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Article contents

Team building and group cohesion in the context of sport and performance psychology.

  • Mark Eys Mark Eys Professor, Kinesiology/Physical Education and Psychology, Wilfrid Laurier University
  •  and  Jeemin Kim Jeemin Kim Wilfrid Laurier University
  • https://doi.org/10.1093/acrefore/9780190236557.013.186
  • Published online: 28 June 2017

Over the past 30 years, researchers studying group dynamics in sport have provided insight regarding the importance of considering a team’s environment, structure, and processes for its effective functioning. An emergent property resulting from activities within the group is cohesion. Cohesion is a dynamic property reflecting members’ perceptions of the unity and personal attractions to task and social objectives of the group. Generally speaking, cohesion remains a highly valued group property, and a strong body of evidence exists to support positive links to important individual and group outcomes such as adherence and team performance.

Given the importance attached to cohesion and other group variables for sport teams, coaches and athletes often attempt to engage in activities that facilitate group functioning. Team building is a specific approach designed to facilitate team effectiveness and individual members’ perceptions of their group. Cohesion has been the primary target of team-building interventions in sport, although recent work on team-building outcomes suggested that the effects of these interventions on cohesion may be limited. The most effective team-building approaches include a goal setting protocol, last at least two weeks in duration, and target a variety of outcomes in addition to cohesion, including individual cognitions and team performance. There is a clear need to identify a team’s requirements prior to intervening (i.e., a targeted approach), consider a variety of approaches to team building, and investigate the effects of team building via more stringent research methods.

  • group dynamics
  • goal setting


The 2016 Football (Soccer) European Championships were notable for the emergence and success of two smaller countries (i.e., Iceland and Wales) competing among the giants of the sport. Commentaries about their accomplishments quite often focused on the teams’ ability to work together as a cohesive unit to overcome any deficiencies in individual talent. For example, in a preview of the Icelandic team leading up to the tournament, the magazine WorldSoccer noted:

Since Lars Lagerback took over as coach in October 2011 he has stuck to the 4-4-2 system that he favoured for so many years with his native Sweden. With his current team, the emphasis has very much been on cohesion and team spirit, both in defence and attack … . The old saying that “a chain is never stronger than its weakest link” is acknowledged by everybody on the team. They all accept that each of them has to give 100 per cent, every game; 95 per cent for a side like Iceland is not enough on the big stage. No one is too big for the team. (Hallgrimsson, 2016 , paras. 13 and 15)

The importance of group cohesion is shared among many performance contexts including sport (e.g., Eys, Loughead, Bray, & Carron, 2009 ), business (e.g., Tekleab, Karaca, Quigley, & Tsang, 2016 ), military (e.g., Kanesarajah, Waller, Zheng, & Dobson, 2016 ), and music (e.g., Dobson & Gaunt, 2015 ). As a result, researchers and practitioners working with performance groups also attempt to facilitate perceptions of cohesion through the process of team building. This article focuses on the physical activity context and will provide an overview of the definition and conceptualization of cohesion, identify measurement tools used to assess athletes’ perceptions of cohesion in sport, highlight the extant literature supporting the importance of cohesion in this context, and discuss the suggestions and protocols that are considered to build high functioning teams within the sport environment (i.e., team building).

Definition and Conceptual Model of Cohesion

Generally speaking, cohesion represents the strength of the bonds among group members or, more informally, the degree to which individuals stick together (Carron & Eys, 2012 ). This group property has been the subject of considerable research over the past 60 years and definitions have indicated differing approaches to understanding cohesion. For example, Gross and Martin ( 1952 ) suggested that cohesion represents the collective resistance to disruption of the group (i.e., the degree to which the group can withstand outside pressures or unfavorable events). Alternatively, Festinger, Schachter, and Back ( 1963 ) defined cohesion as the sum of all the forces that cause members to be attracted to, and remain in, the group, and also considered these forces to be related to task and social aspects of the environment.

In sport and exercise research, the most accepted definition of cohesion was provided by Carron, Brawley, and Widmeyer ( 1998 ): “a dynamic process which is reflected in the tendency for a group to stick together and remain united in the pursuit of its instrumental objectives and/or for the satisfaction of member affective needs” (p. 213). This definition implies several characteristics of cohesion that include an ability to change over the span of group development (i.e., dynamic), a focus on both task (i.e., instrumental objectives) and social aspects of the group (i.e., member affective needs), and, relatedly, an assumption that it is multidimensional.

With respect to the latter points, and following from the varied approaches of earlier cohesion research, Carron, Widmeyer, and Brawley ( 1985 ) proposed a four dimension conceptual model that encompasses two different perceptual orientations (i.e., individuals’ perceptions of their own attractions to the group as well their perceptions about the degree to which the group is integrated) regarding two broad aspects of the group environment (i.e., task and social concerns). In combination, the four dimensions represent individuals’ perceptions of their (a) attractions to task aspects of the group (ATG-T), (b) attractions to social aspects of the group (ATG-S), (c) group’s integration regarding task objectives (GI-T), and (d) group’s integration regarding social objectives (GI-S).

Another interesting aspect regarding the concept of cohesion relates to the dynamism of individuals’ perceptions of their group. McEwan and Beauchamp ( 2014 ) proposed that cohesion is an emergent state resulting from (and influencing) other behavioral processes in which the team engages (e.g., teamwork processes). In this sense, cohesion is proposed to be an outcome/antecedent of several group processes (as opposed to being a process unto itself). Regardless, it is interesting to consider whether the various dimensions of cohesion differ with respect to the speed and/or level with which they initially emerge within a group and their ongoing stability. There is some support in the extant literature to suggest that all dimensions of group cohesion do not progress in lockstep. Arrow, Poole, Henry, Wheelen, and Moreland ( 2004 ) proposed that group members’ attractions to their group have elements that develop at different speeds. More global attractions to the group are proposed to develop quickly while more specific interpersonal attractions (i.e., among group members) need more time to be fostered.

In a physical activity context, Dunlop, Falk, and Beauchamp ( 2012 ) tracked 46 group exercise classes and assessed participants’ perceptions of the four dimensions of cohesion during the 2nd, 5th, and 8th week of the session. They found that perceptions of task cohesion remained relatively stable across exercise sessions, while social cohesion perceptions were more variable over those time points. The researchers suggested that their results had implications toward group interventions in exercise (i.e., opportunities to facilitate social connections within the classes) and provided support that cohesion perceptions are malleable. This result (i.e., greater stability for task cohesion perceptions vs. social cohesion) is consistent with Leeson and Fletcher ( 2005 ), who examined cohesion perceptions of 219 elite female netball players across four time points in a competitive season.

Measurement of Cohesion in Sport

The body of knowledge pertaining to cohesion in sport has been aided by several attempts to measure athletes’ perceptions of this group property. These attempts include the Sport Cohesiveness Questionnaire (Martens, Landers, & Loy, 1972 ), the Multidimensional Sport Cohesion Inventory (Yukelson, Weinberg, & Jackson, 1984 ), and the Group Environment Questionnaire (Carron, Brawley, & Widmeyer, 2002 ; Carron et al., 1985 ). The Group Environment Questionnaire (GEQ) has received the most attention and is the operationalization of the four dimensions of cohesion outlined in the previous section. Specifically, the GEQ is an 18-item measure assessing athletes’ perceptions of their attractions to social (5 items) and task (4 items) aspects of the group, as well as their perceptions of how integrated their group is from both social (4 items) and task (5 items) perspectives. Over time, evidence has been provided regarding the validity and reliability of responses to this assessment tool (see Carron et al., 1998 ; Carron et al., 2002 , for summaries), though certain limitations have been identified. For example, Eys, Carron, Bray, and Brawley ( 2007 ) noted that the strategy of using both positively and negatively worded items might create problems for the internal consistency of certain dimensions.

Furthermore, as Carron et al. ( 2002 ) noted, “The GEQ was specifically developed, its psychometric properties investigated, and norms established with recreational and competitive sport teams composed of North American female and male athletes between the ages of approximately 18 to 30 years” (p. 39) and encouraged careful consideration of the context specificity of the questionnaire. To this end, researchers have translated and adapted the GEQ to ensure they had a relevant measure of cohesion for their population. As just a few examples, Heuzé and Fontayne ( 2002 ) used the GEQ as the basis for a French language cohesion questionnaire (Questionnaire sur l’Ambiance du Group), while Estabrooks and Carron ( 2000 ) adapted the measure for use in an exercise class context (Physical Activity Group Environment Questionnaire).

More recently, efforts have been made to examine cohesion in younger athletes including youth (approximately 12 to 17 years of age; Youth Sport Environment Questionnaire; Eys et al., 2009 ) and children (approximately 9 to 12 years of age; Child Sport Cohesion Questionnaire; Martin, Carron, Eys, & Loughead, 2012 ). Eys and colleagues ( 2009 ) noted several advantages of developing age-appropriate cohesion assessment tools including increased readability. Furthermore, for both questionnaires, the researchers found evidence that younger populations did not distinguish between group integration perceptions and their attractions to the group, but rather viewed their group more globally with respect to task and social cohesion (two dimensions vs. four dimensions). Overall, the efforts of researchers to develop appropriate measures of cohesion have led to a large body of literature within sport. The following section briefly highlights this information.

Research on Cohesion in Sport

Without question, cohesion has been the most heavily researched group dynamics concept in sport psychology. The research questions have tackled a variety of issues including the relationship of cohesion with individual cognition/affect/behavior (e.g., individual effort), other features of the group environment (e.g., motivational climate) and structure (e.g., leadership, roles), and performance. In the following sections, examples of this research are provided to highlight the importance of this emergent state, though we note more extensive coverage can be found in other texts (e.g., Carron & Eys, 2012 ).

Cohesion and the Individual Athlete

Research linking perceptions of cohesion to important individual correlates has been extensive and includes cognitive, affective, and behavioral variables. For example, from a cognitive perspective, Shapcott and Carron ( 2010 ) found that the attributions athletes make regarding team performance were related to task cohesion. As one specific aspect, athletes who had higher perceptions of task cohesion attributed team failures to causes that were controllable and changeable (a more positive attributional approach). Bruner, Eys, Wilson, and Côté ( 2014 ) undertook a study to examine cohesion as it relates to positive youth development. Their findings positively linked both task and social cohesion to the development of personal and social skills, initiative, cognitive skills, and goal setting practices.

