*Result*: The paradox of team conflict revisited: An updated meta-analysis of the team conflict-team performance relationships.
Original Publication: Washington [etc.]
*Further Information*
*The possibility that team conflict, especially task conflict, might improve team performance has stimulated a large body of empirical research that continues to grow to this day. Nevertheless, 12 years has passed since de Wit et al.'s (2012) comprehensive meta-analysis. To synthesize the even larger body of empirical evidence now available, we provide an updated meta-analysis of the team conflict-team performance relationships by revisiting the population average estimates and their effect size heterogeneity. Given the recent developments in the team conflict literature, we also incorporate status conflict into our synthesis. Moreover, to shed light on the contextual factors that may help explain the heterogeneous team conflict-team performance relationships, we examine a host of moderators pertaining to national culture, team features, and research methods. Our results based on psychometric meta-analysis indicate that all four team conflict dimensions (i.e., task conflict, relationship conflict, process conflict, and status conflict) are negatively related to team performance. Moreover, the relationships of task conflict and relationship conflict with team performance have substantial cross-situation heterogeneity. Examining the contingencies of these heterogeneous relationships, our metaregression analyses reveal that national culture (e.g., individualism), team features (e.g., team performance facet), and methodological factors (e.g., team conflict scale) all play important roles in helping to explain the mixed effects of team conflict on team performance. Based on our quantitative synthesis, we discuss the implications for the next waves of team conflict research. (PsycInfo Database Record (c) 2026 APA, all rights reserved).*
The Paradox of Team Conflict Revisited: An Updated Meta-Analysis of the Team Conflict–Team Performance Relationships
<cn> <bold>By: Zhenyu Yuan</bold>>
> <bold>Jingfeng Yin</bold>
>
> <bold>Jiaqing Sun</bold>
>
<bold>Acknowledgement: </bold>Christopher O. L. H. Porter served as action editor.A previous version of this article was presented at the 37th Annual Conference of the International Association for Conflict Management in Singapore.Zhenyu Yuan played a lead role in conceptualization, data curation, formal analysis, investigation, methodology, project administration, visualization, writing–original draft, and writing–review and editing. Jingfeng Yin played an equal role in data curation and project administration. Jiaqing Sun played a supporting role in data curation and project administration.
The increasing prevalence of teams in organizations has produced an unavoidable problem associated with teamwork—team conflict, defined as incompatibilities and disagreements among team members (De Dreu & Gelfand, 2008). Notwithstanding the negative connotations generally associated with the word “conflict,” early theorists astutely pointed out that conflict can be beneficial for team performance under certain circumstances (e.g., Coser, 1956; Pondy, 1967). Accordingly, scholars have distinguished between task conflict and relationship conflict in an attempt to isolate the functional and dysfunctional aspects of team conflict, respectively (Amason, 1996; Amason & Schweiger, 1994; Guetzkow & Gyr, 1954; Jehn, 1994, 1995; Pelled, 1996; Pinkley, 1990; Priem & Price, 1991; Wall & Nolan, 1986). Task conflict refers to disagreements over task-related aspects of teamwork (i.e., content and goals), whereas relationship conflict entails interpersonal incompatibilities. Subsequent research identified a third dimension, process conflict, which refers to disagreements among team members over the process used to carry out team tasks (Jehn, 1997; Shah & Jehn, 1993). Finally, recognizing that the internal structure of a team may be inherently hierarchical, later scholars introduced a fourth dimension, status conflict (i.e., conflict over relative within-team status), into the conflict literature (Bendersky & Hays, 2012).
The tripartite conceptualization of team conflict by Jehn (1994, 1995, 1997; Jehn & Mannix, 2001), as well as the most recent addition of status conflict (Bendersky & Hays, 2012) to this conceptualization, has stimulated a stream of empirical research that continues to grow even today. To date, scholars have conducted three meta-analytic reviews to synthesize the performance implications of team conflict (De Dreu & Weingart, 2003; de Wit et al., 2012; O’Neill et al., 2013). In the earliest meta-analysis (De Dreu & Weingart, 2003), task conflict—the conflict dimension that held the great promise for improving team functioning and hence ignited team conflict research—was found to have a negative relationship with team performance (ρ = −.23, 95% confidence interval [CI] [−.26, −.20],
As this case illustrates, the accumulating body of empirical evidence has continuously informed and updated the performance implications of team conflict, which have in turn shaped subsequent conflict research and influenced business education related to workplace conflict. Given that more than a decade has passed since de Wit et al.’s (2012) review, it is important to further consolidate scholarly understanding of team conflict. Furthermore, the concept of status conflict (Bendersky & Hays, 2012) was introduced into the field after de Wit et al.’s meta-analysis. Incorporating this conflict dimension in an updated meta-analysis can help provide clarity to this emergent body of conflict research.
In addition, conflict scholars have long emphasized that the effects of team conflict on team performance may vary across contexts (Jehn, 1995; Jehn & Bendersky, 2003). Indeed, the team conflict–team performance relationships have demonstrated substantial cross-situation heterogeneity in past meta-analytic reviews (De Dreu & Weingart, 2003; de Wit et al., 2012; O’Neill et al., 2013). As accurately estimating effect size heterogeneity may require a large number of studies (Brannick et al., 2019; Cafri et al., 2010), it is critical to refine scholarly understanding of the heterogeneity of the team conflict–team performance relationships based on the ever-growing body of empirical evidence. Moreover, considering the multitude of conflict dimensions, it is important to investigate which of those dimensions are most prone to having substantial effect size heterogeneity across situations, as these distinctions can lead to a more refined understanding of team conflict.
Relatedly, the existence of cross-situation heterogeneity calls for a continuous stream of research efforts to identify the various contextual factors that can moderate the team conflict–team performance relationships. In that regard, Jehn and Bendersky (2003) advanced a contingency model of team conflict, though they mainly focused on within-study moderators. At the between-study level, de Wit et al. (2012) provided a preliminary meta-analytic test of situational moderators by exploring group contextual (e.g., group task type) and methodological (e.g., study setting) factors. Nevertheless, there has since been a dearth of research efforts to build on and expand their work, which renders scholarly understanding of the between-study contingencies of conflict somewhat fragmented. Importantly, the growing body of literature on team conflict provides a welcome opportunity to further broaden the search for meaningful situational moderators. In the present study, we expand upon previous conflict research by considering both national culture (distal factor) and team features (proximal factor) as contextual influences that may potentially moderate the team conflict–team performance relationships. Additionally, we examine a host of methodological factors. As team conflict research stands at a crossroads (Cronin & Bezrukova, 2019; Shah et al., 2021), such a systematic synthesis can advance scholarly understanding of the pertinent contingencies of team conflict, which may prove useful in directing the next waves of conflict research.
In this study, we aimed to provide an updated quantitative review of the performance implications of team conflict. Capitalizing on the larger body of empirical evidence available to today’s scholars, we seek to update the previous meta-analytic findings regarding the overall effects of team conflict on team performance and the associated effect size heterogeneity. Moreover, we carry out an expanded search for situational moderators to account for the heterogeneous team conflict–team performance relationships. Finally, by incorporating status conflict in our meta-analysis, we provide a timely synthesis of an important team conflict dimension that has attracted increasing scholarly attention.
