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Abstract

Existing research on the relationship between team performance goals and team performance has yielded mixed findings. We argue that these inconsistencies arise in part from the use of two different operationalizations of team performance goals: a collective-based operationalization (averaging team members’ perceptions that their team aims to outperform other teams) and a composition-based one (aggregating team members’ personal aims to outperform other individuals). In this research, we adopted a multiconceptualization approach to examine whether collective and composition performance goals interact in predicting team performance. In Study 1 (98 observations from 73 players across 31 teams), video game teams with higher collective performance goals performed better in a major European tournament when their composition performance goals were lower. In Study 2 (535 observations from 201 players across 22 teams), volleyball teams with higher collective performance goals performed better in the Swiss volleyball championship when their composition in-group performance goals were lower (i.e., fewer members striving to outperform teammates). Because of concerns about statistical power and causality in Studies 1 and 2, we conducted a larger, preregistered experiment. In Study 3 (751 participants from 292 teams), experimentally assembled teams assigned to a collective performance goal condition exhibited less behavioral sabotage during a collaborative task compared to teams instructed to endorse composition performance goals or both goal types simultaneously. This manipulation did not directly affect team performance but indirectly improved it via lower sabotage. Together, our findings suggest that high collective performance goals and low composition performance goals may provide a recipe for fostering team cooperation and performance.

Keywords

collective performance goals composition performance goals sabotage team achievement goals team performance

Social scientists have long tried to identify factors underlying successful sports teams, work units, or academic groups (Deutsch, 1949; Mayo, 1933/2004; Steiner, 1986). Recently, psychologists have highlighted team performance goals, defined as striving to be the best team, as a key predictor of team performance (Chadwick & Raver, 2015). However, empirical evidence is inconsistent: The association between team performance goals and team performance has sometimes been found to be positive (Van Mierlo & Van Hooft, 2020) but other times negative (Davis et al., 2021) or even null (Valcea et al., 2019). We thoroughly reviewed this literature and argue that these contradictory findings may stem from two distinct approaches to operationalizing team performance goals: a collective performance goal-based approach (pooling team members’ perceptions of group goals; Bunderson & Sutcliffe, 2003) and a composition performance goal-based approach (aggregating team members’ personal goals; LePine, 2005; Porter, 2005). Answering calls for integrative research (Porter, 2008; Porter et al., 2019), we adopted a multiconceptualization approach (considering both collective and composition performance goals) to clarify the nature of the association between team performance goals and team performance.

Team Achievement Goals

Individuals in competence-relevant contexts such as athletes, employees, or students can endorse two main types of achievement goals: mastery goals and performance goals (Duda & Nicholls, 1992; Dweck, 1986; VandeWalle, 1997). Mastery goals emphasize developing competence and/or achieving task mastery, while performance goals emphasize demonstrating competence and/or achieving relative performance. These goals can be approach-based (striving to approach competence) or avoidance-based (striving to avoid incompetence; Elliot, 1999). Here, we focus on mastery-approach and performance-approach goals (hereafter referred to as “mastery goals” and “performance goals,” respectively), because avoidance-based goals are unequivocally detrimental for team cohesion and performance (Acton et al., 2020; Lin et al., 2021; Van Mierlo & Van Hooft, 2020; but see Davis et al., 2021).

Importantly, individuals in competence-relevant contexts often interact (Butera, Dompnier, & Darnon, 2024), and their goals can intersect (Weissman & Elliot, 2023). Accordingly, teams in competence-relevant contexts such as sports teams, work units, or academic groups can also pursue team mastery goals and team performance goals (DeShon et al., 2004; LePine, 2005; Porter, 2005). Team mastery goals emphasize developing competence and/or achieving task mastery at the team level, while team performance goals emphasize demonstrating competence and/or achieving relative performance at the team level. Here, we focus on team performance goals, which are central to debates about achievement goals and performance (Crouzevialle & Butera, 2017; Senko et al., 2011; Urdan & Kaplan, 2020).

Two Conceptualizations of Team Performance Goals

Scholars have taken two different theoretical approaches to conceptualizing team performance goals, treating them as either a collective goal-based construct or a composition goal-based one (for a summary, see Figure 1).

Figure 1.

Definitions, sample items, and operationalizations of team-level constructs of collective and composition performance goals, along with definitions and sample items for the team-member-level construct of personal performance goals.

Collective Performance Goals

One approach conceptualizes team performance goals as emerging through a top-down process, whereby higher level phenomena, such as organizational climate or patterns of leader behavior (Dragoni, 2005; Dragoni & Kuenzi, 2012), shape team goals (Bunderson & Sutcliffe, 2003; DeShon et al., 2004). In this view, team members serve as raters evaluating an object, with their shared perceptions enabling researchers to assess the characteristics of the higher level unit (Gong et al., 2013).

This conceptualization uses a team-referent operationalization (a referent-shift consensus model; Chan, 1998), aggregating team members’ perceptions of team goals at the team level (Mehta et al., 2009). For example, Van Mierlo and Van Hooft (2020) asked members of Dutch field hockey teams to report their perceptions of their team performance goals using items such as “It is important for my field hockey team that we perform better this season compared to other teams.” Team performance goals were computed as the within-team mean of these individual perceptions, and the resulting variable was generically labeled “team performance [orientation]” (p. 592). Similar labeling appears in Lin et al. (2021), Shin et al. (2019), and Toader and Kessler (2018). For our part, we refer to this variable as collective performance goals.

Composition Performance Goals

Another approach conceptualizes team performance goals as emerging through a bottom-up process, whereby team member goals combine to form the overall team goals (LePine, 2005; Porter, 2005). In this view, team members function as genes within individuals, combining to make up the characteristics of the higher level unit (Chi & Huang, 2014; Porter, 2005).

This conceptualization uses an individual-referent operationalization (an additive model; Chan, 1998), aggregating team members’ personal goals at the team level (Dierdorff & Ellington, 2012). For example, C.-Y. Huang et al. (2019) asked members of Taiwanese work teams to report their personal performance goals with items such as “I feel successful on my job when I can clearly demonstrate that I am the best qualified person.” Team performance goals were computed as the within-team mean of the individual performance goals, and the resulting variable was generically labeled “teams’ performance goal orientation” (p. 831). Similar labeling appears in Davis et al. (2021), Paunova and Lee (2016), and Unger-Aviram et al. (2018). For our part, we refer to this variable as composition performance goals.

