Team Decision Making: The Dynamic Effects of Team Decision Style Composition and Performance via Decision Strategy

Abstract

As teams continue to become more prevalent in modern-day organizations, researchers and organizations alike can benefit from a more nuanced understanding of teams’ decision-making process, which can ultimately impact organizational effectiveness. Although team processes are conceptualized as dynamic phenomena, they have largely been treated as static in research. In this study, we draw on the input-mediator-output-input and episodic team performance frameworks to advance a theoretical model of the dynamic, reciprocal effects of team rational decision strategy and team performance as well as the role of team composition of individual rational decision style. We sampled 320 participants in 85 teams competing in a 10-week business strategy simulation where teams made weekly strategic decisions that contributed to team performance. Teams composed of individuals with rational decision styles were more likely to adopt rational decision strategies, which led to better team performance. Additionally, results revealed a positive reciprocal effect between rational decision strategy and team performance such that teams with positive prior performance were more likely to engage in subsequent rational decision strategy. As hypothesized, team composition of members’ rational decision style was the primary determinant of team rational strategy during initial stages of team development, but the valence of outcome feedback (i.e., prior performance) took over as the stronger predictor of team rational strategy during later stages of team development. We contribute to the team and decision-making literatures by examining the dynamic process of team decision making and team performance.

Keywords

team composition team decision making reciprocal relationships rational strategy

In today’s fast-paced global economy, high-stakes organizational decisions are seldom made by individuals acting alone; rather, they are made by teams. Team decision making is characterized by a collaborative process that involves gathering, processing, integrating, and communicating information to arrive at a mutually accepted decision (Reader, 2017). Research has shown that team decision making is critical to organizational performance metrics, such as organizational effectiveness and long-term competitiveness (Gavetti, Levinthal, & Ocasio, 2007; Kerr & Tindale, 2004). Furthermore, because teams are dynamic and complex systems that develop and evolve over time, their decision making for consistency can also change over time, as well (Mathieu & Schulze, 2006). Although team processes have been theorized as dynamic phenomena, they have largely been treated as static in research, which has prompted a need for empirical research examining team dynamics (e.g., Cronin, Weingart, & Todorova, 2011; Mathieu, Gallagher, Domingo, & Klock, 2019; Ployhart & Vandenberg, 2010). Given the prevalence and criticality of team decision making, more longitudinal research is needed to investigate the temporal dynamics of how team decision processes develop and identify factors that ultimately improve team performance.

Teams’ ability to make high-quality decisions and thereby improve team performance is contingent on the right combination of members with predispositions for utilizing optimal decision making strategies (Kerr & Tindale, 2004; Kozlowski & Bell, 2003). Therefore, it is important to develop a better understanding of how to assemble and manage the right mix of people that can serve as the foundation for effective team decision-making processes (Bell, 2007; Mathieu, Tannenbaum, Donsbach, & Alliger, 2014). The purposes of this study are twofold. First, we theorize and explore how individual decision style, or “the learned, habitual response pattern exhibited by an individual when confronted with a decision situation” (Scott & Bruce, 1995: 820), aggregates to the team level to form team decision style. Previous research has conceptualized and examined five different decision styles: rational, intuitive, dependent, avoidant, and spontaneous (see Dalal & Brooks, 2013, for a review). Because the decision context in this study is more knowledge intensive and better suited for decision behaviors such as information search, evaluation of alternatives, and logical analyses, we focus upon mechanisms by which team rational style influences team performance via team rational decision strategy. Whereas team members are guided by their own decision styles when making their own individual decisions, it is important to examine whether teams vary on decision strategies and how those strategies impact team performance.

The second purpose of this study is to explore the temporally dynamic, reciprocal effects between team rational strategy and performance. As team members interact, perform different activities, and make decisions together, they cycle through different episodic phases that impact team performance (Mathieu & Schulze, 2006). For instance, according to the input-mediator-output-input (IMOI) framework (Ilgen, Hollenbeck, Johnson, & Jundt, 2005; Marks, Mathieu, & Zaccaro, 2001), outcome feedback (i.e., knowledge of results or outcomes) from prior decision making episodes can subsequently influence later team processes (Mathieu & Schulze, 2006). To examine this, we sampled business student teams competing in a strategic business simulation involving weekly decisions over 10 weeks. We employed a longitudinal design to test the self-reinforcing loop between team rational strategy and performance and expected that past performance would act as outcome feedback to prompt teams to engage in subsequent rational strategy. Additionally, because most dynamic relationships vary in magnitude over time (Mitchell & James, 2001), we examined the relative impact of team composition and past performance on team rational strategy at different stages of team development (i.e., forming, functioning, finishing; Ilgen et al., 2005). Finally, we conducted post hoc analyses to explore (a) how the valence of past performance alters the effect of team rational style on team rational strategy and (b) how changes in rational strategy can subsequently drive changes in team performance (Hollenbeck & Wright, 2017).

This study contributes to the growing literature exploring dynamic effects of team composition, team process, and team performance over time “leveraging a longitudinal design” (Pitariu & Ployhart, 2010). First, we contribute to the team composition literature by exploring how an individual decision style aggregates to the team level to influence team decision process and team performance. We demonstrate that team rational style predisposes teams to engage in team rational strategy, which subsequently has a positive impact on team performance. Second, we contribute to the team process literature by accounting for the dynamic nature of team decision processes to show that the valence of past team performance acts as outcome feedback (i.e., knowledge of results) to alter team rational strategy over time. Additionally, we demonstrate that team rational style has a stronger impact on team rational strategy during early team development, whereas outcome feedback (i.e., past performance) takes over as the stronger predictor in later stages. Being able to pinpoint the team development stages when team composition and outcome feedback are most influential on team decision strategy can be critical for improving team staffing, planning, and management. Finally, we show that the valence of outcome feedback influences the relationship between team rational style and changes in team rational strategy over time. Taken together, we advance a theoretical model of the dynamic process of team decision making.