Team Conflict and Team Performance
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The overall population-level team conflict–team performance relationships and their heterogeneity are both integral components of a meta-analytic synthesis (Borenstein et al., 2009; Schmidt & Hunter, 2015; Whitener, 1990). The population average estimates provide a bird’s-eye view of the direction and the strength of the team conflict–team performance relationships, whereas effect size heterogeneity sheds light on the variation of these relationships across situations. Accordingly, we first turn to the population estimates of the team conflict–team performance relationships and then discuss effect size heterogeneity. Subsequently, we explicate the various contextual influences that can potentially explain the heterogeneous team conflict–team performance relationships.
<h31 id="apl-111-2-195-d126e267">Population Average of Team Conflict–Team Performance Relationships</h31>From a research synthesis standpoint, understanding the population average of the team conflict–team performance relationships has crucial theoretical implications. As mentioned earlier, the negative relationship between task conflict and team performance identified in De Dreu and Weingart’s (2003) meta-analysis led the authors to conclude that “both task and relationship conflict interferes with team performance” (p. 747), which quelled the then-optimistic view of conflict in the research community. Later, the substantively different findings of de Wit et al. (2012) regarding the same relationship, whereby “for task conflict, the overall association with group performance is neither negative nor positive” (p. 372), again shifted scholarly consensus regarding the performance implications of task conflict.<anchor name="b-fn1"></anchor><sups>1</sups> Attesting to the impact of these meta-analytic findings, subsequent reviews have since drawn from de Wit et al.’s study to formulate their conclusions (e.g., Bradley et al., 2015; Cronin & Bezrukova, 2019; Loughry & Amason, 2014; O’Neill & McLarnon, 2018) or conduct secondary analyses (e.g., DeChurch et al., 2013). Considering their theoretical importance, we seek to update the population estimates of the team conflict–team performance relationships.
<h31 id="apl-111-2-195-d126e299">Heterogeneity of Team Conflict–Team Performance Relationships</h31>In addition to determining the population average of the team conflict–team performance relationships, estimating their heterogeneity is important, especially when one considers the sizeable heterogeneity found in previous meta-analyses (De Dreu & Weingart, 2003; de Wit et al., 2012; O’Neill et al., 2013). From a theoretical standpoint, this finding suggests that contextual moderators may underlie these heterogeneous relationships across situations (Whitener, 1990) and, therefore, provides support for the theoretical premise of the contingency model of team conflict (Jehn & Bendersky, 2003). Moreover, more accurate estimates of effect size heterogeneity based on a larger body of empirical evidence can pave the way for the correct identification of such contextual moderators. As such, in addition to updating the population average of the team conflict–team performance relationships, we pay special attention to estimating effect size heterogeneity in the current meta-analysis.
<h31 id="apl-111-2-195-d126e318">Moderators of Team Conflict–Team Performance Relationships</h31>The substantial effect size heterogeneity in the team conflict–team performance relationships calls for systematic efforts to identify contextual moderators (Cortina, 2003). Jehn and Bendersky (2003) put forward the contingency model of team conflict to help identify the circumstances under which team conflict may be beneficial for team functioning. Although their model provided valuable insights, it mainly focused on moderators at the within-study level. To account for effect size heterogeneity between studies, a contingency perspective that delineates the contextual moderators at the between-study level is warranted. Adopting such a viewpoint is important because “context is often a constant within a study … but can vary between studies” (Johns, 2018, p. 22). de Wit et al. (2012) made an important first step in this direction by exploring a host of group contextual factors and methodological factors. Building upon the theoretical richness and empirical growth of the conflict literature, we expand the search for the situational moderators in the team conflict–team performance relationships (Figure 1).
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In addition to recognizing contextual influences, the tripartite conceptualization (Jehn, 1995, 1997; Jehn & Mannix, 2001) was put forward to reconcile, explain, and predict the mixed effects of team conflict, with task conflict theorized to be the most beneficial dimension for team performance (Jehn & Bendersky, 2003). In other words, the heterogeneous team conflict–team performance relationships may (at least partly) depend on “the type of conflict that exists” (Jehn & Bendersky, 2003, p. 197). Accordingly, it is important to differentiate the various conflict dimensions in an updated meta-analysis. Therefore, we first review the differences among the various conflict dimensions and then theorize the contextual influences.
Team Conflict Dimensions
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As work teams are tasked with mobilizing team members’ collective efforts toward their goals (Ilgen et al., 2005), their ability to capitalize on the members’ uniquely valuable perspectives so as to benefit teamwork becomes a crucial consideration (Pelled, 1996). Owing to the informational and cognitive diversity among team members (Amason, 1996; Jehn et al., 1999; Pelled et al., 1999), task conflict invites the sharing of divergent, nonredundant task-related information and facilitates deliberation on task-related matters by team members (Baron, 1984; Todorova et al., 2014). In this way, task conflict may protect team members from groupthink (Janis, 1982) and potentially enhance aspects of team performance such as creativity (Jehn & Bendersky, 2003). In contrast to task conflict, relationship conflict instills a sense of threat and anxiety among team members, which in turn inhibits their cognitive processing of task-related information (Pelled, 1996; Staw et al., 1981). Its interpersonal nature breeds hostility among team members (Jehn, 1995), lowers morale (Jehn et al., 1999), and makes it less likely that team members will work interdependently (Thiel et al., 2019). As a result, relationship conflict distracts team members from their team tasks, debilitates effective work relationships, and undermines team performance.
Whereas the distinction between task conflict and relationship conflict is a long-standing feature of the literature (e.g., Guetzkow & Gyr, 1954), process conflict emerged from inductive, qualitative work by Jehn (1997; cf. Shah & Jehn, 1993). In their subsequent review, Jehn and Bendersky (2003) offered competing hypotheses regarding the process conflict–team performance relationship, which relied heavily on the overlap between process conflict and the other two dimensions. Specifically, “process-related debates” (i.e., overlap between process conflict and task conflict) have been theorized to improve team processes and resource delegation, whereas “process loss” stemming from interpersonal struggles over who should be doing what (i.e., overlap between process conflict and relationship conflict) purportedly harms team performance. In other words, from a theoretical standpoint, the process conflict–team performance relationship may be either positive or negative (cf. Behfar et al., 2011).
Compared with the other three dimensions, status conflict was more recently introduced into the conflict literature. It entails team members’ struggles and clashes over the contested within-group hierarchy—an important aspect overlooked in the tripartite conceptualization of team conflict (Bendersky & Hays, 2012). As status conflict centers on power and influence that are unrelated to team goals, “it should serve as a distraction and harm group performance” (Bendersky & Hays, 2012, p. 332). That is, if team members are vying for relative status and influence within the team, they may be less likely to share information with one another, which may ultimately impede team goal accomplishments.
Taken together, the four team conflict dimensions capture theoretically distinct aspects of disagreements and incompatibilities within the team. This implies that examining team conflict in an undifferentiated manner runs the risk of further contributing to the mixed effects observed in past research. Accordingly, we examine the team conflict–team performance relationships and the associated effect size heterogeneity and investigate the impact of contextual moderators separately for each conflict dimension.
