Can a team have too much cohesion? The dark side to network density
Sean Wise
⇑Ryerson University, Ted Rogers School of Management, 55 Dundas Ave., Toronto, Ontario M5B 2K3, Canada
a r t i c l e
i n f o
Article history: Received 8 August 2013 Accepted 21 December 2013 Available online 18 January 2014 Handling Editor: Sabina Siebert Keywords:
Team performance Social network topology Group cohesion Email analysis Travel agents
a b s t r a c t
The goal of most work teams is high performance. Prior studies suggest that performance within work groups is influenced by that group’s social network topology. Research has generally revealed to date that group cohesion (i.e., network density) is positively related to team performance under certain conditions. However, more recent research has indicated that this is not the full story. Recent research suggests that an inverse curvilinear relationship exists between social network measures (of which group cohesion is one) and team performance. In response to the need for understanding this relationship more fully, and leveraging the promising new insights that can be garnered with the use of social network analysis (SNA), this study employs SNA as a tool to explore the structural cohesiveness of teams of travel agents. This research extends our understanding of the relationship between intragroup social network relations and team performance by confirming an inverse curvilinear relationship exists between group cohesion and team performance. This paper leverages email communication to determine the social networks of each team, and then examines such in light of team performance. In total, an analysis of more than 7 million emails was undertaken. This study was conducted with work teams within a service organiza-tion. Each team in the study carries out the same tasks, i.e., identical task contingency, yet represents a distinct unit of analysis. The study confirms that social network topology is a valuable predictor of team performance and confirms that, like so many other social network measures, group cohesion and team performance share an inverse ‘U’ shaped relationship, not strictly a positive one as previously posited.
Ó 2013 Elsevier Ltd. All rights reserved.
Introduction
Despite the increase of teams and work groups within an orga-nization there has been relatively little social network research on the structural properties of work groups and their consequences for team performance (Cummings & Cross, 2003; Lechner, Frankenberger, & Floyd, 2010). This research attempts to address this deficiency. This research focuses on network topology, in par-ticular the measure of group cohesion, and how such impacts team performance. The Theory of Task Contingency (Donaldson, 2001) postulates that some network topologies are better suited to explo-ration practices, while others are more suited to exploitation practices. That research finds that social network topology can be optimized to generate performance gains (Donaldson, 2001, p. 2). The Theory of Task Contingency (Donaldson, 2001) was extended byLechner et al. (2010)to create the Dark Side of Social Capital theory. Under the Dark Side of Social Capital theory (Lechner et al., 2010) too much of any social network measure is as bad as too little. Put more explicitly, the same network structures that help some teams achieve fit with their environment and accom-plish goals reach a point of diminishing returns, after which
increasing that social measure further leads to negative perfor-mance consequences for groups (Lechner et al., 2010).Fig. 1below illustrates some of the negative and positive influences of intergroup relations on performance.
This research seeks to explore the relationship between a group’s cohesion level and that team’s financial performance, while holding task contingency constant across teams and controlling for team size. By doing so, this research attempts to determine if the relationship between group cohesion and team performance is always positive as found by prior scholars (Beal, Cohen, Burke, & McLendon, 2003; Carron, Brawley, & Widmeyer, 1998; Evans & Dion, 2012) or if the relationship between these variables is actu-ally inversely curvilinear and as found byHardy, Eys, and Carron (2005) and as first suggested by Carron, Prapavessis, and Grove (1994). Further, this research seeks to affirm thatLechner et al. (2010)Dark Side of Social Capital theory holds true for group cohe-sion and team performance.
Theoretical background
Prior research has called on scholars to refine our understand-ing of how network relations contribute to team performance (Lechner et al., 2010). Firms (e.g., Teams or Groups), in the twenty-first century, may be viewed as actors, or indeed players, 0263-2373/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved.
http://dx.doi.org/10.1016/j.emj.2013.12.005
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E-mail address:sean.wise@ryerson.ca
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j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / e m jin evolving socio-economic networks (Hite & Hesterly, 2001). Increasingly, these networks are digitally-enabled, and this rapid, online communication growth provides rich data platforms for so-cial network scholars (Johnson, Kovács, & Vicsek, 2012).
