With the increasing adoption of technology to facilitate communication between workers and team members, the concept of “virtuality” has become an important topic in management and applied psychology. Virtuality, through terms like virtual teamwork, or telework, is most commonly operationalized as the dispersion of workers from their coworkers or teammates, including dispersion across locations, time, and/or organizational boundaries (Gilson, Maynard, Young, Vartiainen, & Hakonen, 2015). Unfortunately, these terms conflate employee distance with virtual interactions, which implies that traditional teams (or workers) rarely interact virtually. However, communication technologies have become commonplace in nearly all aspects of life, including the workplace (Colbert, Yee, & George, 2016), such that employees often interact virtually regardless of geographical distance (Kirkman, Gibson, & Kim, 2012).
This integration of technology into the workplace creates a critical need to distinguish member dispersion from virtual interactions, as well as to develop a deeper understanding of the virtuality construct. Toward this end, substantial theoretical work has elaborated on virtuality as a construct. In keeping with its foundation in computer networking, virtuality is defined as the technology-enabled interactions between two or more people (Dixon & Panteli, 2010). Unlike prior definitions of virtuality, Dixon and Panteli indicated that technology-mediated communication is often used in addition to face-to-face communication. This definition not only rejects the arbitrary dichotomy between “virtual” and “nonvirtual” teams, but also places greater importance on the subjective experience of how technology use influences interactions among team members. However, much of the empirical work on virtual teams has relied on objective measures of dispersion or technology use (Raghuram, Hill, Gibbs, & Maruping, 2019). Although the objective properties of different technology tools can influence work processes, the perceptual experience of technology-mediated communications is uniquely integral in shaping future work processes and performance. For example, users may have different perceptual experiences of technology-mediated interactions (Schmitz & Fulk, 1991), which can influence the perceived functionality of virtual interactions (Carlson & Zmud, 1999). For example, Leonardi and colleagues (2010) observed that people report use communication technology to both increase and decrease their perceived proximity to their colleagues. This suggests that focusing only on objective technology use is likely an oversimplification of how workers experience virtual work. However, research has yet to examine the construct and criterion-related validity of perceived virtuality in teams.
In summary, there is significant value in evaluating virtuality as the perceived experience of technology-mediated interactions within the team. Exclusively focusing on member dispersion prevents researchers from capturing the complexity of technology-mediated interactions, often leads to contradictory findings, and limits the ability to generalize results from research studies to practice (Gibbs, Sivunen, & Boyraz, 2017). Unlike prior experimental studies that focused on the detrimental effects of computer-mediated communication, modern technologies are often used to enable team functioning. For example, Dixon and Panteli (2010) observed that teams can actually use technology-mediated communication to bridge discontinuities (e.g., locational or organizational differences) among team members. Along these lines, many workplaces have adopted new technological tools designed to facilitate communication among team members, such as Slack or Microsoft Teams (e.g., Bunce, Wright, & Scott, 2018; Perkel, 2016). These tools, along with other traditional means of communication (e.g., e-mail or instant message), allow teams with an equivalent degree of objective dispersion to vary greatly in the ways in which members interact. Thus, two teams performing the same task with members who are equally dispersed may use a different combination of tools and experience dramatically different levels of virtuality (Laitinen & Valo, 2018). Likewise, individuals using the same communication tool may experience different degrees of perceived distance (Leonardi et al., 2010). Thus, the perceptual experiences of virtuality may well influence teamwork processes and outcomes to a greater degree than objective indices. Our primary goal in the current study is to investigate how a perceptual measure of team virtuality relates to work-related behaviors and teamwork processes.
Defining Virtuality
Member Dispersion
Much of the research on virtuality or virtual teamwork can be traced back to experimental studies conducted in laboratory settings (e.g., Hollingshead, McGrath, & O’Connor, 1993). In these studies, virtual groups were often distinguished as a separate category of group types, typically defined as consisting of members who were physically separated and thus completely reliant on computer-mediated communication to perform group tasks (e.g., Martínez-Moreno, Zornoza, Gonzalez-Navarro, & Thompson, 2012). Researchers would then compare these virtual groups to collocated groups to extrapolate whether virtual teamwork was beneficial or harmful to team decision making, satisfaction, or performance (e.g., Straus & McGrath, 1994). The comparison of dispersed and collocated groups, as a proxy for testing the effects of computer-mediated communication, allowed for controlled research on member interactions. However, this practice also encouraged researchers (and practitioners) to equate virtual interactions with dispersion (Bell & Kozlowski, 2002), an approach that generally assumes that the nature of communication between group members is similar across groups that are equally dispersed. More importantly, these lab-based studies have been criticized for their lack of ecological validity compared to actual experiences of groups and teams in the field (Purvanova, 2014).
