HOW VIRTUALITY SHAPES EMPLOYEES’ COLLABORATIVE BEHAVIORS: THE MEDIATING ROLES OF SOCIAL PRESENCE AND TRUST IN LEADERS
Master thesis, MSc Human Resource Management University of Groningen, Faculty of Economics and Business
January 17, 2021
HOW VIRTUALITY SHAPES EMPLOYEES’ COLLABORATIVE BEHAVIORS: THE MEDIATING ROLES OF SOCIAL PRESENCE AND TRUST IN LEADERS
ABSTRACT
Virtuality in the workplace has become increasingly important within the last decades. A steady growth in this trend is expected as a response to the governmental measures to fight the COVID-19 pandemic. While team collaboration remains a challenge in virtual work settings, it is a fundamental construct for predicting virtual teams’ and organizations’ effectiveness. This research focuses on whether and how virtuality impacts employees’ collaborative behaviors, conceptualized as knowledge contribution, processing, and not knowledge hiding. As Social Presence Theory emphasizes the importance of social presence to build trust in virtual workplaces, I propose that virtuality negatively impacts employees’ collaborative behaviors, and that this relationship is explained through the lack of social presence and the impaired development of trust in leaders. Moreover, I argue that task interdependence moderates the negative relationship between virtuality and social presence. To test the hypotheses, I conducted a field study by surveying supervisors and subordinates who work in remote settings. Results suggest that only contact dispersion as a dimension of virtuality positively affects knowledge processing. The relationship between spatial dispersion and collaborative behavior, specifically for knowledge processing and not knowledge hiding, is sequentially mediated by social presence and trust. No evidence has been found for task interdependence as a moderator between virtuality and social presence. Implications of these results are discussed.
HOW VIRTUALITY SHAPES EMPLOYEES’ COLLABORATIVE BEHAVIORS: THE MEDIATING ROLES OF SOCIAL PRESENCE AND TRUST IN LEADERS
INTRODUCTION
Due to globalization and the advancement of technology, virtuality in the workplace has become increasingly important within the last decades (Ale Ebrahim et al., 2009). Virtuality in firms refers to a situation “in which members are geographically dispersed and coordinate their work predominantly with electronic information and communication technologies (e-mail, video-conferencing, etc.)” (Hertel et al., 2005: 69). Within the last 10 years, virtual work settings have grown to 91% (Reynolds, 2019), showing benefits for employers and employees. For instance, while workers are given flexibility so they can cope with their private life (Hill et al., 1998; Townsend et al., 1998), companies may cut costs by reducing real estate expenses but also benefit from the increased pool of potential employees as employees with relevant skills from different locations can be hired (Ale Ebrahim et al., 2009; Cascio, 2000). Since March 2020, virtual work arrangements became the new standard, as recent COVID-19 security measures have prompted most people worldwide to stay home, forcing many workers to work from their homeplaces (Baker, 2020). Brynjolfsson et al. (2020) already examined that more than one-third of the American labor force changed to remote work between February and May 2020. A further increase in this growth is expected since 74% of companies plan to introduce permanent remote work modes (Lavelle, 2020).
the absence of real-time interactions, thus requiring higher coordination costs (Siebdrat et al., 2014; Steinfield et al., 1999). This is particularly important because collaboration, referred to as the interactive influence among individuals that facilitates open communication (Peters & Manz, 2007: 119), has proven to be essential for the effectiveness of teams and organizations. For instance, it has shown to enhance creativity (Ocker, 2005) and the quality of an organization’s outcomes by increasing organizational performance, ensuring innovation and competitive advantage (Boughzala & De Vreede, 2015; Montoya-Weiss et al., 2001; Peters & Manz, 2007). According to Holton (2001: 36), “The ability to work collaboratively is recognized as a core competency of a learning organization.”. Nevertheless, researchers highlight the importance of leadership and managerial skills in virtual settings, as leaders are in crucial positions to guarantee the functioning of team collaboration (Lee et al., 2010; Malhotra et al., 2007; Srivastava et al., 2006).
Given the importance of collaboration and the difficulties virtual teams face in creating it, little is known about how virtuality affects individuals’ collaborative behaviors. Therefore, based on the above-mentioned findings, this study addresses the following research question: Does virtuality influence employees’ collaborative behaviors, and if so, how is it affecting it?
other individuals and thus in leaders is more difficult to develop since workers can only rely on the other’s observable actions, and based on that, they evaluate the ability and trustworthiness (Kanawattanachai & Yoo, 2002).
Social Presence Theory (Short et al., 1976) might give a further explanation for the negative impact of virtuality on the collaborative behavior of subordinates. It argues that it is the “awareness of the co-presence of another sentient being” conceptualized as social presence, that enables the evolvement of trust in an individual, thus also in leaders (Biocca et al., 2001: 2). This awareness develops through immediate, real-time communication and, therefore, verbal and non-verbal cue transmission between individuals, which is impeded in virtual work settings (Biocca et al., 2003; Cui et al., 2013; Gunawardena, 1995; O’Leary & Cummings, 2007; Short et al., 1976). As the lack of social presence might demonstrate a reason for the impeded collaborative behavior of subordinates, it is subsequently important to spend thoughts on what would mitigate this negative effect. According to Steinfield (1986), social presence is assumed to reach its maximum when people are geographically close to each other and interact on the same task, namely when there is high task interdependence. Therefore, task interdependence, which refers to the extent of task-related interaction among individuals that affects the others’ work process (Bosch‐Sijtsema et al., 2009: 13), can be implemented to alleviate the negative consequences of virtuality on collaborative behavior through the impaired social presence.