There is a relatively large body of literature on the effects of teamwork from various disciplines, including: sports, organiza-tional behavior, industrial psychology, and management (Carron, Bray, & Eys, 2002; Devine, Clayton, Phillips, Dunford, & Melner, 1999; Sawyer & Guinan, 1998; Straus, 1999). Yang and Tang (2004)found that with social network analysis it is possible to de-scribe the underlying relationships among teammates and to gain a better understanding of its internal processes. These authors sug-gest that various concepts from social network analysis, e.g., cen-trality, network density, be added to the study of work performance (Yang & Tang, 2004, p. 336).
Social Networks represent the outlay of social relationships within groups (Wise, 2012). In this context, a network is defined as a pattern of relationships among groups and institutions (Kogut, 2000). Network analysis is a powerful tool, as it provides a better understanding of networks through the use of sophisticated tech-niques for measuring relationships between different actors in the network (M’Chirgui, 2005). A group’s social network can thus be conceptualized as the lattice of pipes through which knowledge, resources, opportunities, and even social capital may flow (Wise, 2012). These social relationships may extend outside the bound-aries of the group but represent the true avenues and channels through which knowledge is exchanged (Inkpen & Tsang, 2005). Fritsch and Kauffeld-Monz (2009) argue that network structures significantly affect a group’s ability to transfer and absorb knowl-edge, and that this in turn affects team performance. As such, net-work characteristics reflect the social faculty of knowledge exchange. Among the fundamental explanatory tenets of the social network perspective is the idea that the structure of social interac-tions enhances or constrains access to valued resources (Sparrowe, Liden, Wayne, & Kraimer, 2001, p. 316). Social network analysis (SNA) studies the patterns of relations amongst individual actors (Wasserman & Faust, 1994). SNA assumes the structure of interact-ing units (groups in an industry, individuals in a group, groups in a firm) can lend insights into the nature of these relationships (Farrall, 2004). At the group level, effectiveness is measured by such standards as satisfying external client needs, reaching agreed-upon goals, and being able to come together at some future point to do more work if needed.
The Theory of Task Contingency (Donaldson, 2001) postulates that some network topologies are better suited to exploration practices, while others are more suited to exploitation practices. That research finds that social network topology can be optimized to generate performance gains (Donaldson, 2001, p. 2) based on the
type of work being undertaken (e.g., is it more exploitive or more explorative). This view is summarized by the following ‘‘social topology’’ hypothesis:
H1. Groups with similar performance will have similar social network topology. Network topology may be defined as being the arrangement of the various elements (links, nodes, etc.) of a computer (ATIS committee PRQC, 2007; Groth and Skandier, 2005) or indeed a biological network (Proulx, Promislow, & Phillips, 2005). Network characteristics can be divided along three dimensions: structural, relational, and cognitive (Nahapiet & Ghoshal, 1998). The concept of group cohesion, i.e., network density, is a structural measure. The concept of network density refers to the ratio of actual ties to potential ties. The more ties each group member enjoys with other group members, the greater the density of the network. At a group level, team density is analogous to the mean number of ties per group member. From a social network analysis perspective, network density is equivalent to group cohesion, i.e., a denser net-work of strong relationships is taken to be more cohesive). Group cohesion is defined as:
a dynamic process that is reflected in the tendency of a group to stick together and remain united in the pursuit of its instrumen-tal objectives and/or for the satisfaction of member needs ( Car-ron et al., 1998, p. 213).
Thus, group cohesion is a structural measure of a social network which reflects the degree of redundancy occurring within a group and acts as a construct representing group cohesion. That is to say, the number of redundant ties (paths between actors) within a net-work represents netnet-work density (Burt, 1992). If a network is cohe-sive, then it can better tolerate actor defection. Group cohesion (sometimes called network redundancy) has the potential to affect the knowledge processes of a group (Fritsch & Kauffeld-Monz, 2009). Group cohesion is a metric reflective of the entire network and thus must be calculated on a group-wide level. This has been done in past studies through empirical survey-based social net-work analysis (Burt, 1992). The measurement of network cohesion also allows one to account for structural holes occurring in the net-work. Thus, through the use of this metric, it will be possible to identify the presence and frequency of structural holes within a group. Structural holes are disconnections between nodes in a so-cial network (Ahuja, 2000). The theory of network cohesion is often operationalized as either group density or structural holes.
