research paper about teamwork

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Open Access

Peer-reviewed

Research Article

The Effectiveness of Teamwork Training on Teamwork Behaviors and Team Performance: A Systematic Review and Meta-Analysis of Controlled Interventions

* E-mail: [email protected]

Affiliation School of Kinesiology, The University of British Columbia, Vancouver, British Columbia, Canada

Affiliation Departments of Kinesiology/Physical Education and Psychology, Wilfrid Laurier University, Waterloo, Ontario, Canada

Affiliation Department of Educational and Counseling Psychology, Faculty of Education, University of British Columbia, Vancouver, British Columbia, Canada

• Desmond McEwan, 
• Geralyn R. Ruissen, 
• Mark A. Eys, 
• Bruno D. Zumbo, 
• Mark R. Beauchamp

• Published: January 13, 2017
• https://doi.org/10.1371/journal.pone.0169604
• Reader Comments

The objective of this study was to conduct a systematic review and meta-analysis of teamwork interventions that were carried out with the purpose of improving teamwork and team performance, using controlled experimental designs. A literature search returned 16,849 unique articles. The meta-analysis was ultimately conducted on 51 articles, comprising 72 ( k ) unique interventions, 194 effect sizes, and 8439 participants, using a random effects model. Positive and significant medium-sized effects were found for teamwork interventions on both teamwork and team performance. Moderator analyses were also conducted, which generally revealed positive and significant effects with respect to several sample, intervention, and measurement characteristics. Implications for effective teamwork interventions as well as considerations for future research are discussed.

Citation: McEwan D, Ruissen GR, Eys MA, Zumbo BD, Beauchamp MR (2017) The Effectiveness of Teamwork Training on Teamwork Behaviors and Team Performance: A Systematic Review and Meta-Analysis of Controlled Interventions. PLoS ONE 12(1): e0169604. https://doi.org/10.1371/journal.pone.0169604

Editor: Nico W. Van Yperen, Rijksuniversiteit Groningen, NETHERLANDS

Received: September 15, 2016; Accepted: December 19, 2016; Published: January 13, 2017

Copyright: © 2017 McEwan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files. Raw data (taken from the studies in our meta-analysis) are available upon request from the corresponding author.

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

From road construction crews and professional soccer squads to political parties and special operations corps, teams have become a ubiquitous part of today’s world. Bringing a group of highly-skilled individuals together is not sufficient for teams to be effective. Rather, team members need to be able to work well together in order for the team to successfully achieve its purposes [ 1 , 2 ]. As a result, there has been a proliferation of research assessing whether, and how, teams can be improved through teamwork training. A wide range of studies have shown positive effects of teamwork interventions for improving team effectiveness across several contexts such as health care (e.g., [ 3 ]), military (e.g., [ 4 ]), aviation (e.g., [ 5 ]), and academic (e.g., [ 6 ]) settings. Similarly, improvements in teamwork have been observed as a result of training with a variety of team types including new teams (e.g., [ 7 ]), intact teams (e.g., [ 8 ]), and those created for laboratory-based experiments (e.g., [ 9 ]). In sum, the extant empirical evidence to date appears to suggest that teams can be improved via teamwork training.

What is Teamwork?

Within teams, members’ behaviors can be categorized in terms of both taskwork and teamwork processes [ 2 ]. Marks et al. [ 10 ] differentiated between the two by suggesting that “taskwork represents what it is that teams are doing, whereas teamwork describes how they are doing it with each other” (p. 357). Specifically, while taskwork involves the execution of core technical competencies within a given domain, teamwork refers to the range of interactive and interdependent behavioral processes among team members that convert team inputs (e.g., member characteristics, organizational funding, team member composition) into outcomes (e.g., team performance, team member satisfaction) [ 2 , 10 ]. Some examples of teamwork (and respective comparisons to taskwork) include: the seamless communication between a surgeon, nurse, and anaesthesiologist, rather than the technical competencies of these practitioners; the synergy between a quarterback and receiver to complete a passing play, rather than their respective skill sets related to throwing or catching a football; the collaborative adjustments a flight crew makes in response to adverse weather or system problems, rather than each individual’s aviation skills; and so forth. Research from an assortment of studies indicates that teamwork—the focus of the current paper—is positively related to important team effectiveness variables, including team performance, group cohesion, collective efficacy, and member satisfaction [ 1 ].

Teamwork has been conceptualized within several theoretical models. For example, in their review, Rousseau et al. [ 2 ] reported that 29 frameworks related to teamwork have been published. Although there is much overlap across these models, there are also some notable differences. These relate to the number of dimensions of teamwork being conceptualized as well as the specific labelling of these dimensions. One thing that is generally agreed upon, however, is that teamwork is comprised of multiple observable and measurable behaviors . For instance, two highly cited frameworks by Marks et al. [ 10 ] and Rousseau et al. [ 2 ] consist of 10 and 14 dimensions of teamwork, respectively. In general, teamwork models focus on behaviors that function to (a) regulate a team’s performance and/or (b) keep the team together. These two components coincide with the two respective processes that Kurt Lewin, the widely recognized father of group dynamics, originally proposed all groups to be involved in: locomotion and maintenance [ 11 ].

With regard to regulating team performance (i.e., locomotion), teamwork behaviors include those that occur (a) before/in preparation for team task performance, (b) during the execution of team performance, and (c) after completing the team task [ 2 ]. First, with regard to teamwork behaviors that occur before/in preparation for team task performance, these include the active process of defining the team’s overall purpose/mission, setting team goals, and formulating action plans/strategies for how goals and broader purposes will be achieved. These behaviors help ensure that all team members are clear in terms of what is required of them in order for the team to function effectively. Second, teamwork behaviors that occur during the execution of team tasks include actions that correspond to members’ communication, coordination, and cooperation with each other. At this stage, team members translate what they have previously planned (during the preparation phase) into action. Third, in terms of teamwork behaviors that occur after completing the team task (i.e., reflection), these include monitoring important situations and conducting post-task appraisals of the team’s performance and system variables (e.g., internal team resources, broader environmental conditions), solving problems that are precluding team goal attainment, making innovative adjustments to the team’s strategy, and providing/receiving verbal and behavioral assistance to/from teammates. Hence, team members determine whether their actions have moved them closer towards accomplishing the team goals and objectives, and whether any modifications are required in order to facilitate future success. In addition to these three dimensions concerned with the regulation of team performance, a fourth dimension of teamwork involves behaviors that function to keep the team together (i.e., maintenance). These behaviors focus on the team’s interpersonal dynamics , and include the management of interpersonal conflict between members and the provision of social support for members experiencing personal difficulties. Managing interpersonal dynamics is critical as it is theorized that teams cannot operate effectively when these issues are present [ 2 ].

How Can Teamwork Be Trained?

Teamwork interventions have utilized a number of training methods in order to target the regulation of team performance (i.e., preparation, execution, reflection) and management of team maintenance (i.e., interpersonal dynamics) dimensions. These intervention strategies generally fall under one of four categories. First, the most basic approach to training and developing teamwork involves providing didactic education to team members in a classroom-type setting, such as lecturing about the importance of providing social support within the team or promoting ways to manage interpersonal conflict among teammates. This type of training has been found to be useful for enhancing team effectiveness (e.g., [ 12 ]). A second category of team training involves utilizing a more interactive workshop-style format, wherein team members take part in various group activities, such as having discussions about the team’s purposes and goals (e.g., [ 13 ]) or working through case studies together (e.g. [ 14 ]). The third broad category of team training involves simulation training, wherein teams experientially enact various teamwork skills, such as interpersonal communication and coordination, in an environment that mimics upcoming team tasks (e.g., airline simulators or medical patient manikins). Although often used as a means of fostering taskwork competencies (e.g., teaching new surgeons how to perform the technical skills of a medical operation), simulation training has been found to be an efficacious approach to teamwork intervention (e.g., [ 15 ]). In addition to these three training approaches that occur outside of the team task environment (i.e., training within classroom and simulation settings), teamwork can also be fostered by incorporating team reviews in-situ (i.e., where the team actually performs its tasks), which allows teams to monitor/review their quality of teamwork on an ongoing basis. These team reviews involve some form of team briefs before (e.g., creating action plans), during (e.g., monitoring team members’ actions), and/or after (e.g., assessing the team’s performance) team task execution, and have also been shown to be efficacious in previous studies (e.g., [ 16 ]).

The effectiveness of teamwork interventions can be determined with an assortment of criteria, including team- and individually-based behaviors, cognitions, and affective states. Hackman and Katz 2010 [ 17 ] posit that team effectiveness can be determined by examining the extent to which the team has achieved its a priori objectives. Since the broad purpose of forming a team is to produce something of value, it is perhaps unsurprising that the most widely tested criterion of team effectiveness has been team performance [ 18 – 20 ]. Thus, although teams come from an array of settings and are idiosyncratic in their own ways, one question that essentially all teams address at some point during their tenure is whether they are performing well. For example, is that road construction crew fixing potholes adequately? Does the local soccer squad have a respectable winning percentage? Has an elected political party successfully completed the tasks for which they campaigned? Did a special operations corps achieve the mission it set out to accomplish? When taken in concert, questions related to team performance are often of central interest when characterizing a team’s effectiveness.

In addition to assessing the outcome variable of team performance, researchers have also been interested in whether teamwork training actually improves teamwork itself. The efficacy of these interventions can be determined with a number of objective (e.g., products produced by an industry team), self-report (e.g., questionnaires regarding perceived social support amongst team members), and third-party assessments (e.g., expert ratings of team behaviors). Both general/omnibus measures of teamwork (e.g., [ 21 ]) as well as those assessing specific dimensions of teamwork (e.g., communication [ 22 ]) have been operationalized to examine the effectiveness of these interventions. For example, do team goal setting activities actually result in members creating and pursuing effective team goals? Does simulation training improve the requisite coordination processes among aviation cockpit crews? Has a didactic lecture contributed to improved conflict management among team members? Answering these types of questions is important for determining whether an intervention is actually efficacious in changing the variable that is targeted for improvement (i.e., teamwork behaviors).

The Current Review

Prior to outlining the purposes of this systematic review, it is important to recognize that previous quantitative reviews have been conducted that addressed—to some degree—teamwork training. In preparation for this systematic review, we conducted a scoping review which revealed that eight previous meta-analyses have assessed teamwork intervention studies in some way. However, these reviews were delimited based on various sample and/or intervention characteristics. For example, some reviews included studies that were only conducted with certain team types (e.g., intact teams [ 23 ]) or within a particular context (e.g., sports [ 24 ]; medical teams [ 25 ]). Others were delimited to specific training programs/strategies that were restricted to a narrow range of teamwork strategies (e.g., [ 23 , 25 – 29 ]). Finally, studies that used a combination of teamwork and taskwork intervention components have been systematically reviewed [ 30 ]; however, these types of interventions result in a limited ability to determine the extent to which the resulting effects were due to teamwork training versus taskwork training.

It should also be noted that all but one [ 23 ] of these previous reviews pooled together studies that included a control condition (i.e., wherein teams do not receive any type of teamwork training) and those that did not (as mentioned above, that study only analyzed the effects of certain teamwork strategies). This is an important consideration, as it has been suggested that controlled and uncontrolled studies should not be combined into the same meta-analysis due to differences in study quality (which is a major source of heterogeneity) and since stronger conclusions can be derived from controlled interventions compared to uncontrolled interventions (e.g., [ 31 ]). Therefore, while previous systematic reviews have provided valuable contributions to the teamwork literature, a systematic review that assesses the effects of controlled teamwork interventions across a range of contexts, team types, and involving those that targeted diverse dimensions of teamwork appears warranted. In doing so, a more comprehensive assessment of the efficacy of these teamwork interventions is provided, while also having the capacity to look at the potential moderating effects of various sample, intervention, and measurement characteristics. Moreover, by including only controlled studies, one is able to make stronger conclusions regarding the observed effects.

The overall purpose of this study was to better understand the utility of teamwork training for enhancing team effectiveness. Specifically, a meta-analysis was conducted on controlled studies (i.e., comparing teams who have received teamwork training with those who have not) that have examined the effects of teamwork interventions on teamwork processes and/or team performance. To better disentangle the effectiveness of these studies, we also sought to assess potential moderators of these main effects; that is, to determine whether there are certain conditions under which the independent variable of teamwork training more strongly (or weakly) causally influences the dependent variables of teamwork behaviors or team performance [ 32 ]. The specific moderators that we assessed included: (a) the team context/field of study, (b) the type of teams that were trained, (c) the primary type of intervention method employed, (d) the dimensions of teamwork that were targeted in the intervention, (e) the number of dimensions targeted, (f) the types of measures used to quantify the training effects, and (g) in studies where teamwork was assessed as an outcome variable, the dimensions of teamwork that were measured. It was hypothesized that teamwork training would have a positive and significant effect on both teamwork and team performance and that these effects would be evident across a range of the aforementioned sample, intervention, and measurement characteristics/conditions.

Literature Search

Searches for potential articles were conducted in the following databases: PsycInfo , Medline , Cochrane Central Register of Controlled Trials , SportDiscus , and ProQuest Dissertations and Theses . Hand searches were also conducted across thirteen journals that typically publish articles on group dynamics (e.g., Group Dynamics : Theory , Research , and Practice ; Small Group Research , Journal of Applied Psychology ; Personnel Psychology , Human Factors ; Academy of Management Journal , Journal of Sport & Exercise Psychology ). In each database and journal search, the following combination of search terms were used: ( team OR interprofessional OR interdisciplinary ) AND ( intervention OR training OR building OR simulation ) AND ( teamwork OR mission analysis OR goal specification OR goal setting OR planning OR strategy OR coordination OR cooperation OR communication OR information exchange OR information sharing OR monitoring OR problem solving OR backing up OR coaching OR innovation OR adaptability OR feedback OR support OR conflict management OR situation awareness OR confidence building OR affect management ). These terms were based on various models of teamwork that exist within the literature (see Rousseau et al. [ 2 ] for an overview of these models). An additional search was conducted within these databases and journals using the search terms ( TeamSTEPPS OR Crew Resource Management OR SBAR [Situation-Background-Assessment-Recommendation]), as several articles in the initial search used these specific training programs. We also searched the reference sections of the articles from past teamwork training review papers as well as from articles that initially met inclusion criteria to determine if any additional articles could be retrieved. The searches were conducted in September 2015 and no time limits were placed on the search strategy. Each article was first subjected to title elimination, then abstract elimination, and finally full-text elimination.

Eligibility Criteria

To be included in the meta-analysis, a study needed to examine the effects of teamwork training by comparing teams in an experimental condition (i.e., those who received teamwork training) with those in a control condition (i.e., where teams did not receive teamwork training). Cross-sectional/non-experimental studies were excluded, as were intervention studies that did not include a control condition. As this review was only concerned with teamwork interventions, studies that focused on training taskwork—whether independent of, or in addition to, a teamwork intervention—were excluded. For example, as previously mentioned, simulation-based training (SBT) has been used as a means of training individuals to perform technical skills and also to enhance teamwork. In order for a SBT intervention to be included in this meta-analysis, it had to be clear that only teamwork (not technical skills) was being targeted during training. In order to address our primary research question, the study had to provide data on at least one teamwork dimension and/or team performance. The study also needed to provide sufficient statistics to compute an effect size. In cases of insufficient data, corresponding authors were contacted for this information. The articles were delimited to those published in the English language.

Data Analysis

Articles that met the aforementioned eligibility criteria were extracted for effect sizes and coded independently with respect to seven moderators by two of the authors (DM and GR). Interrater reliability for the coding of these moderators was over 90%, kappa (SE) = 0.80 (0.01). The moderators examined were based on a scoping review (the purpose of which included identifying pertinent characteristics that were commonly reported in previous teamwork intervention research), which was conducted in preparation for this systematic review. The moderators that were examined in this review included (1) the context within which an intervention was conducted ( health care , aviation , military , academia , industry , or laboratory experiment) , (2) the type of team targeted ( intact or new ), (3) the primary training method applied to conduct the intervention ( didactic education , workshop , simulation , or team reviews ), (4) the dimension(s) of teamwork ( preparation , execution , reflection , and/or interpersonal dynamics ) targeted in the intervention as well as (5) the number of dimensions targeted (between one and four), (6) the type of measure used to derive effect sizes ( self-report , third party , or objective measures ), and—when teamwork was assessed as the criterion variable—(7) the specific dimension(s) of teamwork that were measured ( general , preparation , execution , reflection , and interpersonal dynamics ).

Once coded, data were entered into the software Comprehensive Meta-Analysis , Version 2 [ 33 ] and analyzed as a random-effects model (DerSimonian and Laird approach). This type of model assumes that there is heterogeneity in the effect sizes across the included studies and is the appropriate model to use in social science research, as opposed to a fixed-effects model (which assumes that effect sizes do not vary from study to study) [ 34 , 35 ]. Where possible, effect sizes for each study were derived from means, standard deviations, and sample sizes at baseline and post-intervention [ 34 , 36 ]. If these statistics were not fully provided, they were supplemented with F -statistics, t scores, correlations, and p -values to compute the effect size. Each study was given a relative weight based on its precision, which is determined by the study’s sample size, standard error, and confidence interval (i.e., the more precise the data, the larger the relative study weight) [ 34 ].

