Effects of ICT Shared Mental Models on Team Processes and Outcomes

Abstract

Although existing studies have shown that shared mental models of information and communication technology (ICT shared mental models) are related to better computer-mediated teamwork, causal effects on team processes and outcomes remain unclear. This study analyzes the effects of ICT shared mental models on team effectiveness indicators via ICT use and team communication. Results based on 69 three-person teams show that manipulated ICT shared mental models significantly influence team performance, coordination effectiveness, and affective team commitment, partially mediated by consistent ICT use in a team and team communication. Study results extend findings on antecedents, mechanisms, and effects of ICT shared mental models.

Keywords

ICT shared mental models computer-mediated communication virtual groups performance commitment

Due to technological progress, many different information and communication technologies (ICTs; e.g., chat programs, ticket systems, and file-sharing software) are increasingly common for teamwork (Gilson et al., 2015). Because of the wide availability and variety of ICTs, disagreements among team members may exist about which one to use and which one is appropriate (Klötzer et al., 2017). These disagreements can result in inconsistent ICT use, inefficient team communication, poor team coordination, and performance (Rice et al., 1998) as well as low team commitment. Thus, a key question remains unanswered: How do common understandings among team members emerge regarding their ICT use in order to improve team processes and outcomes?

Shared mental models, or a common understanding of teamwork, have positive relationships with team communication, team effectiveness, and viability (DeChurch & Mesmer-Magnus, 2010). Shared mental models emerge from individual mental models of team members, which can vary both intra- and inter-individually depending on experience and contextual factors (Cannon-Bowers et al., 1993). Different experiences lead to different mental models of which ICT is useful for a task (cf. Carlson & Zmud, 1999; Handke et al., 2018). According to technology acceptance research, mental models of the usefulness of ICTs are a predictor of actual ICT use (Venkatesh et al., 2003). Therefore, research assumes that for collaborative and consistent ICT use within teams, individual mental models of ICT use must converge to shared mental models (Kock, 2004).

Studies that have supported the relationship between ICT shared mental models, team performance, and coordination effectiveness have used correlational designs with self-ratings or qualitative approaches (Müller & Antoni, 2020a, 2020b; Thomas & Bostrom, 2007). However, these approaches prohibit drawing causal inferences. If these relationships are confirmed to be causal, ICT shared mental models, as an emergent state that manifests over time, could provide an explanation for why virtual team performance increases over time (cf. Fuller & Dennis, 2009). Although affective outcomes, such as trust and commitment, play an important role in virtual team functioning (Breuer et al., 2016), the relationship with ICT shared mental models has not been considered so far. Therefore, investigating ICT shared mental models as an antecedent of affective aspects could provide indications on why virtual team members often identify little with their team (cf. Mannix et al., 2002).

Besides analyzing the influence of ICT shared mental models on team outcomes, we also want to investigate possible mediating mechanisms. Emergent states are supposed to serve as inputs for team processes, such as team communication and ICT use, which are mediating mechanisms for attaining good team functioning and team effectiveness (Marks et al., 2001). Therefore, we investigate, whether team processes mediate the effects of ICT shared mental models on team performance, coordination, and commitment, which has not been investigated until now. Understanding more about how ICT shared mental models influence team processes and outcomes will allow to develop team interventions to support the emergence of ICT shared mental models and effective ICT use in te.

This study aims to contribute to existing research on ICT shared mental models in four ways. First, we identify the causal effects of ICT shared mental models on virtual team performance and coordination effectiveness. Second, we analyze the impact of ICT shared mental models on affective team commitment, which is particularly crucial for virtual team functioning. Third, we clarify the underlying causal mechanisms of ICT shared mental models’ influence on team outcomes by analyzing the mediating role of team communication and ICT use in the team. Fourth, by examining different ICT experiences of team members and planning ICT use in a team as input factors, we provide possible approaches that organizations might consider to support the emergence of ICT shared mental models.

Shared Mental Models

Shared mental models are emergent states that reflect shared knowledge structures among team members about the working environment (Cannon-Bowers et al., 1993). Emergent states describe cognitive, motivational, and affective “properties of the team that are typically dynamic in nature and vary as a function of team context, inputs, processes, and outcomes” (Marks et al., 2001, p. 357). Emergent states are distinguished from team processes. Emergent states are products of teamwork over time, such as the convergence of individual mental models to shared mental models. In contrast, team processes describe interactional processes, such as communication or coordination (Marks et al., 2001).

Team research has divided shared mental models into three subtypes according to their content. Teamwork shared mental models represent a common understanding of team members’ expertise, skills, and abilities. Team members with teamwork shared mental models have the same knowledge (i.e., mental models) of which team member is an expert in which area. Taskwork shared mental models represent a common understanding of the team’s tasks, strategies, and goals. Team members with taskwork shared mental models have the same knowledge of which goal to achieve using which strategies (Cannon-Bowers et al., 1993; Mathieu et al., 2000). Temporal shared mental models represent a common understanding of temporal aspects, such as deadlines or duration of tasks. Team members with temporal shared mental models have the same knowledge of, for example, the next deadline (Gevers et al., 2006; Mohammed et al., 2015).

Previous team research has focused on the positive effects of teamwork, taskwork, and temporal shared mental models on team processes, like team communication (DeChurch & Mesmer-Magnus, 2010). Team communication represents the content of what team members communicate during teamwork. Marks et al. (2001) differentiates action, transition, and interpersonal processes of teamwork. In action phases, team members communicate about their task, that is, what needs to be accomplished to achieve the goal. In transition phases, team members formulate their strategy and communicate about goal specification. In interpersonal phases, team members communicate about conflicts or motivate each other (Marks et al., 2001). In addition, team outcomes, such as performance, coordination effectiveness, and viability, are positively influenced by shared mental models (DeChurch & Mesmer-Magnus, 2010; Mohammed et al., 2015). Team performance refers to the degree to which team results meet or exceed task requirements. Team coordination effectiveness describes how well teams are able to coordinate their interactions. Team viability describes the willingness of team members to continue their teamwork in the future (Antoni & Hertel, 2009).

Shared Mental Models of Information and Communication Technology

It is unclear whether or not shared mental model subtypes apply completely to virtual teams. Schmidtke and Cummings (2017) highlighted that shared mental models become more complex in virtual teams and need to include more aspects than in previously investigated face-to-face teams. They concluded that ICTs should be included in the construct of shared mental models. Shared mental models of ICTs have been considered particularly important for teams using a variety of ICTs (e.g., Müller & Antoni, 2019; Schmitdke & Cummings, 2017; Thomas & Bostrom, 2007). ICTs are collaborative digital tools for team interaction, such as e-mails, chat programs, ticket systems, video conferencing tools, and file-sharing systems. The way they are used by team members influences team interaction and performance (Gilson et al., 2015). Müller and Antoni (2019) defined ICT shared mental models as shared knowledge structures of ICT functionalities, task-specific ICT use, ICT adaptation, and ICT netiquette. In this study, we focus on shared mental models of task-specific ICT use. Shared knowledge of task-specific ICT use describes that team members have similar mental models of combining specific task requirements with specific ICTs (e.g., team members have the same understanding of which ICT to use for urgent queries). Low similarity in ICT shared mental models means that team members think differently about the most appropriate ICT for different tasks.

