Team knowledge representation: a network perspective.

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Team Knowledge Representation: A Network Perspective J. Alberto Espinosa and Mark A. Clark Human Factors: The Journal of the Human Factors and Ergonomics Society published online 27 June 2013 DOI: 10.1177/0018720813494093 The online version of this article can be found at: http://hfs.sagepub.com/content/early/2013/06/26/0018720813494093

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HFSXXX10.1177/0018720813494093Month XXXX - Human FactorsTeam Knowledge Representation: A Network Perspective

Team Knowledge Representation: A Network Perspective J. Alberto Espinosa and Mark A. Clark, American University, Washington, D.C., USA

Objective: We propose a network perspective of team knowledge that offers both conceptual and methodological advantages, expanding explanatory value through representation and measurement of component structure and content. Background: Team knowledge has typically been conceptualized and measured with relatively simple aggregates, without fully accounting for differing knowledge configurations among team members. Teams with similar aggregate values of team knowledge may have very different team dynamics depending on how knowledge isolates, cliques, and densities are distributed across the team; which members are the most knowledgeable; who shares knowledge with whom; and how knowledge clusters are distributed. Method: We illustrate our proposed network approach through a sample of 57 teams, including how to compute, analyze, and visually represent team knowledge. Results: Team knowledge network structures (isolation, centrality) are associated with outcomes of, respectively, task coordination, strategy coordination, and the proportion of team knowledge cliques, all after controlling for shared team knowledge. Conclusion: Network analysis helps to represent, measure, and understand the relationship of team knowledge to outcomes of interest to team researchers, members, and managers. Our approach complements existing team knowledge measures. Application: Researchers and managers can apply network concepts and measures to help understand where team knowledge is held within a team and how this relational structure may influence team coordination, cohesion, and performance. Keywords: team knowledge, shared knowledge, shared cognition, network analysis

Address correspondence to J. Alberto Espinosa, American University, Kogod School of Business, 4400 Massachusetts Ave., N.W., Washington, D.C., 20016-8044, USA; alberto@ american.edu. HUMAN FACTORS Vol. XX, No. X, Month 2013, pp. 1–16 DOI: 10.1177/0018720813494093 Copyright © 2013, Human Factors and Ergonomics Society.

INTRODUCTION Team Knowledge Representation: A Network Perspective

What is the best way to represent the shared mental model of a team? For dyads or threemember teams where knowledge is evenly shared, this question can be readily answered by applying one of the many shared mental model measures in the extant literature. However, what if the team is large and members share knowledge unevenly across multiple domains? In these cases, measuring and representing team knowledge constructs is more complex and simple averages provide an incomplete picture. We propose network analysis methods, used successfully to study social structures, to model knowledge networks and provide a more nuanced understanding of team knowledge. Team knowledge is increasingly recognized as important for organizations in various activities, including research projects and patents (Wutchy, Jones, & Uzzi, 2007), collective behavior (DeChurch & Mesmer-Magnus, 2010), and coordination across systems (Majchrzak, Jarvenpaa, & Hollingshead, 2007). We define team knowledge as “the collection of task- and team-related knowledge held by teammates and their collective understanding of the current situation” (Cooke, Salas, Cannon-Bowers, & Stout, 2000), encompassing not only knowledge held in common, but also individual knowledge and knowledge relationships among members. Although teams possess knowledge structures beyond the simple aggregate of their members (Wooley, Chabris, Pentland, Hashmi, & Malone, 2010), current conceptualizations are primarily based on constructs such as group means or simple distribution, which do not provide nuanced insights into how team knowledge configurations affect outcomes. In this paper, we build on the emergence of social network methods, capturing these configurations, while retaining

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2 Month XXXX - Human Factors

valuable theory and methods used in extant team knowledge research. To illustrate this method, we conduct a simple empirical test of the effects of knowledge network variables on team outcomes. We argue that team knowledge is inherently a social construct—individuals share and exchange knowledge through communication and actions, creating cognitive relationships that help explain team dynamics, process, coordination, and performance. We posit that this exchange can best be represented as a network composed of team members’ knowledge content and relationships, relevant to a particular task. This approach allows the use of methods and tools developed to analyze complex relations in systems of social actors (Carley & Krackhardt, 1996), with team members’ individual knowledge as network nodes (Scott, 1991; Wasserman & Faust, 1994) representing team knowledge content. Links between nodes depict knowledge relationships, such as “shared” knowledge, describing how knowledge is held, organized, and distributed among members. This is similar in concept to individual knowledge structure discussed in cognitive sciences (Cooke et al., 2003; Mathieu, Goodwin, Heffner, Salas, & Cannon-Bowers, 2000; Rentsch & Klimoski, 2001). Such networks can be represented quantitatively (with sociomatrices) and visually (with sociograms). A strength of a network approach is that team knowledge distribution can be analyzed at any level—individual, dyad, subgroup, or team— helping to identify important characteristics such as centralities (e.g., proportion of knowledge ties to other members), isolates (e.g., members with no knowledge ties to other members), and cliques (i.e., subgroups fully interconnected with each other in a given knowledge domain), which can help detect a team’s ability to carry out tasks in ways aggregate team knowledge measures cannot (Tziner & Eden, 1985). This matters because members are knowledge exchange hubs, influencing how members coordinate information (Jarvenpaa & Majchrzak, 2009), which can provide useful knowledge to peers (Wasko & Faraj, 2005), and attain higher individual (Ahuja, Galletta, & Carley, 2003) and team (Huang & Cummings, 2011) performance. Understanding this is especially useful as teams

