Cooperative and Competitive Structures of Trust Relations in Teams

Abstract

This article argues that it is not just trust-generating but also trust-inhibiting mechanisms that operate in teams, and that these cooperative and competitive structures of interpersonal relations of trust within teams may affect team performance. Specifically, we propose that the presence of trust-generating structures (e.g., reciprocity, trusting in the referrals of others we trust, trusting in high performers and more experienced people) and the absence of trust-inhibiting structures (e.g., not trusting in the referrals of others we trust) are more likely to be associated with successful teams. Using exponential random graph models, a particular class of statistical model for social networks, we examine three professional sporting teams from the Australian Football League for the presence and absence of these mechanisms of interpersonal relations of trust. Quantitative network results indicate a differential presence of these postulated structures of trust relations in line with our hypotheses. Qualitative comparisons of these quantitative findings with team performance measures suggest a link between trust-generating and trust-inhibiting mechanisms of trust and team performance. Further theorization on other trust-inhibiting structures of trust relations and related empirical work is likely to shed further light on these connections.

Keywords

social network analysis intra-group relations methodology exponential random graph models

The concept of trust resonates with lay and academic audiences alike because it is so pervasive in our social worlds. Trust is an almost universal part of human social relations, from friends to family to economic exchanges (Fehr, 2009). Trust is seen as an enabler, a lubricant that reduces transaction costs (Arrow, 1974; Gambetta, 1988; Uzzi, 1997) because it can be used as an “alternative control mechanism” to “hierarchical contracts” (Gulati, 1995, p. 93) as well as to coercion and surveillance (Gambetta, 1988). Trust is commonly accepted to be a driving force in teams and by which goals can be obtained, not just individually but collectively. This is especially so in the context of social groups such as teams or organizations where the complexities of trust are multiplied not only by increased numbers of people but also by the need for coordinated social action to achieve group outcomes. The importance of trust and coordinated group action for collective outcomes is evident when considering how trust is required between members of medical teams involved in the care of patients (e.g., surgical teams), or emergency teams responding to natural or human-induced disasters.

Research on the connection between trust and team effectiveness is therefore of great importance, and much has been done already to unpack the ways in which various elements of trust are related to a wide range of team performance measures (Costa, 2003; Costa, Roe, & Taillieu, 2001; De Jong & Elfring, 2010; Jones & George, 1998). However, one avenue that we see missing from the current literature is an understanding of not just how specific structures of trust relations may be associated with team effectiveness (van de Bunt, Wittek, & de Klepper, 2005) but more particularly whether there are specific structures of trust networks that might be associated with hampering team outcomes. By structure of trust we take an explicitly social network approach, referring to the patterns of social network ties (in this case interpersonal social ties of trust between team members) that recur throughout the network. We examine well-accepted patterns of trust that are important for successful group outcomes, such as reciprocity, transitive closure (i.e., trusting in others that those we trust also trust in, such that A trusts B, B trusts C, so A also trusts C), trust in those high in experience/performance, and those of similar experience/performance. We also examine patterns that are to be avoided, such as the non-closure. We do this using a statistical models for social networks approach to help us determine whether such patterns of trust are present more (or less) than we would expect by chance in our data.

To this end, the current study addresses and extends the “need to think of mechanisms and processes which can reinforce and help sustain team-based initiatives” (McHugh, Niehaus, & Swiercz, 1997, p. 47) but also thinking through those mechanisms that undermine them, and articulates such mechanisms and processes theoretically in terms of structures of intra-team trust relations. We examine interpersonal dyadic relations of trust between all team members within a team that together constitute a social network of trust. We argue an array of specific structures of trust relations that we consider may be related to better team performance. We present four trust-generating or cooperative structures of trust that reflect widely accepted mechanisms of trust (reciprocity, transitive closure, trust in high experience/performance, and trust in similar experience/performance). Crucially, we also present a specific structure of trust ties that may be associated with reduced team performance, which we call a trust-inhibiting or competitive structure of trust. The article then examines three teams to search for the presence of such theoretical structures of trust. We use a particular statistical modeling approach to social networks known as exponential random graph models (ERGMs), which are tie-based models for predicting network relations. We then take these quantitative empirical results and qualitatively compare them with team performance measures to see if there is an association between them.

Importantly, it has been noted that “studying multiple mediating mechanisms provides a more nuanced understanding of how trust affects team performance than studying them one at a time” (De Jong & Elfring, 2010, p. 543). Using ERGM, we simultaneously investigate this range of trust-generating and trust-inhibiting network substructures (and as such, the theoretical concepts these substructures represent, such as reciprocity or transitive closure) and thereby assess the importance of all of these concepts, one against the other, within a single analysis for each team. A further decided advantage of ERGMs is that they can specifically take the dependencies inherent in social relations into account in principled ways that make them superior to models that assume independent observations (Snijders, 2011), making them cutting-edge models for social network data (Lusher, Koskinen, & Robins, 2013). There are very few studies that have studied trust using an ERGM methodological approach, and so this method offers potentially new insights into the structure of trust relations. Foreshadowing our results, we have evidence that the presence of cooperative structures of trust and the absence of competitive structures of trust are positively associated with better team performance.

Cooperative and Competitive Structures of Trust Relations in Teams

Trust is a term used knowingly in general parlance but one that also has considerably different definitions depending on the research context, making a unitary definition of trust a difficult task (Costa et al., 2001; Dirks & Ferrin, 2002; Earle & Siegrist, 2006; Krackhardt, 1996; McEvily, Perrone, & Zaheer, 2003). Trust can be seen as a general willingness to be vulnerable (Mayer, Davis, & Schoorman, 1995) involving both risk and interdependence (Rousseau, Sitkin, Burt, & Camerer, 1998). In a recent review, it was found that of the 171 articles examined on trust in organizations, there were 129 different measurements of the concept (McEvily & Tortoriello, 2011). This review found that trust is seen generally in two ways: as a psychological state mainly in the social psychology literature and as a general organizing principle. Recently, Lusher, Robins, Pattison, and Lomi (2012) have argued that it is useful to view trust through both of these lenses at the same time.

