Equipe-article01

Social Networks 25 (2003) 197–210

Structural properties of work groups and theirconsequences for performance�

Jonathon N. Cummingsa,∗, Rob Crossb,1

a Sloan School of Management, Massachusetts Institute of Technology,77 Massachusetts Avenue, Cambridge, MA 02139-4307, USA

b McIntire School of Commerce, University of Virginia, Virginia, USA

Abstract

Over the past several decades, social network research has favored either ego-centric (e.g. em-ployee) or bounded networks (e.g. organization) as the primary unit of analysis. This paper revital-izes a focus on the work group, which includes structural properties of both its individual membersand the collection as a whole. In a study of 182 work groups in a global organization, we found thatstructural holes of leaders within groups as well as core-periphery and hierarchical group structureswere negatively associated with performance. We show that these effects hold even after controllingfor mean levels of group communication, and discuss implications for the future of network analysisin work groups and informal organizations.© 2003 Elsevier Science B.V. All rights reserved.

JEL classification: D23–organizational behavior

Keywords: Work groups; Communication networks; Team performance; Core-periphery; Structural holes

1. Introduction

Despite a tremendous increase in the use of work groups in organizations over the pastseveral decades (Guzzo and Salas, 1995; Hackman, 1990; Sundstrom, 1999), there has beenrelatively little social network research on the structural properties of natural work groupsand their consequences for performance (for some exceptions, seeSparrowe et al., 2001;Reagans and Zuckerman, 2001). Rather, social network scholars have tended to focus on

� An earlier version of this paper was presented at the 22nd Annual International Sunbelt Social Network Con-ference, 13–17 February 2002, New Orleans, LA. This research was supported by the Knowledge and DistributedIntelligence Program of the National Science Foundation (#IIS-9872996).

∗ Corresponding author. Tel.:+1-617-452-3582.E-mail addresses: [email protected] (J.N. Cummings), [email protected] (R. Cross).

1 Tel.: +1-434-924-6475.

0378-8733/03/$ – see front matter © 2003 Elsevier Science B.V. All rights reserved.doi:10.1016/S0378-8733(02)00049-7

198 J.N. Cummings, R. Cross / Social Networks 25 (2003) 197–210

structural properties of ego-centric networks (e.g.Burt, 1992; Podolny and Baron, 1997)or bounded networks within an organization (e.g.Brass, 1984; Ibarra and Andrews, 1993).While early laboratory-based studies (Bavelas, 1950; Shaw, 1964) provide a nice frameworkfor assessing the impact of group communication structure on collective performance out-comes, field research is needed to revisit which structures have meaningful consequencesfor performance in real organizations.

Prior work on small groups in laboratory settings has implications for groups in naturalenvironments, such as the patterns of communication to expect when particular membersare not allowed to interact with other members. However, generalizing these findings tofield settings can be limited due to different time horizons for executing tasks, the physicallocation of different workers, or the ambiguous nature of problems requiring solutions. Forexample, laboratory studies have suggested that some network structures are more effectivethan others for diffusing information throughout a group. Unfortunately, these groups oftenwork to solve a problem pre-defined by the researcher that establishes the correct path ofdiffused information. In field settings, information flow is emergent and depends on theskills and expertise distributed within a group. Thus while certain group structures mightbe effective for diffusion, they might not be effective for leveraging distributed expertise ofa work group in an organizational setting.

Given the increased prevalence of group work in organizations today, it is importantto re-examine the relationship between structural properties of work groups and perfor-mance. We address this linkage with 182 work groups in a global organization (Cummings,2001) and suggest next steps for social network research on work groups and informalorganizations.