become larger, with more complex knowledge distributions (Majchrzak et al., 2007). Furthermore, popular theories successfully applied to social networks, such as “structural holes” (the lack of links between adjacent individuals; Burt, 1995) and “weak ties” (connections to those outside one’s closest members; Granovetter, 1973), can be investigated through team knowledge networks. The network perspective helps reconcile multiple representations of team knowledge in the extant research literature (Cannon-Bowers & Salas, 2001; Salas, Cook, & Rosen, 2008), whether shared (Rentsch & Klimoski, 2001), aggregated as team-level information (Lewis, 2003; Mathieu et al., 2000), or aggregated as a distribution across teams (Cooke, Salas, Kiekel, & Bell, 2004). These separate approaches have been useful to study links of team knowledge content to outcomes such as team performance (Cooke et al., 2003; Cramton, 2001; Nelson & Cooprider, 1996; Rentsch & Hall, 1994), whereas our network approach provides additional insights into how knowledge is structured internally. In the remaining sections of this paper, we describe team knowledge networks conceptually, then empirically illustrate our approach, including examples of network metrics. We conclude with implications and limitations. Team Knowledge Content and Relational Structure

Prior seminal research has employed individual and relational attributes to describe aspects of teamwork and interaction dynamics (Barley, 1986, 1990). Consistent with this, we view team knowledge as having two components: (a) content nodes, for each team member in each relevant knowledge domain (e.g., finance), and (b) relational links among these nodes representing member knowledge relationships in each task and team domain (Klimoski & Mohammed, 1994). Content nodes may represent any number of knowledge dimensions (e.g., task work, teamwork) or knowledge attributes (e.g., accuracy, fleetingness, explicitness; Clark & Espinosa, 2005). Relational knowledge, such as shared knowledge, influences how members interact, exchange knowledge


Team Knowledge Representation: A Network Perspective

(Carley, 1986), communicate (Cramton, 2001), coordinate (Cannon-Bowers, Salas, & Converse, 1993; Rico, Sánchez-Manzanares, Gil, & Gibson, 2008), and perform (Entin & Serfaty, 1999; Stout, Cannon-Bowers, Salas, & Milanovich, 1999). These knowledge relationships create complex team knowledge structures that cannot be explained with simple aggregation of the members’ knowledge (Cooke et al., 2003), more so as we incorporate knowledge attributes such as accuracy (Marks, Zaccaro, & Mathieu, 2000) and relevance. Although we believe that team knowledge per se is neutral with respect to these attributes (Cooke et al., 2000), it is relatively simple to add them into team knowledge network representations (Marks et al., 2000) and use network analysis methods to control for such attributes (Wasserman & Faust, 1994). The network perspective’s value is not just representing heterogeneous team knowledge structures beyond methods more suited to homogeneous knowledge (for further methods, see Cooke et al., 2000; Lewis, 2003), but also modeling dyadic knowledge relationships across multiple content areas in any way suitable to a particular research inquiry. For example, a dyad relationship could be modeled as knowledge similarity (e.g., shared mental model) or as distance or difference. Furthermore, when all the dyadic knowledge relationships are modeled into a single team knowledge network, this approach can be used to derive metrics of heterogeneity using popular measures such as Gini coefficients (Dorfman, 1979). A Network Approach to Team Knowledge Representation

Later we describe the network approach to team knowledge representation, illustrating it by testing simple hypotheses. It is important to note that in this section we are not building new theory or testing novel hypotheses but are demonstrating the added explanatory power provided by this approach when network measures are incorporated into the analysis. We offer network analysis to capture team knowledge represented by content nodes and relational links (Carley, 1997), with multiple layers, one for each knowledge domain represented. For example, one layer could represent