Although geographic dispersion may lead teams to use virtual means of communication, it is not necessary for teams to be dispersed to work virtually (Kirkman & Mathieu, 2005). The increasing adoption of technologies in the workplace has rendered even collocated teams and employees as “virtual” to some extent, relying on a combination of communication methods to perform work (Hertel, Geister, & Konradt, 2005). In support of this point, member dispersion is only weakly correlated with the extent of technology use (Gibson & Gibbs, 2006). Geographic dispersion as an objective measure of virtuality also has displayed weak and somewhat inconsistent relationships with team functioning (Guinea, Webster, & Staples, 2012). Despite these limitations, dispersion remains a common method for studying virtual teamwork (Eisenberg, Post, & DiTomaso, 2019). Moreover, some researchers have suggested that virtuality should only be measured as the physical distance between members (Foster, Abbey, Callow, Zu, & Wilbon, 2015). Instead, we believe that perceptual measures regarding the nature of technology-mediated communication provide a more useful and externally valid operationalization of virtuality.
Virtuality
Recognizing the limitations of prior attempts to categorize or define virtual teamwork, Kirkman and Mathieu (2005) proposed a three-dimensional model of virtuality in teams. Contrary to previous frameworks (e.g., Bell & Kozlowski, 2002), Kirkman and Mathieu (2005) considered dispersion to be a potential antecedent, but not a defining characteristic of virtuality. Synchronicity is defined as the speed with which information is transmitted back and forth between team members, with asynchronous communication is characterized by time lags in the sharing of information (Kirkman & Mathieu, 2005). Reliance on technology refers to the degree to which technology use is necessary for group members to interact when performing. Work groups that rely more heavily on technology are considered more virtual. Finally, information value is defined as the degree to which the technology-based communication provides information that benefits overall work effectiveness. The basis for this dimension originates in media richness theory (Daft & Lengel, 1986), in which a communication medium can be described by its’ carrying capacity, or the amount of information that it can convey. However, information value is defined as a product of the richness of the media being used and the perceived value that it provides (Kirkman & Mathieu, 2005).
Perceived and Objective Virtuality
The difference between objective and perceived virtuality is analogous to the difference between the objective properties of a device and a user’s perceived experience while using that device. Research has largely studied virtuality as an objective construct, most often based on the use of technology (Handke, Klonek, Parker, & Kauffeld, 2020). Although gathered from self-reports, objective-focused measures of virtuality ask teams to estimate the proportion of time spent using specific technological tools (Maynard, Mathieu, Gilson, Sanchez, & Dean, 2019) or time spent working remotely/face-to-face (Golden & Veiga, 2005). Alternatively, researchers may note the objective characteristics of the technological tools that were used and attempt to track the degree that each tool was used (e.g., Cummings, Espinosa, & Pickering, 2009).
Focusing on perceived virtuality provides information on the functionality of virtual interactions that objective indices often do not. This is an important distinction because the ways in which individuals and teams use technology continues to grow and evolve (Tannenbaum, Mathieu, Salas, & Cohen, 2012). For example, delays in communication may only be disruptive when members are perceived to be “late” to respond according to the demands of the task or the expectations of the team. According to channel expansion theory (Carlson & Zmud, 1999), perceptions of communication media are more important than objective qualities in explaining how people use and experience these tools. This notion has been supported by subsequent research, which has identified that perceptions of copresence are more strongly related to relationship quality than objective measures of distance (O’Leary, Wilson, & Metiu, 2014). Thus, perceived synchronicity provides a measure of delay that is more responsive to the different demands and expectations encountered in different contexts. Conversely, simply focusing on the total or average amount of delay (as a more objective index), fails to consider whether the extent of delay is appropriate or even beneficial for a given context. Similarly, the sheer degree that teams rely on technology fails to account for whether the amount of technology reliance was appropriate for the task or context. Whereas these contextual demands are considered by respondents in the case of perceived virtuality, they are neglected by objective virtuality.
Antecedents and Outcomes of Perceived Virtuality
Rather than serving as a defining feature of virtuality, we agree with past research that argues member dispersion is best considered an antecedent of virtuality (Kirkman & Mathieu, 2005). Teams often choose to use technology to communicate or coordinate actions regardless of their location. However, greater distance limits the opportunities for members to meet in person. Team members are expected to be more reliant on technology-mediated interactions as they become increasingly separated across different contextual boundaries. In the current study, we focus on spatial or geographic dispersion across the team as a relevant antecedent to perceived virtuality. Past studies have observed this positive relationship among teams in field (e.g., Gibson & Gibbs, 2006). Although distance is not a necessary condition for technology use, we hypothesize that greater levels of member dispersion should lead to greater reliance on technology use.
Hypothesis 1: Geographic dispersion will be positively related to perceived technology reliance.
Not only is geographic dispersion an antecedent to team virtuality, it is also generally thought to have a negative impact on team functioning. Past studies have typically found that greater geographic dispersion between team members is related to poorer teamwork processes and functioning (Cramton & Webber, 2005). More specifically, greater spatial separation or discontinuities can restrict team members’ ability to perform important transition or action processes, such as task coordination (Cummings et al., 2009) or effective communication between team members (Eisenberg et al., 2019). These challenges can also result in poorer interpersonal relations as indicated by greater conflict and lower satisfaction among team members (Hinds & Bailey, 2003; O’Leary & Mortensen, 2009). Therefore, we expect that geographic dispersion will be negatively related with all measures of teamwork processes.