1.1. Work groups and social networks

The study of work groups has enjoyed a long history in psychology and sociology. Psy-chologists such as Elton Mayo conducted experiments to explore the impact of differentwork conditions on group productivity (Mayo, 1933), while sociologists such as Freed Balesaimed to understand task and social processes in groups (Bales, 1950). As for the examina-tion of network structures in groups, a number of early scholars focused on the associationbetween different communication patterns and performance (Bavelas, 1950; Guetzkow andSimon, 1955; Leavitt, 1951; Shaw, 1964). For example,Leavitt (1951)andGuetzkow andSimon (1955), building on work byBavelas (1950), showed that “communication nets” withcentralized structures (e.g. wheel) improved the diffusion of information insimple taskswhile decentralized structures (e.g. circle) delayed the diffusion of information. However,Shaw (1964)reviewed research demonstrating that groups with decentralized communica-tion nets took less time to finishcomplex tasks than groups with centralized communicationnets.

Researchers have also used sociometry, or the quantitative representation of interpersonalrelationships, to describe group structures (Moreno, 1960; Sorensen, 1971). Yet despite theprospects for synergy between research on work groups and social networks, there have beensurprisingly few field studies over the past several decades. One challenge facing researchersis that developing insight into the relationship between group structure and performance inthe field requires attending to the characteristics of the work itself (Hansen, 1999). We have

J.N. Cummings, R. Cross / Social Networks 25 (2003) 197–210 199

specifically focused here on project-based work that can be characterized as non-routineand complex. Such initiatives may entail new product development, internal improvementinitiatives or other one-time projects critical to an organization. This kind of work oftenrequires effective coordination and integration of ideas, thus making the communicationstructure of the group particularly important for task performance.

Our primary expectation is that more integrative structures should be related to higherperformance, even after controlling for mean levels of communication. Prior work hassuggested that complex, non-routine tasks require more information processing than simple,routine tasks (Daft and Macintosh, 1981). From this perspective, we suggest that structuralproperties of work groups that inhibit lateral communication or integration of expertisewill negatively affect performance. Two streams of research suggest that an integratedcommunication structure would be important for group performance. First, in terms of taskinterdependence, one would expect to see a greater degree of coordination among successfulgroups working on non-routine and complex tasks (Tushman, 1979; Van de Ven et al., 1976).

Second, from a cognitive perspective, research suggests that more integrative structuresmay be effective for leveraging expertise of a group. For example, studies of transactivememory have demonstrated that groups benefit from accurate knowledge of who knowswhat (Liang et al., 1995; Moreland and Myaskovsky, 2000; Rulke and Galaskiewicz, 2000).Similarly, work in diffusion would suggest that longer path lengths are both inefficient andresult in the degradation of information quality (Rogers, 1995; Valente, 1995).

We are not simply suggesting that increased connections among members will improveperformance, but rather that certain network structures will be related to performance. Weinclude a control for the mean level of group communication in our hypotheses below toexplicitly counter the idea that a maximum number of connections among members canexplain away the importance of structure. Specifically, we suggest that groups constrainedby structural properties such as hierarchy, core-periphery, and leader structural holes willperform worse than groups with a more integrative structure.

1.2. Structural properties of work groups

The concept of hierarchical structure characterizes the extent to which relations are or-dered, such as those determined by status or prestige (seeFig. 1; Krackhardt, 1994). Ahujaand Carley (1998)found evidence of hierarchy in a virtual organization and suggest that itmay play a functional role in communication. The division of labor or chain of command mayfacilitate work being delegated among members. However, other discussions of hierarchical

Fig. 1. Hierarchical structure (seven member work group).


Fig. 2. Core-periphery structure (seven member work group).

structures in organizations suggest that connectedness, or members being able to reachother members across the organization, have implications for dealing effectively with crises(Krackhardt and Stern, 1988). In groups working on non-routine and complex tasks, whereinterdependence among members is high, a “flat” hierarchy may permit direct contact amongthose who require flexibility and timely information. Given this latter argument, we propose:

Hypothesis 1. Controlling for mean levels of group communication, greater hierarchicalstructure of the group will be negatively related to group performance.