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the shared mental model for a particular task domain (e.g., marketing), another may represent transactive memory (i.e., knowledge of what other members know; Wegner, 1995), whereas yet another layer could represent shared member characteristics or network context (e.g., firm size and age in biotech coauthor networks in Demirkan, Deeds, & Demirkan, 2012). Multiple network layers of team knowledge may be used independently or be aggregated, depending on the specific research objectives. Our approach involves five steps: (a) identifying relevant knowledge domains for the study, (b) measuring knowledge content in each domain for each team member (nodes), (c) measuring relevant knowledge relations for every dyad in the team (links), (d) incorporating node and link measures into sociomatrices and sociograms (Scott, 1991; Wasserman & Faust, 1994), and (e) applying relevant network analyses to compute and visually represent network metrics (e.g., centralities, isolates, cliques). It is important to note that we don’t advocate any particular set of dimensions or specific measures, but argue that these must be tailored to the specific goals of each research study. Any appropriate measure of dyadic knowledge relationship can be effectively used with our approach. Sociomatrices are key quantitative artifacts used in network analysis, containing one row and one column for each team member, as illustrated in Figure 1 (for simplicity of illustration, all matrix elements are normalized to a 0–1 scale). “Off-diagonal” cells contain values corresponding to specific relationships (e.g., shared task knowledge) between the corresponding row and column members—for example, the value of the shared task knowledge between Members 5 and 6 is 0.84 in the top matrix. “Diagonal” cells contain values for individual knowledge content of the corresponding member—for example, the individual task knowledge of Member 2 in the top matrix is 0.57—and can be left blank when a study doesn’t call for content knowledge. Relational knowledge can be “symmetrical,” wherein the off-diagonal cells above the diagonal are the same as their corresponding cells below (e.g., Member A’s shared knowledge with B is the same as B’s with A), or it can be “asymmetrical” (e.g., A’s knowledge of B’s



Figure 1. An Illustration of sociomatrices and sociograms. For simplicity of illustration all the matrix elements are normalized to a 0–1 scale. Also, for better visual representation, sociograms are often “dichotomized” to show only links when their cell values are above a cutoff value of interest (e.g., median), which is a common practice to avoid cluttering the diagram with weak links (Scott, 1991), as shown in the “valued” diagram. The circles around the nodes represent recursive relationships, which are the respective knowledge attributes (i.e., diagonal value) of those nodes.

expertise is different from B’s knowledge of A’s). Sociograms depict these relations graphically as sets of content nodes with links representing relationships. Sociograms are “undirected” (no arrows) for symmetrical relationships (Figure 1a) or “directed” (arrows from the row member to the column member) for asymmetrical relationships (Figure 1b). Sociograms can be valued—with the link thickness representing the corresponding relationship value—or dichotomized—with a link drawn if the relationship value exceeds a particular threshold of interest, and omitted otherwise. Because valued diagrams can be dense

and confusing (Figure 1a, right diagram), network analysts often dichotomize network diagrams. For example, in the dichotomized graph in Figure 1a (left), a network tie is drawn if the shared knowledge exceeds a particular threshold value of interest (0.5 in this case, boldfaced) and no tie shown otherwise. As shown in Figure 1, dichotomized graphs are more useful in helping visualize and identify network structures. Finally, team knowledge can have one or many layers, depending on how many domains are represented. When multiple layers are represented in various sociomatrices, team knowledge can be depicted as a cube (see Figure 2). Each layer can



Figure 2. Illustration of computation and visual representation of shared task knowledge. The cubes are illustrations of how multidimensional networks can be represented. In this example, the team knowledge network has three layers, one for each task domain. The color in the cube represents the task domain, and the color density of the cube represents the strength of the knowledge relationship (i.e., darker cubes represent more share knowledge). The middle cube shows how layers (i.e., task domains) can be disaggregated to investigate shared knowledge in a particular task domain. The rightmost cube illustrates the network concept of a “slice,” which depicts all the knowledge relationships for one member with all other members for all task domains. Downloaded from hfs.sagepub.com at St Petersburg State University on November 21, 2013

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be modeled as a different variable in network analysis. Because team knowledge is inherently a social construct, network methods are ideally suited to represent team knowledge relationships. This allows for the computation of aggregate measures, while retaining the structural detail that composes knowledge in the team, which is particularly useful for larger teams. Network measures are also computationally simple, based on well-known methods that provide useful information such as “slices” (dyadic relationships across multiple dimensions), cliques, and so on. Figure 1a depicts two shared task knowledge cliques (3-5-6 and 2-5-6). Analyzing these cliques reveals interesting insights. For instance, Members 5 and 6 are in both cliques, and are the most knowledgeable members. Member 5 has higher “degree” centrality (more links than other members) and is the most knowledgeable member. Member 6 is highly knowledgeable and shares task knowledge with three other members. The directional arrows in Figure 1b show who knows which expertise other team members possess. For example, Member 6 has arrows pointing to all other members, indicating that 6 knows the expertise of every member. But only Members 2, 3, and 5 have arrows pointing to 6, indicating that these are the only ones who know Member 6’s expertise. In contrast, everyone but 1 knows Member 5’s expertise, but 5 knows only the expertise of 1 and 6. Such interesting insights into the team’s knowledge structure are not possible with pure aggregate measures. Illustrating Network Analysis of Team Knowledge: Shared Task Knowledge, Network Structure, and Team Outcomes