Theoretical Background and Hypotheses Development

Individual Rational Decision Style

Team composition is the configuration of team member attributes and characteristics within a team that subsequently influence team processes and outcomes (Bell, Brown, Colaneri, & Outland, 2018; Wolfson & Mathieu, 2017). Research on team composition has identified a series of individual difference characteristics that improve team processes and outcomes, such as conscientiousness (Stewart, Fulmer, & Barrick, 2005), agreeableness (Hurtz & Donovan, 2000), extraversion (Kristof-Brown, Barrick, & Stevens, 2005), emotional stability (Mount, Barrick, & Stewart, 1998), and general mental ability (Edwards, Day, Artur, & Bell, 2006). Although decision styles have been shown to impact various individual decision and performance outcomes, decision styles’ compositional impact at the team level has not been widely examined (for an exception, see Fitzgerald, Mohammed, & Kremer, 2017). Given that making effective decisions is critical for many teams (Kerr & Tindale, 2004; Larson, 2017), there is a need not only to consider individual decision making processes but also to identify the team-level factors that can facilitate team-level decision processes that contribute to better team performance.

Individual decision styles capture people’s tendencies to approach decision situations across various domains (e.g., Dalal & Brooks, 2013). Whereas Scott and Bruce’s (1995) taxonomy describes five decision-making styles, we focus on rational decision style, which is characterized as engaging in careful and systematic analysis of information in decision situations (Kahneman, 2011). Specifically, individuals high on rational decision style take time to reflect on decisions, weigh alternatives, and diligently evaluate information in order to problem solve and make decisions (Betsch & Kunz, 2008; Epstein, 2003). In this study, we examine team decision making in a knowledge-intensive and unfamiliar decision situation with low time pressure, which typically prompts rational decision-making behaviors (Phillips, Fletcher, Marks, & Hine, 2016).

Decision Context

Decision making behavior is dependent on the characteristics of the individual decision maker as well as the decision context (Driver, Brousseau, & Hunsaker, 1990). Although individuals tend to have a dominant style when approaching decision situations, they are still malleable to contextual factors, such as time pressure, significance of decision, or familiarity (Elbanna & Fadol, 2016; Fredrickson & Laquinto, 1989). As such, the context in which teams make decisions can also impact the nature of the relationship (i.e., positive vs. negative) between team decision strategy and team performance. Previous work has shown that rational decision making has consistently resulted in higher-quality decisions across different domains (e.g., financial, medical, environmental; Elbanna, 2006) and has been shown to be particularly beneficial in unfamiliar or knowledge-intensive decision situations, especially if the goal is to improve decision quality (Spicer & Sadler-Smith, 2005).

In our study, we examine teams making decisions in a business simulation related to organizational strategy in a knowledge-intensive and unfamiliar environment. In this particular context, the decision makers had access to a large amount of information relevant to the decisions, but they did not have extensive experience with the types of decisions in the simulation. Further, the teams had adequate time to work together to analyze the information to make informed decisions. Given that this decision context was unfamiliar, novel, and knowledge intensive, we expected it to favor the use of rational decision strategy (Salas, Rosen, & DiazGranados, 2010).

Finally, the compatibility between decision style type (e.g., rational) and decision context characteristics determines the optimal or dominant decision strategy for each situation (Kinnunen & Windmann, 2013; Phillips et al., 2016). Specifically, the effect of rational decision making on task performance is strongest when the decision situation calls for rational decision making (Phillips et al., 2016). As mentioned previously, decision tasks that are suitable for rational decision making feature unfamiliarity, availability of information, and opportunities to learn without extensive time pressure (Luan, Reb, & Gigerenzer, 2019). Taken together, the hypotheses we put forth and test are contextualized to a decision context favoring rationality. As such, we focus solely on rational decision making and explore its influence on team performance.¹

Team Rational Decision Style

Research has demonstrated that team decision processes can impact team performance and member satisfaction (Kerr & Tindale, 2004; Reader, 2017). It is crucial to understand how teams can be assembled in order to better facilitate these decision processes. Because many team tasks require members to make decisions together, each member’s preference for how to approach decisions, or their decision style, likely emerges as a salient characteristic in a team setting. Similar to how other individual characteristics (e.g., personality traits, cognitive ability) have been conceptualized as team-level compositional variables (Bell, 2007), we conceptualize team rational style as the aggregate of members’ individual rational styles.

Whereas a team’s decision style composition is a relatively stable aggregate of members’ individual decision styles (Bell, 2007), a team’s decision strategy is a process that can be conceptualized as a type of decision making approach team members collectively adopt (Sonesh, Rico, & Salas, 2014). Accordingly, we examine team rational strategy, which can be characterized as the extent to which the team adopts systematic approaches to collecting, logically evaluating, and analyzing information and choice alternatives relevant to the decision process (Dean & Sharfman, 1996). Similar to individual rational decision making, teams that adopt a rational decision strategy are able to explain the underlying logic for their decisions or courses of action (Hodgkinson & Healey, 2007).

According to the literature on person–team fit, the fit of a member’s individual characteristic relative to the characteristics of the other members on a team predicts important behavioral and attitudinal outcomes (Kristof-Brown, Zimmerman, & Johnson, 2005). For example, supplementary fit occurs when members of a team are similar to each other on a key characteristic. In the context of decision making, individuals prefer decisions made by members who share their cognitive style (Hunt, Krzystofiak, Meindl, & Yousry, 1989). Based on this, team members with high supplementary fit to others on the team would be more likely to agree on the type of decision strategy they would like to adopt. As team members first interact, their decision behaviors are likely to be dictated by the decision approach preference of the majority of members, which can result in a default decision strategy. In this case, we expect that teams with higher proportions of individuals with a rational decision style, or higher team mean rational style, will be more likely to adopt a rational decision strategy than teams with a lower mean rational style.

Hypothesis 1: Team members’ average rational decision style will be positively related to the use of team rational strategy.

Team Rational Decision Strategy and Team Performance

A number of studies have demonstrated that individuals with rational styles are more likely to engage in rational decision behaviors and produce more optimal decision outcomes (e.g., higher decision quality, task performance; Phillips et al., 2016; Wood & Highhouse, 2014). In the organizational context, rational decision making has been linked to improved decision-making competency (Bruine de Bruin, Parker, & Fischhoff, 2007), job performance and firm performance (Riaz, Riaz, & Batool, 2014), and managerial effectiveness (Russ, McNeilly, & Comer, 1996). For example, managers who collected information and analytically evaluated their options made more effective decisions compared to those who did not (Dean & Sharfman, 1996).