The relationship between group cohesion and team success has been widely explored (e.g.,Carron & Chelladurai, 1981; Landers & Lüschen, 1974; Lenk, 1969).Mullen and Copper (1994)carried out meta-analysis of 49 studies of diverse teams (e.g., military, sport, commercial) and concluded that the relationship between group Fig. 1. Negative and positive influences of intergroup relations on initiative performance fromLechner et al. (2010).
cohesion and team performance was explicitly positive. This finding was replicated in more recent works (Beal et al., 2003; Car-ron et al., 1998; Evans & Dion, 2012), which also found a positive relationship between group cohesion and team performance. This makes sense when one considers that high group cohesion repsents many trusted relationships (over which knowledge, re-sources, and opportunities can flow) within the group and it is these trusted relationships that can generate greater performance. Similarly, increases in performance can lead to higher levels of group cohesion as success in turn breeds collegiality. But what of the Dark Side of Social Capital theory: can a team have too much cohesion? Can group cohesion reach a point after which returns diminish?Lechner et al. (2010)Dark Side of Social Capital theory suggests that, in social networks, too much of a good thing can be a bad thing, i.e., when correlating social network measures with performance an inverse U relationship is often found. Put another way, an optimal amount of group cohesion will exist, at which point performance will be maximized. Too little or too much past that optimal point will lead to decreased team performance. Too little cohesion will result in a team riddled with structural holes. These structural holes might serve to undermine the transfer of knowledge, opportunity or resources. On the other side of optimal, too much group cohesion may require extraordinary personal ef-forts to maintain and may lead to groupthink undermining the fric-tion required to spur innovafric-tion (Langfred, 2004).
This research wishes to empirically test if group cohesion (mea-sured as network density) follows suit (e.g., has a dark side) or readily complies with Mullen and Copper’s (1994)findings that the relationship between group cohesion and team performance is strictly positive. In the case at hand, all teams are preforming the same tasks, and thus task contingency is held constant across all teams. From the above, the following hypothesis is derived: H2. There is an inverted U-shaped relationship between group cohesion and team performance.
Methods
Today, gaining access to data held on electronic networks (e.g., email servers) is, from a technological perspective, relatively straightforward though there are of course, a number of ethical is-sues to consider. The surge in data availability, combined with the development of non-intrusive interrogation instruments, allows twenty-first century network scholars to explore the structure of communities through email (Grippa, Zilli, Laubacher, & Gloor, 2006; Kleinbaum, Stuart, & Tushman, 2008; Quintane & Kleinbaum, 2011), phone logs (Eagle, Macy, & Claxton, 2010), or online collaborations (Stewart, 2003). Previously, to obtain information on the social structure of communities, researchers had to conduct research which relied on the accuracy of self-reported data, or conduct time-consuming survey-based, observational, or archival research (Johnson et al., 2012). With newly available non-intrusive digital data, it is hoped that new approaches will be realized. The last decade has witnessed the emergence of social network para-digms through which group activities can be mapped, examined and understood (Gulati, Nohria, & Zaheer, 2006).
Sample selection
The sample population examined in this research is comprised of all the sales associates of a national travel agency. This organiza-tion employs over 1800 individuals in Canada and 20,000 full time employees worldwide. More than 80% of personnel in Canada are exclusively engaged in selling travel products (flights, hotels, car rentals, tours, etc.). Agents are grouped into teams. Each team is
staffed with 5–8 employees on average (some outliers, e.g., the ecommerce team, have 15 full time employees (FTEs). Of the 187 teams reviewed, 17 are Corporate (i.e., selling mostly to pre-estab-lished business clients via phone and email) and 160 teams are Re-tail (i.e., selling mostly to walk-in customers). Corporate teams are assembled into three shared facilities across Canada. Retail teams are located in individual distinct street level (160) storefronts across Canada. Other than size and focus (corporate vs. retail) the groups are extremely similar. Management actively attempts to ensure teams include team members of varied experience. Further teams operate within similar operation environments. All are sell-ing travel to Canadians for recreation or business. All follow the same corporate governance, internal rules and processes and all participate in identical compensation schemes.