In order to test these argumentations, I used empirical research by conducting regression analyses on survey data gathered from supervisors and their subordinates. The sample for the data analyses consists of 40 supervisors and 84 subordinates from 43 different organizations.
This approach enhances research on virtuality by explaining how and under which circumstances the individuals’ collaborative behaviors are impacted by introducing social presence and trust as predictors (Bickle et al., 2019). Along with the theoretical implications, this work gives managers practical insights by introducing what practices to undertake to increase the social presence and trust in supervisors, thus fostering subordinates’ collaborative behaviors.
In the following chapters, the concepts and constructs found in the literature are introduced, the hypotheses are visualized in a conceptual model and tested in regression analyses. In conclusion, the obtained results, the theoretical and practical implications, as well as limitations and future research directions are discussed.
THEORY AND HYPOTHESES Virtuality and Collaborative Behavior
defined virtuality as the “degree of distance between members, the extent that they work face-to-face, the extent of their collocation and the amount of asynchronous work that they do” (Schweitzer & Duxbury, 2010: 278). The higher the expression of each dimension, the higher the level of virtuality.
Resulting from the geographical dispersion and thus the absence of face-to-face interaction, previous researchers have analyzed that virtuality may inhibit collaboration within teams (e.g., Siebdrat et al., 2014), which refers to “the existence of mutual influence among members that enables open and direct communication, resulting in conflict resolution, and support for innovation and experimentation” (Peters & Manz, 2007: 119). Jarvenpaa and Leidner (1999) argue that the higher the level of dispersion, the more difficult are conductions of team processes. For instance, virtual teams face difficulties in fulfilling their coordination as well as communication needs, such as the need to share information, the need for (spontaneous) real-time interaction in order to provide rapid feedback, or the establishment of awareness of day-to-day related activities (Steinfield et al., 1999).
(2007) definition of collaboration implies, it also incorporates the processing of knowledge, which is managed through frequent dialogue and negotiation to achieve sensemaking and a shared understanding (Newell et al., 2004).
Nevertheless, researchers examined that a central problem of virtual teams is maintaining mutual knowledge as information exchange and information-seeking attempts in virtual work environments are hindered (Andres, 2012; Cramton, 2001). This leads to the assumption that workers within virtual work environments rather tend to hide their knowledge instead of exchanging ideas, thereby impeding collaboration. Furthermore, Cramton (2001) also argues that collaboration within virtual wok environments showed a higher risk of misunderstandings and communication malfunctions due to incomprehensible messages, which is why I argue that knowledge processing is also impeded. Such misinterpretations appear due to fewer discussions in virtual settings, which can change people’s impression of one another, their eagerness and manner of cooperation and communication, and thus harm virtual team collaboration in the long-term (Cramton, 2001: 350).
Given the difficulties of exchanging and managing know-how within virtual environments, I argue that virtuality negatively impacts collaborative behavior, conceptualized as knowledge contribution, not knowledge hiding, and knowledge processing. Based on the findings, this work examines the following hypothesis:
Hypothesis 1: Virtuality negatively affects the collaborative behavior of subordinates. Virtuality, Social Presence and Trust
distance incorporates the psychological and functional distance component, both conceptualized by Napier and Ferris (1993) by focusing on the supervisor-subordinate dyad. Functional distance between supervisor and subordinate is regarded as the “degree of closeness and quality of the functional working relationship between the supervisor and the subordinate”, encouraging leader-follower intimacy (Napier & Ferris, 1993: 337). Psychological distance, on the other hand, is described as “psychological effects of actual and perceived demographic, cultural and value differences between the supervisor and subordinate” (Napier & Ferris, 1993: 328-329).
Social presence, conceptualized as one’s personal feeling of closeness and connectedness with others through electronic media (Biocca, 1997; Heeter, 1992), comprises both concepts of subjective distance.
Referring to Short et al.’s (1976) theory on social presence, two fundamental concepts of social presence, particularly immediacy and intimacy, are presented, which are supposed to measure the communication medium’s quality (Gunawardena, 1995). Immediacy addresses the medium’s ability to enable efficient and effective communication by establishing a feeling of closeness and attachment between persons (Biocca et al., 2003, 2003; Cui et al., 2013; Short et al., 1976). More specifically, it represents a channel’s capability to transfer verbal and nonverbal information (Gunawardena, 1995). For instance, communication via videoconference is more efficient as it allows for immediate response, whereas communication via email would limit the efficient transmission of verbal and nonverbal cues, thus demonstrating difficulties in creating psychological and functional closeness.
et al., 1976). Social Presence Theory suggests that communication media that are high on immediacy and intimacy, meaning being able to efficiently transfer both nonverbal and social cues, are considered to be highest in social presence. Accordingly, this would mean that the level of social presence in face-to-face meetings is higher than communication via ICT due to the transmission of nonverbal feedback cues (Keil & Johnson, 2002; Kiesler & Cummings, 2002).