Affective variables have also been considered and, for the most part, the links have been beneficial. Several studies have examined the association between team cohesion and individual athlete satisfaction. Illustrative of this relationship, Spink, Nickel, Wilson, and Odnokon ( 2005 ) found that athletes’ perceptions of how integrated their team was regarding task aspects (GI-T) were positively related to their satisfaction with the group’s contributions and coordination. More recently, Wolf, Eys, and Kleinert ( 2015 ) found that greater cohesion was predictive of athletes’ facilitative interpretations of their precompetitive state anxiety symptoms over and above the contributions of other important variables (e.g., trait anxiety).

Finally, the perceptions individuals hold regarding the cohesion of their team are believed to influence their behaviors. Earlier research provides evidence of positive links with key variables in the sport environment, including adherence (e.g., returning the following season to one’s team; Spink, Wilson, & Odnokon, 2010 ), sacrifice behaviors (e.g., putting aside personal goals for team goals; Prapavessis & Carron, 1997 ), and social loafing (McKnight, Williams, & Widmeyer, 1991 ). Furthermore, Bruner, Boardley, and Côté ( 2014 ) found that perceptions of task and social cohesion played differential mediating roles between social identity and both pro- and anti-social behaviors toward teammates and opponents. For example, Bruner and colleagues found that stronger perceptions of social identity expressed by athletes were positively related to task cohesion that, in turn, were related to greater prosocial behaviors and lesser antisocial behaviors toward teammates. In contrast, social cohesion perceptions promoted by stronger social identities were predictive of more antisocial behaviors toward opponents.

Cohesion and the Team Environment

Given that cohesion is an emergent group state, it is not surprising that researchers have examined it in light of other important group variables. The volume of studies is too large to cover in-depth within this article, but the existing literature highlights numerous associations with structural, leadership, and environmental variables. From a structural standpoint, greater cohesion has been positively linked with perceptions of group status and roles. For example, Jacob and Carron ( 1998 ) found that athletes perceiving higher cohesion attached less importance to status differences within their team. From a different vantage point, participants in a rugged wilderness trek perceived greater cohesion when group members had congruent perceptions of the status structure in their group (Eys, Ritchie, Little, Slade, & Oddson, 2008 ). With respect to roles, Carron and Eys ( 2012 ) summarized that cohesion and role perceptions (e.g., role ambiguity, acceptance, and performance) appear to act on each other in a reciprocal fashion, though Bosselut, McLaren, Eys, and Heuzé ( 2012 ) found that youth athletes’ perceptions of social cohesion were predictive of their subsequent perceptions of role ambiguity.

Leaders play an essential role in the emergence of cohesion within the group. The degree to which leaders (both coaches and athlete/peer leaders) demonstrate behaviors related to training and instruction, social support, and the provision of positive feedback, as well as engage their followers via a democratic style (vs. autocratic), is positively related to perceptions of cohesion (Jowett & Chaundy, 2004 ; Vincer & Loughead, 2010 ). In addition, coaches not only have responsibilities for interacting effectively with each individual athlete, they also need to act in a manner that is helpful in creating a positive motivational climate. Coaches who provide for a stronger task-involving motivational climate have athletes who perceive greater task and social cohesion (Eys, Jewitt, Evans, Wolf, Bruner, & Loughead, 2013 ; Horn, Byrd, Martin, & Young, 2012 ). In contrast, less cohesion is perceived when an ego-involving climate is promoted. On the basis of these previous findings, McLaren, Eys, and Murray ( 2015 ) conducted an intervention with youth soccer coaches to educate them about what constitutes a positive motivational climate and to provide strategies for them to use throughout the season. Compared to a control group, athletes whose coaches took part in the intervention perceived a stronger task-involving motivational climate as well as greater perceptions of cohesion by the end of the season.

Cohesion and Performance

The question pertaining to whether cohesion is linked to team performance has stretched as far back as the 1960s, with individual sets of empirical results yielding a somewhat ambiguous picture of this relationship. In an attempt to rectify this situation, Carron, Colman, Wheeler, and Stevens ( 2002 ) conducted a meta-analysis of sport studies to determine the general relationship between cohesion and performance as well as potential moderators of this relationship. Specifically, Carron and colleagues examined whether the cohesion-performance relationship differed with respect to type of cohesion (task vs. social cohesion), type of sport (interdependent vs. individual sports), gender (male vs. female), skill level and age, and the direction of the relationship using any lagged longitudinal datasets that were available (cohesion leading to performance vs. performance leading to cohesion). Overall, the researchers found that there was a moderate, positive, and significant relationship between cohesion and performance (effect size = 0.655). This particular relationship held regardless of type of cohesion/sport, skill level, or direction of the relationship. However, there was a moderating effect of gender. In essence, while still significant for males (effect size = 0.556), the positive relationship between cohesion and performance was stronger for females (effect size = 0.949). A follow-up meta-analysis (Filho, Dobersek, Gershgoren, Becker, & Tenenbaum, 2014 ), examining studies conducted between 2000 and 2010 , further supported the general positive relationship between these two variables as well as the moderating effect of gender. However, Filho and colleagues demonstrated there were some differences in the strength of the relationship based on skill level and sport type.

The finding that gender moderates the cohesion-performance relationship was discussed by the groups of researchers. Carron and Colleagues ( 2002 ) suggested that this might be important practical knowledge for coaches and sport psychology professionals to consider when working with teams. From a research perspective, Filho and Colleagues ( 2014 ) encouraged investigators to “focus on asking ‘why’ (e.g., Why do women and men differ in cohesion dynamics?) to provide explanation of the mediating mechanisms underlying gender idiosyncrasies” (p. 174). This question pertaining to why there may be gender differences was pursued in a qualitative study conducted by Eys and Colleagues ( 2015 ). These researchers interviewed 22 Canadian and German coaches who had experience coaching both male and female competitive sport teams over the course of their careers. The researchers asked coaches to comment on the findings and to offer their perspectives regarding why cohesion may be a more important group property for female teams as compared to males. While it is beyond the scope of this article to highlight the results in their totality, coaches tended to agree with the empirical results in the sense that they believed that cohesion was important for both males and females, but that there is a tendency for it to be more important in female teams. Furthermore, coaches offered interesting ideas that could form the basis for future research questions. For example, some coaches observed that the direction of the cohesion-performance relationship might differ for males and females; specifically, that cohesion may drive performance for females while performance may drive perceptions of cohesion for males. This is an interesting proposition that has not yet been tested in the previous meta-analyses. As another example, coaches also felt that there may be temporal differences in the development of cohesion. In essence, male and female teams may differ with respect to the speed that cohesion is facilitated (e.g., faster to develop in male teams).

There are a few limitations to previous research examining the cohesion-performance relationship included in the previous meta-analyses. These include a reliance on young adult populations (+18 years), cross-sectional designs, and sub-elite competitive levels. Benson, Šiška, Eys, Priklerovád, and Slepičkab ( 2016 ) sought to address some of these issues in a prospective investigation of the cohesion-performance relationship with elite Czech and Slovak Republic youth football (soccer) and handball teams. Their study included 246 athletes from 18 teams whose perceptions of cohesion were obtained at mid-season and late season along with their team’s performance. In contrast to the general tone of the extant literature suggesting that cohesion leads to performance, Benson and colleagues found evidence that performance outcomes drive perceptions of cohesion in elite youth sport teams. This finding opens up several research questions regarding this relationship across sport and the researchers encouraged continued investigation of the psychological mechanisms (i.e., mediators) and boundary conditions (i.e., moderators) of the cohesion-performance relationship. Certainly, their study had several limitations (e.g., Czech and Slovak Republic athletes only, predominantly male, limited number of sports). Regardless, their result suggesting that performance leads to cohesion in the elite youth sport environment is tantalizing within a body of research that often suggests a bi-directional relationship and/or promotes cohesion as a performance enhancing necessity.

Cohesion as a Potential Disadvantage

As noted, cohesion is believed to be a force for the good of the group. As previous sections have highlighted, cohesion is associated with several important personal, team, and leadership factors, as well as team performance. However, several researchers have cautioned that there are negative aspects to cohesion that need to be considered. This is a concern that is shared and identified by athletes as well. For example, Hardy, Eys, and Carron ( 2005 ) asked 105 intercollegiate athletes if they viewed any downsides to group cohesion and, if so, to further discuss the specific issues. Overall, 56% of the athletes queried noted that they saw potential disadvantages to high social cohesion, and 31% indicated potential problems to high task cohesion. It is important to note that many of the issues raised by the athletes appeared to be interpreted in light of an imbalance of team cohesion (i.e., high social cohesion with a relatively lower amount of task cohesion and vice versa). However, the perceived disadvantages of high social cohesion included the potential for communication problems among friends (e.g., afraid to be critical of those you are close with), challenges in fully focusing on the task at hand (e.g., social issues dominating task concerns), and the exclusion of those individuals who do not adhere to the social norms of the group. From a task perspective, the challenges included perceived increases in pressure to perform as well as decreased social and personal enjoyment.

Rovio, Eskola, Kozub, Duda, and Lintunen ( 2009 ) added further support to the suggestion that group cohesion can be problematic at times. They conducted a qualitative study with an ice-hockey team over the course of one competitive season. In this case study, the team’s performance decreased as the season progressed though group cohesion appeared to be rather resilient. They suggested that the high social cohesion present on the team might have posed some challenges. In particular, they highlighted the occurrence of several established group dynamics phenomena (i.e., pressures to conform, group polarization, groupthink) that may have led to lower standards of performance. Overall, the issues raised in the Hardy et al. ( 2005 ) and the Rovio et al. ( 2009 ) studies are in line with those raised in a review by Pescosolido and Saavedra ( 2012 ), indicating that cohesion can be yet another strong force to contend with in the group environment that can lead to a reluctance on the part of the individual to violate strong normative pressures to be a team player.