After obtaining both ethical approval and management buy in, email logs were provided to the researcher by the IT services firm in the employ of organization being studied. Employee names were converted to anonymous identifiers and all email content (other than: to, from, date) was stripped away. Employees were not in-formed of the data transfer nor were employees asked to consent. The researchers relied instead on the company’s consent, and the fact that the company owns all email created on their servers, which the ethics board deemed sufficient.
For primary data, this research relied on a database containing all emails sent or received for the period beginning January 1, 2011 and ending December 31, 2011. In total, more than 7 million email records (To, From, Date, Time) were reviewed, grouped and organized. Any email deemed external was excluded, as was any email deemed to be inter-team. Only intra-team emails (e.g., amongst members of the same team) were included.
Data collection
For the past two decades, nearly all research conducted in the area of group cohesion has used the conceptual framework introduced by Carron et al. (1998). Group Cohesion within this framework was evaluated based on the widely-used group envi-ronment questionnaire, an 18-item self-report inventory anchored on a 9-point Likert-type scale. While this framework is extensively used within the literature, few researchers have explored alternative tools that may provide new insight and more illustra-tive ways to evaluate team dynamics (Warner, Bowers, & Dixon, 2012, p. 53). Within the more general management literature, SNA research techniques provide metrics for the measurement of the relationships that comprise the social entities to which people belong (Warner et al., 2012, p. 53). In fact,Quatman and Chelladu-rai (2008) characterized SNA as ‘‘a new and promising research lens to the field of sport management’’ (p. 339). Unfortunately, recent studies (Steca, Pala, Greco, Monzani, & D’Addario, 2013; Widmeyer and Brawley, 2007) leveraging SNA did so still using self-reported questionnaires. Thus, while moving forward, these studies still accept the limitations of self-reporting instruments (e.g., self-reported answers may be exaggerated; respondents may not reveal private details; various biases may affect the results, like social desirability bias and recency bias (Junger-Tas and Marshall (1999)).
Following standard social network analysis procedures (Bonsignore et al., 2009; Dunne, Henry Riche, Lee, Metoyer, & Rob-ertson, 2012; Hansen, Shneiderman, & Smith, 2010; Smith et al., 2009) and using an extension to the NodeXLÒ software package
created by the Social Media Foundation and Microsoft, the re-searcher took an ‘X-ray snapshot’ of each team’s intra-team social network. The snapshot was taken on December 31st for each team. The snapshot encapsulates all email activity for the calendar year. This was done to ensure that all email traffic was selected from the same time period, and that the sample period was long enough to
get a response representative of email traffic through the normal working period.
This snapshot is based on underlying data representing a series of social network topology measures, including the number of ties between team members. A tie represents an email from teammate 1 to teammate 2. If teammate 2 replies, a second tie would be cre-ated. A minimum of 10 emails were required before a relationship was inferred. Therefore, the more communication between team-mates, the stronger the relationship is perceived to be. Emails were translated into an aggregate edge list for each team (e.g., an email from teammate 1 to teammate 2 is recorded as an edge between 1 and 2). NodeXlÒgenerates SNA measures based on the edge list
both visually and quantitatively. Measures
Dependent variable
Performance is a multidimensional concept that is difficult to reduce to a single measure (Pallotti & Lomi, 2011). Performance can be defined as the degree to which any expectation is fulfilled (Selnes, 1998; Venkatraman & Ramanujam, 1986). In an organiza-tional sense, the expectations to be met are tacitly understood by the shareholders, who appoint the management of that company to fulfil those organizational aims. Competitive Advantage occurs when an organization acquires or develops an attribute, or combi-nation of attributes, that allows it to outperform its competitors (Porter, 1985). Thus competitive advantage, when harnessed, leads to performance gains. Performance is the dependent variable in this research. To measure performance, the researcher first col-lected the Total Sales Volume per person from management. The researcher then aggregated the data to generate Total Sales Vol-ume per team. Normalized revenue per team was then generated as Nrev, to control for different team sizes.