Another important network construct is the core-periphery structure. Typically in thistype of structure there exists a dense, cohesive core with a sparse, unconnected periphery(seeFig. 2; Borgatti and Everett, 1999). Though there may be multiple cores, it is often thecase that a smaller subset of the total population participates more actively than the rest.A core-periphery structure may be related to better performance because such structureshold the potential to improve the speed and flexibility with which information diffuseswithin a group. However, it is also possible that core-periphery structures could impedeeffectiveness of groups engaged in non-routine, complex tasks. Quite often such groupsare formed of people with unique functional expertise, all of which should be brought tobear on a given problem. A core-periphery structure may limit the contribution of memberswho have valuable input by marginalizing information or opinions coming from peripheralmembers. Given this logic:

Fig. 3. Structural holes (seven member work group).


Hypothesis 2. Controlling for mean levels of group communication, greater core-peripherystructure of the group will be negatively related to group performance.

Finally, one can expect the ego-centric network structure of a group leader to be animportant determinant of collaboration within a group. Previous findings on the role ofleaders in work groups suggest they play an important role for group maintenance and taskaccomplishment (Hackman and Walton, 1986). However, leaders may choose to maintainvery different structures in a group. On the one hand they may choose to create a struc-tural hole within a group and enjoy the concurrent informational and power benefits fromnon-redundant ties (seeFig. 3; Burt, 1992). Alternatively they may actively work to helpconnect members around them, thereby reducing the group’s reliance on them over time. Asscholars have suggested, it is not necessarily the case that a leader’s structural hole positionwould yield benefits for collective performance (Adler and Kwon, 2002; Coleman, 1988;Podolny and Baron, 1997). If members regularly interact with one another and share whatthey know, they are likely to develop and calibrate an awareness of each other’s expertise.For groups working on non-routine or complex tasks, not having to go through a leader toget questions answered may be efficient for the inquirer and may result in a fewer numberof steps or chances of misinformation. It follows that:

Hypothesis 3. Controlling for mean levels of leader communication, greater structuralholes of the leader will be negatively related to group performance.

2. Methodology

The field study used to test the above hypotheses was conducted in a Fortune 500 telecom-munications firm. When the research was conducted, there were over 100,000 employeesin the global organization (40% of whom were engineers). Five divisions were spread overdifferent regions worldwide; members in the sample were from recently completed projectsacross United States/Canada (63%), Latin/South America (3%), Europe (15%), MiddleEast/Africa (5%), India/China (5%), and Japan/Korean/Malaysia (9%). The divisions wereorganized by product-market segments, operated fairly autonomously, and were responsiblefor development, manufacturing, and sales.

2.1. Sample

Two senior executives sponsored this research project and managers of each divisionprovided background information on the nature of work groups we examined. Work groupswere selected for the sample because they participated in a corporate-wide reward and recog-nition program. The program’s goal was to publicly acknowledge the top-performing workgroups in the company. Regional general managers nominated work groups from aroundthe world to participate in a corporate-wide competition. Each group made a 20–30 minpresentation to a panel of 5–12 judges (senior managers) who were given specific instruc-tions and training regarding the judicial process. Given the large size of the corporation,judges were generally unfamiliar with the projects before they made their ratings.


Senior managers in each region first rated 280 groups at over 20 events around the world.The 122 groups at each of the regional events who were judged the highest advanced tofive division-level events, where a different panel of judges rated the groups again. The21 division-level groups that were judged the highest moved on to a final corporate-levelevent, where they made presentations to the company CEO and other senior executives.Background information could not be located for 98/280 work groups, so this sample in-cludes 84/137 (61%) regional-level groups, 77/122 (63%) division-level groups, and 21/21(100%) corporate-level groups.

Each work group in the sample had a “group name,” 4–12 members (average was 8),and a specific project assigned to them. The groups had a designated leader, a general man-ager responsible for providing resources, and an identifiable customer. Members of workgroups generally came from within the same division; almost all were company employees.The groups worked on complex, non-routine projects ranging from product development(e.g. design handheld scanning device for shipping company) to service improvement (e.g.convert client platform for car phones from analog to digital) to process management (e.g.execute separation and sale of business unit to another stakeholder) to manufacturing op-erations (e.g. modify existing factory to support new production of pagers). In this sample,most projects started and ended between January 1998 and January 2000 (average projectlength was 15 months).

