Meeting the challenge of workplace change: Team cooperation outperforms team competition.
Researchers have explored various mechanisms for achieving team adaptation: Team motivation and cognition are recognized as two of the key mechanisms (Maynard, Kennedy, & Sommer, 2015). However, although M. D. Johnson et al. (2006) have shown that a team-based and an individual-based incentive have different effects on team performance, the influence of reward structure on team adaptation has not been examined.
Reward structures impact on outcomes through individuals' perception and cognition (D. W. Johnson & Johnson, 2005). A mental model refers to a simplified, cognitive representation of reality that is used to describe, explain, and predict events, namely, it is individuals' cognitive representation of the world (Johnson-Laird, 1983), and may mediate between reward structure and behavioral outcomes. Shared mental models are organized mental representations of key elements in the team environment shared across team members (Klimoski & Mohammed, 1994), that is, shared cognition among team members.
Mohammed, Ferzandi, and Hamilton (2010) have argued that this has a positive effect on team performance in a stable environment. However, whether an initially effective shared mental model is suitable for a change problem remains doubtful. Uitdewilligen, Waller, and Pitariu (2013) introduced the concept of shared mental model updating to describe the process of aligning a shared mental model and the change problem, namely, making modifications in mental models consistent with task-related changes. We conducted an experimental study to test the inference that shared mental model updating mediates the relationship between reward structure and team adaptation.
Our primary aim in this study was to provide evidence for the function of reward structures in dealing with task changes and the related cognitive mechanism. We set up hypothetical answers based on social interdependence theory (Deutsch, 1949) and team cognition theory (DeChurch & Mesmer-Magnus, 2010; Mohammed et al., 2010), and argued that teams with a cooperative (vs. competitive) reward structure would adapt better to task changes, through shared mental model updating.
Literature Review and Hypothesis Development
Reward Structure and Team Adaptation
In social interdependence theory, Deutsch (1949) formulates that cooperation and competition are interpersonal relationships and that individual team members will behave differently in cooperation and competition orientation situations (D. W. Johnson & Johnson, 2005). Team members who are exposed to the cooperative situation "will perceive themselves to be more promotively interdependent (in relation to the other [team members]) with respect to [the] goal," whereas those exposed to the competitive situation "will perceive themselves to be more contriently interdependent (in relation to the other [team members]) with respect to [the] goal" (Deutsch, 1949, p. 138). Thus, the reward structure (cooperative vs. competitive) that the team adopts may have an important impact on individual members' behavior, which will influence team behavior and performance.
Team studies in which the researchers have applied social interdependence theory have yielded findings that individuals' beliefs about how their goals are related determine the way they understand the task and coordinate their behavior in completing the task (Beersma et al., 2009; M. D. Johnson et al., 2006). Individuals in cooperative (vs. competitive) situations tend to trust each other and be more supportive, and to be more willing to communicate and accept others' help. This is considered beneficial for team adaptation (Christian et al., 2017; Maynard et al., 2015). In contrast, as individuals in competitive (vs. cooperative) situations tend to be more self-interested, and are reluctant to share information and take action to support others, this may lead to team maladaptation (Frick et al., 2018). Thus, we inferred that members in teams with a cooperative (vs. competitive) structure would achieve a relatively higher level of team adaptation. Therefore, we proposed the following hypothesis:
Hypothesis 1: Reward structure will have a significant influence on team adaptation: A cooperative reward structure will promote team adaptation, and a competitive reward structure will inhibit team adaptation.
The Mediating Role of Shared Mental Model Updating
According to D. W. Johnson and Johnson (2005), the impact of reward structure on outcomes (e.g., team members' behavior, affections) depends on individuals' cognitive representation of the situational context. In this study we selected the construct of a shared mental model as proposed by Burke et al. (2006) in their team adaptation model, which is further explained in Zajac, Gregory, Bedwell, Kramer, and Salas's (2014) conceptual work on the cognitive underpinning of team adaptation. Burke et al. proposed four processes in their model: situation assessment, plan formulation, plan execution, and team learning. Zajac et al. (2014) extended the model and contended that a shared mental model was the main contributor in the four adaptive processes.