In instances where a study provided data to calculate multiple effect sizes (such as when several measures of the criterion variable—teamwork or team performance—were examined), these effects were combined into one overall effect size statistic (i.e., a weighted average) for that study. This was done to ensure that those studies that had multiple measures of teamwork or team performance were not given greater weight compared to studies that only provided one effect size (i.e., only had one measure of performance or teamwork), which could potentially skew the overall results [ 34 ]. The exception to this was when articles reported the effects of more than one intervention (i.e., had multiple experimental conditions), each of which had a unique teamwork training protocol. In these cases, an effect size from each intervention was computed. Thus, these articles would contribute multiple effect sizes to the total number of comparisons within the meta-analysis. To correct for potential unit-of-analysis errors in these particular articles, the sample size of the control condition was divided by the number of within-study comparisons [ 31 ]. For example, if three different types of teamwork interventions were compared to one control condition (e.g., which had a sample size of 30 participants), the n of the control condition was divided by 3 (i.e., 30/3 = 10) when calculating the effect sizes of those interventions. Cohen’s d was used as the effect size metric to represent the standardized effect (i.e., the average magnitude of effectiveness) of teamwork interventions on teamwork and team performance [ 37 ]. Standard errors and 95% confidence intervals were computed to test for the accuracy of the standardized effects obtained.

To reduce heterogeneity and improve the interpretability of the results, we pooled studies into those that measured teamwork as its criterion variable and those that measured team performance. Pooling studies in this manner not only reduces heterogeneity but also allowed us to identify the extent to which teamwork interventions impact team performance and, separately, the extent to which they affect teamwork processes. Heterogeneity within the meta-analysis was also assessed by computing a Q value—which estimates the variability in the observed effect sizes across studies—and an I 2 statistic—which estimates the ratio of the true heterogeneity to the total observed variation across studies. High Q and I 2 statistics can be problematic for interpreting the results of a meta-analysis and can also indicate that the meta-analysis includes outlier studies. We also planned to identify and exclude outliers from subsequent moderator analyses in two ways. First, sensitivity analyses were carried out by removing a single intervention from the meta-analysis and noting the resulting effect size—this estimates the impact that each individual intervention has on the overall effect size of teamwork or team performance. If the resulting effect size with an intervention removed (i.e., K– 1) is substantially different than the effect size with that intervention present, this may suggest that it is an outlier and needs to be removed [ 34 ]. Second, we noted any studies that had abnormally high effect sizes and standardized residuals (above 3.0), especially when these values were accompanied by narrow confidence intervals. If heterogeneity ( Q and I 2 ) is substantially reduced upon removal of a study, this further confirms that the study is an outlier and should be omitted from subsequent subgroup/moderator analyses.

Once the two pools of studies were produced, bias within each pool was assessed. First, publication bias was examined by calculating a fail-safe N statistic, which estimates the number of unpublished studies with null findings that would have to exist to reduce the obtained effect size to zero [ 38 ]. If this number is sufficiently large—Rosenberg [ 39 ] recommends a critical value of 5 N +10—then the probability of such a number of studies existing is considered to be low. For example, if 20 studies were included in a meta-analysis, then the resulting fail-safe N should be larger than 110 (i.e., 5*20 + 10); if this value was not larger than 110, then publication bias is likely within this pool of studies. We also obtained two funnel plots (one for studies where teamwork was the outcome variable and one for team performance as the outcome) to provide a visual depiction of potential publication bias. We then conducted an Egger’s test as a measure of symmetry for these two funnel plots. If this test statistic is significant ( p < 0.05), this denotes that the distribution around the effect size is asymmetric and publication bias is likely present [ 34 ].

The literature search from the five databases returned 22,066 articles, while the hand searches of the 13 journals returned 3797 articles, vetting of studies from previous team training reviews returned 191 articles, and the ancestry search of reference lists returned 471 articles (see Fig 1 ). After removing duplicates, 16,849 articles were subject to title and abstract screening, where they were dichotomously coded as ‘potentially relevant’ or ‘clearly not relevant’. 1517 potentially relevant articles were then full-text reviewed and coded as meeting eligibility criteria or as ineligible for the following reasons: (1) not a teamwork intervention; (2) teamwork-plus-taskwork intervention; (3) insufficient statistics to compute an effect size; (4) not including a measure of teamwork or team performance; or (5) not including a control group. As a result of this eligibility coding, 51 articles were included in the meta-analysis. 13 of these studies reported results on two or more interventions, bringing the total number of comparisons ( k ) to 72 with 8439 participants (4966 experimental, 3473 control). See S1 Table for descriptions of each study with regard to study context, type of team and participants, targeted teamwork dimensions of the intervention, number of effect sizes, the criteria measured, and an overview of the intervention.

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https://doi.org/10.1371/journal.pone.0169604.g001

Summary Statistics

Results of the overall effect of teamwork interventions on teamwork processes along with summary statistics and sensitivity analyses (i.e., the final column marked ‘ES with study removed’) for this pool of studies are presented in Table 1 . This pool included a total of 39 interventions from 33 studies. The results revealed that teamwork interventions had a significant, medium-to-large effect on teamwork, d ( SE ) = 0.683 (0.13), 95% CI = 0.43–0.94, Z = 5.23, p < 0.001; Q ( df ) = 660.7 (38), I 2 = 94.2. The funnel plot for this pool of studies is shown in Fig 2 . The fail-safe N was 3598, which is sufficiently large, as it exceeds the critical value of 205 (5*39+10). The funnel plot for this pool of studies is presented in Fig 2 . Egger’s value for this funnel plot was not significant ( B = 0.364, SE = 1.30, 95% CI = -2.26–2.99, t = 0.28, p = 0.78), which also suggests that bias was not present. Two studies were identified as outliers within this pool of studies: Morey et al. [ 3 ] and Marshall et al. [ 22 ]. The resulting effect size when these studies were excluded was d (SE) = 0.550 (0.08), 95% CI = 0.39–0.71, Z = 6.73, p < 0.001; Q ( df ) = 187.53 (36), I 2 = 80.8. Subsequent moderator analyses were conducted with these two outlier studies being omitted.

Circles filled with black indicate outlier studies.

https://doi.org/10.1371/journal.pone.0169604.g002

https://doi.org/10.1371/journal.pone.0169604.t001

Results of the overall effect of teamwork interventions on team performance as well as summary statistics and sensitivity analyses (i.e., the final column marked ‘ES with intervention removed’) for this pool of studies are presented in Table 2 . This pool of studies included a total of 50 interventions from 32 studies. It was shown that teamwork interventions had a significant, large effect on team performance— d ( SE ) = 0.919 (0.14), 95% CI = 0.65–1.19, Z = 6.72, p < 0.001; Q ( df ) = 851.3 (49), I 2 = 94.2. The funnel plot for this pool of studies is shown in Fig 3 . The fail-safe N was 6692, which is sufficiently large, as it exceeds the critical value of 260 (5*50+10). The funnel plot for this pool of studies is presented in Fig 3 . Egger’s value for this funnel plot was not significant ( B = 0.131, SE = 1.19, 95% CI = -2.26–2.54, t = 0.11, p = 0.91), which also implies that bias was not present. There were five outlier interventions (from four studies) in this pool of studies that assessed team performance: Morey et al. [ 3 ], Smith-Jentsch et al. [ 4 ], one of the interventions from Buller and Bell [ 63 ]; teambuilding condition), and both interventions from Bushe and Coetzer [ 43 ]. When these outliers were removed, the resulting effect size was d ( SE ) = 0.582 (0.06), 95% CI = 0.47–0.69, Z = 10.30, p < 0.001; Q ( df ) = 101.1 (44), I 2 = 56.5. Subsequent moderator analyses were conducted with these five interventions omitted.

https://doi.org/10.1371/journal.pone.0169604.g003

https://doi.org/10.1371/journal.pone.0169604.t002

Moderator Analyses

The results of the moderator analyses are shown in Table 3 (for teamwork behaviors) and Table 4 (for team performance). With respect to sample characteristics, significant positive effects of teamwork interventions were found for enhancing teamwork across all contexts ( d s = 0.46–1.23) except for the single effect size from an industry setting ( d = 0.50). In terms of team performance, significant effects were evident across all settings ( d s = 0.40–1.76). In addition, interventions were effective for enhancing teamwork with intact teams ( d = 0.33) and newly-formed teams ( d = 0.67), with the effect size for new teams being significantly larger ( Q = 4.04, p = 0.004) than that for existing teams. Teamwork training was also effective at fostering team performance for both team types; however, in contrast to the findings on teamwork, the effect size for intact teams ( d = 0.99) was significantly larger ( Q = 6.04, p = 0.02) than that for new teams ( d = 0.54).

https://doi.org/10.1371/journal.pone.0169604.t003

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Three intervention characteristics were analyzed as potential moderators. First, with regard to the intervention method utilized, significant effects on teamwork were found for workshop training ( d = 0.50), simulation-based teamwork training ( d = 0.78), and team reviews ( d = 0.64) but not for didactic education ( d = 0.19). All training methods were effective for enhancing team performance ( d s = 0.41–0.69). Second, significant effects of training on teamwork were evident when two or more dimensions of teamwork were targeted ( d s = 0.65–0.98) but not when only one dimension was targeted ( d = 0.05). Team performance, however, improved significantly as a result of teamwork training regardless of the number of teamwork dimensions that were targeted ( d s = 0.46–0.67). Third, significant effects were shown regardless of which dimension (i.e., preparation, execution, reflection, interpersonal dynamics) was targeted for both teamwork ( d s = 0.64–0.75) and team performance ( d s = 0.52–0.60).

With regard to measurement characteristics, significant improvements on teamwork emerged when either third-party ( d = 0.80) or self-report ( d = 0.38) measures of teamwork were utilized; the effect size for third-party measures was significantly larger ( Q = 6.02, p = 0.014) than the effect size for self-report measures. For team performance outcomes, significant effects were shown for both objective ( d = 0.61) and third-party measures ( d = 0.56). Finally, significant effects on teamwork were found when general/omnibus measures of teamwork were taken ( d = 0.71), as well as when a specific dimension of teamwork was measured ( d s = 0.45–0.70).

The purpose of this systematic review and meta-analysis was to quantify the effects of the extant controlled experimental research of teamwork training interventions on teamwork and team performance. We found positive and significant medium-to-large sized effects for these interventions on teamwork and large effects on team performance. When outlier studies were removed, medium-sized effects were found for both criteria. Additional subgroup/moderator analyses also revealed several notable findings, each of which will be discussed in turn. The paper concludes with a discussion of the limitations associated with this meta-analysis as well as considerations for future teamwork training research.

Who Can Benefit From Teamwork Training?

With regard to sample characteristics, teamwork interventions were shown to be effective at enhancing both teamwork and team performance across a variety of team contexts, including laboratory settings as well as real-world contexts of health care, aviation, military, and academia. This highlights the efficacy of teamwork training as a means of improving teams; this is an important finding as effective teams (i.e., those that work well together and perform at a high level) are vital in many of the aforementioned contexts. For example, it has been estimated that approximately 70% of adverse events in medical settings are not due to individuals’ technical errors but, rather, as a result of breakdowns in teamwork [ 78 ]. Thus, there is a critical need to ensure that teams are effective across these settings, as these teams greatly impact (among other things) the welfare of others. The results of this meta-analysis suggest that teamwork training can indeed be a useful way of enhancing team effectiveness within these contexts.

We also examined whether there were differential effects of teamwork training for new teams compared to intact teams. It was shown that these interventions were effective for both team types. The effects of teamwork training on teamwork outcomes were significantly larger for new teams (who showed a medium-to-large effect size) compared to existing teams (who had a small-to-medium effect size). Interestingly, when we examined team performance as the criterion variable, the training effects were significantly larger for intact teams (who showed a large effect size) compared to newly-formed teams (who again showed a medium-to-large effect size). It should be noted that there were many more studies conducted with new teams compared to intact teams—thus, caution should be exercised in directly comparing these findings. Nonetheless, at this point, the existing research seems to suggest that teamwork interventions work particularly well at enhancing teamwork processes for newly established teams—and also work with existing teams—but not the same extent. It is possible that teamwork processes might be more malleable and display greater potential for improvement with new teams compared to more established teams whose teamwork processes may be more entrenched. On the other hand, it is notable that the effects of teamwork training on team performance were stronger for established teams. In line with this, it is plausible that, while intact teams may show less pronounced changes in teamwork, they might be better able to translate their teamwork training into improved team performance outcomes.

What Type of Training Works?

Three moderator variables were assessed with regard to intervention characteristics. First, with regard to the training method utilized, it was shown that all four training methods were effective for enhancing team performance. These included the provision of didactic lectures/presentations, workshops, simulation training, and review-type activities conducted in situ. Although significant effects were shown for the latter three training methods for teamwork outcomes, those interventions that targeted didactic instruction did not result in significant improvements in teamwork itself. This suggests that simply providing educational lectures wherein team members passively learn about teamwork is not an effective way of improving teamwork. When taken together these findings suggest that teamwork training should incorporate experiential activities that provide participants with more active ways of learning and practising teamwork. These may include various workshop-style exercises that involve all team members, such as working through case studies of how teams can improve teamwork, watching and critiquing video vignettes of teams displaying optimal versus suboptimal teamwork, discussing and setting teamwork-related goals and action plans, or other activities that help stimulate critical thinking and active learning of effective teamwork. Teams may also find it useful to conduct simulations of specific team tasks that the group is likely to encounter in-situ, such as aviation teams using an airplane simulator, surgical teams conducting mock-surgeries on medical manikins, military teams practising various field missions, and so on. Teamwork can be also fostered by having team members participate in team reviews/briefings before, during, and/or after the execution of team tasks that occur in-situ. In summary, simply lecturing about the importance of teamwork is not sufficient to create meaningful improvements in teamwork; rather, substantive positive effects can be derived by having team members engage in activities that require them to actively learn about and practise teamwork.

We also sought to assess how comprehensive an intervention should be—specifically, the number of teamwork dimensions that need to be targeted—in order to be effective. With regard to improving team performance, there were significant effects when one or more dimensions were targeted. However, in terms of improving teamwork behaviors, significant effects only emerged when two or more dimensions were targeted. From an applied perspective, individuals concerned with intervention (e.g., team consultants, coaches, managers, team leaders) can utilize these findings by targeting more than one dimension of teamwork within their training protocol. For instance, if the purpose of an intervention is to improve a health care team’s communication, greater effects may be derived by not merely targeting communication during the execution phase alone (e.g., with a structured communication tool), but by also incorporating strategies that target other dimensions of teamwork, such as setting goals and action plans for how communication will be improved (i.e., the preparation dimension of teamwork) as well as monitoring progress towards those goals, resolving any communication-related problems that arise, and making adjustments to action plans as necessary (i.e., the reflection dimension).

Relatedly, we sought to address whether there were differential effects of teamwork interventions on teamwork and team performance based on the dimensions of teamwork that were targeted. It was found that interventions had a significant effect on both teamwork behaviors and team performance when any dimension of teamwork was targeted. This is important as it means that if those concerned with intervention target any one of the four dimensions of teamwork, this will likely result in improvements in team functioning. While the preparation (i.e., behaviors occurring before team task performance such as setting goals and action plans), execution (i.e., intra-task behaviors such as communication and coordination), and reflection (i.e., behaviors occurring following task performance such as performance monitoring and problem solving) dimensions have each been theorized to be implicated in fostering team performance [ 2 , 79 ], is particularly noteworthy that interventions targeting the interpersonal dynamics of a team (i.e., managing interpersonal conflict and the provision of social support between members) also displayed significant effects in relation to team performance. Specifically, efforts to enhance interpersonal processes have generally been theorized to be related to supporting team maintenance more so than supporting team performance [ 2 , 79 ]. However, the results from the current review provide evidence that training teams with regard to social support and interpersonal conflict management processes may actually be a useful way to enhance team performance. While the exact reason for this effect is not immediately clear from this review, it may be that improving interpersonal dynamics has an indirect relationship with team performance. That is, teamwork training focused on improving social support and conflict management may improve the functioning of a team, which, in turn, improves the team’s performance. As Marks et al. [ 10 ] contend, these interpersonal processes “lay the foundation for the effectiveness of other processes” (p. 368). Relatedly, Rousseau et al. [ 2 ] suggest that problems related to social support and conflict management “may prevent team members from fully contributing to task accomplishment or from effectively regulating team performance” (p. 557). Further research examining this potential relationship is required as this would have implications in both research and applied teamwork settings.

Does It Matter How Criterion Variables Are Measured?

Two measurement characteristics were examined as moderators within this meta-analysis. First, significant, large- and small-to-medium sized effects were found for third party and self-report measures of teamwork, respectively. Significant medium effects were also evident for third party and objective measures of team performance. It is worth noting that significantly larger effect sizes emerged for third party assessments of teamwork compared to self-report measures. Taken together, these findings suggest that the positive effects that were found for teamwork interventions are not merely perceptive and/or due to individuals’ self-report biases (i.e., social desirability). Rather, these results indicate that the effects of these interventions on both teamwork and team performance are clearly observable with measures beyond self-report indices.

Finally, we sought to assess whether the effects of teamwork training varied based on which teamwork dimension(s) were measured. Medium-to-large effects emerged when general/omnibus measures of teamwork—that is, those that provided an overall score of teamwork as opposed to examining individual dimensions of teamwork—were taken. Measures that tapped into the specific dimensions of teamwork (e.g., those that provided individual scores on preparation, execution, reflection, and interpersonal dynamics) also yielded comparable effect sizes. Hence, teamwork interventions appear to have a somewhat similar effect on each of the components of teamwork. In summary, the results of the above two moderators (i.e., type of measure and dimension of teamwork examined) suggest that teamwork training has a positive impact on teamwork and team performance regardless of the way in which these variables are assessed.