2.2. Survey data

Group leaders were contacted to verify project descriptions as well as member names andcontact information. Using this information, a survey was sent out in June 2000 as an e-mailattachment to each group member who had a valid e-mail address (1315/1474 or 89%). Thesurvey took 20–30 min to complete, and included a cover letter describing the purpose ofthe study and ensuring confidentiality for group members. The response rate for those whowere sent an e-mail survey, including two follow-up reminders, was 73% (957/1315).2 Atleast one person responded from each group; 86 groups had greater than a 75% responserate; 63 groups had between 50 and 75% response rate; 28 groups had between 25 and 50%response rate; and 5 groups had less than a 25% response rate. A total of 137 out of 182 groupleaders returned the survey, which was sent an average of 6 months after project completion.

3. Measures

3.1. Group size

Group leaders indicated the number of members in the group.

2 Two selection bias analyses were conducted at the group level (N = 182). Correlations were computedbetween group size, project length, manager-rated performance and (1) the percentage of members with a valide-mail address and (2) the percentage of members with survey responses. Neither analysis revealed significantassociations with group size. However, project length (r = 0.21, P < 0.01) and manager-rated performance(r = 0.24,P < 0.01) were associated with a higher percentage of survey responses; group members who weretogether longer and who performed better were more likely to complete the surveys.


3.2. Project length

Group leaders indicated the number of months from the start of the project to the end.

3.3. Years known

Group members indicated how long they knew each other person in the group (1–5 scale;1:<1 year, 2: 1–2 years, 3: 2–3 years, 4: 3–4 years, 5: >4 years), which was averaged acrossthe group to assess years known.

3.4. Psychological closeness

Group members rated how close they felt to each other person in the group (1–5 scale;1: acquaintance, 2: friend, 3: close friend, 4: very close friend, 5: confidant), which wasaveraged across the group to assess psychological closeness.

3.5. Communication frequency

For the measure of overall communication, each group member was asked to indicate“How frequently did you communicate with X during the project?” on a five-point scale(1: never, 2: monthly, 3: weekly, 4: daily, 5: hourly) where X represented the name of everyother member. This question was asked on the survey twice, once for the planning phaseof the project and once for the completion phase of the project. Average communicationfrequency was then computed as the average of the planning phase and the completionphase (correlation between phases for the group-level measure wasr = 0.74).

3.6. Hierarchical structure

For the measure of hierarchical structure, we dichotomized the communication matrixat greater than 2 (i.e. weekly communication, which was the average amount of groupcommunication in the sample, and greater) in UciNet V (Borgatti et al., 1992). We thenran the hierarchy routine in KrackPlot (Krackhardt et al., 1994) for the planning phase,completion phase, and average.

3.7. Core-periphery structure

For the measure of core-periphery structure, we used UciNet V (Borgatti et al., 1992)for the planning phase, completion phase, and average. The continuous measure of fit wasemployed (Borgatti and Everett, 1999).

3.8. Structural holes

For the measure of structural holes, we used the effective size calculation generated withinUciNet V (Borgatti et al., 1992) for the planning phase, completion phase, and average.


3.9. Work group performance

Work groups in the company-wide reward and recognition program were rated by seniormanagers on seven dimensions: (1) teamwork, (2) clearly defined problem selection, (3)appropriateness of method used to solve problem, (4) innovativeness of remedies used tosolve problem, (5) quality of impact from results, (6) institutionalization of solution, and (7)clarity of presentation. A sample analysis of an event (N = 12 judges andN = 33 groups)revealed that judges were able to reliably rate overall performance (α = 0.88 across judges),and provided evidence of a halo effect, whereby all seven dimensions loaded onto one factor(α = 0.80 across dimensions). Ratings of work group performance varied across regionsand divisions, therefore, the 182 groups were given a ranking of 1 (regional-level,N = 84),2 (division-level,N = 77), or 3 (corporate-level,N = 21).