Mohammed et al. (2010) summarized research findings on antecedents, outcomes, and mechanisms of teamwork and taskwork shared mental models. They demonstrated that team member characteristics and team interventions influence team processes and outcomes, such as team coordination, performance, and viability, which is mediated by shared mental models. However, this model focuses only on teamwork and taskwork shared mental models. The research question of this study is therefore which antecedents, mechanisms, and consequences of ICT shared mental models can be found. In the following sections, we will describe antecedents, consequences, and mechanisms that we hypothesize are associated with ICT shared mental models.

Antecedents of ICT shared mental models

Although ICT shared mental models are considered important for virtual teams (Schmidtke & Cummings, 2017; Thomas & Bostrom, 2007), to our knowledge, no study has examined their antecedents to identify implications for how to foster the emergence of ICT shared mental models. This is particularly important as previous research has shown that individuals have different mental models of ICT use (e.g., Fulk et al., 1991; Handke et al., 2018). Team members tend to have different experiences regarding their ICT use (Dennis et al., 2008), leading to different ICT mental models (Kock, 2004). Channel expansion theory (Carlson & Zmud, 1999) suggests that ICT experience positively correlates with perceptions of ICT richness, which describes the extent of relevant cues an ICT can transmit (e.g., textual, auditory, visual information). Accordingly, more experienced team members perceive an ICT as richer than less experienced team members. D’Urso and Rains (2008) as well as Handke et al. (2018) confirm that experience with an ICT increases the perception of its richness. However, the perception of ICT richness is a predictor of which ICT will be used. If team members have different experiences with ICTs, their mental models of the most appropriate ICT for a given task are likely to be different (cf. Chudoba & Watson-Manheim, 2008). Therefore, we assume that different ICT experiences of team members lead to low similarity in their ICT mental models.

To provide an answer to how the emergence of ICT shared mental models can be supported, we can look at research on emergent states. Emergent states are process-oriented, which means that individual mental models of ICT use converge during the process of collaboration and interaction among team members (cf. Kozlowski et al., 2013). If team members have the opportunity to discuss their ICT use, they can develop implicit rules and derogations reflecting situation requirements. We argue that a discussion of ICT use leads to clarification about other team members’ ICT mental models, that is, which ICTs other team members already know and prefer and which ICT they will use for which purpose during their teamwork. Gurtner et al. (2007) found that group discussions about team interaction patterns lead to high similarity of mental models. Marks et al. (2000) have shown that team trainings on effective communication within a team result in more similar team interaction mental models than no team training. Teams that communicate about and plan their ICT use should therefore increase their similarity of ICT mental models. To sum up, we assume that different ICT experiences and planning of ICT use are antecedents of the similarity of ICT mental models within teams.

H1: Teams that plan their ICT use have a higher similarity in ICT mental models than teams with different ICT experiences among team members that have a low similarity in their ICT mental models.

Influence of ICT shared mental models on team outcomes

Although numerous studies have shown that teamwork, taskwork, and temporal shared mental models have positive effects on team outcomes (DeChurch & Mesmer-Magnus, 2010; Mohammed et al., 2015), few correlational studies have investigated the relationship between ICT shared mental models and team effectiveness (e.g., Müller & Antoni, 2020a). More studies are needed to confirm the positive impact of ICT shared mental models on team performance. As described above, team performance is defined as meeting or exceeding task requirements (Antoni & Hertel, 2009). Since ICT shared mental models represent similar knowledge structures of task-specific ICT use, team members can describe, anticipate, and predict other team members’ ICT use for specific tasks. Due to high similarity in their ICT mental models and predictability of ICT use, team members can work in a coordinated way, freeing up more time, and cognitive capacity for task accomplishment (cf. Kock, 2004). Less time and less cognitive effort for planning their ICT use result in more time for task accomplishment and better team performance (Thomas & Bostrom, 2007).

H2a: Teams with high similarity in ICT mental models perform better than teams with low similarity in ICT mental models.

A second result, which has so far only been studied correlatively (Müller & Antoni, 2020a) and should therefore be subject to empirical validation, is the effectiveness of team coordination. Due to similar mental models of ICT use, implicit coordination may occur (cf. Rico et al., 2008). According to Rico et al. (2008), implicit coordination “takes place when team members anticipate the actions and needs of their colleagues and task demands and dynamically adjust their own behavior accordingly, without having to communicate directly with each other or plan the activity” (p. 164). Rico et al. (2008) assume that unshared mental models lead to time-lagged responses and uncoordinated teamwork. Therefore, ICT shared mental models should facilitate effective team coordination because team members can anticipate others’ ICT use and can provide timely responses and information based on their knowledge of the ICT being communicated in (Müller & Antoni, 2019).

H2b: Teams with high similarity in ICT mental models have a better team coordination effectiveness than teams with low similarity in ICT mental models.

Previous research has confirmed that affective aspects are important for team functioning, particularly in virtual teams (Breuer et al., 2016). At the same time, it has been shown that ICT use can have negative influence on affective aspects (e.g., technostress, Ayyagari et al., 2011; well-being and health problems, Day et al., 2012). We hypothesize that ICT shared mental models facilitate high-quality interpersonal interactions via ICTs through similar expectations of ICT use. High-quality interactions promote team members’ positive feelings at work and affective team commitment (Bishop & Scott, 2000). Affective team commitment represents “the relative strength of an individual’s identification with, and involvement in, a particular . . . team” (Bishop & Scott, 2000, p. 439). In addition, Bishop and Scott (2000) found that team commitment is negatively related to inter-sender conflict, which they characterized as differing expectations from multiple team members. As similar ICT mental models represent similar expectations of ICT use, less inter-sender conflicts and higher team commitment should occur. Consistent with this, Klitmøller and Lauring (2013) provided indications that different expectations of ICT use are associated with frustrations among team members. In our study, we extent research on the effects of ICT shared mental models on affective team commitment.

H2c: Teams with high similarity in ICT mental models feel more committed to their team than teams with low similarity in ICT mental models.

Influence of ICT shared mental models on team processes

Previous research only examined the effects of ICT shared mental models on team effectiveness. The question remains whether team communication can also be positively influenced by ICT shared mental models, such as by teamwork and taskwork shared mental models (e.g., Marks et al., 2002; Mathieu et al., 2000, 2005). ICT shared mental models help team members to predict their ICT use without the need to communicate explicitly about it. Team members with a high similarity of ICT mental models can establish a time-saving and efficient team communication and focus on goal- and task-oriented communication rather than on planning aspects (cf. Rico et al., 2019). In contrast, teams with low similarity in their ICT mental models may need to talk explicitly about and plan their ICT use to accomplish the task (cf. Cannon-Bowers et al., 1993) and have less time for task-oriented communication. Transferring this reasoning to the phase model by Marks et al. (2001), less communication about ICT use should reduce transition phases and leave more time for action phases. Teams that differ in their similarity of ICT shared mental models should show differences in their communication content and efficiency, which in turn affects team outcomes.

H3a: ICT shared mental models improve team outcomes via efficient task-oriented team communication.

Because previous research on technology acceptance (e.g., Venkatesh et al., 2003) has shown that the perception of an ICT as useful is an indicator for actual ICT use, the question arises whether ICT use in a team is another team process that can be influenced by ICT shared mental models. Technology acceptance research has so far focused only on individual ICT use as consequences of individual cognitions of ICTs. The perspective of a collaborative ICT use, that is, how uniformly team members use ICTs within their teamwork, has received little attention in research to date. Only Thomas and Bostrom (2007) found that ICT shared mental models are related to successful technology facilitation within a team. However, this finding is based on a qualitative approach that limits generalizability. The question is whether ICT use in a team can be confirmed as a team process that acts as a mediator between ICT shared mental models and team outcomes. We assume that team members who have a low similarity in their ICT mental models use ICTs according to their individual mental models without being able to anticipate the other team members’ ICT use. Different ICT mental models would then lead to inconsistent ICT use. Conversely, team members with a high similarity in their ICT mental models should be able to anticipate their ICT use and use them consistently.