Inside each work team, travel agents field incoming requests for travel bookings (e.g., flights, hotels, car rental) from either Retail (consumer) clients or Corporate (business) clients. For each re-quest, an agent checks the internal cost of the service being sold, then decides on the largest gross margin that the agent believes it can acquire without damaging the client relationship. Agents are incentivised to sell as much travel as possible; while gross mar-gin is not incentivised directly, it does significantly contribute to the Total Sales Volume figures. The ability to manage the client’s elasticity of demand (i.e., how price sensitive the client is) directly impacts the agent’s ability to price effectively and therefore to maximize profit. By definition, pricing travel is a tacit knowledge skill. A new employee who masters this skill earlier, or learns such from her teammates quickly, will generate higher profits and in turn higher performance.
Independent variable
Group cohesion (represented by network density) is the independent variable. Group cohesion is often operationalized as network density (Balkundi & Harrison, 2006; Reagans & McEvily, 2003). Network density is a structural measure of a social network which reflects the degree of redundancy occurring within a team. That is to say, the number of redundant ties (paths between actors) within a network represents group cohesion (Burt, 1992).
Network density was calculated by dividing the number of pos-sible ties amongst all team members by the actual number of ties existing. Network density is a metric reflective of the entire net-work and thus must be calculated on a group-wide level. This has been done in past studies through empirical survey-based so-cial network analysis (Burt, 1992). Network cohesion is often oper-ationalized as density (Balkundi & Harrison, 2006; Reagans & McEvily, 2003). From those articles, the following formula was used to calculate network density:
D ¼ Actual number of connections
Maximum possible connections ð1Þ
where D is density.
To calculate the maximum possible connections, the following formula is used:
C ¼N ðN 1Þ
2 ð2Þ
where N is the number of team members and C represents the max-imum possible connections. D ranges from 0 to 1.0 and can be thought of as the proportion of the primary group that is inter-con-nected. If every member knows every other member, the score is 1.0; if none of them are known to each other, zero.
Analytical procedures
FollowingWarner et al. (2012)after the data were complied, so-cial network analysis specific software, NodeXlÒ (Hansen et al.,
2010; Smith et al., 2009) was used to generate team level social network measures. In this study, group cohesion was measured using the ‘‘density’’ calculation within the software, which deter-mines the proportion of the number of connections or ties that ex-ist between actors in relation to the number of the maximum possible connections or ties in the network (Kilduff & Tsai, 2003). Density was then compared with team performance (i.e., nRev) to determine if any patterns emerged.
Visual procedures
In accordance with standard SNA practice, visual inspections of the generated networks were also included to assess the data ( Kil-duff & Tsai, 2003). Although there is not a particular criterion for analysis in the inspection process, conclusions are drawn through cross-referencing the graphical output with both the generated indices and the researcher’s intuition (Wasserman & Faust, 1994). Measuring networks with SNA software allows a researcher to visually depict a team network in a manner that not only shows the overall structural cohesiveness of a team or network (Warner et al., 2012), but also demonstrates each individual actor’s role, po-sition, and centrality within a network. This method is useful in that it allows researchers to deconstruct the team network down to the individual level (cf.Kozlowski & Klein, 2000).
Findings Visual
As predicted by the Theory of Task Contingency, high performing teams shared a similar network topology.Fig. 2below visualizes the social networks of several high performing teams in the na-tional travel network. Note the dense and redundant number of ties between team members in the social graphs below.
As predicted by the Theory of Task Contingency, lower perform-ing teams shared a similar network topology.Fig. 3below visual-izes the social networks of several low performing teams in the national travel network.
Note some nodes, in the low performing teams above, are iso-lated not cohesively connected to the network. These low perform-ing teams are riddled with structural holes which at this task contingency undermines performance.
Analytical
Descriptive statistics
Table 1displays descriptive statistics for revenues, expressed in 1000s of dollars, and density.1
The revenue numbers are annual and thus represent the same time period (i.e. calendar year) as the emails being used. The aver-age of revenue was slightly more than $4 million, with a standard deviation of $2.4 million. The middle 50% of the observations from the first quartile to the third quartile range from about $2.59 mil-lion to $4.98 milmil-lion. Note that the revenue data are extremely pos-itively skewed, as suggested by the skewness of 2.38 as well as the maximum of $19.66 million, which is more than 6 standard devia-tions above the mean. Not only is the distribution positively skewed, but the kurtosis of 12.39 also indicates that the tails of the distribution have many more observations than we would ex-pect if revenue followed a normal distribution. For density, the
average density is 0.34, the distribution is also positively skewed, and again has too many observations in the tails to be considered a normal distribution.