Hypothesis 2: Geographic dispersion will be negatively related to team processes and performance.
Based on Driskell and colleagues (2003) model of virtual teamwork, we expect that perceived virtuality will influence teamwork processes. According to media synchronicity theory, virtual tools that are more synchronous provide members with more immediate feedback (Dennis & Valacich, 1999). This capability is theorized to allow members to more easily reach a commonly shared understanding of information and the task at hand (Dennis, Fuller, & Valacich, 2008). Not only can this capability help prevent interpersonal conflict by allowing individuals to clarify any perceived misunderstandings with more responsive feedback, but this may allow teams to perform transition processes, such as planning or goal setting, more effectively. Moreover, teams using more synchronous communication tools (face-to-face and video conferencing) also report greater coordination in experimental tasks (Peñarroja, Orengo, Zornoza, & Hernández, 2013) and more highly developed shared mental models (Maynard & Gilson, 2014). Synchronicity has also been discussed as influencing the timeliness of communication, which is considered to be especially important for effective interaction among distributed team members (Marlow, Lacerenza, & Salas, 2017). Likewise, information value or media richness may also play an important role in enabling effective teamwork processes. A lack of media richness (by use of lean communication tools) is thought to increase the likelihood of misunderstandings and attribution biases among team members (Cramton, 2001), decrease the amount of information sharing among team members, and prevent effective coordination (Rosen, Furst, & Blackburn, 2007). This may also hinder the development of shared mental models among team members (Schmidtke & Cummings, 2017). On the other hand, the use of more rich communication tools may allow teams to share information more clearly and effectively (Mesmer-Magnus, DeChurch, Jimenez-Rodriguez, Wildman, & Shuffler, 2011). The use of richer communication tools has also been found to help teams mitigate the challenges of demographic fault lines and infrequent communication (Straube, Meinecke, Schneider, & Kauffeld, 2018). Based on this body of research, we expect that both synchronicity and information value will be positively related to teamwork processes and performance.
Hypothesis 3:Synchronicity (a) and information value/media richness (b) will be positively related to team processes and performance.
Unlike synchronicity and information value/media richness, it is unclear whether technology reliance is generally beneficial or harmful to team functioning. Technology reliance has often been studied as a less desirable alternative to face-to-face communication (e.g., Straus & McGrath, 1994). These studies often indicated that greater reliance was associated with worse coordination and decision making within teams (Thompson & Coovert, 2003) and lower satisfaction with the team (Straus, 1996). Conversely, many technologies provide functionalities that may actually aid teamwork processes, such as planning and coordination. Presently, technological tools can provide a number of benefits including a shared record of interpersonal communication (e.g., e-mail chains or group chat), synchronous exchanges between members (e.g., video chat) or a repository where members can share work and provide feedback (e.g., Google Docs or Dropbox). Along these lines, advanced technological tools may actually enable individuals and teams to perform more effectively (Greer & Payne, 2014; Malhotra & Majchrzak, 2014). At the individual-level, Gajendran, Harrison, and Delaney-Klinger (2015) observed that telecommuting (both whether or not someone telecommutes and the intensity of telecommuting) was positively related to task performance and citizenship behaviors. Yet, other recent work has reported both beneficial and detrimental effects of technology use on employee well-being and burnout due to different mediating factors (Ter Hoeven, van Zoonen, & Fonner, 2016). However, past studies of technology reliance have often measured technology use as proportional to the amount of face-to-face communication. Instead, we are interested in observing the extent to which technology reliance (independent of the amount of face-to-face communication) affects teamwork process and propose competing hypotheses regarding the direction of the relationships.
Hypothesis 4a: Technology reliance will be positively related to team processes and performance.
Hypothesis 4b: Technology reliance will be negatively related to team processes and performance.
Study 1
Method
Sample and procedure
We collected data from teams of undergraduate business students at a public university in the Midwestern US. Teams ranged from 4 to 6 members in size and were tasked with developing a business plan over the course of one academic semester (14 weeks). This project spanned three different classes, in which teams were required to enhance a participating organization’s financial performance by developing strategies for marketing, sales, accounting, and operations. All members attended classes on campus, ensuring that geographic dispersion and temporal boundaries were relatively constant. After removing incomplete responses or teams with only one response, we had a total sample of 282 individual team members representing 94 different project teams. Team members were mostly male (60%) and Caucasian (90%). Median age was 21, ranging from 19 to 31 years of age.
Measures
All variables were measured using online surveys that were collected at the end of the semester, near the completion of the project but before members were aware of their grades. Per university regulations and ethical guidelines, student participation in this project was completely voluntary and occurred outside of class to avoid coercion, which led to some nonparticipating members within some teams. To maintain adequate statistical power for hypothesis testing, teams with responses from at least two members were used for data analysis (Biemann & Heidemeier, 2012). Supplemental analyses showed no notable differences in effect size when teams with only two respondents were removed.