Cohesion as a Target for Intervention

Although there are specific instances in which too much cohesion could be detrimental to a team, the overwhelming evidence suggests that strong group cohesion (both task and social) is a desirable emergent state. The previous section outlining research on cohesion in sport highlighted the many positive individual and group correlates, with arguably the most important connection being the positive association between cohesion and performance. As a result, there have been many attempts designed to enhance athletes’ perceptions of cohesion in their sport teams. In particular, these attempts to improve team effectiveness (i.e., task cohesion) and enhance interpersonal relationships (i.e., social cohesion) are referred to as team building. Brawley and Paskevitch ( 1997 ) provided a specific definition of team building for physical activity contexts that described it as a “method of helping the group to (a) increase effectiveness, (b) satisfy the needs of its members, or (c) improve work conditions” (pp. 13–14).

There is evidence to suggest that cohesion has been the primary target (vs. other group constructs) for team-building interventions. In a unique study examining the origins of team building in sport, Bruner, Eys, Beauchamp, and Côté ( 2013 ) used citation network and citation path analyses to determine the influential texts and articles that have driven team-building research. Essentially, citation network analysis determines the interconnectedness of citations among a series of publications and determines the most central or influential texts. In parallel, citation path analysis links key texts over time to provide a picture of the evolution of thinking around a particular topic. As Bruner and Colleagues ( 2013 ) noted, these two analyses “hold considerable promise to enhance an understanding of [team building] in sport by identifying bodies of literature, and trends, that have shaped the field as well as identifying potential restrictions or omissions that have emerged as the field of enquiry developed” (pp. 31–32).

The major finding from Bruner and Colleagues ( 2013 ) was that the extant literature on team building in sport is largely driven by cohesion-focused research. In particular, the work conducted by Carron and colleagues (e.g., Brawley, Carron, & Widmeyer, 1987 ; Carron et al., 1985 ) was predominant throughout both the citation network and path analyses. On one hand, this result suggests the importance of cohesion as an outcome of team building. On the other hand, the result supports Bruner et al.’s ( 2013 ) cautionary statement that perhaps the field of sport psychology is too narrow with respect to its approach to team building, both in terms of topic (i.e., cohesion) and use of the extant literature (i.e., not giving due consideration to other fields such as organizational psychology). This point was reiterated by McEwan and Beauchamp ( 2014 ) in their review of teamwork processes in sport. They noted that team-building processes should move beyond solely considering cohesion and target additional teamwork behaviors such as coordination, cooperation, and communication.

Critiques concerning the narrowly focused nature of team-building processes aside, if these protocols are focused predominantly on cohesion, what can we say about the effectiveness of intervening with sport teams? Martin, Carron, and Burke ( 2009 ) conducted a meta-analysis to answer this very question. Their analysis included 17 studies and 180 effect sizes emanating from the data in these investigations. Martin and colleagues found a moderate positive effect for team-building interventions when taken in totality across several dependent variables (e.g., social cohesion, task cohesion, performance, enhanced cognitions, roles, anxiety). However, follow-up moderation tests yielded several interesting findings. First, the researchers found that interventions using team goal setting had larger effects than those interventions that took a broader approach to team-building activities (i.e., targeting several components). They surmised that more positive results might be the product of fewer activities that athletes can truly focus on. Second, consistent with past research in both sport psychology and organizational psychology, interventions were less effective when they were shorter in length (i.e., less than two weeks). Third, Martin et al. ( 2009 ) found that team building was particularly effective with independent sport teams (vs. interactive sport teams), but noted that this effect may be due to greater room for developing group interaction in sports that may traditionally offer less opportunities (i.e., a ceiling effect for the interactive teams). Finally, the impact of team-building interventions on perceptions of cohesion (both task and social) were rather muted, which the researchers found interesting given that practitioners and researchers often use team building with the hopes of increasing group cohesion. Certainly, this is a finding with implications that require future research to disentangle and consider regarding the process and targeted outcomes of team building. In the following section, information pertaining to the team-building protocols used in sport are described in further detail.

Team Building Protocols in Sport and Exercise

Given the complex nature of group dynamics and team development, a wide variety of factors must be considered during the creation and delivery of team-building protocols. Thus, researchers and practitioners have taken numerous approaches that have varied on their conceptual basis, delivery medium, types of activities, and outcome measures. This section provides an overview of such protocols and approaches that have been undertaken in the team-building literature in sport and exercise.

At a general level, team-building protocols have largely been categorized as either direct or indirect , based on the role that the interventionist plays in the delivery of the team-building program (Loughead & Bloom, 2013 ). In indirect interventions, the sport psychologist works with the coaching staff to create a team-building program and develop specific strategies, which are subsequently delivered and implemented with the athletes. In other words, the sport psychologist acts as a consultant for the coaching staff, who has the direct responsibility to implement the team-building protocols with their athletes (Loughead & Bloom, 2013 ). On the other hand, direct interventions involve the sport psychologist working directly with all members of the team (i.e., coaching staff and athletes). To this end, the sport psychologist, coaching staff, and athletes share the responsibility of creating and implementing the team-building programs. Thus, the sport psychologist is in direct contact with the athletes during program development and delivery (Loughead & Bloom, 2013 ).

Indirect Interventions

Carron and Spink (Carron & Spink, 1993 ; Spink & Carron, 1993 ) developed and implemented an indirect team-building intervention in exercise settings. Importantly, their intervention was based on a conceptual framework that represented a linear progression of group development that included inputs, throughputs, and outputs (Carron, Spink, & Prapavessis, 1997 ). Specifically, group environment and group structure were the two main categories of input, which influenced the throughput category of group processes . Subsequently, group processes influenced the output, which mainly pertained to the cohesiveness of the group. Each category within the framework included a specific factor that was emphasized and targeted during the team-building intervention. For example, the group environment was targeted by enhancing the group’s distinctiveness , which reflected the extent to which the group appeared unique in comparison to other groups. Group structure mainly related to the norms and positions established within the group, while group processes included interaction, communication , and sacrifices among teammates as the most salient factors. Lastly, the output category of group cohesion included the four sub-dimensions of cohesion (i.e., ATG-T, ATG-S, GI-T, GI-S).

Using this framework, Carron and Spink conducted a set of team-building intervention studies with female exercise class participants over a 13-week period (Carron & Spink, 1993 ; Spink & Carron, 1993 ). In each study, the exercise classes under the experimental condition were led by leaders who were trained to implement team-building protocols in addition to standard exercise programs, whereas leaders in the control condition provided the standard exercise programs only. Specifically, the team-building training was delivered in four stages: introductory, conceptual, practical , and intervention (for full descriptions of the stages, see Carron et al., 1997 ). In the introductory stage, the authors educated the group leaders on the benefits of group cohesion such as greater self-esteem, trust, and adherence to the program, as well as more group stability. In the conceptual stage, the framework of team building was outlined to the group leaders. In this way, the group leaders were able to decide what specific factors within the framework should be targeted in their team-building program. Based on this assessment, in the practical stage, the group leaders brainstormed strategies that would enhance the specific factor. Finally, in the intervention stage, the strategies developed in the previous stage were implemented by the group leader. The four dimensions of group cohesion, as well as satisfaction with the exercise classes, were included as outcome measures. In their results, the participants in the experimental condition showed higher perceptions of ATG-T (Carron & Spink, 1993 ; Spink & Carron, 1993 ) and satisfaction with the classes (Carron & Spink, 1993 ), as well as adherence to the classes represented by the number of dropouts and late arrivals to each class (Spink & Carron, 1993 ). These results provided initial evidence for the usefulness of indirect team-building interventions.

More recently, several sport and exercise psychology researchers extended the early work by Carron and Spink (Bruner & Spink, 2010 ; Bruner & Spink, 2011 ; Newin, Bloom, & Loughead, 2008 ). Bruner and colleagues (Bruner & Spink, 2010 ; Bruner & Spink, 2011 ) used Carron and Spink’s model to conduct team-building interventions in school-based exercise programs. Ten exercise classes with a total of 100 adolescent (13–17 years) participants were randomized into an experimental group or a control group. The exercise classes were run three times per week over a period of eight weeks (i.e., a total of 24 sessions), each lasting approximately an hour. Following Carron and Spink’s protocols, the leaders in the control group ran a standard exercise program only, while the leaders in the experimental condition were trained to conduct team-building activities in addition to the exercise program. Their results revealed that the participants in the experimental condition reported higher task cohesion (Bruner & Spink, 2010 ), group task satisfaction, and session attendance (Bruner & Spink, 2011 ), and that the five specific factors targeted in the intervention significantly improved the prediction of task cohesion (Bruner & Spink 2010 ). Thus, the findings by Bruner and colleagues extended the usefulness of Carron and Spink’s four-stage model of team building to youth populations.

In sport, Newin and Colleagues ( 2008 ) conducted a team-building program with eight youth ice hockey teams. Following Carron and Spink’s ( 1993 ) model, they educated the head coaches on the benefits of team building (i.e., introductory stage), introduced the conceptual framework (i.e., conceptual stage), and developed specific activities that were designed to be engaging and challenging their athletes’ problem-solving and teamwork skills (i.e., practical stage). Then, the coaches led five activities throughout their season, which lasted approximately 30 minutes per activity (i.e., intervention stage). The authors gathered qualitative data using pre- and post-intervention reflection forms completed by coaches, observations of the activities by members of the research team, and individual semi-structured exit interviews with the coaches following the completion of the season. Among their results, coaches reported that their athletes improved their problem-solving skills, abilities to focus and to persist through challenges, and their teamwork skills. Taken together, the recent work by Bruner and colleagues (Bruner & Spink, 2010 ; Bruner & Spink, 2011 ) and Newin et al. ( 2008 ) provide evidence that Carron and Spink’s indirect team-building interventions can be beneficial under both sport and exercise contexts.

