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DOI: 10.1177/0149206305277792

2006 32: 29Journal of ManagementGreg L. StewartA Meta-Analytic Review of Relationships Between Team Design Features and Team Performanc

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10.1177/0149206305277792ARTICLEJournal of Management / February 2006Stewart / Team Design

A Meta-Analytic Review of

Relationships Between Team Design Features

and Team Performance

Greg L. Stewart*College of Business, 108 PBAB, University of Iowa, Iowa City, IA 52242-1000

This article presents a quantitative review of 93 studies examining relationships between team

design features andteamperformance. Aggregated measures of individual abilityanddisposition

correlate positively withteam performance.Team member heterogeneityandperformance corre-

latenear zero,butthe effect varies somewhatby typeof team. Projectand management teams have

slightly higher performance when they include more members. Team-level task meaningfulness

exhibits a modest but inconsistent relationship with performance. Increased autonomy and

intrateam coordination correspond with higher performance, but the effect varies depending on

task type. Leadership, particularly transformational and empowering leadership, improves team

performance.

Keywords: teams; groups; performance; team design

Thirty years ago, Leavitt (1975) wrote an article titled Suppose We Took Groups Seri-

ously. His basic premise was that, contrary to accepted practice, organizations should con-

sider their basic building block to be thegroup rather than the individual. In theensuing years,

the number of organizations adopting team-based structures has steadily increased (Devine,

Clayton, Philips, Dunford, & Melner, 1999). Research related to teams has proliferated along

with this trend in practice. A number of qualitative summaries, many published in theJournal

This article was accepted under the editorship of Daniel Feldman.

*Corresponding author. Tel.: 319-335-1947; fax: 319-335-1956.

E-mail address: [email protected]

Journal of Management, Vol. 32 No. 1, February 2006 29-54

DOI: 10.1177/0149206305277792

2006 Southern Management Association. All rights reserved.

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of Management, have reviewed teams research (e.g., Bettenhausen, 1991; Cohen & Bailey,

1997; Gist, Locke,& Taylor, 1987; Guzzo & Dickson, 1996; Kozlowski & Bell, 2003; Levine

& Moreland, 1990; McGrath & Kravitz, 1982; Shaw, 1981). A few quantitative reviews

have also focused on relationships between team performance and features such as member

homogeneity (Bowers, Pharmer, & Salas, 2000; Webber & Donahue, 2001) and training/

development (Hays, Jacobs, Prince, & Salas,1992; Salas,Rozell, Mullen, & Driskell,1999). I

build on this previous work by quantitatively reviewing studies conducted at the team level of

analysis.

The most recent qualitative review in theJournal of Managementwas conducted 9 years

ago by Cohen and Bailey (1997), who presented a heuristic framework for understanding

teams. This framework suggests that teameffectiveness is a function of environment (industry

characteristics, turbulence), design factors (composition, task features), internal and external

processes (communication, conflict), and psychosocial traits (norms, shared mental models).

Although this heuristic framework may not capture all constructs related to teams, it provides

a guidefor classifying research findings. In thecurrent quantitative review, I focusspecifically

on design factors. Cohen and Bailey defined design factors as those features of the task,

group, andorganization that can be directly manipulated by managers to create theconditions

for effective performance (1997: 243). Within the commonly accepted input-process-output

framework of teams (Gladstein, 1984; Goodman, Ravlin, & Argote, 1986; Hackman, 1987;

McGrath, 1984), design features constitute inputs.

Classification and Theory

I synthesize research findings by classifying previous team studies into categories of

design features and then by using meta-analysis techniques to determine if thedesign features

are consistently linked to team performance. As a beginning point for classification, Cohen

and Bailey (1997) provided three broad categories of design feature: group composition, task

design, and organizational context.

Group compositionlooks at the characteristics of individual team members. One line of

research examines aggregated characteristics to assess whether the inclusion of individuals

with desirable dispositions and abilities improves team performance. A related but somewhat

different area of research looks at how heterogeneity of individual characteristics relates to

team outcomes. Researchers also study how group size correlates with performance. I thus

adopted aggregated member characteristics, member heterogeneity, and team size as catego-

ries for quantitatively reviewing design features associated with group composition.

Task designconcerns how work activities are differentiated and integrated (Lawrence &

Lorsch, 1967). Hackman and Oldhams (1980) notion of task meaningfulness has been

applied at the team level to assess whether differences in the tasks assigned to a team result in

performance variation. Team-level autonomy is thekeyconstruct thathas emerged to describe

how a teams tasks are coordinated with other parts of the organization. Intrateam coordina-tion captures task coordination activities within the team. I thus chose task meaningfulness,

team-level autonomy, and intrateam coordination as constructs within the category of task

design features.

30 Journal of Management / February 2006



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Organizational contextrepresents the third type of design feature. Perceptions of support

from the leader can be nearly synonymous with perceptions of support from the organization

(Eisenberger, Stinglhamber, Vandenberghe, Sucharski, & Rhoades, 2002). Team leadership

provides a point of interfacebetween the team andthebroader organizationalcontext.Leader-

ship receivedfrom thedesignated supervisor thus servedas theclassificationcategory captur-

ing organizational context.

Support for the adoption of the three categories of design feature can be found in Table 1,

which summarizes variables from a number of qualitative reviews. Table 1 suggests that a

majority of research outside of processes, which are defined as interactions within the group

such as communication and conflict (Cohen & Bailey, 1997), can indeed be classified into thecategories of composition (aggregated characteristics, heterogeneity, team size), task design

features (meaningfulness, autonomy, intrateam coordination), and organizational context

(leadership).

