Understanding Work Teams From a Network Perspective: A Review and Future Research Directions

Abstract

As social network theory and methodology advance, scholars in multiple fields have increasingly become interested in examining work teams using network perspectives. Social networks not only enabled work team researchers to theorize about interdependencies and the dynamic interplay of team components (i.e., individuals, dyads, and whole teams) but also provided a methodological tool kit with which to operationalize and test hypotheses about such interdependencies. To this end, the purpose of this article is to conduct an integrative review of organizational teams research that has adopted a social network perspective to highlight what is known and what remains to be addressed. We then outline an agenda for future research that introduces three promising areas to guide researchers to move the field forward. We conclude that a more thorough integration of the networks and teams literatures offer great promise for advancing both our science and practice.

Keywords

teams social networks team dynamics

Work teams are increasingly viewed as complex, adaptive, and dynamic systems (Arrow, McGrath, & Berdahl, 2000; Mathieu, Gallagher, Domingo, & Klock, 2019).¹ In this perspective, a defining element of work teams is that members are interdependent and connected to one another in a variety of ways (Humphrey & Aime, 2014; Kozlowski & Ilgen, 2006). Accordingly, researchers have shown interest in understanding the patterns of formal and informal relationships, interactions, and perceptions among individuals in work groups (Crawford & LePine, 2013). Such patterns are effectively represented by social networks, where a set of actors or nodes is linked with a set of ties and the pattern of those ties yields a particular structure (Borgatti & Halgin, 2011).

Figure 1 shows a frequency graph of work team articles that have used a network perspective, in 3-year periods, since the early days of sociometry research (e.g., Jennings, 1943; Moreno, 1932, 1934). Notably, there has been a surge in work teams research adopting a social network lens to examine particular content areas over the past quarter century. By adopting a social network approach, work team researchers in multiple fields (e.g., management, psychology, sociology) have not only started to theorize about interdependencies and the dynamic interplay of team components (i.e., individuals, dyads, and whole teams) in diverse contexts (Borgatti & Halgin, 2011; Katz, Lazer, Arrow, & Contractor, 2004) but have also utilized a methodological tool kit with which to operationalize and test hypotheses about such interdependencies.

Figure 1

Growth in Studies on Team Networks (1939–2018)

Several recent reviews have addressed the application of social network research to organizational phenomena in general (e.g., Borgatti, Brass, & Halgin, 2014; Burt, Kilduff, & Tasselli, 2013; Soltis, Brass, & Lepak, 2018), and two reviews have specifically addressed work teams applications. Henttonen (2010) provided a selective review of 32 empirical papers that appeared up until 2008, and Wölfer, Faber, and Hewstone (2015) suggested examples of methodological opportunities offered by social network analysis for testing intra- and intergroup phenomena. Curiously, however, recent comprehensive reviews of the work teams literature have given little attention to the network approach (e.g., Kozlowski & Ilgen, 2006; Mathieu et al., 2019; Mathieu, Hollenbeck, van Knippenberg, & Ilgen, 2017). As a result, teams researchers interested in applying a network lens may find it difficult to develop an accurate view of the current state of this research domain. Given the recent increase in network-based teams research, such a review is needed in order to organize this literature and clarify how networks are being applied to teams research.

Therefore, the purpose of this article is to integrate such knowledge by conducting a systematic review of organizational teams research that has adopted a social network perspective. In this review, we also aim to clarify how networks might be integrated into the research programs of those currently studying teams or who intend to take up teams research. Specifically, the goals of this review are (a) to determine how social networks impact and are impacted by teams, (b) to identify questions that have not been adequately answered, and (c) to propose ways in which a network perspective can uniquely further teams research. In doing so, we propose a framework that systematically organizes existing studies along two dimensions: construct level and research model position (i.e., whether the focal network variable was conceptualized as an antecedent, mediator, moderator, or outcome).

The remainder of the article is structured as follows. First, we describe our methodological approach and organizing framework for this review. Second, we synthesize empirical research published in top management journals during the past 25 years. Third, we propose a future research agenda that identifies three major opportunities for team network research to pursue. Our discussion of future research highlights methodological and analytical advancements in the social network domain that can enrich the larger body of teams research.

Method of Review

Search Strategy and Inclusion Criteria

Our review focused on empirical work teams research that used a network perspective between 1994 and 2018. Using the Scopus database, we searched titles, abstracts, and keywords for the terms “team” or “group” and “network” or “social capital” in 20 top management, applied psychology, and social network journals (see Short, 2009, for recommendations on journal selection in review articles), which yielded 489 articles.² To be included in our review, articles must have met all of the following inclusion criteria: (a) sampled work teams or attempted to generalize to work teams, (b) adopted a social network approach, and (c) included empirical analysis. We restricted our search to work teams where members worked interdependently on tasks relevant to organizations. We excluded articles about social groups (e.g., political parties, family groups) and large corporate groups (e.g., a collection of parent and subsidiary corporations). We also excluded papers that collected sociometric (e.g., round-robin) data yet did not use network analysis. On the basis of those criteria, we read the abstracts of all articles and conservatively screened out those that clearly did not fit our criteria. The remaining 236 articles were scrutinized in greater detail, and 120 were excluded because they did not sample or generalize to work settings, did not employ a social network approach, or were strictly methodological in nature. From the remaining 116 articles, we coded each network variable, yielding a total of 279 variables.

Organizing Framework

Work teams research has seen tremendous advancements in the past few decades, with the majority of work being conducted in the context of multilevel designs and analyses (Mathieu et al., 2019; Mathieu, Wolfson, & Park, 2018). This multilevel approach views teams in context and composed of different members, enabling the modeling of upward, downward, and team-level relations (Kozlowski & Chao, 2018; Mathieu & Chen, 2011). Moreover, constructs in teams research are typically categorized according to their position in a research model (Mathieu et al., 2017, 2019).

Accordingly, we adopted an organizing framework with two dimensions. The first dimension, construct level, concerns the level(s) of team components the study was focused on (i.e., individual, dyadic, within-team, or between-team level). It is important to note that the construct level can be different from level of analysis (actor, tie, and network levels) because a node in a network might represent an individual, team, or an entire organization (see Borgatti & Foster, 2003). For example, a study focusing on team centrality in a multiteam system (MTS) would be categorized at the between-team construct level because the focus is on relationships between teams, but the level of analysis would be at the actor level because teams are the focal node in the network and centrality is an actor-level construct. The second dimension, research model position, identifies how authors conceptualized their network variables in the study (i.e., as antecedents, mediators, moderators, or outcomes). It is important to understand how a network variable’s position in a model reflects its causal order and scholars’ conceptualizations of variables. This framework provides a guide for understanding the ways in which network variables have been applied to work teams and enables us to identify fruitful research directions.