Direct Interventions

Based on his work with coaches and athletes at Penn State University, Yukelson ( 1997 ) advocated the use of a direct service approach, where the sport psychologist is in contact with the athletes during team-building interventions. Similar to the indirect approach by Carron and Spink ( 1993 ), Yukelson’s approach consisted mainly of four stages: assessment , education, brainstorm , and implementation (Loughead & Bloom, 2013 ; Yukelson, 1997 ). In the assessment stage, the sport psychologist spends time to learn about the dynamics of the organization, including its goals, needs, norms for productivity, and team atmosphere. Then, the sport psychologist educates the team on the objectives of team building and the nature of group development. Although the brainstorm stage is equivalent to Carron and Spink’s practical stage where specific team-building strategies are developed, Yukelson’s brainstorm stage involves athletes as active participants during strategy development. Finally, the strategies are implemented in the final stage. In addition to the four stages, Yukelson also described the core components that must be included in order to build a successful team. Specifically, the team-building program must promote a shared vision that encompasses the group’s overarching goals and expectations, collaborative and synergetic teamwork as a result of role clarity and acceptance among members, and individual and mutual accountability that reflect their willingness to accept responsibility for their actions and group outcomes. Further, the team must establish a positive team culture and cohesive group atmosphere where the players put the group’s interest ahead of their personal interests, a team identity that includes the team’s distinct characteristics and the extent to which the members feel proud of their membership, and open and honest communication that allows members to freely and effectively express and exchange their feelings and thoughts. Finally, the team members must be willing to provide peer helping and social support (Yukelson, 1997 ).

Following Yukelson’s direct approach, Voight and Callaghan ( 2001 ) conducted a team-building intervention with two NCAA women’s soccer teams. The authors conducted needs assessment for both teams that involved discussions among the coaching staff and the athletes, which led to establishing two primary objectives: team unity and performance. Based on these objectives, the consultant and the team brainstormed specific strategies to be utilized in the team-building interventions, which included individual and team goal setting, pre-performance routines, and establishing re-focusing plans, among others. These interventions were delivered in a four-day workshop during pre-season for the first team, whereas weekly team-building sessions were held for the second. In their results, self-reported intervention feedback revealed that the athletes rated the team-building program generally effective for their team unity, as well as individual and team performance.

More recently, a particular form of team-building activity that involves enhancing mutual understanding among team members has gained research attention (Dunn & Holt, 2003 , 2004 ; Holt & Dunn, 2006 ; Pain & Harwood, 2009 ). According to Crace and Hardy ( 1997 ), team functioning can be improved when individual members go beyond understanding their own values and are able to recognize other members’ values, needs, and strengths. Similarly, Yukelson ( 1997 ) advocated the promotion of mutual understanding among teammates by open and honest communication practices. Building on this approach, Holt and Dunn delivered a pair of personal disclosure mutual sharing (PDMS) interventions, one with a male intercollegiate ice hockey team (Dunn & Holt, 2004 ) and another with a female high performance soccer team (Holt & Dunn, 2006 ). Both teams had qualified to participate in the national championship tournament at the time of the interventions. Specifically, prior to their departure to the national championship tournament, all athletes were asked to prepare a story that was personally significant in their sporting or non-sporting life. Then, the sport psychologist conducted a formal team meeting with the athletes the day before their first game of the tournament, where each athlete shared their stories with their teammates (for detailed descriptions of the intervention, see Holt & Dunn, 2006 ). Following the end of the season, the athletes were invited to participate in semi-structured interviews. Two separate inductive analyses of the interview data revealed that the PDMS intervention had numerous benefits that ranged from understanding self and others, to an enhanced sense of closeness and willingness to play for each other, and to feeling extremely confident in their abilities as a team (Dunn & Holt, 2004 ; Holt & Dunn, 2006 ). These results support the use of PDMS interventions, particularly with an elite group of performers who may benefit from maximizing their group functioning prior to entering a critical performance event.

Despite the encouraging results of the PDMS interventions, Holt and Dunn ( 2006 ) commented that the intervention may not be as useful at other stages of the season where the athletes’ emotional intensity and commitment are not as high, such as mid-season. As such, Pain and Harwood ( 2009 ) took a slightly different approach in their mutual sharing intervention, which involved four weekly team meetings mid-season rather than a single meeting prior to a championship tournament. Further, each meeting involved open team discussions among coaches and athletes regarding various factors related to their team functioning instead of sharing personal stories. The authors collected weekly survey data from the start to the end of the season that captured the athletes’ perceptions of their team environment and performance. Their results suggested that the athletes reported increased social cohesion, trust and confidence in teammates, as well as perceptions of team performance as a result of the intervention. Taken together, although preliminary, research evidence supports the effectiveness of team-building interventions that involve enhancing mutual understanding among the team members. More research is warranted in this regard to establish a stronger base of empirical support and to understand the various contextual factors (e.g., gender, competition level, timing of the season, length of the intervention) that may influence its effectiveness.

Team Goal Setting Approach

Although a sport psychologist may have a long list of team-building strategies to choose from, one particular strategy that seems to have the strongest empirical support is team goal setting. In fact, Martin et al.’s ( 2009 ) meta-analysis of 17 sport team-building interventions revealed that team goal setting was not only one of the most popular strategies employed, it was also one of the most effective strategies.

Based on the early work by Widmeyer and Ducharme ( 1997 ), Eys, Patterson, Loughead, and Carron ( 2006 ) introduced a three-stage team goal setting program. The program starts in stage one by explaining the rationale of the team goal setting to the athletes. Then, the athletes collectively set their team goals, following a sequence of activities that involve breaking down broad, long-term goals into more specific, short-term goals that are more readily achievable by the athletes. Specifically, the athletes first set long-term (e.g., high team standing at the end of the season) and short-term (e.g., winning three out of the next four games) outcome goals. Based on these goals, each individual athlete is then asked to determine specific performance targets (e.g., number of rebounds per game) that must be achieved in order to meet their team goals. These targets are then discussed among a subgroup of three to five players, which are then further discussed and agreed upon the team as a whole. In stage two, these performance targets are monitored on a game-by-game basis, which may involve coach feedback and/or posting the relevant statistics in a locker room. In the final stage, the sport psychologist provides ongoing feedback to the team, and the team can collectively adjust and modify their goals as needed.

An example of a team goal setting program based on the framework by Eys et al. ( 2006 ) was conducted by Senécal, Loughead, and Bloom ( 2008 ) with female high school basketball teams. In their study, eight teams with a total of 86 players were randomly assigned to either an experimental or a control condition. The experimental group was assigned the team goal-setting program described by Eys et al. over a 5-month season, whereas the teams in the control group completed measures of cohesion twice during the season without the team goal setting program. Their results showed that the teams in the experimental condition reported significantly higher perceptions of cohesion on all four dimensions than the control group at the end of the season, a difference that was not observed at the start of the season. A more in-depth analysis of their data showed that the experimental group did not change in their perceptions of cohesion over the course of their season, while the control group significantly decreased their perceptions of cohesion over the course of the season, which was attributed to a ceiling effect due to high levels of cohesion in the beginning of the season (Senécal et al., 2008 ). Thus, it may be concluded that a team goal setting intervention could be useful in maintaining the team’s levels of cohesion over the course of a season, which may naturally decrease otherwise. Similar to other types of team-building interventions, more research studies under various contextual elements (e.g., gender, sport, competition levels) are needed to establish a more solid basis of empirical support and external validity.

Limitations and Future Directions for Team-Building Research

While the team-building literature in sport and exercise has established useful protocols and showed some promising results in enhancing the quality of team functioning, it is also worthwhile to consider several limitations in the current literature as well as directions for future research. First, the most fundamental need within the team-building literature is that more empirical evidence is needed to support the use of team-building protocols with a variety of performance groups. For instance, Martin et al.’s ( 2009 ) meta-analysis of team-building interventions in sport was only able to identify 17 independent studies for review. Although Bruner et al.’s ( 2013 ) recent citation network and path analyses of the team-building literature identified 118 relevant articles, their review included books and book chapters, as well as populations outside sport.

Second, there is clear evidence that most team-building programs in sport have largely focused on group cohesion as an outcome variable (Martin et al., 2009 ). While cohesiveness of a group is an important variable for assessing and improving team functioning, and research based on cohesion has provided fruitful information, this overemphasis on cohesion “suggests that research conducted within the area of team building in sport is relatively narrow” (Bruner et al., 2013 , p. 37), possibly overlooking other important individual (e.g., performance, confidence, anxiety) and team (e.g., role ambiguity, role clarity, collective efficacy) factors that may be affected by team-building interventions. McEwan and Beauchamp ( 2014 ) described in their conceptual framework of teamwork that team-building interventions may benefit from a more process-oriented approach where observable teamwork-related behaviors (e.g., goal setting, member interactions, performance monitoring) are targeted, which could “improve team functioning and effectiveness, with increased cohesion emerging over time as a by-product [emphasis added]” (p. 244). Third, in relation to the second point, future research studies may benefit from employing a more tailored approach. That is, rather than assuming that team functioning will be improved upon increased perceptions of cohesion (or any other variable), a sport psychologist may conduct team-by-team a-priori assessments to understand the specific needs of each team and employ relevant strategies. For instance, a team that needs to improve their communication practices may benefit from conducting formal team meetings to facilitate team discussions, whereas a team with low perceived levels of social cohesion may organize social events to promote positive relationships among team members.

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  • v.3(4); 2016 Apr

Understanding the group dynamics and success of teams

Michael klug.

1 Department of Mathematics and Statistics, The University of Vermont, Burlington, VT, USA

James P. Bagrow

2 Vermont Complex Systems Center, The University of Vermont, Burlington, VT, USA

3 Vermont Advanced Computing Core, The University of Vermont, Burlington, VT, USA

Associated Data

All data analysed are made publicly available by the GitHub Archive Project ( https://www.githubarchive.org ).