Two characteristics of shared mental models are usually evaluated: similarity and accuracy (Mohammed et al., 2010). Similarity refers to the degree to which members' mental models overlap, and accuracy refers to the degree of adequacy in representing the specific content of the mental model. When team members have similar mental models, it is easier for them to form a similar judgment, to decrease conflict, and to develop consensus toward knowledge sharing, leading to a high level of team performance (Xiang, Yang, & Zhang, 2016; Xie, Zhu, & Wang, 2009). An accurate shared mental model contributes to the team forming an accurate judgment, overseeing the completion of the team task, and adopting knowledge appropriately to solve problems, leading to a high level of team performance (Edwards, Day, Arthur, & Bell, 2006; McIntyre & Foti, 2013). Thus, we proposed the following hypotheses:
Hypothesis 2a: Shared mental model similarity will be positively related to team performance. Hypothesis 2b: Shared mental model accuracy will be positively related to team performance.
However, whether the hypothesized relationship between a shared mental model and team performance still holds in a situation of uncertainty is doubtful. The shared mental model may hinder performance in a new situation because of a mismatch (Uitdewilligen et al., 2013). As a task changes, the initial mental model loses its efficiency in explaining and predicting, thus leading to deterioration in team performance (Henrickson Parker, Schmutz, Schmutz, & Manser, 2018). The only way to maintain team performance is to update the shared mental model in the required direction and to ensure that all members' mental models change in the same way so that they can maintain their similarity after the change (Gorman & Cooke, 2011), that is, shared mental model updating.
Shared mental model updating involves team members changing the underlying knowledge structure and matching it with the target task. In this process they develop an adaptive strategy for a change problem in two ways: team members' reflect on their actions, such as whether the actions are helpful or unhelpful, and make decisions about what actions to continue or change (Abrantes, Passos, Cunha, & Santos, 2018). Specifically, shared mental model updating begins with members' awareness of change and judgment of the appropriateness of a current problem-solving strategy, which leads to the revision of the initial mental model and results in a mental model adapted for the new problem (Santos, Uitdewilligen, & Passos, 2015). When a shared mental model changes, the strategy for solving a problem changes accordingly (Randall, Resick, & DeChurch, 2011). Therefore, shared mental model updating facilitates the teams' development of strategies to solve the new problem. Thus, we proposed the following hypothesis: Hypothesis 3: Shared mental model updating will be positively related to team adaptation.
Team members in a cooperative situation tend to be supportive and frequently share high-quality information (Ghobadi, Campbell, & Clegg, 2017), which is positively related to identification of environmental cues (Ellwart, Happ, Gurtner, & Rack, 2015) and team members updating their mental model accordingly, thus contributing to adaptation (Burke et al., 2006). In contrast, team members in a competitive situation tend to keep valuable cues to themselves to achieve their own success, or at least to inhibit others in achieving success (Beersma et al., 2009). Members will thus fail to share relevant information regarding the need for change, and, consequently, fail to update their mental model, leading to maladaptation (Frick et al., 2018). Therefore, we proposed the following hypothesis:
Hypothesis 4: Shared mental model updating will mediate the relationship between reward structure and team adaptation.
The research model is depicted in Figure 1.
Design and Participants
Participants were recruited through an advertisement on an open forum at Zhejiang University, China, and they enrolled through the SOJUMP website. All information was confidential. Participants were 210 undergraduate students aged from 18 to 22 years (M = 19.95, SD = 0.70). We employed a between-subjects design in which participants were randomly allocated to a cooperative or to a competitive situation. We randomly assigned them to groups of three, resulting in 70 teams. Three teams failed to complete Task B, which required the completion of at least 10 rounds in 20 minutes. Five other teams had incomplete data either because of problems with the tracking system or because their survey answers were incomplete. Therefore, we used data from 62 teams in the final statistical analysis. Sample characteristics are shown in Table 1. Neither gender composition nor participants' major being studied had a significant effect on team adaptation according to analysis of variance results, F(3, 58) = 2.189, ns for gender; F(3, 58) = 1.028, ns for major.