Throughout the review, we also highlighted various network measures that were employed in studies. For the sake of clarity and brevity, we define the network measures discussed herein in Table 1. In Table 2, we provide a summary of the number and percentages of study constructs across construct level and research model position.³ The complete list of studies we reviewed and their key findings can be found in Online Supplement B.

Table 1

Network Terminology

Term	Definition
General
Ego	A focal node (i.e., individual) in a network.
Alter	A node with whom ego has a direct tie.
Brokerage	A configuration of relationships whereby one actor mediates the flow of resources or information between two other actors who do not have a direct tie to one another. Brokerage is commonly operationalized with measures of structural holes (see the entry on structural holes below).
Clique	A subset of nodes in which every possible pair of nodes is connected by a direct tie and the clique is not contained in any other clique.
Node level
Centrality	The extent to which a focal node is positioned in a central position in the network. Multiple measures of centrality exist (e.g., indegree, outdegree, and betweenness).
Indegree centrality (popularity)	The number of ties from other nodes that point toward a focal node (i.e., the number of incoming ties a node has).
Outdegree centrality	The number of ties that a focal node sends to other nodes (i.e., the number of outgoing ties a node has).
Betweenness centrality	The extent to which a focal node lies on the shortest path between other nodes in the network (i.e., the extent to which other nodes must go through the focal node to reach each other).
Eigenvector centrality	The extent to which a focal node is connected to other nodes who are themselves well connected.
Weak ties	The number of ties (either incoming, outgoing, or reciprocated) associated with a node that are deemed to be relatively low in either frequency of interaction, emotional closeness, tie duration, or some combination thereof. Weak ties are frequently used to operationalize a node’s potential to bridge across diverse or disconnected groups in a network.
Structural holes	The extent to which a node has ties to other nodes who are not connected to one another. In practice, the construct is operationalized with various measures including network constraint (Burt, 1992), betweenness centrality, and ego network density (a measure of the sparseness of an individual node’s direct ties).
Network heterogeneity	The extent to which a focal node has ties to a diverse set of alters. Heterogeneity can be based on any measurable difference. In practice, network heterogeneity in terms of organizational function, knowledge domain, or demographic background is frequently examined.
Bridging social capital	An overarching construct referring to the extent to which a node possesses a wide range of ties that span diverse boundaries. This construct could be operationalized with a measure of weak ties, structural holes, or the heterogeneity of the nodes to whom a focal node is connected.
Dyad level
Tie strength	The tie strength between two nodes is typically considered to be a function of (a) the dyad’s frequency of interaction, (b) the emotional closeness between the nodes, and (c) the duration of the tie. In practice, tie strength is sometimes operationalized using a subset of these three considerations.
Structural equivalence	Two nodes are structurally equivalent if they send ties to the same third parties and receive ties from the same third parties. They need not be directly connected themselves. In practice, structural equivalence is measured as the extent to which two nodes share ties to common third parties (as opposed to a binary yes-or-no measure).
Third-party cohesion	When two nodes share a tie to a common third party.
Tie multiplexity	When two nodes simultaneously share more than one type of network tie.
Indirect tie	When the path between two nodes is mediated by one or more other nodes (i.e., two nodes do not have a direct tie but can reach each other via a path of two or more steps).
Homophily	The principle that people seek out and connect with similar others. Theorized to be an antecedent to tie formation between two actors.
Network level
Network density	A measure of the connectivity (or cohesion) of a network. Operationalized as the ratio of the number of actual ties in a network to the number of ties that are mathematically possible based on the number of nodes in the network.
Network centralization	A measure of the extent to which ties are organized around particular focal nodes. Calculated by taking the difference of the centrality score of the most central node and those of all other nodes in the network; a ratio is then computed by comparing the actual sum of the differences to the maximum possible sum of differences.
Core–periphery structure	A network structure consisting of a set of densely interconnected core nodes as well as less connected peripheral nodes who tend to have connections only to core nodes and not to other peripheral nodes.
Network transitivity	A triad (A, B, C) is transitive when the following conditions are met: A is tied to B, B is tied to C, and C is tied to A. Network transitivity is a measure of the “clumpiness” of a network. It is operationalized by the ratio of actual transitive triads in a network to the number of potential triads (i.e., the number to triads that could be completed with a single additional tie).

Table 2

Coding Counts

			Research Model Position
Construct Level			Antecedents		Moderators		Mediators		Outcomes
Total	316	100%	192	60.8%	36	11.4%	23	7.3%	65	20.6%
Between team	29	9.2%	22	7.0%	4	1.3%	0	0.0%	3	0.9%
Within team	126	39.9%	74	23.3%	20	6.3%	12	3.8%	20	6.3%
Dyad	63	19.9%	36	11.4%	1	0.3%	0	0.0%	26	8.2%
Individual	98	31.0%	60	19.0%	11	3.5%	11	3.5%	16	5.1%

Note: Among the 317 constructs captured in the 116 empirical articles coded, 27 were counted as multiple substantive variables (e.g., a construct could be both an antecedent and mediator) and six were counted at multiple construct levels (e.g., some studies captured a construct at both the dyad and within-team levels).

Network Thinking in the Work Teams Literature

Individual-Level Constructs

Consideration of individual-level network constructs enables researchers to conceptualize how certain members impact team processes and outcomes and how each member can come to occupy a certain position within a team. Of the studies we reviewed, 31.0% featured individual-level network constructs. Among such variables, members’ degree centrality was frequently employed to index individuals’ structural positions in a team.

Individual-level network variables as antecedents

Many studies have focused on the effects of structural position on individuals’ power and influence and examined how effects can differ by the type of ties examined in a network. For instance, indegree centrality in one’s advice giving network is related to power and influence in the team (Chiu, Balkundi, & Weinberg, 2017; Salk & Brannen, 2000), whereas indegree centrality in negative networks (i.e., networks of who hinders whom, who prefers to avoid whom, who dislikes whom, etc.) has been negatively linked to social power (Chiu et al., 2017). Such negative ties also make individuals more vulnerable to exerted control by others (de Klepper, Labianca, Sleebos, & Agneessens, 2017).

An emerging line of research has specifically focused on the structural positions of team leaders and their effects on both team- and individual-level outcomes. Generally speaking, leader indegree centrality in the team advice network is associated with positive outcomes, such as enhanced team viability and reduced team conflict (Balkundi, Barsness, & Michael, 2009). However, results regarding leader advice network betweenness centrality are more complex. Whereas team leader betweenness centrality may have negative team-level effects (Balkundi et al., 2009), it can be positively linked to individual-level outcomes, such as the radical creativity of team members (Venkataramani, Richter, & Clarke, 2014).