This section is not intended as new theory testing. Rather, we test well-explored theoretical relationships among constructs of shared knowledge, cohesion, and coordination to illustrate the viability of the network approach in providing additional explanatory power beyond that provided by simple aggregate or average measures. Illustration hypotheses and conceptual model. We offer a model (Figure 3a) with relationships generally accepted in the academic

literature: teams as information processors (Hinsz, Tindale, & Vollrath, 1997) within an input–process–output framework (Salas, Stagl, Burke, & Goodwin, 2007), leading to varied team outcomes (cf. Hackman, 1987). More specifically, we model shared task knowledge as an antecedent of team coordination (CannonBowers et al., 1993; Espinosa, Slaughter, Kraut, & Herbsleb, 2007), which in turn affects team outcomes (Mathieu et al., 2000; Mohammed & Dumville, 2001; Stout et al., 1999; Tung & Chang, 2011). We define “shared task knowledge” as the degree to which members hold task-related knowledge in common; “task coordination” as how members work together to accomplish their objectives—that is, coordination process; “strategy coordination” as the accord of the team’s functional strategies—that is, coordination outcome; and team cohesion as feelings of unity and belongingness within the group (cf. Beal, Cohen, Burke, & McLendon, 2003). We formulate hypotheses for the relationships of shared task knowledge to task coordination, strategic coordination, and cohesion to contrast with subsequent hypotheses testing network measures. Because the positive effect of coordination on team performance is established in the literature (Hoegl, Weinkauf, & Gemuenden, 2004), we do not create formal hypotheses for it. A team’s shared task knowledge has positive relationships with cohesion (Mathieu et al., 2000; Tung & Chang, 2011), task coordination, and strategy coordination. Hypothesis 1: The team’s average shared task knowledge is positively associated with (a) task coordination, (b) strategy coordination, and (c) team cohesion.

A team’s ability to use its shared task knowledge will be influenced by how this knowledge is distributed among members. We posit that the degree of members’ knowledge isolation will impede the team’s ability to coordinate its task efforts. When some members hold knowledge not available to others, it is difficult to involve all who are needed to complete the task. Shared knowledge helps teams coordinate because it enables more accurate expectations and explanations about the task, which helps members



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Figure 3. Research models and supported hypotheses.

plan their activities in harmony with the rest of the team (Cannon-Bowers et al., 1993). It follows that when some members don’t share any knowledge with other members, this leads to coordination problems. Therefore, the more knowledge isolates in the team, the less coordinated their task activities. Hypothesis 2: Controlling for the team’s average shared task knowledge, shared task knowledge isolation is negatively associated with task coordination.

Conversely, centrally knowledgeable team members are hubs through which strategic aspects of the task are filtered and exchanged, thus reducing potential inefficiencies and cognitive costs from redundant knowledge overlaps, which could lead to other problems such as groupthink and limited external learning (Cohen & Levinthal, 1990; Hambrick, Cho, & Chen, 1996; Williams & O’Reilly, 1998). A knowledgeable team member who can organize and share relevant information with others can make a difference on the team. Conversely, widespread knowledge distribution

among members can be dysfunctional because there may be too many diverse approaches to the team’s strategy. Team knowledge centrality is therefore a more proximal representation of how knowledge is distributed and available for use in coordinating strategic efforts, compared to aggregate shared knowledge. Therefore, we expect that shared task knowledge centrality will direct team actions into well-coordinated strategies. Hypothesis 3: Controlling for the team’s average shared task knowledge, the centrality of shared task knowledge is positively associated with strategy coordination.

The presence of subgroup partitions (cliques) in a team’s knowledge structure can undercut unity through exclusion of those outside each clique, which will reduce member perceptions of the team as a cohesive whole. Hypothesis 4: Controlling for the team’s average shared task knowledge, the proportion of shared task knowledge cliques is negatively associated with team cohesion.