Limitations

Despite the contributions of this meta-analytic review, it is not without limitations. First, there were additional variables that we had planned to analyze as moderators a priori including team size and length of/contact time within the intervention. However, there was an insufficient amount of reliable data across the studies on these variables to conduct these subgroup analyses appropriately. For instance, although many studies noted the total number of participants within an organization (e.g., a hospital) that took part in an intervention, information on the size of the teams within the organization (e.g., various units within the hospital) was often missing. Team composition variables such as this have been noted as important factors to take into account when examining teams (e.g., [ 30 , 80 ]). Similarly, although some studies were explicit about the total length of the intervention and the contact time between interventionists and participating teams, this information was not provided consistently. This too would have been a valuable feature to analyze in order to provide more specific recommendations about how teamwork training programs should be designed—that is, how long an intervention should last? Unfortunately, due to the paucity of information available in the included manuscripts, we were unable to determine whether these variables moderated the observed effects of teamwork training on teamwork and team performance in the current meta-analysis.

Furthermore, there was a considerable amount of variability within some of the moderator categories that were coded. For instance, with regard to intervention methods, ‘workshops’ consisted of many different types of activities including team charter sessions, strategy planning meetings, case study activities, and so on. Combining these activities into one category was done for the sake of being adequately powered to conduct moderator analyses (i.e., include a sufficient number of studies within each of the resulting categories). However, while the above examples are indeed activities that teams do together, they are of course each different in their own ways. Hence, although it is evident that workshop-type activities are effective overall, it is unclear if specific workshop activities are more effective than others. This example underscores the difficulty that can occur when trying to balance statistical power with accuracy for each moderator category when conducting subgroup analyses in a meta-analysis.

Relatedly, effect sizes were only computed with the statistics that were provided from baseline and post-intervention, even if studies provided additional data on teamwork and/or performance at some other point in between or at a follow-up point in time (although it is worth noting that relatively few studies actually did this). This was done in order to minimize heterogeneity within the meta-analysis and improve the interpretability of the results (i.e., determining the effects of teamwork training from pre- to post-intervention). However, by not taking these measurement time-points into consideration, two questions in particular are raised. First, do certain dimensions of teamwork and team performance evolve differently over time and, if so, how? For instance, do improvements in teamwork occur immediately in response to training and then plateau; or do they improve in a slower, more linear fashion from the onset of training? Second, what are the long-term implications of teamwork training? That is, does teamwork training result in sustained improvements in teamwork and team performance beyond the intervention period or do these effects eventually wane? Answers to these types of research questions would certainly be of interest to teamwork researchers and applied practitioners.

Future Directions

In addition to summarizing the previous research on teamwork interventions for improving teamwork and team performance, the findings from this systematic review also highlight several potential avenues of future research. First, with regard to sample characteristics, the majority of studies that examined the effects of teamwork interventions on team performance were conducted within laboratory settings, with relatively fewer controlled studies having been conducted in real-world settings. Thus, although significant effects on team performance (and teamwork) were found in health care, aviation, military, and academic settings, the extant literature would be strengthened by conducting further controlled intervention research within these contexts. It was also shown that teamwork training was less effective for improving teamwork for intact teams compared to new teams. Since many teams seeking teamwork training are likely to be intact, it is important that future research continue to test various training strategies that can be utilized with these types of teams. In addition, there are other contexts in which controlled interventions have not yet been conducted such as with police squads, firefighting crews, sports teams, political parties, and so on. Research in these areas is clearly ripe for future inquiry.

Further research on the ideal combination of teamwork dimensions (i.e., preparation and/or execution and/or reflection and/or interpersonal dynamics) targeted in an intervention would also enhance our current knowledge in terms of how to train teamwork most effectively and efficiently. We had originally planned to further assess this moderator by conducting a method co-occurrence analysis [ 81 ]. Specifically, since there would likely be a variety of combinations of dimensions that were targeted in the teamwork interventions (e.g., preparation only; preparation and execution; preparation, execution, reflection, and interpersonal dynamics; etc), we had hoped to examine if there would be differential effects of these combinations with regard to intervention effectiveness. Unfortunately, since there were such a large number of combinations of dimensions targeted in the included studies, there was an insufficient number of interventions that fell into each category. We were, therefore, unable to pursue this method co-occurrence analysis [ 81 ] of the various combinations of dimensions. Thus, although our findings suggest that interventions are more effective when two or more dimensions are targeted, further research that examines the effects of the ideal combinations of these dimensions would certainly enhance our current knowledge of teamwork training. For example, if the objective of teamwork training is to improve the coordination and cooperation of the team, should the training also target (in addition to targeting these execution behaviors) both the preparation and reflection dimensions of training (or simply one or the other)? Answering such complex questions will help to advance our understanding of what makes for an effective teamwork training program.

Balanced against the contributions and insights provided by the various moderator analyses conducted in this study, the overall take-home message is that teamwork training is an effective way to foster teamwork and team performance. These effects appear to be evident across a range of samples, utilizing numerous intervention methods, and when considering various measurement characteristics. Interventions appear to be particularly effective when they target multiple dimensions of teamwork and include experiential activities for team members to actively learn about, practise, and continually develop teamwork.

Supporting Information

S1 table. summaries of interventions..

Summaries of each study and intervention included in the meta-analysis is provided in the S1 Table.

https://doi.org/10.1371/journal.pone.0169604.s001

S1 File. PRISMA Checklist.

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Checklist [ 82 ] for this review is presented in the S1 File.

https://doi.org/10.1371/journal.pone.0169604.s002

Author Contributions

• Conceptualization: DM ME BZ MB.
• Data curation: DM.
• Formal analysis: DM.
• Investigation: DM GR.
• Methodology: DM MB.
• Project administration: DM MB.
• Resources: DM MB.
• Supervision: MB.
• Validation: DM GR MB.
• Visualization: DM GR ME BZ MB.
• Writing – original draft: DM MB.
• Writing – review & editing: DM GR ME BZ MB.
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ORIGINAL RESEARCH article

The relationships of team role- and character strengths-balance with individual and team-level satisfaction and performance.

• 1 Department of Psychology, University of Zürich, Zurich, Switzerland
• 2 Clienia Littenheid AG, Littenheid, Switzerland

Teamwork has been argued to play an increasingly important role in numerous jobs, and several studies focused on the effects of team composition for work-related outcomes. Recent research has also identified individuals’ character strengths and positive team roles (e.g., idea creator and relationship manager) as conducive to work-related outcomes. However, there is a scarcity of research on the role of character strengths or positive team roles on the level of teams. In the present study, we extend theoretical assumptions of team role theories to the study of character strengths and positive team roles: We examined the associations between character strengths and team roles with work-related outcomes on the individual (i.e., job satisfaction, self- and supervisor-rated performance) and the team level (i.e., teamwork quality, self- and supervisor-rated team performance). Further, we examined how the team composition relates to the outcomes, that is, whether balanced teams (i.e., all team roles or character strengths are represented in the current team) go along with desired outcomes and whether an overrepresentation of team roles or character strengths in a team (i.e., a team role or character strengths is represented by multiple team members) goes along with undesired outcomes. We studied a sample of 42 teams ( N = 284 individuals) who completed measures of team roles, character strengths, teamwork quality, job satisfaction, and self-rated individual and team performance. Further, supervisor ratings of individual and team performance were collected. Results corroborated the relationships of team roles and character strengths with individual outcomes such as that specific roles and character strengths go along with individual performance and work satisfaction. Further, the results suggested that teams in which more team roles are represented report higher performance and teamwork quality. Also, teams with higher average levels of the character strengths of teamwork and fairness, and teams with more members scoring high in fairness and prudence report higher teamwork quality. Further, there is no evidence that having too many members with a particular character strength has detrimental effects on teamwork quality, work satisfaction, or performance. We conclude that extending the study of character to the level of teams offers an important advancement.

Introduction

Teamwork has often been highlighted as an important factor for the success of projects and organizational performance (e.g., Petty et al., 1995 ; Hoegl and Gmuenden, 2001 ). A considerable body of literature has focused on the composition of successful teams, and several relevant factors for successful teamwork have been proposed. A meta-analysis reported the diversity of education or expertise within teams to go along with qualitatively better team performance, while no effects for the diversity of demographic characteristics were found ( Horwitz and Horwitz, 2007 ). For other variables such as the personality dimensions of the five-factor model, findings were mostly mixed (see Mathieu et al., 2008 for a review).

However, it has been argued for a long time (e.g., Benne and Sheats, 1948 ) that diversity (also referred to as balance) in personality-related individual differences, such as team roles, plays a crucial role for performance and work-related well-being of individuals and teams. Recently, a new framework for studying team roles has been proposed, the VIA team roles. This framework has been developed from a positive psychology viewpoint and distinguishes among seven informal team roles that focus on positive behaviors and contributions to the team ( VIA Institute on Character, 2013 ). Initial studies using this framework suggested positive associations between assuming these team roles and relevant work-related outcomes, such as work satisfaction or calling ( Gander et al., 2018 ; Ruch et al., 2018 ).

Further, within positive psychology, a classification of positively valued personality traits, so-called character strengths, has been suggested ( Peterson and Seligman, 2004 ). This VIA classification encompasses 24 character strengths that are expected to contribute to the “good life” in all its domains. Thus, it is expected that several of these traits also contribute to good work performance and a fulfilling work experience; on the level of individuals, this has been confirmed in earlier studies (e.g., Harzer and Ruch, 2014 ).

In the present study, we aim at providing some information on how teams could be composed regarding team roles and character strengths in order to maximize desirable outcomes. We extend existing findings by studying complete teams and examine whether the configuration of teams with regard to team roles and character strengths relates to work satisfaction, teamwork quality, and performance.

Teams and Team Roles

In the present study, teams are considered groups of at least three people who “exist to perform organizationally relevant tasks, share one or more common goals, interact socially, exhibit task interdependencies, maintain and manage boundaries, and are embedded in an organizational context that sets boundaries, constrains the team, and influences exchanges with other units in the broader entity” ( Kozlowski and Bell, 2003 ; p. 334). Team roles are context-dependent behavior patterns ( Biddle, 1979 ) that people display in such teams.

Several conceptualizations of team roles have been proposed (for an overview see Mathieu et al., 2015 ) with the most influential one suggested by Belbin (1981 , 2010 , 2012) . His framework distinguishes among nine informal roles (i.e., plant, resource investigator, coordinator, shaper, monitor evaluator, team worker, implementer, completer finisher, and specialist). Each of these roles is expected to come along with specific strengths and weaknesses (e.g., coordinators are described as being good at clarifying goals, delegating, and promoting decision making, while also prone to delegating own work to others and being manipulative; Belbin, 2012 ). Based on this model of nine team roles, Belbin (2010) suggested that teams should be balanced with regard to team roles; that is, all team roles should be present in a team, and no relevant role should be missing, while roles should also not be overrepresented (e.g., duplicated) in a team.

Empirical support for this notion is widely mixed. Several studies reported positive findings; for example, Meslec and Curşeu (2015) found positive relationships between teamwork quality and role balance as a configural group property in a student sample. Senior (1997) also reported supporting evidence for the relevance of team role balance for team performance in a sample of 11 management teams. Other studies failed to find any relationships (e.g., van de Water et al., 2008 ; Batenburg et al., 2013 ). Similarly, Meslec and Curşeu (2015) also found no support for the notion that roles should not be duplicated. Overall, results remain inconclusive and research has often relied on very small or student samples. Further, although widely used, Belbin’s model—particularly the associated assessment instrument (Belbin Team Role Self-Perception Inventory; Belbin, 1981 )—has often been criticized, mostly for its allegedly unsatisfactory psychometric properties ( Furnham et al., 1993a , b ; Fisher et al., 2001 ).

The present study employs a different framework for the assessment of team roles, the VIA team roles ( VIA Institute on Character, 2013 ). It assumes the seven following team roles: Idea Creator (thinks of unconventional ways of coming to solutions and great ideas), Information Gatherer (searches for information, for example, on best practices, new trends, potential vendors, competition, etc.), Decision Maker (processes and integrates available information, makes decisions and clarifies the goals), Implementer (controls the current status and takes measures to work toward the goal), Influencer (presents the product for acceptance internally and/or externally), Energizer (infuses energy into their work and others), and Relationship Manager (helps to run relationships smoothly and to resolve conflicts). These team roles were derived rationally based on considerations about relevant skills following a prototypical sequence in a project: At the beginning, a new idea has to be created (Idea Creator), and research conducted on existing information (Information Gatherer). Then, goals have to be set, and decisions made (Decision Maker), which have to be implemented (Implementer), and internal (e.g., supervisors), and external (e.g., customers) stakeholders have to be convinced (Influencer). Throughout the whole process, obstacles have to be overcome, which requires persistence and energy (Energizer), and a productive work atmosphere has to be maintained, and conflicts among team members have to be resolved (Relationship Manager).

While the VIA team roles share many similarities with Belbin’s approach, they represent a more parsimonious model and exclusively focus on strengths (instead of also entailing weaknesses). Further, a psychometrically sound instrument has been developed for their assessment, the VIA Team-Roles Inventory ( Ruch et al., 2018 ). Nonetheless, several of Belbin’s assumptions are also expected for the VIA team roles, mostly the hypotheses that more balanced teams (i.e., teams in which more of the seven VIA team roles are represented), and teams in which team roles are less overrepresented (i.e., duplicate), should perform better in terms of performance and well-being at work (e.g., Senior, 1997 ).

Earlier studies showed that all VIA team roles are positively related to individual work satisfaction ( Ruch et al., 2018 ) and calling (with the exception of Information Gatherer; Gander et al., 2018 ). Further, it has been suggested that the interplay between the team roles one shows in the current job, and the roles one would like to show in an ideal team, also plays a role for job satisfaction: For most team roles (i.e., Information Gatherer, Implementer, Relationship Manager, and partially Idea Creator), a better convergence between current and ideal roles went along with higher job satisfaction. The levels of ideal team roles, however, showed only few comparatively small relationships with job satisfaction or calling— in contrast to the levels of team roles actually shown in the current job that were predictive of job satisfaction.

However, currently there is no data available on the relationships between the VIA team roles and work performance. Further, previous studies exclusively relied on self-ratings of individuals and did also not consider teams. Of course, studying configurations of team roles in existing teams and also considering team-level outcomes is of particular importance for advancing the study of team roles and could help in designing well-functioning teams.

Character Strengths

For studying character, Peterson and Seligman (2004) developed the VIA classification that comprises 24 character strengths (i.e., creativity, curiosity, judgment, love of learning, perspective, bravery, perseverance, honesty, zest, love, kindness, social intelligence, teamwork, fairness, leadership, forgiveness, humility, prudence, self-regulation, appreciation of beauty and excellence, gratitude, hope, humor, and spirituality). For identifying these character strengths, Peterson and Seligman (2004) conducted a comprehensive literature research and applied several criteria (e.g., contributing to fulfillments that constitute the “good life,” being morally valued in its own right, being trait-like, being distinct from other strengths, etc.) to potential candidates for character-relevant traits. In sum, these 24 character strengths represent the predominant model for the empirical study of character.

The relevance of character strengths for work-related outcomes has been emphasized early on. For example, Peterson et al. (2009) suggested that “no matter the occupation, character matters in the workplace (p. 229).” Character strengths have, for example, been shown to go along with well-being at work ( Peterson et al., 2009 ; Gander et al., 2012 ; Harzer and Ruch, 2015 ; Heintz and Ruch, 2020 ; Huber et al., 2020 ). While usually almost all strengths positively relate to well-being, often, the character strengths of zest, hope, love, gratitude, and curiosity yielded the strongest relationships to both, general and work-related well-being. Further, character strengths are also relevant for work performance: Almost all character strengths predicted self-rated work performance, and several strengths also go along with supervisor-rated performance evaluations, including the strengths of perseverance, teamwork, and honesty ( Harzer and Ruch, 2014 ). Perseverance has been suggested to play the most important role for work performance ( Littman-Ovadia and Lavy, 2016 ).

Further, character strengths have also been linked to team roles. On the conceptual level, Ruch et al. (2018) suggested that “character strengths might guide the preference for certain team roles but also help taking on and performing these roles” (p. 2). On the empirical level, Ruch et al. (2018) showed that some strengths (e.g., zest, teamwork, leadership, and hope) were robustly related to most roles, while other strengths were particularly important predictors for specific roles (e.g., creativity for the role of Idea Creator, social intelligence for the role of Relationship Manager). Thus, team roles and character strengths represent distinguishable, but both conceptually and empirically related concepts. In the present article, we aim at studying the relevance of both concepts in teams separately.

While there is a lot of empirical data on the relationships of character strengths and well-being at work, and a few studies that examined their contribution to work performance, all the studies so far are based on individual data and outcomes. However, since work is rarely conducted in isolation, all real-world settings are also affected by the interindividual interplay of individual differences. Thus, an important next step in the study of character at work is to consider levels and configurations of character strengths in teams, and also to take team-level outcomes into account.

The Present Study

The present study examined the role of character strengths and team roles for work-related outcomes. Since some previous studies found effects of team role balance on teamwork quality and team performance, and relationships of character strengths with individual performance and work satisfaction, we considered all these variables: We were interested in individual and team-level performance, individual work satisfaction, and teamwork quality (i.e., comprising several aspects of collaborative team processes related to both tasks and social interactions). Further, we considered data from several sources and levels, namely, individual self-ratings, aggregated self-ratings, and supervisor-ratings.