The experimental task was adapted from the method used in Cohen and Bacdayan's (1994) multiagent problem-solving study. We redesigned it as a computerized card game in which members with different expertise were required to cooperate in replacing the target card with the two of hearts. We used 12 cards in this experiment: the two, three, and four of hearts, two, three, and four of clubs, the two, three, and four of spades, and the two, three, and four of diamonds. Team members were collocated and each worked on a separate networked computer. Apart from the target card, each team member initially received a card, with eight cards remaining as folded cards, which denote unknown resources that can be used to complete a task within the team. The interface of the card game is shown in Figure 2. Each team member followed a specific rule that limited his/her action when exchanging cards, namely, a same suit, a same color, or a same number rule. For example, the member who was assigned the same number rule could exchange his/her card only with a card that had the same number. Team members could perform seven operations to achieve the target: a) exchange the card with the target card if the rule applies; b) exchange the card with the folded card, at which point his/her card will be transferred to the open card pool; c) exchange the card with the open card if the rule applies; d) skip the turn; e) show the card to other team members; f) exchange the card with another team member; g) accept/reject other team members' requests to exchange a card.
To examine the effects of the reward structures and to capture the process of shared mental model updating for a changed task, we manipulated changes as well as the reward structures. Therefore, the game was divided into two main sessions with different criteria for task completion. Achievement was emphasized in Task A: Team players were required to complete as many rounds as possible in 20 minutes. Efficiency was emphasized in Task B: Team players were required to use as few steps as possible to complete 10 rounds. The sequence of Tasks A and B was random to exclude learning effects. Several teams undertook Task A first and then Task B (Task Sequence AB) and others undertook Task B first and then Task A (Task Sequence BA).
The manipulation of the reward structure was consistent with that in previous studies in which different reward structures (Beersma et al., 2009; M. D. Johnson et al., 2006) were used as follows: The cooperative structure was manipulated as a team-level reward and participants were told "The prize will be given to the best team." The competitive structure was manipulated as an individual-level reward and participants were told "The prize will be given to the best player." The decision as to who was the best player was based on two requirements, namely, the player's team had won the game and the player had contributed the most (i.e., she/he completed the final step).
The experiment began with participants being briefed on the instructions of the game. They each also had a reminder of the briefing on their desktop, as well as the game operation and rules. Participants were then given a 5-minute trial period to familiarize themselves with the game, after which the first round of responses to the statements on mental models began. The two formal sessions that made up the experiment began 5 minutes after the trial. Between the end of the first session and the beginning of the second session, we gave the participants a break to provide them with instructions on task changes. There was then another round of responses to statements on mental models and participants also provided background information before their dismissal. The experimental process is shown in Figure 3.
Task performance. Task performance was evaluated based on experimental requirements. The Task A performance was assessed on the total rounds completed, and the Task B performance was assessed on the total number of steps used. Each round began with the four of clubs as the card in the target position and ended with the four of hearts as the card in the target position.
Team adaptation. As participants were required to use different strategies to perform well on Tasks A and B, we used team adaptation as the measure to assess the implementation of efficient strategies for both tasks. After the experimental task analysis, we identified three types of behavior that were efficient in achieving final success. In Task A, these types involved increasing exchange cards with the target (TarA), decreasing exchange cards with the open cards (-OpenA), and acceptance of other members' requests to exchange cards (AcceptA). In Task B, these types involved increasing exchange cards with the target cards (TarB), decreasing exchange cards with the open cards (-OpenB), and increasing the number of skipped turns (Skip B). Thus, for players to perform well in both sessions, the adaptive strategy for the game was the sum of the behavior in the two phases:
Adaptive strategy of Task A = (TarA - OpenA + AcceptA)/SumA
Adaptive strategy of Task B = (TarB - OpenB + SkipB)/SumB
Adaptive strategy of the game = (TarA - OpenA + AcceptA)/SumA + (TarB - OpenB + SkipB)/SumB
Note. SumA refers to the total steps used in completing Task A within 20 minutes; SumB refers to the total steps used in completing Task B in 10 rounds.
To confirm the validation of our task analysis, we tested the correlation between the adaptive strategy in Task A/B and the performance in Task A/B, with results supporting the above analysis (Task A, r = .64, p < .001; Task B, r = -.52, p < .001). The results showed that teams whose members employed adaptive strategies (vs. members who did not employ adaptive strategies) completed more rounds within 20 minutes and required fewer steps to complete 10 rounds.
Shared mental models. The original data that we collected were participants' mental models, which describe a possible way to complete the tasks. The instruction for Task A was "Please write down as many routes as possible that you can use to complete the task," and the instruction for Task B was "Please write down all the potential routes that you can use to complete the task in the quickest way possible." Thus, we obtained each participant's mental model data for the completion of the related task. On the basis of Mohammed et al.'s (2010) definition of team mental model similarity, we compared each team member's mental model with that of the other two team members, and calculated the team's shared mental model similarity as the average of the three comparisons. For example, A, B, and C form a team. First, we compared two of the three members to obtain the similarity of data obtained from AB, AC, and BC. Then we calculated the average of data obtained from AB, AC, and BC to represent the team's shared mental model similarity.