Because social presence relies on immediate, real-time communication and therefore verbal and non-verbal cue transmission, researchers argue that it is crucial for building trust since it strengthens relationship building (Aritz et al., 2018; Croes et al., 2016;Cyr et al., 2007; Kreijns et al., 2004). Williams (2001: 379) argues that “trust develops through repeated social interactions that enable people to update their information about others’ trustworthiness.” This means that individuals need to be “socially present” to be considered trustworthy (Cyr et al., 2007; Kiesler & Cummings, 2002; Short et al., 1976).
Nevertheless, previous research already emphasized the challenge of developing trust in virtual work arrangements, although it is a key factor for successful team functioning, communication, cooperation, and collaboration (Bierly III et al., 2009; Jarvenpaa et al., 1998; Jarvenpaa & Leidner, 1999; Kanawattanachai & Yoo, 2002; Kanter, 1994; McAllister, 1995; Wilson et al., 2006). Researchers even argue that trust within virtual work environments is essential for hindering geographical distance to develop to psychological distance (Gunawardena, 1995; Jarvenpaa et al., 1998). Leaders play a major role in this setting, as they are not only in crucial positions to establish and maintain trust in teams, but they also have to be trustworthy themselves to foster collaboration within virtual work contexts (Malhotra et al., 2007).
work settings, workers refer to cognitive-based trust since benevolence and integrity are more difficult to achieve between dispersed individuals (Kanawattanachai & Yoo, 2002). Due to the lack of social presence through reduced personal interaction and weaker social ties between individuals (Jarvenpaa & Leidner, 1999; Malhotra et al., 2007; McDonough III et al., 2001), it is difficult for them to interpret each other’s facial expressions since it is more critical to transfer emotions and feelings via electronic media. Referring to Social Presence Theory, this would mean that communication media with low intimacy and immediacy would lead to inhibited interpersonal relationship development and thus lower levels of trust.
Researchers analyzed that low levels of interpersonal trust between co-workers induce constrained information and idea exchange and limited expression of opinions, resulting in lower information availability, thus impeding the improvement and accomplishment of tasks within a team (Chi et al., 2012; Mooradian et al., 2006). Conversely, at high levels of interpersonal trust, knowledge sharing is stimulated (Golden & Raghuram, 2010). While previous literature emphasized the role of leaders and effective leadership in order to facilitate collaboration within teams, specifically the distribution and effective utilization of knowledge (Lee et al., 2010; Malhotra et al., 2007; Srivastava et al., 2006), little is known whether trust in leaders would affect knowledge management and therefore collaborative behaviors of subordinates.
Therefore, based on Social Presence Theory, I propose a serial mediation in the following hypothesis:
Hypothesis 2: The relationship between virtuality and the collaborative behavior of subordinates is sequentially mediated by leaders’ social presence and subordinates’ trust in them. Task Interdependence
In order to meet the collaborative challenges resulting from the impaired transmission of social cues faced in virtual work settings, the managerial practice task interdependence has been introduced as it may regulate the negative effects of virtuality on social presence (DeChurch & Mesmer-Magnus, 2010; Hertel et al., 2004).
According to research, high task interdependence, which is defined as the high “degree or requirement of task-driven [and frequent] interaction” among co-workers (Bosch‐Sijtsema et al., 2009: 13), positively affects team effectiveness (Hertel et al., 2005). As the work of the individuals is interdependently connected in such a framework, the accomplishment of one’s work would impact the work process of the other subjects in the group (Bosch‐Sijtsema et al., 2009). This, in turn, raises more awareness about the effects of an individual’s actions on others, creating incentives to increase personal contribution.
where people are geographically close to each other and collaborate on the same task. Referring to Social Presence Theory, working virtually would lower the social presence of a person compared to an onsite worker, but task interdependence as managerial practice would alleviate this negative effect.
Based on these findings, this work argues that task interdependence addresses and advances the two fundamental concepts of social presence theory, immediacy and intimacy, by mitigating the negative consequences of virtuality on collaborative behavior through intensifying social presence. Therefore, I propose the following hypothesis:
Hypothesis 3: The indirect effect of virtuality on collaborative behavior via social presence is moderated by task interdependence. The higher the task interdependence, the less negative the relationship between virtuality and social presence. The lower the task interdependence, the more negative the relationship between virtuality and social presence.
METHOD Sample and procedure
The presented hypotheses, shown in Figure 1, were tested on a sample of employees who were currently working (partly) remotely. In collaboration with a fellow student, we applied the convenience sampling method while searching for respondents using our own personal and professional networks. In case of interest, I distributed an introduction letter of my thesis in which I asked the supervisors to select and provide contact details of subordinates who were willing to participate. The respondents received a personal link via email to the online questionnaire. A translated version of the questionnaire (German and Dutch) was offered using a back-translation procedure. I assured all participants of their anonymity and full confidentiality of their responses. Therefore, I deleted all the respondents’ personal information after receiving and matching the data from supervisors and subordinates. The survey was sent to 56 supervisors and 135 subordinates of 43 different companies. Due to incomplete answers and attention check failures, the data-cleaning process excluded 51 respondents, leading to a new sample size of 84.