Perceived team virtuality
We used an 11-item scale to measure Kirkman and Mathieu’s (2005) three-dimensional model of virtuality (all items are listed in the online supplemental materials). These items were selected from scales developed in a past study of individual team workers (Grossenbacher, Brown, Quinn, & Prewett, 2013). We observed adequate internal consistency for synchronicity (α = .68), technology reliance (α = .84), and information value (α = .88). We also conducted a confirmatory factor analysis (CFA) to observe whether our virtuality scales fit our hypothesized three-factor model. The hypothesized three-factor model provided good fit, χ2(41) = 96.84, p < .01, root mean square error of approximation (RMSEA) = .069, comparative fit index (CFI) = .962, standardized root mean square residual (SRMR) = .060. The hypothesized model provided substantially better fit than both the one-factor (Δ χ2(3) = 242.80, p < .01, ΔCFI = .165) and best fitting two-factor model (Δ χ2(2) = 125.42, p < .01, ΔCFI = .085). Based on these results, we used the hypothesized three factor model for hypothesis testing.
Teamwork processes and conflict
We measured transition, action, and interpersonal teamwork processes using three- to four-item scales based on the framework developed by Marks, Mathieu, and Zaccaro (2001). These scales reflect three clusters of related teamwork processes that have been previously studied in virtuality research. An example transition process item was “Set goals for how/when to complete specific elements of the project.” A listing of all team process items are provided in the online supplemental materials. Internal consistency for the process scales ranged from α = .80 to .88. Task (α = .74) and relationship conflict (α = .85) were measured with three-item scales (Jehn & Mannix, 2001). We calculated within-team agreement for each team-referent outcome variable using intraclass correlations (ICCs). All outcome variables were observed to have ICC(1) values ranging from .10 to .23, indicating that there is an ample amount of between-groups variance in our teamwork process measures for the detection of group-level effects (Bliese, Maltarich, & Hendricks, 2018). In addition, we observed modest reliability of group means based on ICC(2) values ranging from ICC(2) = .25 to .47. We also evaluated within-team agreement by calculating rwg(J) using the uniform null (James, Demaree, & Wolf, 1984). All teamwork outcomes had medianrwg(J) values that exceeded .80.
Results
Prior to testing hypotheses, we first examined the multilevel nature of perceived virtuality by examining interrater reliability among respondents. Using Chan’s (1998) taxonomy of composition models, our virtuality measure is a referent-shift measure where all items ask individuals to rate characteristics of their team. As such, we observe estimates of within-group agreement, using rwg(j), to support the aggregation of individual ratings into team-level scores. Median within-group agreement was observed to be .80 or greater for synchronicity, rwg(j) = .80, technology reliance, rwg(j) = .87, and information value, rwg(j) = .92. In addition, we also calculate interclass correlations to determine the proportion of between-team variability in scores using ICC(1) and the reliability of the team-level scores using ICC(2). ICC(1) values ranged from .07 to .13, indicating that team membership was influential on individual-level virtuality scores. However, these values were also generally lower than those of teamwork criteria, suggesting that individual-level characteristics provide a substantial contribution to virtuality perceptions. Likewise, we also observed relatively low reliability for the group means for each of the virtuality dimensions-ICC(2) ranging from .17 to .30. Based on these results, there is evidence that virtuality perceptions can be conceptualized as shared team properties, characterized by modest within-group agreement.
Correlations among all individual- and team-level variables are reported in Table 1.
When aggregated to the team-level, synchronicity was positively correlated with transition process effectiveness, action process effectiveness, and negatively related to task conflict. Technology reliance also was negatively related to task conflict among individual responses and when aggregated to the team level. We observed similar, negative correlations between information value and task conflict at the individual and team levels. In general, team-level correlations were similar to those at the individual level, suggesting that virtuality operates similarly at both levels of analysis. However, the team-level correlations were generally higher than the individual-level correlations.
Next, we regressed each of our five teamwork process outcomes on the virtuality dimensions (see Table 2).
For these analyses, we were particularly interested in the degree that each dimension uniquely predicted variance in each of the teamwork processes. The full models with the virtuality dimensions were significant predictors of task conflict (R2 = .16, p < .05), transition process (R2 = .03, p < .05), and action process effectiveness (R2 = .08, p < .05). Team synchronicity was positively related to action (β = .29, p < .05) and transition process effectiveness (β = .25, p < .05). Information value was not a unique predictor of any of the five teamwork process outcomes. These results provide partial support for Hypotheses 3a while providing no support for 3b. Technology reliance was negatively related to task conflict (β = −0.50, p < .05) and relationship conflict, although not reaching statistical significance (β = −0.29, p < .10). These regression results, plus the direction of the correlations between technology reliance and our outcomes, provided partial support for Hypothesis 4a and not 4b.