Complex problems often require coordinated group effort and can consume significant resources, yet our understanding of how teams form and succeed has been limited by a lack of large-scale, quantitative data. We analyse activity traces and success levels for approximately 150 000 self-organized, online team projects. While larger teams tend to be more successful, workload is highly focused across the team, with only a few members performing most work. We find that highly successful teams are significantly more focused than average teams of the same size, that their members have worked on more diverse sets of projects, and the members of highly successful teams are more likely to be core members or ‘leads’ of other teams. The relations between team success and size, focus and especially team experience cannot be explained by confounding factors such as team age, external contributions from non-team members, nor by group mechanisms such as social loafing. Taken together, these features point to organizational principles that may maximize the success of collaborative endeavours.

1. Introduction

Massive datasets describing the activity patterns of large human populations now provide researchers with rich opportunities to quantitatively study human dynamics [ 1 , 2 ], including the activities of groups or teams [ 3 , 4 ]. New tools, including electronic sensor systems, can quantify team activity and performance [ 5 , 4 ]. With the rise in prominence of network science [ 6 , 7 ], much effort has gone into discovering meaningful groups within social networks [ 8 – 15 ] and quantifying their evolution [ 15 , 16 ]. Teams are increasingly important in research and industrial efforts [ 3 , 4 , 17 – 21 ], and small, coordinated groups are a significant component of modern human conflict [ 22 , 23 ]. There are many important dimensions along which teams should be studied, including their size, how work is distributed among their members, and the differences and similarities in the experiences and backgrounds of those team members. Recently, there has been much debate on the ‘group size hypothesis’ that larger groups are more robust or perform better than smaller ones [ 24 – 27 ]. Scholars of science have noted for decades that collaborative research teams have been growing in size and importance [ 20 , 28 – 30 ]. At the same time, however, social loafing, where individuals apply less effort to a task when they are in a group than when they are alone, may counterbalance the effectiveness of larger teams [ 31 – 33 ]. Meanwhile, case studies show that leadership [ 3 , 34 – 36 ] and experience [ 37 , 38 ] are key components of successful team outcomes, while specialization and multitasking are important but potentially error-prone mechanisms for dealing with complexity and cognitive overload [ 39 , 40 ]. In all of these areas, large-scale, quantitative data can push the study of teams forward.

Teams are important for modern software engineering tasks, and researchers have long studied the digital traces of open source software projects to better quantify and understand how teams work on software projects [ 41 , 42 ]. Researchers have investigated estimators of work activity or effort based on edit volume, such as different ways to count the number of changes made to a software's source code [ 43 – 46 ]. Various dimensions of success of software projects such as popularity, timeliness of bug fixes or other quality measures have been studied [ 47 – 49 ]. Successful open source software projects show a layered structure of primary or core contributors surrounded by lesser, secondary contributors [ 50 ]. At the same time, much work is focused on case studies [ 45 , 51 ] of small numbers of highly successful, large projects [ 41 ]. Considering these studies alone runs the risk of survivorship bias or other selection biases, so large-scale studies of large quantities of teams are important complements to these works.

Users of the GitHub web platform can form teams to work on real-world projects, primarily software development but also music, literature, design work and more. A number of important scientific computing resources are now developed through GitHub, including astronomical software, genetic sequencing tools and key components of the Compact Muon Solenoid experiment's data pipeline. 1 A ‘GitHub for science’ initiative has been launched 2 and GitHub is becoming the dominant service for open scientific development.

GitHub provides rich public data on team activities, including when new teams form, when members join existing teams and when a team's project is updated. GitHub also provides social media tools for the discovery of interesting projects. Users who see the work of a team can choose to flag it as interesting to them by ‘starring’ it. The number of these ‘stargazers’ S allows us to quantify one aspect of the success of the team, in a manner analogous to the use of citations of research literature as a proxy for ‘impact’ [ 52 ]. Of course, as with bibliometric impact, one should be cautious and not consider success to be a perfectly accurate measure of quality , something that is far more difficult to objectively quantify. Instead this is a measure of popularity as would be other statistics such as web traffic, number of downloads and so forth [ 47 ].

In this study, we analyse the memberships and activities of approximately 150 000 teams, as they perform real-world tasks, to uncover the blend of features that relate to success. To the best of our knowledge this is the largest study of real-world team success to date. We present results that demonstrate (i) how teams distribute or focus work activity across their members, (ii) the mixture of experiential diversity and collective leadership roles in teams, and (iii) how successful teams are different from other teams while accounting for confounds such as team size.

The rest of this paper is organized as follows: in § 2 , we describe our GitHub dataset; give definitions of a team, team success and work activity/focus of a team member; and introduce metrics to measure various aspects of the experience and experiential diversity of a team's members. In § 3 , we present our results relating these measures to team success. In § 4 , we present statistical tests on linear regression models of team features to control for potential confounds between team features and team success. Lastly, we conclude with a discussion in § 5 .

2. Material and methods

2.1. dataset and team selection.

Public GitHub data covering 1 January 2013 to 1 April 2014 was collected from githubarchive.org in April 2014. In their own words, ‘GitHub Archive is a project to record the public GitHub timeline, archive it, and make it easily accessible for further analysis’. These activity traces contain approximately 110M unique events, including when users create, join, or update projects. Projects on GitHub are called ‘repositories’. For this work, we define a team as the set of users who can directly update (push to) a repository. These users constitute the primary team members as they have either created the project or been granted autonomy to work on the project. The number of team members was denoted by M . Activity or workload W was estimated from the number of pushes. A push is a bundle of code updates (known as commits), however most pushes contain only a single commit (electronic supplementary material; see also [ 46 ]). As with all studies measuring worker effort from lines-of-code metrics, this is an imperfect measure as the complexity of a unit of work does not generally map to the quantity of edits. Users on GitHub can bookmark projects they find interesting. This is called ‘stargazing’. We take the maximum number of stargazers for a team as its measure of success S . This is a popularity measure of success; however, the choice to bookmark a project does imply it offers some value to the user. To avoid abandoned projects, studied teams have at least one stargazer ( S >0) and at least two updates per month on average within the githubarchive data. These selection criteria leave N =151 542 teams. We also collect the time of creation on GitHub for each team project. This is useful for measuring confounds: for example, older teams may tend to have both more members and more opportunities to increase success. Of the teams studied, 67.8% were formed within our data window. Beyond considering team age as a potential confounder, we do not study temporal dynamics such as team formation in this work. A small number of studied teams (1.08%) have more than 10 primary members ( M >10); those teams were not shown in figures, but they were present in all statistical analyses. Lastly, to ensure our results are not due to outliers, in some analyses we excluded teams above the 99th percentile of S . Despite a strong skew in the distribution of S , these highly popular teams account for only 2.54% of the total work activity of the teams considered in this study (2.27% when considering teams with M ≤10 members).

2.1.1. Secondary team

GitHub provides a mechanism for external, non-team contributors to propose work that team members can then choose to use or not. These proposals are called pull requests. (Other mechanisms, such as discussions about issues, are also available to non-team contributors.) These secondary or external team contributors are not the focus of this work and have already been well studied by OSS researchers [ 41 ]. However, it is important to ensure that they do not act as confounding factors for our results, as more successful teams will tend to have more secondary contributions than other teams. So we measure for each team M ext , the number of unique users who submit at least one pull request, and W ext , the number of pull requests. We will include these measures in our combined regression models. Despite their visibility in GitHub, pull requests are rare [ 53 ]; in our data, 57.7% of teams we study have W ext =0, and when present pull requests are greatly outnumbered by pushes on average: 〈 W / W ext | W ext >0〉=42.3 (median 16.0), averaged over all teams with at least one pull request.

2.2. Effective team size

The number of team members, M , does not fully represent the size of a team as the distribution of work may be highly skewed across team members. To capture the effective team size m , accounting for the relative contribution levels of members, we use m =2 H , where H = − ∑ i = 1 M f i log 2 ⁡ f i , and f i = w i / W is the fraction of work performed by team member i . This gives m = M when all f i =1/ M , as expected. This simple, entropic measure is known as perplexity in linguistics and is closely related to species diversity indices used in ecology and the Herfindahl–Hirschman index used in economics.

2.3. Experience, diversity and leads

Denote with R i the set of projects that user i works on (has pushed to). (Projects in R i need at least twice-monthly updates on average, as before, but may have S =0 so as to better capture i 's background, not just successful projects.) We estimate the experience E of a team of size M as

and the experiential diversity D as

where the sums and union run over the M members of the team. Note that D ∈[1/ M ,1). Experience measures the quantity of projects the team works on while diversity measures how many or how few projects the team members have in common, the goal being to capture how often the team has worked together. Lastly, someone is a lead when, for at least one project they work on, they contribute more work to that project than any other member. A non-lead member of team j may be the lead of project k ≠ j . The number of leads L k in team k of size M k is

where L ij =1 if user i is the lead of team j , and zero otherwise. The first sum runs over the M k members of team k , the second runs over all projects j . Of course, the larger the team the more potential leads it may contain so when studying the effects of leads on team success we only compare teams of the same size (comparing L while holding M fixed). Otherwise, E and D already account for team size.

We began our analysis by measuring team success S as a function of team size M , the number of primary contributors to the team's project. As S is, at least partially, a popularity measure, we expect larger teams to also be more successful. Indeed, there was a positive and significant relationship ( p <10 −10 , rank correlation ρ =0.0845) between the size of a team and its success, with 300% greater success on average for teams of size M =10 compared with solos with M =1 ( figure 1 ). This strong trend also holds for the median success (inset). While this observed trend was highly significant, the rank correlation ρ indicates that there remains considerable variation in S that is not captured by team size alone.

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Larger teams have significantly more success on average, with a 300% increase in S as M goes from 1 to 10. This correlation may be due to more team members driving project success or success may act as a mechanism to recruit team members. Error bars here and throughout denote ±1.96 s.e. (Inset) Using the median instead of the mean shows that this trend is not due to outliers.

Our next analysis reveals an important relationship between team focus and success. Unlike bibliographic studies, where teams can only be quantified as the listed coauthors of a paper, the data here allow us to measure the intrinsic work or volume of contributions from each team member to the project. For each team we measured the contribution w r of a member to the team's ongoing project, how many times that member updated the project (see Material and methods). Team members were ranked by contribution, so w 1 counts the work of the member who contributed the most, w 2 the second heaviest contributor and so forth. The total work of a team is W = ∑ r = 1 M w r .