Individual-level network variables as mediators

Researchers have primarily investigated how individual centrality can explain why certain team roles and individual characteristics relate to individual performance in different settings. For instance, in a study of virtual R&D teams, M. Ahuja, Galletta, and Carley (2003) explored how individual role characteristics determine individual degree centrality in a communication network, which in turn related to their performance. In a study of networking groups, Ho and Pollack (2014) found that entrepreneurs’ communication network centrality mediated the relationship between two distinct forms of passion (i.e., harmonious and obsessive) and financial performance.

Individual-level network variables as moderators

The influence of team member behaviors and characteristics is contingent upon network position and tie content. For example, researchers have investigated how members’ network position can amplify the effects of extrarole behaviors, such as helping and voice expression (Howell, Harrison, Burris, & Detert, 2015; Li, Zhao, Walter, Zhang, & Yu, 2015). In contrast, centrality in the negative tie network attenuates the positive relationship between workflow network centrality and voice behaviors (Venkataramani, Zhou, Wang, Liao, & Shi, 2016).

Individual-level network variables as outcomes

The primary focus of research on individual-level network variables as outcomes has been on identifying how individuals come to occupy central positions in team networks. For instance, team members’ education level and personality traits (Klein, Lim, Saltz, & Mayer, 2004) have been found to be significant predictors of indegree centrality in team networks. Moreover, identifying with the organization as a whole, as opposed to identifying with one’s subunit, leads to advice indegree centrality (Lomi, Lusher, Pattison, & Robins, 2014), as does having values that are similar to one’s teammates (Klein et al., 2004). Last, the type of relationship a team member has with his or her team leader has also been shown to affect an individual’s indegree centrality (Erdogan, Bauer, & Walter, 2015; Lau & Liden, 2008), suggesting that a network tie from a leader serves as a cue to others on the team. A separate line of research has operationalized leadership emergence as indegree centrality in the team network of leadership perceptions (e.g., Emery, 2012).

Synthesis of individual-level constructs

Individuals’ structural positions in team networks have been frequently studied and conceptualized as antecedents, mediators, moderators, and outcomes by different scholars. For instance, evidence suggests that the central positions in a network may indicate positive (e.g., in advice-giving network) or negative (e.g., in negative tie network) social power or influence in a team. The distribution of incoming network ties on a team—depending on the content of tie—is therefore a reasonable representation of the concentration of influence within a team. Research on the antecedents of indegree centrality suggests that members who “fit in” by sharing common values with a team and having a positive relationship with the leader tend to be more central, while those who may not be seen as “team players” (e.g., members high in neuroticism) are less central.

However, many questions remain unanswered. A central query that has received little attention is the causal direction between malleable individual characteristics and centrality. For example, it remains unclear whether factors such as team identification and one’s relationship with the team leader are a cause or a consequence of indegree centrality. The effects of leader betweenness centrality also deserve additional research attention. Leader betweenness centrality appears to have both positive and negative ramifications, but when this type of centrality is more detrimental than beneficial is still up for debate. A dynamic approach to team networks can potentially address some of these unresolved questions (see Kalish, Luria, Toker, & Westman, 2015, for a recent example). Perhaps, the ramifications of leader betweenness centrality manifest differently depending on the phase of team evolution.

Dyadic-Level Constructs

Dyadic relations are the building blocks of team interactions. Researchers have taken multiple approaches to examining the nature of team member relationships, with the most common approaches being to examine tie strength or the various structural features of a dyadic relationship. Together, dyadic-level constructs constitute 19.9% of our review. None of the studies modeled dyadic network variables as mediators, and only one recent paper examined moderators at the dyadic level.

Dyadic-level network variables as antecedents

Research has sought to examine the network characteristics that lead to similarity in perceptions and judgments between pairs of teammates (Meyer, 1994; Wong, 2008a). More recently, scholars started exploring how dyadic network antecedents affect what flows between individuals. For instance, knowledge transfer between two members was predicted positively by the structural equivalence in advice network and negatively by the distance in advice network (Wei, Zheng, & Zhang, 2011). The degree to which dyads have similar third-party trust relationships was positively associated with performance benefits (i.e., referral received from the other; Gupta, Ho, Pollack, & Lai, 2016).

Dyadic-level network variables as moderators

The one study in our review capturing dyadic-level moderators suggested that sharing a common third-party friendship tie can reduce the likelihood that ego will retaliate against alter when ego perceives that alter has acted unfairly (Goh, Krackhardt, Weingart, & Koh, 2014).

Dyadic-level network variables as outcomes

Homophily, or the tendency for similar individuals to attract, is a powerful predictor of social network ties (McPherson, Smith-Lovin, & Cook, 2001), and multiple studies in the team context demonstrated that various forms of homophily—ranging from demographic similarity to similarity in perceptions—influence the formation of ties between individuals. For instance, being of the same race leads individuals to choose others as teammates for a future task (Hinds, Carley, Krackhardt, & Wholey, 2000). Similarly, being in the gender minority makes individuals less likely to forge advice ties within the group, and having a different tenure level makes it less likely that one will initiate friendship ties (Valenti & Rockett, 2008). Team members tend to maintain communication ties with others feeling similar levels of stress (Kalish et al., 2015) and also tend to initiate friendship ties with teammates who perceive similar levels of psychological safety (Schulte, Cohen, & Klein, 2012).

Synthesis of dyadic-level constructs

Members’ demographic similarity is associated with their forming network ties that serve as conduits that convey knowledge within teams. These findings parallel those observed in terms of tie formation in broader organizational contexts. Team network research at the dyadic level has been increasingly adopting sophisticated exponential random graph models (ERGMs), which are capable of modeling the numerous complex structural effects that may endogenously affect tie formation (e.g., Brennecke & Rank, 2016; Ellwardt, Labianca, & Wittek, 2012; Lusher, Kremer, & Robins, 2014). Recent developments on ERGMs allow for modeling of both structure and node attributes (Lusher, Koskinen, & Robins, 2013). Notwithstanding the positive methodological developments, tie formation in teams has been underexplored. For example, while it appears that sharing a tie is associated with two individuals possessing similar work attitudes, it is not clear whether the similarity is a cause or a consequence of the tie. Future research that leverages longitudinal methodologies and analyses will enable scholars to better disentangle such relationships (e.g., stochastic actor-based models for network dynamics; Steglich, Snijders, & Pearson, 2010). Deciphering causal directions of such effects offers great insights for theoretical progress and applied implications.