Study 2
A notable concern from Study 1 was the high correlation between information value and technology reliance (r = .88). When reviewing the theoretical basis for the model, Kirkman and Mathieu (2005) discuss both the perceived value of the media and the richness of the information provided. As such, we revised our original “information value” scale to a “media richness” scale that assesses the perceived media richness of these technologies in Study 2. Media richness has long been viewed as a critical element of technology-mediated communications (Daft & Lengel, 1986), and media richness theory provides a guiding framework for predicting relationships between media richness and teamwork criteria.
Study 2 also sought to examine the validity of the virtuality dimensions across different levels of member dispersion and to expand the criterion space to include individual-referent measures of performance measures, in addition to team-referent measures of work processes. The inclusion of dispersed teams us to examine the relationships between member dispersion and virtuality and teamwork processes. This allowed us to directly test Hypotheses 1 and 2 and to establish the incremental validity of perceived virtuality measure. The inclusion of peer-rated criteria addresses some concerns that may stem from the self-rated criteria including common method variance (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003). Although these criterion measures still relied upon survey methodology, the use of individual-level item referents for peer evaluations and tailored instructions created an item set that was quite different from the team process criteria.
Method
Sample and procedure
Data were collected from collocated and dispersed project teams of undergraduate business students at a public university in the Midwestern US. Teams ranged from 3 to 6 members in size and were tasked with completing an interdependent project, similar to the task performed by teams in Study 1, over the course of 8 to 14 weeks. All project teams were tasked with developing a business plan for a client organization which included strategies for marketing, sales, accounting, and operations. Fully dispersed teams consisted of students taking courses online. Because many dispersed team members were not living in the same city or even state, these teams were not as likely to meet face-to-face or work from a shared location as the students taking classes on campus (collocated teams). After data cleaning, we had data from 68 teams (30 collocated and 38 dispersed) consisting of 221 individual member responses. A majority of dispersed team members were female (65%), compared to a roughly equal ratio of males (51%) and females (49%) among collocated team members. The median dispersed team member age was 28 years (SD = 9.38), compared to a median age of 21 years for collocated teams (SD = 1.73). Collocated teams were composed of more White members (92%) compared to dispersed teams (59%).
Measures
All observed variables were measured using self-report questionnaires collected online. For both collocated and dispersed teams, team virtuality and process measures were collected at the end of the semester, after completion of the project but before members are aware of their grades. Because of missing data, sample sizes vary for some of our observed variables. Teams with responses from at least two members were used for data analysis (Biemann & Heidemeier, 2012).
Geographic dispersion
To assess geographic dispersion within each team, we used the spatial distance index (SDI) developed by O’Leary and Cummings (2007). This index is calculated using the distance (in miles) between all group members, the number of team members, and the number of work sites within each team. Along with spatial dispersion, a dichotomous measure was also used as a proxy for geographic dispersion (1 = collocated; 2 = dispersed).
Perceived team virtuality
We used nine synchronicity and technology reliance items, adapted from Study 1, and three novel items created to measure media richness (the online supplemental materials). An example media richness item was “the communication technologies that we used in my team provided enough information to prevent misunderstandings between team members.” All technology reliance items were based those used in Study 1 but were rated for frequency instead of agreement. Synchroncity items were revised to be positively worded (framed as synchronicity instead of asynchroncity) to observe how this affects its correlation with the other two dimensions and in attempt to increase internal consistency compared to Study 1. All three scales showed excellent internal consistency (α = .88 −.89). We conducted a CFA to observe whether the virtuality scales fit the hypothesized three-factor model. The three-factor model provided good fit, χ2(51) = 82.94, p < .001, RMSEA = .068, CFI = .972, SRMR = .062. The hypothesized model provided substantially better fit than both the one factor (Δχ2(3) = 332.05, p < .01, ΔCFI = .291) and best fitting two factor model (Δχ2(2) = 134.90, p < .01, ΔCFI = .118).
As in Study 1, we observed good agreement for synchronicity (medianrwg.j = .91), technology reliance (median rwg.j = .90), and media richness (median rwg.j = .78). However, we also observed greater between-groups variance in synchronicity, ICC(1) = .29, and technology reliance, ICC(1) = .24. Likewise, the group means for synchronicity, ICC(2) = .54, and technology reliance, ICC(2) = .48, were more reliable than those observed in Study 1. However, media richness exhibited weaker emergent properties than the other dimensions.
Teamwork processes
Transition processes and interpersonal processes were each measured using three-item scales drawn from the process measures used in Study 1. Internal consistency for the process scales ranged from α = .85 to .89. Instead of a broad measure of action processes, we focused on team coordination (one specific facet of action processes in the Marks et al. [2001] team process framework) using a five-item scale which has been widely used in past research (Lewis, 2003; α = .74). We found acceptable median within-team agreement for each process measure (transition processes = .87; interpersonal processes = .88; coordination = .83). According to ICC(1) estimates, we observed at least 18% of between-team variance across all of our process measures. We also observed adequate reliability for team scores for transition processes, ICC(2) = .40, interpersonal processes, ICC(2) = .46, and coordination, ICC(2) = .59. These values support the aggregation of the individual responses to team-level values (Hox, 2010).