Frequency histograms of the distributions confirm the results suggested by the statistics inTable 1, e.g., these values are indeed skewed to the right and have too many observations in the tails to be considered normal distributions. With regards to outliers, one suggested method for detecting outliers is that any observation that is greater than 1.5 times the interquartile range above the 75th percentile or less than 1.5 times the interquartile range below the 25th percentile be considered an outlier. For revenues, there are 11 outlier observations that are greater than 8573.34, starting with 8617.29 and ending with 19,668.28. No firms had negative revenues, so there are no outliers on the low end of the scale. For density, observations outside the interval [0.33, 0.9995] were considered outliers, but of course none of the densities are nega-tive. There are four firms with densities of 1.0 that are outliers on the high end of the distribution. At this point, we simply note the existence of these outliers and an intention to determine the magnitude of the effect they have on the results that follow. Fig. 2. Social graphs of high performing groups of travel agents.
1
Regression model
Fig. 4below displays a scatterplot of revenues against density, with the smoothed polynomial that represents the best local fit to the data superimposed. What the smoothed fit makes clear is that simply regressing revenue on density and the square of den-sity will not fit the data especially well because the polynomial is not symmetric enough about its maximum. A second notable feature of the graph is the outlier in the northwest, which corre-sponds to the maximum revenue of $19,668.2801 thousands of dollars and has a density of 0.128. Because it turns out not to have a large effect on estimated coefficients, this outlier point was included. It is worth noting, that this outlier responds to the
e-commerce group, a team of 15, with weak cohesion and a busi-ness model different than both the retail groups and the corporate groups.
Following the graph provided byFig. 4, the estimated regression consists of a fourth degree polynomial in the variable density. Regression coefficients are given inTable 2below. The R squared for the regression was 0.35, suggesting that 35% of the variation in revenues is explained by the fourth degree polynomial in den-sity. All of the coefficients inTable 2are significant at the 0.01 level of significance except the constant.
For the fitted quartic equation, there are three extreme values for revenues within the interior of the [0,1] interval for density. Differentiating the quartic equation once provides its slope, which is a cubic equation in density, and the roots of this cubic equation are the maximum and minimum of revenues (i.e., where the slope of the graph is 0).Fig. 5below, presents the fitted values of revenue implied by the estimates inTable 2as a function of density. The re-gion marked as Rere-gion A inFig. 5runs from density equal 0 to den-sity equal to 0.17, and is an interval of denden-sity over which revenue is increasing to a local maximum of $4,043.480 at density equal to 0.17. In Region B, revenues decrease from the local maximum that occurs at density equal to 0.17 until density reaches 0.36, where revenue achieves a local minimum of $3,521,680. In Region C, starting from the local minimum at density equal to 0.36, revenue increases, achieving a global maximum of $8,701,780 at density equal to 0.82. Finally, revenue decreases in Region D, which ex-tends from density equal to 0.82 to density equal to 1.0.
Fig. 3. Social graphs of low performing groups of travel agents.
Table 1
Descriptive statistics for revenues and density.
Revenues, $1000s Density Sample size 187 156 Mean 4059.46 0.34 Standard deviation 2483.20 0.23 Minimum 5.38 0.04 Maximum 19,668.28 1.00 25th percentile 2599.31 0.17 Median 3511.41 0.30 75th percentile 4988.92 0.50 Inter-quartile range 2389.61 0.33 Skewness 2.38 1.01 Kurtosis 12.39 3.42
Finally, estimates for the quartic equation were found to be somewhat sensitive to the inclusion of outliers. The pattern of signs remains the same regardless of whether the largest value of revenue is excluded, the four values of density equal to 1 are ex-cluded, or all of these observations are excluded. Excluding only the largest observation of revenue has very little effect on either the estimates or their significance. Omitting the four observations for which density is 1.0 pulls all of the coefficients on density terms closer to zero, but all estimates are still significant at the 0.05 level of significance. Removing both the largest revenue observation and all four observations of density with a value of 1.0, pulls the coef-ficients even closer to zero, with the result that the linear in den-sity term is significant at the 0.05 level and the higher order terms are significant at the 0.10 level of significance.
