Divergent Mental Models as a Trigger of Team Adaptation
Abstract
Abstract: Due to the continuous implementation of information and communication technologies (ICTs), members of one team often have a different understanding (i. e., mental models) about their ICT use. Consequently, the ability to adapt their ICT use and to converge their divergent mental models is an essential requirement for teams to remain effective. The results of a laboratory experiment examining 198 participants in 66 teams confirm the hypothesis that the higher the initial divergence of mental models about their ICT use, the more the teams engage in team adaptation processes (i. e., situation assessment, plan formulation, and plan execution). Furthermore, the divergent mental models of team members, mediated by sequential team adaptation processes (i. e., situation assessment, plan formulation, and plan execution) and convergence of mental models, lead to a positive change in ICT use. Our results underscore that theories and research on team adaptation should focus more on team members’ divergence of and convergence toward shared mental models.
Zusammenfassung: Die kontinuierliche Einführung verschiedener Informations- und Kommunikationstechnologien (IKT) führt häufig dazu, dass Mitglieder eines Teams unterschiedliche Vorstellungen (d. h., mentale Modelle) über die Nutzung von IKT in ihrem Team haben. Folglich ist die Fähigkeit zur Adaptation der IKT-Nutzung sowie zur Konvergenz unterschiedlicher mentaler Modelle eine wesentliche Voraussetzung für die Leistungsfähigkeit von Teams. Die Ergebnisse eines Laborexperiments mit 198 Versuchspersonen in 66 Teams bestätigen die Hypothese, dass je größer die anfängliche Divergenz der mentalen Modelle über die IKT-Nutzung im Team war, desto mehr Anpassungsprozesse (d. h. Situationsbewertung, Planformulierung, Planausführung, in dieser Reihenfolge) zeigen die Teams. Darüber hinaus führen die divergierenden mentalen Modelle der Teammitglieder, vermittelt durch sequenzielle Teamanpassungsprozesse (d. h. Situationsbewertung, Planformulierung und Planausführung) und die Konvergenz der mentalen Modelle, zu einer positiven Veränderung der IKT-Nutzung. Unsere Ergebnisse unterstreichen, dass Theorien und Forschung zur Teamadaptation sich stärker auf die unterschiedlichen mentalen Modelle und deren Konvergenz zu geteilten mentalen Modellen der Teammitglieder konzentrieren sollten.
The incessant implementation of new information and communication technologies (ICTs) in organizations often entails improvements (Bennett & Lemoine, 2014), but can lead to divergent expectations and understanding (i. e., mental models) among team members regarding the use of ICTs (Müller & Antoni, 2020a). Using different ICTs (e. g., mail vs. chat) for the same purpose (e. g., quick communication) due to divergent mental models can lead to loss of information, redundancies in documentation, or delays. Thus, team members must engage in team adaptation to continuously converge their mental models to remain effective in their ICT use (e. g., Baard et al., 2014). This study addresses the research question of whether team members’ divergent mental models about their ICT use trigger team adaptation and whether successful completion of the adaptation processes leads to a convergence to shared mental models (SMM) and a more effective ICT use.
Based on an input–mediator–output framework (IMOI), research on team adaptation (e. g., Rico et al., 2020) has differentiated team adaptation triggers (i. e., events that make an adjustment necessary), processes (i. e., behaviors that teams engage in to react to an adjustment), and outcomes (i. e., results due to the adjustment). Research has shown that if a trigger occurs, teams that engage in specific team processes (e. g., situation assessment, plan formulation) are more effective than teams that do not engage in these processes (Christian et al., 2017). Further, theoretical frameworks of team adaptation propose a reciprocal relationship between team adaptation processes and SMM updating, and assume that team members’ mental models must converge for successful team adaptation (e. g., Burke et al., 2006; Rosen et al., 2011).