The outcomes were (i) self-rated individual performance, (ii) supervisor-rated individual performance, (iii) self-rated team performance on both the level of the individual (How does a team member perceive the performance of his or her team?), and (iv) aggregated on the team level (How do the team members perceive their performance on average?), (v) supervisor-rated team performance, (vi) self-rated individual work satisfaction, (vii) self-rated teamwork quality on both the level of the individual (How does a team member perceive the teamwork quality in his or her team?), and (viii) aggregated on team level (How do the team members perceive their teamwork quality on average?). The outcomes are summarized in Table 1 .

Table 1. Outcomes in the present study.

The present study had six main aims: first, we aimed at examining the relationships between current and ideal team roles and character strengths with work-related outcomes. Thereby, we intended to corroborate earlier findings on positive relationships of team roles ( Gander et al., 2018 ; Ruch et al., 2018 ) and character strengths (e.g., Harzer and Ruch, 2014 ; Gander et al., 2020 ; Heintz and Ruch, 2020 ; Huber et al., 2020 ) with work-related outcomes and extending these findings by analyzing hitherto not studied outcomes, such as team performance and teamwork quality, and by additionally considering the team-level perspective. In line with previous findings, we expected positive relationships of all current team roles with work satisfaction, teamwork quality, and performance because enactment of these roles is considered conducive to achieving work tasks as well as to being satisfied with one’s work. For character strengths, we expected positive relationships of work satisfaction and teamwork quality with the strengths of teamwork, zest, love, curiosity, gratitude, and hope, and a positive association between performance and the strength of perseverance.

Second, we aimed at studying whether a good convergence between ideal and current team roles goes along with better outcomes. We examined this research question on both the level of individuals (i.e., whether the convergence between an individual’s ideal and current team role goes along with better outcomes), and the level of teams (i.e., whether teams with higher average levels of convergence between the team member’s ideal and current team roles report better outcomes). While earlier studies ( Gander et al., 2018 ) analyzed the relationships of current-ideal convergence with job satisfaction and calling, no study has addressed the relevance of this convergence for performance, or on the level of the team. Based on the findings by Gander et al. (2018) , we hypothesized higher levels of performance, work satisfaction, and teamwork quality for more convergent individuals and teams.

Third, we examined whether the number of team roles represented in the current team goes along with the outcomes. In line with theoretical assumptions for the VIA team roles (adapted from Belbin, 2010 ), we hypothesized higher levels in all outcomes in more balanced teams in which more of the team roles are represented.

Fourth, we studied for each team role separately, whether the outcomes are affected by the number of team members representing this role. In line with theoretical assumptions for the VIA team roles (adapted from Belbin, 2010 ), we expected that having multiple team members assuming the same roles might have detrimental effects on the outcomes (i.e., that the number of team members representing this role would be negatively related to the outcomes).

Fifth, we examined whether balance in teams with regard to character strengths (i.e., how many character strengths are represented in a team) also relates to the outcomes. This idea was examined on an exploratory basis, and we did not formulate specific hypotheses.

Finally, we tested for each character strength separately, whether there are detrimental effects on the outcomes, when a strength is represented by several team members. Based on theoretical considerations ( Peterson and Seligman, 2004 ) and earlier empirical findings on the individual level for other outcomes, such as life satisfaction ( Park et al., 2004 ) and calling ( Harzer and Ruch, 2012 ), we expected that this is not the case and that there is no such thing as “too much” of a character strength, also with regard to teams. Thus, we conducted these analyses on an exploratory basis. The hypotheses and findings are summarized in Table 2 .

Table 2. Overview over hypotheses and findings.

Materials and Methods

Participants, individuals.

The sample of team members consisted of 284 (41.2% men) participants aged between 16 and 66 ( M = 42.18, SD = 10.62). Most participants (69.4%) held a degree from a university or a university of applied sciences, 6.7% held a diploma allowing them to attend such universities, 19.4% completed vocational training, and 4.6% completed mandatory school. Most participants (82.7%) completed the German version of the survey; the remaining participants completed an English version. On average, participants had been working for M = 4.48 years ( SD = 5.54 years) in the team, with a broad range from less than 1 year up to 34 years.

The 284 team members were working in N = 42 teams. Team sizes varied between 3 and 15 members ( M = 8.49; SD = 3.25 members). Teams were from a broad array of occupations and sectors, including public administration (38.1%), international corporations (21.4%), health care (14.3%), technology and engineering (11.9%), education and research (7.1%), law firms (4.7%), and one team from the service sector.

Supervisors

The 42 teams were led by N = 42 supervisors (61.9% women) aged 28–62 ( M = 47.31, SD = 9.18). These supervisors represented the direct supervisors and were not team members themselves but represent a separate sample.

Instruments

The VIA Team-Roles Inventory ( Ruch et al., 2018 ) assesses the degree to which one masterfully performs the seven VIA team roles (i.e., Idea Creator, Information Gatherer, Decision Maker, Implementer, Influencer, Energizer, and Relationship Manager) in the current team with five items each. Respondents read a short description of the roles and then are asked about their ability to perform this role, and their enjoyment and engagement/flow in performing this role. All items used a seven-point Likert-style scale, ranging from 1 (“strongly disagree”) through 7 (“strongly agree”). A sample item is, “In my current team, I’m at my best when coming up with ideas” (Idea Creator). Internal consistencies in the present study were high (all α ≥ 0.92).

The VIA Ideal Team-Roles Inventory ( Gander et al., 2018 ) assesses the degree to which one would perform the seven VIA team roles in an ideal team. Participants were asked to think of an ideal team, i.e., a team in which they could apply all their strengths and do what they do best. All items used a seven-point Likert-style scale, ranging from 1 (“strongly disagree”) to 7 (“strongly agree”). A sample item is, “If I would be in my ideal team, I’d be at my best when coming up with ideas” (Idea Creator). Internal consistencies in the present study were high (all α ≥ 0.93).

The Values in Action Inventory of Strengths (VIA-IS; Peterson and Seligman, 2004 ; German version by Ruch et al., 2010 ) assesses the 24 character strengths of the VIA classification with 10 items per character strength. It uses a five-point Likert-style scale ranging from 5 (=“very much like me”) to 1 (=“very much unlike me”). A sample item is, “I find the world a very interesting place” (curiosity). Internal consistencies in the present study ranged from α = 0.68 to α = 0.91 (median α = 0.76).

The Teamwork Quality Questionnaire (TWQ; Hoegl and Gmuenden, 2001 ) assesses six facets of collaborative team process (i.e., communication, coordination, balance of member contributions, mutual support, effort, and cohesion) capturing both task-related and social interaction within teams with 38 items. The questionnaire uses a five-point Likert-style scale ranging from 1 (“strongly disagree”) through 5 (“strongly agree”). A sample item is “There was frequent communication within the team” (communication). In the present study, we only analyzed general teamwork quality (i.e., the total score across all items). Internal consistency was high (α = 0.95), and there was a good inter-rater reliability among team members (ICC [2]; one-way random effects, absolute agreement, average of multiple raters = 0.82), and there was a considerable amount of variance attributed to group membership (ICC [1] = 0.40). Inter-rater-agreement for the individual teams ranged from r WG(J) = 0.96 to 0.99 [median r WG(J) = 0.99].

For the assessment of Work Satisfaction , we selected the 11 items out of the 15 items suggested by Warr et al. (1979) that clearly loaded on a general job satisfaction factor and did not show secondary loadings in a previous study ( Parker, 2000 ). All items are rated on a seven-point Likert-style scale ranging from 1 (“extremely dissatisfied”) to 7 (“extremely satisfied”). A sample item is, “How satisfied are you with the opportunity to use your ability?” Internal consistency was high (α = 0.87).

For the assessment of self- and supervisor-rated Team Performance and Individual Performance , we adapted five items suggested by Hoegl and Gmuenden (2001) . The items for the assessment of team performance, rated both by each team member and the team supervisor, were: “Going by the results, the work of the team can be regarded as successful,” “The work of the team is of high quality,” “The team was satisfied with the results of the team’s work,” “The team achieves its goals,” and “The team completes its tasks within schedule.” Further, we adapted these five items for the assessment of self- and supervisor-rated work performance: “Going by the results, my work can be regarded as successful,” “My work is of high quality,” “I am satisfied with the results of my work,” “I achieve my goals,” and “I complete my tasks within schedule.” Internal consistencies were high (team performance self-rating: α = 0.87, team performance supervisor rating: α = 0.78, individual performance self-rating: α = 0.81, individual performance supervisor rating: α = 0.91), while inter-rater reliability for self-rated team performance was moderate (ICC [2] = 0.64), and 21% percent of the variance could be attributed to team membership (ICC [1]). Inter-rater agreement for the individual teams ranged from r WG(J) = 0.79 to 0.99 [median r WG(J) = 0.96].

According to the university’s ethics guidelines, no formal ethics proposal was needed for the present study. All data was collected online. We recruited participants via their supervisors who were contacted through professional networks, psychology mailing lists, psychology magazines, and meet-up groups. Individuals who are currently members of a work team of three or more people were eligible for participation. A work team is defined as a group of people that comprise a set of complementary skills and whose members interact with each other to achieve an—at least partially—common goal.

First, the team supervisor received a link to an online survey, asking for the e-mail addresses of all team members. The supervisors completed performance evaluations of the individual team members and the team as a whole. Afterward, each team member received an invitation to participate in an online survey in which they provided demographic information and completed the measures on character strengths, team roles, job satisfaction, teamwork quality, and individual and team performance. Before the start of the questionnaire, all supervisors and team members provided written informed consent. All questionnaires could be completed in German or English. Upon request, each participant received a feedback on his or her individual character strength profile and a team-based feedback on the team role balance, character strengths balance, and aggregated levels of teamwork quality. No other incentives for participation were offered.

Data Analysis

Convergence between current and ideal team roles.

For computing an overall indicator of convergence between current and ideal team roles, we computed the Euclidian distance, that is, the square root of the sums of the squared differences between every current (VIA Team Roles Inventory) and ideal (VIA Ideal Team Roles Inventory) team role. The resulting indicator is a measure of discrepancy: lower scores denote a better convergence between ideal and current team roles. While earlier studies suggested more complex relationships between current and ideal team roles, also depending on the type of role ( Gander et al., 2018 ), we used this measure as an overall indicator of convergence.

Team Role/Character Strength Balance

For studying the effects of balance with regard to team roles and character strengths, we computed two different types of indices: The first type of indices indicates how many of the seven team roles or the 24 character strengths are represented in a team. Thus, for every team role (and character strength), we determined that it was present in a given team, when at least one of the members scored among the highest 10% in this scale. For each team role (and character strength), the team received one point if the role/strength was present—regardless of how many team members represented the role/strength—and zero points if the role/strength was represented by none of the team members. This resulted in two overall balance indices for each team; one for team roles and one for character strengths. These indices ranged from 0 to 7 for team roles and from 0 to 24 for character strengths. The overall balance indices were used for determining whether individuals and teams are more satisfied and perform better when all roles are represented.

The second type of indices indicated by how many times a team role or character strength was represented by a team member. Thus, for each team member who represented the role/strength of interest, the team received one point. This resulted in seven indices for team roles, and 24 indices for character strengths, each ranging from 0 to the total number of team members. We tested for linear and quadratic trends in these indices, for examining whether there are negative effects on the outcomes when some roles are represented several times in a team. All analyses using these balance indices were controlled for the number of team members (team size).

Statistical Analyses

We had data on the team-level (Level 2; i.e., team size, gender ratio, average age of team members, average educational level of team members, average duration of team membership, average fit between ideal and current roles, supervisor ratings of team performance, and number of team roles/character strengths present in the team) and on the person-level (Level 1; i.e., gender, age, education, duration of team membership, fit between current and ideal roles, self-ratings of work satisfaction, individual performance, team performance, teamwork quality, and supervisor-ratings of individual performance), with the person-level nested within the team-level. We used the R-package lme4 ( Bates et al., 2015 ) for analyzing multilevel models, and lmerTest ( Kuznetsova et al., 2017 ) for computing p -values for the fixed effects. All models with Level 1 outcomes (i.e., predicting self- and supervisor-rated individual performance, self-rated team performance, work satisfaction, and teamwork quality) were estimated using a restricted maximum likelihood estimation and allowed random intercepts for the teams. Since preliminary analyses suggested relationships of several demographic variables (e.g., gender and education) and objective team characteristics (e.g., gender ratio and average education level) with the outcomes, we controlled all subsequent analyses for team size, as well as individual and team-level gender, age, education, and duration of team membership.

The only exceptions were the analyses with supervisor-rated team performance as outcome (Level 2). For these analyses, we computed ordinary least squares regressions using only aggregated Level 2 data as predictors and control variables (i.e., team size, gender ratio, average age, average education, and average duration of team membership).

Zero-order correlations between all variables in the study on both the individual level, and on the aggregated team-level are given in online Supplementary Table A .

Levels of Current and Ideal Team Roles

First, we inspected the relationships between the levels of current and ideal team roles with the outcomes by computing a set of multilevel models predicting the outcomes by each team role separately, and the control variables (see Table 3 ).

Table 3. The relationship of current and ideal team role levels with the outcomes.

Table 3 shows that most current team roles positively related to self- and supervisor-rated individual performance (exceptions were Information Gatherer and Relationship Manager), and to self-rated team performance, but not supervisor-rated team performance. Overall, the numerically strongest relationships were found for the Idea Creator and Implementer roles. All seven team roles contributed to individual work satisfaction, while all roles but Information Gatherer related to self-rated teamwork quality. At the team-level, higher average levels of Idea Creator, Information Gatherer, and Influencer were associated with higher average scores of teamwork quality.

Only a few relationships were found for the levels of ideal roles. Some roles were related to self- (Idea Creator, Decision Maker, Implementer, and Influencer) or supervisor-rated (Influencer and Energizer) individual performance, work satisfaction (Energizer and Relationship Manager), or self-rated teamwork quality (Relationship Manager), while all roles were unrelated to supervisor-rated team performance.

For analyzing the relevance of the convergence between current and ideal team roles, we computed a set of multilevel models, predicting the outcomes by the indicator of convergence, and the control variables. Results are given in Table 4 .

Table 4. The relationships of discrepancy between current and ideal team roles and team role balance with self- and supervisor-rated performance, work satisfaction, and teamwork quality.

Table 4 shows that with regard to outcomes on the level of individuals, the smaller the discrepancy between current and ideal roles, the higher the supervisor-rated—but not self-rated—performance, and the higher the self-rated work satisfaction and perceived teamwork quality. On the level of teams (i.e., using aggregated outcomes), no effects of current/ideal-convergence were observed.

Team Role Balance

The index of team role balance ranged between 0 and 7, with an average of M = 4.31 roles ( SD = 2.23) represented in each team. For analyzing the effects of team role balance, we computed the same analyses, predicting the outcomes by the team role balance and the control variables.

Table 4 shows that the more the seven VIA team roles are represented in each team, the better the self-rated team performance. Further, the number of team roles represented also went along with higher reported work satisfaction and teamwork quality. No relationship was found for supervisor-rated individual performance. On the level of teams, the number of team roles represented showed positive effects on self-rated team performance and teamwork quality.

Further, for each team role, we looked at how many times they were represented in a team. These indices ranged from the minimum of 0 (for all team roles) to the maxima of 4 (Idea Creator, Information Gatherer, and Relationship Manager), 6 (Energizer), 7 (Decision Maker and Implementer), and 8 (Influencer) persons in a team representing these roles. Averages ranged from M = 0.76 roles (Information Gatherer) to M = 1.64 roles (Implementer) with standard deviations between SD = 0.96 (Information Gatherer) and SD = 1.45 (Influencer).

For examining whether there is a satiation point of the number of people representing a team role, we computed a set of multilevel models, and estimated both linear and quadratic trends. Thus, we predicted the outcomes by the number of team members representing this role, and the squared number of team members representing this role (predictors were mean-centered for avoiding issues of multicollinearity), and the control variables. Results are given in Table 5 .

Table 5. The relationships of the number of team roles represented in each team with self- and supervisor-rated performance, work satisfaction, and teamwork quality.

Table 5 shows that for individual performance, there were only effects for the team role of influencer (Influencer): Results suggested an inverted u-shape relationship between the number of people representing the role of influencer and the supervisor-rated individual performance. Figure 1 shows an example of the nature of this u-shaped relationship.

Figure 1. Relationship of the number of information gatherers per team and teamwork quality (standardized coefficients).

Similar patterns were also observed for self-rated team performance (for the roles of Information Gatherer, Decision Maker, and Influencer), while for the roles of Idea Creator and Implementer, only a positive linear effect was observed, while the quadratic effects did not reach significance. For work satisfaction, again, inverted u-shaped relationships were found for Idea Creator, while linear effects were obtained for Information Gatherer, Energizer, and Relationship Manager roles. For teamwork quality, u-shaped relationships were found for Information Gatherer and Decision Maker, and linear relationships for Idea Creator and Influencer. Finally, on the level of teams, we found the same linear and quadratic effects for the roles of Decision Maker and Influencer for supervisor-rated team performance. Further, aggregated self-ratings were mostly parallel to the findings for individual self-ratings.

Levels of Character Strengths

As for team roles, we computed a set of multilevel models predicting the outcomes by the level of each character strength separately and the control variables (see Table 6 ).

Table 6. The relationship of character strength levels with self- and supervisor-rated performance, work satisfaction, and teamwork quality.

