. C. (2007). Work group diversity. Annual Review of Psychology, 58, 515-541. doi:10.1146/annurev.psych.58.110405.085546
Watson, W. E., Kumar, K., & Michaelsen, L. K. (1993). Cultural diversity’s impact on interaction process and performance: Comparing homogeneous and diverse task groups. Academy of Management Journal, 36, 590-602.
Webber, S. S., & Donahue, L. M. (2001). Impact of highly and less job-related diversity on work group cohesion and performance: A meta-analysis. Journal of Manage- ment, 27, 141-162. doi:10.1177/014920630102700202
Williams K. Y., & O’Reilly, C. A. (1998). Demography and diversity in organiza- tions: A review of 40 years of research. In B. M. Staw & L. L. Cummings (Eds.), Research in organizational behavior (Vol. 20, pp. 77-140). Greenwich, CT: JAI Press.
Woodman, R. W., Sawyer, J. E., & Griffin, R. W. (1993). Toward a theory of organiza- tional creativity. Academy of Management Review, 18, 293-321. doi:10.2307/258761
Zakaria, N., Amelinckx, A., & Wilemon, D. (2004). Working together apart? Build- ing a knowledge-sharing culture for global virtual teams. Creativity & Innovation Management, 13, 15-29. doi:10.1111/j.1467-8691.2004.00290.x
Martins and Shalley 561
Zhou, J. (1998). Feedback valence, feedback style, task autonomy, and achievement orientation: Interactive effects on creative performance. Journal of Applied Psy- chology, 83, 261-276. doi:10.1037/0021-9010.83.2.261
Bios
Luis L. Martins is an associate professor at the McCombs School of Business, Uni- versity of Texas at Austin, USA. He received his PhD from the Stern School of Business, New York University. His research examines the dynamics of diversity, particularly in the context of virtual teams and global virtual work.
Christina E. Shalley is the Thomas R. Williams-Wachovia and ADVANCE profes- sor at the College of Management at the Georgia Institute of Technology, USA. She received her PhD in business administration from the University of Illinois, Urbana– Champaign. Her research examines the effects of social and contextual factors on creativity and innovation.