H3b: ICT shared mental models improve team outcomes via consistent ICT use in the team.

Representing our hypotheses, Figure 1 displays the research model of this study. Different ICT experiences and planning the ICT use influence the similarity of ICT mental models, which then influences team performance, team coordination effectiveness, and affective team commitment via team communication and consistent ICT use in a team.

Method

Participants and Procedure

In total, 207 undergraduate students (79% females, M_age = 22.31 years) from a German university participated in the experiment. Based on results of studies using similar team decision-making tasks (e.g., Ellwart et al., 2015), we calculated the required sample size a priori (using GPower 3.1.9.2, F tests, MANOVA: Global effects, f²(V) = 0.25, α = .05, 1−β = .80 with two groups and five response variables). Students were randomly assigned to teams of three participants and the teams were randomly assigned to two ICT shared mental models conditions (shared vs. unshared). Eight teams had to be excluded due to missing data, abandonment, or wrong material. After exclusion, 69 teams remained for analyses. In total, 34 teams were assigned to the shared condition and 35 teams were assigned to the unshared condition. For participation, students received course credit or monetary compensation (15 €).

After being welcomed, participants were placed at three separate laptops. Each laptop was equipped with the following ICTs: Slack (chat program), Trello (ticket system), Outlook (e-mail program), InVision (online whiteboard), and Microsoft Office. It was impossible for participants to have any face-to-face contact during the experiment, as there were privacy shields between them. Teams can be described as virtual teams as the only way to communicate with each other was using the aforementioned ICTs (cf. Breuer et al., 2016). Screen recordings were taken to detect potential disruptions (e.g., computer crashes) during the experiment.

Participants received information about the aim of the experiment and signed a consent form. Afterwards, a training phase of around 10 minutes followed, in which participants performed some exercises with the ICTs to get used to them. After training, participants received instructions for their team decision-making task. Participants were told that they work in a student consultancy and they had to select an applicant for a management position. Participants received information about seven potential applicants and the required criteria for the position. The information about the required criteria was partially the same for all team members, but some information was distributed among participants to create interdependence among participants (i.e., hidden profile paradigm; Stasser, 1992). To solve the task, team members had to exchange all distributed information about the requirement criteria. Only one applicant met the required criteria. The solution should be presented in a PowerPoint presentation including the recommended applicant and each exclusion criterion for the other six applicants. Teams had 40 minutes for task execution. After 40 minutes, participants received a questionnaire measuring the dependent variables.

Manipulation of ICT Shared Mental Models

Before team members began the task, we manipulated ICT shared mental models with two conditions: planning of ICT use (shared condition) and different ICT experiences (unshared condition). Teams in the shared condition had the opportunity to discuss their ICT use face-to-face 5 minutes prior to task execution. The face-to-face discussion about ICT use should ensure that the individual ICT mental models of the team members converge. This interaction displays the process of convergence (cf. Kozlowski et al., 2013). At the end of the discussion, each team member documented the ICT use on paper. In the unshared condition, each participant received a cover story with different descriptions of their own previous experience with ICTs. In each of these descriptions, one ICT (e-mail, chat program, or ticket system) was presented as beneficial and the other available ICTs as ineffective. To standardize the study design and control the influence of face-to-face interaction, teams of the unshared condition should discuss their experience with student consultancies face-to-face for 5 minutes prior to task execution. At the end of the discussion, each team member documented the ICT use on paper.

To check if the manipulation of ICT shared mental models worked, all participants had to indicate which ICT they intended to use and their commitment to their ICT use or experiences prior to task execution. After task execution, participants rated whether they had adhered to their ICT use or experiences and which ICTs they had actually used during teamwork.

Dependent Measures

If not otherwise described, all items were answered using a five-point rating scale ranging from 1 (strongly disagree) to 5 (strongly agree). All items are presented in the Appendix.

ICT shared mental models were assessed subjectively and objectively. To measure the subjective similarity of ICT mental models, we used a four-item scale (e.g., “During team collaboration, team members knew which ICT we used for which tasks.” α = .75) from Müller and Antoni (2020b). The objective similarity of ICT mental models was assessed by a task-ICT matrix each team member had to fill out before (T0) and after (T1) task execution (“Which ICT will you use/did you use for the following purposes?”) – matching ICTs with purposes of the decision-making task. For each team, two agreement indices (Congers Kappa; Conger, 1980) were calculated to analyze the objective similarity among the individual ICT mental models after the instruction (T0) and after task execution (T1). Congers Kappa can reach values between −∞ and 1, where high values display high agreement within teams on which ICT to use for which purpose, representing ICT shared mental models.

Team effectiveness indicators

Team Performance was assessed subjectively and objectively. To assess subjective performance, we adapted the five-item scale from Jung and Sosik (2002) to our team context (e.g., “We did an excellent job of getting the task done,” α = .96). The objective team performance was assessed by points achieved in the task. Teams could reach up to seven points for the correct solution: one point for selecting the correct applicant and six points, one each for the correct exclusion criterion for the other six applicants. To assess team coordination effectiveness, we adapted the five-item scale from Lewis (2003) to our team context (e.g., “During collaboration in the team, we worked together in a well-coordinated fashion,” α = .90). To assess affective team commitment, we adapted the six-item scale from Dunham et al. (1994) to our team context (e.g., “I enjoyed working in the team,” α = .92).

Team processes

Team communication was assessed subjectively and objectively. For subjective team communication, we used two separate subscales measuring content and efficiency. Participants rated content via two items (e.g., “During team collaboration, we mainly discussed goal- and task-oriented aspects (e.g., requirement criteria, applicants’ data),” α = .51) and efficiency via six items (e.g., “During team collaboration, we often talked at cross-purposes, that is, my team members often discussed other topics than I did.” α = .84). After recoding respective items, high values display task-oriented content as well as high efficiency. Objective team communication was assessed by two external ratings analyzing the communication content of each team. Raters used a deductive coding schema including “action,” “transition,” “questions about ICTs,” “discussion about ICT preferences,” “hints about ICT use,” and “interpersonal” (cf. Marks et al., 2001). We quantified the deductive codes of the content. First, we calculated the percentage of the different codes in the total communication. Afterwards, we used a two-step cluster analysis to analyze communication patterns of the teams based on the percentage distribution of the content. Results of the cluster analysis are presented in the results section. Consistent ICT use in a team was assessed by a self-created six-item scale (e.g., “Team members used different ICTs for the same purpose,” α = .87). High values displayed inconsistent ICT use within the team.

Team specialization

To assess whether the teams had recognized the hidden profile paradigm, that is, whether they recognized that they had to share the distributed information to accomplish the task, we captured team specialization as a control variable. If team members did not recognize that they had distributed information, this might be an additional influencing factor for the effectiveness of team processes and outcomes. To ensure that the effects in the dependent variables are due to ICT shared mental models and not due to non-recognition of the hidden profile paradigm, we included this as a covariate. We adapted the five-item scale based on Lewis (2003) to our team context (e.g., “Each team member had specialized knowledge for different aspects of the task,” α = .82.).