In general, the subordinate sample consisted of 58,33% full-time employees, 21.43% working students, and 14.29% part-time employees. Subordinates had a mean age of 31.2262 (SD = 10.1784)1, and 59.50% were male. This sample’s nationality was mainly German (40.48%) and Dutch (29.76%), who resided in six different countries, whereas 60.71% lived in Germany and 33.33% in the Netherlands. Most of the subordinates held a bachelor’s degree (47.62%) or master’s degree (29.76%). Furthermore, 64.29% of the subordinates worked entirely remotely.
Measures
To prevent common method variance through self-reporting data (Kline et al., 2000), subordinates reported virtuality, social presence and trust, whereas supervisors rated task interdependence and collaborative behavior of employees. In the following, I explain the measurement of the variables. Unless otherwise noted, all items were measured using a 7-point Likert scale (1 = strongly disagree, 7 = “strongly agree”). The questions from the survey are attached in the appendix (Appendix A).
Dependent Variable
Collaborative Behavior. No scale for measuring individual collaborative behavior was readily available. Therefore, I decided to conceptualize collaborative behavior as a subsumed label for employee knowledge contribution, not knowledge hiding and processing. The scale consisted of 4-items (α = .6353)2 adapted from Elsbach and Flynn (2013), Tsui et al. (1997), and Zhang and Min (2019). Knowledge contribution was measured using a two-item scale. One of the example items is “When working virtually, this subordinate provided ideas to help others improve their work.” Knowledge processing and not knowledge hiding were measured using a single-item scale. The constructs were measured using the items “When working virtually, this subordinate actively discussed ideas offered by others.” and “When working virtually, this subordinate sometimes communicated only parts of the relevant information to others.” (reversed).
Independent variables
Virtuality. According to theory, the construct of virtuality exists of several dimensions. Based on Schweitzer and Duxbury’s (2010) definition of virtuality, I conceptualized virtuality as
2 Although Cronbach’s alpha of collaborative behavior increased to .7272 when excluding not knowledge hiding,
spatial dispersion and contact dispersion3. Since the Cronbach’s alpha coefficient of these measurements is .0003, I decided to segregate the construct into two separate dimensions.
The formula of Spatial dispersion based on O’Leary and Cummings’ (2007) conceptualization was adapted to a dyadic level. It measures the sum of the physical distance in kilometers between the subordinate to the main office or the supervisor. The higher the spatial dispersion, the more virtual the relation.
Contact dispersion examines the extent of co-location of supervisor and their subordinates by indicating the possibility of face-to-face interactions. It is operationalized as the subordinate’s hours worked remotely per week, expressed as a proportion of the total weekly worked hours. The higher the contact dispersion, the more virtual the relation. Similar measures had been used, for instance, by Arling and Subramani (2011).
Social Presence. Social Presence was measured using Biocca et al.’s (2001) scale of social presence. I slightly adapted it to the supervisor-subordinate relationship by asking subordinates about their supervisors’ social presence, using a 6-items scale (α = .7728). Example items for social presence were: “I didn’t notice my supervisor while working remotely.” and “I was often aware of my supervisor while working remotely.”.
Trust in leaders. The construct of trust was measured by a slightly altered 4-item scale derived from De Jong and Elfring (2010), having a Cronbach’s alpha of .8660. Example items were: “When working remotely, I am able to count on my supervisor for help if I have difficulty with my job.” and “When working remotely, I am confident that my supervisor will keep me informed about issues that concern my work.”.
3 Temporal dispersion, quantified as the aggregation of all time zones between supervisors and their subordinates
Task Interdependence. Task interdependence was measured using a scale consisting of four items (α = .9073) developed by Pearce and Gregersen (1991) and Van der Vegt and Janssen (2003). It measured whether a team is dependent on consultation with the supervisor. Two examples of the items were: “When working remotely, my subordinates need information and advice from me to perform their jobs well.” and “When working remotely, my subordinates must frequently coordinate their efforts with me.”.
Control variables. Various control variables were tested to eliminate the possibility of alternative explanations for the hypotheses. All control variables were tested in the initial analyses, but I continued the tests with the controls that showed significance.
I controlled for subordinate’s age to exclude the possibility that age influences collaborative behavior, as in previous research, it has been related to innovative behavior (Guillén, & Kunze, 2019). I controlled for subordinate’s gender as females are generally more prone to be more relation-oriented, and their presence showed to improve team collaboration (Bear & Woolley, 2011; Eagly, 2009). I controlled for tenure, as employees with higher organizational tenure showed to generate more ideas (Woods et al., 2018). Further, I controlled for tenure with leaders and time spent on leaders’ work as it might indicate frequent interactions, thus enabling relationship and trust-building.
RESULTS
TABLE 1
Descriptive Statistics and Intercorrelations
M SD 1 2 3 4 5 6 7 1. Subordinate’s Age 31.2262 10.1784 1 2. Spatial Dispersiona 81.6744 324.9432 -.0870 1 3. Contact Dispersion .7995 .3338 -.1896† .0802 1 4. Social Presence 5.4187 .9090 -.0392 .0930 -.0819 1 (.7728) 5. Trust 6.2113 .7590 .0089 -.0690 -.1669 .7128** 1 (.8660) 6. Task Interdependenceb 4.8512 1.4386 -.3474** -.0715 .2940** -.0243 -.0950 1 (.9073) 7. Collaborative Behavior 5.5000 .9043 .4369** .1025 .0076 -.0806 .0088 -.2066† 1 (.6353) Notes. N = 84. Cronbach’s Alpha between parentheses on the diagonal. †p < .10, *p < .05, **p < .01, ***p < .001.
a = Subordinate’s age was included in the final analyses as it was the only control variable that significantly influenced the dependent variable.
b = Task Interdependence is rated only once by supervisors for the entire team, not for each subordinate, actual N = 40.