In this study, we take an explicitly relational (and social network) perspective on trust, in line with previous research (Becerra & Gupta, 2003; Burt, 2001; Burt & Knez, 1995; Buskens, 1998; Ferrin, Dirks, & Shah, 2006; Mayer et al., 1995; van de Bunt et al., 2005). We do so because this permits us to look for specific patterns of trust relations—patterns that represent social processes of trust—and assess whether these structures of trust are associated with team performance. Importantly, the notion of mixed-motive interactions highlights the dilemma of some situations in which people are conflicted between desires to cooperate or compete with one another (Davis, Laughlin, & Komorita, 1976; Komorita & Parks, 1995; Schelling, 1960). In the case of sporting teams, teams that are ongoing (De Jong & Elfring, 2010), the simultaneous operation of cooperation and competition among team members is quite evident:

The public face of the team attempts to present to the rest of the world that of a “family,” whose shared goal of winning games and championships bonds its individual members together. But the structure of athletic careers is such that individuals on teams are constantly competing against each other—first for a place on the team, then for playing time, for public recognition and star status, and eventually, just to stay on the team. (Messner, 1997, p. 344)

Teams are not purely cooperative enterprises (Messner, 1997) and have resultant competition between players, which others have noted as antagonistic cooperation (Riesman, 1953). While we could restrict such sentiments to sporting teams, it is clear that within any group that people have agendas which clash with those of others, and so the notion of antagonistic cooperation can possibly be seen to reflect more general conceptions of teams or groups (e.g., Labianca & Brass, 2006; Sampson, 1968; Tsai, 2002). Clearly some aspects of competition are fundamental to team development, improvement, and outcomes. Yet competition so fierce that it has an impact on trust is likely to have a deleterious effect on the team (or on the other hand, be indicative of existing problems within the team). An example of this could be two players in which many others trust both who do not trust one another. There is an obvious tension to balancing these cooperative and competitive relations between team members.

While it is obvious that trust relations can be viewed as collaborative, what is less clear is how they might be seen as competitive. To be clear, we are not so much suggesting that team members are competing for trust, but that competition between team members is associated with a lack of trust between them, and this inhibits trust not just between these competing team members but may also cause split loyalties among their supporters and lessen trust throughout the team. We argue that certain structures of trust relations can be evaluated as competitive, and this begins to answer the question of how the structure of trust relations may impinge on team performance. Such a view is informed by the insight that it is not just important where social ties are but also where they are not (White, Boorman, & Breiger, 1976). In the case where social ties are expected to be present, but instead are absent, we can interpret this as indicative of a lack of trust. And it is this lack of trust that we argue is indicative of competition among team members and has trust-inhibiting effects.

We note here that we are not examining negative ties, such as distrust, negative gossip, work difficulty or conflict, on which much is written (Ellwardt, Labianca, & Wittek, 2012; Huitsing et al., 2012; Labianca & Brass, 2006; Labianca, Brass, & Gray, 1998). We are instead examining positive ties, but noting that the absence of some trust ties in specific structures can be informative of trust within a team. As such, this is a univariate analysis of a standard positive tie network of trust relations, but one that can inform the sorts of issues that a simultaneous analysis of negative and positive ties can bring. We do not argue it as a replacement for studying negative ties, but simply as another avenue into this terrain.

Given the multifaceted nature of trust (Mayer et al., 1995), it is expected that there are differing explanatory causes or social mechanisms (Hedström & Swedberg, 1998) that explain the presence of interpersonal trust relations in teams. Drawing on the trust literature from organizational research and the sporting literature on cooperation and competition within sporting teams, we hypothesize five such social mechanisms reflective of cooperative and also competitive structures of trust relations within teams. We note that of these five mechanisms, one corresponds to a competitive (or trust-inhibiting) structure of trust (not trusting in the referrals of others we trust) and the other four to cooperative (or trust-generating) structures of trust (reciprocity, trusting in the referrals of others we trust, trusting in high performers and more experienced people, and trusting in others of similar performance/experience). Our hypotheses relate to network structures (or network configurations) presented later in Table 1 and explained in more detail in the section on social network analysis (SNA), and in the methods. The expectation of the presence of these structures is not just that we will see some of them in a network of trust relations, but more of such structures than would be expected than by chance (and thus a statistically significant number of such network structures—see the “Methods” section). Our major argument here is that trust relations that are of value to the team should be reflected by an absence of an effect for trust-inhibiting structures (i.e., Hypothesis 3 [H3]) and a presence of trust-generating structures (i.e., Hypothesis 1 [H1], Hypothesis 2 [H2], Hypothesis 4 [H4], and Hypothesis 5 [H5]). Trust is equally about the absence of negatives as it is the presence of positives. We now outline these social mechanisms of trust relations.

Table 1.

Summary of Network Effects Evaluated Using ERGM.

Parameter	Image	Explanation
Purely structural effects
1. Arc		One actor nominating another actor (baseline propensity to form ties)
2. Mutual		Mutual ties between two actors (models the tendency for reciprocation across the graph)
3. Simple connectivity (2-path)		Chain-like or simple hierarchy formation of ties
4. Popularity spread		Indicative of the presence of highly nominated individuals within a network (models the indegree distribution)
5. Activity spread		Indicative of the activity of actors to engage many others (models the outdegree distribution)
6. Path closure		Triadic closure (i.e., a friend of a friend is a friend)
7. Popularity closure		Triadic closure where two nodes share high popularity
8. Cyclic closure		The propensity for ties to form as part of a cyclic triad or a multiple cyclic configuration
9. Multiple connectivity		The propensity for ties to form as part of formations involving multiple short paths between actors
10. Shared popularity		The propensity for popularity based structural equivalence involving multiple short paths between actors
Actor-relation effects
11. Sender		The attribute of the sender of the tie, which may be continuous, categorical or binary (models the propensity of an actor with the attribute to send ties, i.e., to be active in network terms)
12. Receiver		The attribute of the receiver of the tie, which may be continuous, categorical or binary (models the propensity of an actor with the attribute to be popular)
13. Homophily		A selection of a person with similar levels of the same continuous attribute (e.g., age)

Note. ERGMs = exponential random graph models.

First, trust is commonly seen as reciprocal (Burt & Knez, 1995; Gambetta, 1988; McEvily et al., 2003), specifically as an expectation that others will reciprocate such behavior (Tyler & Kramer, 1996). According to Blau (1964), reciprocity is a universal human activity, representing social exchange. Indeed, as trust is seen as risky or creating uncertainty (Jones & George, 1998), the expectation that trust is reciprocated is a psychological heuristic that can be used to ameliorate such concerns (Lusher et al., 2012). Within a team, we argue it would be expected that if trust is conferred upon another that it will be reciprocated. We note that such an explanation relates to the structure of social ties regardless of the individual qualities of team members. In reciprocal relations, a social tie (in this case, trust) comes about because of another already existing social tie. That is, it is because “you scratch my back” that “I will scratch yours.” Trust may be reciprocated due to signaling by one partner in the dyad to the other, or it may simply be inferred. We do not claim to know the underlying psychology of such relationship formation (though for an interesting take on this issue, see Lusher et al., 2012). This leads us to our first hypothesis:

H1: Trust relations will be reciprocated.