The breadth of the classification categories does create some potential difficulty for inter-

preting theconstructs being assessed,as diverse measures might be classified into a singlecat-

egory. Nevertheless, onepurpose of meta-analysis is todeterminethe extent to which different

measures can be grouped together to form a broad construct that exhibits consistent relation-

ships with other variables. Theexistence of a broad constructunderlying differentmeasures is

supported when the various measures grouped into that broad classification exhibit a con-

sistent relationship with another variable such as performance (Hunter & Schmidt, 1990). In

contrast, the improper grouping of nonsimilar measures is a possible explanation why vari-

ance across studies remains after correcting for statistical artifacts. A starting point for meta-

analysis is thus to begin with broad classification and then attempt more specific classifica-

tion in instances where variance across studies remains. Beginning with broad constructs is

also supported by research in personality that has examined trait bandwidth and concluded

that both broad and narrow constructs have value, and that research can often advance best by

Stewart / Team Design 31

Table 1

Summary of Variables Included in Review Articles

Study Composition Task Design Context and Leadership

Gist, Locke, & Taylor

(1987)

Group structure, size, abil-

ity, personality, gender,

race

Leadership, rewards

Levine & Moreland

(1990)

Size, demographics, abil-

ity, personality

Ecology (physical sur-

roundings), leadership

Bettenhausen (1991) Size, heterogeneity Structure, interdepen-

dence, self-managing

groups

Guzzo & Dickson (1996) Size, heterogeneity,

familiarity

Leadership training, auto-

mation, computers

Cohen & Bailey (1997) Size, tenure, diversity, age Autonomy, task

characteristics

Rewards, supervision

Kozlowski & Bell (2003) Size, diversity, personal-

ity, ability

Team type, self-managing

teams

Team leadership, team

training



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first examining broad constructs and then more specific classifications (Ashton, Jackson,

Paunonen, Helmes, & Rothstein, 1995; Ones & Viswesvaran, 1996; Stewart, 1999).

Group Composition

Group composition concerns what individual members bring to the group in terms of skill,

ability, and disposition (Driskell, Hogan, & Salas, 1987; Hollenbeck et al., 1995; Tesluk &

Mathieu, 1999). A great deal of research has linked individual abilities and dispositions with

individual performance (Barrick & Mount, 1991; Hunter & Hunter, 1984; Gatewood & Feild,

2000). A meta-analysis by Mount, Barrick, and Stewart (1998) specifically concluded that

certain personality traits correlate with high individual performance in team settings. How-

ever, this evidence does not automatically guarantee that teams composed of individuals

selected for high individual performance will perform better as collective units (Stewart,

2003). As pointed out by Schneider, Smith, and Sipe, The effects of a valid selection proce-

dure can be nullified by any lack of cooperation within groups and by bottlenecks, shirking,and social loafing (2000: 99). They also pointed out that the positive impact of some dis-

positions and abilities might be intensified in group settings.

The important question for teams is therefore whether individual dispositions and abilities

combine to precipitate high performance for the group as a whole. This question of relating

individual dispositions and ability to collective performance has only recently begun to

receive substantial research attention. A quantitative review of these findings can help assure

that researchers and practitioners are not erroneously applying individual-level findings at the

team level.

Aggregated characteristics. Groupcomposition research requires individual differences to

somehow emerge to form team-level constructs that in turn relate to collective performance

(Kozlowski & Klein, 2000). One form of emergence occurs when individual characteristics

combine in a linear fashion such that the mean or sum of individual characteristics represents

the team-level construct (Chan,1998; Stewart, 2003). Theassumption underlying this form of

aggregation is that desirable dispositions and abilities of individuals provide the team with a

resource of talent and that more talent is always better. Specific studies from this perspective

have focused on assessing member skill and ability levels (LePine, Hollenbeck, & Ilgen,

1997; Tziner & Eden, 1985; Yetton & Bottger, 1982), member personality traits (Barrick,

Stewart, Neubert, & Mount,1998; LePineet al., 1997; Neuman& Wright, 1999),andmember

background and experience (Bantel & Jackson, 1989). At the individual level, some of these

categories of individual difference exhibit stronger relationships with performance than oth-

ers(Bobko, Roth, & Potosky, 1999).However, littleis known about therelative valueof differ-

ent characteristicswhen they areaggregated to predict collective performance. I thus assessed

the extent to which individual characteristics cumulate linearly to influence collective perfor-

manceand explored potential differences among types of disposition and ability (i.e., person-ality, cognitive ability, expertise).




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Member heterogeneity. A differentperspectiveof group composition suggests that individ-

ual characteristics do not aggregate in a linear fashion. This perspective highlights the impor-

tance of assessing fit between team members (Kristof-Brown & Stevens, 2001). In essence,

the desirability of a specific individuals traits depends on the traits of other team members

(Barry& Stewart, 1997; Stewart,2003).Much of theresearchfrom this perspectivefocuses on

the heterogeneity or homogeneity of team member characteristics and uses variance-based

measures of individual characteristics to form team-level indicators of composition. Hetero-

geneity of team members has been measured along demographic lines (e.g., race, sex),

psychosocial traits (e.g., intelligence, personality), and background characteristics (e.g.,

career paths, education).

Some studies support the proposition that heterogeneity is best (Bantel & Jackson, 1989;

Magjuka & Baldwin, 1991). Other studies suggest that homogeneity is most desirable

(Campion, Medsker, & Higgs, 1993; Wiersema & Bird, 1993). Theoretical arguments sup-

porting heterogeneity focus on thecreativity associated with diverse viewpoints andskill sets,

whereas arguments supporting homogeneity focus on the notion that highly similar people

experience less conflict. Heterogeneity of team members is thus usually advocated for teams

engaged in creative tasks but not for teams engaged in routine tasks (Guzzo & Dickson, 1996;

Jackson, May, & Whitney, 1995). Recent research has also emerged to suggest that heteroge-

neityof sometraits is morebeneficial thanheterogeneityof other traits.Specifically, heteroge-

neity of characteristics that are clearly job related is argued to be more beneficial than hetero-

geneity of less direct characteristics such as demographic variables (Pelled, Eisenhardt, &

Xin, 1999; Webber & Donahue, 2001; Williams & OReilly, 1998). I thus assessed the extent

to which team member heterogeneity is related to team performance. I also conducted analy-

ses to determine if heterogeneity matters more for teams engaged in creative work and if job-

related heterogeneity matters more than demographic heterogeneity.