While the effect of team adaptation processes on the convergence of SMM has already been shown (e. g., Ellwart et al., 2015), the effect of the reciprocal relationship between mental models and team adaptation processes on successful team adaptation has, to our knowledge, never been tested empirically. To date, most research focuses on the relationship between team adaptation processes and outcomes (e. g., maintaining team performance) or on triggers, processes, or outcomes (e. g., Georganta et al., 2019). Team adaptation is often only inferred from an observed change in outcomes following a trigger (Maynard et al., 2015) rather than examining actual team adaptation that comprises the sequence of triggers, processes, and outcomes. So far little empirical, longitudinal research has been carried out on team adaptation triggers as causes of team adaptation processes and outcomes (Maynard et al., 2015). Divergent mental models among team members have already been proposed as a trigger of team adaptation (Ellwart et al., 2015), but have not yet been empirically investigated.
This study contributes to existing research in three ways. First, we analyze divergent mental models among team members as a trigger of team adaptation processes. Following the IMOI of team adaptation, we secondly examine the sequential mediating mechanisms of adaptation processes (i. e., situation assessment, plan formulation, and plan execution) and convergence of mental models between a team adaptation trigger (i. e., divergent mental models) on an adaptation outcome (i. e., change in effective ICT use). Third, we analyze team adaptation processes objectively with external ratings using a behaviorally anchored rating scale (Georganta & Brodbeck, 2018).
Team Adaptation
One of the most important abilities of today’s teams is to adapt quickly and successfully to events that require changes or modifications of existing structures, capacities, and/or behavioral or cognitive goal-directed actions (Burke et al., 2006). Burke and colleagues (2006) describe team adaptation as part of a multilevel nomological network of different constructs. In their IMOI model of team adaptation, they present this network, in which they differentiate, among others, the adaptive cycle, adaptative team performance, and team adaptation. The adaptive cycle is characterized by the recursive team processes of situation assessment, plan formulation, plan execution, and team learning. The adaptive cycle and its reciprocal relationship with emergent states (e. g., SMM) constitute adaptive team performance, which is not the consequence of action, but the action itself. The result of this adaptive team performance is team adaptation, which they define as “a change in team performance, in response to a salient cue or cue stream, that leads to a functional outcome for the entire team [which is manifested in] the innovation of new or modification of existing structures, capacities, and/or behavioral or cognitive goal-directed actions” (Burke et al., 2006, p. 1190).
One construct related to team adaptation is team resilience (van der Kleij et al., 2011). Both constructs focus on a team’s ability to adapt to disruptions and difficult situations, and become stronger afterward (Maynard et al., 2020). While team adaptation is defined as a change of team performance in response to salient cues, whether aversive or not, team resilience focuses on the capacity or process to adapt to adverse events, and reach again a positive result (Raetzeke et al., 2022). Team resilience as a capacity to cope with aversive events can be, on the one hand, a beneficial antecedent of team adaptation, and on the other hand, the long-term result of several successful team adaptations (Gucciardi et al., 2018). The relationship between team resilience and adaptation is therefore reciprocal (Kennedy et al., 2016).
Based on the IMOI model of team adaptation by Burke et al. (2006), other reviews on team adaptation have further elaborated the underlying team processes and variables involved. In their integrative framework of team adaptation in complex environments, Rico et al. (2020) differentiated between team adaptation triggers (i. e., events that make an adaptation necessary), team adaptability (i. e., teams’ inherent ability to adapt), team adaptation processes (i. e., behaviors that teams perform during adaptation, which are different from normal taskwork processes), and team adaptation outcomes (i. e., results due to the adaptation). In reviews on team adaptation (Maynard et al., 2015), team adaptation processes act as mediators between adaptation triggers and outcomes.
Team adaptation processes are conceptualized as a four-phase process of situation assessment, plan formulation, plan execution, and team learning with reciprocal relationships to SMM (Burke et al., 2006). Situation assessment is defined as screening the environment and gathering relevant information. Plan formulation is defined as formulating a strategy to cope with the changed situation, including specifying responsibilities, tasks, and goals. Plan execution is defined as actively engaging in actions to execute the formulated plan and team learning is defined as the acquisition of competencies as a result of the reflection of team processes and team adaptation (Burke et al., 2006). An empirical study has shown that the four phases of team adaptation are distinct and that teams undergo the first three phases consecutively (Georganta et al., 2021).
