Extreme teams work in challenging environments in which there are considerable demands and ineffective performance can have severe, potentially life-or-death consequences (Maynard et al., 2018). The perceived urgency, importance, and impact of an emergency change teams’ decision-making dynamics (Meslec et al., 2020). Despite increased recent interest, empirical research on the operation of extreme teams remains limited (Brown et al., 2020), with the extant literature focusing more on leadership behaviors (e.g., Minor, 2021) than on team dynamics during a crisis.

Most studies of team responses to adversity have taken a functional stance (Stoverink et al., 2020), following the assumption that coping with adversity is generally appropriate. However, Staw et al. (1981) argued for also identifying maladaptive behaviors, proposing that a “threat to the vital interests of an entity, be it an individual, group, or organization, will lead to forms of rigidity” (p. 502). Decision-making groups in a stressful situation may ignore new information and control deviant responses (Janis, 1972). According to the threat-rigidity thesis, threat will lead to restriction in information and constriction in control (Staw et al. 1981). This study focused on the constriction in control as one reaction to threat.

The threat-rigidity thesis has been tested at the organizational level in many studies (e.g., Barnett & Pratt, 2000; Kreiser et al., 2020; Shimizu, 2007), but team-level tests remain scant. At the team level, constriction in control means that people with power dominate the decision-making process. To test this proposition, most team-level studies have assigned two roles in their teams: leader and team member. However, another important role might also exist in crisis decision-making teams. French and Raven (1959) identified several bases of social power as important during the process of social influence. One base is legitimate power, which is derived from an individual’s position or role and legitimized by policies, rules, and laws (Magee et al., 2010). Another base is expert power, which is derived from an individual’s special knowledge or expertise. In most crisis decision-making teams, high-level officials with legitimate power by virtue of their position are involved in decision making. However, individuals with expertise relative to the tasks of the crisis situation, and people who have valuable problem-solving expertise are indispensable for teams to cope effectively with a crisis. What happens if there are both legitimate and expert power types in a team? Will influence be centralized on the leader or the expert? Following the threat-rigidity thesis, I proposed two possible power dynamics: leader-centralized or expert-centralized influence.

Leader-Centralized Power Dynamic Hypothesis

In response to a crisis, power may become centralized when leaders make decisions on their own without consulting their teammates (Staw et al., 1981). Group members rely on their leaders to make decisions (Driskell & Salas, 1991). As a result, leaders become increasingly influential. Kamphuis et al. (2011) assigned a formal leader to each team in a laboratory experiment and found that experiencing threat led to more autocratic leadership and less team discussion, thereby degrading team performance. Halverson et al. (2004) showed that team members perceived group leaders as more charismatic during stress situations than at other times, which, in turn, led to other team members being more susceptible to leaders’ influence. In teams experiencing a high-level threat, Harrington et al. (2002) found rigidity effects of constriction in the control of leaders. Argote et al. (1989) showed that a perceived high-level threat was related to leader-centralized communication. Further, Clarke et al. (2021) suggested that formal team leaders seek informal influence through the occupation of central positions in social networks. Consistent with the leader-centralized power dynamic hypothesis, I inferred that during a crisis leaders would exert more influence over group decisions than would other members; thus, I anticipated that group decisions would be more consistent with leaders’ initial preference in this context.
Hypothesis 1a: In a crisis situation, leaders will be perceived as more influential than are experts during the group decision-making process.
Hypothesis 1b: In a crisis situation, group decisions will be consistent with leaders’ initial preferences rather than with those of experts.

Expert-Centralized Power Dynamic Hypothesis

Gladstein and Reilly (1985) explored the relationship between external threat and group decision-making processes through a management simulation game called Tycoon. In the simulation, groups of five or six people work closely together for 6 days, managing their company and making decisions covering all areas of business. During the process, multiple external threats are introduced (e.g., strikes, storms, terrorist attacks) that have potential financial consequences for the company. These researchers found no change in the centralization of influence within the groups, which suggests that group response is not necessarily rigid when facing adversity. Crises impose high demands on groups. Group members are sensitive to others’ expertise when they realize that experts’ advice could help their group reach a better decision. Lee (2019) investigated collaborative disaster management of floods in Malaysia using semistructured interviews and secondary data from news reports and government documents, and found that the perceived status of other group members influenced the collaboration process among agencies when managing a disaster. Hollingshead (1996) showed that team members who had (vs. did not have) critical information participated more and achieved more influence over group decisions. Therefore, I proposed that experts would be perceived as being as important in crises as they are in noncrisis situations—perhaps even more so, because a crisis creates greater pressure for making a correct team decision to avoid calamity. Thus, during crises, groups are likely to rely more on experts than they are at other times. Research has suggested that team members who are perceived as possessing expertise have the power to influence team performance (Sinaceur et al., 2010).