Team size. Research findings related to team size are mixed. Some studies find large teams

to suffercoordinationandprocess losses(Gooding & Wagner, 1985; Markham, Dansereau, &Alutto, 1982; Mullen, Symons, Hu, & Salas, 1989). Other studies find large teams to be more

effective (Magjuka & Baldwin, 1991; Yetton & Bottger, 1982). Kozlowski and Bell (2003)

pointed out that the benefits of a larger team likely depend on the nature of the team and its

environment. In particular, they build on thework of Hill (1982) and suggest that larger teams

may be more able to obtain resources such as time, energy, money, and expertise. These

resources are expected to be particularly beneficial for completing difficult tasks in complex

and uncertain environments. The possibility of additional members being more beneficial for

some types of teams has not, however, been quantitatively explored. The final area of review

for group composition research was thus to assess whether variation in team size is related to

differences in team performance andif increasedteam size is indeedmore important for teams

performing difficult tasks in uncertain environments.




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Team-Level Task Design

Task meaningfulness. Task design consists of guidelines and task-driven prescriptions for

coordinating various work tasks (Campion et al., 1993; Cohen & Bailey, 1997; Hollenbeck

et al. 1995). One team-level focus concerns the characteristics of the tasks pursued by thecol-

lective group. Specifically, Hackman and Oldham (1980) theorized that their individual-level

job characteristics could be applied at the team level of analysis and that teams perform better

when their work provides meaning through skill variety, task identity, and task significance.

Meaningfulness is enhanced by teams perceiving their work to be worthwhile and important.

Teamsengaged in meaningful tasksshould perform better because theyexperience higher lev-

elsof internal motivation (Batt & Applebaum, 1995; Campion et al., 1993).Evidencesuggests

that providing individuals with meaningful tasks does indeed increase individual productivity

(Fried & Ferris, 1987; Kopelman, 1986). However, the extrapolation of this finding to the

team level of analysis is uncertain. A clear understanding of the relationship between team-

leveltask meaningfulness and team performance does not exist. I thus quantitativelyreviewedresearch concerning collective task meaningfulness to provide insight at the team level of

analysis.

Autonomy. Whereas task meaningfulness focuses on perceptions about the nature of the

tasks themselves, autonomy is concerned with how tasks are coordinated with other parts of

theorganization. The relevant focus is on the level of autonomy for thecollective group rather

than on autonomy for individuals. Teams with more autonomy aregivenincreased freedom to

make decisions, to plan work activities, and to adapt to changing conditions. Autonomy is fre-

quently achieved through empowering workers withgreater information and decision-making

authority, so that they experience heightened self-determination (Spreitzer, 1995). In fact,

researchers frequently use the terms autonomy and empowermentinterchangeably.Both refer

to the concept of providing workersgroups of workers in the present contextwith in-

creased opportunity to determine their own courses of action.Autonomy at the team level not only increases internal motivation (Cohen & Ledford,

1994; Wall, Kemp, Jackson, & Clegg, 1986) but also allows teams to improve performance

through localized adaptation to variation in work environments and demands (Manz &

Stewart, 1997; Pearce & Ravlin, 1987). Facilitating processes and interactions develop

because the team as a whole rather than a hierarchical leader is responsible for performance

(Beekun, 1989). Higher levels of collective autonomy should also improve team performance

by increasing the information held by team members (Hollenbeck, Ilgen, LePine, Colquitt, &

Hedlund, 1998).

Yet,some research suggests that high autonomy maynot be universally desirable for teams

(Stewart & Barrick, 2000). Specifically, autonomy may not be as beneficial when teams per-

form tasks that are clearly understood and optimized (Manz & Stewart, 1997). In these situa-

tions, following hierarchically prescribedprocesses can leadto increasedefficiency and supe-

rior performance (Adler & Cole, 1993). High autonomy may make it difficult to coordinatetheeffortsof multiple teams.The valueof team-levelautonomy maydepend on team tasks and

purposes, with autonomy being more beneficial when work conditions and requirements are




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uncertainand dynamic. This assumptionhas not, however, received adequateempiricalexam-

ination. I thus determined theextent to which autonomy links with collectiveperformance and

assessed whether the autonomy-performance relationship is stronger for teams engaged in

creative and dynamic work.

Intrateam coordination. Intrateam coordination focuses on task coordination within the

team. Thefocus of coordinationdiffers from autonomy in that it concerns relationships within

the team rather than relationships between the team and other parts of the organization. This

means that teams have high intrateam coordination when they are interdependent and mem-

bers depend on each other for information, materials, and reciprocal inputs (Campion et al.,

1993; Emery & Trist, 1969). Designing teams with high coordination encourages team mem-

bers to work together closely and develop shared expectations and norms for appropriate

behavior (Bonacich, 1987; Stewart & Barrick, 2000). High coordination improves intrateam

processes by opening communication channels, building feelings of esprit de corps, and re-

ducing social loafing.

However, high intrateam coordination creates high interdependence among team mem-

bers, and research studies have shown that interdependence among team members canexhibit

a curvilinear relationship with collective performance (e.g., Saavedra, Earley, & Van Dyne,

1993; Stewart & Barrick, 2000; Wageman, 1995). Both high and low levels of interdepen-

dence correspond with higher performance than moderate levels of interdependence. In con-

trast to the community-building aspects of high interdependence, the benefits of low interde-

pendence include efficiency and creative problem solving by individuals (Wageman, 1995).

Interdependence thus ranges along a continuum from low where people function as individu-

als, to moderate where some interaction takes place, and finally to high where extensive inter-

action and relationships occur. Performance varies nonlinearly with these forms of inter-

dependence, suggesting that meta-analytic assessment of linear relationships with intrateam

coordination may be somewhat difficult.

A critical issue associated with intrateam coordination in field settings is, nevertheless,whether people operating with very low coordination are included in team studies. It takes at

least a minimum level of coordination for a collection of people to be classified as a group or

team. Although an earlier review by Goodman et al. (1986) reported some team studies based

on entire departments or individuals reportingto a common supervisor, recent studieswhich

represent the majority of studies included in this reviewtend to include groups with at least

moderate amounts of intrateamcoordination. This suggests that teams included in this review

will mostly fall in the range from moderate to high intrateam coordination. Increased coordi-

nation for included teams is thus likely to correspondwith improvedcollectiveperformance.