Within-Team-Level Constructs

Network constructs were the most frequently studied (39.9%) at the within-team level in our review. Of those, the majority of studies were devoted to how the density or centralization of networks was associated with team-level outcomes. Within-team level network variables were mostly studied as antecedents, which encompasses 58.7% of within-team level studies (23.3% of all variables).

Within-team-level variables as antecedents

Generally speaking, team network density of positively valent ties (e.g., friendship, advice) has shown positive relationships with team outcomes, such as performance (Grund, 2012; Parise & Rollag, 2010) and viability (Balkundi & Harrison, 2006). Notable exceptions to this include Oh, Chung, and Labianca (2004), who found an inverted U-shaped relationship between expressive tie (e.g., support and friendship) density and team effectiveness, and Sparrowe, Liden, Wayne, and Kraimer (2001), who failed to find a significant relationship between team advice network density and team performance. Furthermore, network density has been studied as a predictor related to the emergence of other group structures. For instance, density of warmth ties (i.e., socioemotional perceptions related to liking) was found to predict the emergence of leadership structure density overtime (DeRue, Nahrgang, & Ashford, 2015). Elsewhere, past team cohesion (i.e., density of positively valent relations among team members) negatively predicted the formation of current structural holes (Zaheer & Soda, 2009).

The effects of team network centralization are also largely dependent on the context and content of network ties examined. For instance, centralization in team knowledge exchange networks was negatively associated with team performance, and the relationship was more negative for diverse teams (Huang & Cummings, 2011). Another study found a negative relationship between the centralization of soccer teams’ pass networks (who passes the ball to whom) and team performance (Grund, 2012). In contrast, Argote, Aven, and Kush (2018) found that perfectly centralized communication networks can potentially mitigate the negative effect of turnover in groups because incumbents tend to ignore new members who may not readily understand the implicit coordination norms of the group. Researchers also investigated how network structure may change members’ leadership schemata. In both experimental and survey data, Brands, Menges, and Kilduff (2015) revealed that women leaders are perceived as less charismatic than their male counterparts in teams with a more centralized advice network.

Within-team-level network variables as mediators

At the within-team level, network mediators have been used to explain how differing network structures can explain distinct social processes. A notable research stream has identified team network density as a mediator for the relationship between transformational/charismatic leadership and outcomes such as team performance (Varella, Javidan, & Waldman, 2012; Zhang & Peterson, 2011) and perceptual consensus among team members (e.g., safety climate: Zohar & Tenne-Gazit, 2008). Other work focusing specifically on transactive memory systems (TMSs) as an outcome has examined team-level network characteristics as an intervening variable (e.g., Argote et al., 2018; Lee, Bachrach, & Lewis, 2014).

Within-team-level network variables as moderators

Within-team network variables were frequently introduced as cross-level moderators. For instance, dense advice networks enhance the chances of knowledge being transferred between team members who are not directly connected (Wei et al., 2011), suggesting that dense networks promote a cooperative team climate. Other research has shown that in dense teams, individual abilities and leadership-relevant characteristics were more likely to become salient (Serban et al., 2015), and creative synthesis of diverse information was more effective (Han, Han, & Brass, 2014). In contrast, research has primarily found that network centralization plays a weakening moderating role (e.g., Zhang & Peterson, 2011), which indicates that network centralization may cause information blockages and social fragmentation, which in turn attenuate the positive effects of density on team performance.

Within-team-level network variables as outcomes

When predictors of team network density and/or centralization have been examined, the tie content of the networks has varied considerably. These studies can be placed into two general categories. The first category encompasses studies that have examined how task characteristics and environmental context affect the structure of interaction networks (task complexity and time pressure; Brown & Miller, 2000; learning task complexity; Tirado-Morueta, Maraver-López, & Hernando-Gómez, 2017). Taken together, these studies show that task type exerts a significant influence on team collaboration structures and provided indirect support for the notion that centralized networks are an efficient structure for simple tasks, while dense networks are necessary for the intensive collaboration required of complex tasks (Shaw, 1964). The second category comprises studies that explored networks of perceptions within a team. As opposed to reflecting a specific type of relationship (e.g., friend, mentor, adversary) or interaction (e.g., task communication, advice seeking, problem solving), the network ties in this category captured the way in which team members perceive the characteristics of another (e.g., as a leader; DeRue et al., 2015; or fair; Liu, Hernandez, & Wang, 2014). Collectively, these studies show that the network structure of perceptual networks is often influenced by perceptions regarding the social dynamics of the overall team.

Synthesis of within-team-level constructs

In general, the research on within-team networks has explored multiple types of ties (e.g., advice, friendship, workflow, leadership) in a variety of contexts (e.g., teams in multiple industries in Asia, Europe, and North America). However, this variety has both positive and negative aspects. On one hand, it inspires confidence in the consistent findings that have emerged. For example, there is a general consensus that density of positive working relationships is positively related to team performance outcomes, suggesting that it is a robust signifier of beneficial team functioning (Parise & Rollag, 2010; Sparrowe et al., 2001). Empirical findings also suggest that transformational team leadership promotes density of advice network (Zhang & Peterson, 2011). On the other hand, such variety has likely made it difficult to isolate certain effects. For example, there is little consensus about the effects of centralization on team outcomes. As classic work by Bavelas (1950) suggests, it may be that task complexity plays a moderating role, with centralization leading to team performance only when tasks are low in complexity.

The effect of team diversity also appears to play a moderating role, though the nature of this effect is unclear at this stage. Identifying the boundary conditions that affect centralization is therefore an important endeavor that will help to clarify its relationship with team performance outcomes. In addition, despite its demonstrated effects on team performance, it is possible that team density may promote potentially dysfunctional behaviors that have yet to be fully explored, such as outgroup bias and insularity (Hansen, Mors, & Løvås, 2005; Oh et al., 2004). Although any conclusions about such a claim are premature at this stage, exploring potential downsides to team density will be a useful direction for future research. In sum, it is clear that implications of team network configurations, such as density or centralization, are contingent on tie content and context.

Between-Team-Level Constructs

We found the between-team construct level to be the least explored (9.2% of total). When it has been studied, researchers have largely focused on the advantage of nonredundant information that results when members create ties externally. No article in our review considered network variables as mediators at the between-team level.

Between-team-level network variables as antecedents

Researchers have found that team connections to a heterogeneous set of external groups positively impact group effectiveness as they provide access to nonredundant information (Oh et al., 2004; Wong, 2008b). Similarly, weak ties to units outside the team have been linked to decreased project completion time for low-complexity tasks (Hansen, 1999), and structural holes in the interteam co-membership network have been positively linked to team performance (Zaheer & Soda, 2009). Moreover, team leaders’ betweenness centrality in the interteam idea exchange network of peer leaders was found to be a positive antecedent of the radical creativity of team members (Venkataramani et al., 2014).