Confirmatory Factor Analysis (CFA)
Based on the implications of Kline (1998), a model needs to fulfil certain model-fit criteria to be a regarded as good-fitting: χ2/Df ratio should be less than 3, the values of CFI and TLI should
be greater than .9004, and RMSEA should not exceed .080.
The goodness-of-fit of the two-factor model is slightly off standards (χ2/Df [34] = 3.214;
RMSEA = .162; CFI = .836; TLI = .783) but it fits the data significantly better than the single-factor solution model (χ2/Df [35] = 4.120; RMSEA = .193; CFI = .762; TLI = .694). The ANOVA
results showed a significance of the differences between the two-factor model and the single-factor model at a 99.9% confidence interval (p < .001), χ2(1) =34.904.
A confirmatory factor analysis was also conducted for the variables task interdependence and collaborative behavior, both reported by the supervisors (see Table 2). The goodness-of-fit of the two-factor model is not meeting the RMSEA standard but is satisfactory for the remaining criteria (χ2/Df [19] = 2.144; RMSEA = .117; CFI = .930; TLI = .897). This model fits the data
significantly better than the single-factor solution model (χ2/Df [20] = 4.684; RMSEA = .209; CFI
= .763; TLI = .668). The significance of the differences between the two-factor model and the one-factor model showed that these differences are all significant at a 99.9% confidence interval (p < .001), χ2(1) =52.937.
TABLE 2
Results Confirmatory Factor Analysis (CFA)
Index Trust and Social Presence
χ2 Df χ2/Df RMSEA CFI TLI
2-factor-model 109.281 34 3.214 .162 90% [.129, .197] .836 .783 1-factor-model 144.185 35 4.120 .193 90% [.161, .226] .762 .694 ***
Index Task Interdependence and Collaborative Behavior
χ2 Df χ2/Df RMSEA CFI TLI
2-factor-model 40.742 19 2.144 .117 90% [.067, .166] .930 .897 1-factor-model 93.679 20 4.684 .209 90% [.168, .253] .763 .668 *** Notes. †p < .10, *p < .05, **p < .01, *** p < .001. Significance of difference compared to 2-factor model.
Hypotheses Testing
In the following paragraph, the results are presented per hypothesis. I standardized the measure constructs that are relevant for the mediated-moderation analyses. Each of the dimensions of virtuality had been tested solely and separately. Further, I made use of 5,000 bootstrap samples with confidence interval levels of 90%.
Test of Hypothesis 1
To test whether the first hypothesis is supported, I performed two regression analyses to predict subordinates’ collaborative behavior resulting from virtuality. Results of the regression analyses are presented in Table 3.
The first model demonstrates the prediction of collaborative behavior resulting from spatial dispersion, whereas the second model displays the results for contact dispersion as a predictor of collaborative behavior. In contradiction to Hypothesis 1, the results show neither evidence for a significant effect of spatial dispersion (model 1: β = .0004, SE = .0003, t(81) = 1.4294, p = .1567, 90% CI = [-.0001, .0009]), nor for a significant effect of contact dispersion on collaborative behavior (model 2: β = .4471, SE = .2712, t(81) = 1.6483, p = .1032, 90% CI = [-.0042, .8984]).
TABLE 3
Regression Results for Linear Regressions
DV: Collaborative Behavior
Model 1 Model 2
Controls Subordinate’s Age .0399 (.0088)** .0416 (.0089)** Predictors Spatial Dispersion Contact Dispersion .0004 (.0003) .4471 (.2712) R2 .2108 .2171 ΔR2 .0063
Notes. N = 84. Standard Errors between parentheses †p < .10, *p < .05, **p < .01, ***p < .001
Test of Hypothesis 2
Hypotheses 2 predicts that the negative relationship between virtuality and collaborative behavior is sequentially mediated by supervisors’ social presence and the trust in them. In order to examine whether this hypothesis is supported, I perform regression analyses using model 6 of the PROCESS macro by Hayes (2017). Table 4 summarizes the results of the regression analyses. Estimates of the indirect effects are provided along with the 90 percent bootstrapped confidence intervals for the path estimates.
As to the influence of contact dispersion, no mediation effect was found. Although the direct effect of contact dispersion on collaborative behavior is significant (β = .4745, SE = .2768, t(79) = 1.7144, p = .0904, 90% CI = [.0138, .9352]), social presence (Ind1 β = .0369, BootSE = .0745, 90% BootCI = [-.1728, .0681]) and trust (Ind2 β = -.0398, BootSE = .0662, 90% BootCI = [-.1662, .0364]) do not significantly mediate the relationship between contact dispersion and collaborative behavior. Results of the serial mediation did not find support either (Ind3 β = -.0245, BootSE = .0423, 90% BootCI = [-.1036, .0287]). Based on the findings, Hypothesis 2 can be partially supported, since the serial mediation through social presence and trust show to be significant in the relationship between spatial dispersion and collaborative behavior.