However, similar to autonomy, evidencesuggests that increasedcoordination within teams

may not be as beneficial for teams performing routine work (Adler & Cole, 1993; Stewart &

Barrick, 2000). In these teams, moderate levels of coordination allow task specialization of

ongoing work, which increases efficiency as long as task demands and environmental condi-

tions remainstable (Thompson, 1967). I thus assessed theoveralleffect of intrateamcoordina-tion on teams, as well as the question whether high coordination is indeed more beneficial for

teams engaged in creative tasks.




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Leadership

The final type of design feature concerns the context of the surrounding organization.

Although topics from the qualitative reviews reported in Table 1 suggest that some studies

have focused on other context variables such as training, a majority of contextual research has

been in the area of leadership. A majority of the interactions between a team and the larger

organization occur through the individual who is designated as the team leader. Specifically,

Eisenberger et al. suggested that because leaders act as agents of the organization, who have

responsibility for directingand evaluating subordinatesperformance, employeeswouldview

theirsupervisors favorable or unfavorableorientation toward themas indicativeof theorgani-

zations support (2002: 565). The leadership that a team receives from its designated leader

who represents the larger organization thus represents much of theorganizational context and

represents contextual factors in the current review.

Theeffectsof leadership at theteam levelare notas well understood as at theorganizational

and individual levels. In general, research has demonstrated that effective leadership canimprove performance of individuals (Wayne, Liden, Kraimer, & Graf, 1999) and organiza-

tions (Waldman, Ramirez, House, & Puranam, 2001). However, little is known about whether

the benefits of leadership also occur for teams. One reason why teams might differ is the

potential inabilityof a team leader to createan overall vision, which is a critical component of

effective organizational leadership (Bass, Avolio, Jung, & Berson, 2003). Given potential dif-

ferences, a quantitative review of the effects of leadership at the team level seems especially

warranted.

The overall category of leadership is, nevertheless, a broad classification that includes a

wide variety of leadership activities. Because this broad category may contain measures that

are not homogeneous, I also reviewed findings related to twospecific forms of leadership fre-

quently advocated for teams: transformational leadership and empowering leadership.

Transformational leadership usescharismaand intellectual stimulation to encourage teamfol-

lowers to transcend personal self-interest in order to accomplish team goals (Bass, 1985;Keller, 1992; Ross & Offermann, 1997). Empowering leadership develops follower self-

capacity to achieve a state where teams actually lead themselves (Manz & Sims, 1987).

Although transformational and empowering leadership are developed and advocated from

different theoretical perspectives, they are expected to have somewhat similar outcomes in

teams, as a primary objective of each is empowerment of the team (Burns, 1978; Druskat &

Wheeler, 2003; Kark, Shamir, & Chen, 2003).

Transformational leadership. Bass described transformational leadership as the moving

of followers beyond their self-interests for the good of the group, organization, or society

(1997: 130). Transformational leadership includes exhibiting charisma through conviction

and purpose, articulating an appealing vision of the future, stimulating new perspectives, and

dealing with others as individuals (Bass, 1985). Effective transformational leaders communi-

cate strong ideology, emphasize collective identity, and display exemplary behavior (Shamir,Zakay, Breinin, & Popper, 1998). This increases internal motivation for followers (Bono &

Judge, 2003) and builds higher cohesion and potency within teams (Bass et al. 2003). A criti-

cal focus of transformational leadership is thus using idealized influence to encourage team




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members to transcendself-interests for thegood of thecollective team (Bass,1999). To review

the effects of this form of influence, I examined the relationship between transformational

leadership and team performance.

Empowering leadership. Manz and Sims suggested that for highly empowered teams, the

dominant role of the external leader then, is to lead others to lead themselves (1987: 119).

The primary objective of leadership from this perspective is the successful transfer of power

and responsibility away from the leader to the collective of followers. This is done primarily

by building self-management practices of both individuals and the collective group. These

practicesincludeactivities likeself-observation,goal setting,and self-reinforcement (Manz&

Sims, 1987). Druskat and Wheeler (2003) also suggested that empowering leadership aids

teams through boundary-spanning activities such as seeking information from, and develop-

ing political and/or social relationships with, other parts of the organization. Compared with

transformational leadership, empowering leadership moves the focus from the charismatic

vision created by the leader to the ownership and commitment generated by teams creating

and pursuing their own visions (Manz & Sims, 1991). As a final category, I assessed the

impact of this empowering form of leadership on team performance.

Method

I conducteda literature search to identify published studies of teams andgroups. I used two

strategies to search the literature. I first did a computer search with PsycINFO and ABI/

Inform. Keywords included the termsgroup,team,effectiveness,teamwork,team decision

making,productivity, andperformance. I also manually searched several leading academic

journals from the beginning of their publication. Journals searched include the Journal of

Applied Psychology,Academy of Management Journal, Organizational Behavior and Human

Decision Processes, Administrative Science Quarterly, Personnel Psychology, Journal of

Occupational and Organizational Psychology, Group Dynamics, Small Group Research,

Journal of Organizational Behavior,Journal of Management,Human Relations,and Group&

Organization Management.

I adopted two criteria to determine whether a study should be included in the meta-

analyses.The firstcriterionwasa participant sampleof intact teams performing real-life tasks

in a natural setting. Although laboratory simulations provide an excellent forum for theory

development, numerous researchers have posited substantial differences between intact and

laboratory teams (e.g., McGrath, 1984). Natural settings arealso preferredforobtainingaccu-

rate estimatesof actual relationships (Dobbins, Lane, & Steiner,1988). Theanalyses reported

here thus use intact teams performing relevant tasks in theirnatural environments. Teams per-

forming simulated tasks, or tasks in artificial environments, are not included. The second cri-

terion was that the unit of analysis be teams rather than individuals. Measures obtained from

individuals are only included if the study reported a measure aggregated to the team level ofanalysis.

I included studies through the end of 2003. Ninety-three studies met the outlined criteria.