We found that the distribution of work over team members showed significant skew, with w 1 often more than two to three times greater than w 2 ( figure 2 a ; electronic supplementary material). This means that the workloads of projects are predominantly carried by a handful of team members, or even just a single person. Larger teams perform more total work, and the heaviest contributor carries much of that effort: the inset of figure 2 a shows that w 1 / W , the fraction of work carried by the rank one member, falls slowly with team size, and is typically far removed from the lower bound of equal work among all team members. See the electronic supplementary material for more details. This result is in line with prior studies [ 51 ], supporting the plausibility of our definition of a team and our use of pushes to measure work.

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Teams are focused, and top teams are more focused than other teams of the same size. ( a ) The average fraction of work w r / W performed by the r th most active member, where W is the total work of the team, for different size teams. Larger teams perform more work overall, but the majority of work is always done by a small subset of the M members (note the logarithmic axis). Inset: the fraction of work performed by the most active team member is always high, often larger than half the total. The dashed line indicates the lower bound of uniform work distribution, w r / W =1/ M . ( b ) A team is dominated when the most active member does more work than all other members combined. Top teams are significantly more probably to be dominated than either average teams or bottom teams for all M >2. ( Top team : above the 90th percentile in S ; average team : greater than the 40th percentile of S and less than or equal to the 60th percentile of S ; bottom team : at or below the 10th percentile of S .) ( c ) The effective team size m (see Material and methods), a measure that accounts for the skewed distribution of work in ( a ), is significantly smaller than M . Moreover, top teams are significantly more focused, having smaller effective sizes, than average or bottom teams at all sizes M >1. This includes the case M =2, which did not show a significant difference in ( b ). The dashed line denotes the upper bound m = M . ( d ) Success is universally higher for teams with smaller m / M , independent of M , further supporting the importance of focused workloads. The solid lines indicates the average trend for all teams 2≤ M ≤10. These results are not due to outliers in S ; see the electronic supplementary material.

This focus in work activity indicates that the majority of the team serves as a support system for a core set of members. Does this arrangement play a role in whether or not teams are successful? We investigated this in several ways. First, we asked whether or not a team was dominated , meaning that the lead member contributed more work than all other members combined ( w 1 / W  > ½). Highly successful ‘top’ teams, those in the top 10% of the success distribution, were significantly more likely to be dominated than average teams, those in the middle 20% of S , or ‘bottom’ teams, those in the bottom 10% of the S ( figure 2 b ).

Can this result be due to a confounding effect from success? More successful projects will tend to have more external contributors, for example, which can change the distribution of work. For example, in one scenario a team member may be a ‘community manager’ merging in large numbers of external contributions from non-team members. To test this we examined only the 57.7% of teams that had no external contributions ( W ext =0) and tested among only those teams whether dominated teams were more successful than non-dominated teams. Within this subset of teams, dominated teams had significantly higher S than non-dominated teams (Mann–Whitney U test (MWU) with continuity correction, p <10 −8 ). The MWU is non-parametric, using ranks of (in this case) S to mitigate the effects of skewed data, and does not assume normality. We conclude from this that external contributions do not fully explain the relationship between workload focus and team success.

Next, we moved beyond the effects of the heaviest contributor by performing the following analysis. For each team we computed its effective team size m , directly accounting for the skew in workload (see Material and methods for full details). This effective size can be roughly thought of as the average number of unique contributors per unit time and need not be a whole number. For example, a team of size M =2 where both members contribute equally will have effective size m =2, but if one member is responsible for 95% of the work the team would have m ≈1.22. Note that M and m are positively correlated ( ρ =0.985).

Figure 2 c shows that (i) all teams are effectively much smaller than their total size would indicate, for all sizes M >1, and (ii) top teams are significantly smaller in effective size (and therefore more focused in their work distribution) than average or bottom teams with the same M . Further, success is significantly, negatively correlated with m , for all M ( figure 2 d ). More focused teams have significantly more success than less focused teams of the same size, regardless of total team size.

Further analyses revealed the importance of team composition and its role in team success.

Team members do not perform their work in a vacuum, they each bring experiences from their other work. Often members of a team will work on other projects. We investigated these facets of a team's composition by exploring (i) how many projects the team's members have worked on, (ii) how diverse the other projects are (whether the team members have many or few other projects in common) and (iii) how many team members were ‘leads’ of other projects.

An estimate of experience, E , the average number of other projects that team members have worked on (see Material and methods), was significantly related to success. However, the trend was not particularly strong (see the electronic supplementary material) and, as we later show via combined modelling efforts, this relationship with success was entirely explainable by the teams' other measurable quantities.

It may be that the volume of experience does not contribute much to the success of a team, but this seems to contradict previous studies on the importance of experience and wisdom [ 37 , 38 ]. To investigate, we turned to a different facet of a team's composition, the diversity of the team's background. Successful teams may tend to be composed of members who have frequently worked together on the same projects in the past, perhaps developing an experiential shorthand. Conversely, successful teams may instead have multiple distinct viewpoints, solving challenges with a multi-disciplinary perspective [ 54 ].

To estimate the distinctness of team member backgrounds, the diversity D was measured as the fraction of projects that team members have worked on that are unique (see Material and methods). Diversity is low when all M members have worked on the same projects together ( D =1/ M ), but D grows closer to 1 as their backgrounds become increasingly diverse. A high team diversity was significantly correlated with success, regardless of team size ( figure 3 ). Even small teams seem to have benefited greatly from diversity: high- D duos averaged nearly eight times the success of low- D duos. The relationship between D and S was even stronger for larger teams ( figure 3 , inset), implying that larger teams can more effectively translate this diversity into success. Even if the raw volume of experience a team has does not play a significant role in the team's success, the diversity of that experience was significantly correlated with team success. See also our combined modelling efforts.

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Teams whose members belong to more diverse sets of other teams tend to be more successful, regardless of team size. The dashed line denotes the average success of all teams. (Inset) The rank correlation ρ between diversity and success grows with team size. Teams above the 99th percentile in S were excluded to ensure the trend is not due to outliers.

Considerable attention has been paid recently to collective leadership, where decision-making structures emerge from the mass of the group instead of being imposed via a top-down hierarchy [ 34 , 36 ]. The open collaborations studied here have the potential to display collective leadership due to their volunteer-driven, self-organized nature. The heaviest contributor to a team is most likely to occupy such a leadership role. Further, as teams overlap, a secondary member of one team may be the ‘lead,’ or heaviest contributor to another. This poses an interesting question: Even though teams are heavily focused, are teams more successful when they contain many leads, or few? A team with many leads will bring considerable experience, but most of its members may also be unable to dedicate their full attention to the team.

To answer this, we measured L , the number of team members who are the lead of at least one project (1≤ L ≤ M , see Material and methods) and found that teams with many leads have significantly higher success than teams of the same size with fewer leads ( figure 4 ). Only one team member can be the primary contributor to the team, so a team can only have many leads if the other members have focused their work activity on other projects. Team members who are focused on other projects can potentially only provide limited support, yet successful teams tend to arrange their members in exactly this fashion. Of course, the strong focus in work activity ( figure 2 ) is probably interrelated with these observations. However, we will soon show that both remain significantly related to success in combined models.

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Teams with more leads have higher success than teams of the same size with fewer leads. A lead is someone who contributes more work to at least one team he or she belongs to than any other members of that team. Outliers in S were removed as before.

Expanding on this observation, table 1 illustrates the extreme case of teams of size M with a single lead ( L =1) compared with teams of the same size composed entirely of leads ( L = M ). The latter always displayed significantly higher success than the former (MWU test, see table 1 ), independent of team size, underscoring the correlations displayed in figure 4 . Often the difference was massive: teams of size M =7, for example, averaged more than 1200% higher success when L =7 than when L =1.

Teams composed entirely of leads ( L = M ) are significantly more successful (MWU test on S ) than teams of the same size with one lead ( L =1), regardless of team size M . Teams above the 99th percentile in S were excluded to ensure the differences were not due to outliers.

a When M ≥8, the number of teams with L = M is too small ( N <20) for us to reasonably conclude the difference in S is significant, despite the small p -values.

These results on team composition cannot be easily explained as a confound with success or secondary contributions as they study specific features and projects of the individuals who comprise a team, those features are not related to the successes of other projects an individual may work on, and they strictly control for total team size M (e.g. we only compare teams with different values of L when they have the same value M ). These results further amplify our findings on team focus, and augment important existing research [ 3 , 4 , 36 , 37 , 54 ].

Taken together, our results demonstrate that successful teams tend to be focused ( figure 2 ), successful teams tend to be experientially diverse ( figure 3 ) and successful teams tend to have many leads ( figure 4 ). We have found that teams tend to do best when optimized along all three of these dimensions. Of course, it is necessary to explore the joint effects of quantities, to see if one relationship can be explained by another, which we will do with multivariate statistical models.

4. Combined models and confounds

One important aspect of the individual team measurements is that they do not exist in isolation. For example, successful teams also have high work activity (high W ). This can correlate with effective team size m as the potential inequality between team members can grow as their total activity grows. In other words, we need to see how our team measures relate to success together .

To understand the relative effects of these team composition measures, we fitted a linear regression model of success as a function of all explored measures ( table 2 ). Not only did this regression allow us to determine whether a variable was significant or if it was confounded by the other measures, but the coefficients (on the standardized variables) let us measure the relative strengths of each variable. We also included the age of a project T (measured as the time difference between the recorded creation time of the project and the end of our data window; see Material and methods) as this may also be a potential confounding factor (older projects have had more time to gain members and to gain success).

OLS regression model on team success, S = α + β M M + β m m + β W W + β E E + β D D + β L L + β T T . Outliers (above the 99th percentile in S ) were filtered out to ensure they do not skew the model.

a Variables are standardized for comparison such that a coefficient β x implies that increasing a variable x by one standard deviation σ x corresponds to a β x σ S increase in S , holding other variables fixed.