Members were also asked to rate the group’s overall performance with three items(10-point scale; 1: not at all, 5: somewhat, 10: very much): (a) Our team was efficientin our performance, (b) Our team adhered to schedules and budgets, and (c) Our team pro-duced excellent work (Ancona and Caldwell, 1992). For the average of group (membersand leader) responses,α = 0.84, and for the average of group leader responses,α = 0.86.In the analyses below, we report results for manager-rated performance, member-ratedperformance, and leader-rated performance.

4. Results

Table 1provides means and standard deviations for the main study variables, whileTable 2provides the correlations. The inter-correlations among structural variables and

Table 1Means and standard deviations (in parenthesis) for main study variables

Variable Phase

Planning Completion Average

Work groupSize 8.10 (1.94)Length 14.96 (10.53)Years known 2.82 (0.82)Psychological closeness 1.81 (0.46)Communication 2.90 (0.41) 3.11 (0.50) 3.01 (0.42)Hierarchy 0.60 (0.27) 0.58 (0.27) 0.57 (0.28)Core-periphery 0.60 (0.10) 0.60 (0.10) 0.60 (0.11)Perform (manager) 1.65 (0.68)Perform (members) 8.09 (1.16)

LeaderYears known 3.03 (0.95)Psychological closeness 1.90 (0.74)Communication 3.07 (0.54) 3.23 (0.60) 3.15 (0.51)Structural holes 2.88 (1.54) 2.62 (1.34) 2.71 (1.32)Perform (manager) 1.71 (0.69)Perform (leader) 8.21 (1.41)

N = 182 work groups;N = 137 leaders.

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Table 2Correlation matrix with (average) main study variables

Variable 1 2 3 4 5 6 7 8 9 10 11 12 13 14

Work group1. Size –2. Length 0.02 –3. Years known −0.05 0.19∗∗ –4. Psychological closeness −0.14t 0.02 0.24∗∗ –5. Communication −0.20∗∗ −0.10 0.18∗ 0.43∗∗ –6. Hierarchy 0.00 −0.09 −0.09 −0.04 −0.15∗ 0.23∗∗ –7. Core-periphery 0.23∗∗ −0.10 0.02 −0.07 −0.17∗ –8. Perform (manager) 0.19∗∗ 0.01 0.04 0.08 0.21∗∗ −0.19∗∗ −0.31∗∗ –9. Perform (members) −0.05 0.00 0.17∗ 0.36∗∗ 0.38∗∗ −0.05 −0.23∗∗ 0.41∗∗ –

Leader10. Years known −0.07 0.06 –11. Psychological closeness 0.02 −0.03 0.12 –12. Communication −0.02 −0.10 0.08 0.30∗∗ –13. Structural holes 0.42∗∗ −0.07 −0.10 0.10 0.06 –14. Perform (manager) 0.14 0.03 0.01 0.22∗∗ 0.29∗∗ −0.10 –15. Perform (leader) 0.00 −0.12 0.13 0.21∗∗ 0.22∗∗ −0.12 0.24∗∗

N = 182 work groups;N = 137 leaders.t P < 0.10.∗ P < 0.05.∗∗ P < 0.01.


Table 3Regression analyses predicting manager-rated performance

Variables Model (phase)

1 (planning) 2 (completion) 3 (average)

B S.E. B S.E. B S.E.

Constant 1.25 0.58 0.98 0.51 0.91 0.53Size 0.09∗∗ 0.03 0.09∗∗ 0.03 0.10∗∗ 0.03Length 0.00 0.01 0.00 0.00 0.00 0.01Years known 0.00 0.06 −0.02 0.06 0.00 0.06Psychological closeness 0.08 0.11 −0.01 0.12 0.02 0.11Communication 0.15 0.13 0.30∗∗ 0.11 0.31∗ 0.13Hierarchy −0.58∗∗ 0.18 −0.52∗∗ 0.18 −0.58∗∗ 0.17Core-periphery −0.86t 0.49 −1.07∗ 0.48 −1.07∗ 0.46d.f. 7 7 7R2 0.16 0.20 0.20