8 Month XXXX - Human Factors TABLE 1: Descriptive Statistics and Correlation Matrix Variable

M

1. Firm performance 2. Team cohesion 3. Strategy coordination 4. Task coordination 5. Perceived substitutability 6. Shared task knowledge 7. Degree of isolation 8. Proportion of task cliques 9. Team knowledge centrality 10. Communication frequency

0.00 5.76 5.41 5.67 4.99 0.54 0.17 0.13 0.12 4.19

SD

1

2

0.92 0.85 .12 0.72 .29** .63** 0.66 .02 .65** 0.62 .20* .62** 0.10 .05 .35** 0.26 –.08 –.29** 0.19 .09 .26** 0.11 .15 .35** 0.72 –.19 .04

3

4

.57** .89** .53** .52** .32** –.37** –.39** .32** .26** .42** .24** .24** .02

5

6

7

8

9

.55** –.33** –.38** .39** .62** –.39** .47** .37** –.40** .35** .28** .53** –.11 .19* .08

*p < .05. **p < .01.

METHOD Setting and Sample

The data were drawn from 57 teams of four to six students engaged in a graduate-level 10-week management simulation at a Midwestern university. Most participants did not know each other beforehand and formed their own teams. Each team managed a simulated firm, competing against each other by formulating strategies based on more than 70 decision variables entered into a simulation model in 14 consecutive quarters, yielding quarterly financial performance results, typical of real companies. We collected data during the simulation from (a) student questionnaires at three time periods (the average response rate was more than 80%, and we retained data for teams with three or more responses) and (b) objective team financial performance. Measures. Our survey items and variables are listed in Appendix A; the descriptive statistics and correlation matrix are in Table 1. We constructed an aggregate shared task knowledge measure using responses to peer ratings of each other’s knowledge in specific task domains (i.e., financial, production, and marketing management of the team’s simulated companies). Each member rated others’ knowledge about their company’s management in each task domain. We computed their knowledge similarity as a measure of knowledge overlap, first calculating the average knowledge rating for each member in each task domain, then deriving a

measure of knowledge overlap for each dyad in each task domain as the lowest knowledge rating of the two members (i.e., the knowledge overlap between any two members is no greater than the knowledge of the least knowledgeable member). We normalized all measures to a 0 to 1 scale by dividing the respective rating averages by the scale range used to measure task knowledge. We evaluated the validity of this measure in Appendix B. It is important to note that we are not measuring team members’ knowledge in these domains directly, but the members’ evaluation of what other members know. We kept our measure relatively simple to illustrate our approach, but more sophisticated measures of dyadic knowledge relationships can be substituted. The team’s strategy coordination was assessed as the average response to six questionnaire items (α = .84) that requested members’ perceptions of the coordination of their functional strategies. Task coordination was measured with five questionnaire items (α = .71) that asked about how members worked together. The team performance variable was constructed as an average of normalized z scores of three key objective financial performance indicators of the firm yielded by the game simulation—firm price, profits, and rates of return on investment (α = .90)—which are the three financial performance components used for the final course grade. Illustrating dyads, sociomatrices, and sociograms. To compute our network variables, we first measured shared task knowledge for each



dyad in a team. We then used these values to create sociomatrices and sociograms representing the team knowledge network. Figure 2 illustrates this computation and visual representation for a six-member team with three task knowledge areas: financial management, production, and marketing. Relational links were drawn if the value met the midpoint of the 7-point scale labeled “average” expertise in the rating scale. Typically such cutoff values are set at a value with meaning for the research, such as a particular percentile important in the relationship, and are often varied in network analysis to better understand how strong, medium, or weak ties affect the results. With this information we created three shared task knowledge (STK) network layers, each corresponding to a focal task in the simulation. Interesting insights in Figure 2 include the sparsity of the STKFINANCE network, with only Members 5 and 6 sharing substantial task knowledge, reducing the likelihood of full group discussions about finance. The STKPRODUCTION network shows a fair amount of task knowledge shared by some members only. In contrast, STKMARKETING yields a fully connected network, providing evidence of shared knowledge and common ground for marketing discussions. We also see that Member 1 is isolated from the aggregate STK, whereas 5 and 6 have the highest amounts of task knowledge shared (i.e., high knowledge centrality) with others across all task areas. This information could be valuable when analyzing decision flow or team leadership factors. Illustrative analysis of network measures. To evaluate the additional explanatory power of network measures beyond aggregate knowledge measures, we constructed a hierarchical regression model with shared task knowledge modeled as an antecedent to task and strategy coordination. We then entered the network variables: (a) degree of isolation—the number of members not sharing financial, production, or marketing task knowledge, divided by team size; (b) proportion of task cliques—the number of three-member cliques in each of the three task areas, divided by the maximum number of three-member cliques possible; and (c) team knowledge centrality—the standard deviation of the degree centrality (i.e., shared knowledge