Contextual Moderators of the Team Conflict–Team Performance Relationships
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In light of their substantial cross-situation heterogeneity, a systematic examination of the contextual moderators of the team conflict–team performance relationships can greatly advance scholarly understanding of team conflict. Building upon previous research (de Wit et al., 2012), we consider both distal (i.e., national culture) and proximal (i.e., team features) moderators.
<h31 id="apl-111-2-195-d126e438">National Culture</h31>To the extent that teams are embedded in the broader organizational and societal context (Kozlowski & Bell, 2003), national culture may serve as a distal source of influence and inform the corresponding norms for handling interpersonal disagreements and clashes at work (Gelfand et al., 2008; House et al., 2004). Following previous research (de Wit et al., 2012), we operationalize these cultural dimensions as a contextual variable that reflects aspects of the national culture wherein the sample is collected. In other words, across samples collected from different cultures, team members may react in systematically different ways, which in turn may influence the strength and/or direction of the team conflict–team performance relationships. To provide a comprehensive test of national culture, we examine the moderating effects of Hofstede’s (2001) cultural dimensions: power distance, masculinity–femininity, individualism–collectivism, uncertainty avoidance, and long-term versus short-term orientation. In light of recent developments in cross-cultural research (Gelfand et al., 2006, 2021), we also incorporate the tightness–looseness dimension to furnish a comprehensive test of national culture.
First, in high-power-distance cultures, team members are more accepting of the social hierarchy and inequalities (Hofstede, 2001). As team members openly clash with one another owing to power struggles and/or interpersonal differences, team conflict (e.g., status and relationship conflict) may be seen as normalized. Moreover, team members may view arguments over task-related details and processes as a necessary way to establish and maintain inequalities of power (Caputo et al., 2018; Gunkel et al., 2016). Conversely, in low-power-distance cultures wherein individuals value equality, interpersonal disagreements and incompatibilities may prove very disruptive for teamwork. Consequently, the detrimental impact of team conflict on team performance may be more pronounced in low-power-distance cultures compared with high-power-distance cultures.
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In highly masculine cultures, people place great emphasis on material success and achievement by being very direct, competitive, and assertive (Hofstede, 2001). In turn, they may have greater tolerance of interpersonal friction (Gelfand et al., 2008). In this context, team members may value the expression of divergent opinions and accept disagreements and arguments as the preferred way to get ahead (Gunkel et al., 2016). Therefore, teams in highly masculine cultures may be more likely to benefit from conflict. In feminine cultures that value interpersonal harmony, team members may be very concerned with accommodating one another at the expense of fully taking in the informational value of different opinions (Gabrielidis et al., 1997). Moreover, interpersonal frictions and clashes may prove highly aversive in feminine cultures, thereby accentuating the negative effect of team conflict on team performance.
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Owing to the interdependent self-construal associated with collectivism (Markus & Kitayama, 1991), team members in collectivistic cultures may hold negative beliefs about conflict (Sanchez-Burks et al., 2008) and, therefore, rely on indirect and passive ways to express and handle team conflict (Gelfand et al., 2001; Ren & Gray, 2009). Consequently, they may be less likely to reap the benefits associated with task-related disagreement, which hinge upon direct idea exchanges and information sharing (Tsai & Bendersky, 2016). Moreover, as interpersonal frictions and clashes over power are believed to be fundamentally harmful for group well-being (Hofstede, 2001), teams in collectivistic cultures may be especially vulnerable to the disruptive effect of team conflict compared with their counterparts in individualistic cultures. As such, the negative team conflict–team performance relationships may be stronger in collectivistic cultures than in individualistic cultures.
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In high-uncertainty-avoiding cultures, people prefer structured solutions to problems (Hofstede, 2001). In this context, team members may be ill equipped to deal with the uncertainty instilled by task-related conflict, which entails the expression of divergent ideas. Moreover, interpersonal clashes and incompatibilities may threaten established routines and norms. Consequently, team members in high-uncertainty-avoiding cultures may find the disruptive effect of team conflict especially distressing, such that it interferes with their team functioning. By contrast, in low-uncertainty-avoiding cultures, as team members are more at ease with uncertainty and unpredictability, they may be more receptive to opinions that differ from the established routines and thus invite the exchange of different ideas (Gunkel et al., 2016). Consequently, team conflict is more likely to prove detrimental to team performance for teams in high-uncertainty-avoiding cultures than in low-uncertainty-avoiding cultures.
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Moreover, in cultures characterized by a long-term orientation, individuals tend to be tenacious because they generally believe their hard work will be rewarded in the future (Hofstede, 2001). By contrast, in short-term-oriented cultures, individual behaviors are primarily motivated by quick results. Indeed, cross-cultural research indicates that individuals from long-term-oriented cultures are more likely to be persistent in their attempts to solve disagreements (Caputo et al., 2018; Gunkel et al., 2016). Considering that effectively dealing with disagreements and incompatibilities calls for sustained efforts from team members (e.g., Jehn & Mannix, 2001), team members in long-term-oriented cultures are more likely to benefit from conflict, whereas their counterparts in short-term-oriented cultures may struggle when faced with conflict. Thus, the negative team conflict–team performance relationships may be stronger in short-term-oriented cultures than in long-term-oriented cultures.
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Last, compared with loose cultures, tight cultures greatly value social norms and sanction deviance from such established norms (Gelfand et al., 2006). In tight cultures, the expression of divergent ideas and incompatibilities may be viewed as less socially acceptable and even aberrant. In turn, team members may quickly shun any potential sign of conflict and suppress and even sanction those who voice different opinions. Thus, cultural tightness may create an environment where team conflict is either frowned upon or dealt with in a very passive way (Gelfand et al., 2008). When team members’ limited attention is diverted toward suppressing conflict and sanctioning dissenters, team performance may deteriorate. By contrast, in loose cultures, team members may feel encouraged to voice and listen to different opinions. Consequently, the team conflict–team performance relationships are less likely to be negative among teams in loose cultures than in tight cultures.
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Team features represent proximal situational moderators in the team conflict–team performance relationships. To provide a comprehensive test of such influences, we examine four factors: team performance facet, the intercorrelations among conflict dimensions, the sample mean of team conflict, and team conflict symmetry.
First, as different team performance facets are not interchangeable (Mathieu et al., 2008), the multifaceted nature of team performance may contribute to the heterogeneity of team conflict–team performance relationships. The same work team may, for example, be expected to meet multiple, yet not completely congruent, goals. As a result, the team may rely on a proven successful way of doing things in pursuit of efficiency at the cost of being innovative (O’Reilly & Tushman, 2008). In this case, task disagreements may hurt team efficiency but nonetheless promote team creativity. Given that not all facets of team performance may benefit from conflict, the team conflict–team performance relationships may depend on the specific facet of team performance under investigation (De Dreu, 2008; Mathieu et al., 2008). As such, it is important to consider the potential differences among team performance facets as a moderator in the team conflict–team performance relationships (de Wit et al., 2012; O’Neill et al., 2013). In the preceding example, the task conflict–team performance relationship may be more positive for team creativity than for team efficiency, which is a key moderator according to Jehn and Bendersky’s (2003) contingency model of conflict. However, de Wit et al. (2012) did not find empirical support for this notion in their meta-analysis. In light of the inconclusive evidence in the literature, we seek to further explore the moderating role of the team performance facet.