Although the benefits of rational decision making have been demonstrated at the individual level, how individual decision styles function at the team level as well as the effects on team decision-making process and performance are not yet well understood. Decision making in a team context may inherently involve individuals with dissenting viewpoints, and team members may need to influence each other’s decisions. For instance, rational persuasion is an influence tactic that draws upon the use of logical arguments and explanations to persuade others on a course of action (Lee, Han, Cheong, Kim, & Yun, 2017; Yukl & Falbe, 1990). When examining this at the team level, because team members who engage in rational decision-making behaviors can justify their choice and provide rationale for their decisions, they may be able to influence others through rational persuasion more effectively. By persuading others to adopt a rational strategy, teams are more likely to make higher-quality decisions that contribute to better team performance. Therefore, we expect that team rational strategy will positively impact team performance.

Hypothesis 2: Team rational strategy will be positively related to team performance over time.

Reciprocal Effects of Team Rational Strategy and Team Performance

Teams are complex systems that develop and evolve over time, and prior research has demonstrated that feedback loops are essential components for understanding dynamics associated with team decision-making processes (e.g., Ilgen et al., 2005; Kozlowski & Ilgen, 2006). Cronin and colleagues (2011) argued that team processes evolve because they retain and carry forward memories of what has happened, which will result in team-level variables to reciprocally influence each other. One way for teams to understand and evaluate their team behavior is through valence of performance feedback, which has been shown to be a powerful tool that can shape a team’s behavior and subsequent performance (e.g., Peterson & Behfar, 2003). Specifically, we focus on outcome feedback, or information about the results of a team’s performance, such as whether the action (i.e., decisions) resulted in success or a failure (Gabelica, Van de Bossche, Segers, & Gijselaers, 2012). Outcome feedback has been shown to change people’s actual decisions and decision approaches (Taylor, Hall, Cosier, & Goodwin, 1996).

Outcome feedback has been theorized to influence team decision making differently compared to individual decision making (Taylor et al., 1996). In the team setting, outcome feedback can highlight the decision outcome and stimulate reflection on the team’s previous decision behaviors. Team regulatory theory (DeShon, Kozlowski, Schmidt, Milner, & Wiechmann, 2004) posits that outcome feedback will impact teams’ regulatory process and performance, such that the teams can utilize the knowledge of their previous team decision outcome to regulate how they will approach future decisions in order to achieve their goals (Schippers, Edmondson, & West, 2014). As such, we draw upon team regulatory theory to advance a framework of dynamic reciprocal effects between team decision strategy and team performance. To do this, we also draw on Ilgen et al.’s (2005) IMOI model and Marks et al.’s (2001) episodic performance logic to examine how prior team performance can function as outcome feedback to serve as the antecedent to subsequent team rational strategy and thus establish a self-reinforcing feedback loop.

We explicitly model episodic relationships as a series of input-processes-output (IPO) stages and formally incorporate prior performance as a driver of teams’ decision-making strategies. As Mathieu et al. (2008: 414) noted, “Such feedback actually occurs as a team transitions from one episode to another . . . and teams may readily adopt different processes as a function of outcomes.” Because people utilize outcome feedback to assess their chance of future success, when previous decision strategy results in success, they are more likely to commit to that strategy in the future. As such, we anticipate that implementation of team rational strategy will lead to greater team performance, which, once solidified, should perpetuate a positive loop (Ilgen et al., 2005; Mathieu, Kukenberger, D’Innocenzo, & Reilly, 2015).

Notably, teams do not have access to outcome feedback when they start; they need to interact and work together before receiving initial information regarding their performance. As teams form, team composition becomes critical as it sets the stage for their initial decision approach. As outcome feedback becomes available, teams can incorporate the feedback by engaging in team reflection and regulation (DeShon et al., 2004), which can serve as mechanisms for adjusting subsequent strategies in hopes of improving future performance. Indeed, research in team reflexivity has demonstrated that when teams reflect upon how they function, they are more likely to perform better subsequently (De Jong & Elfring, 2010). Specifically, positive outcome feedback signals to teams that they are doing well and reinforces the previously used decision making strategy (Passos & Caetano, 2005). We argue that the valence of outcome feedback (i.e., positive or negative team performance) will trigger team regulatory processes that will prompt teams to reflect on past team decision strategy and engage in behaviors that can enhance future team performance. In the case that a team is performing well, their positive outcome feedback acts as a reaffirmation that the team is implementing a successful strategy, which will allow them to continue on their respective course.

As mentioned previously, the decision task in this study (i.e., business simulation) matches key components of rational decision making, which should prompt teams to engage in rational decision strategy. Given that we expect past performance to serve as outcome feedback to enable teams to reflect and prepare for future decision making, positive outcome feedback will reinforce teams to maintain a rational strategy for the next performance episode. As such, we hypothesize the following:

Hypothesis 3: Prior performance will have a self-reinforcing effect on team rational strategy such that it will be positively related to team rational strategy over time.

Relative Importance of Team Composition and Past Performance on Rational Strategy

Mitchell and James (2001) argued that most dynamic relationships are unlikely to be of the same magnitude over time and called for for research to specify instances where those changes occur. Although prior performance is expected to influence team rational strategy, we explore the magnitude of the impact of team composition and prior performance on team rational strategy. Specifically, we explore the extent to which stable team composition and dynamic prior performance impact team rational strategy when teams are first formed (i.e., initial stages), have interacted for some time (i.e., middle stages), and are about to disband (i.e., final stages). For instance, when individuals first come together in a team, they do not have any information on team performance; as such, time within a team is needed for members to acquire task-relevant information and become familiar with how decision strategies impact team performance. In the initial stages of team development, the default is to rely on the members’ propensity to engage in rational decision-making behaviors such that teams composed of members who are more rational are likely to engage in rational strategy from the start. More formally, we expect team rational style to predict team rational strategy in newly formed teams.