Table 6 shows that several character strengths (including perseverance, perspective, leadership, hope, self-regulation, honesty, zest, and gratitude) predicted self-rated individual performance; only perseverance was associated with supervisor-rated individual performance. A similar picture was obtained for team performance, where several character strengths were associated with self-rated individual team performance (mostly teamwork, love, and fairness), but no strengths were related to supervisor-rated or aggregated self-rated team performance. Work satisfaction and teamwork quality were predicted by several character strengths (strongest relationships for teamwork and love) in self-ratings, while on the level of teams, only teamwork and fairness were significant predictors of teamwork quality.

Character Strength Balance and Number of Character Strengths Represented

We computed the same analyses for character strengths as for team roles, for examining whether the character strength balance, that is, how many of the 24 character strengths of the VIA classification are represented in each team, relate to the outcomes. Between 3 and 24 of the character strengths were represented in each team ( M = 13.31; SD = 5.84). Results are given in Table 7 .

Table 7. The relationships of the character strengths balance and the number of character strengths represented in each team with self- and supervisor-rated performance, work satisfaction, and teamwork quality.

Table 7 shows that no relationships were observed between character strength balance and the outcomes.

Next, we analyzed whether the number of members in each team representing each of the 24 character strengths relates to the outcomes. Since analyses suggested no quadratic effects of character strengths, only linear effects were examined. Only for the character strengths of fairness (positive relationships with teamwork quality) and prudence (positive relationships with self-rated individual and team performance and teamwork quality) effects were observed.

The present study examined the contributions of team roles and character strengths to well-being and performance at work on both the levels of individuals and teams. Overall, our expectations were mostly confirmed for self-ratings of the outcomes, while they only were partially confirmed for supervisor ratings and team-level aggregated self-ratings. On the level of teams, this can mostly be explained by insufficient power due to the small sample size on the level of teams, since most effects were in the expected direction but failed to reach significance. Also, especially in the supervisor ratings, there was less variance, and potential relationships might be hidden by ceiling effects. Nonetheless, it is also possible that self-ratings of performance (on individual and team-level) assess somewhat different constructs than supervisor ratings and that the former are more strongly influenced by perceptions of teamwork quality and satisfaction than the latter. In the following, we summarize and discuss our main findings.

Effects of Team Roles on Performance, Work Satisfaction, and Teamwork Quality

First, higher levels in most current team roles—but not ideal team roles—went along with higher levels of work satisfaction and teamwork quality, individual performance (both self- and supervisor-rated), and self-rated team performance, thus, widely confirming our expectations. On the level of teams, however, although the effects of self-ratings on team performance and teamwork quality went into the expected direction, only a few effects reached significance, and no relationships with supervisor-rated team performance were observed. Since these analyses were performed at the level of teams, the statistical power was determined by the sample size of teams and was likely not sufficient to detect the effects—even though the sample size of teams was considerably larger than in many previous studies. Compared to the other team roles, Information Gatherer and Relationship Manager seemed to be least important for performance, and Information Gatherer for well-being at work, while the most robust results across all outcomes were found for Idea Creator. One might argue that this is due to the sample that consisted mostly of higher-level occupations where coming up with new, innovative approaches is a core requirement of the job, while gathering information might be considered a more basic skill that several people should be able to perform, and that is therefore less appreciated.

Convergence Between Current and Ideal Roles

Further, a better convergence between current and ideal team roles went along with higher work satisfaction and better teamwork quality, thus confirming previous findings ( Gander et al., 2018 ) and our expectations. For individual and team performance, we found some support for positive relationships, although they did not show up in all different data sources and levels of analysis considered. Nonetheless, we conclude that increasing the convergence between current and ideal roles might offer a valuable starting point for interventions aimed at fostering individual work satisfaction. Although team roles represent informal roles that cannot be assigned, one still might consider ways to craft someone’s job in order to increase the fit to his or her ideal team role ( Wrzesniewski and Dutton, 2001 ). Further research is needed on formal roles that facilitate the display of team roles; based on such information team roles might also be considered in selection procedures, for maximizing the person-job fit.

Team role balance showed the expected positive relationships to work satisfaction, teamwork quality, and performance on the level of teams; no effects were observed for supervisor ratings. Thus, how many of the seven VIA team roles are represented in a team is an important information for the well-being of the team members, although this does not necessarily translate to effects on performance that could also be perceived by external evaluators, such as the team supervisor. Nonetheless, a satisfying work experience can be considered an important factor for attracting and retaining employees (e.g., Michaels et al., 2001 ). Therefore, designing teams with the intention to have all team roles represented could be a helpful endeavor for the benefit of both the individual and the organization. In the present study, the operationalization of team role balance allowed each team member to represent multiple roles; thus, a balanced team of five members can theoretically consist of one member representing all seven roles and four members representing no roles at all. It is up to future studies to examine whether the degree to which the team roles are evenly distributed among the members also plays a role—one might expect that this is indeed the case, and that it is beneficial for a team when all individuals contribute to the representation of the roles in the team.

Further, the study provided some evidence on the question whether having more team members assuming the role goes along with positive or detrimental effects. Results suggest a complex relationship: For several roles (i.e., Information Gatherer, Decision Maker, and Influencer), quadratic relationships between the number of team members with this role and team performance, and teamwork quality was found, suggesting that while it is beneficial to have some team members in this role, there is also a maximum that should not be surpassed in order to avoid detrimental effects. For Idea Creator and Implementer roles, mostly linear effects were found, while there were also trends for quadratic effects that did not reach significance, however. The number of Energizers and Relationship Managers showed the weakest relationships to the outcomes. Thus, we tentatively conclude that when designing teams, one should particularly pay attention to avoid an overrepresentation of Information Gatherer, Decision Maker, and Influencer roles. One possible reason for these effects might be that, on the one hand, these roles might be more prone to competition and rivalry that lead to internal conflicts when assumed by several members of a team. On the other hand, having more people to create and implement ideas might be beneficial since these roles could often be more directly related to the success of the team and go along with mutual inspiration. However, at this point, we can only speculate about possible processes; more information on the processes and mechanisms of team role and character strength balance is desirable. For example, conflicts might also trigger reflection and contribute to team learning (e.g., Schley and van Woerkom, 2014 ).

How many of these roles are to be considered an overrepresentation, however, cannot be answered by this study. In the present study, we controlled for the effects of team size in all our analyses. However, one might assume that this strongly depends on the team size, and larger teams might be able to need or accommodate more people with Decision Maker roles without detrimental effects, while for very small teams, one person might be enough.

Effects of Character Strengths on Performance, Work Satisfaction, and Teamwork Quality

The present study also underlined the relevance of character strengths for work-related outcomes. Our findings were in line with previous studies (e.g., Heintz and Ruch, 2020 ) with regard to the contributions of strengths such as love, gratitude, zest, and curiosity for work satisfaction, and also teamwork quality. However, the strengths of teamwork and fairness also contributed to both variables, and were the only two strengths that yielded significant effects on teamwork quality on the team level. Both strengths also yielded the highest numerical relationships to self-rated team performance, which is in line with findings on the relationships of character strengths with students’ performance in group work ( Wagner et al., 2020b ). For individual performance, perseverance was found to be most important and related to both self- and supervisor-rated performance, in line with earlier findings. Thus, we conclude that perseverance is the single most relevant strength when interested in maximizing individual performance in selection decisions (in line with earlier findings; Harzer and Ruch, 2014 ; Littman-Ovadia and Lavy, 2016 ), while teamwork and fairness should be considered when selecting employees for tasks involving high amounts of cooperation in order to expect high levels of well-being in the teams.

Character Strength Balance

When looking at configurations of character strengths in teams, no support was found for the idea that all character strengths should be present in a team for all considered outcomes. One might assume that while some character strengths are highly relevant to work-related behavior and experiences in most occupations (i.e., persistence) several other character strengths are of lesser relevance in many occupations (e.g., spirituality, appreciation of beauty and excellence). However, one would also expect variation among jobs regarding the character strengths of most relevance (see e.g., Heintz and Ruch, 2020 ). Thus, not all 24 strengths of the VIA classification might be relevant in all jobs; in future studies, one might consider determining in a first step how many character strengths are potentially relevant in a particular team and examining in a second step whether those teams in which all relevant character strengths are represented outperform teams in which only few relevant strengths are represented.

Also, in line with our expectations, we found no evidence for detrimental effects when there are many team members with the same character strength in a team. This supports the idea that character strengths represent positive characteristics and that there is no such thing as having too much (or, in this case, too many) of a character strength. For two strengths, we found positive (linear) relationships between some outcomes and the number of people with the strengths in the team: this was the case for the strengths of prudence (self-rated individual and team performance, teamwork quality) and fairness (teamwork quality). This is especially interesting, since these relationships were also observable on the team levels: Thus, teams with more people who score high in prudence or fairness report better functioning. Both these character strengths might help in preventing conflicts within the team (i.e., being more careful in one’s actions and treating other members just). As opposed to team roles, having multiple members with these strengths might not lead to conflicts due to rivalry but instead could allow for a mutual support.

Although these findings should not be overinterpreted due to the large number of comparisons, they underline the relevance of character strengths such as fairness and prudence that are often overlooked or considered of lesser relevance when only positive outcomes on the individual level are considered (see e.g., Wagner et al., 2020a ).

Limitations

Of course, several limitations of the present study have to be addressed. First, the sample size of the teams was relatively small and only allowed for the detection of medium to large effects. Further, the present study pursued a quasi-experimental approach and studied real, existing teams. While studying real teams also represents the strength of the current study, no conclusions about directionality or causality of the findings can be made. Studies using experimental assignments of team roles or intervention studies aiming at changing team role behavior and/or balance are warranted that would allow for looking at causal influences of team roles on the outcomes. Further, most effects were found for self-reports that are prone to biases. While we also considered supervisor ratings for the performance-related outcomes, these ratings showed a slight negative skew and a restricted range. This limited variability in the supervisor-ratings might have led to an underestimation of the relationships. Also, one might argue that information from peers on the team members’ assumed team roles might also be considered for providing an additional perspective—in many teams, other team members might be able to provide a more accurate picture of a team member’s contributions than the supervisors who interact less frequently with the team members. Thus, future studies might also consider additional data sources. Finally, for examining the effects of team role and character strengths balance, we computed one index for counting how many of the seven team roles/24 character strengths are represented in a team, and indices for determining the number of roles/strengths represented by each team member. These indices rely on cutoff scores that were empirically derived for the present study; of course, such cutoff scores are always somewhat arbitrary and drastically reduce the amount of available information. Also, one might argue that different cutoffs for every team role/character strength would yield stronger effects—it is possible that for some roles/strengths, relatively low levels suffice for a team to function well, while for other roles/strengths, higher levels are needed. Thus, it is possible that a more sophisticated approach for measuring team role/character strength balance might yield even larger effects regarding the studied outcomes.

In summary, the present study corroborated earlier research on the relationships of the VIA team roles and the convergence between current and ideal team roles with work satisfaction. Further, earlier findings of the relationships between character strengths and work satisfaction and performance were widely replicated. Additionally, we extended previous findings on team roles in the following main aspects: (1) The VIA team roles go along with better self- and supervisor-rated individual performance, and self-rated teamwork quality; (2) a better fit between current and ideal roles goes along with better supervisor-rated performance; (3) teams in which more team roles are represented report higher team performance and teamwork quality, both on the levels of individual and aggregated ratings; and (4) having too many team members sharing the same team role can go along with reduced levels of team performance and teamwork quality.

Further, previous research on character strengths was extended by also considering the team level: (5) We found that teams with higher average levels of teamwork or fairness report higher teamwork quality; (6) teams with more members with high levels in prudence or fairness report better teamwork quality and aggregated self-ratings of team performance (only prudence); and (7) there is no evidence that having too many members with high levels in a particular strength goes along with negative effects. We conclude that extending the study of character to the level of social systems, such as teams, provides a highly relevant new perspective, and more studies should examine the effects of different configurations of character strengths in such systems.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation, to any qualified researcher.

Ethics Statement

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

FG, IG, and WR conceptualized and designed the work. FG and IG analyzed and interpreted the data. FG drafted the article. FG and IG made critical revisions of the article. All authors contributed to the article and approved the submitted version.

This study has been supported by the Manuel D. and Rhoda Mayerson Foundation and the VIA Institute on Character and a research grant from the Swiss National Science Foundation (100014_172723 awarded to WR).

Conflict of Interest

WR is a Senior Scientist at the VIA Institute on Character, which holds the copyright of the VIA-IS and this study has been supported by the Manuel D. and Rhoda Mayerson Foundation and the VIA Institute on Character.

The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2020.566222/full#supplementary-material

FIGURE S1 | Zero-order correlations among all study variables, on individual level (above diagonal) and team level (below diagonal). Above diagonal: correlations based on individual data ( N = 277–284). Below diagonal: correlations based on aggregated team-level data ( N = 36–42). ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001.

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Keywords : character strengths, team roles, team role balance, work performance, work satisfaction, teamwork quality

Citation: Gander F, Gaitzsch I and Ruch W (2020) The Relationships of Team Role- and Character Strengths-Balance With Individual and Team-Level Satisfaction and Performance. Front. Psychol. 11:566222. doi: 10.3389/fpsyg.2020.566222

Received: 27 May 2020; Accepted: 16 October 2020; Published: 30 November 2020.

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Copyright © 2020 Gander, Gaitzsch and Ruch. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Fabian Gander, [email protected] ; orcid.org/0000-0002-2204-8828

† ORCID: Willibald Ruch, orcid.org/0000-0001-5368-3616

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Cover Story

What makes teams work?

Psychologists are pinpointing the factors that make teams gel—research that has far-reaching implications for health care, education, research, industry and more

By Kirsten Weir

September 2018, Vol 49, No. 8

Print version: page 46

15 min read

• Healthy Workplaces

The lone wolf is becoming an endangered species. In fields from health care to hospitality, startups to big business, teamwork has become the favored way to get things done. "The world is so complex, no one person has the skills or knowledge to accomplish all that we want to accomplish," says Susan McDaniel, PhD, a psychologist at the University of Rochester Medical Center and 2016 APA president known for her dedication to team-based work. "Interdisciplinary teams are the way to make that happen."

While humans have always joined forces with one another to achieve shared goals, psychologists are zeroing in on the methods and processes that make those collaborations more efficient and successful. "What's changing is the understanding and appreciation that there is a science behind how to manage teams," says Suzanne Bell, PhD, an associate professor of industrial/organizational (I/O) psychology at DePaul University in Chicago.

Now, a special issue of American Psychologist (Vol. 73, No. 4, 2018) details what psychologists have learned—and need to learn—about working in teams. "The Science of Teamwork," co-edited by McDaniel and colleague Eduardo Salas, PhD, of Rice University in Houston, in cooperation with American Psychologist editor-in-chief Anne E. Kazak, PhD, offers 21 articles that delve into the theory, research and applications of team science.

Here, we look at some of the most significant findings in the special issue, particularly the ways that team processes matter for psychologists, whether they're working in health care, research, industry, the military or education.

Building a dream team

Sometimes teams seem to click without too much effort, working together seamlessly and producing great work as a result. Other collaborations crash and burn. A team's success often depends on its composition, as Bell and her co-authors describe in their contribution to the special issue.

Surface-level attributes of individual team members—such as age, gender and reputation—can be important to the team's overall function, but they aren't necessarily the factors that matter most, Bell says. Instead, it's the "deep-level" factors you can't see at a glance, such as the members' personality traits, values and abilities, that tend to have a much bigger impact on work teams, studies suggest.

Those deep-level factors shape what researchers call the ABCs of teamwork: the attitudes, behaviors and cognitive states that collectively influence whether a team achieves its goals. Those elements depend to some degree on the context and on the team's objectives, Bell says. If the goal is to design an innovative new digital device, it's a good idea to build a team with diverse thinkers who bring a range of knowledge, skills and abilities to the project. But if a team's goal is to be more efficient, diverse attitudes might be less critical.

Team success also hinges on some basic tenets of team composition, say Bell and her co-authors. One person's mood and outlook can spread within a team, so a pessimistic team member could negatively influence the way the whole group views its goals. Individuals who value working in groups tend to be both more confident and more cooperative in a team setting. When team members are high in conscientiousness, they are better at self-regulating their teamwork. And groups composed of high-ability members who are able to learn, reason, adapt and solve problems are more likely to work well together.

Researchers are working to design algorithms that help organizations create effective teams for specific goals. In a project with NASA, for instance, Bell and colleagues are developing algorithms to identify crew members suited to working together on long-distance space missions.

Ultimately, such tools can help organizations create the best possible teams from the outset and tailor interventions for the unique needs of a team with a specific composition. "Teams are complex systems," Bell says. "The more you can manage them using a scientific basis, the better your teams will be."

The secret sauce: Cooperation in the military

Using scientific methods to understand teams isn't a new trend. Military researchers have been studying teamwork systematically for more than half a century, as Gerald F. Goodwin, PhD, of the U.S. Army Research Institute for the Behavioral and Social Sciences, and colleagues describe in an article in the special issue. "The military has been really central in supporting and executing research on teams since the 1950s," he says. "That support has been critical to moving this science forward."

That distinction might seem obvious, says Goodwin, but understanding the elements that allow teams to function well—team cohesion and shared mental models, for example—is important for training teams as well as evaluating their performance. "How well people work together may be more important than how well they work on the tasks," he says. "The secret sauce comes from the teamwork."

Research from military settings has also clarified the importance of team cognition—what teams think, how they think together and how well synchronized their beliefs and perceptions are. Team cognition is what allows team members to understand intuitively how their teammates will think and act, whether on the battlefield, in a surgical suite or on a basketball court. "Team cognition is really important for teams that have to quickly adapt to dynamic circumstances without having the opportunity to communicate a lot," Goodwin says.