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concentration) of all members in all areas. (Degree centrality for one member is computed as the number of links that the member has to other members, divided by the total number of members [minus one]. The standard deviation of these measures provides an indication of whether knowledge is widely shared [i.e., high dispersion] or concentrated in one or more centrally knowledgeable members [i.e., low dispersion].) The coordination variables were then entered into the model as antecedents to team cohesion and team performance. To test all paths of the model, team knowledge variables were also included as antecedents of the outcome variables. We modeled general task activity coordination (i.e., process)—as an antecedent of strategy coordination (i.e., outcome), analyzing the measurement model for survey variables using factor analysis with varimax rotation. The survey items and factor loadings presented in Appendix A show acceptable reliability factors greater than .70 and factor loadings grouping items into the study variables as expected. RESULTS

We used a fixed effects model, which is appropriate with panel data (i.e., three survey waves). Our results (Table 2 and Figure 3) show that, consistent with Hypothesis 1, without accounting for network variables, shared task knowledge was a significant predictor of task coordination, strategy coordination, and team cohesion, but had no effect on firm performance. When the network variables were entered, we used an F test to evaluate if the network variables increased the R-squared significantly. We found that the predictive power of the models with the network variables included was significantly higher than the models without the network variables, in all three regression models—task coordination (R2 change = .07, p = .007), strategy coordination (R2 change = .05, p = .004), and team cohesion (R2 change = .03, p = .018)—indicating that network variables provide additional explanatory power. Furthermore, the effects of shared task knowledge became nonsignificant in both coordination models, degree of isolation became significant in the task coordination model, team knowledge centrality became significant in the strategy


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Sig

Coef

Sig

Coef

Sig Coef Sig Coef Sig

Strategy Coordination Coef

Sig

Coef

Team Cohesion Sig

Coef

Sig Coef Sig

Firm Performance

156

156

153 .21 .07 .01

156 0.14

.599

0.45

155

.5 .05 .00

152

.55

154

.58 .03 .02

151

.13

99a

.144 .015 .647

96a

0.56 .578

0.49 .330

1.53 .001

0.27

0.86 .248

−0.80 .030

0.01 .977

−0.10 .798

0.04 .925

0.50 .023

−0.17 .384

−0.73 .001

0.64 .000 2.93 .000 1.68

Note. Boldface indicates p < .05. Italics indicate p < .10. a The degrees of freedom for firm performance are lower because there is not financial data for T1 at the beginning of the game simulation.

156

.000 1.88 .000

.000 0.53 .000 −0.34 .045 −0.34 .053 .030 3.37 .001 −0.46 .685 –2.22 .242

Coef

Task Coordination

0.50 .000 0.46 .000 0.49 .115 2.38 .000 1.20 .196 1.43

Sig

Centrality

.007 −0.59 .000 .237 −0.17 .141 −0.11 .552 −0.10 .591 .000 0.46 .000 0.64 .000 0.63 .001

Coef

Cliques

.000 −0.23 .000 −0.07 .001 −0.35 .010 −0.20 .247 0.08 .518 0.22 .142 −0.35 .207 −0.01 .826 −0.01 .565 0.14 .250 0.18 .125 0.07 .525 0.10 .339 −0.14 0.47

Coef Sig

T1 to T2 0.22 T2 to T3 0.06 Strategy coordination Task coordination Shared task –1.72 knowledge Degree of isolation Proportion of task cliques Team knowledge centrality Degrees of freedom R2 R2 increase p value of R2 increase

Variable

Isolation

TABLE 2: Regression Analysis Results With Standardized Beta Coefficients


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coordination model, and the proportion of task cliques became significant in the team cohesion model. The degree of task knowledge isolation had a negative association with task coordination (β = −0.73, p = .001), supporting Hypothesis 2. Team knowledge centrality had a positive association with strategy coordination (β = 1.53, p = .001), supporting Hypothesis 3. Hypothesis 4 was also supported, as the proportion of team knowledge cliques was negatively associated with team cohesion (β = −0.80, p = .030). Task coordination influenced strategy coordination (β = 0.46, p < .001) and strategy coordination in turn affected both team cohesion (β = 0.46, p < .001) and firm performance (β = 0.63, p = .001), whereas shared task knowledge affected only team cohesion. It is interesting to note that, surprisingly, the degree of isolation also had a positive effect on team cohesion (β = 0.50, p = .023). As this test is an illustration of the network method, rather than strictly theory testing, we do not conduct formal tests of mediation by network measures in the relationships of shared task knowledge to firm performance. However, we also note that this relationship is not significant, negating the need for further mediation tests.