Examining the regression coefficients showed that the number of leads L was the variable most strongly correlated with team success. Team age T , effective team size m and team size M play the strongest roles after L in team success, and all three were also significant in the presence of the other variables. The coefficient on m was negative while for M it was positive, further underscoring our result that, while teams should be big, they effectively should be small. Next, the diversity D of the team, followed by the total work W done on the project, were also significant measures related to success. Finally, overall team experience E was not significant in this model ( p >0.1). We conclude that, while S and E are correlated by themselves, any effects of E are explained by the other quantities.

What about secondary contributions, those activities made by individuals outside the primary team? We already performed one test showing that dominated teams are more successful than non-dominated teams even when there are no secondary contributions. Continuing along these lines, we augmented this linear model with two more dependent variables, M ext and W ext . Regressing on this expanded model (see the electronic supplementary material for details) did not change the significance of any coefficients at the p =0.05 level; E remained insignificant ( p >0.1). Both new variables were significant ( p <0.05). Note that there were no multicollinearity effects in either regression model (condition numbers less than 10). We conclude that secondary contributions cannot alone explain the observations relating team focus, experience and lead number to team success.

5. Discussion

There has been considerable debate concerning the benefits of specialization compared with diversity in the workplace and other sectors [ 39 ]. Our discoveries here show that a high-success team forms a diverse support system for a specialist core, indicating that both specialization and diversity contribute to innovation and success. Team members should be both specialists, acting as the lead contributor to a team, and generalists, offering ancillary support for teams led by another member. This has implications when organizations are designing teams and wish to maximize their success, at least as success was measured in these data. Teams tend to do best on average when they maximize M ( figure 1 b ) while minimizing m ( figure 2 d ) and maximizing D ( figure 3 ) and L ( figure 4 ).

Of course, some tasks are too large for a single person or small team to handle, necessitating the need for mega teams of hundreds or even thousands of members. Our results imply that such teams may be most effective when broken down into large numbers of small, overlapping groups, where all individuals belong to a few teams and are the lead of at least one. Doing so will help maximize the experiential diversity of each sub-team, while ensuring each team has someone ‘in charge’. An important open question is what the best ways are to design such pervasively overlapping groups [ 14 ], a task that may be project- or domain-specific but which is worth further exploration.

The negative relationship between effective team size m and success S (as well as the significantly higher presence of dominated teams among high success teams) further belies the myth of multitasking [ 39 ] and supports the ‘surgical team’ arguments of Brooks [ 17 ]. Focused work activity, often by even a single person, is a hallmark of successful teams. This focus both limits the cognitive costs of task switching, and lowers communication and coordination barriers, as so much work is being accomplished by one or only a few individuals. We have provided statistical tests demonstrating that the relationship between focus and success cannot be due to secondary/external team contributions alone.

Work focus could possibly be explained by social loafing where individual members of a group contribute less effort as part of the group than they would alone, yet loafing does not explain the correlation between e.g. leads and success ( figure 4 ). Likewise, our team composition results on group experience, experiential diversity and the number of leads cannot be easily explained as a confound with success or secondary contributions: they study specific features of the individuals who comprise a team, those features are not related to the successes of other projects an individual may work on, and they strictly control for total team size M (except for the number of leads L , so for that measure we only compared teams with the same M ). The measures we used for external team contributions, M ext and W ext , may be considered measures of success themselves, and studying or even predicting their levels from team features may prove a fruitful avenue of future work.

Lastly, there are two remaining caveats worth mentioning. We do not specifically control for automatically mirrored repositories (where a computer script copies updates to GitHub). Accurately detecting such projects at scale is a challenge beyond the scope of this work. However, we expect most will either be filtered out by our existing selection criteria or else they will probably only have a single (automated) user that only does the copying. The second concern is work done outside of GitHub or, more generally, mismatched assignments between usernames and their work. This is also challenging to fully address (one issue is that the underlying git repository system does not authenticate users). We acknowledge this concern for our workload focus results, but even it cannot explain the significant trends we observed on team composition such as the density of leads. Noise due to improperly recorded or ‘out-of-band’ work has in principle affected all quantitative studies of online software repositories.

Supplementary Material


We thank Josh Bongard, Brian Tivnan, Paul Hines, Michael Szell and Albert-László Barabási for useful discussions, and we gratefully acknowledge the computational resources provided by the Vermont Advanced Computing Core, supported by NASA (NNX-08AO96G).

1 For examples, see https://github.com/showcases/science .

2 See https://github.com/blog/1840-improving-github-for-science .

Data accessibility

Authors' contributions.

M.K. participated in data collection and data analysis, and helped draft the manuscript; J.B. conceived the study, designed the study, carried out data collection and analysis, and drafted the manuscript. All authors gave final approval for publication.

Competing interests

We have no competing interests.

J.B. has been supported by the University of Vermont and the Vermont Complex Systems Center.

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The Fruitful Toolbox

Case Study- Microsoft: Team Building and Group Dynamics

Aug 15, 2016 | Everything DiSC , Five Behaviours

Case Study- Microsoft: Team Building and Group Dynamics

Creating and fostering a culture of teamwork requires trust, collaboration, and accountability.  Forty exceptional employees, six short months, and the responsibility to one reputable Fortune 100 company: That is what Darci Kleindl was handed when she accepted a job as the general manager of sales excellence and sales enablement for the Microsoft Business Solutions (MBS) group. She inherited a global team of customer support managers, all of whom work with diverse clientele, including engineers, marketing organisations, consultants, and external customers. She also manages a team of leaders who drive worldwide standards for sales basics, internal and external readiness, and the business rhythm and communications to accelerate the MBS group.

group dynamics case study

To add to the chaos, this group, which had operated autonomously within Microsoft, faced a large organisational shift when she started: “Our enterprise and small business teams integrated our CRM solutions into the overall business strategy and execution. It took our work from a priority state of 4 to 9 overnight,” she says.

As daunting as her situation seemed, Kleindl wasn’t new to managing dynamic groups or governing complex situations. Her career often has included strategising on the higher executive level. This time, however, would be different. She planned to implement a comprehensive leadership and team development programme that would transform her work culture and provide a foundational structure for her team.

Strategising for success Kleindl was resolute in making a lasting impact. “What happens so often is people do a one-time hit when it comes to team and leadership development. People are put in one class and we expect it to change their world overnight,” she explains.

Her vision involved stabilising a culture of trust, collaboration, and accountability—one that would resonate beyond just her direct reports. To do this, she sought the help of a trusted advisor—Bruce Leamon, an authorised partner of The Five Behaviours of a Cohesive Team —who jumped at the opportunity.

“Darci understands that you can have great products, you can have phenomenal software, you can have the smartest people, but if they’re not playing together, it’s never going to get you ahead,” Leamon says. “I recommended that she use The Five Behaviours; it’s the operating system for teamwork. Once that’s established you have a framework to build from.”

The Five Behaviours of a Cohesive Team is a comprehensive team development programme based on the work of Patrick Lencioni. It helps both team members and leaders understand how their unique group dynamic can work together to build a more effective team and achieve sustainable results.

The facilitated sessions help teams to comprehend where they fall within five distinct pillars of teamwork: trust, conflict, commitment, accountability, and results. It uses the Everything DiSC behavioural assessment to establish a neutral language and help participants have productive conversations to develop within these five competency areas.

With The Five Behaviours as the foundation, Kleindl and Leamon quickly developed the Organisational Capital Programme. The six-month programme establishes a common language for the team to communicate, standardise expectations to improve accountability, strengthen leadership abilities, and create an emotionally intelligent culture.

“I wanted to give my colleagues an end-to-end picture of who they are, from their own understanding as well as from other people’s perspective—to learn that maybe you have a dominant personality style and be able to understand how that relates to your emotional intelligence or to your leadership skills,” Kleindl explains.

The team training session Thirty-six tentative colleagues participated in the two-day, off-site training session. Leamon started by establishing conversation based on vulnerability-based trust. Before they could get into discussions on dealing with conflict, their collective idea of commitment, or holding one another accountable, the team needed to trust that what they were saying would be respected and heard. They shared personal backgrounds to learn about one another’s perspectives and how they might relate to their colleagues in ways beyond work. They spoke candidly on what makes them comfortable and uncomfortable in a work environment. They discussed their team’s core values and their vision for themselves and the company.

“It allowed us to understand who people are. Combined with information from DiSC , we could establish a common language and move swiftly through the rest of the programme,” Leamon says.

The impact The Organisational Capital Programme is built on the lessons learned from that initial The Five Behaviours session. The impact seen within the MBS group has been multifaceted, explains Kleindl. “I think this team has increased in visibility substantially as far as what they do and the impact. Our relationship network has grown, systems have been put in place to help with productivity, our planning is more succinct, and the team is connecting more one-on-one with people to explain their roles.”

In terms of accountability, there was a notable shift. “They’ve come to understand that they don’t always have to say ‘yes.’ … They trust each other and understand how to have healthy conflict when situations are off-loaded to us that really should not be our job in the first place. We’re clear on what we’re accountable for.”

The Five Behaviours is a reliable tool that helped lay the foundation for further development. But a large reason why it was successful was because people felt heard. “I got emails from people saying, ‘No one has ever invested in me in this way. It’s fantastic!’” That sort of investment in individuals served well for Kleindl, her team, and also the network beyond her team. The MBS group now has a reputation for sustaining an enviable culture of trust, collaboration, and accountability.

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group dynamics case study

Group Dynamics: A Difficult Day in the Office Case Study

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Karen group, the diagnosis of the problem in the group, conflict resolution, communication, negotiations and decision making, managing and taking part in the meetings.

The concept of group dynamics has been of great significance in the modern day study of organizational behavior. A group is an important aspect of any organization. Synergy is always achieved through group work (Elloy, 2005, p. 68).

In addition, a group working together is able to come up with more diverse and better results to a problem at hand (Ramayah & Koay, 2003, p. 102). The essence of group work can therefore not be over emphasized. It is due to the value that is attached to group work that many organizations commission different groups to perform different tasks.