Main Effects of Collective Performance Goals and Composition Performance Goals on Team Performance

In this section, we present a thorough review of studies on team performance goals and team performance published between 2004 and 2021. We selected 2004 as the starting point because it marks the publication of the first paper on team performance goals (DeShon et al., 2004). We show that collective and composition performance goals cannot be treated as interchangeable constructs, as they act as different predictors of team performance.

Collective Performance Goals Often Positively Predict Team Performance

Most studies documented positive effects of collective performance goals on team performance. DeShon et al. (2004; 79 teams) reported that collective performance goals positively predicted team efficacy, which was linked to team-focused efforts and performance (see also Mehta & Mehta, 2018; 90 teams). You (2021; 175 teams) reported that collective performance goals positively predicted academic team creativity and achievement (see also Mehta et al., 2009 [68 teams]; Shin et al., 2017 [91 teams]). Van Mierlo and Van Hooft (2020; 29 teams) reported a similar association in sports teams. However, these positive effects were not always replicated. A longitudinal study documented a positive effect at some time points but not others (Maltarich et al., 2016; 64 teams), while five studies reported null effects (Porter et al., 2010 [137 teams], 2016 [48 teams]; Toader & Kessler, 2018 [33 teams]; Van Hooft & Van Mierlo, 2018 [209 teams]; Yu, 2005 [73 teams]).

Composition Performance Goals Often Negatively Predict Team Performance

Most studies documented negative effects of composition performance goals on team performance. LePine (2005; 64 teams) reported that composition performance goals hindered team adaptation to unforeseen change during a difficult decision-making task, which was linked to team performance (see also Dierdorff & Ellington, 2012; 64 teams). Davis et al. (2021; 29 teams) reported that composition performance goals negatively predicted team efficacy and performance in a business simulation task (see also C.-Y. Huang et al., 2019; 64 teams). Unger-Aviram and Erez (2016; 40 teams) reported that teams assigned to a composition performance (vs. mastery) goal condition performed worse at a bridge-planning task. However, these negative effects were not always replicated. One study found a positive bivariate correlation but no effect in more advanced analyses (Valcea et al., 2019; 82 teams), while four other studies reported null effects (J.-C. Huang, 2012 [110 teams]; Porter, 2005 [80 teams]; Spara, 2007 [70 teams]; Unger-Aviram et al., 2018 [35 teams]).

The Interplay Between Collective Performance Goals and Composition Performance Goals in Predicting Team Performance

Porter (2008) suggested that a multiconceptualization approach taking both collective and composition performance goals into account had “the potential to promote our understanding of achievement . . . in groups and teams” (p. 164). While factors such as sample size and achievement domain may have contributed to the inconsistencies highlighted earlier, we propose that a multiconceptualization approach could clarify why collective performance goals produce either positive or null effects on team performance, whereas composition performance goals produce either negative or null effects. Specifically, we argue that collective performance goals only produce positive effects when composition performance goals are low.

Collective Performance Goals With Low Composition Performance Goals Should Be Predictive of Team Performance

Because collective performance goals use a team-referent focus, they shift team members’ attention toward winning intergroup competitions (Shin et al., 2019; for a classic work, see Sherif, 1966/2015). This focus enhances commitment to team objectives over individual ones (DeShon et al., 2004), fostering efficient communication, coordination, and cooperation among team members (Gong et al., 2013; Mehta & Mehta, 2018; Shin et al., 2017), thus facilitating team performance (Van Mierlo & Van Hooft, 2020).

In team settings, individuals must choose between prioritizing team or individual goals. Teams with high collective performance goals but low composition performance goals should experience minimal conflict between group and individual goals, enabling members to devote all their efforts to outperforming other teams without being encumbered by the goal of outperforming other individuals. As each team member’s success serves the entire team, everyone should be motivated to collaborate toward a common aim, allowing the team to reap the full benefits of their collective performance goals.

Collective Performance Goals With High Composition Performance Goals Should Not Be Predictive of Team Performance

Because composition performance goals use an individual-referent focus, they shift team members’ attention toward winning interpersonal competitions (Butler, 1992; for relevant reviews, see Butera, Świątkowski, & Dompnier, 2024; Conroy et al., 2009; Darnon et al., 2012). Personal performance goals are well known to have detrimental interpersonal consequences within dyads/groups, predicting tactical deception, low cooperativeness, and within-group sabotage (Levy et al., 2004; Poortvliet et al., 2007; Sommet et al., 2015, 2019), which undermines team performance (Janardhanan et al., 2020).

As previously mentioned, individuals in teams must choose between prioritizing team or individual goals. Teams with high collective performance goals and high composition performance goals should experience conflict between group and individual goals, leading members to shift away from outperforming other teams toward outperforming other individuals (including their teammates). As the success of other team members does not necessarily align with the individual goal of being the best, some team members might hesitate to help others (or even sabotage them), and the team may not be able to reap the full benefits of their collective performance goals.

Study Overview and Hypotheses

Three studies aimed to test the following general hypothesis: The association between collective performance goals and team performance is stronger when composition performance goals are lower.

In Study 1, we surveyed international video game teams involved in a major European tournament. We hypothesized that teams with higher collective performance goals would perform better in the tournament as their composition performance goals decreased.

In Study 2, we surveyed national volleyball teams involved in the Swiss volleyball championship. Performance goals are well known to be intrinsically tied to social comparison processes (Ames, 1992; Darnon et al., 2010; Elliot et al., 2021). However, the specific targets of comparison used by individuals focused on performance goals have rarely been studied (for a notable exception, see Kim et al., 2012). This issue seems particularly important in team settings, as striving to outperform teammates should be more detrimental to team collaboration than striving to outperform opponents (Rogat & Linnenbrink-Garcia, 2019). In Study 2, we differentiated between composition in-group performance goals (teams with members striving to outperform teammates) and composition out-group performance goals (teams with members striving to outperform opponents). We hypothesized that teams with higher collective performance goals would perform better in the championship as their composition in-group performance goals decreased. We did not formulate a hypothesis about composition out-group performance goals.