A second social mechanism of trust refers to the fact that trust has also been viewed as involving transitive closure or triadic relations; that is, if A trusts B, and B trusts C, then A will also trust C, or more colloquially referred to as a friend of a friend is a friend (Burt, 2005; Buskens, Raub, & van der Veer, 2010; Granovetter, 1985; Kipnis, 1996; McEvily et al., 2003). Of course, the importance of triadic relations goes back to ideas of the famous sociologist Simmel (1950), and the fact that this constitutes the smallest group with a majority. Heider’s (1958) well-known balance theory, as too Cartwright and Harary’s (1956) elucidation of Heider’s idea into structural balance theory (though with less of a focus on the psychological aspects), have demonstrated the importance of triadic relations for over 50 years. Granovetter’s (1973) strength of weak ties argument further illustrated the importance of triadic relations, highlighting the discomfort faced when two of our friends are not at least connected by a weak tie. Trust triads therefore indicate trust beyond the dyad and at a group level. Robins, Pattison, and Wang (2009) demonstrated for a trust network within an organization in the presence of transitive closure (but not cyclic closure), concluding that trust was structured hierarchically for triadic relations. Yet Robins et al. (2009) also noted the presence of reciprocity, and hence of balance with regard to trust relations. As such, trust may be both balanced and hierarchical simultaneously within the one social context. Both of the above hypotheses, for reciprocal trust and transitive closure trust, we view as indicative of cooperative structures of trust. While one mechanism is balanced and the other is hierarchical, both mechanisms demonstrate cooperation—the adherence to mutual structures of trust or to hierarchical structures of trust.

H2: Trust relations will involve transitive closure of ties.

The third social mechanism of trust can be seen as the opposite of the second, and as indicative of a competitive structure of trust relations. This third social mechanism draws on the absence of social ties—that is, where trust relations may be expected but are not present. While it is noted above that trust should be transitive (i.e., if A trusts B, and B trusts C, then A should also trust C), where it is not (i.e., where A does not also trust C) there is likely to be tension (Granovetter, 1973; Heider, 1958). As an example, consider two prominent and influential team members who are both trusted by many teammates (indeed, these two influential players are trusted by the same teammates). If these two prominent team members do not trust one another when it is expected they should (i.e., a trust relation is absent when it is expected to be present), this is likely to indicate a division in the team, and result in tension. Krackhardt’s (1987) cognitive social structures (CSS) are informative here. CSS refers to the ways in which individuals within a network perceive of the social ties of others within that network, such that actor i reports on social ties from actor j to k. People are aware of social ties beyond their own specific relations and these are important to consider (Ferrin et al., 2006).

H3: Trust-inhibiting relations will involve the non-closure of trust ties.

A fourth social mechanism of trust refers to the fact that some team members with certain individual-level characteristics are also likely to be trusted more than others because of these qualities. Clearly, the uncertainty or risk inherent in trusting another can be alleviated by investing trust in those who are high performers or who have status within the team (Jones & George, 1998; Mayer et al., 1995). Gorgenyi (1998) highlights the importance of performance and experience to informal hierarchical relations in sporting teams, though clearly in teams more general factors such as ability (Mayer et al., 1995) and reputation (Burt, 2005) are valued. We would expect that those team members who are high on performance are more likely to be trusted because it is these people who play a critical role in the success of the team. Furthermore, we expect that team members who are high in experience are also likely to garner trust because they can pass on knowledge to younger team members and provide the cool head in a crisis. Furthermore, their longevity in the team shows a commitment to organization (Costa, 2003). Drawing on social identity theory and the related self-categorization theory, there is evidence to suggest that identification with group causes preferential choices of others (Tajfel & Turner, 1979; Turner, Hogg, Oakes, Reicher, & Wetherell, 1987). Self-categorization theory details the process by which

people cognitively represent social groups in terms of prototypes. A prototype is a subjective representation of the defining attributes (e.g., beliefs, attitudes, behaviours) of a social category, which is actively constructed from relevant social information in the immediate or more enduring interactive context. (Hogg, Terry, & White, 1995, p. 261)

People resembling the prototype are more likely to be liked, admired, and indeed trusted than those who do not fit the prototype. It is through this identification process that individuals and groups align, so trust in the prototype is trust in the group itself. Freeman’s (1977) concept of central people within networks (i.e., those who receive more ties) being advantaged can also tie in here. In this case, such people are central due to their individual-level attributes—they receive more ties due to their individual-level qualities.

H4a: Trust relations will be afforded to team members high in experience.

H4b: Trust relations will be afforded to team members high on performance.

Finally, the concept of birds of a feather flock together is known as homophily (McPherson, Smith-Lovin, & Cook, 2001) that appears to be a universal human tendency for people to connect with similar others. Research suggests that shared membership of a group makes people more trustworthy toward one another because of this social similarity in characteristics (Earle & Siegrist, 2006; Lincoln & Miller, 1979; McAllister, 1995; Sheppard & Tuchinsky, 1996). Again, the cognitive heuristic of choosing people like me reduces uncertainty with regard to trust, and is a way of overcoming tensions involved in the risk of trusting others, and a way of expressing one’s confidence in others (Axelrod, 1984). As a result, we expect that team members are more likely to trust others with similar experience to themselves. Furthermore, team members will also trust others of similar performance level to themselves.

H5a: Trust relations will be afforded to other team members of similar experience.

H5b: Trust relations will be afforded to other team members of similar performance.

In summary, we have hypothesized multiple reasons of how trust relations are structured within teams that represent competing social mechanisms regarding how interpersonal trust occurs in teams. The first, second, fourth, and fifth hypotheses relate to cooperative structures of trust relations because they are seen as trust-generating mechanisms. Conversely, the third hypothesis is seen as a competitive structure of trust because it is a trust-inhibiting mechanism—resisting the formation of trust ties. We expect that teams that have more trust-generating cooperative structures of trust and an absence of trust-inhibiting competitive structures of trust are likely to be better functioning teams and be more successful. Conversely, those teams that lack cooperative structures of trust and/or have competitive structures of trust are less likely to be functioning well and perform more poorly.

To be able to make inferences about such social mechanisms we need to test them, one against the other, empirically. It is for this reason that we employ SNA and use a particular class of statistical model for social networks, ERGMs, so that we can determine statistically that explanations for trust relations are more (or less) likely. Finally, we compare the quantitative network models results of these structures of trust qualitatively with team performance measures to see if this thesis is supported.