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Second, given the multidimensional nature of team conflict (Jehn, 1995), the empirical overlap among conflict dimensions can shed light on an important aspect of the team conflict phenomenon (De Dreu & Weingart, 2003; de Wit et al., 2012). Specifically, the correlation among conflict dimensions may indicate the co-occurrence of conflict (K. Choi & Cho, 2011; Greer et al., 2008; Mooney et al., 2007; Pelled et al., 1999). As the burden of dealing with co-occurring conflict may exceed the team’s information processing capacity (de Wit et al., 2013), it may eradicate any benefits of team conflict, resulting in negative team conflict–team performance relationships. Moreover, when team members experience both task and relationship conflict, they may misattribute benign, task-based disagreement to interpersonal clashes (Simons & Peterson, 2000; Yang & Mossholder, 2004). When divergent task-related ideas are interpreted with a hostile intent, team members are much less likely to appreciate the informational value of task conflict. In turn, task conflict is more likely to hinder (rather than boost) team performance. Thus, in samples with higher levels of correlation among conflict dimensions, the effects of team conflict on team performance may be more destructive (i.e., negative).
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Third, the sample mean of team conflict, which captures the overall level of conflict in a given context, may represent a moderating influence (e.g., Schneider et al., 2017). In a study context characterized by an overall lower level of conflict, team members may have become accustomed to uniformity. Under this circumstance, when disagreements and incompatibilities arise, team members may be ill equipped to handle what they perceive as a threatening situation. In turn, team conflict is likely to prove disruptive for the team, resulting in a strong, negative relationship with team performance. However, in a context characterized by an overall higher level of conflict, team members may have overcome inertia and developed a collective capacity to work through disagreements (e.g., De Dreu, 2006; J. L. Farh et al., 2010). In this situation, different ideas and opinions can quickly mobilize team members and spark collaborative efforts to solve such disagreements. As a result, team members are more likely to reap the benefits rather than the detriments associated with team conflict. In turn, the negative team conflict–team performance relationships will be attenuated. Taken together, we posit that if an overall higher (vs. lower) level of team conflict is present in a given study, the negative relationships between team conflict (e.g., task conflict) and team performance may be much weaker.
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Last, in recognition of the limitations of the consensus-based team conflict conceptualization (Jehn, 1995), researchers have started to incorporate conflict asymmetry (i.e., the degree to which team members have divergent conflict experiences; Jehn et al., 2010) into their research in this area. To wit, among work teams that report the same level of mean conflict, members in some teams may have more divergent experiences than their counterparts in other teams. Notably, to the extent that seeing eye to eye allows team members to develop shared team cognitions (DeChurch & Mesmer-Magnus, 2010), conflict symmetry, compared with asymmetry, may be conducive to effective team processes and make it more likely that the team will benefit from conflict. That is, the performance benefits associated with team conflict may be more pronounced in samples with higher (vs. lower) levels of conflict symmetry.
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Additionally, we consider a series of methodological factors that may help explain the heterogeneous team conflict–team performance relationships. Specifically, we examine whether the study sampled (a) top management teams, (b) student teams, (c) average team tenure, (d) average team size, (e) team task type, (f) the team conflict scale, and (g) performance measurement method and (h) whether the study used a cross-sectional design. Accounting for their potential influences can help disentangle their effects from the substantive contextual moderators. Figure 1 summarizes the moderators examined in this research.
Method
> <h31 id="apl-111-2-195-d126e675">Transparency and Openness</h31>
In this section, we describe our literature search, inclusion criteria, coding process, and meta-analytic procedure. While conducting our research, we adhered to the
We first identified empirical studies to be considered for inclusion in the current meta-analysis by searching ABI/INFORM Global, APA PsycInfo, and ProQuest Dissertations and Theses up to January 2023, using “conflict” in combination with “team,” “task,” “relationship,” “process,” “status,” “cognitive,” and “affective” as keywords. To supplement this search, we closely examined the reference lists of previous meta-analyses (De Dreu & Weingart, 2003; de Wit et al., 2012; O’Neill et al., 2013) to identify additional studies that we could access. To ensure we covered the studies conducted since the publication of de Wit et al.’s (2012) comprehensive meta-analysis, we carried out a backward search by going through all the studies that cited de Wit et al. Additionally, we searched the Academy of Management Annual Meeting proceedings and the conference programs of the Society for Industrial and Organizational Psychology (2012–2022) for relevant studies. Upon identification of such studies, we emailed the author(s) to request a copy of the article. In an effort to identify other unpublished research, we made requests for unpublished studies and data sets via email listservs to members of the Conflict Management, Human Resources, and Organizational Behavior Divisions of the Academy of Management and the International Association for Conflict Management.
<h31 id="apl-111-2-195-d126e705">Inclusion Criteria</h31>Given the focus of our research, each independent sample needed to have measured at least one of the four conflict dimensions and team performance to be included in our meta-analysis. Complete information regarding the sample size (i.e., number of teams) as well as the respective correlation between team conflict dimension(s) and team performance must be available from the study. Team performance should be measured either concurrently with or after team conflict. Similar to previous reviews (de Wit et al., 2012), we excluded studies that were not at the team level (e.g., individual experiences of task conflict) or that did not report team-level correlation between conflict and team performance. Likewise, we excluded studies that did not differentiate conflict dimensions but simply collapsed them into one composite. Following Jehn’s (1995) conceptualization, we focused on team conflict as team members’ reported experience of disagreement and incompatibilities, so we did not include studies that inferred team conflict via an external rater (e.g., observer rating). To ensure the independence of the included samples, we followed the screening heuristic advocated by Wood (2008) to detect studies that used the same data set and, upon their identification, included only one study. Using these criteria (see Figure 2), we identified a total of 268 independent samples (i.e., 211 for the task conflict–team performance relationship, 204 for the relationship conflict–team performance relationship, 40 for the process conflict–team performance relationship, and 15 for the status conflict–team performance relationship) from 251 articles.
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We first coded sample size (i.e., number of teams), the correlation between team conflict dimension(s) and team performance, the intraclass correlation coefficient(2) of the team conflict dimensions, and coefficient α of team performance from each independent sample. This information was used to conduct psychometric meta-analyses of the team conflict–team performance relationships.
Next, we coded information pertaining to study-level moderators whenever such information was available in the study. Regarding culture, we coded the country/region where the sample was collected and then used the corresponding country-level scores of national cultural dimensions (i.e., power distance, masculinity–femininity, individualism–collectivism, uncertainty avoidance, and long-term vs. short-term orientation) from Hofstede’s (2001) study. For tightness–looseness, we used the country-level scores validated by Eriksson et al. (2021).