As the teams develop, outcome feedback becomes available based on previous team decisions such that prior team performance sets the stage for subsequent team rational strategy. In later stages of team development, we expect that the magnitude of prior performance → team rational strategy will exceed that of team rational decision style composition → team rational strategy. See Figure 1 for our conceptual model. As such, we expect that positive outcome feedback predisposes teams to engage in subsequent team rational strategy. For these reasons, we hypothesize that team mean rational style will have a greater impact on team rational strategy initially, whereas prior performance will take over as the stronger predictor in later stages.

Hypothesis 4: Team mean rational style will be a stronger predictor of team rational strategy during the initial stages of team development, whereas prior performance will be a stronger predictor of team rational strategy during later stages.

Figure 1

Conceptual Model

Method

Participants and Procedure

The study sample included 320 business students in 85 teams enrolled in eight sections of an undergraduate capstone course at a large northeastern university in the United States. Each student was randomly assigned to a “management team” within his or her class section at the beginning of the semester. Each team participated in a complex business strategy simulation entitled StratSim (James & Deighan, 2008) over the course of the semester (i.e., 10 weeks). The team development process in the simulation context mirrored processes similar to those of management teams and have been argued as an ideal setting to study basic construct relationships (Mathieu et al., 2015; Zhu, Barnes-Farrell, & Dalal, 2015). The teams disbanded when the semester was over. Team size was three or four students (M = 3.85), and participants were on average 21.51 years old (SD = 1.71), 58.5% male, and mostly Caucasian (70.2%), with the other race-ethnicity categories being 19.7% Asian, 5.0% Hispanic/Latino, 2.2% African American, and 2.8% Other.

Simulation

Each team in the StratSim simulation acted as a firm in the automotive industry and started with a base stock price of $50. Each week, teams had to incorporate information about the industry environment (e.g., customer preferences, previous performance feedback, market conditions) from sources such as industry report analysis, research studies, and decision simulators to make nine major decisions involving operations, sales, marketing, research and development, and finances across 10 weeks (which corresponds to 10 years in the simulation). For instance, teams can launch a new car model or increase production on an existing model to expand their firms. These decisions directly impacted their firm’s performance in terms of weekly stock price, which was made available to each team at the end of each performance period via a financial statement (i.e., outcome feedback). The simulation’s stock price has been shown to be a useful outcome to examine teamwork (e.g., Maltarich, Greenwald, & Reilly, 2016; Maltarich, Kukenberger, Reilly, & Mathieu, 2018). Finally, the relative performance in the form of team stock price at the end of the simulation accounted for a substantial portion of the students’ course grades. Hence, the team decisions made through the simulation had real reward implications for the students. Although the nature of the simulation may prompt students to engage in rational decision strategy, the instructors in the course did not specifically guide the teams on how they should make decisions (e.g., promote the use of rational decision strategies).

Measures

Participants were surveyed four times throughout the semester. The first survey was administered prior to the beginning of the simulation and gathered members’ demographic information, including sex, race, age, grade point average (GPA), and individual decision styles. The subsequent three surveys were collected between Weeks 3 and 4, between Weeks 6 and 7, and finally, between Weeks 9 and 10. The temporal spacing between surveys was intended to allow enough time for team members to interact and engage in meaningful team processes as well as to incorporate information regarding their prior performance. Doing so, we were able to establish temporal precedence of each performance episode to examine the reciprocal relationships and relative impact of team rational style as well as prior performance on team rational strategy.

Team competence

Team competence was indexed using the team members’ mean GPA (Mathieu et al., 2015); they were asked to report GPA on a 4-point scale (M = 3.48, SD = 0.14).

Team rational style

Individual rational decision style was measured with Scott and Bruce’s (1995) five-item scale (α = .85) using a 5-point agreement scale. A sample item is “I make decisions in a logical and systematic way.” Team rational style composition was operationalized using additive models to index team-level mean rational style (Chan, 1998). Team mean rational style was calculated by taking the average of members’ individual rational styles within each team. Because team rational style was conceptualized as an additive model, agreement within teams was not required (Chen, Bliese, & Mathieu, 2005).

Team rational strategy

Team rational strategy was operationalized using a referent-shift consensus model (Chan, 1998) and therefore required within-group agreement to justify aggregation. We use James, Demaree, and Wolf’s (1993) r_wg(j) agreement index to justify aggregating individual members’ responses to the team level. Median r_wg(j) values > .70 are considered acceptable agreement among members to justify aggregation. Additionally, we calculated intraclass correlations (ICCs). ICC1 represents the percentage of members’ response variance attributable to unit membership (i.e., simulation team), whereas ICC2 is a reliability index of mean scores. We measured team rational strategy using Dean and Sharfman’s (1996) four-item measure; median r_wg(j)s were .92, .92, and .92; ICC1 (ps < .05) = .10, .08, and .11; ICC2 (ps < .05) = .30, .26, and .32; and α = .80, .77, and .84 for Times 1 to 3, respectively. A sample item is “How extensively did the group analyze the relevant information before making a decision?” The rating anchor ranged from 1 (not at all) to 5 (to a great extent). Given sufficient intermember agreement to warrant aggregating to the team level of analysis, we indexed it as the average of members’ responses at each of the three time points.

Team performance

We used team stock price as a measure of team performance. Notably, for modeling purposes, we leveraged performances occurring just before and following survey administrations when team decision-making strategies were indexed. Accordingly, teams’ stock prices from Weeks 3, 6, and 9 were employed as prior outcome feedback (i.e., predictor) and from Weeks 4, 7, and 10 as team performance criteria.

Analysis Strategy

Given the temporal design of our study, we employed a two-level framework. Simulation performance and team rational strategy constitute within-team, or temporally varying, measures via three Level 1 repeated measures. These are subject to between-team influences of team rational style composition. We modeled both linear and quadratic trends at the within-team level (Level 1) and coded time as 0 for the survey administered between Weeks 3 and 4, as 1 for the survey administered between Weeks 6 and 7, and as 2 for the survey administered between Weeks 9 and 10. We z-scored our Level 1 variables using their total distributions over time in order to create a common metric for comparison purposes and to maintain between-team and temporal-based variances in scores. We also z-scored between-team variables at Level 2 and employed a homogeneous error-variance structure (Bliese & Ployhart, 2002).