SNA and ERGMs

If we are interested in trust as a relational construct, then it is necessary to have a methodology capable of dealing with relational data. One such methodology is SNA, which by formal definition is a set of techniques that focus on the “relationships [sic] among social entities, and on the patterns and implications of these relationships” (Wasserman & Faust, 1994, p. 3). As such, the term social network can be moved beyond use as a metaphor of the social world and implemented as a specific, local-level relational methodology (Emirbayer & Goodwin, 1994). So what we examine within network analysis is not individuals but the collection of relations between the individuals.

In a social network, individuals (or actors) are represented as nodes in a graph, and the relations between them are represented as edges or lines. In a very practical sense, a social network can be measured by asking all individuals in a particular social context (e.g., a team) about a particular social relation with others in the network (e.g., “Who do you trust?”). The network is then the combination of all possible nominations among all pairs of actors in the network. In addition, individual-level variables (e.g., years with the team) can be incorporated and investigated within a social network.

Our primary aim in using SNA is to predict how and why social relations occur—in this case, trust. Networks are relational (i.e., dependent) by nature, but standard statistical tests assume the independence of observations, which necessarily results in a disjunction between theory and method. ERGMs provide a means to resolve this tension between theory and method. ERGM are a particular class of statistical model for social networks that were originally developed by Frank and Strauss (1986), and further refined by Wasserman and Pattison (1996) and Pattison and Wasserman (1999). These models have the capacity to address complex social structures. Recent model derivations have the capability of examining both individual-level variables and structural relations simultaneously (Daraganova & Robins, 2013; Robins & Daraganova, 2013; Robins, Elliott, & Pattison, 2001; Robins, Pattison, & Elliott, 2001). New specifications for ERGM allow for the examination of higher order social structures (Robins et al., 2009; Snijders, Pattison, Robins, & Handcock, 2006) and have permitted a more detailed investigation of social structures and individual attributes together, providing a possible means to handle both structure and agency. The assumption of interdependency which underpins the ERGM framework, and which suggests that in social contexts individuals (or groups) are not independent of one another, is seen as advantageous and complex dependency assumptions have been proposed for this purpose (Pattison & Robins, 2002; Snijders et al., 2006). More detailed introductions to ERGMs are provided by Contractor, Wasserman, and Faust (2006) and Robins, Pattison, Kalish, and Lusher (2007) and Lusher et al. (2013).

The ERGM class of statistical models for social networks essentially works as a pattern recognition device for predicting why social network ties occur. We call these patterns of social network ties network configurations, and a range of such configurations are presented in Table 1. Network configurations (or effects) give us an understanding of those structural processes necessary to explain how the network came about. A network configuration is a consequential pattern that may represent an underlying social process (or social mechanisms), and as such it is claimed that a network structure is the consequence of a dynamic process. The cooperative and competitive structures of trust relations outlined previously are themselves social mechanisms that can be examined in a network by including network configurations that represent such processes. So the processes of H1 through H5 are aligned with network substructures that we then look for in our model to see if such substructures occur at greater (or less) than chance levels.

While one could simply count the number of reciprocated ties or the number of triangles in a network, one would not know if there were more triangles (or less) than expected by chance. It is important to note that networks formed completely at random (i.e., networks without structure) still have some reciprocated ties, and some triangles. For example, in the simulation of Bernoulli random graphs, in which ties are added to a graph independently of one another (i.e., ties are added with equal probability between any pair of actors), we are still likely to observe a number of triangles. So to claim that there is an effect for transitive path closure (the notion that a friend of a friend is a friend, the formation of a triad), we would need to see more triangles than we would expect to see by chance (i.e., if the network ties were formed randomly). ERGM therefore permits the statistical evaluation of competing hypotheses for why social ties may be present. Using ERGM, we can examine a trust network for reciprocity, transitive closure, and homophily all at the same time and evaluate if these structures occur at greater (or less than) we might expect in a random network.

Finally, ERGMs permit differentiation between endogenous network processes and processes related to actor attributes (Lusher et al., 2013). Endogenous (or purely structural network effects) reflect the ways in which network ties self-organize. As such, purely structural effects reflect processes in which ties form due to the presence or absence of other ties. Conversely, ties may form due to actor attributes, and are known as actor-relation effects, highlighting the association of a tie with the qualities of an actor. This is particularly important for H4 and H5 in which we state that trust occurs due to experience and performance of team members. It is known that when endogenous processes are not controlled for, the effects of actor attributes may be overestimated and therefore spurious claims may be made about the impact of individual-level qualities (Lusher & Ackland, 2011). The ERGM framework therefore provides a more principled way of making inferences about the association of actor attributes and network ties because it can distinguish between whether ties were formed due to the attributes of the actors or whether simply the actor’s popularity is the result of being embedded within many purely structural network structures. Other approaches, such as linear regression using attributes and degree centrality, cannot delineate these processes.

Finally, we reiterate that ERGMs are models for predicting the presence of social ties. This makes the unit of analysis not the number of individuals within the team (n) but the number of possible ties between all players, or n(n − 1), with n − 1 accounting for the fact that people cannot make self-nominations. In the case of a 34-player team as in Club A, there are 1,122 observations, thereby providing plenty of statistical power for the analysis.

Methods

Participants

Three Australian Football League (AFL) teams from Melbourne, Australia, participated in this research. Excluding missing data, there were 107 male athletes from three AFL clubs (Club A, n = 34; Club B, n = 36; Club C, n = 37).¹ Based on a potential 40-person playing list, participation rates ranged from 85% to 92.5%, with data collected between December 2004 and January 2005. The AFL has representative teams in most states of Australia making it a national competition and the pre-eminent sport in the country. These clubs were chosen due to their perceived variability in performance and culture.

Materials

A pen-and-paper survey instrument was administered as part of a larger study that focused on social structures among sports teams (Lusher, Robins, & Kremer, 2005). A staff roster that contained a list of names of all playing and coaching staff with assigned numeric identifiers was provided so responses for network questions could be indicated. The trust network was measured by asking participants with respect to their particular team “Who do you trust?” This network is binary (so a relationship is either present or absent) and directed (so A trusts B is different from B trusts A), and self-nominations (i.e., I trust myself) were not permitted. We have deliberately kept the definition of trust open here to include instrumental and expressive trust because in these intense team environments where players are constantly interacting, there is good reason to believe that both may be important. Restricting trust nominations only to work-related matters might possibly discount the emotional bonding and strong interpersonal ties that should be promoted to develop trust among team members (De Jong & Elfring, 2010). Trust networks for the three clubs are graphically presented in Figures 1, 2, and 3.