However, as group interaction experience increases, members gain information about the actual level of other members’ expertise. Consequently, they may question the legitimacy of the status of an incompetent expert when their contribution is not as valuable as expected in the team decision-making process (Bendersky & Shah, 2013). Since these contributions are an important basis for reallocating status (Pai & Bendersky, 2020), when experts lack information that is key to making correct decisions, they may be perceived as incompetent and lose their potential to dominate the team decision-making process.
Hypothesis 2a: In a crisis situation, team members will perceive experts as more influential than leaders during the group decision-making process, with the boundary condition that experts are competent rather than incompetent.
Hypothesis 2b: In a crisis situation, group decisions will be consistent with experts’ initial preferences rather than with those of leaders, but this will hold only when the experts are competent rather than incompetent.

To test the hypotheses I developed a fire-rescue decision-making task and used a bases of power (legitimate power vs. expert power vs. powerless member) × expert (competent expert vs. incompetent expert) mixed factorial design, with the expert-condition factor occurring at the group level. The research was conducted in collaboration with local fire stations, and training programs for firefighters and their leaders were developed based on the results as well as the results of other studies conducted as part of the same big project but out of the scope of the current paper.

Method

Participants

The extant literature suggests that people of different genders behave differently in crisis situations (e.g., Yang & Zheng, 2009). Thus, I recruited only male students from local universities, through each university’s bulletin board system.

Forty-seven teams (141 individuals, M_age = 21.73 years, SD = 2.13) participated in the study. Of these, seven teams were eliminated because one or more members chose the wrong answer at the personal decision-making stage. Thus, the final valid sample comprised 40 three-member teams (120 individuals), with 20 teams in the competent-expert condition and 20 teams in the incompetent-expert condition.

Procedure

The project was reviewed and approved prior to data collection by the Academic Ethics Committee of China National Academy of Governance. When participants arrived at the laboratory, they signed an informed consent form then read the directions for the experiment, which stated that each participant would be assigned to a three-person team comprising a leader, an expert, and a regular member. As a team, they would engage in a fire-rescue decision-making task. Participants were told that it was vital that they take the experiment seriously because the research results would be used as guidelines for future fire-rescue practices. Both the quality of the team’s collective decision making and how quickly they solved the problem were crucial.

Participants were randomly assigned the role of leader, expert, or powerless member. I used the hidden profile paradigm (Stasser & Titus, 1985) to distribute information, so that the initial judgment of each member could be expected to be different from that of the other members in a given team, allowing me to eliminate confounding explanations such as coalitions (Krausburg, 2021).

To assign the role of leader in each team, I followed the procedure of Burris et al. (2009). First, participants completed a personality questionnaire. The experimenter explained that certain personality characteristics are associated with more effective leadership. Then, the experimenter scored the three team participants’ responses in front of them and announced that one of them had been chosen to be the leader according to their scores. In fact, the leader was chosen randomly. The leader was told that he was responsible for the team’s outcome and should report the team’s final decision to the experimenter as soon as the team members reached an agreement.

After assigning the leadership role, the experimenter stated that there would also be an expert in their group. Participants were told that members of a real decision-making team in a fire command center rarely have identical information about burning buildings. The expert on the team might receive supplementary information relevant to the task. Then, the experimenter randomly appointed one of the other two group members as the expert.

Next, the groups entered the laboratory room, where a simulated fire command center was set up, in which fire videos were projected on the walls and live firefighting noise was played continually. Typical firefighting slogans and photographs were pasted on the walls. This scene was designed to ensure that participants would feel as though they were working in a real fire command center. The room contained a round table with a computer for each of the three team members. Each participant took a seat according to their assigned role.