Between-team-level network variables as moderators

Research has explored how external ties affect the relationship between team characteristics and team success. For example, Shah, Dirks, and Chervany (2006) found that betweenness centrality in the interteam friendship network affected the relationship between internal friendship density and team performance only when density was low. This result suggests that teams low in internal friendship density are ill equipped for the information integration and coordination demands associated with interteam betweenness centrality. Elsewhere, Heavey and Simsek (2015) explored how environmental dynamism, operationalized as the average duration, frequency, and emotional intensity of external ties, moderates the relationship between top management team’s TMS and firm performance.

Between-team-level network variables as outcomes

Perhaps not surprisingly, member interaction networks were predicted by workflow interdependencies. That is, ties between teams tend to form when the nature of tasks demand it. In rare cases, teams fail to coordinate with one another despite having workflow interdependencies. Sosa, Eppinger, and Rowles (2004) examine the conditions under which such coordination failures occur. Some work has also examined how structural holes form in a between-team network. Research on networks of production teams in the television production industry over a 12-year period demonstrated that a team’s number of structural holes in the interteam network were significantly influenced by team members’ prior team affiliations (Zaheer & Soda, 2009). Specifically, teams with members who had prior affiliations to high-status and highly central teams had more structural holes.

Synthesis of between-team-level constructs

Although there has been relatively little work done on between-team networks, existing evidence suggests that external tie range—operationalized as weak ties, structural holes, or network heterogeneity—is positively related to team effectiveness outcomes. We would submit, however, that these findings have been found with teams for whom boundary spanning is a beneficial activity. Moreover, it appears that a certain amount of information integration capacity (reflected by high within-team density) is necessary for teams to capitalize on the resources that external ties provide (van Knippenberg, 2017). It also appears that work-related interdependencies are a reliable predictor of tie formation between teams. We know little, however, about the dynamics of team external ties. At the individual level, for example, personality has been shown to predict network churn (i.e., change to who one is connected to; Sasovova, Mehra, Borgatti, & Schippers, 2010), and network churn has been associated with employee performance (Burt & Merluzzi, 2016). Little is known about the outcomes or antecedents of network churn at the team level, though. For example, are the effects of structural holes on team effectiveness stronger if new ties are constantly being forged as old ties are dissolved? Likewise, little has been done on what mediates the relationship between external ties and team effectiveness. For instance, are informational resources the explanatory mechanism, or do external ties precipitate an emergent state in teams that explains the relationship?

Assessment of the Current Literature

In the past 25 years, a great deal of insightful work has been done on work teams with network perspectives. Our review revealed that network constructs are most frequently studied at the within-team level and as antecedents, whereas certain areas (e.g., between-team mediators, dyad moderators, dyad mediators) were relatively understudied. It is critical to note that employing a social network approach demands different theoretical assumptions and measures, which in turn necessitates new conceptualization and assessment of team phenomena (e.g., Carter, DeChurch, Braun, & Contractor, 2015; D’Innocenzo, Mathieu, & Kukenberger, 2016). Moreover, researchers should consider temporal aspects when addressing their research questions as the predictive power of network approaches may differ based on the team’s life cycle stage (e.g., Lin, Yang, Arya, Huang, & Li, 2005).

Our review also revealed a few notable opportunities across levels and research positions. First, researchers have largely examined team networks cross-sectionally.⁴ This is indeed a key limitation considering the long-acknowledged notion that teams undergo significant changes over the course of their life cycle (Marks, Mathieu, & Zaccaro, 2001). Dynamic social networks enable teams researchers to understand those changes with the coevolving actors and various structures in teams (G. Ahuja, Soda, & Zaheer, 2012). Second, it is notable that little of the research we reviewed examines tie multiplexity, or the existence of more than one type of relationship between two individuals. Given the likely prevalence of multiplex relationships in teams (Crawford & LePine, 2013), more attention is needed on multiplex relationships that may have unique effects on team processes and outcomes. Similarly, the network connections among different types of nodes within teams has received little attention despite the promise this notion holds for contemporary teams research. Finally, the network approach holds much promise for its ability to answer open questions about the unique ways in which organizations have been using teams in recent years. Modern teams are unique in that (a) individuals can be a member of more than one team simultaneously; (b) teams can be temporary, with new teams frequently being formed in organizations; and (c) teams are small organizational units that can interact closely with one another as interdependent “systems” that may simultaneously cooperate and compete with one another. Bearing in mind these three themes, the next section addresses research avenues that represent opportunities to advance teams research by taking a social network approach.

Advancing Work Teams Research With Network Thinking

In reviewing empirical work team studies across levels and positions of network constructs, we identified three avenues that are likely to advance teams research in unique ways. Specifically, we highlight (a) coevolutionary relationships in temporal dynamics, (b) complex webs of relationships in work teams, and (c) the fuzzy boundaries of work teams. The recommendations we offer in the next sections are meant to illustrate how network research on work teams can come closer to modeling the complexity that is a reality for modern teams. Table 3 summarizes research questions in need of future research attention.

Table 3

Promising Research Questions

Research Area	Questions
Coevolutionary relationships
Emergence	How do team member characteristics affect the emergence of trust networks within teams?
Reciprocal coevolution	How do various types of conflict ties coevolve between individuals over time? Do common third-party ties affect this evolution?
Network churn	Are the effects of structural holes on team effectiveness stronger if new ties in the between-team network are constantly being forged as old ties are dissolved?
Team adaptation	What team network structures are most beneficial for successfully adapting to various types of events?
Complex webs of relationships
Multiplexity	Is the negative effect of relationship conflict dependent on whether or not a workflow tie is present (i.e., are conflict ties detrimental only if they are multiplex with workflow ties)?How common is relational ambivalence (i.e., both positive and negative ties) within teams, and how does it influence both teams and individuals?
Multidimensionalnetworks	How does technological change within a team affect how members interact with technological artifacts, and how does this subsequently affect the network ties among individuals on a team? Do changes in interpersonal networks mediate the relationship between technological change and distal team outcomes?On innovation-focused teams, what is the optimal multidimensional network structure among team members (modeled as one type of node) and innovation roles (modeled as a second type of node)? Does the optimal structure change as the team moves between idea generation and idea implementation phases?
Fuzzy boundaries of work teams
Multiple-team membership	Do teams composed of members with many common team memberships (i.e., teams composed of relationally embedded members) have a performance advantage?Do individuals who are connected to multiple teams that each contain different individuals more likely to exhibit higher creative performance?Do teams that are composed of highly popular members (i.e., members with membership on many other teams) perform better?
Teaming and subgrouping	How important are prior social network connections to the success of teaming instances?How does teaming affect individuals’ social capital (i.e., network size, structural holes) over time? What are the ultimate effects of individual social capital growth that is driven by teaming?What outcomes accrue to individuals who broker between subgroups within a team?
Multiteam system (MTS)	How does a change in team power and resource allocation affect between-team MTS ties?Does an accurate perceptual map of the MTS workflow network aid in effective resource allocation?Does a difference in the internal network structure of two teams in an MTS affect the quality and stability of the between-team network tie? Do teams with similar internal network structures coordinate more effectively?