We compared each member's mental model with the expert mental model that was based on task analysis from the definition of team mental model accuracy (Mohammed et al., 2010). As we gave participants only 5 minutes to write down the routes and it was impossible to write down all the routes with more than four cards, we set up all the possible routes with up to four cards as the expert mental model in Task A, and we set up all the shortest routes with only three cards in Task B. Then, for each member, we obtained comparison data, which we averaged to the team level to represent the team's shared mental model accuracy.
As shared mental model updating involves making a change in the correct direction for the team to meet task requirements, it is different from team mental model differences (Uitdewilligen et al., 2013). Thus, we first calculated the differences in each member's mental model between the first and second sessions. We then assigned a value of either +1 or -1 to the difference in his/her mental model according to the direction of change. The positive value represented correct updating and a negative value denoted failure to update. Finally, we averaged the values of each team member in each team to obtain data for the team's shared mental model updating.
Reward structure. The reward structure was manipulated as two different settings among all the teams. For each team, a cooperative structure was marked as 1 and a competitive structure was marked as 0.
Control variables. Game experience was an important variable that may have influenced our results (Uitdewilligen et al., 2013). Participants rated three items on a 5-point scale from 1 = the least similar to 5 = the most similar to measure game experience. The items are "The degree of similarity between the games I have played and this game," "The degree of similarity between the games I have seen and this game," and "The degree of similarity between the games I have known and this game." We also included education background and gender as control variables as they are considered to influence the performance of problem-solving tasks (Gallagher et al., 2000). The effect of task sequence was also controlled. We assigned 1 for Task Sequence AB and 0 for Task Sequence BA.
The descriptive analysis results are presented in Table 2. They show that teams with a cooperative (vs. competitive) structure performed both tasks better, and that teams with a cooperative (vs. competitive) structure demonstrated better team adaptation and shared mental model updating. The correlation coefficients showed that shared mental model updating was positively related to team adaptation. The results also showed that shared mental model accuracy and similarity were related to task performance for Task A, and that accuracy, but not similarity, was related to task performance for Task B. These results provide preliminary support for the hypotheses.
We tested Hypothesis 1 by performing an analysis of variance (ANOVA), the results of which for team adaptation supported Hypothesis 1, F(1, 60) = 45.48, p < .01. There was a significant difference in team adaptation between teams with a cooperative reward structure and teams with a competitive reward structure. This means that teams rewarded as a whole could better adapt to new tasks than those in which members were rewarded as individuals. We employed a linear regression model to test the other hypotheses, the results of which are reported in Table 3. The reward structure was controlled when analyzing the effect of the accuracy and similarity of the shared mental model on the teams' task performance. Results showed that the relationship between the shared mental model and task performance was at a significant level only for shared mental model accuracy and performance in Task A. In Task B, the coefficient of shared mental model accuracy was marginally significant, but the explained variance that it added to performance in Task B was not substantial enough to indicate an impact. Thus, Hypotheses 2a and 2b were not supported. In Hypothesis 3 we predicted a positive relationship between shared mental model updating and team adaptation. According to the results of Model 7 in Table 3, Hypothesis 3 was supported.
We examined Hypothesis 4, in which we predicted that shared mental model updating would mediate between reward structure and team adaptation, through PROCESS software (Preacher & Hayes, 2004). According to the results in Table 4, reward structure was positively related to shared mental model updating, as indicated by a significant unstandardized regression coefficient (B = 0.22, t = 5.27, p < .001). Reward structure was also positively associated with team adaptation (B = 0.13, t = 6.7, p < .001), providing further support for Hypothesis 1. Finally, reward structure had an indirect effect on team performance as hypothesized (effect value = .05, z = 3.55, p < .001). Bootstrap results confirmed the Sobel test, and a bootstrap 95% confidence interval (CI) for the indirect effect did not contain zero. Thus, Hypothesis 4 was supported.
In this study we addressed the importance of cooperation for successful adaptation to changes by conducting an examination of the relationships between reward structure, shared mental model, and team adaptation. We conducted our examination in a laboratory experiment that involved completing team tasks in a computerized card game, and we found that in a task change situation, teams with a cooperative (vs. competitive) reward structure performed better in contributing to team adaptation through shared mental model updating. These results offer insight into the development of social interdependence theory and add to team adaptation research, as well as providing suggestions for helping teams to deal successfully with task-related changes.