Studies 1 and 2 had high ecological validity but were constrained by small higher level sample sizes and low internal validity. In Study 3, which was preregistered, we experimentally assembled a large number of teams to collaborate on an anagram task. We manipulated collective and composition goals, and aimed to conceptually replicate the findings observed in the two observational studies. Additionally, we hypothesized that this effect would be explained by differences in behavioral sabotage.

Study 1: Video Game Teams

In Study 1, we hypothesized that teams oriented toward collective performance goals would perform better as their composition performance goals decreased.

Method

Open Science statement

Across all studies, all data exclusions, variables analyzed, and manipulations are reported. Questionnaires, de-identified datasets, and scripts to reproduce findings are available on the Open Science Framework repository (OSF; https://osf.io/7uchv/).

Participants and teams

Our sample included 73 eSports players (66 men, seven unspecified; M_age = 24.88 ± 3.91) from 31 of the 65 international teams involved in a major European online video game (“Quake Live”) tournament held in 2011 (team participation rate: 47.7%).¹ A simulation-based sensitivity analysis showed that the sample size provided 80% power to detect the hypothesized interaction if the true effect in the population were ln(OR) = −0.56 or stronger. This represented a fully attenuated interaction with a large simple effect of ln(OR) = 1.54 (d ≈ 0.85) and a null simple effect (see details in the Supplemental Material).

Procedure

Data collection

The study was conducted in collaboration with ClanBase, the organization coordinating the European tournament (now closed), and ESReality, the largest “Quake Live” newsfeed. Two weeks before the tournament, we posted an online survey on ClanBase and ESReality to reach the competing teams.

The tournament

The semiannual tournament aimed to determine the European champion teams of “Quake Live.” In this video game, players navigate maps containing various items (e.g., weapons). Eliminating an opponent is recorded as a “frag,” while being eliminated results in a “loss”—the loss of all equipment and a “respawn” (reappearance) at another location on the map. In the tournament, four-player teams competed in best-of-three matches (with any additional team members sitting out) across two categories: (a) “death-match mode,” where teams have to “frag” more than they are “fragged” (18 teams); and (b) “capture-the-flag mode,” where teams have to capture the enemy’s flag while defending their own (13 teams). Teams first played round-robin matches within pools, followed by single-elimination brackets for the top-performing teams. After the tournament, we collected screenshots of match results, and two student assistants built the dataset.

Variables

All measures used a 7-point response scale (1 = not at all, 7 = completely). For the correlation matrix, see Table S1.

Collective performance goals

We assessed team members’ perceived team performance goals using an adaptation of the three performance-approach goal items from Elliot and Murayama’s (2008) Achievement Goal Questionnaire–Revised (e.g., “With my clan, we want to perform better than the other clans”; α = .87, M = 5.54, SD = 1.14). Responses deviated from the group mean by an average AD_M(J) = 0.56 points; AD_M(J)⩽ 1.2 indicates acceptable Responses deviated from the group within-team agreement (Burke & Dunlap, 2002). Moreover, and the r_wg index was .69; r_wg ∈ [0.51, 0.70] indicates moderate agreement, whereas r_wg ∈ [0.71, 0.90] indicates strong agreement (Woehr et al., 2015). Collective performance goals were operationalized as the within-team mean of perceived team performance goals.

Composition performance goals

We assessed team members’ personal performance goals using the same measure (e.g., “When I play in a tournament, I want to perform better than the other players”; α = .63, M = 5.28, SD = 1.12). Since personal performance goals are assumed to be homogeneous within teams (as in any additive composition model), we did not calculate within-team agreement indices (Davis et al., 2021). Composition performance goals were operationalized as the within-team mean of personal performance goals.

Team performance

We assessed team performance using the official result of each match. Each team played between one and five matches (99 matches in total; 58 defeats, 41 victories; ICC = .24, 95% CI [0.05, 0.64]).

Prior team performance

We assessed prior team performance using team members’ online gaming profiles, aggregating the proportion of past matches won at the team level (M = 0.51, SD = 0.11). One missing value was replaced with the mean.

Results

Preliminary exploratory and confirmatory factor analysis

We performed an exploratory factor analysis (EFA) and a confirmatory factor analysis (CFA) to determine whether perceived team performance goals and personal performance goals represented separate constructs. Regarding the EFA, we identified two factors: (a) the first factor, comprising the three perceived team performance goal items, explained 56.8% of the variance; (b) the second factor, comprising two of the three personal performance goal items, explained 17.6% of the variance (the last item did not load on either factor). The correlation between factors was .49 (full results in Table S2). Regarding the CFA, we built two models: (a) a one-factor model with all six items belonging to a single latent factor (CFI = .90, RMSEA = .17, SRMR = .07); (b) a two-factor model differentiating the perceived team performance goal items from the personal performance goal items (CFI = .94, RMSEA = .15, SRMR = .06). The two-factor model provided a better fit, χ²(1) = 7.14, p = .008.

Overview of the multilevel logistic regression analysis

Table 1 (left panel) presents the full results. We treated matches as within-team observations (N = 98 Level 1 units) nested in teams (K = 31 Level 2 units). We used multilevel logistic regression modeling (Sommet & Morselli, 2017) while including a first-order autoregressive disturbance term to address temporal autocorrelation² (nonindependence of matches adjacent in time). A preliminary analysis revealed that all teams’ Cook’s D values were below the conservative threshold of 1; nevertheless, we used robust estimation of standard errors to minimize the impact of influential higher level observations (Yuan & Zhong, 2008).

Table 1.

Odds ratios and 95% CIs from the multilevel logistic regression model testing the hypothesized interaction between composition and collective performance goals on team performance (i.e., odds of winning a match): Studies 1 and 2.