To code team performance, we followed Mathieu et al.’s (2008) suggestion to clearly differentiate various team performance facets. Specifically, we coded indicators of the extent to which the team worked effectively to meet their goals as team effectiveness, whereas performance aspects related to team decision-making were coded as decision quality. Objective financial outcomes were coded as financial performance, and performance components dealing with the creativeness and innovativeness of team output were coded as innovativeness. Moreover, we distinguished between grades and simulation results among student team samples. If raters (e.g., course instructors, judges) provided subjective performance evaluations of student team projects, those ratings were coded as grades. If student team performance was captured through established simulation exercises and activities (i.e., no raters were involved), we coded them as simulation results. Last, in studies that examined multiple team performance facets, we created performance composites and coded them as team effectiveness.
When a study included more than one team conflict dimension, we recorded the intercorrelation(s) among conflict dimensions. We recorded the reported sample mean of team conflict dimensions, as well as the scale range and the starting point of the scale, and converted the sample mean into a 0–1 metric (Cohen et al., 1999). Moreover, we used
As for methodological factors, we recorded whether the teams studied were top management teams (1) or not (0). Similar to de Wit et al.’s (2012) study, if a study relied on student teams in a classroom setting, we coded it as “not applicable” and excluded it when analyzing the organizational level of teams, as the sample was not from an organizational setting. Next, we coded whether the study sampled student teams (yes = 1; no = 0). When doing so, we considered both undergraduate and professional student samples (e.g., Master of Business Administration students) to be student teams, as the nature and environment of these teams are substantially different from intact teams in the workplace (Poitras, 2012). Based on the information available from each study, we recorded the average team tenure (in months) and average team size. Whenever possible, we relied on sample descriptions in the research report to code the average team size. In studies wherein this information was not available, we calculated the average team size using the number of individuals divided by the number of teams.<anchor name="b-fn2"></anchor><sups>2</sups> Following previous meta-analyses (e.g., De Dreu & Weingart, 2003; de Wit et al., 2012), we drew from McGrath’s (1984) group task circumplex and distinguished four types of tasks: creativity tasks (i.e., generating and developing new ideas, solutions, and/or products), decision-making tasks (i.e., reaching consensus for dynamic and complex tasks that do not have a right answer), production and service tasks (i.e., manufacturing products and/or delivering services to meet certain standards), and project tasks (i.e., engaging in problem solving for specific assignments). If teams completed a combination of different tasks, we coded the task type as mixed.
In terms of conflict scales, Jehn’s instruments (Jehn, 1995; Jehn & Mannix, 2001) continue to have a dominant role in research, though some scholars have used alternative measures. Among them, Behfar et al. (2011) provided the most substantial revision to the measurements of task and process conflict. Therefore, for task and process conflict, we coded whether the study used Jehn’s scales, Behfar et al.’s scale, or other scales (i.e., three categories). For relationship conflict, we differentiated between studies that used Jehn’s scales and those that did not. Moreover, we distinguished between the rating source of performance as team members’ self-rating (1) versus nonself-rating (0). Finally, we coded whether the team conflict–team performance relationship was cross-sectional (1) or time separated (0).
All members of the author team took part in coding. We first developed a detailed coding scheme, with which we test coded several articles to calibrate our shared understanding of the coding processes. After the coding training, each author coded a subset of the included studies. Next, a different coder was assigned to the subset of studies to cross-check the previous coding and identify coding inconsistencies and errors (interrater agreement = 98%). The team then met and discussed the reasons for inconsistencies to ensure the accuracy of coding. Prior to performing the data analyses, we conducted a final round of cross-checking (see the Supplemental Material for the complete coding file).
<h31 id="apl-111-2-195-d126e822">Meta-Analytic Procedure</h31><bold>Psychometric Meta-Analysis</bold>
To examine the overall team conflict–team performance relationships and their heterogeneity, we conducted psychometric meta-analyses (Schmidt & Hunter, 2015). For team performance facets that were based on objective measurements (i.e., financial performance, simulation results), we used 1 for their reliability estimates; for performance facets that relied on subjective ratings, we conducted reliability generalization analyses (Rodriguez & Maeda, 2006) and used the mean estimate (.88) for the small number of studies that did not report reliability information. Compared with sample size weighted averages of reliability estimates, reliability generalization estimates are more accurate because they take into account the precision of reliability estimates from different samples (Greco et al., 2018). As many studies did not report ICC(2) for the team conflict dimensions, we performed psychometric meta-analyses based on artifact distributions<anchor name="b-fn3"></anchor><sups>3</sups> using the open-source
When a study included more than one team performance indicator, we first created the composite score correlation (Nunnally, 1978; Schmidt & Hunter, 2015).<anchor name="b-fn4"></anchor><sups>4</sups> This correlation was then used to analyze the team conflict–team performance relationship for that study. Similar to the case of multiple performance indicators, some studies had multiple effect sizes available for the same team conflict–team performance relationship because more than one entity (e.g., team members and team leader) rated team performance and/or study variables were measured at multiple time points. Accordingly, we also created composite score correlations in these cases.
<bold>Metaregression Analyses</bold>
Although psychometric meta-analysis is suitable for testing the overall population-level team conflict–team performance relationships and their heterogeneity, metaregression offers many advantages when it comes to moderator testing (e.g., Gonzalez-Mulé & Aguinis, 2018; Steel & Kammeyer-Mueller, 2002). Thus, to examine the effects of the proposed moderators, we conducted a set of random effect metaregression analyses (Hedges & Olkin, 1985; Hedges & Vevea, 1998) with the open-source
To enhance the robustness of the metaregression findings, we first tested the effect of each moderator individually by regressing the observed effect sizes on the respective moderator. For moderators that contained more than two categories (i.e., task type, team performance facet, and task and process conflict scales), we created a set of dummy variables for the corresponding categories (e.g., four dummy variables for the five task types). When testing their effects, we entered the whole set of dummy variables and interpreted the results of the omnibus test first (i.e.,
Our methodological choices (i.e., combining the psychometric meta-analyses and metaregression analyses, the coding procedure of study-level moderators) are consistent with those of de Wit et al. (2012). This allows us to attribute the substantively different findings (if any) to the enlarged body of evidence instead of to divergent research practices.