Figure 4. Illustration of Shared Task Knowledge Networks for High-Performance Teams

DISCUSSION

Figure 5. Illustration of Shared Task Knowledge Networks for Low-Performance Teams

Our illustration shows that network variables add to our understanding of how knowledge operates within a team, influencing coordination, cohesion, and, indirectly, team performance. It is important to note that we used a particular measure of knowledge overlap only as an illustration of how to take dyadic relational knowledge measures and incorporate them into a network. Any other relational knowledge measure of interest can also be used with this approach. Although shared knowledge has been represented through a variety of measures and methods in the past— for example, task relatedness matrices (Cooke et al., 2003), quadratic assignment procedure (Mathieu et al., 2000), and schema agreement (Rentsch & Klimoski, 2001)—our approach allows the incorporation of any of these measures into a team knowledge network that includes all dyads, allowing not only for richer analysis and more nuanced explanations of team processes

and outcomes but also for aggregation into more general measures. Without a network perspective, aggregate measures provide an incomplete picture of a team’s knowledge structure, especially for larger teams. To illustrate this, Figures 4 and 5 depict the shared task knowledge networks for two highperformance and two low-performance teams. The aggregate shared task knowledge networks of both high-performance teams are dense, but the individual task domain knowledge networks tell different stories. Team 1 has four members fully connected on their financial knowledge. It also has four members fully connected on their production knowledge, but including Member 4 rather than 1. Only three members are fully connected on their marketing knowledge, including 5, who does not share knowledge with others in either finance or



production. Finally, 3 is the only member who shares knowledge with others in all task areas, making the team vulnerable to negative consequences of member turnover. On the other hand, Team 2 has more widespread shared knowledge about finance and marketing. Furthermore, since Members 5 and 3 share knowledge in all task areas, the team has some level of knowledge redundancy, making it less vulnerable to member turnover. In contrast, the low-performing teams have more disconnected shared task knowledge. Notice that although Team 3 has a three-way clique in finance and marketing, there are no dyads who share knowledge in all task areas, which may account for the low performance of this team. In contrast, Team 4 has a three-way clique in finance and only two members connected through production issues, but it has a fully connected knowledge network on marketing. This team’s low performance may have been due to an excessive focus on marketing, perhaps neglecting finance and production issues. Such explanations are not possible with aggregate measures alone, underscoring the importance of the network approach. Awareness of a team’s knowledge configuration of knowledge can also be helpful to leaders when assigning tasks and influencing members (Balkundi & Kilduff, 2006) and in understanding team behavior and

motivation, and performance (DeChurch & Mesmer-Magnus, 2010; Rico et al., 2008). LIMITATIONS AND IMPLICATIONS

Our study used self-report questionnaire data, although we reduced the potential response bias by using a mix of dyadic, team-level, and objective performance data. Another limitation is that some team knowledge content measures may be difficult to collect. Despite these limitations, we believe that this paper makes important contributions to team cognition research, in that it leverages the power of network analysis theories and methods; builds on strengths of current team cognition measures; is computationally simple; can be used at both aggregate and detail levels; incorporates both individual and relational knowledge attributes, providing a complete picture of the team’s knowledge; allows the computation and visual representation of the team’s knowledge with multiple dimensions; and provides a richer explanation of how different structural aspects of team knowledge affect team outcomes. Our preliminary evidence confirms that this network perspective adds explanatory value. Further development and testing of network methods to study team knowledge are still necessary, but our study shows some promise to inform research and practice how team knowledge can influence performance.



13

APPENDIX A Questionnaire Items for Study Variables Factor Analysis Component Matrix Component Survey Item

1

Perceived substitutability (Cronbach’s α = .75) Members of my team know a lot about each others’ .759 areas of expertise (e.g., marketing, finance, production). Members of my team don’t know much about the tasks .748 others are working on. If a member of my team couldn’t finish his/her tasks, the .710 rest of us know enough to take over. Strategy coordination (Cronbach’s α = .84) My team has a clear idea of what our financial strategy .362 should be. My team has a clear idea of what our marketing strategy .082 should be. My team has a clear idea of what our production strategy .260 should be. Members of my team have a clear idea of our team’s goals. .195 My team knew exactly what it had to get done in order to .030 succeed in the game. Members of my team fully understand how competitors’ .168 actions will impact our performance. Task coordination (Cronbach’s α = .71) Members of my team often disagreed about who should be .258 doing what task. Members of my team did their jobs without getting in each .011 others’ way. Members of my team often duplicated each others’ work. –.113 Tasks were clearly assigned to specific team members. .155 My team wasted a lot of time. .006 Team cohesion (Cronbach’s α = .86) I felt I was really part of my team. .257 I am very satisfied with my team. .238 I looked forward to being with my team. .076 I’m extremely glad I got this team of people to work with. .211

2

3

4

.287

–.084

.175

.172

.161

.161

.177

.071

.246

.669

.043

.075

.729

.161

.281

.688

.081

–.004

.559 .775

.262 .079

.467 .263

.619

.202

.255

.011

.698

.077

.112

.551

.333

.098 .402 .247

.776 .511 .554

–.012 .234 .372

.172 .273 .181 .160

.172 .208 .085 .163

.666 .796 .766 .843

Note. Items loading into factor groupings shown in boldface.