Individual-level influence and behaviors
To account for potential biases in team-referent, self-report measures, peer ratings of performance that used individual-referent items, were also included as criteria. Peer influence was measured using the General Leadership Impressions (GLI) Scale. There were total of six items that measured GLI (α = .93). An example GLI item was, “How much leadership did this member show?.” Unlike the teamwork process measures, GLI items were peer-referent instead of team-referent and potentially introduce a lesser degree of common method variance. Peer ratings were aggregated for team-level analyses and represent the average leadership influence among team members.
Individual-referent performance behaviors were measured using behaviors adapted from Morgeson, DeRue, and Karam’s (2010) scale of team leadership. These behaviors include the task-oriented behaviors (eight items, α = .90), derived from the transition and action phase, and relational-oriented behaviors (seven items, α = .91), derived from the interpersonal dimension. We aggregated these ratings to produce an estimate of the input from the average team member for each team. We consider both of these measures to be additive composites which are aggregated regardless of the within-group variance or agreement (Chan, 1998).
Results
Correlations for all Study 2 variables are reported in Table 3.
Geographic dispersion (as measured by the spatial distance index) was not significantly correlated with any of the perceived virtuality dimensions. On the other hand, collocated teams reported greater synchronicity than dispersed teams, d = 1.21. Somewhat surprisingly, collocated teams also reported greater technology reliance, d = .83. Member dispersion was also negatively related to technology reliance, but this did not reach statistical significance, r = −.21, p < .10. No significant differences in media richness was observed between dispersed and collocated teams. These results failed to provide support for Hypothesis 1.
Our sample of collocated and dispersed teams also provided the opportunity to test Hypothesis 2. Contrary to our expectations, we did not observe any significant differences in teamwork process effectiveness or coordination between team types. We also did not observe any significant correlations between continuous measures of geographic dispersion and these process measures. On the other hand, we did find significantly lower average leadership ratings (d = −.98, p < .05) and member contribution ratings among distributed teams (d = −.55, p < .05). We also replicated these findings using our continuous measure of geographic dispersion (correlations of r = −.41 and −.39, respectively, p < .05). Our results provide only partial support for Hypothesis 2.
Regarding the relationships between virtuality and team processes, the virtuality dimensions combined to predict all five team process outcomes (see Table 4).
As in Study 1, we found that synchronicity was related to greater coordination (β = .32, p < .05) and more effective transition processes (β = .27, p = .09). Likewise, media richness was a significant predictor of interpersonal processes (β = .38, p < .05), transition processes (β = .37, p < .05), and coordination (β = .20, p < .05). These results provided partial support for Hypotheses 3a and 3b. Unlike Study 1, we found unique effects for technology reliance. This dimension was positively related to greater average team member contributions (β = .34, p < .05) and average leadership behaviors (β = .48, p < .05). However, technology reliance was not a unique predictor of any of the remaining process measures. Our findings provide partial support for Hypothesis 4a and not 4b.
To further analyze these relationships, we ran additional regression models where each teamwork criterion was regressed onto the three virtuality dimensions after controlling for team type. Perceived virtuality predicted incremental variance beyond team type for all five of our outcome measures (Tables 5
and 6).
Although not reported here, we also found similar results when using a continuous measure of geographic dispersion among team members. Not only did the virtuality dimensions account for incremental prediction beyond team type, but the dimensions also provided greater prediction (greater model R2) compared to team type for all outcomes excluding leader behaviors. These results indicate that the perceived virtuality dimensions are more proximal to various teamwork processes than objective measures of geographic dispersion (either dichotomous or continuous measures).
General Discussion
The primary goal of the present study was to observe how perceived virtuality relates to important teamwork processes and emergent states, independent of geographic dispersion. In Study 1, we held geographic dispersion constant by sampling collocated teams working from the same university campus. In Study 2, we measured geographic dispersion as a means to statistically control for differences within a sample of collocated and distributed teams. In both samples, we observed strong, positive relationships between perceived synchronicity and various teamwork processes and states. We provide a summary of our hypothesis test results in Table 7.
In particular, collocated and dispersed teams that reported greater synchronicity also were more effective in managing transition (e.g., planning and goal setting) and action (e.g., coordination) processes. This supports propositions of media synchronicity theory, which posits that synchronicity provides more rapid feedback which allows for greater information sharing (Mesmer-Magnus et al., 2011) and convergence among team members (DeLuca & Valacich, 2006). These results also reinforce the role of synchronicity as an important dimension of Kirkman and Mathieu’s (2005) team virtuality model. In particular, this dimension may be especially useful in the early stages in a team’s development, when the group is focused on assigning roles, defining group norms, and setting goals for the team. Along these lines, past research has reported that synchronicity has stronger beneficial effects on team performance in the short-term (Fuller & Dennis, 2009).