Given the multilevel design, we used a three-stage model-building approach to test the hypothesized relationships using a random-coefficients growth-modeling technique in the form of hierarchical multivariate linear modeling (HMLM; Raudenbush, Bryk, & Congdon, 2004). We first fitted a baseline, or “null,” model to determine the percentage of outcome variance that resides in our criterion within and between units. In the second stage, we included temporal trends and covariates. In the third stage, we included lower-level and cross-level direct effects. In the next section, we present our results in the following order. First, we show results predicting team rational strategy testing Hypotheses 1 and 3. Second, we show results predicting team performance testing Hypothesis 2. Finally, we conclude with results of our reciprocal relative importance analysis and test Hypothesis 4. Because HMLM does not provide an R² statistic, we calculate a pseudo R² (~R²) according to Snijders and Bosker (1999: 102-103).

Results

Table 1 reports descriptive statistics and correlations among all variables in the model at the team and episode levels.

Table 1

Descriptive Statistics and Correlations

Variable	M	SD	1	2	3	4	5	6	7	8	9	10
Between teams (Level 2)
1. Mean GPA	3.48	0.14	—
2. Rational style mean	4.11	0.29	.13	—
Within teams (Level 1)
Rational strategy
3. Time 1	3.88	0.37	.01	.38	.80^a	.92^b	.10^c	.30^d
4. Time 2	3.91	0.35	.04	.19	.50	.77^a	.92^b	.08^c	.26^d
5. Time 3	3.91	0.42	−.11	.12	.33	.53	.84^a	.92^b	.11^c	.32^d
Pre-performance
6. Time 1	44.71	9.17	−.10	−.07	.09	−.02	−.00	—
7. Time 2	40.00	21.88	−.11	−.02	.17	.33	.15	.17	—
8. Time 3	56.45	36.95	−.11	.01	.12	.44	.44	−.08	.70	—
Post-performance
9. Time 1	45.87	15.76	−.07	−.06	.23	.09	−.04	.69	.57	.23	—
10. Time 2	41.15	26.76	−.17	−.02	.18	.43	.28	−.00	.91	.85	.35	—
11. Time 3	63.38	42.35	−.07	.06	.14	.44	.47	−.13	.67	.97	.19	.82

Note: N = 85 teams. GPA = grade point average.

Cronbach’s α.

Median r_wg(j).

Intraclass correlation 1.

Intraclass correlation 2.

r > |.21|, p < .05

r > |.28|, p < .01

r > |.36|, p < .001

Models Predicting Team Rational Strategy

The baseline model with team rational strategy as the outcome indicated that 44% of the variance resided between teams, with the remaining 56% residing within teams over time. As summarized in Table 2, Models 2 and 3, adding team temporal trends did not result in any significant effects and did not account for significant variance in team rational decision strategy. Notably, although this context may prompt all teams to move toward a more rational approach over time, the lack of significant temporal trends demonstrates a lack of a uniform learning trend or maturation effect. In Model 4, we added team competence (β = –.05, SE = .10, p = .768), which did not account for additional significant variance in team rational strategy. In Model 5, we added team rational style mean (Hypothesis 1: β = .23, SE = .08, p = .005), which explained 6.89% of the total variance in team rational strategy (Snijders & Bosker, 1999) and provided support for Hypothesis 1. In Model 6, we added past performance (Hypothesis 3: β = .29, SE = .06, p < .001), which explained a total of 18.74% of the variance in team rational strategy and provided support for Hypothesis 3.

Table 2

Effects of Temporally Varying and Fixed Team Characteristics on Dynamic Team Rational Strategy

Variable	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7^b
Intercept	.13 (.08)	.09 (.10)	.08 (.11)	.07 (.11)	.07 (.10)	.10 (.10)	.10 (.10)
Linear		.04 (.06)	.13 (.20)	.13 (.20)	.13 (.18)	.31 (.20)	.32 (.20)
Quadratic			−.05 (.10)	−.05 (.10)	−.05 (.10)	−.17 (.10)	−.17 (.10)
Mean GPA				−.02 (.08)	−.05 (.08)	−.03 (.07)	−.02 (.07)
Rational style mean					.23 (.08)**	.23 (.08)**	.24 (.08)**
Past performance						.29 (.06)***	.31 (.06)***
By Mean GPA							.06 (.05)
By rational decision style mean							−.11 (.06)*
~R²		.06%	.09%	.17%	6.89%	18.74%	19.51%
Δχ² (df)^a		.47 (1)	.23 (1)	.09 (1)	7.54 (1)**	24.00 (1)***	4.35 (2)

Note: N = 85 teams over three occasions each. Values are parameter estimates with standard errors in parentheses. ~R² denotes total variance explained in the model (Snijders & Bosker, 1999: 102-103). GPA = grade point average.

Nested chi-square difference tests from previous step.

Model 7 depicts results from post hoc analysis.

p < .05

p < .01

***

p < .001

Models Predicting Episodic Team Performance

The baseline model with team performance as the outcome indicated that 34% of the variance resided between teams, with the remaining 66% residing within teams over time. In Table 3, Models 2 and 3, regressing team performance onto temporal trends for both linear (β = –.59, SE = .20, p = .003) and quadratic (β = .44, SE = .09, p < .001) trends jointly accounted for 6.44% of the variance in team performance. In Model 4, we added team competence as a covariate, which did not explain additional significant variance in team performance (β = –.08, SE = .08, p = .289). In Model 5, we added team mean rational style (β = .02, SE = .08, p = .800), which explained 7.43% of variance in team performance. In Model 6, we added team rational strategy (Hypothesis 2: β = .37, SE = .06, p < .001), which explained 23.11% of the total variance in team performance and provided support for Hypothesis 2.²

Table 3

Effects of Temporally Varying and Fixed Team Characteristics on Dynamic Team Performance

Variable	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
Intercept	−.00 (.08)*	−.29 (.10)**	−.14 (.10)	−.14 (.10)	−.14 (.10)	−.17 (.09)
Linear		.39 (.06)***	−.59 (.20)**	−.59 (.20)**	−.59 (.20)**	−.64 (.18)***
Quadratic			.44 (.09)***	.44 (.09)***	.44 (.09)***	.46 (.09)***
Mean GPA				−.08 (.08)	−.09 (.08)	−.07 (.07)
Rational style mean					.02 (.08)	−.07 (.07)
Rational decision strategy						.37 (.06)***
~R²		3.60%	6.44%	7.38%	7.43%	23.11%
Δχ² (df)^a		22.71 (1)***	17.80 (1)***	1.12 (1)	.06 (1)	39.78 (1)***

Nested chi-square difference tests from previous step.

p < .05

p < .01

***

p < .001.