Analysis

As all variables focus the team level, we aggregated them. To justify aggregation on team level, we analyzed r_wg(j) (>.70) and ICC₁ (>.10) as agreement indices (LeBreton & Senter, 2008). To test whether the manipulation of ICT shared mental models worked, we calculated Congers Kappa of the task-ICT matrices measured after the instructions (T0) and after task execution (T1). Teams in the shared condition should have high values (k ≥ .40) indicating a high agreement and teams in the unshared condition should have low values (k < .40) indicating low agreement about their task-specific ICT use (cf. Greve & Wentura, 1997). Data of team members who reported that they did not adhere to their ICT use or ICT experience were included in the analyses to avoid the deletion of the whole team. Keeping this data blurs the difference between the shared and unshared condition, making it more difficult to find differences between the two conditions and leading to more conservative estimates¹.

To test our directed H1 and H2, we used one-sided significance tests (p < .05) in two one-way between-subject multivariate analyses of covariance (MANCOVA) with team specialization as a covariate. The analyses were done with SPSS 25.0. For H1, we used the manipulation of ICT shared mental models (shared vs. unshared condition) as categorical independent variable and subjective and objective ICT shared mental models as continuous dependent variables. To test H2, we used the manipulation of ICT shared mental models (shared vs. unshared condition) as categorical independent variable, and objective and subjective team performance, team coordination effectiveness, and affective team commitment as continuous dependent variables. To test H3, we ran a structural equation model (SEM) using MPlus. We used the experimental manipulation of ICT shared mental models as exogenous variable, objective and subjective team communication and consistent ICT use in a team as mediators, and objective and subjective team performance, team coordination effectiveness, and affective team commitment as continuous dependent variables. As mediational models are saturated, we compared our hypothesized model with three alternative models comparing model fit indices (SABIC). The best fitting model is the one with lowest value (cf. Kenny, 2018).

Results

Preliminary Analyses

All constructs reached acceptable values in inter-rater agreement, except of ICT shared mental models, (r_wg(j) = .62), and team specialization (r_wg(j) = .63), although these values can be still regarded as moderate (LeBreton & Senter, 2008). The teams in the conditions did not differ significantly with regard to age, team specialization (F < 2.50, p > .121) or gender (χ² (2, 191) = 0.47, p = .790). In average, participants felt moderately committed to their ICT use or experiences prior to task execution (M (SD)_shared = 4.13 (0.80), M (SD)_unshared = 3.31 (0.98)). After task execution, participants stated that they had adhered to the ICT use in an acceptable way (M (SD)_shared = 4.17 (0.76), M (SD)_unshared = 3.41 (1.13)). Descriptive statistics of both conditions are presented in Table 1. Table 2 presents the intercorrelations of variables on team level.

Table 1.

Descriptive Statistics in the Experimental Conditions.

Variable	Shared condition M (SD)	Unshared condition M (SD)
Objective ICT SMM (T0)	0.96 (0.10)	0.13 (0.18)
Objective ICT SMM (T1)	0.77 (0.19)	0.39 (0.35)
Subjective ICT SMM	3.71 (0.53)	3.16 (0.58)
Communication content^a	4.12 (0.37)	3.74 (0.72)
Communication efficiency	3.24 (0.73)	3.08 (0.79)
Consistent ICT use in a team^b	2.96 (0.46)	3.68 (0.85)
Objective team performance	3.50 (1.96)	2.54 (1.92)
Subjective team performance	2.96 (0.97)	2.34 (0.88)
Team coordination effectiveness	3.33 (0.67)	2.49 (0.65)
Affective team commitment	3.37 (0.54)	2.60 (0.72)
Team specialization	4.12 (0.47)	3.88 (0.76)
ICT commitment	4.13 (0.80)	3.31 (0.98)
ICT adherence	4.17 (0.76)	3.41 (1.13)

Note. N_shared = 34 teams, N_unshared = 35 teams. SMM = shared mental models.

High values = task-oriented content, low values = transition-oriented content.

High values = inconsistent ICT use in a team.

Table 2.

Intercorrelations of all Variables on Team Level.

Variable	1	2	3	4	5	6	7	8	9	10	11	12
1. Condition
2. Objective ICT SMM (T0)	−.95**
3. Objective ICT SMM (T1)	−.57**	.62**
4. Subjective ICT SMM	−.45**	.50**	.49**
5. Communication content^a	−.31**	.35**	.28*	.34**
6. Communication cluster	.40**	−.46**	−.43**	−.29*	−.47**
7. Communication efficiency	−.11	.11	.01	−.09	−.07	.14
8. Consistent ICT use in a team	.47**	−.54**	−.54**	−.50**	−.46**	.40**	−.12
9. Objective team performance	−.24*	.27*	.40**	.15	.11	−.19	−.06	−.18
10. Subjective team performance	−.32**	.36**	.49**	.57**	.35**	−.35**	−.11	−.52**	.46**
11. Team coordination effectiveness	−.54**	.61**	.63**	.69**	.44**	−.50**	−.01	−.69**	.44**	.83**
12. Affective team commitment	−.52**	.60**	.57**	.69**	.38**	−.35**	−.02	−.60**	.14	.51**	.71**
13. Team specialization	−.19	.20	.30*	.27**	.15	−.12	.21	−.06	.26*	.19	.33**	.22

Note. N = 69 teams. SMM = shared mental models.

High values = task-oriented content; low values = transition-oriented content.

p < .05. **p < .01.

The two-step cluster analysis of objective team communication resulted in two clusters (Table 3). The first cluster describes teams that have a high percentage of task- and transition-oriented communication. The second cluster describes teams that have a high proportion of discussion about ICT preferences and interpersonal issues. A total of 56 teams were allocated into the first and 13 teams were allocated into the second cluster. A χ² test showed that the communication clusters significantly differed between the experimental conditions (χ² (1) = 11.08, p = .001). A total of 33 teams of the shared condition were allocated in the first cluster; one team was in the second cluster. About 22 teams of the unshared condition were allocated in the first cluster; 12 teams were in the second cluster. Teams in the unshared condition communicated significantly more about their ICT preferences (F(1, 67) = 25.63, p < .001, η² = .28), transition-oriented topics (F(1, 67) = 3.33, p = .036, η² = .05), and the hidden profile paradigm (F(1, 68) = 10.15, p = .001, η² = .13) than teams in the shared condition.

Table 3.

Communication Pattern of the Two Clusters (M (SD) in %).

Content domain	1. Cluster (%)	2. Cluster (%)
Task-oriented	60.81 (10.54)	49.45 (15.97)
Transition-oriented	22.61 (9.58)	14.09 (6.37)
Questions about ICTs	1.86 (2.21)	1.62 (1.26)
Discussions about ICT preferences	1.30 (2.29)	9.17 (5.77)
Hints about ICT use	5.75 (4.26)	4.65 (4.52)
Interpersonal-oriented	5.19 (3.50)	5.07 (4.43)
Hidden profile paradigm	2.49 (2.12)	15.32 (8.77)

Note. N_{first cluster} = 56 teams, N_{second cluster} = 13 teams.