	Study 1 Video game teams		Study 2 Volleyball teams
	OR	95% CI	OR	95% CI
Composition in-group performance goals	0.88	[0.59, 1.34]	0.59*	[0.39, 0.88]
Collective performance goals	1.64	[0.91, 2.97]	1.67*	[1.11, 2.51]
Composition In-Group × Collective Performance Goals	0.43***	[0.27, 0.68]	0.72*	[0.55, 0.95]
Composition out-group performance goals			0.79	[0.47, 1.30]
Composition In-Group × Composition Out-Group Performance Goals			0.26***	[0.14, 0.46]
Composition Out-Group × Collective Performance Goals			2.86***	[1.69, 4.85]
Composition In-Group × Composition Out-Group × Collective Performance Goals			2.50***	[1.54, 4.04]
Team category (+0.5 = capture-the-flag in Study 1; fixed effects in Study 2)	1.71*	[1.06, 2.75]	χ²(3) = 22.10***
Prior team performance	1.00	[0.56, 1.77]	1.72***	[1.39, 2.14]

Note. A first-order autoregressive disturbance term was included to account for temporal autocorrelation. Robust estimation of standard errors was used. All variables were standardized. The boldfaced row indicates the hypothesized interaction.

p < .050. ***p < .001.

We regressed team performance (match result: 0 = defeat, 1 = victory) on collective performance goals, composition performance goals, and their interaction. We controlled for team category (−0.5 = death-match mode, +0.5 = capture-the-flag mode) and prior team performance (a common confounder in the link between performance goals and performance; Hulleman et al., 2010). All continuous variables were mean-centered.

We performed a preliminary analysis using Aiken et al.’s (1991) step-down approach for model selection. This analysis revealed a marginal higher order interaction between collective performance goals, composition performance goals, and team category, OR = 1.77, 95% CI [0.99, 3.13], p = .051. Since this interaction was only marginally significant, it was not included in the main analysis, but Figure S1 shows that the hypothesized interaction occurred primarily in the death-match mode.

It should be noted that no unbiased effect size estimate is currently available for multilevel modeling (LaHuis et al., 2014). We therefore rescaled all our variables so that the odds ratios for main and simple effects reflected changes in the odds of winning a match for a +1 SD change in the predictor, with OR ≈ 1.5, 3.5, and 6.5 generally viewed as small, medium, and large effects, respectively (Chen et al., 2010). To aid interpretation, we converted the odds ratios for each simple effect into Cohen’s d and included these values in parentheses (conversion formula in the Supplemental Material).

Main analysis

Consistent with our hypothesis, we observed an interaction between collective performance goals and composition performance goals, OR = 0.43, 95% CI [0.28, 0.68], p < .001. Simple slopes were calculated using the delta method (Norton et al., 2013). Collective performance goals significantly predicted the odds of winning a match when composition performance goals were low (−1 SD), OR = 3.78, 95% CI [1.54, 9.27] (d ≈ 0.73), p = .004, but not when they were high (+1 SD), OR = 0.71, 95% CI [0.41, 1.24] (d ≈ −0.19), p = .231 (see Figure 2, left panel). Findings using an alternative decomposition of the interaction are described in the Supplemental Material.

Figure 2.

Association between collective performance goals and team performance (the likelihood of winning a match in the tournament) as a function of composition performance goals for video game teams (left panel) and volleyball teams (right panel): Studies 1 and 2.

Robustness checks

A limitation of our multilevel model was its inability to account for the nonindependence of residuals between matches involving similar opposing teams. Although the ICC associated with this form of clustering was five times smaller than the ICC for teams, it was still not negligible (ICC_opp = .05). To address this, we built a model where matches were nested in teams and cross-classified by opposing teams. Due to the complexity of this model, it was not possible to include the autoregressive disturbance term or use robust estimation, making the estimates sensitive to nonindependence between adjacent matches and to influential observations. Despite these limitations, the hypothesized interaction did not differ significantly from that in the main model and remained significant, OR = 0.48, 95% CI [0.25, 0.95], p = .036 (full results in Table S3).

Discussion

Study 1 showed that collective performance goals are stronger predictors of team performance as composition performance goals decrease. However, it had two limitations. First, it used a small, predominantly male sample, providing only sufficient power to detect a fully attenuated interaction involving a large simple slope. Second, the items measuring composition performance goals were ambiguous, failing to clearly specify the targets of comparison. For example, “I want to perform better than the other players” could reflect a desire to outperform opponents, teammates, or both. Given that people’s goal profiles often involve varying degrees of intragroup and intergroup competitiveness (Rogat & Linnenbrink-Garcia, 2019), and that competition among teammates is common in sports teams (Harenberg et al., 2016; Keegan et al., 2010; van Mierlo & van Hooft, 2024), it seemed crucial to account for comparison targets when assessing performance goals.

In Study 2, we used a larger, more gender-balanced sample and drew on prior research (Kim et al., 2012; Rogat & Linnenbrink-Garcia, 2019) to differentiate between composition in-group performance goals (outperforming teammates) and composition out-group performance goals (outperforming opponents). Because the goal of outperforming teammates is largely incompatible with working as a group to outperform opposing teams, we expected collective performance goals to be stronger predictors of team performance as composition in-group performance goals decrease. However, because the goal of outperforming opponents does not inherently conflict with working as a group to outperform opposing teams, we made no predictions regarding composition out-group performance goals.

Study 2: Volleyball Teams

In Study 2, we hypothesized that teams oriented toward collective performance goals would perform better as their composition in-group performance goals decreased.

Method

Participants and teams

Our sample included 201 volleyball players (47.26% men; M_age = 23.74 ± 5.61) from 22 out of the 53 national teams involved in the 2015–2016 Swiss volleyball championship (team participation rate: 41.5%).³ The median number of surveyed players per team was 11, with only 5% of teams having five or fewer players surveyed. A simulation-based sensitivity analysis showed that the sample size provided 80% power to detect the hypothesized interaction if the true effect in the population were ln(OR) = −0.56. This represented a fully attenuated interaction with a medium-to-large simple effect of ln(OR) = 1.09 (d ≈ 0.60) and a null simple effect (for details, see the Supplemental Material).

Procedure

Data collection

The study was conducted in collaboration with Swiss Volley, the federation coordinating the Swiss volleyball championship, and the presidents of all National League A (NLA) and National League B (NLB) volleyball clubs. Two weeks before the championship, we sent questionnaires to the coaches, requesting that they distribute them to their players and return the completed forms in prestamped envelopes. The questionnaires were made available in French, German, and English.

The championship

The annual championship aimed to determine the Swiss national indoor volleyball champion teams. In the championship, six-player teams competed in a series of best-of-five set matches across four categories: (a) men’s NLA (five teams); (b) women’s NLA (five teams); (c) men’s NLB (six teams); and (d) women’s NLB (six teams). During qualification, each team played all other teams twice in two-legged ties. Playoff games followed, culminating in a two-legged final. After the tournament, Swiss Volley provided us with the official results, and a student assistant built the dataset.