Team adaptation triggers, defined as “events that give rise to team adaptation” (Georganta et al., 2019, p. 230), have undergone minimal empirical study (Maynard et al., 2015). In their meta-analysis, Christian et al. (2017) considered triggers only as moderators, and distinguished between internal and external triggers. External triggers are, for example, a change in the collective task environment (e. g., situational contingencies, resources, interventions, occurrence of non-routine events). Internal triggers are, for example, changes in roles or membership, team structure, rewards, or interpersonal dynamics, such as disagreement among team members (Georganta et al., 2019). Disagreements among team members can result from divergent mental models among team members (DeChurch & Mesmer-Magnus, 2010).
Mental models are cognitive representations of relevant work characteristics, such as other team members’ expertise, teams’ tasks and goals, temporal aspects, or different ICTs (Mathieu et al., 2000; Mohammed et al., 2015; Müller & Antoni, 2022). Mental models emerge from prior experiences and influence how people process, conceptualize, and interpret new information (Levesque et al., 2001). Team members who are “on the same page” have SMM, resulting in similar expectation regarding the specific work characteristic (Cannon-Bowers et al., 1993). If team members have divergent mental models about a specific aspect, they are likely to interpret new information regarding this aspect in different ways, leading to unexpected behaviors of other team members (Mohammed et al., 2010).
Previous research on SMM has shown their importance to team processes and outcomes (DeChurch & Mesmer-Magnus, 2010), by creating similar expectations for team members’ behavior during teamwork (Rico et al., 2008). If team members have SMM, they can coordinate their action implicitly (Rico et al., 2019). If team members have divergent mental models, team members probably pass the wrong information to the wrong receiver at an inappropriate time using an inadequate ICT (cf. Klimoski & Mohammed, 1994). When the convergence of mental models was examined empirically, studies provided inconsistent results, with team members’ divergent mental models converging with the increasing duration of teamwork (Liang et al., 1995; Moreland, 2006) or mental models not converging over time (Mathieu et al., 2000). Other studies showed that mental models even diverge over time (Levesque et al., 2001). These inconsistent results regarding the convergence of mental models show that it is not a matter of time, but it may be due to the variety of team processes that teams (do not) engage in during teamwork.
In their theoretical work on mental model convergence, Kennedy and McComb (2010) have postulated that the higher the divergence of team members’ mental models, the higher the need for explicit team communication about the content of the mental models to build SMM. In other words, divergent mental models require explicit team communication about team roles, tasks, time, or ICT use for effective teamwork. Although theoretically assumed, to our knowledge, no empirical study exists that has investigated the causal effect of initial divergence of mental models on explicit team communication. As teams need to adapt to divergent mental models, this explicit team communication should encompass team members recognizing their dysfunctional team emergent state (i. e., situation assessment), formulating a plan to optimize their team emergent state (i. e., plan formulation), and adhering to this plan (i. e., plan execution). Thus, if team members have divergent mental models, members should engage in team adaptation processes to overcome their dysfunctional team emergent state.
Hypothesis 1: The higher the team members’ initial divergence of mental models, the more likely teams will engage in sequential team adaptation processes (situation assessment, plan formulation, plan execution).
Theoretical reviews on team adaptation (e. g., Rosen et al., 2011) have explained that only if teams recognize that a situation has changed, formulate a plan on how to react to this change, and adhere and execute this plan are they able to successfully update their mental models and achieve positive outcomes. Empirical findings have already supported the fact that some team adaptation processes are associated with convergence of mental models (Resick et al., 2010; Waller, 1999). Ellwart et al. (2015) have shown that teams that engage in situation assessment and plan formulation have a higher convergence of their mental models than teams that engage in none of these processes or only in situation assessment. However, they did not investigate a team adaption trigger or plan execution. Further, as they used the manipulation of team adaptation processes as the independent variable, they have not investigated whether team adaptation processes are the mediating mechanisms between the initial divergence of mental models on their convergence. We combine the theoretical considerations of Hypothesis 1 and the empirical findings from Ellwart et al. (2015) and hypothesize that team adaptation processes, caused by the initial divergence of mental models, are sequentially and positively associated with the convergence of mental models.