N = 182 work groups.∗ P < 0.05.∗∗ P < 0.01.t P < 0.10.

communication frequency were weak to moderate, suggesting that group structure cap-tures more than the average amount of communication. For the complete network vari-ables, overall group communication was negatively associated with hierarchical groupstructure (r = −0.15) and core-periphery group structure (r = −0.17), while the hier-archical group structure was positively associated with core-periphery group structure (r =0.23). For the ego-centric network variables, overall leader communication was unrelatedto structural holes of the leader (r = 0.06). As for performance, the correlation between

Table 4Regression analyses predicting member-rated performance




Constant 5.69 0.96 4.77 0.84 4.77 0.89Size 0.01 0.04 0.01 0.04 0.02 0.04Length 0.00 0.01 0.00 0.01 0.00 0.01Years known 0.11 0.10 0.07 0.10 0.07 0.10Psychological closeness 0.75∗∗ 0.19 0.51∗∗ 0.19 0.60∗∗ 0.19Communication 0.33 0.22 0.71∗∗ 0.18 0.69∗∗ 0.21Hierarchy −0.83∗∗ 0.31 −0.71∗ 0.30 −0.78∗∗ 0.29Core-periphery 0.40 0.82 0.51 0.80 0.45 0.78d.f. 7 7 7R2 0.19 0.25 0.23

N = 182 work groups.∗ P < 0.05.∗∗ P < 0.01.


Table 5Regression analyses predicting manager-rated performance




Constant −0.10 0.46 0.16 0.44 −0.03 0.46Size 0.09∗∗ 0.03 0.08∗∗ 0.03 0.09∗∗ 0.03Length 0.00 0.01 0.00 0.01 0.00 0.01Years known −0.02 0.06 −0.03 0.06 −0.03 0.06Psychological closeness 0.16∗ 0.08 0.16∗ 0.08 0.16∗ 0.08Communication 0.37∗∗ 0.11 0.28∗∗ 0.10 0.36∗∗ 0.11Leader structural holes −0.11∗∗ 0.04 −0.10∗ 0.05 −0.12∗∗ 0.05d.f. 6 6 6R2 0.16 0.14 0.17

N = 137 leaders.∗ P < 0.05.∗∗ P < 0.01.

manager- and member-rated performance wasr = 0.41, the correlation between manager-and leader-rated performance wasr = 0.24, and the correlation between member- andleader-rated performance wasr = 0.71.

Ordinary least squares regression analyses were used to test the three hypotheses. First,there was full support for the hypothesis that, controlling for mean levels of group commu-nication, greater hierarchical structure of the group would be negatively related to groupperformance. Greater hierarchical structure of the group was negatively related to bothmanager- and member-rated group performance (seeTables 3 and 4).

Second, controlling for mean levels of group communication, there was partial support forthe hypothesis that greater core-periphery structure of the group would be negatively related

Table 6Regression analyses predicting leader-rated performance




Constant 5.78 0.97 6.47 0.91 6.04 0.97Size 0.03 0.07 0.08 0.07 0.07 0.07Length −0.01 0.01 −0.02 0.01 −0.02 0.01Years known 0.17 0.13 0.14 0.13 0.14 0.12Psychological closeness 0.29t 0.16 0.33∗ 0.17 0.31t 0.17Communication 0.51∗ 0.23 0.28 0.21 0.44t 0.24Leader structural holes −0.06 0.09 −0.22∗ 0.10 −0.20∗ 0.10d.f. 6 6 6R2 0.10 0.11 0.12

N = 137 leaders.∗ P < 0.05.t P < 0.10.


to group performance. While greater core-periphery structure was significantly associatedwith manager-rated performance (seeTable 3), it was not significantly associated withmember-rated performance (seeTable 4).