An analysis of the publication dates of the studies suggests that a majority of this research




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was done in the past 10 years (studies before 1975 = 9, 1975-1979 = 3, 1980-1984 = 5, 1985-

1989 = 3, 1990-1994 = 16, 1995-2000 = 27, 2001-2003 = 30). The 93 studies contain

correlational information for 697 measures. To conduct the meta-analyses, the 697 measures

were coded into the design feature categories (aggregated characteristics, heterogeneity, team

size, task meaningfulness, autonomy, intrateam coordination, leadership), a performance cat-

egory, a broad category of team processes (which was not in the content domain of the present

study), andan other category. Performance measures includedbothsupervisor/observer rat-

ings and objective indicators such as output levels and financial indicators. Because the num-

ber of studies was expected to be limited in many categories, I did not plan analyses for differ-

ent types of performance measure. Indeed, an examination of the results confirms that there

are too few studies to conduct separate meta-analyses for different types of dependent vari-

ables.However, toeliminatethe bias of a commonmeasurementsource, I didnot include team

member self-ratings of performance.

Two raters familiarwith the teams literature coded each measure. Raters initiallyclassified

626of the697 measures into thesame categorization(90% agreement). Ratersthenmetto dis-

cuss the 71 measures that were not placed into a common cluster. Discussion resulted in a

mutually agreedupon classificationfor46 of themeasures,but25 measures could not be clas-

sified accurately and were eliminated from the data. The 93 studies thus yielded 672 useable

measures.

Within several studies, more than one measure was classified into a given category. For

instance, one study correlated seven measures of team diversity with team performance.

Because measures in a common study are not independent, bias is introduced if each measure

of a construct is included separately in a meta-analysis. I thus calculated the mean correlation

coefficient for each category within each study and adopted this as a coefficient representing

the relationship for that study. In cases where a large number of measures were grouped into a

single classification, I conducted additional analyses to determine whether measures such as

different forms of heterogeneity should be grouped together.

I adopted Hunter and Schmidts (1990) methods for meta-analysis, weighting study effectsizesby sample size. The meta-analyses also include corrections for unreliablemeasurement.

The best indicators of reliability are a pair of intraclass correlation (ICC) coefficients. The

first, which James (1982) labeled ICC(1), indicates the reliability of ratings from members of

the same team. The second, labeled ICC(2), indicates the extent that teams can be systemati-

cally differentiated on the construct of interest. ICC(1) is essentially the reliability of a single

rater, whereas ICC(2) represents the reliability of the measure based on the number of team

members providing assessment, which is the measure that is actually used to represent the

team-level construct. I thus chose ICC(2) as the appropriate indicator of reliability.

Because values for ICC(2)are notincludedin allstudies, I used artifactdistributions to cor-

rect for unreliability. I calculated distributions for task meaningfulness (mean reliability of

.77,SD= .08), autonomy (mean reliability of .76,SD= .03), intrateam coordination (mean

reliability of .61,SD= .16), aggregate team member characteristics (mean reliability of .75,

SD = .10), andteam leadership (mean reliabilityof .82, SD = .05). Only onestudy hasassessedreliability of performance at the team level, with an estimate for reliability of .72 over a 1-year

period (Landis, 2001). Rothstein (1990) reported a value of .50 for the reliability of perfor-

manceat the individual level. Salgado (1997) also provided a distribution for the reliability of




12/28

individual performance (mean reliability = .63,SD= .12). To provide conservative correc-

tions, I used the estimate for the reliability of performance from Landis (2001) for all meta-

analyses. Because reliability indexes were unavailable, and because they usually used objec-

tive measurement, I did not make corrections for unreliability of measurement with team

heterogeneity and team size.

Results

Group Composition

Aggregated characteristics. Table 2 presents results of the initial meta-analyses. Aggre-

gated team member characteristics exhibit a significant relationship with team performance

(= .27, credibility value [CV] is .05 to .50). A substantial amount of variance (60%) is not

explained by sampling error or unreliability. The search for an explanation of this variationacross studies focused on determining if nonequivalent constructs had been grouped into the

broad category. Specifically, Hunter and Schmidt suggested that if the meta-analysis does

find varianceacross studies, then thehypothesizednonequivalenceof variables canbe used as

a moderator variable (1990: 496). I thus conducted separate analyses for aggregated mea-

sures of personality, cognitive ability, and expertise. Analyses based on these categorizations

arereported inTable 3. Taken as a whole, themeta-analyses using subcategories of aggregated

member characteristics suggest the amount of variance across studies is less when character-

istics are grouped into specific categories. This supports the notion that aggregations of per-

sonality, cognitive ability, and expertise are indeed nonequivalent constructs.

The meta-analysis for personality included aggregated measures of team member traits

such as conscientiousness and agreeableness. The results are similar to those for the broader

classification in that a moderately strong relationship ( = .26, CV is .12 to .41) exists. More-

over, variance across studies (40%) still cannot be explained simply by sampling error andunreliability, suggesting moderation. Unfortunately, the small number of studies using per-

sonalitymeasurespreventsfurther searchingfor a moderatoruntiladditional research is done.

The meta-analysis for aggregated cognitive ability included studies using basic measures

of team member mentalability. The results support a strong relationship(=.40, CVis .31 to

.50) that has limited variance across studies.

The meta-analysis for aggregated expertise included measures of teammember experience

and education. The results support a moderate relationship ( = .16, CV is .16 to .16) that is

consistent across studies.