Following the classic hidden profile paradigm, each group member first made their personal decision independently on their computer. There was a computer screen positioned in front of each person on which the information that they received was displayed. Screens were set up in such a way that team members could not see one another’s information. There was only one right choice given the information set each person received. The right choice varied according to the assigned role because each of the three team members received different information. Only those teams whose members’ initial judgments were all correct based on the information they had received could proceed and discuss their information to make a unanimous group decision. Upon completing the discussion, group members filled in questionnaires that included manipulation checks and an assessment of the perceived social influence of their teammates. At the end of the study, they were debriefed, thanked for their participation, and paid RMB 30 (USD 4.45).

Task

To develop the fire-rescue decision-making task used in this study, four fire chiefs were interviewed, and 88 firefighting cases in the Handbook of Chinese Fire Protection (Guo, 2008) were studied. The scenario was as follows: “Three buildings at three different locations are burning, and people at all these locations are asking for rescue from the fire command center. However, only one building can receive immediate assistance because of a temporary shortage of rescue resources.” The participants played the role of members of fire command center decision-making teams, and it was their task to choose the building most urgently in need of help.

I collected case studies about fires at a chemical factory, a skyscraper, and a hospital (Locations X, Y, and Z, respectively), and compiled 26 informational items for each location. Participants (n = 81) were invited to evaluate the valence and importance of each item. To measure valence, participants were asked “Do you think this item is low-risk, neutral, or high-risk information about the scene of the inferno? It should be noted that low-risk information suggests that the situation is not so bad; thus, they can afford to wait for rescue. High-risk information suggests that the situation is very serious; thus, immediate rescue is needed. Neutral information makes it difficult to tell whether the situation is good or bad.” This is an example of low-risk information: “The chemical plant was built with fireproof wood, and the windows are all fire-resistant. In sum, its fire-protection rating is high.” This is an example of high-risk information: “There is a river not far away from the burning chemical plant. If the chemical solutions keep on leaking, the river will soon be polluted.” This is an example of neutral information: “The skyscraper was built by China Construction 4th Division 6th Construction Engineering Company.”

To measure the importance of each item in making a decision, participants were asked “To what extent do you think this information is important for you to make a final decision?” They provided their response on a 7-point Likert scale (1 = not important at all, 7 = extremely important). Those items with importance scores beyond M ± 2 SD were excluded because, according to the principles of the hidden profile paradigm, items should not differ considerably in their importance (Stasser & Titus, 1985). The final result was 12 items for each location. Specifically, Location X had eight high-risk, two neutral, and two low-risk informational items, whereas Locations Y and Z each had four high-risk, two neutral, and six low-risk informational items. Thus, based on the full information set, Location X was the correct answer in regard to which building was most urgently in need of help.

In line with the hidden profile paradigm, I manipulated the distribution of the information for each location in the following ways (see Table 1): In the competent-expert condition, experts received all information about the task, and if they made the correct choice, their initial independent preference should have been Location X. Leaders and regular team members received only partial information. On the basis of the information they obtained, before the group discussion stage the leaders should have preferred Location Z, and regular team members should have preferred Location Y. In the incompetent-expert condition, the expert, the leader, and the regular team member each received 18 pieces of information and no one received the full information set. Further, the information distribution was designed in such a way that before the group discussion stage the incompetent experts would prefer Location Y, leaders would prefer Location Z, and regular team members would prefer Location X.

Measures

Individual Decision Making
Each team member was asked to type in their personal decision on the computer based on the information they had received before they proceeded to the group discussion stage.

Group Decision Making
Teams were asked to reach an agreement in answer to the question “According to your group discussion, which location is the most urgently in need of rescue?”

Perceived Social Influence
Participants were asked to answer the question “Who was the most influential person on your team during the group decision-making process?”

Results

Manipulation Check

To check the manipulation of experts’ competence level, the leader and the regular team member rated the perceived expertise of the expert in their team with two items (“He was knowledgeable about the task” and “He possessed expertise needed for the task”; Burris et al., 2009). Items (r = .93) were rated on a 7-point Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree), so that a higher score indicated a higher level of perceived expertise of the team expert. The results of an independent samples t test revealed that group members in the competent-expert condition believed that their expert demonstrated a higher level of expertise (M = 5.48, SD = 1.02) than did those in the incompetent-expert condition (M = 3.17, SD = 0.98), t(38) = 3.25, p < .05, which suggests that the manipulation was effective.