TABLE 4
Regression Results for Serial Mediation
M: Social Presence M: Trust DV: Collaborative Behavior Model 3 Model 4 Model 4 Model 5 Model 6 Model 7
ΔR2 .0002 -.0071 .0018
Regression results serial mediation (Spatial Dispersion)
β SE t p
Direct effect of Spatial Dispersion on
Collab. Behavior .0005 .0003 16.585 .1013
Indirect effect of Spatial Dispersion
on Collab. Behavior LLCI ULCI
Total effect of Spatial Dispersion on
Collab. Behavior -.0001 .0001 -.0003 .0000
Social Presence .0000 .0001 -.0004 .0000
Trust -.0001 .0001 -.0002 .0001
Social Presence and Trust .0000 .0001 .0000 .0003
Regression results serial mediation (Contact Dispersion)
β SE t p
Direct effect of Contact Dispersion
on Collaborative Behavior .4745 .2768 17.144 .0904
Indirect effect of Contact Dispersion
on Collaborative Behavior LLCI ULCI
Total effect of Contact Dispersion on
Collaborative Behavior .0274 .0745 -.1728 .0681
Social Presence .0369 .0566 -.0401 .1379
Trust -.0398 .0662 -.1662 .0369
Social Presence and Trust -.0245 .0423 -.1036 .0287
Notes. N = 84. Standard Errors between parentheses †p < .10, *p < .05, **p < .01, ***p < .001
Test of Hypothesis 3
I used model 7 of the PROCESS macro by Hayes (2017) to test the predicted moderated mediation, where social presence mediates the negative relationship between virtuality and collaborative behavior, and task interdependence moderates the relationship between virtuality and social presence. Table 5 presents the mediator and the dependent variable model.
was not significant (β = .0550, SE = .0819, t(79) = .6720, p = .5035, 90% CI = [-.0812, .1912]), shown in model 8 (R2 = .0272). Further, the index of moderated mediation was non-significant (β
= -.0042, BootSE = .1520, 90% BootCI = [-.0300, .0724]), showing no evidence for a moderated mediation relationship at high levels (β = -.0274, BootSE = .0613, 90% BootCI = [-.1056, .0277]), at average (β = -.0261, BootSE = .0684, 90% BootCI = [-.1113, .0260]), and at low levels of task interdependence (β = -.0254, BootSE = .0823, 90% BootCI = [-.1164, .0261]).
Model 9 and 11 demonstrate the effect of contact dispersion on social presence and collaborative behavior. The interaction effect of contact dispersion and the moderator task interdependence on the mediator social presence was not significant (β = .0249, SE = .0935, t(79) = .2668, p = .7903, 90% CI = [-.1306, .1805]), shown in model 9 (R2 = .0156). Further, the index
of moderated mediation was non-significant (β = -.0019, BootSE = .0113, 90% BootCI = [-.0238, .0126]), showing no evidence for a moderated mediation relationship at high levels (β = .0023, BootSE = .0123, 90% BootCI = [-.0199, .0193]), at average (β = .0034, BootSE = .0131, 90% BootCI = [-.0179, .0234]), and at low levels of task interdependence (β = .0046, BootSE = .0131, 90% BootCI = [-.0201, .0333]).
Based on these findings, no evidence was found in this sample to support Hypothesis 3 TABLE 5
Regression Results for Moderated Mediation
M: Social Presence
DV: Collaborative Behavior Model 8 Model 9 Model 10 Model 11
Spatial Dispersion .3447 (.2635) .1344 (.0902) Contact Dispersion -.0551 (.1259) -.1451 (.0914) Moderators Task Interdependence -.0411 (.4277) -.1056 (.1698) Spatial Dispersion x Task Interdependence .0550
(.0819)
Contact Dispersion x Task Interdependence .0179 (.0967) Mediators Social Presence -.0765 (.0989) -.0490 (.0090) R2 .0272 .0168 .2166 .2195 ΔR2 -.2552 .0029
Regression results mediation (Spatial Dispersion)
β SE t p
Direct effect of Spatial Dispersion on
Collaborative Behavior .1344 .0902 14.897 .1402
Conditional indirect effects
Task Interdependence Effect SE BootLLCI BootULCI
-1SD -.0254 .0823 -.1164 .0261
Mean -.0261 .0684 -.1113 .0260
+1SD -.0274 .0613 -.1056 .0277
Index of moderated mediation -.0042 .1520 -.0300 .0724 Regression results mediation (Contact Dispersion)
β SE t p
Direct effect of Contact Dispersion on
Collaborative Behavior .0634 .1002 .6328 .5287
Conditional indirect effects
Task Interdependence Effect SE BootLLCI BootULCI
-1SD .0046 .0171 -.0201 .0333
Mean .0034 .0131 -.0179 .0234
+1SD .0023 .0023 -.0199 .0193
Supplementary Analysis
In this section, an analysis of the results using segregated dimensions of collaborative behavior is provided. Given that collaborative behavior is a multi-dimensional construct and its relatively low reliability as a unitary construct, I tested the models again but split the variable into the separate dimensions knowledge contribution, knowledge processing, and not knowledge hiding. Since knowledge contribution is measured with two items, a new variable is computed. Tables of the supplementary analysis can be found in Appendix C.