Results
> <h31 id="apl-111-2-195-d126e922">Population Average and Heterogeneity of Team Conflict–Team Performance Relationships</h31>
We report the psychometric meta-analysis results in Table 1. Across the four team conflict dimensions, their overall relationships with team performance were all negative (task conflict, ρ = −.09, 95% CI [−.14, −.04]; relationship conflict, ρ = −.28, 95% CI [−.32, −.24]; process conflict, ρ = −.33, 95% CI [−.41, −.24]; status conflict, ρ = −.38, 95% CI [−.50, −.26]). Moreover, the task conflict–team performance relationship had substantial cross-situation heterogeneity (
>
><anchor name="tbl1"></anchor>
<bold>Moderators of the Task Conflict–Team Performance Relationship</bold>
Metaregression results for the moderators of the task conflict–team performance relationship are reported in Table 2. Individualism weakened the negative task conflict–team performance relationship (
>
><anchor name="tbl2"></anchor>
Regarding team features, omnibus tests indicated that team performance facet,
>
><anchor name="fig3"></anchor>
Last, we examined all of the significant moderators from the individual tests simultaneously (
<bold>Moderators of the Relationship Conflict–Team Performance Relationship</bold>
We report the metaregression results for the moderators in the relationship conflict–team performance relationship in Table 3. Several national cultural dimensions emerged as pertinent moderators. In cultures that were more masculine (
>
><anchor name="tbl3"></anchor>
Moreover, team performance facet moderated the relationship conflict–team performance relationship,
When significant moderators from the individual tests were examined simultaneously (
<bold>Moderators of the Process Conflict–Team Performance Relationship</bold>
The metaregression results for the moderators of the process conflict–team performance relationship are reported in Table 4. National cultural dimensions did not moderate the negative process conflict–team performance relationship (power distance:
>
><anchor name="tbl4"></anchor>
The status conflict–team performance relationship demonstrated the smallest amount of heterogeneity (see Table 1). Moreover, the number of independent samples for this relationship was too small to yield robust metaregression results (Gonzalez-Mulé & Aguinis, 2018), so we did not examine moderators of the status conflict–team performance relationship.
In summary (see Table 5), regarding culture, we found support for individualism (Hypothesis 3), partial support for masculinity (Hypothesis 2) and uncertainty avoidance (Hypothesis 4), and no support for power distance (Hypothesis 1) or tightness (Hypothesis 6). The moderating impact of long-term orientation on the relationship conflict–team performance relationship was contrary to Hypothesis 5. As for our research question, the negative team conflict–team performance relationship was stronger for team effectiveness than for other performance facets for both task conflict and relationship conflict. The moderating impact of the correlation among conflict dimensions (Hypothesis 7) and the sample mean of conflict (Hypothesis 8) received support for task conflict. Conflict symmetry weakened the relationship conflict–team performance relationship—a finding that supported Hypothesis 9.
>
><anchor name="tbl5"></anchor>
<bold>Methodological Factors</bold>
Among teams that were larger (
<bold>Supplemental Analyses</bold>
Although not the main focus of this research, we also explored (a) the intercorrelations among conflict dimensions, (b) the moderators of the task conflict–relationship conflict relationship, and (c) the temporal dynamics of team conflict. Due to space constraints, we summarize our notable findings here and report the complete results in the Supplemental Material. First, we conducted psychometric meta-analyses and found that the intercorrelations among team conflict dimensions were strong (ρ ranged from .49 to .79). Moreover, some of these intercorrelations demonstrated substantial heterogeneity, for example, the 80% CV of the correlation between task conflict and relationship conflict was wide [0.06, 1.00]. Second, in light of the robust moderating role of the sample correlation between task conflict and relationship conflict, we explored which factors might explain its variation across samples. Of note, studies that used Behfar et al.’s (2011) task conflict scale reported weaker correlations between task conflict and relationship conflict compared with those that used Jehn’s scales (
Discussion
> <h31 id="apl-111-2-195-d126e1471">Theoretical Implications and Extension of de Wit et al. (2012)</h31>
<bold>Population Average of Team Conflict–Team Performance Relationships</bold>
The performance ramifications of team conflict, especially task conflict, have been a key driver of the continuous growth of this research domain. Improved understanding of the team conflict–team performance relationships has crucial implications for organizational scholars and practitioners alike. Using a much larger set of independent samples (
In particular, the current meta-analysis reveals that, on average, the population relationship between task conflict and team performance was negative, not near zero as in de Wit et al.’s (2012) meta-analysis. As such, it is warranted to update scholarly understanding of the overall impact of task conflict on team performance. Moreover, we provided the first empirical synthesis of the effect of status conflict on team performance, which substantiates its detrimental impact on team functioning (Bendersky & Hays, 2012). Consistent with de Wit et al., we found that relationship conflict is a destructive aspect of team conflict, evidenced by its moderate, negative relationship with team performance. Similarly, the negative process conflict–team performance relationship identified by de Wit et al. received support in the current meta-analysis. In light of the convergent evidence, perhaps it is time to shift away from the two-sided view of process conflict (Jehn & Bendersky, 2003) and instead recognize it as a dysfunctional aspect of team conflict in general (Behfar et al., 2011).
<bold>Heterogeneity of Team Conflict–Team Performance Relationships</bold>
Importantly, our characterization of the overall performance implications of team conflict should be qualified by their effect size heterogeneity, especially for task conflict and relationship conflict, as their 80% CVs were wide and crossed zero. These results, which are consistent with de Wit et al.’s (2012) findings, highlight that both task conflict and relationship conflict have heterogeneous effects on team performance across situations. Although there is broad consensus regarding the mixed effects of task conflict, the current research reveals that the performance implications of relationship conflict may also depend on the context. Furthermore, the current meta-analysis extends de Wit et al.’s study by showing that both process conflict and status conflict have less heterogeneous relationships with team performance, as evidenced by the narrower 80% CVs that did not include zero.
When viewed together, our results regarding the population average and heterogeneity of the team conflict–team performance relationships deepen scholarly understanding of the nature of team conflict. Specifically, considering their negative and relatively homogenous effects on team performance, both process conflict and status conflict may be understood as destructive aspects of team conflict. Meanwhile, task conflict and relationship conflict appear to be more paradoxical aspects of intrateam conflict, as their detrimental influence on team performance varies across contexts.
<bold>Contextual Moderators of the Team Conflict–Team Performance Relationships</bold>
National Culture
>
Our expanded investigation of contextual moderators revealed the systematic influence of cultural dimensions (Gelfand et al., 2008; Hofstede, 2001; House et al., 2004). Specifically, in cultures that more strongly seek to avoid uncertainty, team members may react very poorly to task conflict, augmenting its negative impact on team performance. In highly masculine and individualistic cultures, where conflict may be viewed as socially acceptable, such tolerance (and even preference) for disagreements may buffer the negative team conflict–team performance relationships. Contrary to our hypothesis, we found that long-term orientation accentuates the negative impact of relationship conflict on team performance. This may indicate that interpersonal frictions are especially troublesome in cultures that emphasize sustained efforts to achieve long-term goals. Of note, individualism emerged as the most potent moderator, as it consistently buffered the negative team conflict–team performance relationships for both task conflict and relationship conflict. This suggests that interpersonal disagreements and clashes may be fundamentally at odds with the strong emphasis on unity and interpersonal harmony in collectivistic cultures (Hofstede, 2001).
Although the distal influence of national culture received very little support in de Wit et al.’s (2012) study, our expanded analyses highlight that culture merits attention in future conflict research. Collectively, these findings suggest that researchers may need to pay close attention to how conflict is viewed and interpreted in the respective culture in which they are conducting their studies (e.g., Gelfand et al., 2001). Moreover, cross-cultural research aimed at illuminating the impact of cultural differences is warranted (e.g., Gunkel et al., 2016). Relatedly, the influence of national culture suggests that its more proximal counterpart—organizational conflict culture (Gelfand et al., 2008, 2012)—might potentially play an even bigger role. Therefore, we encourage scholars to devote more attention to this important topic as well.