Member heterogeneity. Member heterogeneity exhibits a rather small, negative relation-

ship with team performance (= .04, CV is .04 to .04), and all of the variance across stud-

ies can be explained by sampling error. Nevertheless, I tested for moderator effects because

using the amount of variance explained to assess moderation has low statistical power whenthe number of studies is relatively small (Sackett, Harris, & Orr, 1986). Potential moderators

include differences in the work and tasks that a team is asked to do. One variable that poten-

tially captures many of these differences is team type. On the basis of the framework of




13/28

40

Table2

Meta-AnalyticResultsforRelati

onshipsBetweenTeamDesignFeatu

resandTeamPerformance

Variable

K

N

r

s2

r

s2

e

%VE

SD

80%CV

Groupcomposition

Aggregatedcharacteristics

38

3,03

7

.20

.030

.012

40

.27

.18

.0

5.5

0

Heterogeneity

26

1,83

0

.0

4

.009

.015

100

.0

4

.00

.0

4.0

4

Teamsize

26

1,86

2

.03

.009

.014

100

.04

.00

.0

4.0

4

Taskdesign

Taskmeanin

gfulness

15

1,32

7

.12

.020

.011

55

.16

.13

.0

2.3

2

Autonomy

19

1,41

0

.19

.036

.013

36

.25

.20

.0

1.5

1

Intrateamco

ordination

11

574

.16

.016

.019

100

.25

.00

.2

5.2

5

Leadership

Overall

53

2,43

7

.20

.046

.021

46

.26

.20

.0

0.5

1

Transformat

ional

12

541

.26

.011

.012

100

.33

.00

.3

3.3

3

Empowering

9

580

.25

.047

.014

30

.32

.23

.0

3.6

2

Note:K=numberofcorrelations;N=totalsamplesize;r=mea

nobservedvalidity;s

2 r

=observedvariance;s

2 e=samplingerrorvariance;%VE=percentageof

variance

explainedbyartifactualerrors;=estimatedtruevalidity;SD

=populationstandarddeviation;CV=credibili

tyvalue.



14/28

41

Table3

Meta

-AnalyticResultsforSubcategories

Variable

K

N

r

S2

r

S2

e

%VE

SD

80%CV

Aggregatedcharacteristics

Personality

20

1,84

2

.20

.018

.010

60

.26

.11

.1

2.4

1

Cognitiveab

ility

10

791

.30

.015

.011

78

.40

.08

.3

1.5

0

Expertise

14

1,21

2

.12

.010

.011

100

.16

.00

.1

6.1

6

Heterogeneity

Managemen

t

8

804

.0

3

.004

.010

100

.0

3

.00

.0

3.0

3

Production

10

561

.0

6

.011

.018

100

.0

7

.00

.0

7.0

7

Project

8

461

.03

.012

.018

100

.04

.00

.0

4.0

4

Demographics

14

774

.0

6

.010

.018

100

.0

7

.00

.0

7.0

7

Expertise

11

1,04

6

.0

5

.010

.011

100

.0

5

.00

.0

5.0

5

Organizationtenure

10

906

.0

7

.025

.011

44

.0

8

.14

.2

5.1

0

Teamsize

Managemen

t

6

624

.05

.004

.010

100

.06

.00

.0

6.0

6

Production

12

749

.0

1

.012

.016

100

.0

1

.00

.0

1.0

1

Project

8

489

.08

.007

.016

100

.09

.00

.0

9.0

9

Autonomy

Knowledgework

11

684

.19

.031

.015

50

.26

.17

.0

4.4

7

Physicalwork

6

380

.27

.049

.014

29

.36

.25

.0

4.6

7

Intrateamcoordination

Knowledgework

6

308

.19

.022

.018

90

.29

.07

.2

0.3

8

Physicalwork

3

161

.08

.002

.019

100

.12

.00

.1

2.1

2

Note:K=numberofcorrelations;N=totalsamplesize;r=mea

nobservedvalidity;s

2 r

=observedvariance;s

2 e=samplingerrorvariance;%VE=percentageof

variance

explainedbyartifactualerrors;=estimatedtruevalidity;SD

=populationstandarddeviation;CV=credibili

tyvalue.



15/28

Sundstrom, DeMeuse, and Futrell (1990), Cohen and Bailey (1997) advocated four types of

team: production (work), project, management, and parallel teams. Production teams consist

of people working together in ongoing relationships to continuously produce goods or ser-

vices. Project teams consist of knowledge workers organized for a finite period to complete a

specific assignment. Management teams are ongoing groups of managers who work together

to developstrategy andcoordinate work. Parallel teams aredecision-making andadviceteams

that exist outside the formal organizational structure. Studies were sorted into these catego-

ries, with 10 studies based on production teams, 8 studies each representing project and man-

agement teams,and0 studies representingparallel teams.Table 3 shows meta-analytic results

within each of the three represented categories.

Project teams engage in nonrepetitive tasks and usually require application of knowledge

and expertise (Cohen & Bailey, 1997), suggesting a need for heterogeneity. Production teams

are generally stable and usually engage in short work cycles that require continuous comple-

tion of a small number of fairly routine tasks (Sundstrom et al., 1990), suggesting that hetero-

geneity maybe detrimental. The results suggest a small positive relationship for heterogeneity

in project teams ( = .04, CV is .04 to .04) and a small negative relationship for production

teams ( = .07, CV is .07 to .07). The magnitude of these relationships is not strong, but

the results are consistent with heterogeneity being more desirable for teams engaged in cre-

ative tasks. To the extent that management teams engage in diverse and turbulent activities,

they should also benefit from heterogeneity. However, the results suggest a slight negative

relationship for management teams ( = .03, CV is .03 to .03). The analyses separating

heterogeneity effects by team type thus yield small effects but do provide limited support for

the notion that heterogeneity is more desirable in the creative setting of project teams than in

the routine setting of production teams.

I also probed for a confound created by classifying different types of heterogeneity into a

single category. Because they are frequently studied in the existing literature, heterogeneity

measures were classified as capturing either fewer job-related demographics or more job-

related measures of expertise and tenure. Demographics and expertise continue to exhibitrather small negative relationships (= .07, CV is.07 to.07;= .05, CV is.05 to.05,

respectively) that do not vary across studies. The relationship with organizational tenure is

similar in magnitudeanddirection(= .08, CV is.25to .10) but variesacrosssettings(56%

of variance unexplained). Because all effects remain quite small in magnitude and similarly

negative, the supplemental analyses based on more specific classifications of heterogeneity

thus do not support the prediction that some types of heterogeneity are more beneficial than

others.