14 Month XXXX - Human Factors APPENDIX B Questionnaire Items for Study Variables

Figure B1. Shared task knowledge over time. Shaded regions represent quartiles above and below the median, whereas the lines represent the 25th and 75th percentiles.

Evaluation of shared task knowledge measure. Before we conducted the analysis we evaluated the validity of the measure we used for the team’s average shared task knowledge measure. Measures achieve convergent validity when their values move in the expected direction and when they actually measure the characteristics we intend to measure (Ghiselli, Campbell, & Zedeck, 1981). Because shared knowledge of the task is expected to develop through working together over time (CannonBowers et al., 1993), we tested for knowledge increase as the task progressed chronologically. Results are shown in the box plots in Figure B1. Shared task knowledge increased steadily and significantly over the task period (F = 50.902, p < .001), providing some evidence of convergent validity. Next, we checked for convergence of shared task knowledge with communication frequency, which is expected to lead to more task domain knowledge exchange. The within-team average of self-reported communication frequency (a 1–6 Likert-type scale) had a positive significant correlation with shared knowledge of the task (r = .53, p < .001). We then tested for convergence of shared task knowledge with perceived member substitutability, based on the expectation

that members of teams with stronger shared task knowledge would perceive more overlapping knowledge with others and could, therefore, substitute for each other more easily. Member substitutability was measured as the average response to three questionnaire items (Appendix A, α = .75) that asked the member’s perceptions of task knowledge overlap and member substitutability. This measure was positively correlated to shared task knowledge (r = .55, p < .001), supporting convergence. Following Ghiselli et al. (1981), we tested preliminary concurrent validity by exploring the correlation of shared task knowledge with process variables of “appropriate team strategy” and “task coordination” (Klimoski & Mohammed, 1994). Overall, we found a significant positive correlation of shared task knowledge with both strategy coordination (r = .59, p < .001) and task coordination (r = .40, p < .001). Finally, strategy coordination had a significant positive correlation with the firm’s performance reported in the simulated financial statements (r = .29, p < .001), suggesting an indirect association between shared knowledge and team performance, which provides some empirical validation for our measures. KEY POINTS •• Our research takes advantage of the powerful analytical insights that can be gained from network analysis theories, methods, and tools. •• Our research also builds on strengths of current measures. •• Our research provides computationally simple methods to study team knowledge in the sense that no specialized statistical or network analysis software is necessary and any of the current and popular network analysis tools can be employed. •• Our methods can be applied to study team knowledge at the highest aggregate level, or at any sublevel of detail (members, dyads, slices or cliques), and across multiple team knowledge dimensions of interest. •• Our methods allow us to model individual knowledge attributes that describe content dimensions along with relational attributes that describe structure dimensions, thus providing a more complete picture of the team’s knowledge. •• Our methods provide for the computation and visual representation of various dimensions of team knowl-


Team Knowledge Representation: A Network Perspective edge, helping better understand how this knowledge is distributed in the team, thus providing richer and more nuanced explanations of how the distribution of knowledge within a team influences performance and its antecedents.

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J. Alberto Espinosa is a professor of information technology and Kogod Research Professor with the Kogod School of Business, American University. He earned his PhD in information systems from the Tepper School of Business at Carnegie Mellon University. His research focuses on coordination and performance in global technical projects across global boundaries, particularly spatial and temporal distance. His current research areas include coordination of technical work across time zones and coordination in large-scale technical collaboration tasks like enterprise architecture. His work has been published in Management Science, Organization Science, Information Systems Research, Journal of Management Information Systems, IEEE Transactions on Engineering Management, and Communications of the ACM, among other outlets, and has been presented internationally to both academic and business audiences. Mark A. Clark is an associate professor of business management with the Kogod School of Business, American University in Washington, D.C. His research centers on team performance processes and contexts, including the effects of knowledge, diversity, culture, and strategic human capital practices. His work has appeared in Group Dynamics, Human Resource Management, Academy of Management Journal, and Journal of Applied Psychology, among other outlets, and has been presented internationally to both academic and business audiences. His background includes experience as a group treatment specialist, trainer, community development program administrator, and consultant. He earned his PhD from Arizona State University. Date received: December 11, 2011 Date accepted: May 6, 2013


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