Relative Effects of Predictors of Team Rational Strategy

To illustrate the shifting influences on team rational strategy, we tested Hypotheses 4 by conducting relative weight analyses (e.g., Tonidandel & LeBreton, 2015).³ Relative weight analysis is valuable in modeling the unique contributions of a set of potentially correlated predictors. We adopted the Tonidandel and LeBreton (2011) framework, which allows for estimating the amount of outcome variance explained by each predictor relative to the overall R² while providing significance tests based on 95% confidence intervals. Further, we incorporated multiple time periods in our relative weights analysis in order to highlight the shifting influences of the predictors over time.

At Time 1, team mean rational style explained 14.93% of the total variance with 95% confidence interval [.02, .31] and was a stronger predictor of team rational strategy than prior performance, which accounted for 1.14% [–.03, .10] but was not significant (Table 4). Notably, whereas prior performance did not significantly predict team rational strategy at Time 1, team mean rational style accounted for more than 13 times the variance than team performance. However, at Times 2 and 3, prior performance accounted for, respectively, 11.30% [.01, .25] and 18.86% [.08, 32] and was a stronger predictor of team rational strategy than team mean rational style, which accounted for 3.73% [–.03, .15] and 1.36% [–.04, .11] (Table 4). These results suggest that team mean rational style significantly predicted the use of team rational strategy in the initial stages, but prior performance (i.e., outcome feedback) took over as the stronger predictor during middle and later stages of team development, thus supporting Hypothesis 4.

Table 4

Relative Effects of Team Rational Style Composition and Past Performance on Team Rational Strategy

Parameter	Time 1	Time 2	Time 3
Rational style mean	14.93% [.02, .31]	3.73% [–.03, .15]	1.36% [–.04, .11]
Past performance	1.14% [–.03, .10]	11.30% [.01, .25]	18.86% [.08, .32]
R ²	16.07%	15.03%	20.23%

Note: Percentages represent raw weights relative to total R². Numbers in brackets represent the 95% confidence interval.

Post Hoc Exploratory Analysis

Hollenbeck and Wright (2017: 9) suggested that “THARKing [Transparently Hypothesizing After Results are Known] should be part of every published empirical study for any authors who do not have perfect ability to omnisciently predict the future.” Essentially, they argued for the value of post hoc exploratory analysis. Endorsing this notion and combined with the insights of one of our anonymous reviewers, we sought to explore how prior performance (i.e., outcome feedback) may influence the effects of stable team rational style on dynamically changing team rational strategy. Earlier, we argued for the reciprocal direct effect of prior performance on team rational strategy, but the valence of the feedback (e.g., high vs. low performance) could differentially impact the relationship between team rational style and team rational strategy over time as well. Outcome feedback can either reinforce an effective strategy or alter the team’s strategy by inducing behavioral change (Passos & Caetano, 2005). However, as teams differ in their composition, and given the positive loop of team mean rational style → rational strategy → performance, the valence of prior performance is likely to differentially impact the strategy implementation of more versus less rational teams. Teams with a predisposition (higher team mean rational style) are inherently more likely to engage in the dominant rational strategy in this context. However, for teams lacking this predisposition, positive team performance could act as the impetus for subsequent rational strategy implementation.

Building on the results in Table 2, Model 6, we regressed team rational strategy onto team competence by past performance interaction in Model 7 (β = .06, SE = .05, p = .285), and team rational style mean by past performance interaction (β = –.11, SE = .06, p = .049), which jointly accounted for 19.51% of the total variance in team rational strategy. We plotted the significant interaction effect of team mean rational style on team rational strategy at the mean and plus and minus one standard deviation of past performance (Figure 2).

Figure 2

Reciprocal Influences of Team Mean Rational Style by Episodic Past Performance as Related to Episodic Team Rational Strategy

Furthermore, we conducted regions of significance tests (Preacher, Curran, & Bauer, 2006) to illustrate the nature of the interaction effect. Regions of significance offer a more precise depiction of interactions because they define the exact level of the moderator at which the predictor–criterion relationship is significant (Gardner, Harris, Li, Kirkman, & Mathieu, 2017). The shaded area represents the observed range of our data for the moderator, whereas the red dashed and green cross-hashed subareas represent the regions below and above the mean level of past performance within the observed range of data for which the relationship between team mean rational style and rational strategy is significant.

Past performance exhibits a substituting effect on the team mean rational style → team rational strategy relationship (Figure 2). Prior performance moderated the positive relationship between team mean rational style and team rational strategy such that this effect weakens This past performance increases. This effect was significant for teams less than .70 standard deviations above the mean on past performance (i.e., 83% of the 255 performance episodes).

As the prior analysis indicated that only positive past performance encouraged greater use of rational strategy, in further post hoc analyses, we wanted to address whether changes in rational strategy use over time predict subsequent improvements or detriments in performance. Regardless of the team’s rational style, teams that increased their rational strategy use in early periods saw an average increase of their stock price of $3.60 and a further increase of $26.60 in later periods compared to their counterparts who decreased rational strategy use in early periods and saw a decrease of $14.50 in early periods, t(77) = 3.27, p = .002, d = .75, and a lesser growth in later periods of $18.00, t(77) = 1.40, p = .166, d = .32. We saw a similar trend for teams that increased their rational strategy use in later periods, where these changes were associated with a stock price increase of $28.75 as compared to $16.75 for their counterparts who decreased their rational strategy use, t(79) = 2.28, p = .025, d = .51. Furthermore, teams that continued an upward trajectory in rational strategy use throughout saw an average increase of $34 in their final stock price as compared to an increase of $10 for those who did not, t(75) = 3.33, p = .001, d = .77. These results suggest that regardless of team rational style, increased use of rational strategy can result in improved team performance over time.