Figure 1.

Trust network for Club A (n = 34).

Figure 2.

Trust network for Club B (n = 36).

Figure 3.

Trust network for Club C (n = 37).

A number of individual-level measures were also included. A measure of AFL playing ability (hereafter labeled performance) was included as a continuous variable for each player. Performance was djudged by asking the social network question “Who are the best players at your club?” of the players themselves and where a player could nominate more than one player. As such, each player scored between 0 (for no nominations) and n − 1 (for nominations by all others in the team, except himself, as no self-nominations were permitted; n = number of players in the team). This resulted in a count of the number of nominations received for each player that was then included as a continuous node-level variable for each player. Players who received greater numbers of best player network nominations were considered to have higher playing performance or, in other words, to be better or more skilled players. Experience was measured as the self-reported number of AFL games played by respondents.

As a measure of general team performance, we examined the ladder (or points table) for the 16-team competition across the years 2004 and 2005, which followed and preceded the data collection, respectively. Our lack of precision, for example, by not mentioning which quartile of the ladder a team finished in, is deliberate and required so that the identities of teams are protected and remain confidential. We found that Club A finished near the bottom in both the years, Club B can be described as finishing mid-range on the ladder, and finally Club C can be described as finishing near the top of the ladder (and so having higher team performance).

The profile for each club is presented in Table 2. Preliminary analyses (ANOVA) indicated that there were no differences in individual performance between clubs for experience, performance, and age.

Table 2.

Summary of Attributes for the Three AFL Clubs (M ± SD).

	Club A	Club B	Club C
	M (SD)	M (SD)	M (SD)
Age	22.8 (3.7)	22.6 (3.2)	22.7 (3.7)
Experience (games played)	73.3 (70.0)	60.6 (71.0)	65.0 (71.4)
Performance (peer-rated nominations)	4.4 (8.5)	3.9 (7.8)	4.9 (10.3)
Overall team performance	Low	Medium	High
Density	0.1791	0.0833	0.1111
Highest in degree	14	14	10

Note. AFL = Australian Football League.

Procedure

Participants were briefed on the purpose of the study and then completed the survey in a single session of approximately 30 min duration. The club-specific rosters were used by players to record responses (i.e., numeric codes for individual players) to the network questions. All surveys were completed in a club meeting room during the off-season period from December 2004 to January 2005. Participants were able to spread out to some degree when responding to the survey, in much the same way that school students in a classroom are spread out in an exam. Two researchers were present, overseeing data collection and encouraging self-completion and non-monitoring of others. Players gave their de-identified completed surveys directly to the researchers to ensure confidentiality. Ethics approval for the study was provided by the University of Melbourne Human Research Ethics Committee.

Data Analysis and ERGM Specification

Data analyses of interpersonal trust relations (i.e., the trust network) involved ERGMs using the PNet freeware program (Wang, Robins, & Pattison, 2009). We included the network effects from Table 1 into our models. All 10 of the purely structural parameters were included to examine the self-organizing properties of network ties. For the actor-relation parameters, we included separate sender, receiver, and homophily² effects for the variables’ performance and experience. Broadly, this class of effects examines the formation of relations of trust due to individual qualities of players.

We fixed (or conditioned on) the outgoing (sent) trust ties of three players in Club A as these players indicated that they trusted everyone (or nearly everyone) in their team. For excessive network nominations for trust, it is a reasonable approach to fix³ the outgoing ties of such nodes (i.e., making them exogenous to the modeling process) as a method to deal with such high numbers of nominations (see Lusher et al., 2012).

Importantly, each team is statistically examined separately. Therefore, we do not make statistical comparisons across teams, but rather examine statistically whether the presence of reciprocity, transitivity, or receiving of ties due to experience occurs at greater than chance levels. As such, we essentially compare each club with other possibilities of how its social ties might be arranged. We note that as age and experience were highly correlated (r = .93, p < .05), we only included experience in our social network models to avoid problems associated with co-linearity. The correlation between experience and performance was also significant but more moderate, r = .57, p < .05. The densities of the three trust networks were Club A = .1791, Club B = .0833, and Club C = .1111.

Finally, we note that we do not include a measure of team performance in our models, so there is no statistical relationship between the ERGM estimates and team-level success. It would be possible to run some form of multilevel analysis of the networks to determine this if there were enough teams to justify it (e.g., Lubbers, 2003). However, with only three teams, this was not possible. Rather, in the current study, we qualitatively align team-level success data with the network measures of Hypotheses 1 through 5 that arise from the ERGMs and look for associations between the two.

Results

We separate the results here into (a) model estimates and (b) qualitative implications.

Model Estimates

Network parameter estimates for the ERGMs are presented in Table 3 (with associated standard errors in parentheses). As a general rule, an estimate greater (in absolute value) than 2 times standard error is regarded as statistically significant (denoted by an asterisk *). Estimates are provided in square brackets [], and their standard errors in parentheses () within these brackets.

Table 3.

ERGM Parameter Estimates for Trust Relations and Player Characteristics for Three AFL Clubs (Standard Errors in Parentheses).

	Club A	Club B	Club C
Hypothesized effects
(H1) Mutual	1.43 (0.45)*	2.02 (0.50)*	1.91 (0.46)*
(H2) Path closure	1.01 (0.27)*	0.37 (0.37)	0.539 (0.266)*
(H3) Shared popularity	0.82 (0.35)*	0.10 (0.03)*	0.07 (0.06)
(H4a) Receiver–experience	0.0053 (0.0025)*	0.002 (0.002)	0.007 (0.002)*
(H4b) Receiver–performance	0.026 (0.021)	0.06 (0.02)*	0.025 (0.012)*
(H5a) Homophily–experience	0.006 (0.002)*	0.005 (0.002)*	0.009 (0.002)*
(H5b) Homophily–performance	−0.009 (0.020)	0.02 (0.02)	0.005 (0.01)
Control effects
Arc	−11.44 (2.85)*	−4.11 (0.62)*	−3.99 (0.65)*
Simple connectivity	−0.12 (0.08)	−0.47 (0.23)	−0.12 (0.09)
Popularity spread	4.60 (1.45)*	0.61 (0.29)*	0.52 (0.30)
Activity spread	−0.49 (0.39)	0.19 (0.28)	0.45 (0.30)
Popularity closure	−0.31 (0.73)	0.74 (0.38)	0.20 (0.25)
Cyclic closure	−0.259 (0.127)*	−0.19 (0.19)	−0.07 (0.14)
Multiple connectivity	0.08 (0.09)	0.31 (0.26)	−0.05 (0.11)
Sender-performance	0.016 (0.017)	0.01 (0.02)	0.01 (0.01)
Sender-experience	0.0037 (0.0020)	0.000 (0.001)	−0.0033 (0.0018)

Note. Each club is modeled separately. ERGM = exponential random graph models; AFL = Australian Football League.