Hypothesis Testing

Perceived Social Influence
To test which of the team members was considered most influential, I ran a chi-square analysis. The results show that the expert condition was significantly related to perceived social influence, χ²(2) = 14.80, p < .01. When experts were competent, compared with the leader (28.33%) and the regular team member (8.33%), the leader (63.33%) was more likely to be nominated as the most influential person, χ²(2) = 27.9, p < .001. However, when experts were incompetent, compared with the expert (28.33%) and the regular team member (16.67%), the leader (55.00%) was more likely to be nominated as the most influential person, χ²(2) = 13.90, p < .01. These results supported Hypothesis 2a, but did not support Hypothesis 1a.

Group Decision
The results suggest that when experts were competent, the group decision was more likely to comply with the expert’s choice. Among the teams, 75% chose Location X, 20% chose Location Y, and 5% chose Location Z (χ² = 16.30, p < .001). In contrast, when experts were incompetent, the group decision was consistent neither with that of the leader nor of the expert, but was the correct choice after group discussion. Among the teams, 75% chose Location X, 10% chose Location Y, and 15% chose Location Z (χ² = 15.70, p < .001). These results supported Hypothesis 2b but did not support Hypothesis 1b.

Discussion

To understand power dynamics in crisis decision-making groups, this study tested the threat-rigidity thesis by examining if influence is centralized in legitimate power or in expert power for decision-making teams in crisis situations. Two competing hypotheses were proposed involving (a) leader-centralized and (b) expert-centralized power dynamics. The results generally supported the expert-centralized power dynamic hypothesis, with the boundary condition that the expert should be competent.

Theoretical and Practical Implications

This study has provided insight into the power dynamics of decision-making groups in a crisis context. The results complicate the proposition of the threat-rigidity thesis by differentiating among sources of power and demonstrating an adaptive team decision-making process: The group dynamic depended on whether the expert in the team was competent. However, the good news is that whether or not the expert was competent, most group decisions were satisfactory. I found that a competent expert dominated the decision-making process; however, an incompetent expert had diminished status, so that the leader in such a team dominated the decision-making process. This finding is consistent with that of Lee (2019), who suggested that perceived status influences the collaboration process among agencies during a disaster. However, Lee’s study was based on qualitative methodology and focused on general organizational status without differentiating between competent and incompetent experts. My study extends the literature by suggesting that experts will play an important role in crisis situations only when other team members perceive them as being competent.

My findings show that teams can act adaptively in simulated crisis situations. Other researchers have similarly found that stressful situations may lead to positive and active changes in both organizations and teams. For example, Driskell and Salas (1991) found that both high- and low-status members were more likely to accept input from one another under threat of tear gas. Lanzetta (1955) found that stress led to decreased conflict, fewer self-focused behaviors, better cooperation, and increased synergy and reconcilement. These studies suggest that threat leads to loosening rather than constriction of control, in that team members are likely to accept one another’s task input during crises (Driskell & Salas, 1991). I found that when the other team members perceive an expert as competent, the team will rely on their guidance, whereas when they are perceived as incompetent, together the team members can still make the decision that will result in the best outcome possible. I will refrain from asserting that in the simulated crisis situations of this study the teams demonstrated a rigid response to the crisis, because the decision-making quality in both cases was good.

Limitations and Directions for Future Research

There are some limitations to this study. First, the participants were all men. However, in many teams, the gender composition is diversified. In addition, all participants were college students with limited working experience, whereas crisis teams in the real world generally consist of people with enriched working experience. Finally, as there were only three members in the team, there was little scope for nuances of opinion in the discussion. The powerless member had few opportunities to disagree with either the leader or the expert, gain support from a number of other powerless team members, and advocate for a decision different from that of either the leader or the expert. To increase generalizability and for replication, further studies are needed, based on samples comprising people with diverse genders and who are not students.

Further, trust may have a positive effect on the degree to which sufficient resources are developed to address a crisis in a timely fashion by enhancing decentralization, undistorted communication, and collaboration (Mishra, 1996). In addition, it has been found that trust promotes information sharing and fosters a climate supportive of creativity, both of which encourage correct decisions (Sommer & Pearson, 2007). In future studies, testing could be conducted to establish if trust among team members ameliorates the rigid response of teams in crisis situations.