Hypothesis 2: Team adaptation processes (i. e., situation assessment, plan formulation, and plan execution) sequentially mediate the effect of team members’ initial divergence of mental models on the convergence of mental models.
Further, some studies have already shown significant relationships of the convergence of mental models with team adaptation outcomes (Uitdewilligen et al., 2013). For example, teams that rely on previous SMM without updating them fail to remain effective (Stachowski et al., 2009). Therefore, we assume that teams with high initial divergent mental models that adapt to converge their mental models show an improvement in team outcomes. The higher the initial divergence of mental models the stronger the change should be. If teams with high initial divergent mental models fail to adapt and to converge their mental models (i. e., do not engage in team adaptation processes), they show no improvement or even a worsening in team outcomes. Following an IMOI on team adaptation (cf. Burke et al., 2006), we hypothesize that team adaptation processes and convergence of mental models sequentially mediate the effect of initial divergence of mental models on team adaptation outcome.
Hypothesis 3: Team adaptation processes (i. e., situation assessment, plan formulation, and plan execution) and convergence of mental models sequentially mediate the effect of team members’ initial divergence of mental models on the change in ICT use.
Method
Participants
This study was part of a larger research project (10/2019 – 03/2020). An ethics committee reviewed and approved the procedure of the research project (08/2019). Here, we only address the relevant information for this study. In total, 227 undergraduate students in 77 teams from a German university participated in the experiment (57.09 % psychology, 21.99 % education science, 20.94 % others). For participation, students received course credit or monetary compensation. Participants were randomly assigned to teams of three members. A total of 11 teams had to be excluded, because four teams consisted only of two members and technical problems occurred in seven teams. After exclusion, 66 teams consisting of 198 members remained for analyses, 79 % females, M(SD)age = 22.31 (3.27) years).
Procedure
The experiment consisted of an introduction and two taskwork phases. This study focused mainly on Phase 2. However, we describe the whole procedure. After being welcomed, the participants sat in front of one of three laptops with privacy shields between them. Participants received information about the study and signed a consent form. Afterward, they had to do exercises in four ICTs (mail, chat, ticket system, whiteboard), so that each participant got used to the ICTs they would use for the taskwork phases. After the exercises, each participant received information about the task in Phase 1. They were told that they are a student consultancy group that has to select one of seven possible applicants for a managerial position for a company. To ensure some variance in the initial divergence of mental models across teams, we manipulated SMM regarding the ICT use before phase 1. A total of 33 teams were guided to plan their ICT use. For that, they talked about their ICT use for 5 min in a face-to-face setting. The other 33 teams talked about another topic for 5 min in a face-to-face setting and each member of these teams received a different description of their previous ICT experience.
After filling out the manipulation check, the teams started to work on Task 1 for 40 min. The task was designed as a decision-making task with one correct solution. The information about the task was distributed among team members to create interdependence. As Phase 1 ended, participants filled out a questionnaire (T1) assessing, among others, the structure of their mental models about the ICT use, the perception of SMM about their ICT use, and the effectiveness of their ICT use.
After T1, teams received instructions for their task in Phase 2. In this brainstorming task, teams should develop a fact sheet on the required criteria of management positions. For this, teams should brainstorm on required criteria for management positions and then select and prioritize the four most important criteria. To signal that team adaptation can take place (i. e., to give a cue for team adaptation; Burke et al., 2006; Louis & Sutton, 1991), the instructions of Task 2 included a hint that teams were allowed to change their previous ICT use if necessary. Teams had 10 min to accomplish Task 2. Afterward, participants again filled out a questionnaire (T2) assessing, among others, the perception of SMM about their ICT use and the effectiveness of their ICT use.