Discussion and conclusions
This research explored the relationship between a group’s cohe-sion level and that team’s financial performance, while holding task contingency constant across teams and controlling for team size. The relationship between group cohesion and team success has been widely explored (Beal et al., 2003; Carron & Chelladurai, 1981; Carron et al., 1998; Evans & Dion, 2012; Landers & Lüschen, 1974; Lenk, 1969; Mullen & Copper, 1994) all of which found a strictly positive relationship between group cohesion and team performance. This research contributes to the conversation of how group cohesion and team success are correlated by showing that there is more to the relationship than previously established.
Fig. 4. Scatterplot of revenues against density.
Table 2
Estimated coefficients of the regression of revenue on a fourth degree polynomial in density. Robust
Coefficient Standard error P-Value [95% confidence interval]
Constant 1421.58 856.56 1.66 0.100 270.82 3113.98 Density 38,820.05 13,138.81 2.95 0.004 12,860.41 64,779.70 Density2 190,303.30 5,9146.40 3.22 0.002 307,164.70 73,441.94 Density3 341,828.30 94,963.79 3.60 <0.001 154,199.00 529,457.70 Density4 188,147.30 48,382.50 3.89 <0.001 283,741.40 92,553.20
This research found:
o Groups with similar performance have similar social network topology. High performing teams had high group cohesion and a dense and redundant number of ties between team mem-bers. Low performing teams shared a social network topology, specifically one riddled with structural holes.
o This research found the relationship between group cohesion and team performance was found to be inversely curvilinear. Prior research has suggested that a positive relationship (shown as Region B inFig. 5) exists between team performance and group cohesion. Notwithstanding, this research showed while such is generally true, it does not address the whole picture. Like most measures in social network analysis (e.g., Trust, Tie Strength, Cen-trality, etc.) this research found that too much of a good thing, can be negative (shown as Region D inFig. 5). Just as too much trust can undermine performance (as will too little trust) too much cohesion can lead to less than optimal performance. Just as one can imagine the benefits of cohesion (e.g. lower friction in knowl-edge transfer, more job satisfaction, etc.); one can also imagine that too much cohesion can lead to negative results (e.g. group think, stagnation of innovation, etc.). In finding such, the author confirms that cohesion follows the Dark Side of Social Capital theory put forth byLechner et al. (2010).
This research confirms that optimal social network topologies exist at which performance can be maximized it also contributes by illustrating that the Dark Side of Social Capital Theory is true for group cohesion (i.e., network density). Specifically this research finds issue with the findings of: Carron et al., 1998; Beal et al., 2003; Evans & Dion, 2012, all of which suggested that group cohe-sion and team performance were always positively related. This re-search supports the more inclusive view of the Dark Side of Social Capital theory (Lechner et al., 2010) and findings herein support such. This research confirms findings byHardy et al. (2005)and, as first suggested byCarron et al. (1994), this research shows that group cohesion may be both positive (i.e., provide benefits) and negative (i.e., have costs) to members of teams. Group cohesion is generally seen as a worthy goal of managers globally. A cohesive group will generate many positive benefits, including but not lim-ited to: easier knowledge transfer, internal support, higher individ-ual satisfaction, lower team conflict, and lower team member turnover. Over time, members of a cohesive group develop shared values and team loyalty (Tekleab, Quigley, & Tesluk, 2009). The familiarity of team members creates smoother, more effective communication. When working toward a common goal, team members bring diverse skills and varied points of view to their work. While these are key benefits, all describing the upside of group cohesion, they do not describe the full picture. Perhaps teams that are too cohesive spend too much time building internal ties? Perhaps teams that are too cohesive lack the impetus to inno-vate? Perhaps prior art only viewed the left hand side of the in-verse U? The population studied all reside within the same national travel firm. This study looked at only one firm (with 180 + teams), over one year, in one industry, in one country. This myopic focus may greatly undermine the generalization of the findings.
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