Many of the empirical findings from military research apply to civilian teams as well. From the earliest studies, military and civilian researchers have openly shared findings and worked together to grow the science of teamwork, Goodwin says. The military, for instance, has made use of results from team research in aviation. Meanwhile, findings from military-funded research have informed processes in many industries, health care in particular.

Teaming up for better health

Teams in the military and in health care share an important commonality: They can be operating in situations in which team coordination can be a matter of life or death. Some of the earlier research on health-care teams focused on hospital settings, where teamwork failures can lead to patient harms such as misdiagnoses, medication mistakes, surgical errors and hospital-acquired infections. In a paper for the special issue, Michael Rosen, PhD, an associate professor of anesthesiology and critical care medicine at the Johns Hopkins University School of Medicine, and colleagues describe how medical team coordination affects patient safety and the quality of patient care.

Unlike teams in a business setting that might collaborate with one another for months at a time, health-care teams are often fluid, especially in hospitals. Medical personnel including physicians, nurses, surgical assistants and pharmacists might have to jump into a new care team at each shift change or for each new patient. The fundamentals of good collaboration are the same no matter how transient the team, Rosen says: "It's about having clear roles, clear goals and a clear plan of care."

Teams are also becoming increasingly important in primary-care settings. "I think the industry is recognizing that we don't have a choice. Health care has become too complex, and the current model isn't working very well," says Kevin Fiscella, MD, a professor of family medicine at the University of Rochester Medical Center who co-authored a special issue article with McDaniel on the science of primary-care teams. "It's not a question of whether we adopt teamwork [in primary care], but how we do it—and how we begin addressing the barriers to teams."

Unfortunately, those barriers are not insignificant, Fiscella adds. One challenge is simply changing the way that many physicians think about primary care. "I graduated from medical school in 1980, and our whole training was that care is about me and the patient, and everybody else is there to support that relationship," he says. While that mentality is changing, it's not dead yet. "That unfortunate mental model of what it means to provide primary care can make it difficult" to move toward team thinking, he adds.

Systemic challenges also make collaboration difficult in primary-care settings. Clinicians such as family physicians, specialists and mental health professionals might be spread out in different locations. "It makes it harder to support other team members who are making important contributions," Fiscella says.

The traditional fee-for-service payment model also makes it difficult for medical professionals to prioritize teamwork, Fiscella and McDaniel add. Research has shown, for example, that when primary-care teams have short "huddles" before a visit to coordinate their care plans, they routinely report better teamwork and more supportive practice climates. Similarly, short team debriefings at the end of the day to hash out what worked and what didn't can boost learning and performance among team members and improve outcomes for patients.

Yet due to scheduling challenges, it can be tough for primary-care teams to find even a few minutes to come together for huddles or debriefings. "Time is money. If you take time out for a team meeting, that's lost revenue," Fiscella says.

Teamwork in the lab

Academia is famous for its departmental silos, but that, too, is changing as multidisciplinary research becomes the norm across all fields of science. Team science is gaining momentum for good reason, says Kara Hall, PhD, director of the Science of Team Science Team at the National Cancer Institute and co-author of a special issue article about collaboration in science.

Globalization and technology have made the pressing problems of society ever more complex, Hall says. Take the public health problem of reducing tobacco use, for instance. To address that challenge, you need an understanding of the genetic, neural, psychological and behavioral factors related to tobacco dependence, not to mention related social forces and the public policy context. "If you want to solve an applied global health problem, you need people who can bring their specialized knowledge to bear," she says. "Multidisciplinary teams can really [create] movement on these big problems."

Research on team science has found that collaborating across organizational and geographic boundaries increases productivity and scientific impact. And cross-disciplinary teams produce more academic publications and publish in more diverse outlets, Hall and her colleagues report.

Despite proven benefits, it can be hard for a researcher to wrap his or her head around team science. Most scientists were trained in an apprenticeship model, learning the ropes from a single mentor. "Historically, our scientists haven't been trained to work in teams or to lead teams," Hall says.

Even if scientists are prepared to take the leap to team-based research, their institutions might not be. Tenure and promotion are usually based on outputs such as academic publications, with more weight given to a paper's lead author and to articles published in journals in a researcher's own discipline. That model rewards competition, with the potential for tension as team members hash out who should be credited as first author. Team science is built instead on interdisciplinary cooperation—but so far, only a few academic institutions reward those cooperative efforts.

Because of the lack of team training and the institutional hurdles, Hall says, a research project may be technically and scientifically well-conceived yet fail to yield anticipated outcomes. If a cross-disciplinary team fails to meet its goals, was it because the topic was better suited to intradisciplinary science? Was it a problem with the way the team was composed? Or could the team have succeeded if members had received more institutional support and training?

Still, some early patterns are emerging to guide the way toward improved science teams, Hall and her co-authors report. Some studies have found that small teams are best for generating ideas that shake up the status quo, for instance, while larger teams are better at further developing those big ideas. And while cultural diversity can increase a science team's impact, diverse teams might benefit from more team science principles to head off challenges such as miscommunication.

Multicutural questions

The role of diversity in teamwork is a topic that needs a lot more attention, not just for scientific teams but in all areas of teamwork, says Jennifer Feitosa, PhD, an assistant professor of I/O psychology at the City University of New York, Brooklyn College. In the special issue, Feitosa and her co-authors describe the ways in which multicultural teams may function differently from teams in which all members share the same cultural background.

Yet it can take longer for a diverse team to find its groove than a team with similar backgrounds and mindsets. Individuals in multicultural teams are more likely to have different ways of doing things and might not understand where their fellow team members are coming from. "If you take a snapshot of a multicultural team at the beginning, it doesn't look so promising. They often need more time to all get on the same page," Feitosa says.

In both multicultural teams and more homogenous teams, trust is a key component for effective collaboration, Feitosa and her colleagues reported. But because of their differences, members of multicultural teams might have difficulty trusting each other at first.

"Focusing on shared goals can really help to develop that trust," she says.

In other ways, diverse teams operate quite differently from teams with cultural similarities. In the general teamwork literature, for example, research suggests that it's important to address and manage team conflict head-on. But when team members come from cultures that emphasize harmony and avoid conflict, calling out the elephant in the room can make people extremely uncomfortable and interfere with the teamwork dynamic, Feitosa says.

Differences in leadership style can also hinder multicultural teams. In North America, organizations are moving toward giving individuals greater autonomy and opportunities for self-management, Feitosa notes. "In very collectivistic and high power-distance cultures, people might rely more heavily on direction from team leaders and might rather be told what to do."

Fortunately, teams can prevent cultural differences from becoming obstacles by creating a "hybrid" culture, the authors report. "It's about establishing team norms that aren't entirely your culture or entirely my culture, but a little bit of everyone's," Feitosa says.

The research on multicultural teams can guide those looking to create collaborations that are both diverse and high-functioning. But to fully harness the value of cross-cultural perspectives and talents, Feitosa and her colleagues conclude, much more needs to be done to integrate findings from research on single-culture teams and multicultural teams. "Teamwork is a complex phenomenon, so we need to get more creative in how we look at this," she says.

Intervening to improve teamwork

Although researchers have more work to do to fully understand team processes, especially in multicultural contexts, it's not too early to apply what we know, Salas says. For the special issue, Salas and colleagues described evidence-based approaches for improving teamwork.

Organizations are clamoring for tools to make their teams more effective. "Team building is probably the No. 1 human resources intervention in the world," Salas says. Yet the results of such programs are mixed. If you send a group of executives into the wilderness for two days, they might have fun and learn something about one another—but it doesn't mean they'll magically develop new teamwork skills.

Put them into evidence-based team trainings, however, and the story is different. "Team training works," Salas says. "We know how to design, develop and evaluate it."

In particular, Salas and his colleagues describe four types of team development interventions that have been shown to benefit team performance: team training, team building, leadership training and debriefing.

Team training describes formalized learning experiences that aim to improve specific team skills or competencies. Structured team training has been shown to improve teamwork functioning and outcomes in industries such as education, engineering and health care. A prime example is TeamSTEPPS , an intervention to reduce medical errors by improving communication and teamwork skills among health-care professionals (see sidebar). Team-building interventions, meanwhile, aim to better teams by improving interpersonal relationships, clarifying roles and improving problem-solving. Such interventions might focus on increasing trust or setting challenging yet specific goals, for example. Leadership training targets a team leader's knowledge, skills and abilities, and improvements to these areas have been shown to support effective overall team processes. When leaders are trained in occupational safety, for instance, their teams exhibit safer behaviors on the job. Finally, team debriefings of the sort used in primary-care settings have been shown to improve performance in a variety of settings, including aviation and military teams.

There's power in numbers, and high-performing teams can be more than the sum of their parts. It's fortunate, then, that teamwork processes can be measured and improved with targeted interventions. But to keep sharpening the science, psychologists must continue exploring the conditions that allow teams to succeed, Salas says.

There's certainly no shortage of demand, he adds. "There's a tremendous amount of interest in trying to understand collaboration and teamwork—in health care, aviation, academia, the military, space exploration, the corporate world. I hope this special issue will inspire people to improve their teams, and to look for new ways of motivating their teams using psychological science."

To read the full American Psychologist special issue on teamwork, go to http://psycnet.apa.org/PsycARTICLES/journal/amp/73/4 .

Further resources

APA: A Curriculum for an Interprofessional Seminar on Integrated Primary Care www.apa.org/education/grad/curriculum-seminar

National Cancer Institute: Team Science Toolkit www.teamsciencetoolkit.cancer.gov

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The Secrets of Great Teamwork

Collaboration has become more complex, but success still depends on the fundamentals. by Martine Haas and Mark Mortensen

Summary .   

Over the years, as teams have grown more diverse, dispersed, digital, and dynamic, collaboration has become more complex. But though teams face new challenges, their success still depends on a core set of fundamentals. As J. Richard Hackman, who began researching teams in the 1970s, discovered, what matters most isn’t the personalities or behavior of the team members; it’s whether a team has a compelling direction, a strong structure, and a supportive context. In their own research, Haas and Mortensen have found that teams need those three “enabling conditions” now more than ever. But their work also revealed that today’s teams are especially prone to two corrosive problems: “us versus them” thinking and incomplete information. Overcoming those pitfalls requires a new enabling condition: a shared mindset.

This article details what team leaders should do to establish the four foundations for success. For instance, to promote a shared mindset, leaders should foster a common identity and common understanding among team members, with techniques such as “structured unstructured time.” The authors also describe how to evaluate a team’s effectiveness, providing an assessment leaders can take to see what’s working and where there’s room for improvement.

Today’s teams are different from the teams of the past: They’re far more diverse, dispersed, digital, and dynamic (with frequent changes in membership). But while teams face new hurdles, their success still hinges on a core set of fundamentals for group collaboration.

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Committee on the Science of Team Science; Board on Behavioral, Cognitive, and Sensory Sciences; Division of Behavioral and Social Sciences and Education; National Research Council; Cooke NJ, Hilton ML, editors. Enhancing the Effectiveness of Team Science. Washington (DC): National Academies Press (US); 2015 Jul 15.

Enhancing the Effectiveness of Team Science.

• Hardcopy Version at National Academies Press

3 Overview of the Research on Team Effectiveness

This chapter summarizes the research literature on team effectiveness, highlighting findings on the key features that create challenges for team science outlined in Chapter 1 . Based on its review of the literature (e.g., Marks, Mathieu, and Zaccaro, 2001 ; Kozlowski and Ilgen, 2006 ; Salas, Goodwin, and Burke, 2009 ), the committee defines team effectiveness as follows:

Team effectiveness, also referred to as team performance, is a team's capacity to achieve its goals and objectives. This capacity to achieve goals and objectives leads to improved outcomes for the team members (e.g., team member satisfaction and willingness to remain together) as well as outcomes produced or influenced by the team. In a science team or larger group, the outcomes include new research findings or methods and may also include translational applications of the research.

More than half a century of research on team effectiveness ( Kozlowski and Ilgen, 2006) provides a foundation for identifying team process factors that contribute to team effectiveness, as well as actions and interventions that can be used to shape the quality of those processes. As noted in Chapter 1 , this evidence base consists primarily of studies focusing on teams in contexts outside of science, such as the military, business, and health care. These teams share many of the seven features that can create challenges for team science introduced in Chapter 1 . For example, in corporations, top management teams and project teams are often composed of members from diverse corporate functions, and these teams seek to deeply integrate their diverse expertise in order to achieve business goals. Therefore, the committee believes the evidence on teams in other contexts can be translated and applied to improve the effectiveness of science teams and larger groups.

This chapter begins by presenting critical background information—highlighting key considerations for understanding team effectiveness and presenting theoretical models that conceptualize team processes as the primary mechanisms for promoting team effectiveness. The chapter then highlights those team process factors shown to influence team effectiveness ( Kozlowski and Bell, 2003 , 2013 ; Ilgen et al., 2005 ; Kozlowski and Ilgen, 2006 ; Mathieu et al., 2008) , based on well-established research (i.e., meta-analytic findings [see Box 3-1 ] or systematic streams of empirical research). Next, the discussion turns to interventions that can be used to improve team processes and thereby contribute to team effectiveness; these are discussed in greater detail in subsequent chapters. This is followed by a discussion of how this foundational knowledge can inform team science, a description of models of team science and effectiveness, and a discussion of areas in which further research is needed to address the challenges emerging from the seven features outlined in Chapter 1 .

What Is a Meta-Analysis. The foundation of scientific research is based on primary studies that collect data under a given set of conditions (i.e., experiments or field studies) and examine effects on, or relationships among, the observed variables of (more...)

• BACKGROUND: KEY CONSIDERATIONS AND THEORETICAL MODELS AND FRAMEWORKS

Key Considerations

One key consideration regarding team effectiveness is that it is inherently multilevel, composed of individual-, team-, and higher-level influences that unfold over time ( Kozlowski and Klein, 2000 ). This means that, at a minimum, three levels of the system need to be conceptually embraced to understand team effectiveness (i.e., within person over time, individuals within team, and between team or contextual effects; Kozlowski, 2012) . Broader systems that encompass the organization, multiple teams, or networks are obviously even more complex. Moreover, individual scientists may be part of multiple research projects spread across many unique teams and thus are “partially included” in their teams ( Allport, 1932) . As noted in Chapter 1 , a recent study suggests that scientists' level of participation (i.e., inclusion) in a team is related to team performance, with higher participation related to increased performance ( Cummings and Haas, 2012) .

A second critical consideration for understanding, managing, and improving team effectiveness is the degree of complexity of the workflow structure of the team task ( Steiner, 1972) . In simple structures, team members' individual contributions are pooled together or constructed in a fixed serial sequence. For example, in a multidisciplinary team, members trained in different disciplines combine their expertise in an additive way. Complex structures incorporate the integration of knowledge and tasks through collaboration and feedback links, making the quality of team member interaction more important to team effectiveness.

A final key consideration is the dynamic interactions and evolution of the team over time. According to Kozlowski and Klein (2000 , p. 55):

A phenomenon is emergent when it originates in the cognition, affect, behaviors, or other characteristics of individuals, is amplified by their interactions, and manifests as a higher-level, collective phenomenon.

In other words, emergent phenomena arise from interactions and exchange among individuals over time to yield team-level characteristics. Emergent phenomena unfold over time as part of the team development process. Time is also pertinent with respect to how teams themselves evolve. For example, Cash et al. (2003) reported on the evolution of a transdisciplinary group focused on developing improved varieties of wheat and corn. The authors reported that a strictly sequential approach—in which scientists first developed new crops in the laboratory or field and then later handed them over to native farmers—did not lead to widespread use of the new crops. However, when the native farmers were brought into the research at an earlier point in time, as valued participants and partners with the scientists, the group produced new crops that were widely used. Relatedly, teams have different time frames for interaction (i.e., their life cycle or longevity), and this too will alter the emergent dynamics (e.g., Kozlowski et al., 1999 ; Kozlowski and Klein, 2000 ; Marks, Mathieu, and Zaccaro, 2001 ).

Theoretical Models and Frameworks

Most of the research on team effectiveness has been substantially influenced by the input-process-output (IPO) heuristic posed by McGrath (1964) . Inputs comprise (a) the collection of individual differences across team members that determine team composition; (b) team design characteristics (e.g., information, resources); and (c) the nature of the problem that is the focus of the team's work activity. Processes comprise the means by which team members' cognition, motivation, affect, and behavior enable (or inhibit) members to combine their resources to meet task demands.

Although team processes are conceptually dynamic, researchers generally assess them at a single point in time. Hence, they are often represented in the research literature by static perceptions or emergent states ( Marks, Mathieu, and Zaccaro, 2001 ). More recently, team processes have been represented by dynamic or sequential patterns of communications ( Gorman, Amazeen, and Cooke, 2010 ) or actions ( Kozlowski, in press ). In this report, the committee uses the term “team processes” to refer to both dynamic team processes (e.g., communication patterns) and the emergent perceptual states that result from these processes (e.g., cohesion).

Contemporary theories of team effectiveness build on the IPO heuristic but are more explicit regarding its inherent dynamics. For example, Kozlowski et al. (1996 , 1999 ) and Marks, Mathieu, and Zaccaro (2001) emphasized the cyclical and episodic nature of the IPO linkages. Similarly, Ilgen et al. (2005) and Mathieu et al. (2008) are explicit about the feedback loop linking team outputs and subsequent inputs. Accordingly, various authors have urged more attention to team dynamics in research (e.g., Cronin, Weingart, and Todorova, 2011 ; Cooke et al., 2013) and advances in research design ( Kozlowski et al., 2013 ; Kozlowski, in press ) to better capture these dynamics and more clearly specify the relationships between variables. Moving from broad heuristics to more well-defined theoretical models would benefit the field.