Dependent Measures
The initial divergence of mental models was assessed at T1. Each participant had to rate which ICT (i. e., mail, chat, ticket system, whiteboard, or PowerPoint) he or she used for different purposes (i. e., team communication, communication with the company, allocation of tasks, documentation of required criteria and applicant data, presentation) during Task 1 (“Which ICT did you use for the following purposes?”). For each team, an agreement index (Conger’s kappa; Conger, 1980) was calculated to analyze the objective initial divergence of mental models prior to Phase 2. Conger’s kappa can reach values between −∞ and 1. For easier interpretation, we recoded this variable by subtracting the respective value from 1, so that high values display highly divergent mental models among team members. Kappa is calculated with the following equation, where p0 describes the observed agreement on the 5×5 matrix (5 ICTs × 5 purposes) between team members (i. e., how high is the agreement among members for the purposes regarding the used ICT) and pe describes the expected agreement of team members, if members judge it completely randomly (i. e., how often was, e. g., chat used for all purposes). Table E1 in the ESM 1 provides an example calculation of one team and the detailed equations.
Kappa = 
We assessed team adaptation processes using the Behaviorally Anchored Rating Scale (BARS) for team adaptation developed by Georganta and Brodbeck (2018). In BARS, the respective behavior is defined and explained, and the external rating focuses on whether the team has engaged in this defined behavior to a high (= 5) or a low (= 1) extent. For more information on how BARS is used, please see Georganta and Brodbeck (2018). Since we focused on the adaptation of ICT use, we adapted the original BARS toward the teams’ ICT use (see Table E2 in the ESM 2). For this purpose, the communication content and ICT use of each team in Phase 2 were analyzed regarding situation assessment, plan formulation, and plan execution. Each process was rated from 1 (i. e., the team had not engaged in the described behavior) to 5 (i. e., the team had engaged in the described behavior to a great extent). We validated the external rating with participants’ subjective perception on whether their team engaged in the described behavior. Team members assessed three items using a 5-point Likert scale (1 = do not agree, 5 = agree) for situation assessment (“During the brainstorming task, we gathered information about which ICT with which features are available to us”; intraclass correlation coefficient [ICC][1] = .16, ICC[2] = .36), plan formulation (“During the brainstorming task, we planned our ICT use, i. e., what ICT we use for what purpose”; ICC[1] = .26, ICC[2] = .52), and plan execution (“During the brainstorming task, each member adhered to the rules we made regarding ICT use”; ICC[1] = .40, ICC[2] = .67).
Convergence of mental models was assessed using the difference score from T1 to T2 regarding subjectively measured ICT SMM. We used a 4-item scale (e. g., “During team collaboration, team members knew which ICT we used for which tasks”; αT1 = .75; αT2 = .84) from Müller and Antoni (2020b) at T1 (ICC[1] = .16; ICC[2] = .36) and T2 (ICC[1] = .29; ICC[2] = .55). Participants rated the items on a 5-point Likert scale (1 = do not agree, 5 = agree). We aggregated the values on team level and subtracted the value of T1 from T2. Negative values represent a perceived divergence and positive values represent a perceived convergence of team members’ mental models from T1 to T2.
Change in ICT use was assessed by the difference score from T1 to T2 regarding subjectively measured effectiveness of ICT use. We used a self-created 6-item scale (e. g., “During team collaboration, our ICT use was effective”; αT1 = .78; αT2 = .70) at T1 (ICC[1] = .60; ICC[2] = .82) and T2 (ICC[1] = .40; ICC[2] = .67). Participants rated the items on a 5-point Likert scale (1 = do not agree, 5 = agree). We aggregated the values on team level and subtracted the value of T1 from T2. Negative values represent a perceived deterioration and positive values an improvement in the ICT use from T1 to T2.
Data Analysis
As all variables referred to the team level, we performed the analyses on team level. To test our directed hypotheses, we report all exact p values and 90 % confidence intervals (CI) of the relationships assumed in our hypotheses. To assess our hypotheses, we calculated one sequential mediation model including one independent variable (i. e., initial divergence of mental models), four sequential mediators (i. e., situation assessment, plan formulation, plan execution, convergence of mental models), and one dependent variable (i. e., change in ICT use), using the SPSS Macro Process Model 6 for sequential mediation analysis (CI = 90 %; number of bootstrap samples = 10,000; Hayes, 2021).