Likewise, media richness was a unique predictor of both interpersonal process effectiveness and coordination among collocated and dispersed teams in Study 2. Based on these results, media richness seems most relevant when teams are aligning their actions and addressing social phenomena within the group. This general finding is unsurprising, as there is an ample amount of past literature concerning media richness to help inform researchers and practitioners how the richness of communication may influence the development of group norms or shared understanding among team members (Lewis, 2003). Moreover, greater richness likely allows for a broader expression of nonverbal communication which may help prevent interpersonal conflict or misunderstandings from occurring (Cramton, 2001). Based on our findings, we suggest that media richness is an important aspect of virtuality, provides a more specific operationalization than Kirkman and Mathieu’s (2005) information value, and is empirically distinct from synchronicity.
We also found that the degree of technology reliance was positively, rather than negatively, related to effective teamwork behaviors. One explanation for this finding is that our technology reliance measure may reflect communication frequency within the team, which generally has positive effects on team processes and outcomes (Marlow, Lacerenza, Paoletti, Burke, & Salas, 2018). Unlike other previous operationalizations of technology use, our measure does not assume that face-to-face communication and technology use is ipsative (e.g., what proportion of your communication was computer-mediated compared to face-to-face; Maynard et al., 2019). However, like Maynard and colleagues, we also observed significant, positive correlations between technology reliance and effectiveness measures. We believe that our findings indicate that greater technology reliance may also represent a greater use of technology to augment other means of communication. Especially in cases where the technology is well-suited for the team’s task, technology use may enable teams to perform more efficiently and effectively (Maruping & Agarwal, 2004). Although we did not directly measure task-technology fit in the present study, this may be a useful direction for future research to clarify the means in which technology use can be advantageous to teams.
Construct Validation of Perceived Virtuality
Results suggested that perceived virtuality can be empirically measured, distinguished from measures of dispersion, and account for variance in teamwork processes beyond other measures. The presence of variance at both levels of analysis support the notion of a multilevel construct, in which common team experiences and interactions lead to emergent, shared perceptions of virtuality across the team. Among collocated teams in Study 1, we observed modest within-group agreement for each of the virtuality dimensions but a clear influence of group membership on virtuality scores. We replicated these findings in Study 2 with a mix of distributed and collocated teams. In Study 2, we observed a greater amount of between-team variance in our virtuality dimensions. This may be explained by our inclusion of dispersed teams compared to only sampling from collocated teams in Study 1. This result may suggest that dispersed teams tend to communicate as a unit, whereas collocated teams may communicate more frequently as dyads, leading to more unique within-team experiences. These differences in communication patterns may create within-team subgroups and faultlines which may subsequently affect group dynamics (Thatcher & Patel, 2012). However, future research is warranted to verify this speculation.
Study 2 yielded novel and important findings regarding virtuality and geographic dispersion. First, we found that evidence that the virtuality scale is distinct from two different geographic dispersion measures. Although geographic dispersion has been theorized as an antecedent of team virtuality, we actually observed that greater dispersion was related to less frequent use of technology. This may be the result of a general tendency for dispersed groups to communicate less often but this finding also indicates that collocated groups commonly make use technology to communicate. This makes it all the more important to stop relying on measures of dispersion or discontinuities as proxies for virtuality or technology reliance. Instead, we hope that further use of perceptual measures of virtuality will allow researchers to better understand the ways in which teams use technology and how specific capabilities may be well suited for overcoming specific teamwork challenges including dispersion, member diversity, or multiteam membership. Second, we also found that virtuality provides greater, and incremental, prediction of most teamwork processes beyond measures of dispersion. Even though several past studies have reported a negative effect of dispersion on teamwork effectiveness (e.g., Cramton & Webber, 2005; Gibson & Gibbs, 2006), we only observed deficits in coordination and average member contribution. On the other hand, perceived virtuality accounted for significant, incremental prediction of all five of our outcome measures. These results indicate that perceived virtuality is likely more proximal to teamwork processes than dispersion.
An important benefit of our perceived virtuality measure is that it does not directly reference any specific form of technology or application (e.g., e-mail, voice chat, or Slack). This allows our measure to be more flexible to the changing trends and adoption of communication tools compared to objective, checklist approaches used in prior research (Cummings et al., 2009). We also hope that this flexibility enables researchers in different fields to use these methods in order to help bridge the long existing segmentation of research on virtual teamwork (Raghuram et al., 2019). Moreover, these measures do not make the assumption that a communication tool has a fixed level of synchronicity or media richness. Instead, perceptual measures can accommodate the fact that individuals often use the same tool in different ways in order to create different experiences of connectivity or distance (O’Leary et al., 2014).
Despite some of the advantages of our perceived virtuality measure, further evidence for the construct validity of this measure is needed. To our knowledge, we are one of the first studies to design perceptual measures based on Kirkman and Mathieu’s (2005) model. As such, we believe that our measure is a first step in improving the ways in which team virtuality is operationalized. In Study 1, we observed only modest internal consistency for our synchronicity measure and weaker between-groups variability in virtuality scores compared to Study 2 which may have attenuated some of our results. We also observed relatively strong correlations among our perceived virtuality dimensions. This multicollinearity among the dimensions led to fewer, unique effects in our multiple regression results despite the fact that each individual dimension was more strongly related to team outcomes than dispersion measures. Yet, we did find that a more narrowly defined measure of media richness (in Study 2) provided more discriminant validity than attempting to measure Kirkman and Mathieu’s more broadly defined information value. We hope that future scale development research will be conducted to improve discriminant validity by.