Third, controlling for mean levels of leader communication, there was full support for thehypothesis that greater structural holes of the leader would be negatively related to groupperformance. Greater structural holes of the group leader was negatively related to bothmanager- and leader-rated group performance (seeTables 5 and 6).3

5. Discussion

A primary goal of this paper was to relate structural properties of groups engaged incomplex, non-routine work to performance. In full support of the first hypothesis, greaterhierarchical structure was negatively related to manager- and member-rated performance.In partial support of the second hypothesis, greater core-periphery structure was negativelyrelated to manager-rated performance. Finally, in full support of the third hypothesis, struc-tural holes of the leader was negatively related to manager- and leader-rated performance.The weaker finding for core-periphery structure is consistent with the argument that moreintegrative structures are better for performance in complex, non-routine group work. (i.e.,core-periphery structures are more integrative than hierarchy or structural holes). Thoughthere does not appear to be a typical integrative structure for high-performing groups, suf-ficient ties among members to facilitate information flow, without over-reliance on onemember, does seem important.

An important strength of this research lies with the data itself. We know of very fewstudies that have been able to empirically link structural properties of a network to theperformance of that collective (cf.Sparrowe et al., 2001; Reagans and Zuckerman, 2001).While a great deal of recent social network research has focused on costs and benefits ofego-centric networks or actors embedded within bounded networks or organizations, therehas been less focus on structural properties of work groups and performance. By virtueof this data we are able to offer empirical evidence, though cursory, regarding structuralproperties of groups and the relationship to performance for a particular kind of work.

Clearly this research has limitations. A primary concern of ours throughout this researchinvolves missing data. Extensive efforts were undertaken to promote as high a response rateas possible within this organization. Nevertheless, incomplete data were used in the analyses,and interpretations are made with caution, though we did perform several analyses withvarying levels of complete data that suggest the results are reliable (see Footnote 2). A secondlimitation of the research lies with its scope. We only investigated relatively successful workgroups engaged in temporary project-based work. Further, these groups all came from thesame organization and might be affected by unique features of the organization such asits culture. However, we feel that this limitation is mitigated along two fronts. First, it is

3 Concerns about missing data were addressed in two ways. First, the statistical analyses were run with workgroups where at least the leader returned a survey (N = 137), and second, the statistical analyses were run withwork groups where at least 50% of the members returned a survey (N = 149). In both situations, the results didnot change.


important to better understand the work these kinds of groups engage in given the increasingprevalence of complex, non-routine activities within organizations. Second, the focus onone organization allowed for a meaningful and consistent performance metric. Nevertheless,we hope that future field research will take on the challenge of assessing groups completingdifferent forms of work and in different organizations.

Despite limitations the consistency of our results suggest an important link betweenstructural properties of work groups and performance. Certain network structures mightbe effective for diffusion of information (e.g. hierarchical or core-periphery structures) orindividual accomplishment (e.g. leader structural holes). However, our results suggest thatthese structures might be detrimental to group performance in field settings where bothtasks and required information are emergent (though it is possible that structural holes andclosure can play complimentary roles, seeBurt, 2000). In such settings, it might be moreeffective to engage in processes that help to promote lateral connectivity so that groups canleverage their collective intellect.

These findings hold implications for a contingency model of the relationship betweengroup network structure and performance. Of course as we did not vary the type of task inthis study, we can only speculate about this link. However, our results suggest that integra-tive structures yield higher performance in non-routine, complex work by joining uniqueexpertise within a group. We also think these findings might further illuminate networkresearch at the crossroads of formal and informal structure in organizations. Formal hier-archy and standard procedures within organizations likely have a strong effect on both thesocial and cognitive processes involved when people exchange information. Unfortunately,little inquiry exists at the intersection of formal structure and the informal relationshipsuncovered via social network analysis (cf.Stevenson and Gilly, 1991). Our research sug-gests that it might be important to better understand informal organizations where thereare performance goals, yet there are not constraints from formal hierarchy or other culturalnorms that impose structures. Like work groups, the study of informal organizations shouldfurther our understanding of the interplay between ego-centric and bounded networks.

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