Results
Preliminary Analyses
Descriptive statistics as well as intercorrelations of all relevant variables are presented in Table 1. The correlations between BARS and subjective perception were significant for situation assessment, (r = .38, p < .001), for plan formulation (r = .43, p < .001), and plan execution (r = .28, p < .05). This indicates that the external rating corresponds with the subjective perceptions of team members about the team adaptation processes.
Hypotheses Testing
Figure 1 presents the results of the mediation model. It shows that the higher the team members’ initial divergence of mental models, the more teams engage in situation assessment (β = .25, p = .046), which leads to higher plan formulation (β = .61, p < .001) and this, in turn, results in higher plan execution (β = .51, p < .001). As there are no significant associations between team members’ initial divergence of mental models and plan formulation or plan execution, or between situation assessment and plan execution, the sequential process of situation assessment, plan formulation, and plan execution can be assumed. Thus, Hypothesis 1 is supported.
Figure 1 further shows that the higher the team members’ initial divergence of mental models, the higher the convergence of mental models (β = .37, p = .003). We also found a positive association between plan execution and convergence of mental models (β = .28, p = .067). The sequential mediation of situation assessment, plan formulation, and plan execution between team members’ initial divergence of mental models and convergence of mental models led to a significant indirect (β = .02, CI [0.001; 0.051]) and total effect (β = .37, p = .003). Thus, Hypothesis 2 is supported.
Finally, results showed a significant total (β = .52, p = .001, CI [0.985; 2.020]) and direct effect (β = .30, p = .002) of team members’ initial divergence of mental models on the change in ICT use. The sequential indirect effect via all three team adaptation processes and convergence of mental models was significant (β = .01, CI [0.001; 0.032]). Another indirect effect (i. e., team members’ initial divergence of mental models → convergence of mental models → change in effective ICT use) was significant (β = .22, CI [0.108; 0.329]). Thus, Hypothesis 3 is supported. All results are also presented in a table in the ESM 3.
Discussion
Although many teams face the necessity to adapt during their lifetime (Ilgen, 1999; Kozlowski & Bell, 2003) and reviews postulate that SMM are an important emergent state between different phases of team adaptation processes (Burke et al., 2006; Rosen et al., 2011), divergent mental models as a trigger of team adaptation processes have not been investigated. Since previous research showed that disagreements among team members can act as an internal trigger of team adaptation (Georganta et al., 2019), the aim of this study was to investigate whether team members’ divergent mental models act as a trigger of team adaptation. Following the IMOI of team adaptation (e. g., Maynard et al., 2015; Rico et al., 2020), we differentiated between team adaptation trigger (i. e., divergent mental models), processes (i. e., situation assessment, plan formulation, and plan execution), convergence of mental models, and outcome (i. e., change in ICT use). Our results support all hypotheses that team adaptation processes and the convergence of mental models mediate the effect of team members’ initial divergence of mental models on the change in ICT use.
Theoretical Implications
The quality of cognitive emergent team states (i. e., shared vs. divergent mental models) can act as a trigger of team adaptation processes and outcome. In our study, we found significant influences of team members’ initial divergence of mental models on situation assessment, convergence of mental models, and change in ICT use. In this way, our study extends previous research on team adaptation triggers. In their review on team adaptation, Maynard et al. (2015) used the taxonomy of team processes suggested by Marks et al. (2001) and postulated that external triggers lead to action processes (i. e., monitoring, backup behavior, coordination). Internal triggers should lead to interpersonal processes (i. e., conflict management, motivation, affect management); afterwards, both processes should lead to transition processes. Based on these assumptions regarding internal and external triggers and team processes, it is difficult to categorize team members’ initial divergence of mental models as either internal or external. In our study, a dysfunctional team emergent state (operationalized as team members’ divergent mental models about ICT use) has led to transition processes (i. e., mission analysis, plan formulation). This indicates that a third type of trigger might exist that directly leads to transition processes.