Discussion

Given the growing reliance on teams to make key organizational decisions, there is a critical need to identify factors that improve team decision processes that subsequently improve team performance. Four main findings from this study augment our knowledge of the effect of team composition on dynamic team decision process and performance. First, teams composed of more rational members tended to adopt more rational decision strategies, which subsequently led to better team performance. This finding further supports the notion that rational decision making improves decision quality at both individual and team levels (e.g., Dean & Sharfman, 1996; Wood & Highhouse, 2014).

Second, past performance was positively related to subsequent team rational strategy, meaning that positive past performance, for some teams, acted as a reinforcing mechanism to either maintain or move teams toward the dominant decision strategy. Specifically, past performance signaled the implementation of rational strategy, which was better suited for the decision context of this study. This finding suggests that decision context matters, and it contributes to a growing number of studies adopting more nuanced views of decision making such that performance resulting from a particular type of strategy is dependent on the decision environment (e.g., Elbanna & Fadol, 2016; Larrick, 2016). Although we focused on rational decision making in this study, other decision contexts characterized by time pressure, uncertainty (lack of information), or lack of learning opportunities may invoke an intuitive decision making strategy, which is characterized by relying on emotion and instincts (Luan et al., 2019). For instance, decisions toward a new entrepreneurial venture or decisions in highly uncertain situations with extreme time constraints may warrant intuitive decision making approaches.

Third, the relative impact of team decision composition and past performance on subsequent decision strategy use changed depending on the stages of team development. The results suggest that during early stages of team development, a team’s decision style composition, driven by team members’ individual decision styles, dictates the team’s decision-making approach. However, in later stages of team development, once team members have interacted with each other and become aware of the effectiveness of their decision strategy, outcome feedback takes over as the stronger predictor of team rational strategy.

Finally, in a post hoc analysis, we tested whether past performance could influence the relationship between static team rational style and dynamic team rational strategy. Both team rational style and past performance were positively related to team rational strategy. Furthermore, teams with either a higher rational style or greater past performance subsequently used a more rational strategy. The attenuating effect, however, indicated that there was no additional benefit to being high on both, but there was a cost to being low on both. For teams with low rational style, better past performance was associated with subsequent increases in rational strategy use; lower past performance was associated with the lowest subsequent rational strategy use. In sum, teams with high rational style, regardless of past performance, engage in more rational strategy use. It is important to point out that some teams, despite their suboptimal composition (i.e., low rational style), still implemented rational strategy when their performance was high initially.

For those teams not predisposed to using a rational strategy from the start, it is possible that they may not even attribute negative outcomes to their decision-making processes. In line with the fundamental attribution bias (Jones & Harris, 1967), decision makers are likely to take credit for their decision-making process when outcomes are successful and attribute negative outcomes to factors external to their decision-making process (Highhouse, 2008). A similar process may occur in team settings whereby positive performance triggers attribution of success to the team’s decision processes and prompts reflection on implementing the appropriate strategy for the decision context. For the current study, this may explain why some teams with low rational style who performed well initially gravitated toward rational strategy subsequently. In contrast, negative performance may be attributed to other external factors, thus not prompting reflection on team processes and changes to decision strategy. Future research should further examine how teams’ attribution processes may impact team behaviors and team performance.

The question this raises is, how do we set these teams with suboptimal compositions onto positive performance trajectories? Team development interventions have been shown to influence team performance trajectories in a variety of positive ways (Shuffler, DiazGranados, Maynard, & Salas, 2018). Future research can examine whether team development interventions in the early stages of the team development process can prompt teams to engage in rational strategy, therein improving and supporting struggling teams, maintaining adequately performing teams, and even maximizing team capabilities.

Theoretical and Practical Implications

Understanding and improving team decision-making processes and effectiveness have implications for both scholars and practitioners. Previous research has called for more temporal integration in team process research and emphasized the importance and need to examine dynamic effects in teams (Cronin et al., 2011; Mohammed, Hamilton, & Lim, 2009). Our study draws on the IMOI framework and episodic performance logic (Ilgen et al., 2005; Marks et al., 2001) to advance a theoretical model of dynamic team decision making. We investigated the degree to which team members’ decision styles, as a compositional factor, influence team decision strategies and performance, and how those relationships vary over time. By featuring the reciprocal loop from performance directly onto strategy and as a moderator of the input–mediator path, we highlight the critical value of theorizing and testing the effects of outputs as subsequent inputs in temporally disaggregated models. Practically, in situations where complex decisions are crucial to team performance, assembling a team with members who are highly rational will increase the likelihood that the team adopts rational strategies earlier, which will likely result in better team performance. Our results may also inform team membership churn, as the individual decision style of incoming team members, relative to those leaving, may alter the existing composition (Hausknecht & Holwerda, 2013).

By modeling the temporal influence of team decision style and past performance as outcome feedback on decision strategy over time, we were able to examine the relative impact between stable team characteristic and dynamic performance. Although team composition may lead to beneficial decision strategies as teams begin to develop, knowledge of how their current decision strategy impacts performance is necessary to ensure that teams continue to adopt beneficial decision strategies. This is well aligned with other work suggesting the potential for feedback loops in team dynamics (e.g., Ilgen et al., 2005). Practically, organizations or team leaders can use after-action reviews or debriefs as a formal forum at different team stages to discuss how to improve future decisions (Eddy, Tannenbaum, & Mathieu, 2013). Additionally, establishing team charters may provide opportunities for teams to lay out each member’s responsibilities and how the members plan to function as a team, particularly related to how the team will approach major decisions (Mathieu & Rapp, 2009).