The main difference between previous findings and our findings was the nonsignificant relationship between the teams' shared mental model and task performance in this study. Previous researchers have confirmed the positive effects of similarity and accuracy of shared mental models on team performance across various settings (DeChurch & Mesmer-Magnus, 2010; Mohammed et al., 2010). A key difference in this study was its counterbalanced design that created an uncertain task environment. The nonsignificance of the relationship between the teams' shared mental model and task performance actually provides indirect evidence for the argument of an unstable relationship between a shared mental model and task performance. Even when a team's shared mental models are similar and accurate, they may not perform well when a task changes. In addition, shared mental models usually influence team performance through facilitating important team process activities, such as coordination and backup behavior (Burke et al. (2006). However, these processes were not necessary in this study, thus decreasing the explanatory power of shared mental models.
Our results also showed that the correlation of shared mental model accuracy with both tasks was negative. We were interested in this result as it may be evidence for the strategy paradox (Raynor, 2007). That is, committing to previously successful strategies may lead to failure. In this study, for example, for teams who experienced task sequence AB, the high accuracy of Task A's mental model led to success in the first round. These team members may have retained their mental models for the second round because there was little time for them to contemplate and adjust. When the task changed in the second round, the initial accurate mental model became inaccurate. Therefore, the more accurate the shared mental model in the first round, the less accurate was the shared mental model in the second round.
Theoretical Implications and Contributions
We have mainly contributed to two fields: team adaptation research and social interdependence theory (Deutsch, 1949). Team adaptation research is relatively new, as it has emerged only within the last two decades (Maynard et al., 2015), whereas social interdependence theory is a longstanding research topic and has been applied to various disciplines (D. W. Johnson & Johnson, 2005). We have contributed to the former in terms of dynamic processes and to the latter in terms of the theoretical expansion to multigoal situations. According to Deutsch's (1949) social interdependence theory, individual team members focus on a single goal. However, D. W. Johnson and Johnson (2005) wrote that "the implementation of social interdependence theory in business and industrial organizations has highlighted the need to expand the theory to take into account multiple goals" (p. 336). Thus, the multigoal situation needs to be examined for the generalization of this theory. In our study design, we created a situation in which the team goal was shared, indicating cooperative interdependence for all the team members, as well as creating their reward interdependence. Participants had to act within two common or conflicting goals (task goal and reward goal). The results showed that in a shared task goal condition, members still acted differently when the reward structure was different. The reason for such difference may be positive interaction among members (e.g., they publicized their card when they transferred their card to another team member who could make a winning move), but also may be the positive effect of congruence (when both goals are consistent vs. inconsistent). Future researchers need to explore the underlying reasons for such differences and the effect of multigoal congruence.
The aim of team adaptation researchers is to resolve uncertainties for, and threats to, team performance from environmental dynamics, emergent cases such as change of leader and team downsizing, and general changes in task environments (Frick et al., 2018). Our results contribute to this research by clarifying the role of reward structure and the process of shared mental model updating. We found that shared mental model updating acts as the proximal antecedent of team adaptation and mediates the relationship between reward structure and team adaptation. To our knowledge, our study is the first in which an attempt has been made to explain the mechanism of the effect of reward structures on team adaptation.
We also make a contribution to management practice. In modern organizations where the environment is characterized by dynamic, complex, and uncertain situations, teams are employed to deal with changes and to solve problems, which are an important issue for team management. Our findings help team managers to better deal with task changes in terms of team design and process management.
We suggest that team managers emphasize cooperation both in goal setting and reward structure for teamwork. The competitive structure may be productive and efficient for routine tasks in a static environment, but does not function well in a dynamic environment (Beersma et al., 2009; M. D. Johnson et al., 2006). Therefore, the reward should be given to each member of the team instead of to individual members, tasks should be assigned to groups, thus ensuring that everyone gets involved through collective discussions, and activities that are beneficial for developing shared understanding should be organized. Thus, team managers should encourage teamwork, cooperation, and sharing, and guide team members' behavior toward achieving the same goal.