Hypotheses Testing

In H1, we assumed that teams in the shared condition have a higher similarity in their subjective (T1) and objective ICT mental models (T0 and T1) than teams in the unshared condition. Subjective ICT shared mental models differed between experimental conditions, F(1, 66) = 14.38, p < .001, η² = .18. Objective ICT shared mental models differed between experimental conditions after the instruction (T0), F(1, 66) = 528.22, p < .001, η² = .89, and after task execution (T1), F(1, 66) = 27.61, p < .001, η² = .30. Teams in the shared condition had subjectively and objectively more similar ICT mental models than teams in the unshared condition. The effect sizes represent large effects, supporting H1.

In H2, we assumed that teams in the shared condition have better (a) subjective and objective team performance, (b) team coordination effectiveness, and (c) affective team commitment than teams in the unshared condition. Objective team performance differed between experimental conditions, F(1, 66) = 2.88, p = .048, η² = .04, indicating a small effect. Subjective team performance differed between experimental conditions, F(1, 66) = 6.21, p = .001, η² = .09 indicating a medium effect. Team coordination effectiveness (F(1, 66) = 24.00, p < .001, η² = .27) and affective team commitment (F(1, 66) = 22.20, p < .001, η² = .25) differed between experimental conditions indicating large effects. Teams in the shared condition performed better, had a more coordinated teamwork and felt more committed to their team than teams in the unshared condition, supporting H2.

In H3, we assumed that efficient task-oriented team communication and consistent ICT use in a team mediate the relationship between ICT shared mental models and team outcomes. Our hypothesized models include potential direct effects between ICT shared mental models and team outcomes, assumed effects between ICT shared mental models and team processes as well as effects between team processes and outcomes. As team specialization did not differ between the conditions significantly, we excluded it as a control variable for further analyses.

In our hypothesized mediational model, ICT shared mental models had a significant influence on consistent ICT use in a team (β = .47, p < .001, CI [0.28, 0.67]), communication content (β = −.31, p = .001, CI [−0.51, −0.12]), and communication clusters (β = .40, p < .001, CI [0.23, 0.57]). Teams in the shared condition reported that they had a more consistent ICT use, communicated more about task-oriented topics and were allocated significantly more into the first communication cluster. None of the mediators was significantly related to objective team performance. However, consistent ICT use in a team fully mediated the relationship between ICT shared mental models and subjective team performance (β = −.44, p < .001, CI [−0.66, −0.22]). Communication efficiency was also related to subjective team performance (β = −.15, p = .048, CI [−0.33, −0.00.]). Consistent ICT use in a team (β = −.50, p < .001, CI [−0.68; −0.32]) and communication clusters (β = −.18, p = .025, CI [−0.36; −0.03]) partially mediated the relationship between ICT shared mental models and team coordination effectiveness. There was still a direct effect of ICT shared mental models on team coordination effectiveness (β = −.22, p = .011, CI [−0.41, −0.03]). Consistent ICT use in a team (β = −.42, p < .001, CI [−0.62; −0.23]) partially mediated the relationship between ICT shared mental models and affective team commitment. There was still a direct effect of ICT shared mental models on affective team commitment (β = −.31, p = .001, CI [−0.50, −0.11]).

Besides this hypothesized mediational model, we tested three alternative models omitting effects sequentially: (a) model without direct effects, (b) model without effects between ICT shared mental models and team processes, and (c) model without effects between team processes and team outcomes. Our hypothesized mediational model reached a value in SABIC = 1063.14. The alternative models reached partly higher values in SABIC indicating worse model fits (a = 1071.74; b = 1082.47, c = 1063.14). Since the model without effects between team processes and team outcomes had the same value in SABIC, that is, the assumed effects between processes and outcomes did not reach a better model fit, hypothesis 3 is only partially supported. Figure 2 represents the results of H2 and H3. To simplify, we have only drawn those lines that reached as least values of p ≤ .05, whereas the dashed lines displays direct effects (H2) and solid lines indirect effects (H3).

Discussion

Our study aimed at clarifying antecedents, mechanisms, and consequences of ICT shared mental models in virtual teams. We extend previous research that is based only on correlational designs by providing a causal investigation of ICT shared mental models. Results confirm ICT experiences and team planning of ICT use as antecedents of ICT shared mental models, which emerge from individual cognitions and manifest at the team level. While different ICT experiences among virtual team members lead to lower ICT shared mental models, team planning of ICT use leads to a convergence of team members’ individual ICT mental models to shared mental models supporting H1. Findings also show that ICT shared mental models improve virtual team performance, coordination effectiveness, and team commitment supporting H2. In particular, we demonstrated that team commitment can be influenced by the extent of agreement among team members about their ICT use. Lower levels of team commitment can be explained by different perceptions of task-specific ICT use among team members. These results do not only add to research by confirming similar effects of teamwork and taskwork shared mental models on team performance and coordination effectiveness, but also by showing that also affective team commitment are influenced by ICT shared mental models, as affective outcomes had not been in the focus of prior research on shared mental models. Finally, our findings contribute to research by showing that the convergence in ICT mental models improves team processes, such as a more task-oriented team communication and consistent ICT use in a team, partially supporting H3. This shows that emergent states, such as ICT shared mental models, which support consistent ICT use in a team, can also contribute to technology acceptance research on predictors of technology acceptance and use.

Theoretical Implication

Our results support that the manipulation of ICT shared mental models leads to different degrees of similarity of ICT mental models. Our manipulation of different ICT experience broadens the assumption of channel expansion theory (Carlson & Zmud, 1999) that next to actual ICT experience, communication about advantages and disadvantages of ICTs can influence ICT mental models and ICT use, too. Communication about advantages and disadvantages of ICTs (in our study, the cover story of team members’ previous ICT experiences) can influence and shape individual mental models about ICT use. Even in teams in which team members adhere to their ICT experience only sometimes, priming is sufficient to lead to different ICT mental models among team members. This implies that social aspects, such as reported experiences or vicarious learning by other people, can also influence the perception of an ICT. The social influence model of technology use (Fulk et al., 1991) explains how ICT evaluations are formed, including social influence, such as direct statements and vicarious learning. However, these social influences as well as emergent states, such as ICT shared mental models, are still unconsidered in technology acceptance research (Venkatesh et al., 2003).

Explicit planning is an antecedent of high similarity in ICT mental models, which is in line with research on shared mental models (e.g., Gurtner et al., 2007). As the explicit planning in our study was in a face-to-face mode and shared mental models convergence has been proven to be more difficult in virtual collaboration (Andres, 2011), the mode of planning seems to be crucial. The significant increase of objective ICT shared mental models from T0 (prior to teamwork) to T1 (after teamwork), F(1, 34) = 21.85, p < 001, η² = .39, in teams of the unshared condition indicates that their ICT mental models likely converge during teamwork. As the objective ICT shared mental models at T1 still differed significantly between the two conditions, we assume that planning of ICT use in a face-to-face mode is better for ICT shared mental models emergence than in a computer-mediated mode.

In a computer-mediated mode, the convergence of ICT shared mental models probably takes more time than in a face-to-face mode, which research has already shown for other team processes (e.g., team learning and reflexivity, Andres, 2013). As face-to-face communication is the most efficient way for humans to communicate (cf. Kock, 2004), it is particular difficult for virtual teams to establish a common ground, which is necessary for shared mental models (Brennan, 1998; Hantula et al., 2011). Communication via ICTs is restricted as communication partners can transfer fewer cues (e.g., non-verbal or para-verbal cues). However, the media compensation theory (Hantula et al., 2011) assumes that humans can compensate the ICT based restriction in communication reaching a similar efficiency than in face-to-face interaction. According to these researchers, similar individual schemas about ICTs, which are comparable with ICT shared mental models, is one aspect to compensate the ICT based restrictions and to yield communication efficiency even via ICTs. The collaboration mode and the adaptability to change the ICT use might moderate the relationship between planning of ICT use and similarity of ICT mental models.