This notwithstanding, the groups do not come not without their fair share of disadvantages. Since a group is composed of various individuals whose inputs are of equal importance and value, challenges always arise whenever a group is incepted to work on whatever assignment (Arnold, et al., 2010, p. 93).

These challenges if not handled well may result in the frustration of the subject matter of the formation and subsequently lead to a failure in the pursuit of the set targets.

This management report seeks to carry out an expository diagnosis of the problems that are marring the ‘New-Product-Group’ headed by Karen. It will further discuss the possible reasons as to why the group is dysfunctional. Finally a recommendation will be given on the possible way forward to deal with the jinx.

All this will be accomplished with reference to the key indicative topics that have been learnt in class namely managing and taking part in meetings, communications, conflict resolution, negotiation, and decision making.

The group members

The group is composed of four members; three gentlemen; Ben, James, and Charles and one lady; Karen. Karen is the head of the group and is charged with the responsibility of coming up with the final report and make a presentation to the management the following day. The different attributes of each member are discussed in the ensuing text.

Karen is a female member of the group. She is in charge of the group and is charged with the responsibility of ensuring that the group succeeds in its assignment. She is the one answerable to the management and is expected to make the presentation the following day. She is dealing with a difficult group though as one of the members namely Ben has a superiority complex and feels that he should be the one in charge of the group.

Ben is a man who is said to have a chauvinistic regard on women. He claims to have had a four year Navy service experience and this makes him think that he is the best positioned to handle the group leadership role. He openly expresses his disrespect and dishonor to the group leader and thinks that he ought to be in charge of the group.

He does not believe in being led by a woman and bears the views that the position of the woman is raising children. Once his contributions are criticized by Karen, he quickly takes it to a personal level rather than a work related issue. He is said to be the hardest obstacle to overcome in achieving the group’s objective.

James is a more open minded member as compared to Ben. He seems more agreeable though the problem with him is that he takes sides with Ben so as they can together impose their wills on the group. He is therefore portrayed as Ben’s puppet.

Charles is the group member who is in charge of the collection of data. He is posited as a person who is more willing to listen and thus probably the most cooperative of the two other friends. He however brings into the group a lot of data such that it is impossible to make conclusions from the amount of data he presents. This leads to a situation of analysis paralysis during the meetings.

A look at each member’s attributes leads to the group being dysfunctional and as such not much has been able to be achieved by the group so far. There is only one day remaining before the presentation of the final report by the group leader and this seems to be a repeat of the previous meetings which end in unproductive confrontations between Ben and Karen.

A look at the situation at hand reveals that the group is dysfunctional and as such it is unable to fulfill its mandate if the status quo remains. For any group to be successful in meeting its objectives, several important factors have to be put into consideration. It must be noted that a group is not formed by merely collecting several individuals and assigning them a task to fulfill (Proehl, 1997, p. 39).

A group ought to be comprising of individuals who fully and clearly understand the task at hand and therefore work for the common goal while putting individual differences aside (Sosik & Jung, 2002, p. 133). The following part discusses the various indicative points that should shed light on the Karen’s problem as well as giving the possible solution to the problem.

Due to the diverse nature of the modern day organizations and the complexity of the members, it is inevitable to work without conflicts emerging among the members.

A conflict is defined as an antagonistic force that repels another person’s point of view and seeks to advance the bearer’s interests (Hellriegel & Slocum, 2011, p. 259). There are various forms of conflicts. These include opinion based conflicts, personality based and group based.

According to the case study, there are two types of conflicts; opinion based and personality based, the biggest culprit being Ben and the biggest victim being Karen. Whichever the type and whoever is affected, a common truth is that conflicts ought and must be resolved. There are several ways in which this conflict can be resolved.

One way is through empowering every member of the group to take a certain responsibility in the achievement of the common goal (Wood & Bandura, 1989, p. 263).

This would help in a great manner since a sense of personal responsibility will be instilled in each member of the organization. This would further bring about respect among the members and as such reduce the potential conflict of interest between the conflicting parties.

While segregating of duties may solve the group related conflict, the other form of conflict relating to the personal opinion on Ben towards Karen will have to be resolved through a dialogue between the two persons. Dialogue is known to be a very effective tool of conflict resolution more so when it comes to personal differences (Nina, 1992, p. 101).

However, this may need a third person who may act as the dialogue moderator or an arbitrator if the two individuals cannot peacefully talk on their own. The effectiveness of such a dialogue would be judged by the two parties condoning differences without taking personal opinions on the other person.

Once the conflict has been resolved the boons that come along with the result are collaboration, conflict avoidance, compromise, and frequent agreement (Lent & Hackett, 1987, p. 298).

This seems to be the ingredient that is conspicuously lacking in this group and a proper management of the conflict will set the group on for a successful attainment of the goal. It will also boost trust among the member and this will begin to reflect in the decision making process as decisions will be made at a more efficient manner.

Compromise helps a person to accommodate the other person’s views and collaboration helps in coming up with a high quality work through contribution by each of the members. Conflict avoidance is only possible after dealing with all the triggers of conflict. In the end, decision making becomes an easier task by the group and the results are achieved with a lot of ease.

Communication is defined as the sending and receiving of information through a set channel. It is the single most important aspect of organizational management that ensures that the organization is run in the set path. Communication helps in all areas of the organization in that the expectations and the feedback of any activity has to be passed to the relevant persons (Nina, 1992, p. 180).

As such, communication is an important aspect of any meeting since through communication, ideas are shared, consultations are carried out and decisions are made (Newstrom & Davis, 1993, p. 69). Effective communication always results in mutual understanding among the discussing members and also results in more efficient decision making.

The communication channels usually depend on the nature and the context of the subject matter and as such, individuals should properly understand the dynamics which come along with the different audiences and adjust to the dynamics where necessary.

A look at the case study reveals a breakdown in communication. The decisions take too long to make and this is a clear indication that the members of the group do not communicate the expectations clearly.

It is said that sometimes verbal exchanges between Karen and Ben become too heated that Ben ends up shouting at Karen. This indicates that the group communication skills are poor and as such there need to be a complete overhaul of the communication strategies being employed by the group.

Negotiation skills are usually borne by an individual who has proper communication skills. This is because for an individual to be able to effectively negotiate, he/she must be able to effectively communicate as well. Negotiation therefore comes in hand in hand with the communication.

It helps a group to be able to reach a consensus as well as make decisions. Decision making therefore comes hand in hand with negotiations as the members of the group are able to reach agreements and choose a common course of action.

A group is termed as dysfunctional when the members are unable to carry out negotiations and effectively reach compromise and make decisions and this is the situation in the case study involving Karen’s group (Sosik & Jung, 2002, p. 133).

One member Charles comes up with numerous data to analyze such that he literally paralyzes the decision making. Even worse, the members are unable to effectively negotiate and this has caused poor relationship among the group members. Ben and James link up to impose their will on other members and this often results in a stalemate.

The group should embrace the art of negotiation and as such, make every member’s contribution to the discussion important and valuable. In doing this, they will be able to reach a compromise soon and also make decisions at a more efficient pace. The making of decisions is therefore dependent on the level of compromise reached.

Since the meetings that have happened so far have failed to reach a consensus and hence a decision, there is a dire need to change the various approaches to the meetings that will ensure that the locker heads among the member s is resolved as soon as possible. If the above issues are paid close attention to, it is possible to resolve the problem that is facing Karen

It is clear from the above discussion that the group headed by Karen has a lot of problems that are rendering it dysfunctional. The main problem in the group relates to conflict in leadership and communication breakdown. Although these are two very important aspect of any group, they are conspicuously absent in this group and are mainly responsible for the dysfunctional state of the group.

All the members need to be approached separately outside the meeting context and explained the importance of co-operation at least to ensure that the deadline is met by the group. Afterwards, clear communication ought to be given before instituting ant group as in doing this the structure of the group should be clearly spelt out to ensure avoidance of any unapparent conflict.

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Group Dynamics and Socialization

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Group dynamics refers to interactional and psychological behaviors within a group of people. It sheds light on the fundamental processes that generate norms, roles, relations, objectives, or ideologies within social groups.

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Matusitz, J. (2020). Group Dynamics and Socialization. In: Global Jihad in Muslim and non-Muslim Contexts. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-47044-9_4

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Prison group dynamics: Political and criminal inmates in Indonesia. A case study

This study is a case study of Indonesian prisoners. It explores the life of both political and criminal inmates within the walls. Thus, similarities and dissimilarities of their behavior in everyday life within the prison are the main scrutiny of this study. In sum, this study focuses much on the dynamic of group relationships. To this end, I employed a field research method to gather my data by asking 15 open-ended questions to both groups of criminal and political inmates. The outcome is very fascinating. These two group of inmates, in many ways, share some similarities and differences. In addition, they dislike and distrust each other. However, both criminal and political inmates agree with the fact that the guards and officials of the prison are corrupt and power abusers. In the eyes of the guards and officials, both criminal and political inmates are law breakers who need to be incarcerated in order to fix their mental attitude. "We uphold the law and they break the law," said one official. Even though both criminal and political inmates live within the same environment, political inmates are much more able to cope with their lives within the walls than criminal inmates. Political inmates are able to confront their realities maturely while criminal inmates live their lives with high tension and anxiety. Furthermore, the demarcation line between criminal and political inmates also lies in the strong beliefs that political inmates hold. Political inmates are committed to bettering society, while criminal inmates are self-interest oriented and can be considered against society. In addition, political inmates perceive their fellow criminal inmates as destroyers of society, while the latter see the former as the illusionists who did not do anything to improve the quality of life of the people whom they always claim to represent. In the eyes of criminal inmates, political inmates are the elite who think only of themselves and never care about the rest. Overall, my research found that both criminal and political inmates basically live in an environment of anxiety, tension, and fear. One of the key problems is law of the prison that is truly in favor of the guards and officials. This law was enacted during the Dutch occupation, which automatically reflects the attitude of the Dutch regime toward its colony. That is why the inmates live with unforgotten days with unforgiven men. Unfortunately, their grievances or voices are unheard.

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