Opportunity 1: Coevolutionary Relationships

Time-based approaches to the understanding of network development and evolution may provide additional insights into dynamic team phenomena along with coevolving environmental pressures and challenges (Mathieu et al., 2019). Indeed, Mathieu and Luciano (2019: 167) submitted that “network measurements and conceptions liberate investigators to consider how the pattern of [group] relationships potentially evolve, solidify, transform, and dissipate over time (Borgatti & Foster, 2003)” and thereby are particularly well suited for representing dynamic changes in configural constructs. For instance, recently, leadership has been understood less as a hierarchy of authority or power and more as a complex interpersonal adaptive process that is socially constructed and driven by interactions within relations (DeRue, 2011). Scholars have adopted network perspectives to explore antecedents of leader emergence (Carnabuci, Emery, & Brinberg, 2018; DeRue et al., 2015; Kwok, Hanig, Brown, & Shen, 2018) as well as consequences of leadership structures (Mukherjee, 2016; Wang, Han, Fisher, & Pan, 2017). Future research can further explore whether a traditional vertical leadership structure can naturally transform into a structure where leader roles can be carried out by multiple individuals in teams (e.g., Fransen, Delvaux, Mesquita, & Van Puyenbroeck, 2018) and, if so, why multiple leaders can arise and how those structures impact team processes and outcomes differently than a solo leader structure.

One important methodological approach with the potential to yield new insights into how networks and teams evolve falls under the family of models known as stochastic actor-oriented models (SAOMs; Snijders, 2001; Snijders, van de Bunt, & Steglich, 2010). Within this modeling framework, researchers can simultaneously consider the dynamic influence of node attributes (e.g., individual differences; knowledge, skills, abilities, and other characteristics) and local processes (i.e., member interactions) on global structure (i.e., a team network). This approach provides the unique ability to isolate the effects of selection versus influence. That is, do individuals form ties with others because of shared similarities (selection), or do individuals with network ties grow increasingly similar over time (influence)?

Additional research is needed as to how employees’ perceptions coevolve together in a reciprocal manner within teams (Kalish et al., 2015; Schulte et al., 2012). Take interpersonal trust in teams as an example. Trust perceptions are formed via microprocesses where the interactions between trustors and trustees as well as their individual characteristics continuously influence the emergence and evolution of trust (Ferrin, Bligh, & Kohles, 2008). Dynamic network approaches to team trust will help to clarify issues such as how trust spreads among individuals within teams and how much trust ties between team members are affected when a team encounters difficulty (e.g., negative performance feedback). Similarly, changing conflict relations can be understood by investigating a series of behavioral actions and reactions. For instance, research has demonstrated that whether one type of conflict would be transformed into another type is affected by emotional processes (e.g., King & Emmons, 1990; Yang & Mossholder, 2004). Also, the manner in which conflict is expressed has a significant influence on the way it is interpreted and reacted to by others over time (Tsai & Bendersky, 2015; Weingart, Behfar, Bendersky, Todorova, & Jehn, 2015). Combining the two perspectives, one can investigate whether the expression of one type of conflict (e.g., task conflict) would be reciprocated in kind or spark another type of conflict (e.g., relationship conflict) over time between parties in teams. Moreover, the network approach enables researchers to examine whether contextual network conditions, such as shared third-party ties, affect the evolution of these conflict behaviors.

Another area of research that looks into temporal relationships in teams is the study of team adaptation from a network perspective. Given the increasing evidence that team adaptation is a critical element of team success (Maynard, Kennedy, & Sommer, 2015), examining how team networks adapt in response to disruptions is a line of research that could shed light on team effectiveness. For example, Stuart (2017) examined the network dynamics of professional hockey teams during games over the course of one season and found that teams that adapted by experimentally testing various team network configurations performed better than ones that did not engage in such experimentation. Additional longitudinal research that identifies how teams may structure themselves to most effectively respond to various types of events would help to advance our understanding of team adaptation. It may be, for example, that high-performing teams exhibit a low level of communication centralization with more flexible problem-solving strategy and information-processing paths when reacting to a novel and disruptive event. Future research can further investigate how different structural characteristics (e.g., structural holes) may be more suitable to teams in such disruptions.

Last, it is important to note that traditional measurement schemes, such as surveys, may not be a viable solution for studying these dynamic relationships. Having members of even small groups complete network surveys concerning a team phenomenon at multiple time points is a taxing exercise. Researchers should consider modern-day methodologies, such as wearable sensors (Pentland, 2007), computer-aided text analysis (Pollach, 2012) on digital communication traces, and emotional facial recognition (F. Liu & Maitlis, 2014) to index high frequencies of member interactions in a more systematic and less time-consuming manner. In terms of analytical tools, relational event modeling (Butts, 2008; Quintane, Conaldi, Tonellato, & Lomi, 2014) can be useful for researchers to take advantage of continuous streams of time-sequenced network data to model the dynamic effects of employee interactions within teams (e.g., Leenders, Contractor, & DeChurch, 2016).

Opportunity 2: Complex Webs of Relationships

Work teams are composed of complex webs of relationships that involve not only multiple types of relationships (addressed by multiplexity) but also multiple types of entities (addressed by multidimensionality). Network research that considers multiplexity and multidimensionality can help to capture the complexity of teams.

Multiplexity

Teams are complex adaptive systems that serve as a context for multiple types of relationships among members (Arrow et al., 2000). Although there is a growing appreciation for the fact that individuals in teams often have multifaceted relationships with one another (Crawford & LePine, 2013), few of the empirical studies in our review simultaneously consider multiple types of relationships. A multirelational network is a network that contains two or more relationship types (see Figure 2). One reason to examine multirelational networks is because it enables researchers to isolate tie multiplexity, which refers to the degree to which two or more types of ties exist between two actors (Agneessens & Skvoretz, 2012). That is, multiplexity is a dyadic phenomenon and the multirelational construct is at the network level. A multirelational network may therefore consist of uniplex ties (e.g., within the same team, members A and B share a friendship tie only, where members C and D share a conflict tie only) as well as multiplex ties (e.g., within the same team, members A, B, C, and D all share friendship and task ties). Multiplex ties can be qualitatively different from uniplex ties. For example, a uniplex advice tie between two individuals is likely to be less durable and trusting than a multiplex advice and friendship tie. The utilization of multiplex ties and multirelational networks will further enrich teams literature as teams are thought to be complex entities and not well understood via single-variable or single-network investigations.