We do not go through each of the effects in the model, but instead focus on those effects directly related to our hypotheses. However, we do note that we have included a number of control effects so that we account for complex interdependencies and self-organizing tendencies of network ties (Rank, Robins, & Pattison, 2010). The specific variables selected for the set of control variables follows guidelines on ERGM (Lusher et al., 2013, p. 175). We note all parameters in our models indicated good convergence of the Markov chain Monte Carlo maximum likelihood estimation (MCMCMLE) algorithm, and that in the goodness of fit the convergence t-ratios for all included effects <.1 and for all non-included effects <2.0. This suggests that these three models represent our data well, and that other network effects that were not specifically modeled are not extreme and are therefore well captured by the model (Koskinen & Snijders, 2013).

Trust relations will be reciprocated

There was a significant and positive effect for reciprocity for all three clubs [A: 1.43 (.45)*, B: 2.02 (.50)*, and C: 1.91 (0.46)*], which means that, given the other effects in the model, players reciprocate their trust nominations more than expected by chance. This supports H1 regarding structures of mutual trust, and aligns with a considerable body of literature on trust that notes the importance of reciprocity.

Trust relations will involve transitive closure of ties

In Club A, the significant and positive effect for path closure [1.01 (.27)*], and the significant and negative effect for cyclic closure [−.259 (.127)*], indicate that hierarchical multiple triadic closure is very likely and generalized (or cyclic) triads are very unlikely. As such, trust is not given in a general sense to others in the team, but is given in a hierarchical manner, and this is support for H2. In Club B, there was no significant effect for path closure. However, Club C did show a significant and positive effect [.539 (.266)*] for path closure, indicating support for H2.

Trust-inhibiting relations will involve the non-closure of trust ties

The third hypothesis examines a trust-inhibiting structure of trust, as opposed to all other hypotheses that examine trust-generating structures. For Club A, there is a significant and positive shared popularity effect [.82 (.35)*], indicating that it is common for players to agree on the same players as team members they trust (see Table 1 effect 10 for a visualization of this effect). This is support for the third structure of trust (H3) and indicates that some highly popular teammates do not trust one another. For Club B, we also find a significant and positive effect for shared popularity [.10 (.03)*], demonstrating support for a trust-inhibiting structure of H3. Unlike Clubs A and B, for Club C there is no shared popularity effect (H3), and importantly this indicates the absence of trust-inhibiting structures of trust relations for Club C.

Trust relations will be afforded to team members high in experience and performance

With regard to trusting team members high in experience (H4a), Club A demonstrates a positive and significant receiver effect for experience [.0053 (.0025)*], indicating that players with more experience are significantly more likely to be trusted than others. For Club B, there is a significant and positive receiver effect for performance (H4b) [.06 (.02)*], indicating that players of higher rated performance were more likely to be trusted. Finally, for Club C, there are two significant and positive receiver effects: one for experience (H4a) [.007 (.002)*] and one for performance (H4b) [.025 (.012)*]. These separate main effects for receiving ties indicate that players high in performance are more likely to be trusted, and also players high in experience are more likely to be trusted by others. Again, these are separate effects, and it may be that they are not the same people (i.e., the people who are trusted for high performance may not have high experience, and vice versa).

Trust relations will be afforded to other team members of similar experience and performance

For Club A there is a significant and positive homophily effect for experience [.006 (.002)*], indicating that players trust others of a similar level of experience to themselves (supporting H5a). This effect is over and above the receiver effect for experience, and each effect must be interpreted in light of the other effect, and so in conjunction these two effects are interpreted as players trust others of similar or greater experience to themselves. However, there are no such effects for peer-rated individual performance. Club B also has a significant and positive homophily effect for experience [.005 (.002)*], indicating that players trust others with similar levels of experience to themselves. Furthermore, Club C also shows a positive and significant homophily effect for experience [.009 (.002)*]. We note that there was no support for H5b regarding trust homophily due to performance in any of the clubs.

Qualitative Implications

In this section, the quantitative results from the ERGMs are compared qualitatively with the team performance measures we have for the three AFL teams. A summary of the results for each hypothesis by club is given in Table 4 below.

Table 4.

Summary of Support for Each of the 5 Hypotheses for Each Club (O = Hypothesis Supported) for Cooperative and Competitive Structures of Trust.

Club	Network configuration graphic	Club A	Club B	Club C
Overall team performance	Network configuration graphic	Low	Medium	High
Trust generating structures
H1 (mutual)		O	O	O
H2 (path closure)		O		O
H4a (receiver experience)		O		O
H4b (receiver performance)			O	O
H5a (homophily experience)		O	O	O
H5b (homophily performance)
Trust-inhibiting structures
H3 (shared popularity)		O	O

The most interesting effect is the difference between teams with respect to the trust-inhibiting structure of trust relations. While it may be seen as only one effect among many, to make this importance of such a trust-inhibiting effect more concrete, consider the effects on a team if the captain and the vice captain do not trust one another. The presence of network structures like this for Clubs A and B suggest that such a situation is actually taking place in both of these clubs. The importance of the perception of social ties between alters has been demonstrated by Krackhardt (1987). If in the above example tensions between captain and vice captain were not masked and those other players who trusted both were aware of the absence of trust between the two, this might create psychological tension for these other players (Heider, 1958) and create a situation of conflicted loyalties. This situation of trusting two other people who do not trust one another is typically resolved by either (a) trust occurring between captain and vice captain or (b) by players who trust the captain and vice captain withdrawing trust for one of these two. It is for this reason that we refer to such a structure as trust-inhibiting and competitive because it drives divisions between team members.

Importantly, for Club C, there was an absence of the hypothesized trust-inhibiting structure (H3), which differentiates this club from the other two. Furthermore, it is worth reiterating that with respect to their overall team performance, Club C was rated highest, followed by Club B and then Club A. We note that team performance was not measured quantitatively against the presence/absence of these structures of trust, but rather that such judgments were made qualitatively and based on comparing the quantitative network results with team performance. In any case, while a myriad of factors can contribute to the overall team performance, there is some face validity to the claim that teams that do well are not hampered by trust-inhibiting structures of trust relations. The fact that the club who finished highest overall of the three teams on the points table did not have trust-inhibiting mechanisms is promising. Clearly, it may not simply be just the presence of ties but also in some cases the absence of ties that is important (White et al., 1976).