Finally, research has demonstrated that people can be primed to engage in rational decision making in a task regardless of their default decision-making style (Rusou, Zakay, & Usher, 2013). As such, the decision environment can be designed to shape certain decision processes. For instance, choice architecture, a growing field in the decision-making literature, has been successful in modifying decision behaviors in a variety of contexts (e.g., financial, medical; Thaler & Sunstein, 2008). From a potential intervention standpoint, the results of our second set of post hoc analyses revealed that timeliness of strategy implementation may play a critical role. Although future research is warranted to further explore these effects, the preliminary findings in the post hoc analyses suggest that an early and continued shift toward a more rational strategy may be particularly advantageous for positive team performance.

Limitations and Future Research

We examined team rational decision style and strategy in a complex, knowledge-intensive decision context, which likely favors rational decision approaches. Our study may be limited in that we focused on teams with lower levels of skill and authority differentiation over 10 weeks (Hollenbeck, Beersma, & Schouten, 2012). It is possible that these results do not generalize to teams with a greater temporal stability or other decision contexts. As such, future research could explore additional decision factors that may act as boundary conditions for the present findings, such as varied decision situations (i.e., extreme time pressure or limited information) and longer time frame. Similar to how some studies have manipulated team tasks (e.g., De Dreu & Beersma, 2010), future studies can manipulate these decision context factors in order to examine their impact on team decision making and performance. Further, each team was self-managed and did not have a formal leader who was responsible for the team’s decisions. Research on hierarchical team decision making (Humphrey, Hollenbeck, Meyer, & Ilgen, 2002) has found that team decision making can be more efficient when a leader is designated as the final decision maker and incorporates information from other team members. Future research could explore if leaders highlighting outcome feedback (i.e., positive or negative team performance) or providing process feedback (i.e., specific information regarding team processes intended to alter behavior or improve processes in subsequent episodes; Geister, Konradt, & Hertel, 2006; Taylor et al., 1996) to the rest of the team on decision process would prompt members to engage in team reflection.

Although process feedback has been shown to reduce overconfidence and improve decision-making accuracy, individuals have been shown to rely on outcome feedback given its ease of interpretation (Kuncel, 2008). Additionally, process feedback is typically provided in controlled contexts (e.g., training or development). Teams in organizations rarely receive process feedback after each decision; rather, they receive knowledge of their decision outcome. As such, future research could examine the optimal time and type of feedback to provide teams to train them to engage in effective decision-making processes in real decision situations. Finally, engaging in learning experiences, such as error training, where team members explore the consequences of different performance strategies, can clarify when and why certain alternatives are more optimal (Wilson, Burke, Priest, & Salas, 2005).

In addition to the rational style discussed in this study, Scott and Bruce (1995) identified four other individual decision styles that have important implications for decision outcomes. Intuitive decision style is characterized by behaviors that are emotion based, automatic, and effortless. Avoidant decision style is characterized by an effort to abscond responsibility from making the decision. This style has been shown to be negatively related to task performance (Russ et al., 1996) and could further exacerbate process loss through social loafing in team settings. Individuals with a dependent style tend to gather advice and input from others before making decisions. This style has been shown to positively relate to performance (Riaz et al., 2014), but gathering information from others can take time, which may not be advantageous in all decision situations. Finally, spontaneous decision style is characterized by wanting a sense of immediacy and prioritizes making decisions as quickly as possible, which differentiates it from intuitive decision style, where the priority is the emotion-based gut feeling as opposed to speed of the decision. Research has found that spontaneous decision making can be detrimental to decision outcomes (Parker, De Bruin, & Fischhoff, 2007). These different types of individual decision styles can be examined in team composition models to expand on static predictors of team process and performance.

Finally, there are a number of additional team-level factors that can be examined in a dynamic team decision-making model. The results of this study found that supplementary fit of rational decision styles resulted in beneficial decision processes, but Humphrey, Hollenbeck, Meyer, and Ilgen (2007) theorized that optimal team configuration may include a combination of supplementary (homogeneity) and complementary (heterogeneity) models (e.g., minimizing one trait’s variance and maximizing another’s variance). Furthermore, the nature of the task, individual, and team characteristics may simultaneously influence whether supplementary or complementary fit is ultimately advantageous (Soda & Furlotti, 2017; Wolfson & Mathieu, 2018). As previously mentioned, teams with members who are similar in their rational decision styles may engage in rational persuasion, which would limit conflict through implicitly agreed-upon decision approaches. However, teams composed of members with dependent or avoidant decision styles may benefit from complementary models, where variance on these traits may contribute to better team performance. Having multiple people who have dependent or avoidant decision styles may slow down the decision process and put unnecessary onus on a single individual, which could vary in utility based on the nature of leadership structure within the team.

Conclusion

In this study, we set out to examine whether team composition of decision styles impacted team performance through team decision strategies. We find that teams composed of individuals with higher rational decision styles are more likely to engage in rational decision strategies, which ultimately results in superior team performance. Subsequently, positive team performance leads to engagement in rational decision strategy. Additionally, we find that the effects of team composition on decision strategy are particularly salient in the early stages of team development, whereas outcome feedback emerges as a stronger predictor of decision strategies in later stages. Overall, we explore the dynamic nature of team decision making while examining a previously neglected aspect of team composition that is particularly relevant to team decision processes and performance.

Supplemental Material

Team_Decision_Making_JOM_Online_Supplement_FINAL – Supplemental material for Team Decision Making: The Dynamic Effects of Team Decision Style Composition and Performance via Decision Strategy

Supplemental material, Team_Decision_Making_JOM_Online_Supplement_FINAL for Team Decision Making: The Dynamic Effects of Team Decision Style Composition and Performance via Decision Strategy by X. Susan Zhu, Mikhail A. Wolfson, Dev K. Dalal and John E. Mathieu in Journal of Management

Footnotes

Acknowledgements

The authors would like to thank Brian Fox, Don Zhang, Daroon Jalil, and Elsheba Abraham for their helpful comments on earlier drafts of this article. We would like to thank our associate editor and three anonymous reviewers for their constructive comments and developmental feedback.

Supplemental material for this article is available with the manuscript on the JOM website.

ORCID iD

X. Susan Zhu

Notes

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