Figure 2

Illustrations of Network Terminology

Although the number of possible multiplex ties is seemingly endless, we identify three specific forms of multiplexity that deserve additional attention. First, multiplex friendship and workflow ties have been shown to have both positive (e.g., affect) and negative (e.g., exhaustion) ramifications for individuals (Methot, LePine, Podsakoff, & Christian, 2016). It will therefore be useful for future research to examine whether the positive relationships between team workflow density and team potency (Tröster, Mehra, & van Knippenberg, 2014) and between workflow centrality and voice behavior (Venkataramani et al., 2016) hold when multiplex workflow ties are considered. Second, a multiplex consideration of conflict network ties in teams would be beneficial. For example, multiplex friendship and conflict ties are found to affect team performance differently than uniplex conflict ties (Hood, Cruz, & Bachrach, 2017). Furthermore, recent theorizing suggests that the effect of conflict ties will be mitigated to the extent that they do not overlap with workflow ties and that examining how workflow multiplexity affects conflict relationships within teams is a fruitful research avenue (Park, Mathieu, & Grosser, 2020). Finally, researchers should investigate how ambivalent relationships (e.g., positive and negative affect) are likely to occur in organizations (Methot, Melwani, & Rothman, 2017). Although research suggests that ambivalent ties may have both negative (e.g., stress) and positive (e.g., cognitive flexibility) effects (Methot et al., 2017), virtually no research has examined how these ties affect both teams and individuals. We therefore suggest that future research look at positive and negative affective tie multiplexity in order to examine the effect of relational ambivalence in teams.

Multidimensional networks

A multidimensional network view of teams involves acknowledging that teams are both multirelational and multimodal. A multimodal network includes more than one type of node. Most of the network research on teams examines one-mode networks, with people constituting the nodes. It is also the case, however, that other types of nodes interact within teams. For example, inanimate entities (e.g., databases, tools, computer programs) that facilitate team goal accomplishment can be included in a multimode network (Arrow et al., 2000; Contractor, Monge, & Leonardi, 2011). In a multidimensional network, people may interact with one another and with the inanimate entities, and there may be interactions among the inanimate entities themselves. Moreover, these various entities may be linked by multiple types of relationships. It is also the case that the multiple roles or tasks that individuals participate in can constitute unique types of nodes in a team network. For example, Contractor, DeChurch, Carson, Carter, and Keegan (2012) demonstrate how the interactions between people and various leadership roles can be examined via social network methodology to uncover unique insights about collective team leadership.

An example of a multidimensional network is depicted at the bottom of Figure 2. In it, multiple types of relationships exist (e.g., friendship, workflow), and there are multiple modes that constitute nodes in the network (e.g., people, roles, and machines). Modeling team networks in this fashion allows researchers to empirically study them as complex adaptive systems (Arrow et al., 2000). Although many scholars conceptualize teams in this way, few actually attempt to empirically model them as such (Ramos-Villagrasa, Marques-Quinteiro, Ramon, & Rico, 2018). Taking a dynamic multidimensional perspective is an especially powerful approach that can yield new insights into team processes, such as leadership (Carter et al., 2015), innovation, and decision making. By way of a brief example, we posit that such an approach can also yield insight into technological change within teams. Although it is clear that change in technology affects the processes and interpersonal relationships within a team (e.g., Barley, 1990; Burkhardt & Brass, 1990), the mechanisms accounting for this process have been difficult to systematically model quantitatively. Using a dynamic multidimensional approach, researchers can examine how technological change affects how team members (modeled as one set of nodes) relate to the technological systems (modeled as a second set of nodes) and whether this has a subsequent impact on the multiple relationships that exist in the team. For instance, Kirkman and Mathieu (2005) discussed both the nature (i.e., tie content) of interactions among members of virtual teams and the various tools and technologies (e.g., text, video conference, knowledge repositories) that they employed. Having a better model of how virtual team members and technology dynamically interrelate with one another will clarify the theoretical mechanisms behind effective change and will have useful practical implications.

Emerging developments in SAOMs are making this type of empirical approach possible (e.g., Snijders, Lomi, & Torló, 2013). However, a dynamic multidimensional approach to team networks means that researchers must be creative in how they gather data. The rich longitudinal data required by this approach are typically possible only via unobtrusive data collection approaches. Our review revealed that researchers are gathering this type of data by utilizing archival data collection techniques, including online data trace (e.g., email; Quintane, Pattison, Robins, & Mol, 2013; discussion forum; Tirado-Morueta et al., 2017), archival sports data (e.g., cricket; Mukherjee, 2016; National Basketball Association; Fonti & Maoret, 2016; Major League Baseball; Wang & Cotton, 2018), and online databases (e.g., patent and trademark; Singh & Fleming, 2010; movie database; Cattani & Ferriani, 2008). Moreover, aforementioned new and evolving measurement techniques (e.g., wearable sensors, computer-aided text analysis, and facial recognition technique) may permit more elaborate modeling of team networks than has been feasible in the past (Kozlowski, 2015; Luciano, Mathieu, Park, & Tannenbaum, 2018).

Opportunity 3: Fuzzy Boundaries of Work Teams

The traditional multilevel paradigm (Mathieu & Chen, 2011) assumes that lower-level entities (e.g., team members) are neatly nested in higher-level units (e.g., teams, organizations). Nevertheless, the tenor of more recent research on teams clearly suggests that the nature of collaboration and teamwork has changed (Wageman, Gardner, & Mortensen, 2012) and the distinction between teams is often blurred (Mortensen & Haas, 2018). Fuzzy boundaries of work teams warrant at least three distinct areas to explore further: (a) multiple-team membership, (b) teaming and subgrouping, and (c) MTSs.

Multiple-team membership

Contemporary teams tend to overlap, with members working simultaneously on more than one team (i.e., multiple team membership: O’Leary, Mortensen, & Woolley, 2011). For example, an individual can be involved in multiple project teams at the same time, with some of the team members overlapping. Conceptually, it becomes increasingly complex to understand phenomena at different levels because it is difficult to differentiate effects between and within teams. For instance, an individual’s motivation may stem from the anticipation of one team activity and spill over and motivate the person’s behavior on another team. In a similar way, one may experience negative conflict with members of one team and spread it to members of a second team via emotional contagion-type processes. Last, it might be impossible to get at clear team-level phenomena, as team states or processes cannot be entirely independent from the other teams.