Directions for Future Research
One important area for future research is the role of training and organizational support in team virtuality. Training is a common method for improving technology-related skills in employees, yet these training programs should move beyond the functionality and use of a tool and seek to integrate these tools with the team context in which their use may occur. Even though research provides ample evidence for the importance of training in virtual teams (Gilson et al., 2015), a past study estimated that nearly 60% of the organizations surveyed did not provide training to virtual team members (Rosen et al., 2007). In our study, we were not able to assess the extent to which teams had received any prior support or training in using technology to collaborate with team members. Thus, our results cannot identify whether any prior experiences or targeted training may be useful for improving the experience of perceived virtuality. This future research may identify a great practical need for training team members not only in how to use certain technologies, but how to align technology use with task demands and member needs. Training may also help in the development of group norms regarding technology use, which can lead to greater trust among team members (Breuer, Hüffmeier, Hibben, & Hertel, 2020).
Another area for future research is to investigate the role of individual factors in the emergence of team virtuality. Although we focused on the team-level effects, we also found an appreciable degree of variance in virtuality both within- and between-teams. This is unsurprising, as virtuality perceptions likely reflect both shared group phenomena and individual differences in the experiences and appraisals of team interactions. Outside of team-based work, individual perceptions of virtuality may also be useful in better understanding the conditions where telecommuting is beneficial or detrimental to employee well-being or performance (Allen, Golden, & Shockley, 2015). Future research should also investigate the role of individual differences in explaining perceived team virtuality. Not only can the subjective experience of a communication medium vary across individuals (Sacau, Laarni, & Hartmann, 2008) but individuals may also vary in the extent to which they prefer to synchronous communication with others (Leroy, Shipp, Blount, & Licht, 2015). Moreover, past research suggests that distributed work may require different forms of knowledge, abilities, or skills compared to working face-to-face (Schulze, Schultze, West, & Krumm, 2017). This future work would help further our understanding of the important antecedents and outcomes of virtuality perceptions at the individual level (Gibson, Gibbs, Stanko, Tesluk, & Cohen, 2011) while also helping inform best practices for selecting individuals for virtual work assignments (D’Souza, Prewett, & Colarelli, 2017).
Limitations
There are a few limitations that should be acknowledged when evaluating the findings of our studies. Although the number of groups in Study 2 is not substantially smaller than what is commonly reported in published research (Biemann & Heidemeier, 2012), many of the groups only had responses from either two or three members. The lack of observations within groups leads to less reliable group means, which can attenuate relationships between group-level variables (Hox, 2010). Another limitation is that we only collected data from team members at one time point in both studies. This approach provides only a static view of team virtuality which may fluctuate during the life span of the team or its project (Handke, Schulte, Schneider, & Kauffeld, 2019). Furthermore, our ability to generalize our findings may be limited by our reliance on short-term, student teams in both studies. Although our teams were similar to those often studied in virtual team research (Gilson et al., 2015) and worked together for several weeks as part of an interdependent project, additional work is needed to determine whether our results can be generalized across other types of teams, tasks, or conditions of member dispersion or tool use. One final limitation is that we did not sample enough teams in Study 2 in order to adequately test for any potential interactions between perceived virtuality and team type. Although we observed stronger effects for the virtuality dimensions when sampling both collocated and dispersed teams in Study 2 compared to only collocated teams in Study 1, we did not have enough statistical power to test for interaction effects. We hope that future research is conducted in order to test for these effects.
Conclusions
Overall, we find that measures of perceived virtuality are strongly related to effective teamwork processes among collocated and dispersed project teams. Not only do these measures display acceptable within-group agreement, but they also predict process ratings beyond dichotomous or continuous measures of geographic dispersion. This approach represents a perspective on virtuality that is more externally valid than prior methodologies which have focused on objective measures of technology use or geographic dispersion. We hope that future researchers continue to use this approach to better understand how teams use technology and how technological capabilities enable different forms of process functions.
Supplemental material:https://doi.org/10.1037/gdn0000120.supp
Acknowledgements
Corresponding Author
We thank Jared Quinn, Stephen Attar, Ki Ho Kim, and Misty Bennett for their work in collecting and organizing data for this project. Portions of this article were presented as a symposium at the 29th Annual Meeting for the Society for Industrial and Organizational Psychology in April 2014. Portions of this paper were also used as part of Matt I. Brown’s doctoral dissertation.Correspondence concerning this article should be addressed to Matt I. Brown, Autism and Developmental Medicine Institute, Geisinger Health System, 120 Hamm Drive, Suite 2A, MC 60-36, Lewisburg, PA 17837
Email: [email protected]
Publication History
Received November 15, 2019
Revision received April 2, 2020
Accepted April 8, 2020
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