With regard to trust-generating structures of trust relations, it is again important to note that Club C has all four types of such structures present with respect to trust relations. In all clubs, there is support for reciprocity of trust relations, and thus H1 relating to trust-generating mechanisms. It is no surprise that we found the significant presence of mutual trust ties in all three clubs, especially given that the reciprocal nature of trust is seen as fundamental to the definition of trust itself. Indeed, reciprocity can almost be seen as the fundamental structure for trust relations, and the absence of such patterns from a network of trust would indicate an incredible lack of trust within the network.

However, for transitive path closure (H2) —or the idea that a friend of a friend is a friend—we find evidence of this in Clubs A and C, but not Club B. We noted earlier that path closure (otherwise known as triadic closure, which is about the formation of triads/triangles) is another fundamental element of groups (Heider, 1958; Simmel, 1950), and a structure that has been shown to be extremely important in social network (Burt & Knez, 1995; Granovetter, 1973; Robins et al., 2009). That it is not significantly present in Club B is concerning, because as Granovetter (1985) notes, “Better than the statement that someone is known to be reliable is information from a trusted informant that he has dealt with that individual and found him so” (p. 490). A triad (in the form of path closure⁴) essentially represents Granovetter’s sentiments above regarding the referral of trust. The absence of such an effect indicates that team members in Club B are reluctant to believe the recommendations of their teammates regarding trust, indicating that while team members may be willing to take risks in trusting at the level of the dyad (i.e., reciprocity), they are unwilling to risk referrals, or such referrals are not being made. In any case, team members are uncomfortable investing trust based on the views of another, or offering their view of another. This suggests trust in the form of path closure involves more risk than reciprocal trust, where judgments are made by oneself and not through the investment of third parties.

It is almost a given, within the context of these sporting teams, that you would trust your best players and also your more experienced colleagues because their value to the team in their ability to help win games (the team’s primary group outcome) is higher. These structures of trust seem to be fundamental along the lines of mutual trust ties, such that if players in a team did not trust those high in performance or experience you would be worried—such lack of trust would be conspicuous in its absence. Again, Club C meets both of these expectations, with separate effects showing players investing trust in those high in experience (H4a) and those high on performance (H4b). Yet, once again indicating some issues, Club A players trust in experience, but not performance, and Club B players trust in performance, but not experience. The implication is that for Club A high performers may be unreliable, and that for Club B more experienced players may be seen as past it, rather than older and wiser.

Finally, indicating that similarity breeds trust among the players, in all three clubs players trusted others of similar levels of experience to themselves. This effect is over and above the receiver effect for experience, so that beyond trusting highly experienced team members, team members also trust other team members who are similar in experience. It is also over and above effects of reciprocity and transitive closure. Given the correlation between experience and age, this may not be so surprising. Yet curiously, this is not the case for performance, and in this case it seems that team members trust up the hierarchy toward better performing players, not across the hierarchy to those similar to themselves. This may be because experience is a more easily defined commodity than performance. However, experience is also somewhat less contested than performance, with players competing with one another in their performances to be a part of the team (Messner, 1997).

In summary, our method of analysis (ERGM) has been used to differentiate different structures of trust relations to unpack the subtle ways that trust is afforded to teammates. It is only Club C that demonstrates the presence of all four types of trust-generating structures of trust relations but also the absence of trust-inhibiting structures of trust relations. This same club has the highest overall team performance. As such, the qualitative comparison of the quantitative network model findings for these structures of trust with team performance does suggest an interesting association between these mechanisms of trust and team performance.

Discussion

The current study is primarily concerned with the examination of a trust-inhibiting (or competitive) structure of trust relations in teams, and its association with team performance. Trust-inhibiting structures were not found in the club that had the highest overall team performance, but were found in teams that performed more poorly. The inclusion of trust-generating structures of trust adds validity to our findings regarding the trust-inhibiting structure. All types of trust-generating structures were also found in the team with the highest overall team performance, though in the other two teams these trust-generating structures occurred to a lesser extent. The trust-generating hypotheses were indeed nothing new and replicate well-established findings regarding trust. The observed trust-generating and trust-inhibiting effects align, and give credence to the suggestion that negative relations are indeed powerful and are worth avoiding (Labianca & Brass, 2006).

The issue of interpersonal trust in teams is not merely an academic question. Determining how and why some teams work effectively together while others do not is of wider interest given that many teams, or groups more generally, place significant importance on collective outcomes that depend on the cooperation and trust of its members. Many teams institute mentor programs for the purposes of team building and cohesion. If trust is based more on experience then matching older team members with younger ones would seem a sensible approach to mentoring. On the other hand, if trust is (peer-referenced) performance related then it may well be worth using this quality as a characteristic for matching team members. Indeed, there may also be other qualities on which one is trusted, such as attitudes like identification to the team. The statistical ERGM approach is able to uncover consequential patterns of network trust ties and their association with personal-level attributes, thereby giving insights into potential underlying social processes that are not visible with standard statistical techniques. The ERGM approach gives us the ability to look, one team at a time, at social mechanisms that inhibit or generate trust in teams. This ability to postulate nuanced theory about trust relations and test it empirically within the context of teams is an important contribution of this article.

Jones and George (1998) discuss the utility of conceiving of trust as being in one of three forms: distrust, conditional trust, and unconditional trust. It is particularly unconditional trust that they suggest can “fundamentally change the quality of the exchange relationship and convert a group into a team” (Jones & George, 1998, p. 539). Where trust is expected but withheld is suggestive that unconditional trust is not evident, particularly in cases where it is between prominent and highly trusted members of the team. By conceptualizing trust in network terms, and thinking of certain micro structures as describing “distinct states or forms of the trust experience” (Jones & George, 1998, p. 537) we have potentially shown here that distinct states may operate simultaneously within the one team. In Club C we would argue that there are many example structures of trust (H1, H2, H4, and H5) and an absence of distrust (H3). Together, these effects could be interpreted as representing unconditional trust, and the value of the current study shows that we are able to measure empirically structures of trust that relate to this concept and evaluate teams on trust using a network approach. There are of course other possible structures that trust can take, and theoretical work needs to be done to articulate these and then examine trust relations in teams empirically for their presence.