Our results show that the manipulation of the development of ICT shared mental models has a significant influence on team outcomes. Previous field studies with employees as participants have reached similar results (DeChurch & Mesmer-Magnus, 2010; Müller & Antoni, 2020a). A quite new finding is that ICT shared mental models have a significant influence on team commitment. This is in line with research on ICT use as indicator of stress (e.g., technostress, Ayyagari et al., 2011; information overload, Ellwart et al., 2015). In virtual teams, frustrations due to, for example, inconsistent ICT use are more difficult to clarify. We argue that teams in the shared condition were less frustrated by their teamwork because they used the ICTs consistently and achieved a good solution in the task. These aspects represent positive teamwork behavior, which increases the likelihood of high team commitment. Although affective aspects, such as trust or commitment are particularly important in virtual teams (Breuer et al., 2016), team commitment has not yet been the focus of research on shared mental models in virtual teams. Research and theories on virtual teams and well-being should therefore consider ICT shared mental models as a crucial cognitive emergent state regarding to team performance, team coordination, and affective team commitment. To sum up, our study results on antecedents, mechanisms, and consequences of ICT shared mental models support that ICT shared mental models are another important emergent state for virtual teamwork.

Practical Implication

The findings of our study have some implications for team members, leaders, and organizations. First, it is important that organizations comprised of virtual teams using different ICTs, are aware that team members may have different ICT experiences, which can results in inconsistent ICT use and poor teamwork. Without discussing their ICT use explicitly and defining ICT rules at the beginning of teamwork, the likelihood that team members use different ICTs for collaboration increases. Different ICT mental models result in different ICT use and negative consequences for task execution, team coordination, and team members’ commitment. To overcome these problems, teams with different ICT mental models have to discuss their ICT use during teamwork. However, up to that point, they may have lost time due to redundant communication, inefficient coordination processes, and members might have become frustrated. It is advisable that newly formed teams should discuss their ICT experiences and preferences and agree on common ICT rules when they start to collaborate, for example, during a face-to-face or computer-mediated kick-off meeting.

Different ICT experiences are not only a challenge for newly formed teams, but also for long-term teams. In long-term teams, it is more difficult to change which ICTs are used, as team members have established routines regarding ICT use. Further, some teams receive external rules about which ICTs they have to use. Under such conditions, team trainings about available ICTs could be helpful. During the team training, each team member receives the same information about different ICTs and their features. This increases the similarity of ICT mental models of team members. During a team training, team members have the opportunity to become aware of which team member is an expert for which ICT and which team member has difficulties in dealing with a specific ICT. To summarize, it is important that organizations are aware that implementing or using computer-mediated teamwork is more than transferring face-to-face teams in a virtual context using different ICTs. The way of using ICTs by different team members has an impact on relevant team processes and outcomes.

Limitations and Future Research

When interpreting the results of this study some limitations have to be discussed, which can stimulate future research. Although research using field studies with employee samples (e.g., Müller & Antoni, 2020a; Thomas & Bostrom, 2007) reached similar results, the laboratory design using a student sample in an experimental task allows for causal interpretation but needs external validation of the results with employee teams in organizations. Future field studies using quasi-experimental designs with employee samples to investigate the consequences of ICT shared mental models on team outcomes would be helpful to generalize our findings. Furthermore, our study was limited to a short period of teamwork (40 minutes), which was unable to detect developmental and learning processes, which are important in mental models convergence (Mohammed et al., 2010). Future research should use longitudinal designs with repeated tasks to investigate how ICT shared mental models establish and develop in virtual teams based on learning processes over time as well as analyze the effects of ICT shared mental models on team processes and outcomes over time.

Our study design has another methodological limitation, which limits the validity of our results. The decision-making task with a hidden profile paradigm made it possible to establish the necessary interdependence among team members. Further, as there was only one correct solution, performance could be operationalized objectively, independently of subjective ratings. However, the task was designed that some ICTs (e.g., e-mail and chat program) were more suitable than others were (e.g., ticket system). It was particularly difficult for team members who were primed to prefer the ticket system to enforce their ICT preferences, because it was less appropriate for the task compared to team members who were primed to use the e-mail or chat program, which were more suitable for the task. Although team members were primed to use different ICTs, it happened that teams in the unshared condition only used one ICT (e-mail or chat program). This limits our manipulation, as the difference between the shared and unshared condition is blurred by the actual use of ICTs. However, it makes it less probable to find differences between the two conditions. Consequently, the reported findings of the effects of ICT shared mental models are probably rather conservative estimates. Future research should develop tasks that require using all available ICTs in a balanced way to estimate the effects of shared and unshared ICT mental models more accurately.

What we did not investigate was the individual perception of ICT richness. Future research on ICT use should investigate whether the degree of similarity among individual ICT mental models could influence the perceptions of richness. ICT richness functions as an indicator of ICT appropriateness (Daft & Lengel, 1986). If this is the case, popular media theories (e.g., Daft & Lengel, 1986; Dennis et al., 2008) should be revised to take into account that emergent states, such as ICT shared mental models can influence ICT appropriateness.

Finally, as we focused on ICT shared mental models, we did not analyze the interplay with teamwork, taskwork, and temporal shared mental models and their differential and interactive effects on team processes and outcomes. It might be interesting for future research to integrate all subtypes of shared mental models to assess the incremental variance explained by teamwork, taskwork, temporal, and ICT shared mental models on team processes and outcomes and their potential interactive effects.

Conclusion

This study contributes beyond existing research by showing the causal effects of ICT shared mental models on virtual team performance, coordination effectiveness, and commitment as well as by clarifying the team processes of team communication and consistent ICT use in teams as underlying causal mechanisms. Furthermore, our results inform organizations that interventions to influence ICT experiences and ICT use can be helpful to support the emergence of ICT shared mental models, and thus team processes and outcomes. Future field studies integrating all subtypes of shared mental models and analyzing their mutual effects using longitudinal and (quasi-) experimental designs seem to be promising.

Footnotes

Appendix

Appendix.

Items.