Variables

All measures used a 7-point response scale (1 = not at all, 7 = completely). For the correlation matrix, see Table S1.

Collective performance goals

We assessed team members’ perceived team performance goals using the same measure as in Study 1 (e.g., “With my team, our goal is to perform better than the other teams”; α = .80, M = 6.05, SD = 1.06). Within-team agreement was acceptable, AD_M(J) = 0.59, r_wg = .77. Collective performance goals were operationalized as the within-team mean of perceived team performance goals.

Composition in-group and out-group performance goals

We assessed team members’ personal performance goals using the same measure as in Study 1. However, this time, we distinguished in-group goals (e.g., “My goal is to perform better than my teammates”; α = .84, M = 5.15, SD = 1.42) from out-group ones (“My goal is to perform better than the players of the opposing teams”; α = .83, M = 5.93, SD = 1.20). Composition performance goals were operationalized as the within-team mean of personal performance goals.

Team performance

We assessed team performance using the official result of each match. Each team played between 22 and 30 matches (535 matches in total; 290 defeats, 245 victories; ICC = .14, 95% CI [0.06, 0.29]).

Prior team performance

We assessed prior team performance using the proportion of matches won by each team during the previous championship (M = 0.48, SD = 0.19). Two missing values were replaced with the mean.

Results

Preliminary EFA and CFA

As in Study 1, we performed an EFA and CFA to determine whether perceived team performance goals and personal performance goals represented separate constructs. Regarding the EFA, we identified three factors: (a) the first factor, comprising the three in-group performance goal items, explained 48.3% of the variance; (b) the second factor, comprising the three perceived team performance goal items, explained 15.4% of the variance; (c) the third factor, comprising the three out-group performance goals, explained 11.7% of the variance. The correlations between factors ranged from .33 to .43 (full results in Table S2).

Regarding the CFA, we built three models: (a) a one-factor model with all nine items belonging to a single latent factor (CFI = .71, RMSEA = .21, SRMR = .10); (b) a two-factor model differentiating the three perceived team performance goal items from the six personal performance goal items (CFI = .86, RMSEA = .15, SRMR = .07); and (c) a three-factor model further differentiating the in-group from the out-group performance goal items (CFI = .97, RMSEA = .07, SRMR = .04). The three-factor model provided a better fit than the one-factor, χ²(3) = 218.71, p < .001, and the two-factor model, χ²(2) = 98.57, p < .001.

Overview of the multilevel logistic regression analysis

Table 1 (right panel) presents full results of the analysis. As in Study 1, we treated matches as within-team observations (N = 535 Level 1 units) nested in teams (K = 22 Level 2 units), and we built a multilevel logistic regression model while including an autoregressive disturbance term. A preliminary analysis revealed that one team’s Cook’s D was close to the conservative threshold of 1 (D = 0.93). Removing this pseudoinfluential observation rendered the hypothesized interaction marginal, OR = 0.76, 95% CI [0.55, 1.05], p = .095. As in Study 1, we used robust estimation of standard errors to minimize its impact.

We regressed team performance (match result: 0 = defeat, 1 = victory) on collective performance goals, composition in-group performance goals, composition out-group performance goals, and all their interactions. As in Study 1, we controlled for team category and prior team performance using fixed effects for category (4 categories – 1 = 3 dummy variables), thus estimating pooled within-league effects on team performance (Allison, 2009). As in Study 1, all continuous variables were centered and rescaled.

We again performed a preliminary analysis using Aiken et al.’s (1991) step-down approach for model selection. This analysis showed that including the higher order interaction improved model fit, χ²(1) = 12.08, p < .001, so it was incorporated into the final model (for related research, see Hull et al., 1992; Yzerbyt et al., 2004).

Main analysis

Consistent with our hypothesis and replicating Study 1, we observed an interaction between collective performance goals and composition in-group performance goals, OR = 0.72, 95% CI [0.55, 0.95], p = .018. Simple slopes were again calculated using the delta method. Collective performance goals were a significant positive predictor of the odds of winning a match when composition in-group performance goals were low (−1 SD), OR = 2.31, 95% CI [1.51, 3.53] (d ≈ 0.46), p < .001, but not when they were high (+1 SD), OR = 1.29, 95% CI [0.69, 2.05] (d ≈ 0.09), p = .540 (see Figure 2, right panel). Findings using an alternative decomposition of the interaction are described in the Supplemental Material.

Interestingly, we observed an unexpected interaction between collective performance goals and composition out-group performance goals, OR = 2.86, 95% CI [1.69, 4.85], p < .001. Simple slopes analyses revealed that collective performance goals were a marginally negative predictor of the odds of winning a match when composition out-group performance goals were low (−1 SD), OR = 0.60, 95% CI [0.35, 1.02] (d ≈ −0.28), p = .060, and a positive predictor when they were high (+1 SD), OR = 4.72, 95% CI [2.19, 10.16] (d ≈ 0.86), p < .001.

We also observed an interaction between composition out-group performance goals and composition in-group performance ones, OR = 0.25, 95% CI [0.14, 0.46], p < .001. Simple slopes analyses revealed that composition out-group performance goals were a positive predictor of the odds of winning a match when composition in-group performance goals were low (−1 SD), OR = 3.05, 95% CI [2.15, 4.32] (d ≈ 0.61), p < .001, but became a negative predictor when they were high (+1 SD), OR = 0.20, 95% CI [0.07, 0.56] (d ≈ −0.89), p = .002.

Finally, there was a second-order interaction, OR = 2.50, 95% CI [1.54, 4.04], p < .001. Although controlling for this term was required by the step-down approach for model selection, the sample size was insufficient for reliable interpretation of such a complex, unpredicted interaction (see Dawson & Richter, 2006). Consequently, we chose not to elaborate further on it.