In their monograph, Kozlowski and Ilgen (2006) adopted the dynamic IPO conceptualization and focused on those team processes with well-established, empirically supported contributions to team effectiveness. They then considered actions and interventions in three aspects of a team—composition, training, and leadership—that shape team processes and thus can be used to enhance team effectiveness (as shown in the shaded areas of Figure 3-1 ). Given the preponderance of literature that follows the IPO conceptualization, we emulate that approach in this chapter.

Theoretical framework and review focus. SOURCE: Reproduced from Kozlowski and Ilgen (2006). Reprinted with permission.

• TEAM PROCESSES: THE UNDERPINNINGS OF TEAM EFFECTIVENESS

Team processes are the means by which team members marshal and coordinate their individual resources—cognitive, affective, and behavioral—to meet task demands necessary for collective goal accomplishment. When a team's cognitive, motivational, and behavioral resources are appropriately aligned with task demands, the team is effective. Thus, team processes are the primary leverage point for enhancing team effectiveness. The committee's review in this section examines team cognitive, motivational and affective, and behavioral processes, discussed below.

Cognitive Team Processes

Teams have been characterized as information processing systems ( Hinsz, Tindale, and Vollrath, 1997 ) such that their collective cognition drives task-relevant interactions. Here we discuss several cognitive and perceptual processes that are related to team effectiveness: team mental models and transactive memory, cognitive team interaction, team climate, and psychological safety.

Team Mental Models and Transactive Memory

Team mental models are conceptualized as shared understandings about “task requirements, procedures, and role responsibilities” that guide team performance ( Cannon-Bowers, Salas, and Converse, 1993 , p. 222). Whereas team mental models represent common understandings, transactive memory captures the distribution of unique knowledge across team members ( Wegner, Giuliano, and Hertel, 1985 ), especially their shared understanding of “who knows what” such that they can access and direct relevant knowledge ( Liang, Moreland, and Argote, 1995 ; Austin, 2003 ; Lewis, 2003 , 2004 ; Lewis, Lange, and Gillis, 2005 ; Lewis et al., 2007) . Meta-analytic findings indicate that both processes are positively related to team processes (ρ = .43) and team performance (i.e., effectiveness) (ρ = .38) ( DeChurch and Mesmer-Magnus, 2010) .

Studies of science teams and larger groups have also found that shared mental models enhance team effectiveness. To cite just a few examples, a study of research and development teams in India ( Misra, 2011) found that shared mental models were positively related to team creativity. A study focusing on larger groups of European scientists participating in interdisciplinary and transdisciplinary environmental research found that those groups whose members developed a shared understanding of the research goals were much more likely to succeed in synthesizing their perspectives to achieve those goals than those who did not develop shared understandings ( Defila, DiGiulio, and Scheuermann, 2006 ). In a recent qualitative study of the National Cancer Institute's Transdisciplinary Research on Energetics and Cancer Center, investigators and trainees reported that articulating concrete shared goals (through grant applications, for example) and investing time and effort in developing mutual understanding were essential to successfully carrying out their research projects ( Vogel et al., 2014) .

Both team mental models and transactive memory have the potential to be shaped in ways that enhance team effectiveness. For example, a number of studies demonstrate that mental models can be influenced by training, leadership, shared or common experiences, and contextual conditions ( Cannon-Bowers, 2007 ; see also Kozlowski and Bell, 2003 , 2013 ; Kozlowski and Ilgen, 2006 ; Mathieu et al., 2008 ; Mohammed, Ferzandi, and Hamilton, 2010 , for reviews). Similarly, transactive memory systems are formed through shared experiences in working together and training ( Bell et al., 2011 ; see also Blickensderfer, Cannon-Bowers, and Salas, 1997 ; Kozlowski and Bell, 2003 , 2013 ; Kozlowski and Ilgen, 2006 ; Mathieu et al., 2008 ; Mohammed, Ferzandi, and Hamilton, 2010 , for reviews). Accordingly, it is often recommended that training be designed to foster development of appropriate team mental models and transactive memory systems and that leaders shape early team developmental experiences to build shared mental models and transactive memory ( Kozlowski and Ilgen, 2006) .

Cognitive Team Interaction

Team mental models and transactive memory focus on cognitive structure or knowledge and how that knowledge is shared or distributed among team members. Although knowledge certainly contributes to team cognition, it is not equivalent to team-level cognitive processing. Teams often actively engage in cognitive processes, such as decision making, problem solving, situation assessment, planning, and knowledge sharing ( Brannick et al., 1995 ; Letsky et al., 2008) . The interdependence of team members necessitates cognitive interaction or coordination, often manifested through communication, the essential building block of team cognition ( Cooke et al., 2013) . These interactions facilitate information and knowledge sharing processes that are foundational to decision making, problem solving, and the other collaborative cognitive processes mentioned above ( Fiore et al., 2010a) .

The theory of interactive team cognition proposes that team interaction, often in the form of explicit communication, is at the heart of team cognition and in many cases accounts more than knowledge inputs for variance in team effectiveness ( Cooke et al., 2013) . In addition, unlike internalized knowledge states, team interaction in the form of communication is readily observable and can be examined over time, thus providing ready access to the temporal dynamics involved ( Cooke, Gorman, and Kiekel, 2008 ; Gorman, Amazeen, and Cooke, 2010 ).

Another approach to team cognition, focused more on the development of shared problem models, is the macrocognition in teams model ( Fiore et al., 2010b) . This model is based upon a multidisciplinary theoretical integration that captures the cognitive processes engaged when teams collaboratively solve novel and complex problems. It draws from theories of externalized cognition, team cognition, group communication and problem solving, and collaborative learning ( Fiore et al., 2010a) . It focuses on team processes supporting movement between internalization and externalization of cognition as teams build knowledge in service of problem solving. Recently the model has been examined in complex contexts such as problem solving for mission control, in which scientists and engineers were required to collaborate to understand and solve problems on the International Space Station (Fiore et al., 2014).

As with other interpersonal processes, interventions can improve cognitive interaction and ultimately team effectiveness. Training that exposes teams to different ways of interacting ( Gorman, Cooke, and Amazeen, 2010 ), as well as team composition changes ( Fouse et al., 2011 ; Gorman and Cooke, 2011) , have been found to lead to more adaptive and flexible teams. Similarly, training or professional development designed to support knowledge-building activities has been shown to enhance collaborative problem solving and decision making, leading to improved effectiveness ( Rentsch et al., 2010 , 2014 ). These and other professional development approaches are discussed in more detail in Chapter 5 .

Science teams and larger groups, like teams in general, are interdependent and require interaction to build new knowledge. They need to manage a range of technological and social factors to coordinate their tasks and goals effectively. Salazar et al. (2012) have proposed a model of team science, discussed later in this chapter, in which social integration processes support cognitive integration processes. These processes can help foster deep knowledge integration in science teams or larger groups.

Many of the features that create challenges for team science described in Chapter 1 introduce challenges to cognitive interaction, and, therefore, interventions that bolster cognitive interaction, such as professional development or training to expose teams to different ways of interacting, may be particularly helpful for science teams.

Team Climate

Climate represents shared perceptions about the strategic imperatives that guide the orientation and actions of team or group members ( Schneider and Reichers, 1983 ; Kozlowski and Hults, 1987) . It is always shaped by a particular team or organizational strategy. For example, if a team's goal is to innovate, then the team may have a climate of innovation ( Anderson and West, 1998) ; if the goal is to provide high-quality service, then the team may have a service climate ( Schneider, Wheeler, and Cox, 1992 ); if safety is critical for team or organizational success, then the team or the larger organization may have a safety climate ( Zohar, 2000) .

Climate has been studied for more than seven decades, and the relationship of climate to important work outcomes is well established (e.g., Carr et al., 2003 ; Zohar and Hofmann, 2012 ; Schneider and Barbera, 2013) .

Several types of interventions can shape team or group climate. For example, organizations communicate strategic imperatives through policies, practices, and procedures that define the mission, goals, and tasks for teams and larger groups within the organization ( James and Jones, 1974) . Team leaders shape climate through what they communicate to their teams from higher levels of management and what they emphasize to their team members ( Kozlowski and Doherty, 1989 ; Zohar, 2000 , 2002 ; Zohar and Luria, 2004 ; Schaubroeck et al., 2012) . And team members interact, share their interpretations, and develop shared understandings of what is important in their setting (Rentsch, 1990).

Psychological Safety

Psychological safety is a shared perception among team members indicative of an interpersonal climate that supports risk taking and learning ( Edmondson, 1999) . The research on psychological safety has been focused primarily on its role in promoting effective error management and learning behaviors in teams ( Bell and Kozlowski, 2011 ; Bell et al., 2011) . Learning from errors (i.e., to identify, reflect, and diagnose them and develop appropriate solutions) is particularly important in science as well as in other teams charged with innovation ( Edmondson and Nembhard, 2009) , and therefore, fostering psychological safety may be uniquely valuable for science teams and larger groups. Although research on this process has not yet been summarized in a published meta-analysis, support for its importance is provided by a systematic stream of theory and research (e.g., Edmondson, 1996 , 1999 , 2002 , 2003 ; Edmondson, Bohmer, and Pisano, 2001 ; Edmondson, Dillon, and Roloff, 2007 ).

Research on psychological safety has focused on the role of team leaders in coaching, reducing power differentials, and fostering inclusion to facilitate psychological safety, so that team members feel comfortable discussing and learning from errors and developing innovative solutions (e.g., Edmondson, Bohmer, and Pisano, 2001 ; Edmondson, 2003 ; Nembhard and Edmondson, 2006) . Hall et al. (2012a) proposed that creating an environment of psychological safety is critical to lay the groundwork for effective transdisciplinary collaboration. Thus, the research base suggests that appropriate team leadership is a promising way to promote psychological safety, learning, and innovation in science teams and larger groups.

Motivational and Affective Team Processes

Key factors that capture motivational team processes—team cohesion, team efficacy, and team conflict—have well-established relations with team effectiveness.

Team Cohesion

Team cohesion—defined by Festinger (1950 , p. 274) to be “the resultant of all the forces acting on the members to remain in the group”—is among the most frequently studied team processes. It is multidimensional, with facets focused on task commitment, social relations, and group pride, although this latter facet has received far less research attention ( Beal et al., 2003) . Our primary focus is on team task and social cohesion because that is where most of the supporting research is centered.

There have been multiple meta-analyses of team cohesion, with two of the more recent ones ( Gully, Devine, and Whitney, 1995 ; Beal et al., 2003) being the most thorough and rigorous. Both papers concluded that team cohesion is positively related to team effectiveness and that the relationship is moderated by task interdependence such that the cohesion-effectiveness relationship is stronger when team members are more interdependent. For example, Gully et al. (1995) reported that the corrected effect size (ρ) for cohesion and performance was .20 when interdependence was low, but .46 when task interdependence was high. Because high task interdependence is one of the features that creates challenges for team science, fostering cohesion may be particularly valuable for enhancing effectiveness in science teams and larger groups.

Remarkably, although team cohesion has been studied for more than 60 years, very little of the research has focused on antecedents to its development or interventions to foster it. Theory suggests that team composition factors (e.g., personality, demographics; see Chapter 4 ) and developmental efforts by team leaders (e.g., Kozlowski et al., 1996 , 2009 ) are likely to play an important role in its formation and maintenance.

Team Efficacy

At the individual level, research has established the important contribution of self-efficacy perceptions to goal accomplishment ( Stajkovic and Luthans, 1998) . Generalized to the team or organizational level, similar, shared perceptions are referred to as team efficacy ( Bandura, 1977) . Team efficacy influences the difficulty of goals a team sets or accepts, effort directed toward goal accomplishment, and persistence in the face of difficulties and challenges. The contribution of team efficacy to team performance is well established (ρ = .41) ( Gully et al., 2002) , across a wide variety of team types and work settings ( Kozlowski and Ilgen, 2006) . As with team cohesion, Gully et al. (2002) reported that team efficacy is more strongly related to team performance when team members are more interdependent (ρ = .09 when interdependence is low, and ρ = .47 when interdependence is high).

Antecedents of team efficacy have not received a great deal of research attention. However, findings about self-efficacy antecedents at the individual level can be extrapolated to the team level. These antecedents include individual differences in goal orientation (i.e., learning, performance, and avoidance orientation; Dweck, 1986 ; VandeWalle, 1997) and experiences such as enactive mastery, vicarious observation, and verbal persuasion ( Bandura, 1977) . To develop team efficacy, leaders may consider goal orientation characteristics when selecting team members, but these characteristics can also be primed (i.e., encouraged) by leaders. Similarly, leaders can create mastery experiences, provide opportunities for team members to observe others succeeding, and persuade a team that it is efficacious (see Kozlowski and Ilgen, 2006 , for a review).

Team Conflict

Team or group conflict is a multidimensional construct with facets of relationship, task, and process conflict:

Relationship conflicts involve disagreements among group members about interpersonal issues, such as personality differences or differences in norms and values. Task conflicts entail disagreements among group members about the content and outcomes of the task being performed, whereas process conflicts are disagreements among group members about the logistics of task accomplishment, such as the delegation of tasks and responsibilities ( de Wit, Greer, and Jehn, 2012 , p. 360).

Although conflict is generally viewed as divisive, early work in this area concluded that although relationship and process conflict were negative factors for team performance, task conflict could be helpful for information sharing and problem solving provided it did not spill over to prompt relationship conflict (e.g., Jehn, 1995 , 1997 ). However, a meta-analysis by De Dreu and Weingart (2003) found that relationship and task conflict were both negatively related to team performance. A more recent meta-analysis ( de Wit, Greer, and Jehn, 2012 ) has shown that the relationships are more nuanced. For example, all three types of conflict had deleterious associations with a variety of group factors including trust, satisfaction, organizational citizenship, and commitment. In addition, relationship and process conflict had negative associations with cohesion and team performance, although the task conflict association with these factors was nil. Thus, this more recent meta-analysis suggests that task conflict may not be a negative factor under some circumstances, but the issue is complex.

Group composition that yields demographic diversity and group faultlines or fractures is associated with team conflict ( Thatcher and Patel, 2011) . Because diverse membership is one of the features that creates challenges for team science introduced in Chapter 1 , science teams and groups can anticipate the potential for conflict. Many scholars suggest that teams and groups should be prepared to manage conflict when it manifests as a destructive and counterproductive force. Two conflict management strategies can be distinguished ( Marks, Mathieu, and Zaccaro, 2001 )—reactive (i.e., working through disagreements via problem solving, compromise, and flexibility) or preemptive (i.e., anticipating and guiding conflict in advance via cooperative norms, charters, or other structures to shape conflict processes) ( Kozlowski and Bell, 2013 ).

Team Behavioral Processes

Ultimately, team members have to act to combine their intellectual resources and effort. Researchers have sought to measure the combined behaviors of the team members, or team behavioral processes, in several ways, including by looking at team process competencies and team self-regulation.

Team Process Competencies

One line of research in this area focuses on the underpinnings of good teamwork based on individual competencies (i.e., knowledge and skill) relevant to working well with others. For example, Stevens and Campion (1994) developed a typology of individual teamwork competencies with two primary dimensions (interpersonal knowledge and self-management knowledge) that are each assessed with a set of more specific subdimensions. Based on this typology, they also developed an assessment tool, although empirical evaluations of this tool have yielded somewhat mixed results ( Stevens and Campion, 1999) .

Others have focused on behavioral processes at the team level. Integrating many years of effort, Marks, Mathieu, and Zaccaro (2001) developed a taxonomy of team behavioral processes focusing on three temporal phases: (1) transition, which involves preparation (e.g., mission, goals, strategy) before task engagement and reflection (e.g., diagnosis, improvement) after; (2) action, which involves active task engagement (e.g., monitoring progress, coordination); and (3) interpersonal processes (e.g., conflict management, motivation), which are viewed as always important.

A recent analysis by LePine and colleagues (2008) extended the Marks, Mathieu, and Zaccaro (2001) taxonomy to a hierarchical model that conceptualized the discrete behavioral processes as first-order factors loading onto second-order transition, action, and interpersonal factors, which are then loaded onto a third-order, overarching team process factor. Their meta-analytic confirmatory factor analysis found that the first- and second-order processes were positively related to team performance (mostly in the range of ρ = .25 to in excess of .30.).

Team Self-Regulation

For teams focused on reasonably well-specified goals, team processes and performance can be related to the team's motivation and self-regulation, similar to models of the relationship between motivation and performance at the individual level. Feelings of individual and team self-efficacy, discussed above ( Gully et al., 2002) , are jointly part of a multilevel dynamic motivational system of team self-regulation. Team self-regulation affects how team members allocate their resources to perform tasks and adapt as necessary to accomplish goals ( DeShon et al., 2004 ; Chen, Thomas, and Wallace, 2005 ; Chen et al., 2009) . In addition, there is meta-analytic support for the efficacy of group goals for group performance ( O'Leary-Kelly, Martocchio, and Frink, 1994 ; Kleingeld, van Mierlo, and Arends, 2011 ).