This article has not delved directly into values, attitudes, moods and emotions, other psychological mechanisms involved with trust by including such actor-level measures on these issues (Mayer et al., 1995). However, our results do offer possibilities to understand trust within teams from a psychological perspective. Building on the work of Heider (1958), Krackhardt’s (1992) seminal study on the strength of strong ties among managers clearly shows the psychological strain that a person endures when he is pulled in differing directions from others with differing views. We have shown here that it is possible to measure such tensions at a level that includes multiple individuals who might be in the same conflicted position by including a parameter in an ERGM to see if such structures occur more than we might expect. So we are able to include measures of tension as parameters in a model and test them statistically. The results of this study suggest that within the observed networks there are multiple people in potentially tense positions regarding trust relations.

When using an ERGM to examine social network data, the researcher is made to consider the multiple and intersecting reasons why network ties (i.e., trust) occur. This multiple explanation approach sits very comfortably with agreed conceptions of the multifaceted nature of trust (Mayer et al., 1995; McEvily et al., 2003). As such, different structures of trust ties can relate to different theoretical conceptions of trust, potentially accommodating a range of theoretical perspectives simultaneously, and comparing them one against the other, statistically. Such an approach allows for a complex analysis of trust that does not take one particular process at a time, single it out, and potentially overestimate its importance (De Jong & Elfring, 2010), but instead forces an explanation to appear above and beyond the importance of competing reasons. Within the ERGM framework, network ties are seen as locally emergent and the agglomeration of these local patterns forms the global structure of trust relations observed for each network/team. This is both a methodological viewpoint and also a theoretical statement about network tie formation (Robins & Lusher, 2013). As noted, there are at least two broad types of local patterns. In one of these types, network effects occur because of the presence of other network ties known as purely structural network effects, or network self-organization (e.g., “you scratch my back, I’ll scratch yours,” or reciprocity). Such ties occur regardless of their personal qualities of the people involved—one tie comes about because of the presence of another tie; that is, one tie depends on the other tie. H1, H2, and H3 are such explanations of how trust ties occur, and give insights into the dependencies of trust relations. Other network effects occur due to the individual-level qualities of individuals and are called actor-relation effects (e.g., a team member may be popular because she is a high performer). H4 and H5 are examples of such actor-relation effects for trust. This study has shown evidence that both of these types of reasons for trust to occur are important, and that purely structural explanations do not wash away actor-relation explanations, or vice versa. A focus only on endogenous process or only on individual attributes is likely to insufficiently explain trust ties.

A further strength of the study is that ERGMs are appropriate and fine-grained methods for investigating specific structures of trust relations among team members. We have detailed specific ways in which trust is structured within each of the three teams, including how trust relations align with individual attributes. We reiterate that we have conducted separate analyses for each club, in a similar vein to running separate regression analyses for each club. However, it is also worth noting that in the current research we have controlled for the structure of social ties and their dependencies in these models in a way that a regression cannot (because it assumes independent observations).

There are of course certain limitations to this article. The observations made are specific to the teams we have observed and are not generalizable to other contexts and further investigation in other settings would be useful. We do not make causal claims here, such that trust relations have impacted on performance, for it could also be the case that team performance is generative of trust. Clearly longitudinal analysis is required to unpack such issues. Furthermore, we do not have detailed information about the clubs themselves. Further research is needed to better clarify how the proposed network structures for trust relations are associated with team cohesion and other metrics on which teams can be measured. And there are surely other structures of trust relations that may be of consequence for teams, and these need to be elaborated. In addition, the measurement of trust through a single item might be improved on using a multi-item scale that more specifically defines trust for the participants instead of leaving the definition up to the participant to work out for themselves. We have not delineated instrumental and expressive trust in our network measure. Certainly it may be informative to do so in future research, and we see possibilities here with regard to using a multivariate ERGM to unpack the structures related to each type of trust network and how these two might align and diverge. The current research also opens up other possibilities. For instance, just as experience and performance have been analyzed with respect to their co-occurrence with interpersonal trust relations, it is also possible to investigate how individual-level perceptions of trust co-occur with such structures. Specifically, in relation to the link between trust and team performance, it would be possible to measure, at the level of the individual, team satisfaction or perceived task performance (Costa, 2003), or team reflexivity and team effort (De Jong & Elfring, 2010), and how these may be related to structures of trust. This would allow a further unpacking of the “mechanisms and processes which can reinforce and help sustain team-based initiatives” (McHugh et al., 1997, p. 47), and to develop a greater understanding of the organizing principles of trust (Gambetta, 1988).

Conclusion

Many insights into trust in teams point to mechanisms that can be seen as trust generating, but less attention is directed toward trust-inhibiting structures that may undermine trust within teams. The current research argued that trust-inhibiting (or competitive) structures of trust may indeed be deleterious to team performance. We hypothesized one such trust-inhibiting structure, which entails highly trusted team members not trusting one another, and examined three teams for its presence. An analysis of trust networks using ERGMs indicates that this trust-inhibiting structure of trust was absent in the most successful team but present in the two less successful teams. Furthermore, that well-accepted trust-generating mechanisms of trust (e.g., reciprocity) were highly present in the most successful team, but less so in the less successful teams. We qualify our findings by noting that our analyses of the mechanisms of trust were quantitatively derived, and then compared qualitatively with team performance. Nonetheless, we do suggest there is an association between the presence of trust-generating mechanisms of trust, the absence of trust-inhibiting mechanisms of trust, and team performance. We suggest that trust-inhibiting structures offer an importance to pursue a line of inquiry through which to understand tensions regarding trust in teams because these may have a significant relationship with team performance. Further theorization on other trust-inhibiting structures of trust relations and related empirical work is likely to shed further light on the connection of trust in teams and team performance.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Funding by the Australian Football League (AFL) Research and Development Board that assisted this research is gratefully acknowledged.

Notes

Author Biographies

Dean Lusher is a Senior Research Fellow in the Swinburne Business School at Swinburne University of Technology, Australia. His research interests are in the theory and application of statistical models for social networks, in regard to organizations, innovation and organizational culture, and issues of social inclusion.

Peter Kremer is a Senior Lecturer in Sport Behaviour in the School of Exercise and Nutrition Sciences at Deakin University, Australia. His research interests focus on behavioral and psychological factors associated with participating in sport and exercise and the role of sport and exercise in promoting positive mental health outcomes.

Garry Robins is a mathematical psychologist in the School of Psychological Sciences, University of Melbourne, Australia, whose research focuses on statistical models for social networks. He has been involved in a wide variety of empirical studies dealing with social networks.

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