Robustness checks

As in Study 1, a limitation of our multilevel model was its inability to account for the nonindependence of residuals between matches involving similar opposing teams. This time, the ICC related to this form of clustering was about the same as the ICC for teams, with ICC_opp = .26. As in Study 1, we built a model where matches were nested in teams and cross-classified by opposing teams, though this approach again prevented us from accounting for temporal autocorrelation or using robust estimation. The hypothesized interaction did not differ significantly from that in the main model but became marginally significant, OR = 0.69, 95% CI [0.44, 1.06], p = .093 (full results in Table S3).

Discussion

Study 2 showed that collective performance goals are stronger predictors of team performance as composition in-group performance goals decrease, indicating that these goals are most beneficial when team members do not strive to outperform one another. Study 2 also documented two unexpected findings. First, collective performance goals were stronger predictors of team performance as composition out-group performance goals increased, suggesting that these goals are even more beneficial when team members individually strive to outperform opponents. Second, composition out-group performance goals were a positive predictor of performance only when they were not paired with composition in-group performance goals, highlighting the incompatibility between striving to outperform opponents and striving to outperform teammates.

Studies 1 and 2 had two limitations: the higher level sample sizes were small, and our observational design prevented us from drawing causal inferences. In Study 3, we therefore randomly assigned a large number of teams to one of three conditions: (a) collective performance goals only, (b) composition (in-group) performance goals only, or (c) both types of goals. We aimed to conceptually replicate the findings of the observational studies while investigating behavioral sabotage as the mediator.

Study 3: Experimentally Assembled Teams

In Study 3, we hypothesized that (a) teams instructed to pursue collective performance goals would perform better in an anagram task than teams instructed to pursue composition performance goals or both types of goals, and (b) behavioral sabotage would mediate this effect. The study was preregistered (https://osf.io/29365/).

Method

Power analysis

An a priori power analysis revealed that 227 teams were needed to detect a small-to-medium effect (f² = .035) with power = .80 (α = .05).

Participants and teams

We recruited U.S. participants using CloudResearch. We oversampled by one third to account for the exclusion of participants, opening the experiment to 300 four-member teams. As preregistered, we excluded team members who failed the instructional check, leaving a sample of 292 teams (56.68% men; M_age = 37.21 ± 8.16; 66.92% had a 4-year college degree or higher).

Procedure

Data collection

Participants were told that they belonged to a four-member team testing anagrams for a board game publisher. To foster a sense of team belonging, each team was assigned a name (e.g., “The Amarna Team”), presented with a detailed diagram emphasizing interdependence among team members (highlighting how their performance could combine and how hints could be exchanged), and informed that their performance would be assessed at both individual and team levels.

Participants solved the same set of 10 anagrams and chose 10 hints (one per anagram) to pass to another team member. Data spanned 4 weeks. In Week 1, 75 “first members” solved the 10 anagrams and chose 10 hints to give to the second team member; then, 75 “second members” solved the 10 anagrams using the 10 hints provided by a first member and provided 10 hints to the third team member; and so on until the fourth member (who did not provide hints) participated. We repeated this procedure for Weeks 2–4, surveying 75 × 4 team members each week.

Performance goal manipulation

Before the anagram task, teams were randomly assigned to one of three conditions. In the collective-only condition (n = 98), instructions were as follows: “As an individual, your goal is to follow the instructions and complete the task [control-like individual instructions]” and “As a group, your goal is to perform better than the other teams [collective performance goal instructions].” In the composition-only condition (n = 97), instructions were as follows: “As an individual, your goal is to perform better than the other team members [composition in-group performance goal instructions]” and “As a group, your goal is to follow the instructions and complete the task [control-like team instructions].” In the mixed-goal condition (n = 97), instructions were as follows: “As an individual, your goal is to perform better than the other team members [composition in-group performance goal instructions]” and “As a group, your goal is to perform better than the other teams [collective performance goal instructions].”

Pilot study

Ninety-eight participants (36.73% men; M_age = 41.21 ± 13.52) were randomly assigned to one of these conditions and completed the perceived team performance goal (α = .89, M = 5.99, SD = 1.50) and the personal (in-group) performance goal (α = .85, M = 5.54, SD = 1.60) measures from Study 2. Perceived team performance goals were higher in the collective-only, M = 6.44, 95% CI [5.98, 6.90], and mixed-goal, M = 6.53, 95% CI [6.06, 7.00], conditions than in the composition-only condition, M = 5.01, 95% CI [4.55, 5.47], F(2, 95) = 13.40, p < .001, η²_p = .22. Conversely, personal performance goals were higher in the composition-only, M = 6.42, 95% CI [5.99, 6.86], and mixed-goal, M = 6.07, 95% CI [5.63, 6.52], conditions than in the collective-only condition, M = 4.19, 95% CI [3.76, 4.61], F(2, 95) = 30.62, p < .001, η²_p = .39. This provides support for the effectiveness of the manipulation.

Instructional check

In the main study, following the manipulation, participants were asked to rephrase in their own words (a) their individual goal and (b) their team goal. Two independent coders, who were blind to the experimental conditions, categorized the first 100 responses as corresponding to a collective performance goal, a composition in-group performance goal, control-like instructions, or as undetermined. Interrater reliability was perfect (κ = 1.00), and the second coder categorized the remaining responses alone. A total of 37.41% of the participants failed at least one check and were excluded. While this indicates a notable proportion of participants may not have completed the study conscientiously, this only led to the exclusion of 2.67% of the teams (where all members failed the checks). Retaining all participants reduced the strength of the hypothesized effects, B = −0.19, 95% CI [−0.52, 0.31], p = .662 (team performance); B = −0.06, 95% CI [−0.12, 0.00], p = .064 (behavioral sabotage).

The anagram task

The task was adapted from Rudman et al. (2012).

Team performance

Team members had 5 minutes to solve a set of 10 anagrams. For each anagram, they had to unscramble all the letters to form the correct word (with only one correct answer). Team performance was operationalized as the within-team mean number of correctly solved anagrams (M = 5.90, SD = 2.17, adjusted for team size).

Behavioral sabotage

After completing the task, participants were presented with possible hints alongside the correct answers. For each anagram, they selected one hint to share with the next team member from three options varying in helpfulness. For instance, for the anagram “CPESNRAA” (answer: “PANCREAS”), the hints included: “It’s the organ in your body that starts with ‘P’” (helpful), “It’s an organ in your body” (somewhat helpful), and “It starts with the letter ‘P’” (unhelpful). As preregistered, we adopted the operationalization used by Rudman et al. (2012): Hints were scored from 1 (helpful) to 3 (unhelpful), and a behavioral sabotage index was calculated based on the within-team mean (M = 13.37, SD = 3.60, adjusted for team size).