Finally, there is meta-analytic support ( Pritchard et al., 2008) for the effectiveness of an intervention designed to increase team regulation by measuring performance and providing structured feedback—the Productivity Measurement and Enhancement System (ProMES; Pritchard et al., 1988) . On average and relative to baseline, productivity under ProMES increased 1.16 standard deviations.

Measuring Team Processes

To assess team processes and intervene to improve them, team processes must be measured. Team process factors such as making a contribution to the team's work, keeping the team on track, and appropriately interacting with teammates have traditionally been measured through self or peer reports of team members ( Loughry, Ohland, and Moore, 2007 ; Ohland et al., 2012) .

Instruments relying on behavioral observation scales and ratings of trained judges have also been used to measure processes associated with collaborative problem solving and conflict resolution as well as self-management processes such as planning and task coordination ( Taggar and Brown, 2001) . Brannick et al. (1995) evaluated judges' ratings of processes of assertiveness, decision making/mission analysis, adaptability/flexibility, situation awareness, leadership, and communication. The ratings were found to be psychometrically sound and with reasonable discriminant validity, though the importance of task context was also noted: that is, process needs to be assessed in relation to the ongoing task. “Team dimensional training” was developed to measure a set of core team processes of action teams (e.g., Smith-Jentsch et al., 1998) and has since been validated in numerous settings (e.g., Smith-Jentsch et al., 2008) . Another approach that provides for context is the use of checklists of specific processes that are targeted for observation ( Fowlkes et al., 1994) .

Researchers have measured cognitive processes somewhat differently, relying typically on indirect knowledge elicitation methods such as card sorting to identify team mental models ( Mohammed, Klimoski, and Rentsch, 2000 ) and assess their accuracy (e.g., Smith-Jentsch et al., 2009) . In addition, concept maps corresponding to team member mental models have been developed by instructing participants to directly create them (e.g., Marks, Zaccaro, and Mathieu, 2000 ; Mathieu et al., 2000) or by indirectly creating them through similarity ratings of pairs of concepts analyzed using graphical techniques such as Pathfinder ( Schvaneveldt, 1990) . Transactive memory systems focusing on team members' knowledge of what each member knows have been measured both via self-assessment ( Lewis, 2003) and via communications coding ( Hollingshead, 1998 ; Ellis, 2006) . Cooke et al. (2000) reviewed different measurement approaches for measuring team mental models (including process tracing and conceptual methods), pointing out challenges related to knowledge similarity for heterogeneous team members and methods of aggregation.

Recent work in this area has focused on developing measures that are unobtrusive to the teamwork and can capture its complex dynamics (e.g., videorecording, team work simulations, and sociometric badges; Kozlowski, in press ). Communication data, for example, can be captured with relatively little interference and provide a continuous record of team interaction ( Cooke, Gorman, and Kiekel, 2008 ; Cooke and Gorman, 2009) . This research has identified changes in patterns of simple communication flow (who talks to whom) that are associated with changes in the state of the team (such as loss of situation awareness or conflict). These continuous methods provide a rich view of team process, not captured by static snapshots in time.

• INTERVENTIONS THAT SHAPE TEAM PROCESSES AND EFFECTIVENESS

Table 3-1 identifies actions and interventions that have been found to influence team processes related to three aspects of a team—its composition, professional development, and leadership. This section and the associated three chapters that follow provide detail on each of these three aspects.

Team Processes Related to Team Effectiveness: Interventions and Support.

Team Composition: Individual Inputs to Shape Team Processes

Team composition results from the process of assembling a combination of team members with the expertise, knowledge, and skills necessary for accomplishing team goals and tasks. At the individual level, the logic of staffing is based on selecting individuals with knowledge, skills, abilities, and other characteristics that fit job requirements. At the team level, staffing is more complex because one is composing a combination of members who must collaborate well, not merely matching each person to a well-defined job ( Klimoski and Jones, 1995) . Chapter 4 takes a detailed look at how team composition and assembly are related to team processes and effectiveness.

Professional Development to Shape Team Processes

Once a team has been assembled, its effectiveness can be facilitated by formal professional development programs (in the research literature, these are referred to as training programs). Although much of the research on team training has focused on programs developed for military teams ( Swezey and Salas, 1992 ; Cannon-Bowers and Salas, 1998 ), these teams face many of the same process challenges as science teams and groups, resulting from features, such as high diversity of membership, geographic distribution, and deep knowledge integration. Further evidence supporting training as an intervention to facilitate positive team processes is reviewed in Chapter 5 , along with discussion of educational programs dedicated to preparing individuals for future participation in team science.

Leadership to Shape Team Processes

Research has shown the influence of leadership on team and organizational effectiveness. Most of this research, however, focuses on the leader, rather than the team, and measures the effectiveness of the leader based on individual perceptions rather than measuring team effectiveness. The leadership literature is also rich with theories of leadership, some of which seem particularly relevant for science teams and larger groups. There is also promising new work on the concept of shared leadership by all team members. Moreover, recent meta-analytic findings provide support for the positive relationship between shared leadership and team effectiveness (42 samples, ρ = .34; Wang, Waldman, and Zhang, 2014 ), suggesting that it may be a useful concept for science teams. Team science leadership is discussed further in Chapter 6 .

• CONNECTING THE LITERATURE TO TEAM SCIENCE

New Models of Team Science

Researchers have developed and begun to study models of team science and effectiveness. Moving beyond traditional models of group development, such as Tuckman's (1965) phases of storming, norming, forming, and performing, these models incorporate elements specific to science teams and larger groups, such as deep knowledge in interdisciplinary teams, to meet scientific and societal goals. They provide different windows into team science and serve different purposes with respect to team science practice and policy. For instance, Hall et al. (2012b) proposed a model that serves as a heuristic for considering the broad research process. The model delineates four dynamic and recursive phases: development, conceptualization, implementation, and translation (see Box 3-2 ). Key team and group processes from the literature on teams and organizations are then linked to each of four phases. One of the unique contributions of this model is to highlight the breadth of collaborative and intellectual work that can be done in the early stages of developing a team science research project. Currently, such work in the development phase is often carried out hastily because of resource constraints. This part of the model helps to highlight the need for planning, institutional support, and funding specifically for the development phase. Overall, the model emphasizes key team and larger group processes that may, across the four phases, increase the comprehensiveness and sophistication of the science and effectiveness of the collaboration.

Two Models of Team Science. In the first model, Hall et al. (2012b) proposed that transdisciplinary team science includes four phases: development, conceptualization, implementation, and translation: In the development phase, the primary goal is to define (more...)

In contrast, Salazar et al. (2012) presented a model that specifically focuses on enhancing a team's integrative capacity through the interplay of social, psychological, and cognitive processes (see Box 3-2 ). Hadorn and Pohl (2007) presented a model of the transdisciplinary research process that discusses elements of both research and integration processes. The three phases of the model include (1) problem identification and structuring, (2) problem analysis, and (3) bringing results to fruition. This model is specifically designed for incorporating the community perspective (i.e., via “real-world actors”) and includes strategies linked to these phases. It draws heavily on a European perspective of transdisciplinarity, science policy, and sustainability research. Reid et al. (2009) and Cash et al. (2003) also discussed models of engaging and integrating knowledge from community stakeholders for sustainability. For instance, Cash et al. (2003) identified key mechanisms for information exchange, transfer, and flow that facilitate communication, translation, and mediation across boundaries in transdisciplinary team science projects.

Existing models of team science have primarily focused on specific aspects of research and knowledge integration processes, but work has recently begun on a team science systems map project that would provide a broader, holistic understanding of the system of factors involved in the context, processes, and outcomes of team science ( Hall et al., 2014 a). Such a map would aid in identifying possible leverage points for interventions to maximize effectiveness, as well as areas where further research is needed.

Features That Create Challenges for Team Science and Team Processes

Most of the key features that create challenges for science teams and larger groups have direct impacts on team processes:

• As noted by Hall et al. (2012b) and Salazar et al. (2012) , science teams or larger groups with high diversity of membership (feature #1) face challenges particularly in the area of team process. Communication across scientific disciplines or university boundaries, for instance, may prove difficult.
• Deep knowledge integration (feature #2) is required to achieve the objectives of interdisciplinary or transdisciplinary team science projects, yet also points to team process as a central mechanism for effectiveness. Strategies and interventions to foster positive team processes (described more fully in Chapters 4 , 5 , and 6 ) are critical for effective collaboration within science teams and larger groups that have diverse membership and seek to foster deep knowledge integration.
• The research on how team process influences effectiveness described in this chapter has primarily been based on relatively small teams of 10 or less, as few researchers have attempted to conduct empirical team research on larger groups (feature #3). As noted in Chapter 1 , most science teams include 10 or fewer members, suggesting that the findings in this chapter are relevant to science teams. Although it is unclear whether the findings scale to larger groups, the committee assumes that increasing size poses a challenge to group processes and ultimately group effectiveness.
• Large science groups composed of subteams that may be misaligned with other subteams (feature #4), as well as teams or groups of any size with permeable boundaries (feature #5), may also be less cohesive than other teams or groups. When team or group membership changes to meet the changing goals of different phases of a transdisciplinary research project, leaders need to make renewed efforts to develop shared understandings of the project goals and individual roles ( Hall et al., 2012b) . Such efforts, along with other leadership strategies described in Chapter 6 , can help to address these features.
• Geographic dispersion (feature #6) limits face-to-face interaction and development of transactive memory and thereby places a toll on cognitive interaction in a team or group. Some ways to address this particular challenge are described in Chapter 7 .
• High task interdependence (feature #7) is often exaggerated in science teams or groups because of the complex demands of scientific research that may involve sharing highly sophisticated technology or carrying out tasks with experts from a different discipline. Increasing task interdependence creates increasing demand for such team processes as shared mental models (shared understanding of research goals and member roles) and transactive memory (knowledge of each team members' expertise relevant to the research goals).

The seven features create challenges through the processes in which science teams engage. The features of diversity, large size, permeable boundaries, and geographic dispersion push team or group members apart, impacting cohesion and conflict and generally challenging cognitive interaction. On the other hand, features such as the need for deep knowledge integration in interdisciplinary and transdisciplinary team or groups and high task interdependence demand enhanced team processes. Thus these features demand high-quality team processes while also posing barriers that thwart them, creating a team process tension.

• SUMMARY AND CONCLUSION

Based on its review of the robust research on teams in contexts outside of science and the emerging research on team science, the committee concludes that team processes (such as shared understanding of goals and team member roles, team cohesion, and conflict) are related to effectiveness in science teams and larger groups, and that these processes can be influenced. The committee assumes that research-based actions and interventions developed to positively influence these processes and thereby increase effectiveness in contexts outside of science can be extended and translated to similarly increase the effectiveness of science teams and larger groups. Actions and interventions targeting team composition, team leadership, and team professional development are discussed further in the following chapters.

CONCLUSION. A strong body of research conducted over several decades has demonstrated that team processes (e.g., shared understanding of team goals and member roles, conflict) are related to team effectiveness. Actions and interventions that foster positive team processes offer the most promising route to enhance team effectiveness; they target three aspects of a team: team composition (assembling the right individuals), team professional development, and team leadership .

• Cite this Page Committee on the Science of Team Science; Board on Behavioral, Cognitive, and Sensory Sciences; Division of Behavioral and Social Sciences and Education; National Research Council; Cooke NJ, Hilton ML, editors. Enhancing the Effectiveness of Team Science. Washington (DC): National Academies Press (US); 2015 Jul 15. 3, Overview of the Research on Team Effectiveness.
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Smart and Efficient Economy : Preparation for the Future Innovative Economy

Joanna Samul

Purpose of the article There has been many studies focus on the effectiveness of teamwork, the factors determining it and the impact on the results of the organization. However the variability of the nature of team caused new challenges in this field. The purpose of the article is a review of existing indicators of teamwork effectiveness and to determine the used of the teamwork measures and to test if these indicators are correlated with the applied assessment of organizational performance. Methodology/methods The study was conducted using a paper and pencil questionnaire. The final sample consisted of 161 Polish companies from the public and private sectors. The survey was conducted in 2014. Senior executives were approached to respond to a survey. Scientific aim The main aim is to carry out an evaluation in terms of use the teamwork measures in the practice of the enterprises and to determine the relation between the dimensions of team effectiveness: member behavior, team attitudes, team productivity and organizational performance like output measures and competitiveness. Findings The research results indicate the use of teamwork measures and the existence of relations between the dimensions of measuring teamwork effectiveness. In addition, the relations between the measurement of the dimensions and the measurement of organizational results are also significant, but analysis does not indicate any links between the used indicators of team effectiveness and the measures of competitive position. Conclusions The research results indicate the direction of improving the existing indiactors for team measurement, which should not be unnecessarily extended with many measures, but should focus on the most important indicators for the organization like output measures. Further studies should take into consideration the process approach to determine the relations between measuring the team effectiveness and the competitiveness.

Zubair Hassan

This research examined the impact of Teamwork on employee performance. The study adopted descriptive and explanatory research design. Further this study used a cross sectional survey methods using a survey questionnaires, containing 35 items with Likert Scale (Disagree-1 and 5 for Agree). A questionnaire was developed based on past literature and numerous tests were done to test the normality, reliability and validity of the data. The independent variables to measure effective teamwork are Effective communication, Team Cohesiveness, Accountability, Interpersonal skills, Leadership and Level of trust. The dependent variable used in this research is employee performance. The samples of 107 employees from an entertainment company in Kuala Lumpur capital of Malaysia were selected using simple random probability sampling technique. The collected data was analysed using descriptive means and regression via SPSS.20. This study found that all the chosen factors have significant relationship with teamwork. This research find Efficient Communication, Level of trust, Leadership and Accountability has a positive and significant impact on employee performance. While we found no significant influence of Intrapersonal skills and Cohesiveness on Employee Performance. Though this research included only one entertainment organisations, future studies may include larger sample by conducting the study on more organisations including manufacturing industry, Financial firms etc. to see the variation in the results. The future studies may compare differences based on socio-demographic profile and might examine the similarities and difference of motivational factors in different sectors in Malaysia.

michiya morita

This research&#39;s objectives are to propose a concept of teamwork linkage for the organizational effectiveness and to extract implications to build up teamwork competence supported by the concept. Many researches pay attention to specific teamwork like teamwork of people on the factory floor, or technical people like medical staffs and engineers when they make experimental research works on the determinants of teamwork. In this research main focus is put on various types of teamwork of the firm and their interactions to derive implications for teamwork effectiveness and build-ups. Under then New Economy, teamwork, internal and external, should be a key for success. The competence to design and implement effective teamwork will be critical for competitiveness.

International Journal of Learning and Development

Murad Hussain

M.E. Akinade

Effective teams in organizations make all the differences in the achievement of corporate value creation, growth, and attainment of the predetermined and emergent goals and objectives of any organization. This paper examines the impact of teambuilding and teamwork in organizations and their implications to managers and employees. The paper notes that team building stimulates organizational productivity, service quality and general positive performances and enhances organizational development and efficiency. It also note that team building encourages continuous growth, open and positive communication, and development of trust and leadership potentials of organizations members. It however pointed out that team building encounter serious challenges in employee resistance, lack of trust, virtual workplace and globalization. The paper concluded that team building promotes effective collaboration of all team members, and also make organizations better places of work. Additionally, the sus...

Indian J.Sci.Res.

parisa yazdanian

The structure of modern organizations is changing towards team orientation. Work groups are one of the major organizational units whose application ensures the improvement of organizational performance and efficiency. Dynamic organizations depend on groups to survive. By making teamwork the axis, achievement of organizational objectives is facilitated through the improvement of the efficiency and effectiveness of teams. Considering the various factors influencing the improvement of performance and effectiveness of teams, the present article used the review method to refer to the concept of work teams and their types and present a brief introduction of team performance and effectiveness and factors influencing the promotion of team performance. Recognition and consideration of factors such as leadership, bonus, team objectives, training, team composition and size, effectiveness and performance assessment models can have a significant effect on the realization of the desired result an...

Ada Mac-Ozigbo

This paper explored Team Building and Performance in Organizations: An Exploration of Issues. It describes team building: nature and characteristics, types of teams, stages of team development, team building objectives, building effective teams, effects of team building on performance, challenges to team building; and thus offered a number of positive results/benefits as well as the challenges which team building bequeaths/poses to organizations.

Procedia Computer Science

Hamid Tohidi

Carlos María Alcover de la Hera

Teams do not always provide the diversity of knowledge, attitudes, skills and experience required to generate an innovative response to challenges or perform according to expectations. This paper summarizes the key results of research on work teams carried out over the decade from 1999 to 2009. To this end, we set out a brief explanatory framework for the effectiveness of work teams based on a differentiated analysis of inputs, mediators and outcomes. Our approach uses the SWOT technique, which identifies strengths, weaknesses, opportunities and threats in relation to teamwork research for the new decade. Finally, we integrate and discuss the key challenges facing the field if it is to turn threats into opportunities.

ASMARA HABIB

The teams of people working together for a common purpose have been a centerpiece of human social organization ever since our ancient ancestors first banded together to hunt game, raise families, and defend communities. Human history is generally story of individuals working together in bunches to investigate, accomplish, and overcome. The purpose of this research work is to understand the aspects effecting team effectiveness through this we come to know that how Individuals’ talents and skills are pooled. Through this Members can see the bigger picture. Members can develop their skills. Tasks can be completed more quickly. This research was based on primary data, which was collected by means of questionnaire, the questionnaire was developed by the author. After checking the reliability of questionnaire was floated to selected 130 respondents from banking and telecom sector. The received 114 accurate responses were used for regression and correlation analysis by using SPSS software....

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