Team size. A small but positiveeffect (= .04, CVis .04 to.04) isfound for teamsize,with

all variance across studies again being explained by sampling error. Yet, on the basis of theo-

retical arguments, I probed for a moderator effect. Studies were coded based on team type,

with 12 studies being classified as production teams, 8 as project teams, and 6 as manage-

ment teams. As shown in Table 3, team size is positively related to performance for projectteams (= .09, CV is .09 to .09) and management teams (= .06, CVis .06 to.06), butnotfor

productionteams(= .01, CV is .01 to .01). Project teams by definition,andmanagement

teams in many cases, differ from production teams in that they work on ill-defined tasks and




16/28

require acquiring resources through substantial interaction withpeople and groups outside the

team (Sundstrom et al., 1990). The magnitude of the positive relationships remains small, but

the results areconsistent with thenotion thatadditional members aredesirable when the team

is required to interact with, and obtain, resources such as supplies and expertise from a com-

plex environment.

Task Design

Task meaningfulness. As shown in Table 2, the relationship between task meaningfulness

and performance is modestly positive (= .16, CV is .02 to .32). Unexplained variance

(45%) exists across studies, supporting moderation. Most studies related to meaningfulness

adopt Hackman andOldhams (1980) measure, suggesting that more specific classificationof

study variables is not possible. Moreover, all but two of the studies measuring task

meaningfulness arebased on production teams,making it impossible toexplore team type as a

potential moderator. Team-level task meaningfulness does, therefore, have a modest relation-ship with performance in many teams, but the relationship is neither universally positive nor

consistent across settings.

Autonomy. The relationshipbetween team autonomy and performance is positive (= .25,

CV is .01 to .51). Moderation is likely, as the credibility interval includes zero, and substan-

tial variation (64%) remains after correcting for artifacts. I began the search for moderation

with a classification of team type. Seventeen of the 19 studies were classified as production

teams,preventing analysis based on differences in team task. Building on the theoretical work

of Devine (2002), studies were then coded into two broad classifications related to primary

work tasks. One type is knowledge work, which consists primarily of thinking, mental skills,

and information production. The other type is physical work that involves physical skills, a

linear workflow, and tangible products. Eleven studies used teams engaged primarily in

knowledge tasks, 6 studies used teams doing mostly physical tasks, and 2 studies used teams

that could not be classified. Table 3 shows meta-analytic results within the categorizations of

knowledge work and physical work. Counter to expectations, the relationship is stronger for

teams engaged in physical work ( = .36, CV is .04 to .67) than for teams engaged in knowl-

edgework ( = .26, CV is .04 to .47). However, a great dealof variance remains across studies

in both categories (50% for knowledge work, 71% for physical work), suggesting that addi-

tional moderators exist. The positiveeffect of autonomy thus varies across settings, but oppo-

site the predicted manner.

Intrateam cooperation. Intrateam cooperation exhibits a positive relationship with perfor-

mance (= .25, CV is .25to .25). Variance across studies is explained by sampling error, but I

still conducted moderator analysis based on theoretical arguments. Similar to autonomy,

almost all studies examiningintrateamcoordination usedproductionteams. Studies were thuscategorized based on differences in knowledge work and physical work. Results are shown in

Table 3. Sixstudies used teams engaged in knowledgework, three studies used teams engaged

in physical work, and two studies have teams that could not be classified. Based on the small




17/28

number of studies included in thecategories, results arespeculative. However, consistent with

expectations, the relationship between intrateamcoordination and performance is stronger for

teams engaged in knowledgework (= .29, CV is .20 to .38) than for teams engaged inphysi-

cal work (= 12, CV is .12 to .12). The results suggest that for teams operating in natural set-

tings, increased intrateam coordination is indeed positively related to performance, and initial

evidencesuggests theeffect maybe stronger when thepredominant work tasks areknowledge

based.

Leadership

As shown in Table 2, the relationship between leadership and performance is consistently

positive (= .26, CV is .00 to .51). Considerablevariation in this relationshipdoes existacross

studies (54%), suggesting the possible existence of moderators. One possible moderator is

type of leadership. Subsequent analyses based on transformational and empowering forms of

leadership thus provide valuable insight into differences across studies.

Transformational leadership. Transformational leadership exhibits a consistently positive

relationship withcollective performance (= .33, CV is .33to .33). Variance for this relation-

ship can be explained by sampling error and unreliability. Transformational leadership thus

appears to be particularly potent for teams regardless of situational differences.

Empowering leadership. Empowering leadership also exhibits a positive relationship with

team performance ( = .32, CV is .03 to .62). Yet, this relationship is likely moderated, as

studies contain variation beyond sampling error and unreliability. This suggests that empow-

ering leadership is likewisebeneficial for teams,but it is more beneficial in some settings than

others.

Discussion

Meta-analysis can be an especially valuable tool for teams research. Many primary studies

are based on rather small samples of teams, calling into question the validity of their conclu-

sions. This quantitative review thus provides insight by identifying important patterns of

results and increasing confidence in conclusions by summarizing results from severalprimary

studies.

Group Composition

Aggregated characteristics. Aggregations of personality, cognitive ability, and expertise do

correspond with team performance. Who is included in the team matters. This finding at theteam level of analysis represents a critical expansion of individual-level research andsupports

the argument that individual characteristics emerge to form a collective construct that links

with higher team-level performance. Moreover, the pattern of relationship strength for these




18/28

aggregatedcharacteristics is similar to thepattern at the individual level.Aggregated cognitive

ability hasthe strongestandmost consistent positive relationshipwith team performance. Per-

sonality has a moderate relationship that varies across settings. Expertise has a small positive

relationship. As a whole, team performance is improved when members have high cognitive

ability, desirable personality traits, and relevant expertise.

Theone typeof aggregatedcharacteristic withvariation across settings is personality, high-

lighting the need for additional team-level research in this area. A likely explanation for the

inconsistent relationship is that some traitsmatter more than others. Studies suggest that inter-

personal traits such as agreeableness matter more in teams than in other organizational set -

tings. Yet, our understanding of which traits are most beneficial for collective performance of

teams is still evolving. Researchers shouldcontinue to examine a variety of traitsso that future

meta-analyses can specifically determine which aggregated traits exhibit the strongest rela-

tionships with collective performance. The current results do, nevertheless, support the gen-

eral usefulness of aggregations of personality as predictors of team performance.