Table C1 demonstrates the results of the linear regressions. Whereas the results of the analyses with spatial dispersion showed to have no significant effects on the dimensions knowledge contribution (model 12: β = .0003, SE = .0003, t(81) = 1.0629, p = .2910, 90% CI = [-.0002, .0001]), knowledge processing (model 14: β = .0003, SE = .0004, t(81) = .8704, p = .3866, 90% CI = [-.0003, .0009]) and not knowledge hiding (model 16: β = .0006, SE = .0005, t(81) = 1.1063, p = .2719, 90% CI = [-.0003, .0015]), the effect of contact dispersion significantly influenced knowledge processing (model 15: β = .7541, SE = .3439, t(81) = 2.1930, p = .0312, 90% CI = [.1819, 1.3263]). No evidence has been found for the effect of contact dispersion on knowledge contribution (model 13: β = .4690, SE = .3122, t(81) = 1.5024, p = .1369, 90% CI = [-.0504, .9884]) and not knowledge hiding (model 17: β = .0962, SE = .5302, t(81) = .1814, p = .8565, 90% CI = [-.7860, .9784]).
being non-significant (βKP = .0004, SE = .0004, t(79) = 1.0592, p = .2927, 90% CI = [-.0002,
.0010]; βKH = .0007, SE = .0005, t(79) = 1.3233, p = .1896, 90% CI = [-.0002, .0016]). The results
of contact dispersion did not show evidence for a serial mediation.
The results of the moderated mediation analyses with spatial dispersion as independent variable are summarized in Tables C5 to C7. The results demonstrate non-significant indices of moderated mediation for knowledge contribution (model 24: β = -.0036, BootSE = .1279, 90% BootCI = [-.0219, .0682]), knowledge processing (model 26: β = -.0058, BootSE = .1806, 90% BootCI = [-.0368, .0861]) and not knowledge hiding (model 28: β = -.0039, BootSE = .2276, 90% BootCI = [-.0691, .0801]), showing no evidence for a moderated mediation relationship for each of the dimensions. The indices of moderated mediation with contact dispersion as independent variable were also non-significant for knowledge contribution (model 25: β = -.0007, BootSE = .0086, 90% BootCI = [-.0138, .0137]), knowledge processing (model 27: β = -.0013, BootSE = .0115, 90% BootCI = [-.0210, .0159]) and not knowledge hiding (model 29: β = -.0009, BootSE = .0193, 90% BootCI = [-.0311, .0233]), showing no evidence for a moderated mediation relationship for each of the dimensions.
DISCUSSION
The purpose of this paper was to analyze whether the relationship between virtuality and employees’ collaborative behaviors can be explained through the social presence of leaders and the development of trust in them.
significantly mediate the relationship between spatial dispersion and collaborative behavior, concretely for the behaviors knowledge processing and not knowledge hiding, the indirect effects of contact dispersion and collaborative behaviors via social presence and trust remained non-significant. Nevertheless, contact dispersion only significantly influences the component knowledge processing. No evidence has been found for the moderated mediation through task interdependence, neither for spatial dispersion nor for contact dispersion.
An explanation for the lack of consistency in the results for virtuality significantly affecting collaborative behaviors might be that collaborative behaviors are characterized by complexity and show multidimensionality. The results demonstrated that not all facets of collaborative behaviors could be predicted by virtuality, but if they are, they may show differences in the impact. For instance, in this sample and this conceptualization, knowledge processing and not knowledge hiding seemed to significantly vary as a result of the virtuality dimension spatial dispersion, whereas knowledge processing only results from contact dispersion.
absence of social-emotional communication to form group cohesion, I argue that it can also be explained through the increased possibility of miscommunication (Cramton, 2001).
Theoretical Implications
The current paper contributes to the understanding of virtuality and collaboration in various meaningful ways.
Given the importance of collaboration at the team level, the study extends current literature by showing that virtuality impacts collaborative behaviors at the individual level but not equally in all dimensions. Different collaborative behaviors may have different antecedents. For instance, while the positive effect on knowledge processing can be explained through the virtuality dimension contact dispersion, it showed no significant evidence when testing for the direct effect of spatial dispersion. Nevertheless, the relationship between spatial dispersion and knowledge processing is sequentially mediated through social presence and trust in leaders, while the direct effect remained non-significant. This was also the case for the effect of spatial dispersion on not knowledge hiding. For knowledge contribution, no antecedents could be identified. Therefore, this study contributes to current research by arguing for the multidimensional character of collaborative behavior and demonstrating its different antecedents.
supervisors impacts the collaborative behaviors of individual team members. Whereas previous studies on virtuality and collaboration mainly focused on the team setting and the social presence of team members (Bickle et al., 2019; Furst et al., 1999; Giesbers et al., 2009; Gressgård, 2011; Sallnäs, 2005), the current paper extends the collaboration literature by helping to explain that the awareness and trust in leaders within virtual work environments also matter for the collaborative behaviors of subordinates.