The multilevel paradigm can accommodate some degree of multiteam memberships with analytic techniques such as cross-classified designs (Rapp & Mathieu, 2019) and random-effects models (Cafri, Hedeker, & Aarons, 2015), but they are cumbersome and become unwieldy with differing degrees of inclusion per grouping. As an example, a recent paper by Chen, Smith, Kirkman, Zhang, Lemoine, and Farh (2019) examined how empowering leadership exerted by two different team leaders influenced an employee’s psychological empowerment and subsequent proactive behaviors. Though insightful, the study was limited to two teams and not able to dissect the complex nesting arrangements with other team members. A key benefit of the networks approach is that it enables researchers to efficiently examine multiple-team memberships. With a network approach, one could examine individuals as one type of node in an organizational network and teams as a second type. This approach will allow researchers to pursue open questions pertaining to multiteam membership. For example, do teams that are composed of highly popular members (i.e., members with membership on many other teams) perform better?

Teaming and subgrouping

Another area where the social network approach can add value to teams research can be found in the concept of “teaming,” an active process of flexible gathering and disbanding of diverse employees as needed (Edmondson, 2012). Rather than conceiving of teams as being composed of members who are designated via formal channels (Mathieu & Chen, 2011), teaming would suggest that a natural occurring pattern of relationships among employees may constitute a “teaming instance” that may be very transient in nature. The network perspective will be beneficial in helping to address some of the pressing questions in this emerging line of research. For example, researchers have acknowledged that transient teams must be able to integrate diverse information quickly to be effective, yet we know little about how such teams overcome diversity and effectively synthesize information rapidly (Edmondson & Harvey, 2018). This question can be profitably tackled using network analysis to examine whether or not preexisting network ties among members of temporary cross-boundary teams contributes to their success. Teaming is also thought to be a way for individuals to quickly build social capital as they form ties with many others on the various temporary teams in which they reside. This may have a beneficial skill-building effect as individuals learn to function as boundary spanners (Edmondson & Harvey, 2018). Such rapid social capital growth may also have a detrimental effect, as individuals feel overloaded when faced with managing a large social network (Oldroyd & Morris, 2012). Exploring how teaming affects individuals’ social networks will therefore help to illuminate how teaming ultimately affects individuals in organizations. We should note, however, that such investigations will need to adopt high-frequency longitudinal methodologies to index dynamic network relationships over time.

Although teams research often examines teams as integrated units, it is also true that teams may be composed of subgroups (e.g., Carton & Cummings, 2012; Mathieu, Maynard, Rapp, & Gibson, 2008). While the idea of subgroups is by no means new in the teams literature, the tendency has been to infer cleavages within teams based on demographic profiles (i.e., fault lines) rather than directly identifying subgroups by observing relational patterns (see Reagans, Zuckerman, & McEvily, 2004). Network perspectives can add value to this literature by directly measuring relationships between and within subgroups. For example, Ren, Gray, and Harrison (2015) found that relational bridges of social ties between subgroups (also known as cliques) can change the relationship between compositional fault lines and team performance. Future research can further expand this to explore disproportionate influences within and between subgroups. In other words, not all subgroupings are likely to be equal, as some members may devote more time and energy to, or identify more with, some subgroupings or teams than others (Rapp & Mathieu, 2019). Therefore, social structures in a team can reveal various issues associated with reciprocal influence within and between subgroups (Thatcher & Patel, 2012).

MTSs

Teams are seldom stand-alone units but often are highly interdependent and influenced by actions and processes of other teams in the pursuit of shared superordinate goals in addition to their component team goals. Mathieu, Marks, and Zaccaro (2001) referred to this kind of structure as a “multiteam system,” which is becoming increasingly popular in management studies as researchers address issues regarding complex problems and rapid adaptation (Zaccaro, Marks, & DeChurch, 2012). But how can we study the synchronization of the effort and processes in a meaningful way? As a starter, by adopting recent developments in multilevel network analysis (Zappa & Lomi, 2015), one can conceptualize and examine the dynamism (i.e., the variability or instability) of MTSs over time (Luciano, DeChurch, & Mathieu, 2018). For example, it has been established that changes to the goal hierarchy frequently lead to changes in the power levels of teams in an MTS (Davison, Hollenbeck, Barnes, Sleesman, & Ilgen, 2012). The network approach will be helpful in examining how such power shifts affect the network of interteam relationships in an MTS, which will ultimately help researchers understand how teams can mitigate any negative effects of such power shifts. Efficient resource allocation among teams has also been identified as a practical problem in MTSs. Given that an accurate understanding of the structure of a social network can lead to more effective coordination (Enemark, McCubbins, & Weller, 2014), future research should explore whether providing an MTS with a “map” of the interteam workflow network might improve the efficiency and effectiveness of resource allocation among teams.

Conclusion

Organizational researchers have increasingly adopted network approaches to enrich our understanding of work teams as dynamic and complex systems. In this article, we reviewed the network-based work teams literature, selectively detailing what we currently know and what questions remain unanswered. Overall, our review suggests that social networks have a significant impact on teams. For example, the network position of individuals in teams and the overall network structure of teams affect outcomes at multiple levels of analysis. We then proposed an agenda for future research that focuses on how network approaches to teams can move work teams research forward by uniquely addressing open questions currently facing the field. We suggested that future scholars should better represent the coevolution of network relationships within and between teams, consider the fuzzy boundaries of teams, and model team phenomena in terms of multiplex or multidimensional networks. In order to effectively pursue these opportunities, we highlighted recent network methodologies that are available to researchers. In addition, we noted that researchers can leverage archival and digital trace data sources to effectively model the complex and dynamic relationships that exist in modern work teams. We believe that continued application of network thinking and methodology to the study teams holds great promise for advancing our understanding of organizational work team functioning and effectiveness.

Supplemental Material

JOM_R3_Online_Supplements – Supplemental material for Understanding Work Teams From a Network Perspective: A Review and Future Research Directions

Supplemental material, JOM_R3_Online_Supplements for Understanding Work Teams From a Network Perspective: A Review and Future Research Directions by Semin Park, Travis J. Grosser, Adam A. Roebuck and John E. Mathieu in Journal of Management

Footnotes

Supplemental material for this article is available with the manuscript on the JOM website.

ORCID iD

Semin Park

Notes

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