Conclusion

This study was an exploration of the power dynamics of the group decision-making process in crisis situations. The results show that the power of crisis decision-making teams was centralized on experts when the team expert was competent, but was centralized on leaders when the team expert was incompetent. In addition, whether the expert was competent and dominant in the group, or incompetent and the leader was therefore dominant, most group decisions were satisfactory, suggesting that teams behaved adaptively to solve problems when the situation was pressing. These findings challenge the threat-rigidity thesis and extend understanding of power dynamics among team members during crisis situations.

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https://doi.org/10.1016/0749-5978(89)90058-7

Barnett, C. K., & Pratt, M. G. (2000). From threat-rigidity to flexibility - Toward a learning model of autogenic crisis in organizations. Journal of Organizational Change Management, 13(1), 74–88.
https://doi.org/10.1108/09534810010310258

Bendersky, C., & Shah, N. P. (2013). The downfall of extraverts and rise of neurotics: The dynamic process of status allocation in task groups. Academy of Management Journal, 56(2), 387–406.
https://doi.org/10.5465/amj.2011.0316

Brown, O., Power, N., & Conchie, S. M. (2020). Immersive simulations with extreme teams. Organizational Psychology Review, 10(3–4), 115–135.
https://doi.org/10.1177/2041386620926037

Burris, E. R., Rodgers, M. S., Mannix, E. A., Hendron, M. G., & Oldroyd, J. B. (2009). Playing favorites: The influence of leaders’ inner circle on group processes and performance. Personality and Social Psychology Bulletin, 35(9), 1244–1257.
https://doi.org/10.1177/0146167209338747

Clarke, R., Richter, A. W., & Kilduff, M. (2021). One tie to capture advice and friendship: Leader multiplex centrality effects on team performance change. Journal of Applied Psychology. Advance online publication.
https://doi.org/10.1037/apl0000979

Driskell, J. E., & Salas, E. (1991). Group decision making under stress. Journal of Applied Psychology, 76(3), 473–478.
https://doi.org/10.1037/0021-9010.76.3.473

French, J. R. P., Jr., & Raven, B. (1959). The bases of social power. In D. Cartwright (Ed.), Studies in social power (pp. 150–167). University of Michigan.

Gladstein, D. L., & Reilly, N. P. (1985). Group decision making under threat: The Tycoon Game. Academy of Management Journal, 28(3), 613–627.
https://doi.org/10.5465/256117

Guo, T. N. (2008). Handbook of Chinese fire protection [In Chinese]. Shanghai Scientific & Technical Publishers.

Halverson, S. K., Murphy, S. E., & Riggio, R. E. (2004). Charismatic leadership in crisis situations: A laboratory investigation of stress and crisis. Small Group Research, 35(5), 495–514.
https://doi.org/10.1177/1046496404264178

Harrington, R., Lemak, D., & Kendall, K. W. (2002). The threat-rigidity thesis in newly formed teams: An empirical test and theoretical extension. Journal of Business and Management, 8(2), 127–146.
htps://bit.ly/3cLyRjh

Hollingshead, A. B. (1996). Information suppression and status persistence in group decision making: The effects of communication media. Human Communication Research, 23(2), 193–219.
https://doi.org/10.1111/j.1468-2958.1996.tb00392.x

Janis, I. L. (1972). Victims of groupthink: A psychological study of foreign-policy decisions and fiascoes. Houghton-Mifflin.

Kamphuis, W., Gaillard, A. W. K., & Vogelaar, A. L. W. (2011). The effects of physical threat on team processes during complex task performance. Small Group Research, 42(6), 700–729.
https://doi.org/10.1177/1046496411407522

Krausburg, T. (2021). Hierarchical coalition formation in multi-agent systems. Proceedings of the 17th International Conference on Distributed Computing and Artificial Intelligence, Special Sessions (pp. 210–214). Springer International Publishing.
https://doi.org/10.1007/978-3-030-53829-3_23

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Gang Xue, Department of Public Administration, China National Academy of Governance, 6 Changchunqiao Road, Beijing, 100089, People’s Republic of China. Email: Email: [email protected]