Results

Table S4 presents the full set of results.

Condition → team performance

We treated teams as the units of analysis. As preregistered, we used contrast analysis (Rosenthal & Rosnow, 1985), breaking down conditions into two contrasts: (a) the planned contrast compared the collective-only condition to the composition-only and the mixed-goal conditions (weights: ⁺²/₃, ⁻¹/₃, and ⁻¹/₃); (b) the orthogonal contrast ensured that there was no difference between the composition-only and the mixed-goal conditions (weights: 0, ⁻¹/₂, and ⁺¹/₂). We regressed team performance on these contrasts.

Inconsistent with our main hypothesis, the planned contrast was not significant, B = 0.12, 95% CI [−0.41, 0.65], p = .662 (c path in Figure 4). Team performance did not differ across the collective-only, M = 5.98, 95% CI [5.54, 6.41], the composition-only, M = 5.79, 95% CI [5.35, 6.23], and the mixed-goal, M = 5.93, 95% CI [5.50, 6.37] conditions.

Condition → behavioral sabotage → team performance

A follow-up mediation analysis repeated the main analysis while (a) using behavioral sabotage as the outcome, and (b) including behavioral sabotage as an additional predictor while using team performance as the outcome.

In the first analysis, the planned contrast was significant, B = −1.31, 95% CI [−2.20, −0.43], p = .004, η²_p = .04 (a path). Behavioral sabotage was lower in the collective-only condition, M = 12.49, 95% CI [11.77, 13.21], than in the composition-only, M = 14.21, 95% CI [13.49, 14.93], and the mixed-goal, M = 13.40, 95% CI [12.69, 14.12] conditions (see Figure 3).

Figure 3.

Behavioral sabotage (average unhelpfulness of the hints shared within the team) as a function of experimental condition: Study 3.

In the second analysis, behavioral sabotage was negatively associated with team performance, B = −0.11, 95% CI [−0.18, −0.04], p = .003, η²_p = .03 (b path). Although the total effect was not significant, there was a positive indirect effect (calculated using the percentile bootstrap method with 50,000 repetitions; Yzerbyt et al., 2018) of the planned contrast on team performance via a decrease in behavioral sabotage: indirect effect = 0.14, 95% CI [0.03, 0.29] (a × b path; see Figure 4).

Figure 4.

Indirect effect of condition (collective-only vs. composition-only and mixed-goal conditions) on team performance via behavioral sabotage: Study 3.

Robustness checks

Focusing on the second and third members

The “assembly-line” structure of our procedure made the first and fourth members less dependent on the rest of the team than the others, as the first members did not receive hints and the fourth members did not send hints. Thus, we replicated the analysis while focusing on the second and third members. The conclusions were similar to those of the main analysis: The total effect on performance was nonsignificant, B = 0.00, 95% CI [−0.75, 0.74], p = .992, but the condition did influence sabotage, B = −1.38, 95% CI [−2.38, −0.39], p = .007, η²_p = .04, which in turn was associated with performance, B = −0.25, 95% CI [−0.33, −0.16], p < .001, η²_p = .10 (indirect effect = 0.34, 95% CI [0.09, 0.63]).

Multilevel modeling using prior member’s sabotage score as mediator

Given the hierarchical structure of the data, multilevel regression with team members nested in teams (Sommet & Morselli, 2021) while using time fixed effects (Allison, 2009) was a suitable alternative to the preregistered approach. This strategy also made it possible to use the prior member’s sabotage score as the mediator instead of the aggregated score. The conclusions were again similar to those of the main analysis: The total effect on individual performance was nonsignificant, B = −0.03, 95% CI [−0.50, 0.43], p = .878, but the condition did influence sabotage, B = −1.19, 95% CI [−1.97, −0.43], p = .002, with the sabotage score of the prior member negatively affecting performance, B = −0.12, 95% CI [−0.21, −0.04], p = .004 (indirect effect = 0.15, 95% CI [0.03, 0.31]).⁴

Discussion

Study 3 showed that teams instructed to pursue collective performance goals displayed less behavioral sabotage than teams instructed to pursue composition performance goals or both collective and composition performance goals. This demonstrates that collective performance goals are beneficial for teamwork only when unencumbered by composition performance goals. Importantly, we did not observe a total effect on team performance but rather an indirect effect: Collective performance goals reduced behavioral sabotage, which in turn predicted team performance.

General Discussion

In this paper, we presented two observational studies involving real-world teams participating in high-stakes eSports and sports competitions, along with one preregistered experiment involving a large number of competing teams, to clarify the association between team performance goals and team performance.

Summary of the Findings

In Study 1, video game teams striving to outperform other teams performed better in a European tournament when their members did not strive to outperform other individuals. Thus, high collective performance goals and low composition performance goals may help teams perform better.

In Study 2, volleyball teams striving to outperform other teams performed better in the Swiss volleyball championship when their members did not strive to outperform their teammates. Thus, high collective performance goals and low composition in-group performance goals may more specifically contribute to stronger team performance.

In Study 3, experimentally assembled teams instructed to outperform other teams showed less behavioral sabotage than teams instructed to outperform teammates or both teammates and other teams; this, in turn, predicted higher team performance. High collective performance goals and low composition in-group performance goals may foster a cooperative environment that is associated with better performance outcomes.

Discrepancy Between Observational and Experimental Studies

The discrepancy between the observational studies, which documented an interaction between collective and composition performance goals on team performance, and the experimental study, which only found such an interaction for sabotage behaviors, may stem from differences in team characteristics. The observational studies involved real-world teams competing in real time for tangible prizes. Conversely, the experiment used ad hoc teams engaged in asynchronous collaborations over a 4-week period for symbolic rewards (i.e., high-performance scores). Additionally, video games and volleyball require a high degree of coordination, while the anagram task used in the experiment primarily required collaboration for hint-giving and was otherwise more independent in nature.