Practical Implications
Several scholars have demonstrated the practical importance of understanding virtual work arrangements due to the recent governmental safety measures that addressed a minimum of social contacts. More and more companies were forced to switch to remote work to guarantee the businesses functioning. According to Lavelle (2020), a further increase in this growth is expected since 74% of companies plan to introduce permanent remote work modes. Therefore, it is essential for companies to understand the impact of remote work on individuals, especially on collaborative behaviors, since it is essential for organizational outcomes (Boughzala & De Vreede, 2015; Montoya-Weiss et al., 2001; Peters & Manz, 2007).
members. According to research, the use of videoconferencing is likely to positively influence the participants’ social presence (Giesbers, et al., 2009; Sallnäs, 2005). Moreover, leaders should react more often in online meetings in order to appear more present. When reacting, they should make sure that they respond with audio instead of only text messages or emoticon reactions, since in this way the communication is clearer, enabling the participants to correctly interpret but also demonstrate social and emotional cues (Giesbers et al., 2009).
Limitations and Directions for Future Research
In this last section, limitations of the current study are addressed, and directions for future research are provided.
A significant limitation of the current study is the low response rate and the considerable amount of missing data, which may affect the findings. The data was gathered in times of the COVID-19 pandemic in 2020, which reduced the sample size since many respondents withdrew their engagement in the study. Increasing the number of participants would allow for more representative and meaningful results, thus being more generalizable. Future research should also address respondents who work in different time zones in order to include another significant dimension of virtuality. According to Mell et al. (2020), temporal distance is a more influential factor in virtual teams than spatial distance since it narrows the time frame for synchronous communication. In this study, temporal dispersion was measured but excluded because only two respondents reported a different time zone location.
dispersed one is from the workplace, the more virtual the relation to co-workers and the supervisor would be (O’Leary & Cummings, 2007). However, this does not apply to this setting. The majority of the respondents used to work onsite before companies had to react to the governmental COVID-19 measures by turning virtual. Therefore, the spatial distance to the workplace is generally low, but since social contacts are reduced to their minimum, one can assume that the relation can be seen as virtual due to the reduced chances to meet face-to-face (Cooper, 2020; McNamara, 2020; Roberts, 2020). Therefore, future research on virtuality should develop alternatives in order to measure virtuality, even after relaxations in the governmental orders, since working remotely will become a common work arrangement (Lavelle, 2020). For instance, although other researchers have argued that the dependence on technology is rather a consequence than a prerequisite of virtuality (e.g., Schweitzer & Duxbury, 2010), I argue that this measure will play a more crucial role in future virtual settings, while the degree of spatial dispersion will lose of importance. Spatial dispersion remains a criterion of virtuality, however, it will not directly give an indication of the degree of virtuality.
Furthermore, besides the dependency on ICT, future research should take the different communication media that are used (face-to-face interaction, video calls, email, chats, etc.) into account since this has a meaningful effect on the social presence as well as trust level. Whereas videoconferencing resembles real-life face-to-face interactions in the workplace, social and nonverbal cues cannot be transmitted via email communication (Sallnäs, 2005). I argue that contact dispersion as the dimension of virtuality shows inconsistencies in the evidence since the use of different communication media was not included.
interactions. However, in the case of partial remote work, the interpretation of the variable might be inaccurate as it only indicates the possibility of to-face interactions and not the actual face-to-face interaction. Therefore, future research should use the extent of remote work as a dimension of virtuality, as it measures the extent of collocation. However, future research should additionally include another dimension that measures the actual face-to-face interaction (Schweitzer & Duxbury, 2010).
Another limitation of the virtuality measurement is that it does not include the change from onsite work to a remote setting. Previous research oftentimes examined teams that were from the beginning virtual (Ale Ebrahim et al., 2009; Carlson et al., 2013; Malhotra et al., 2007). However, a measurement that takes the change into consideration would reduce biases. For instance, employees may have a better comparison to onsite work, and based on the previous experience, they decide to work more collaboratively to overgo communication and coordination problems.
Another explanation might give the work of Siebdrat et al. (2014). According to them, it is not the spatial dispersion per se that impacts employees’ eagerness to collaborate, but rather the subjective distance. As most of the respondents used to work onsite before, there is a higher possibility that a basis of trust had been built already, thus having lower chances of developing functional and psychological distance when turning virtual (Gunawardena, 1995; Jarvenpaa et al., 1998). Therefore, the direction of future research is to conduct a longitudinal study and include the change to remote work by examining the variables over time (before and after turning virtual).
Furthermore, linked with the findings, another explanation for the insufficient Cronbach’s Alpha can demonstrate the multidimensional character of collaborative behavior, which results from the different effects of virtuality on the different collaborative behavior components. Therefore, future research should not treat collaborative behavior as a unitary measure but separate the construct into its dimensions.
Lastly, this study did not consider multilevel issues, although task interdependence was measured at a team level, whereas the remaining variables had been measured at the individual level. Since multilevel analysis would go beyond the scope of this work, it was not included in this study. However, this might have influenced the results of the analyses. Therefore, a direction for future research is to validate whether my claim holds that task interdependence moderates the relation between virtuality and social presence by either conducting a multilevel analysis or measuring task interdependence between a supervisor with the individual subordinate.
CONCLUSION
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