Scale	Items
ICT shared mental models Müller and Antoni (2020b)	During team collaboration, everybody knew the features and functionalities of the particular ICTs we used.
	. . .everybody knew the possibilities and limitations of ICTs.
	. . .everybody knew which ICT we used for which tasks.
	. . .everybody knew how to apply the ICT we use adequately.
Team performance Jung and Sosik (2002)	We worked efficiently on our task.
	We did an excellent job of getting the task done.
	We were efficient in fulfilling the task.
	We reached our goal.
	We finished the task successfully.
Team coordination effectiveness Lewis (2003)	During team collaboration, we worked together in a well-coordinated fashion.
	. . .we had very few misunderstandings about what to do.
	. . .we often had to pause to discuss certain aspects of the task and start again. (R)
	. . .we accomplished the task smoothly and efficiently.
	. . .there was rarely confusion about how to accomplish the task.
Team commitment Dunham et al. (1994)	I would like to work on further projects with this team in the future.
	I enjoyed working in the team.
	I felt like a part of the team.
	I felt emotionally connected to the team.
	I felt a strong sense of belonging to the team.
	In the team, I felt like a family member.
Communication content^a	During team collaboration, . . .
	. . .we mainly discussed goal- and tasked-related aspects (e.g., requirement criteria, applicants’ data).
	. . .we often discussed organizational aspects (e.g., who uses which kind of ICT, organizational matters). (R)
Communication efficiency^a	. . .we often talked at cross-purposes, that is, my team members often discussed other topics than did I. (R)
	. . .we received the information, which was necessary for solving the task.
	. . .we received the information we needed out of mutual questioning.
	. . .our interaction via ICTs led to time delays. (R)
	. . .we had to wait for answers from another team member. (R)
	. . .our communication via ICT was extensive and complicated. (R)
Consistent ICT use in a team^a	Team members used different ICTs for the same purpose.
	We used the available ICTs uniformly. (R)
	We had to look for relevant information in the various ICTs.
	We had to switch back and forth between ICTs unnecessarily.
	Our ICT use was effective. (R)
	It was difficult to keep track of all relevant information in the ICTs.
Team specialization Lewis (2003)	Each team member had specialized knowledge about the requirements for the manager position and/or the applicants.
	I had knowledge about the requirements for the manager position and/or the applicants, which no other team member had.
	Each team member had specialized knowledge for different aspects of the task.
	It was necessary to combine the special knowledge of all team members to solve the task.
	I knew which team member had which particular knowledge that was relevant for the task.
ICT commitment^a	How strongly do you feel committed to the ICT rules developed in the team? (shared condition)
ICT commitment^a	How strongly do you feel committed to use ICTs according to your experiences? (unshared condition)
ICT adherence^a	I have followed the rules/my previous experience of ICT use.

Note. Reversed items are marked with a (R) and were recoded for analyses.

Self-created.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Rebecca Müller

Notes

Author Biographies

Rebecca Müller is a research associate in the Department of Industrial and Organizational Psychology at the University of Trier, Germany. She is involved in the research project AdaptPRO; her doctoral thesis focuses on team processes in virtual teams and the influence of digital media on shared mental models.

Conny Herbert Antoni is professor of Industrial and Organizational Psychology at the University of Trier, Germany. His work focuses on teamwork, leadership, stress and work-life balance, reward, and change management.

References

Andres

H. P.

(2011). Shared mental model development during technology-mediated collaboration. International Journal of e-Collaboration, 7(3), 14–30. https://doi.org/10.4018/ijec.2011070102

Andres

H. P.

(2013). Team cognition using collaborative technology: A behavioral analysis. Journal of Managerial Psychology, 28(1), 38–54. https://doi.org/10.1108/02683941311298850

Antoni

Hertel

(2009). Team processes, their antecedents and consequences: Implications for different types of teamwork. European Journal of Work and Organizational Psychology, 18(3), 253–266. https://doi.org/10.1080/13594320802095502

Ayyagari

Grover

Purvis

(2011). Technostress: Technological antecedents and implications. MIS Quarterly, 35(4), 831–858. https://doi.org/10.2307/41409963

Bishop

J. W.

Scott

K. D.

(2000). An examination of organizational and team commitment in a self-directed team environment. Journal of Applied Psychology, 85(3), 439–450. https://doi.org/10.1037//0021-9010.85.3.439

Brennan

S. E.

(1998). The grounding problem in conversations with and through computers. In Fussell

S. R.

Kreuz

R. J.

(Eds.), Social and cognitive psychological approaches to interpersonal communication (pp. 201–225). Lawrence Erlbaum.

Breuer

Hüffmeier

Hertel

(2016). Does trust matter more in virtual teams? A meta-analysis of trust and team effectiveness considering virtuality and documentation as moderators. Journal of Applied Psychology, 101(8), 1151–1177. https://doi.org/10.1037/apl0000113

Cannon-Bowers

J. A.

Salas

Converse

(1993). Shared mental models in expert team decision making. In Castellan

N. J.

(Ed.), Individual and group decision making: Current issues (pp. 221–246). Lawrence Erlbaum.

Carlson

J. R.

Zmud

R. W.

(1999). Channel expansion theory and the experiential nature of media richness perceptions. Academy of Management Journal, 42(2), 153–170. https://doi.org/10.5465/257090

10.

Chudoba

K. M.

Watson-Manheim

M. B.

(2008). Shared communication practices and mental models in the virtual work environment. In Panteli

Chiasson

(Eds.), Exploring virtuality within and beyond organizations (pp. 55–72). Palgrave Macmillan.

11.

Conger

A. J.

(1980). Integration and generalization of kappas for multiple raters. Psychological Bulletin, 88(2), 322–328. https://doi.org/10.1037/0033-2909.88.2.322

12.

Daft

R. L.

Lengel

R. H.

(1986). Organizational information requirements, media richness and structural design. Management Science, 32(5), 554–571. https://doi.org/10.1287/mnsc.32.5.554

13.

Day

Paquet

Scott

Hambley

(2012). Perceived information and communication technology (ICT) demands on employee outcomes: The moderating effect of organizational ICT support. Journal of Occupational Health Psychology, 17(4), 473–491. https://doi.org/10.1037/a0029837

14.

DeChurch

L. A.

Mesmer-Magnus

J. R.

(2010). The cognitive underpinnings of effective teamwork: A meta-analysis. Journal of Applied Psychology, 95(1), 32–53. https://doi.org/10.1037/a0017328

15.

Dennis

A. R.

Fuller

R. M.

Valacich

J. S.

(2008). Media, tasks, and communication processes: A theory of media synchronicity. MIS Quarterly, 32(3), 575–600.

16.

Dunham

R. B.

Grube

J. A.

Castaneda

M. B.

(1994). Organizational commitment: The utility of an integrative definition. Journal of Applied Psychology, 79(3), 370–380. https://doi.org/10.1037/0021-9010.79.3.370

17.

D’Urso

S. C.

Rains

S. A.

(2008). Examining the scope of channel expansion: A test of channel expansion theory with new and traditional communication media. Management Communication Quarterly, 21(4), 486–507. https://doi.org/10.1177/0893318907313712

18.

Ellwart

Happ

Gurtner

Rack

(2015). Managing information overload in virtual teams: Effects of a structured online team adaptation on cognition and performance. European Journal of Work and Organizational Psychology, 24(5), 812–826. https://doi.org/10.1080/1359432X.2014.1000873

19.

Fulk

Schmitz

Steinfield

C. W.

(1991). A social influence model of technology use. In Fulk

Steinfield

(Eds.), Organizations and communication technology (pp. 117–140). Sage.

20.

Fuller

R. M.

Dennis

A. R.

(2009). Does fit matter? The impact of task-technology fit and appropriation on team performance in repeated tasks. Information Systems Research, 20(1), 2–17. https://doi.org/10.1287/isre.1070.0167

21.

Gevers

J. M.

Rutte

C. G.

Van Eerde

(2006). Meeting deadlines in work groups: Implicit and explicit mechanisms. Applied Psychology, 55(1), 52–72. https://doi.org/10.1111/j.1464-0597.2006.00228.x

22.

Gilson

L. L.

Maynard

M. T.

Jones Young

N. C.

Vartiainen

Hakonen

(2015). Virtual teams research: 10 years, 10 themes, and 10 opportunities. Journal of Management, 41(5), 1313–1337. https://doi.org/10.1177/0149206314559946

23.

Greve

Wentura

(1997). Wissenschaftliche Beobachtung. Eine Einführung (2nd ed.). Beltz.

24.