Contributions

Collective performance goals are team performance goals

Our studies demonstrated that collective and composition performance goals were operationally distinct (Studies 1–2) and produced opposing empirical effects (Studies 1–3). Although we concur with other scholars that both collective and composition performance goals are needed to better understand and predict team-level outcomes (Porter, 2008, Porter et al., 2019), we argue that they cannot serve as alternative or complementary operationalizations of team performance goals.

Conceptually, collective performance goals are “gestaltic”: they correspond to a shared other-team-referenced standard in competence evaluation. However, composition performance goals are “a-gestaltic”: they merely correspond to the combination of team members’ other-individual-referenced standards in competence evaluation (for a description of the achievement goal standard model, see Elliot & Murayama, 2008). Because a team (goal) is more than the mere sum of its members (goals; Wetherell, 1996), collective performance goals arguably represent a more suitable operationalization of team performance goals, whereas composition performance goals should preferably not be labeled as such.

Collective and composition performance goals are functional antagonists

The fact that collective performance goals predict team performance (and reduce behavioral sabotage) only when unencumbered by composition performance goals can be interpreted through the lens of self-categorization theory. A focus on group performance is typically associated with individuals’ social identity (the intermediate level of self-categorization; Turner & Oakes, 1989), whereas a focus on self-performance is associated with individuals’ personal identity (the subordinate level of self-categorization; Mackie, 1986). Traditionally, the collective and personal levels of the self are conceived as functional antagonists (Turner et al., 1987) and, in a goal endorsement context, team members’ commitment to personal goals may undermine coordination at the group level (Tjosvold, 1984).

Members of teams pursuing collective performance goals may focus on a shared sense of identity and common fate, which facilitates selfless coordination to achieve team goals that seem “bigger than them.” However, when these individuals also pursue personal performance goals, they may face challenges balancing (a) coordinating with teammates to achieve the team objectives and (b) striving to outperform their teammates to achieve their personal objectives (Giel et al., 2021; Schreuder et al., 2019). This aligns with findings suggesting that while pursuing personal performance goals enhances performance in individual tasks (Murayama & Elliot, 2012), it fosters a perception of others as “rivals” rather than “allies” (Ryan & Pintrich, 1997), which undermines performance and learning in collaborative tasks (Gabriele, 2007; Lim & Lim, 2020).

Two boundary conditions should be noted. First, in Study 2, teams pursuing both collective performance goals and composition out-group performance goals demonstrated higher team performance. While unexpected, this finding suggests that collective and personal goals are not inherently misaligned: Striving to outperform a rival team as a group is compatible with aiming to outperform opponents individually. This highlights the importance of clearly defining the targets of comparison when conceptualizing and operationalizing performance goals (Kim et al., 2012). It also aligns with research showing that performance goals focused on out-group members predict a sense of shared goals within one’s group (Kim et al., 2015) and the quality of group regulation (Rogat & Linnenbrink-Garcia, 2019).

Second, some tasks may allow composition performance goals to coexist with collective performance goals without undermining team performance. While personal performance goals often harm interpersonal outcomes (Poortvliet & Darnon, 2010), they are robust predictors of positive performance-based intrapersonal outcomes such as perceived self-efficacy (C. Huang, 2016) or persistence (Elliot et al., 1999; Guan et al., 2006; Sommet & Elliot, 2017). This suggests that personal performance goals may enhance team performance in tasks where team members cannot sabotage one another and where individual contributions combine to produce the group’s overall output.

Limitations

In Studies 1–2, although the lower level sample sizes were satisfactory, the team-level sample sizes were small (31 and 22 teams, respectively). In multilevel modeling, adequate numbers of higher level units are important, particularly when testing interactions involving higher level variables (Arend & Schäfer, 2019). Since these observational studies were conducted, expectations regarding higher-level sample sizes have become more demanding, and replications with larger team samples are needed to better capture these interaction patterns.

In Study 3, although the number of teams was more satisfactory, the performance goal manipulation did not produce a total effect on team performance but rather an indirect effect via reduced behavioral sabotage. The use of experimentally assembled teams with low-stakes, single-point, asynchronous collaborations among unknown team members may have prevented us from directly observing the downstream effects of our manipulation on team performance. Replications using longitudinal experimental designs are warranted.

Conclusion

Our results suggest that in team competition settings, individualistic pursuits aimed at outperforming others do not necessarily lead to optimal outcomes for the group as a whole. However, when team members prioritize the collective goal of outperforming other teams over personal ambitions, sabotage behaviors decrease and team performance may improve. This suggests that creating organizational climates that encourage between-team competition while discouraging interindividual competition (Balaguer et al., 1999; Dragoni, 2005; Meece et al., 2006) may help foster team cooperation and achievement.

Supplemental Material

sj-pdf-1-gpi-10.1177_13684302251413705 – Supplemental material for Team performance goals and team performance: A multiconceptualization approach

Supplemental material, sj-pdf-1-gpi-10.1177_13684302251413705 for Team performance goals and team performance: A multiconceptualization approach by Nicolas Sommet, Ocyna Rudmann, Vincent Pillaud and Fabrizio Butera in Group Processes & Intergroup Relations

Footnotes

Acknowledgements

We thank ClanBase and ESReality for facilitating player recruitment for Study 1, as well as Swiss Volley, the presidents of national leagues, and the coaches of all volleyball teams for their support in recruiting players for Study 2. We extend our gratitude to Georges-André Carrel, former coach of the Lausanne Swiss team, and Alessandro Raffaelli from Swiss Volley, whose contributions were instrumental to this study.

Authors’ Contributions

NS: conceptualization, methodology, formal analysis, investigation, data curation, writing: original draft, visualization. OR: data curation, writing: review and editing. VP: conceptualization, methodology, investigation, writing: review and editing. FB: conceptualization, methodology, writing: review and editing, supervision.

Ethical Approval and Informed Consent

The research was conducted in Switzerland. The national law governing ethical issues is the Federal Law on Research Involving Human Beings, which requires authorization from a responsible ethics committee only when the research is health-related (). Since the material did not include health-related measures, the three studies were de facto exempt. However, all procedures performed in studies involving human participants were in accordance with the 1964 Helsinki Declaration and its later amendments. Informed consent was obtained from all individual participants included in the study.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Nicolas Sommet

Ocyna Rudmann

Fabrizio Butera

Supplemental Material

Supplemental material for this article is available online.

Notes

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