As our results showed that reward structure contributes to team adaptation through shared mental model updating, our second suggestion to team managers and members is for them to pay attention to the identification of potential cues in the environment, which may help them update their mental model for problem solving. Team members may develop mental models to represent the task environment, which help them to solve the problem quickly and accurately (Mohammed et al., 2010). However, when elements in the task are changed (e.g., scope changes for software development teams, budget changes for project teams), the current mental model would be inefficient. Team managers should encourage team members to share relevant cues that indicate changes and updated mental models with other members to obtain consensus for the development of a new shared mental model at the team level. This model would be tailored to fit the new task environment as it represents the changed task environment.
There are several limitations in this study. We examined team adaptation in an experimental laboratory setting to observe the causal relationship between reward structure and team adaptation. However, as an experimental study lacks external validity, this is a concern common to team adaptation research (Beersma et al., 2009). Future researchers should conduct team adaptation field studies, and examine the external validity of the reward structure. They should also investigate the boundaries and conditions of our model. We adopted a simple design to build the causal relationship and exclude noise variables, but we may have ignored important boundaries. For example, Stachowski, Kaplan, and Waller (2009) confirmed the important roles of team communication and interaction in their positive relationship with team adaptation. As we discussed only the role of team cognition in this study, a parallel team process needs to be explored. Future researchers can conduct an empirical study to clarify the role of team process and team cognition (or other emergent states) in the reward structure-team adaptation relationship. In addition, team members' traits have an important input as they are an indicator of team composition (Neuman, Wagner, & Christiansen, 1999). Future researchers should discuss the interaction between team members' traits and the reward structure and their effect on team adaptation. For example, the negative effect of the competitive reward structure may be alleviated in teams comprising members high in extraversion, because they share information frequently, regardless of the situation. Future researchers should test this in an empirical study.
Finally, we are concerned about the measurement tool for team adaptation in future empirical studies. Burke et al. (2006) noted that the challenges in measuring team adaptation are assessing the types of adaptation and selecting the best times to collect data. It is difficult to collect data in field studies, as the time and number of changes cannot be controlled. If these challenges can be addressed, future field study researchers can make a substantial contribution to team adaptation research.
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Chen Yue (1), Patrick S.W. Fong (2), Teng Li (1)
(1) School of Management, Zhejiang University, People's Republic of China
(2) Department of Building and Real Estate, Hong Kong Polytechnic University, Hong Kong
How to cite: Yue, C., Fong, P. S., & Li, T. (2019). Meeting the challenge of workplace change: Team cooperation outperforms team competition. Social Behavior and Personality: An international journal, 47(7), e7997
CORRESPONDENCE Chen Yue, School of Management, Zijingang Campus, Zhejiang University, Hangzhou 310058, People's Republic of China. Email: firstname.lastname@example.org
Table 1. Sample Characteristics Indicator Information Gender Men Women Gender composition Three men Two men and one woman Two women and one man Three women Major Science and engineering Art and humanities Social sciences Agriculture and medicine Major composition Three members major = science and engineering Two members major = science and engineering One member major = science and engineering No members major = science and engineering Total 3 x 62 = 186 Indicator P % Gender 97 52.2 89 47.8 Gender composition 5 teams 8.1 30 teams 484 22 teams 35.5 5 teams 8.1 Major 110 59.1 13 7 20 10.8 43 23.1 Major composition 14 teams 22.6 24 teams 38.7 20 teams 32.2 4 teams 6.5 Total Table 2. Correlations Among Study Variables M(SD) 1 2 3 1. SMMA accuracy 0.30 (0.18) 