Team Features
>
We utilized a more systematic way of operationalizing team performance facets compared with de Wit et al. (2012). Notably, we found largely convergent moderating effects of team performance facet across task conflict and relationship conflict, thereby bringing clarity to the conflict literature. Specifically, the negative team conflict–team performance relationship was stronger for team effectiveness compared with other team performance facets. Moreover, the team conflict–team effectiveness relationship itself was negative. Thus, as far as team effectiveness is concerned, both task conflict and relationship conflict may be detrimental. Given that team effectiveness is a broadly relevant performance criterion for work teams (Mathieu et al., 2008), this finding underscores that the potential benefits associated with team conflict may be extremely circumscribed (De Dreu, 2008). In turn, this requires future theorizing about the performance benefits of team conflict to be very precise—that is, to clearly specify the performance facets of interest. Failure to do so runs the risk of contradicting the large body of empirical evidence accumulated in the literature.
Similar to what de Wit et al. (2012) found, our analysis showed that the correlation between task conflict and relationship conflict strengthened the negative task conflict–team performance relationship. Considering the robust effect of this factor, conflict scholars may want to further investigate ways to mitigate the strong correlation between task conflict and relationship conflict (de Wit et al., 2012). Notably, the substantial heterogeneity of their intercorrelation identified in our supplemental analyses underscored the urgency of addressing this research question. We think there are at least two promising avenues to do so. First, as our supplemental analyses indicated, focusing on concrete conflict expressions such as debates and discussions (as in Behfar et al.’s, 2011, task conflict scale), compared with the general notion of conflicts and disagreements (as in Jehn’s, 1995, task conflict scale), may help reduce the magnitude of the correlation between task conflict and relationship conflict. It is also worth noting that studies that used Behfar et al.’s (2011) scale reported more positive task conflict–team performance relationships than those that used Jehn’s (1995) instrument. On these grounds, we recommend using Behfar et al.’s task conflict scale in future research. Moreover, this finding points to the need to further refine the conceptualization and measurement of task conflict by explicitly considering the role of conflict communication and expression (Barki & Hartwick, 2004; Bendersky et al., 2014; Brykman & O’Neill, 2023; Todorova et al., 2014; Tsai & Bendersky, 2016; Weingart et al., 2015). Second, the empirical overlap between the two conflict dimensions calls for a continuous stream of research on ways to de-escalate conflict (e.g., Greer et al., 2008) and minimize misattribution of conflict (e.g., Simons & Peterson, 2000).
The task conflict–process conflict correlation played a moderating role similar to that of the overlap between task conflict and relationship conflict. This new finding from the current meta-analysis underscores the insidious impact of co-occurring conflict in accentuating the detrimental impact of task conflict on team performance. Furthermore, in response to a reviewer’s inquiry, we explored the moderating role of average team conflict (i.e., the average correlation among task conflict, relationship conflict, and process conflict) and observed a similar pattern (for detailed results, see the Supplemental Material). Therefore, it may be fruitful to further investigate the situation of “all-out” conflict, in which teams are fraught with all types of conflict. This line of inquiry can extend conflict research by revealing the best and/or worst configurations of team conflict (e.g., O’Neill, McLarnon, Hoffart, Woodley, & Allen, 2018).
In the current meta-analysis, we heeded the research call by de Wit et al. (2012) to consider the issue of conflict (a)symmetry. The empirical support for conflict symmetry’s ability to buffer the negative relationship conflict–team performance relationship is noteworthy, as it indicates that the mixed effects of relationship conflict may partly depend on whether team members see eye to eye regarding their interpersonal chasm. As scholars have started to address the limitations associated with the predominant consensus-based team conflict conceptualization (Jehn et al., 2010; Korsgaard et al., 2014; Shah et al., 2021), our analyses suggest that conflict (a)symmetry may provide a useful theoretical lens.
Last, our exploration of the effect of the sample mean of task conflict showed that it weakened both the negative task conflict–team performance relationship and the positive task conflict–relationship conflict relationship. These findings suggest that in a context characterized by an overall higher level of task conflict, this type of conflict is more likely to be beneficial than harmful.<anchor name="b-fn10"></anchor><sups>10</sups> In other words, an overall higher level of task conflict in a given context may overcome team members’ inertia and activate its beneficial potential (e.g., J. L. Farh et al., 2010).
Taken together, these findings regarding team features echo the notion that “conflict within groups is not an ‘all or nothing’ event” (Korsgaard et al., 2014, p. 51). That is, researchers need to carefully consider the various team features, including the nature of team performance, the co-occurrence of team conflict, the symmetry of team members’ conflict experiences, and the overall level of conflict in the respective context to enhance the precision of their theorizing.
<bold>Methodological Moderators of the Team Conflict–Team Performance Relationships</bold>
Our meta-analysis provides the most comprehensive examination of methodological moderators of the team conflict–team performance relationships undertaken to date. These results are important from the standpoint of research design, as they illuminate how methodological choices may affect the relationship between team conflict and team performance. Notably, the relationship between task conflict and team performance depended on the type of team task. Specifically, production and service tasks that rely on routine coordination among team members may be especially vulnerable to the disruptive effect of task disagreements. In samples with a larger (vs. smaller) average team size, the negative task conflict–team performance relationship was stronger. Together, the moderating effects of these factors suggest that conflict scholars may need to pay close attention to the characteristics of teams that they recruit for their research. Furthermore, the negative effect sizes were stronger when team performance was self-rated by team members (vs. nonself-rated) and when the study was cross-sectional (vs. time separated). In light of these findings, we encourage conflict scholars to continue to improve their research rigor using nonself-rated team performance and adopting either a time-separated or longitudinal design. Parsing out the potential influence of these methodological factors can help researchers correctly identify the pertinent theoretical moderators for the team conflict–team performance relationships.
<h31 id="apl-111-2-195-d126e1661">Practical Implications</h31>In the past, the idea that task conflict might be beneficial for team performance has heavily shaped practitioner-oriented writings and conflict-related business education (e.g., Gallo, 2018; George & Jones, 2005; McShane & Von Glinow, 2000; Robbins, 2000; Rollinson, 2002). Our meta-analytic findings provide a good opportunity to update the evidence-based guidance for managerial practices regarding team conflict, especially task conflict. Considering the overall negative relationship between task conflict and team performance, we would caution against adopting a stance of unreserved optimism—that is, the view that task conflict is always beneficial for team functioning (De Dreu, 2008). Moreover, among the various team performance facets, team effectiveness demonstrated the most negative relationship with task conflict. Meanwhile, task conflict did not have any significant, positive relationships with any other team performance facets. Thus, it appears that task conflict is more damaging for team effectiveness than for other team performance aspects. However, this should not necessarily be taken as evidence that task conflict is more beneficial for other team performance aspects than for team effectiveness. Furthermore, given that all four team conflict dimensions were negatively related to team performance and that the intercorrelations among the conflict dimensions were strong, we worry that truly “good types” of team conflict may be hard to come by. As De Dreu (2008) suggested, a more realistic goal for organizations may be to seek to minimize the harmful impacts of team conflict rather than to aim to maximize its potential benefits.