In line with the extension of team adaptation triggers, our findings provide theoretical implications for the definition of team adaptation. Previous research mainly defined team adaptation as an adjustment to novel, unexpected, or sudden changes in the teams’ environment (cf. Burke et al., 2006; Burtscher et al., 2010; Rico et al., 2019). However, team members’ initial divergent mental models do not necessarily occur unexpectedly or suddenly. Particularly in newly formed teams or when tasks change, the likelihood of divergent mental models is high. Thus, our findings provide theoretical implications for the definition of team adaptation, which should be broadened to include team adaptation triggers that do not occur suddenly. Including triggers that do not appear suddenly or unexpectedly, such as when teams are newly formed, would then also provide starting points for research on team development interventions. The introduction of team adaptation processes in the initial teamwork can then reduce the probability of divergent mental models in the long term.
A third theoretical implication can be derived for the occurrence of team adaptation processes. Our results validate that the three team adaptation processes occur sequentially (cf. Georganta et al., 2021). This finding is in line with theoretical models of team adaptation (Burke et al., 2006; Rico et al., 2020; Rosen et al., 2011) that adaptation triggers influence situational assessment directly, but not plan formulation and execution. It is also in line with theoretical models of teamwork cycle (e. g., Hagemann & Kluge, 2017; McGrath & Tschan, 2004). McGrath and Tschan (2004) describe task performance as recurrent cycles of processes called “orient, enact, monitor, and modify.” These processes can be compared to plan formulation (i. e., orient) and plan execution (i. e., enact). Later models (e. g., Hagemann & Kluge, 2017) included situation assessment as an important starting point of an ideal teamwork cycle. McGrath and Tschan (2004) highlighted that task performance is not reflected by a single sequence of actions, which is in line with the temporally based framework of team processes by Marks et al. (2001). Marks and colleagues (2001) describe the rhythm of phases that teams go through in accomplishing tasks. The rhythm comprises recurring transition (e. g., situation assessment and plan formulation) and action phases (e. g., plan execution). This framework implies that it is possible for teams to engage in the three team adaptation processes in a more dynamic and reciprocal way. For example, during plan execution, a team might shift back to situational assessment because the team has noticed that the formulated plan is unfeasible, unrealistic, and does not fit the changed situation (cf. monitor and modify from McGrath & Tschan, 2004). Thus, the reciprocal occurrence of situation assessment, plan formulation, and plan execution should be analyzed in more detail to reveal differences among teams that differ in the rhythms of these processes (cf. Marks et al., 2001) and to update previous frameworks on team adaptation.
To date, the convergence of mental models is postulated to happen due to the verbal communication during teamwork, as “communication provides a window into the cognitive processes of the team” (McComb & Kennedy, 2012, p. 551). That is, mental models should converge if team members communicate with each other about the mental model content during teamwork (Kennedy & McComb, 2010). Our results imply that other mediating variables between team members’ initial divergence of mental models on the convergence of mental models might exist. The convergence of mental models can happen not only due to explicit team communication (cf. Kennedy & McComb, 2010), but due to other nonverbal cues, such as other team members’ behaviors (in our study: ICT use). Other studies have shown that nonverbal communication modalities can lead to perceived SMM (Hanna & Richards, 2018). Thus, convergence of mental models could also occur implicitly, such that team members observe the ICT use of the other team members and adapt their mental model without overt communication. Regardless of how mental models converge, mental model convergence seems to be an important intermediate step in the process of team adaptation and teamwork in general (cf. Hagemann & Kluge, 2017). An empirical study on continuous improvement processes (Kaizen) showed that the explicit group consensus after each Kaizen phase is crucial for team performance and that poor-performing teams spent too little time on establishing a group-shared understanding (Franken et al., 2021).
Practical Implications
Our results have some practical implications for team adaptation. Since team members’ divergent mental models act as a trigger of team adaptation, teams should not only focus on external triggers, such as changes in resources (budget or time), but should also be aware of other triggers, such as dysfunctional team emergent states. For this, team leaders should emphasize the fact that team members should screen both their working environment and interpersonal dynamics repeatedly.