Gurtner

Tschan

Semmer

N. K.

Nägele

(2007). Getting groups to develop good strategies: Effects of reflexivity interventions on team process, team performance, and shared mental models. Organizational Behavior and Human Decision Processes, 102(2), 127–142. https://doi.org/10.1016/j.obhdp.2006.05.002

25.

Handke

Schulte

E. M.

Schneider

Kauffeld

(2018). The medium isn’t the message: Introducing a measure of adaptive virtual communication. Cogent Arts & Humanities, 5(1), 1–25. https://doi.org/10.1080/23311983.2018.1514953

26.

Hantula

D. A.

Kock

D’Arcy

J. P.

DeRosa

D. M.

(2011). Media compensation theory: A Darwinian perspective on adaptation to electronic Communication and collaboration. In Saad

(Ed.), Evolutionary psychology in the business sciences (pp. 339–363). Springer. https://doi.org/10.1007/978-3-540-92784-6_13

27.

Jung

D. I.

Sosik

J. J.

(2002). Transformational leadership in work groups: The role of empowerment, cohesiveness, and collective-efficacy on perceived group performance. Small Group Research, 33(3), 313–336. https://doi.org/10.1177/10496402033003002

28.

Kenny

D. A.

(2018, September 25). Mediation. Retrieved August 13, 2020, from http://davidakenny.net/cm/mediate.htm#OS

29.

Klitmøller

Lauring

(2013). When global virtual teams share knowledge: Media richness, cultural difference and language commonality. Journal of World Business, 48(3), 398–406. https://doi.org/10.1016/j.jwb.2012.07.023

30.

Klötzer

Hardwig

Boos

(2017). Gestaltung internetbasierter kollaborativer Team-und Projektarbeit. Gruppe. Interaktion. Organisation. Zeitschrift für Angewandte Organisationspsychologie (GIO), 48(4), 293–303. https://doi.org/10.1007/s11612-017-0385-3

31.

Kock

(2004). The psychobiological model: Towards a new theory of computer-mediated communication based on Darwinian evolution. Organization Science, 15(3), 327–348. https://doi.org/10.1287/orsc.1040.0071

32.

Kozlowski

S. W.

Chao

G. T.

Grand

J. A.

Braun

M. T.

Kuljanin

(2013). Advancing multilevel research design: Capturing the dynamics of emergence. Organizational Research Methods, 16(4), 581–615. https://doi.org/10.1177/1094428113493119

33.

LeBreton

J. M.

Senter

J. L.

(2008). Answers to 20 questions about interrater reliability and interrater agreement. Organizational Research Methods, 11(4), 815–852. https://doi.org/10.1177/1094428106296642

34.

Lewis

(2003). Measuring transactive memory systems in the field: Scale development and validation. Journal of Applied Psychology, 88(4), 587–604. https://doi.org/10.1037/0021-9010.88.4.587

35.

Mannix

E. A.

Griffith

Neale

M. A.

(2002). The phenomenology of conflict in distributed work teams. In Hinds

P. J.

Kiesler

(Eds.), Distributed work (pp. 213–233). The MIT Press.

36.

Marks

M. A.

Mathieu

J. E.

Zaccaro

S. J.

(2001). A temporally based framework and taxonomy of team processes. Academy of Management Review, 26(3), 356–376.

37.

Marks

M. A.

Sabella

M. J.

Burke

C. S.

Zaccaro

S. J.

(2002). The impact of cross-training on team effectiveness. Journal of Applied Psychology, 87(1), 3–13. https://doi.org/10.1037/0021-9010.87.1.3

38.

Marks

M. A.

Zaccaro

S. J.

Mathieu

J. E.

(2000). Performance implications of leader briefings and team-interaction training for team adaptation to novel environments. Journal of Applied Psychology, 85(6), 971–986. https://doi.org/10.1037/0021-9010.85.6.971

39.

Mathieu

J. E.

Heffner

T. S.

Goodwin

G. F.

Cannon-Bowers

J. A.

Salas

(2005). Scaling the quality of teammates’ mental models: Equifinality and normative comparisons. Journal of Organizational Behavior, 26(1), 37–56. https://doi.org/10.1002/job.296

40.

Mathieu

J. E.

Heffner

T. S.

Goodwin

G. F.

Salas

Cannon-Bowers

J. A.

(2000). The influence of shared mental models on team process and performance. Journal of Applied Psychology, 85(2), 273–283. https://doi/10.1037/0021-9010.85.2.273

41.

Mohammed

Ferzandi

Hamilton

(2010). Metaphor no more: A 15-year review of the team mental model construct. Journal of Management, 36(4), 876–910. https://doi.org/10.1177/0149206309356804

42.

Mohammed

Hamilton

Tesler

Mancuso

McNeese

(2015). Time for temporal team mental models: Expanding beyond “what” and “how” to incorporate “when”. European Journal of Work and Organizational Psychology, 24(5), 693–709. https://doi.org/10.1080/1359432X.2015.1024664

43.

Müller

Antoni

C. H.

(2019). Einflussfaktoren und Auswirkungen eines gemeinsamen Medienverständnisses in virtuellen Teams. Gruppe. Interaktion. Organisation. Zeitschrift für Angewandte Organisationspsychologie (GIO), 50(1), 25–32. https://doi.org/10.1007/s11612-019-00447-3

44.

Müller

Antoni

C. H.

(2020a). Individual perceptions of shared mental models of information and communication technology (ICT) and virtual team coordination and performance—The moderating role of flexibility in ICT use. Group Dynamics: Theory, Research, and Practice, 24(3), 186–200. https://doi.org/10.1037/gdn0000130

45.

Müller

Antoni

C. H.

(2020b). Scale development and validation of shared mental models of information and communication technology (ICT SMM). Team Performance Management: An International Journal, 26(7/8), 391–407. https://doi.org/10.1108/TPM-03-2020-0025

46.

Rice

R. E.

D’Ambra

(1998). Cross-cultural comparison of organizational media evaluation and choice. Journal of Communication, 48(3), 3–26. https://doi.org/10.1111/j.1460-2466.1998.tb02757.x

47.

Rico

Gibson

C. B.

Sánchez-Manzanares

Clark

M. A.

(2019). Building team effectiveness through adaptation: Team knowledge and implicit and explicit coordination. Organizational Psychology Review, 9(2), 71–98. https://doi.org/10.1177/2041386619869972

48.

Rico

Sánchez-Manzanares

Gil

Gibson

(2008). Team implicit coordination processes: A team knowledge–based approach. Academy of Management Review, 33(1), 163–184. https://doi.org/10.5465/amr.2008.27751276

49.

Schmidtke

J. M.

Cummings

(2017). The effects of virtualness on teamwork behavioral components: The role of shared mental models. Human Resource Management Review, 27(4), 660–677. https://doi.org/10.1016/j.hrmr.2016.12.011

50.

Stasser

(1992). Information salience and the discovery of hidden profiles by decision-making groups: A “thought experiment”. Organizational Behavior and Human Decision Processes, 52(1), 156–181. https://doi.org/10.1016/0749-5978(92)90049-D

51.

Thomas

Bostrom

(2007, January 3–6). The role of a shared mental model of collaboration technology in facilitating knowledge work in virtual teams [Conference session]. 40th Annual Hawaii International Conference on System Sciences (HICSS’07), Waikoloa, HI, United States. https://doi.org/10.1109/HICSS.2007.552

52.

Venkatesh

Morris

M. G.

Davis

G. B.

Davis

F. D.

(2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540