0.26 (0.11) 0.34 (0.23) 2. SMMA similarity 0.35 (0.17) 0.31 (0.17) 0.39 (0.17) .07 3. SMMB accuracy 0.29 (0.15) 0.26 (0.16) 0.31 (0.13) -.40 (**) .24 4. SMMB similarity 0.33 (0.15) 0.34 (0.18) 0.31 (0.12) .06 .10 .20 5. Taskper A (total rounds) 32.77 (25.19) 18.26 (7.92) 47.29 (28.14) .40 (**) .29 (*) -.004 6. TaskperB (Total steps) 104.16 (28.38) 112.13 (29.08) 96.79 (25.70) -.02 .12 -.35 (*) 7. SMMU 0.23 (0.40) 0.00 (0.37) 0.45 (0.29) .14 .15 .08 8. Team adaptation 0.38 (0.20) 0.25 (0.14) 0.51 (0.16) .14 .14 .23 4 5 6 7 1. SMMA accuracy 2. SMMA similarity 3. SMMB accuracy 4. SMMB similarity 5. Taskper A (total rounds) -.17 (*) 6. TaskperB (Total steps) -.11 -.15 7. SMMU -.27 (*) .49 (*) -.18 8. Team adaptation -.17 .64 (**) -.52 (**) .71 (**) Note. The first mean and standard deviation represents the total sample, the second in bold represents teams with a competitive reward structure, and the third in italics represents teams with a cooperative reward structure. SMMA accuracy = shared mental model accuracy for Task A; SMMA similarity = shared mental model similarity for Task A; SMMB accuracy = shared mental model accuracy for Task B; SMMB similarity = shared mental model similarity for Task B; Taskper A (total rounds) = performance in Task A; Taskper B (total steps) = performance in Task B; SMMU = shared mental model updating. (*) p < .05, (**) p < .01. Table 3. Regression Results Predictor Team outcomes Task A Task B Control variables M1 M2 M3 M4 Game experience .020 .033 .021 -.009 Task sequence .247 .172 .681 .651 Major composition .038 .045 .097 .136 Gender composition -.067 -.090 .013 -.002 Indicator variable Reward structure (dummy variable) .397 (*) .352 (*) -.773 (*) -.700 (**) Mediators SMMA similarity .143 SMMA accuracy .275 (*) SMMB similarity .092 SMMB accuracy -.217 SMMU Total [R.sup.2] .371 .460 .318 .360 [DELTA][R.sup.2] .099 .042 F 6.60 (**) 6.56 (**) 5.22 (**) 4.34 (**) Predictor Team adaptation Control variables M5 M6 M7 Game experience -.069 -.107 -.075 Task sequence .497 (**) .055 -.019 Major composition .007 -.030 -.058 Gender composition -.146 -.094 -.115 Indicator variable Reward structure (dummy variable) .628 (**) .394 (**) Mediators SMMA similarity SMMA accuracy SMMB similarity SMMB accuracy SMMU .501 (**) Total [R.sup.2] .265 .456 .623 [DELTA][R.sup.2] .191 .167 F 5.14 (**) 9.39 (**) 15.12 (**) Predictor Shared mental model updating Control variables M8 M9 Game experience -.035 -.063 Task sequence .478 (**) .149 Major composition .083 .056 Gender composition .003 .042 Indicator variable Reward structure (dummy variable) .467 (**) Mediators SMMA similarity SMMA accuracy SMMB similarity SMMB accuracy SMMU .231 .337 Total [R.sup.2] .106 [DELTA][R.sup.2] -.035 -.063 F 4.29 (**) 5.68 (**) Note. Task A = total rounds; Task B = total steps; Task sequence, either AB or BA. SMMA accuracy = shared mental model accuracy for Task A; SMMA similarity = shared mental model similarity for Task A; SMMB accuracy = shared mental model accuracy for Task B; SMMB similarity = shared mental model similarity for Task B; SMMU = shared mental model updating. (*) p < .05, (**) p < .01. Table 4. Regression Results for Simple Mediation Direct and total effects Variable B SE Team adaptation regressed on reward structure 0.13 0.02 Shared mental model updating regressed on reward structure 0.22 0.04 Team adaptation regressed on shared mental model updating, controlling for reward structure 0.25 0.05 Team adaptation regressed on reward structure, controlling for shared mental model updating 0.08 0.02 Indirect effect and significance using normal distribution Value SE Sobel .05 0.02 Bootstrap results for indirect effect M SE Effect 0.05 0.01 Direct and total effects Variable t P Team adaptation regressed on reward structure 6.7 .001 Shared mental model updating regressed on reward structure 5.27 .001 Team adaptation regressed on shared mental model updating, controlling for reward structure 4.89 .001 Team adaptation regressed on reward structure, controlling for shared mental model updating 3.8 .001 Indirect effect and significance using normal distribution z p Sobel 3.55 .001 Bootstrap results for indirect effect 95% CI LL UL Effect 0.03 0.09 Note. N = 62 teams. CI = confidence interval; LL = lower limit, UL = upper limit. Bootstrap sample size = 5,000.
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|Author:||Yue, Chen; Fong, Patrick S.W.; Li, Teng|
|Publication:||Social Behavior and Personality: An International Journal|
|Date:||Jul 1, 2019|
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