Our results provide practical implications on how to handle divergent mental models of ICT use. It is important that team members, as they may have different ICT experiences, and thus mental models, have the opportunity to engage in situation assessment. For this purpose, teams need time between action phases for discussing their experience and use of ICTs. In so-called transition phases (cf. Marks et al., 2001), situation assessment and plan formulation can occur. In the subsequent action phase, the execution of the newly formulated plan can take place, which can lead to a convergence of mental models of ICT use. The significant indirect effect of team members’ initial divergence of mental models via team adaptation processes on the convergence of mental models shows that all three team adaptation processes contribute sequentially to successful convergence in mental models. Hence, we recommend leading teams by the implementation of transition phases including situation assessment and plan formulation, so that an elaborate plan can be executed during action phases. This practical implication is in line with the theoretical model by Hagemann and Kluge (2017), which describes that achieving a shared situation assessment as the comparison between team goals and the current state of the teams should be the main goal of the transition phase so as to show an ideal teamwork cycle (i. e., the following action processes of coordination and cooperation).
Limitations and Future Research
Our study has some limitations that have to be discussed and may stimulate future research. The first limitation is that we used a student sample in a laboratory experiment, which allows for causal inference but needs external validation using field studies with employee teams in organizations. Further, our study contained only a relatively short teamwork time. This may have prohibited the finding of other mediating variables and limited the analysis of team learning. Further, our results should only be applied for ad hoc teams, as the duration of working together as a team (i. e., familiarity) might play an important moderating role in the relationships found here. Teams with extensive familiarity may draw more on their shared procedural knowledge of how to adapt to changes, rather than formally assessing, planning, and executing a change. Future research should employ longitudinal designs over a longer period of teamwork (e. g., several months) to investigate how the relationships found in this study change or are moderated by specific aspects.
Second, our operationalizations of variables differ in their objectivity. While team members’ initial divergence of mental models and the team adaptation processes were objectively measured, convergence of mental models and change in ICT use were based on self-report ratings. Further research should combine objective and subjective measurement methods to operationalize the dependent variables.
The third limitation focuses on the changes from Task 1 to Task 2. In our study, also the teams’ tasks changed from the first to the second phase (i. e., decision-making to a brainstorming task), representing an external trigger. Thus, the processes of team adaptation may be due to the external trigger. However, McComb and Kennedy (2012) have postulated that the task types of generation (i. e., brainstorming) and choosing tasks (i. e., decision-making) require team members to communicate about the same mental model content and use of the same ICTs. This indicates that these two task types are quite similar regarding the convergence of mental models and the change in ICT use. Thus, the change in task type from Task 1 to Task 2 should not have triggered team adaptation processes, the convergence of mental models, or the change in ICT use. However, a study using the same task type in both phases should be employed to replicate our results.
Fourth, although postulated in reviews on team adaptation (Burke et al., 2006; Rosen et al., 2011), we were not able to analyze the intervening update of SMM between team adaptation processes. Future studies combining team adaptation and SMM should investigate the update of team members’ mental models after each adaptation process. This analysis could clarify the dynamic nature of convergence and divergence of mental models (cf. Kozlowski et al., 2013) associated with the respective team (adaptation) processes. This would allow researchers to provide practical implications on which team (adaptation) processes lead to convergence and which lead to divergence of team members’ mental models.
Finally, as we focused on divergent mental models of ICT use, we did not focus on team members’ divergent mental models about teamwork, taskwork, or temporal aspects (Cannon-Bowers et al., 1993; Mohammed et al., 2015). It might be interesting for future research on team adaptation to assess which subtypes of SMM lead to which team adaptation processes and outcomes. However, as different subtypes of SMM correlate (Konradt et al., 2015; Müller et al., 2020b) and their convergence processes are considered to be similar, one could expect that the other subtypes of SMM lead to similar adaptation processes and outcomes.
Conclusion
This study contributes to existing research by emphasizing that team members’ initial divergence of mental models regarding their ICT use acts as a trigger of team adaptation. The effect of team members’ initial divergence of mental models on the change in effective ICT use was sequentially mediated by situation assessment, plan formulation, plan execution, and the convergence of mental models. Further, team members’ initial divergence of mental models directly leads to the convergence of mental models and to changes in ICT use. Future studies using employee samples and longitudinal designs should be conducted to validate our research findings.
Electronic Supplementary Materials
The electronic supplementary material is available with the online version of the article at https://doi.org/10.1026/0932-4089/a000421
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