Toward that end, we encourage organizations operating in cultures that are highly uncertainty avoiding, long-term oriented, collectivistic, and feminine to devote resources to helping their work teams handle conflict, as the effects of team conflict may be especially negative in these cultures. Moreover, teams that are larger in size and those that are working to meet production and service goals may be especially prone to the disruptive effects of conflict. In these kinds of teams, managers may want to prioritize establishing effective intrateam processes over encouraging divergent opinions and ideas among team members. Furthermore, conflict may spiral out of control when team members escalate task disagreements into interpersonal clashes and/or misinterpret differences of opinion as interpersonal incompatibilities. This situation calls for timely managerial intervention so that team members can work through their task disagreements without taking things personally. Relatedly, teams that experience more than one type of conflict may require special attention, as they are especially vulnerable to its detrimental impacts. Otherwise, with the onset of entrenched, “all-out” conflict, effective teamwork may come to a standstill as the team descends into total chaos. Last, when team members do not see eye to eye regarding their conflict, this discordance can compound the disruptive effects of relationship conflict. In this situation, managers are encouraged to help team members by “bringing conflicts into the open” (Jehn et al., 2010, p. 610) so that they can establish convergent conflict experiences and minimize any potential misunderstandings.
<h31 id="apl-111-2-195-d126e1692">Research Limitations</h31>The current meta-analysis has several limitations worth noting. First, given the nature of our synthesis, the level of analysis for all moderators was the between-study level. Although it can reveal meaningful contextual factors that might be otherwise masked at the within-study level (Johns, 2018; Yuan, 2021), capturing certain moderating influences at the between-study level has some inherent limitations. For example, we relied on the sample mean to test the effect of team tenure and team size, which may have limited utility, especially if the within-sample distribution is nonnormal (e.g., Yuan et al., 2023). Relatedly, some moderators could be examined only when the sample was homogeneous in that respect. For example, if a sample consists of teams from multiple countries, it would be difficult to include it in analyses of national cultural dimensions. Nevertheless, given the expanded scope of our analyses, the current meta-analysis should be considered an important step toward illuminating the broader impact of context on the team conflict–team performance relationships.
Second, the statistical power for some of the metaregression analyses may be somewhat low. Specifically, for the moderator analyses involving process conflict, the number of independent studies ranged from 20 to 40. Given that some of these analyses may have been somewhat underpowered, their results should be viewed with caution (Gonzalez-Mulé & Aguinis, 2018). Similarly, as status conflict is the dimension most recently introduced into the literature, the number of independent studies of the status conflict–team performance relationship was very small. Consequently, the psychometric meta-analytic results regarding status conflict may not be very stable (e.g., Cafri et al., 2010). In a similar vein, the results (e.g., CVs; Brannick et al., 2019) in our subgroup psychometric meta-analyses that were based on a small number of independent samples may be prone to estimation error. As the conflict literature continues to grow, we encourage researchers to provide meta-analytic updates in the future, especially for process conflict and status conflict. That said, for the analyses involving task conflict and relationship conflict, we do not think statistical power was a great concern, as the number of independent studies far exceeded the benchmarks commonly reported in past metaregression research (Gonzalez-Mulé & Aguinis, 2018). Furthermore, our systematic analytic approach, whereby we conducted both individual and simultaneous tests, screened for outliers, and tested for publication bias, further enhanced the robustness of our findings.
Third, as meta-analytic findings are based on a quantitative synthesis of primary studies, the quality of the included studies may directly affect the validity of the meta-analytic conclusions (Hunt, 1997). Toward that end, although we considered some prominent study design features, other extraneous factors may have contributed to the mixed effects of team conflict on team performance (Schmidt & Hunter, 2015). We call for more scientific rigor and transparency in future conflict research, which can improve the robustness of the cumulative body of empirical findings and facilitate the identification of additional moderators.
Last, we emphasize that our findings related to team conflict should not be erroneously overgeneralized to other levels (e.g., the dyad level). Although the vast majority of conflict research has been conducted at the team level, conflict at other levels also warrants attention in future research. We hope that by offering a comprehensive account of the team conflict–team performance relationships, the current meta-analysis joins conflict research at other levels (Humphrey et al., 2017; Park et al., 2020; Shah et al., 2021; Sinha et al., 2016) to support a truly multilevel view of conflict (Korsgaard et al., 2008). Relatedly, although we operationalized team conflict as team members’ reported conflict experiences (Jehn, 1995), there may be value in investigating conflict in other ways, such as via behavioral coding (e.g., Park et al., 2024) and/or experimental manipulations (Minson et al., 2023).
Footnotes
<anchor name="fn1"></anchor><sups> 1 </sups> Unlike de Wit et al. (2012), who searched multiple databases, O’Neill et al. (2013) searched only in APA PsycInfo. We mainly discuss the findings of de Wit et al., as their meta-analysis is the most comprehensive to date.
<anchor name="fn2"></anchor>
<sups>
2
</sups> As pointed out by a reviewer, the calculated team size may be a conservative estimate due to imperfect response rates from team members. In turn, this may potentially affect the robustness of our findings (e.g., Hirschfeld et al., 2013). Therefore, we reran the analyses while excluding those studies that did not report the average team size (
<sups>
3
</sups> ICC(2) distribution statistics:
<sups>
4
</sups> As an example, in a study that measured task conflict and two team performance facets, the correlation between task conflict and the team performance composite was calculated as 
Relatedly, the reliability of the team performance (
<sups> 5 </sups> Consistent with our psychometric meta-analyses, we used ICC(2) and coefficient α when performing the psychometric corrections of team conflict and team performance, respectively. For studies that did not report ICC(2), we imputed the sample size weighted mean of ICC(2). To triangulate the robustness of our findings (e.g., Yuan et al., 2020), we ran metaregression analyses using the observed effect sizes and the effect sizes corrected for measurement error of team conflict based on coefficient α, which led to largely convergent findings (see the Supplemental Material).
<anchor name="fn6"></anchor><sups> 6 </sups> For the countries included in the current meta-analysis, these five cultural dimensions ranged from 8 to 104. In light of how they were scored, we report the results for cultural dimensions to three decimal places.
<anchor name="fn7"></anchor><sups> 7 </sups> For the sake of completeness, complete paired comparison results are reported in the Supplemental Material.
<anchor name="fn8"></anchor><sups> 8 </sups> As including the correlation between task conflict and process conflict would greatly reduce the sample size and hence the statistical power, we did not include it in the simultaneous test. Given that its theoretical implication is similar to the correlation between task conflict and relationship conflict (which was included in the simultaneous test), we believe this decision should be considered an acceptable trade-off to ensure adequate statistical power.
<anchor name="fn9"></anchor><sups> 9 </sups> Complete paired comparison results are reported in the Supplemental Material.
<anchor name="fn10"></anchor><sups> 10 </sups> Given that a lower level of sample mean may also indicate a restricted range of team conflict, we further explored its effect by controlling for the sample standard deviation. Additionally, we tested the curvilinear effect of sample mean. The complete results are reported in the Supplemental Material.
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