Member heterogeneity. Member heterogeneity exhibits consistently weak relationships

with team performance. This is similar to results reported in two other independent meta-

analyses (Bowers et al., 2000; Webber & Donahue, 2001). The current results do, however,

provide an extension and clarification of previous meta-analytic conclusions. Some support

wasfound for theargument that heterogeneity is most beneficial when teams operate in uncer-

tain and dynamic environments. Nevertheless, the magnitude of relationships with heteroge-

neity is still rather small, suggesting that forming teams based on heterogeneity of member

characteristics is not as important as choosing team members with high ability, expertise, and

desirable personality traits.

Team size. As predicted by Kozlowski and Bell (2003), larger team size is more helpful for

management and project teams. However, even with this finding, the optimal number of team

members may differ across team type. I explored this concern by determining the averagenumber of members for particular teams. Production teams had an average of 12 members.

Project teams had an average of 7 members. Even though team size corresponds with perfor-

mance in project teams, the optimal number of members for project teams may thus still be

smaller than theoptimal number for production teams.A clear prescription for optimum team

size is thus difficult and appears to depend on the purpose and responsibilities of the team.

Future research examining team size should take relevant differences into account and focus

on why optimal team size might vary across types of teams.

Task Design

Task meaningfulness. Task meaningfulness at the group level links positively with collec-

tive performance. This relationship is stronger for some teams than for others. Unfortunately,thecurrent data do notallow formeaningful moderatoranalysis. Additional studies might pro-

vide more data that yield greater insight. Yet, the modest magnitude of the relationship sug-




19/28

gests that research in other areas of task design such as autonomy and intrateam coordination

may be more fruitful.

Autonomy. Autonomy exhibits a moderately strong relationship with team performance.

Teams benefit from having greater information and freedom to makedecisions. This relation-

ship is stronger for physical work than for knowledge work, which is opposite the theoretical

prediction of autonomy being most beneficial for teams performing creative and dynamic

work. As noted by an anonymous reviewer, perhaps this is because such routine work requires

lessdirectionandsupervision, whichis consistent with thepath-goal perspectiveof leadership

(House, 1971). Increased autonomy thus appears to be helpful for teams, but additional

research is needed to understand the environmental conditions that influence the extent to

which autonomy improves performance.

Intrateam coordination. The results show that increased intrateam coordination is desir-

able for teams operating in natural work environments. Greater cooperation is most beneficial

when teams perform creative and dynamic work. Team designers should encourage coopera-

tion among team members, and as expected, the benefits of this coordination are greater for

teams performing complex and creative work.

Leadership

Leadership correlates positively with teamperformance. The morespecificperspectives of

leadershiptransformational and empoweringalso correspond with improved perfor-

mance.Relationships for the twospecific forms of leadership aresimilar andstronger than the

relationship with the broad leadershipcategory. Both transformational and empowering lead-

ers have positive effects on team performance.

Transformational leaders appear to be universally capable of consistently influencing oth-

ers, even when they operate withina largerorganizational structure. Thebenefits of empower-

ing leadership are also positive but vary more across settings, highlighting the need for addi-

tional research that clearly identifies the conditions where empowerment is most beneficial.

The end conclusion, however, extends research at the individual and organizational levels by

showing that leadership does indeed matter for teams.

It is also interesting to note that a goal of both transformational andempowering leadership

is increased empowerment for teams. Very few studies simultaneously measure leadership

and team empowerment, but combining results for transformational and empowering leader-

ship with the results for autonomy illustrates a clear pattern of findings. Teams perform better

when they have increasedopportunity to control their work activities.This effect is found both

when opportunity is measured directly (i.e., autonomy) and when it is measured indirectly via

leader actions that empower teams.

Of course, several leadership studies were not classified as either transformational orempowering. These studies measured leadership areas such as provision of consideration,

structuring of work, performance monitoring, leader experience, and leader technical compe-




20/28

tence. Unfortunately, the studies were so varied that additional classification categories could

not be reliably created, but the substantial variance across the results of these studies suggests

that thevarious measures maynot be assessinga commonconstruct. This problem of prolifer-

ating constructs is particularlyacute for leadership research.Perhaps team settings canbe use-

ful for determining similarities and integrating concepts, as teams present an environment

where different leaderactions canbe linked to group characteristics. Understanding theextent

to which various forms of leadership similarlyinfluence groups might provide insight into the

not yet understood mechanisms by which leadership operates and should be a focus of future

research.

Limitations and Conclusions

This review has several limitations. First, several of the meta-analyses are based on a rela-

tively small number of studies. Findings related to these constructs are tentative. Future meta-

analyses based on additional studies will provide more definitive results in these areas.Several of the classification categories are also quitebroad. As noted, this has the potential

to create construct difficulty. Throughout this study, analyses with broad classifications have

been accompanied by analyses with more specific classifications where possible. Analyses

based on both broad and narrow categorizations provide a balance between classifying

research into categories where enough studies exist to provide a meaningful review andassur-

ingthat classificationis made into homogeneous categories. In theend, theresults suggest that

more specific classification is needed for aggregated personality traits and leadership,

whereas measures of member cognitive ability and task characteristics (meaningfulness,

autonomy, intrateam coordination) appear to assess rather homogeneous constructs.

A third potential limitation is the inclusion of only published studies. The primary goal of

this study is to quantitativelyreview published research. There is a potentialtrade-off between

the quality of unpublished studies and their possible contribution. Not including unpublished

studies can lead to somewhat inflated parameter estimates, as studies reporting weak results

are less likely to get published.

Taking the limitations into account, it seems reasonable to conclude that teams can indeed

be designed for high performance. Important design factors exist for group composition, task

design, and the organizational context of leadership. Teams perform better when members

have higher levels of cognitive ability and expertise. They also perform better when members

have desirable personality traits, but the extent of this benefit varies across team settings. The

task design factors of greater autonomy and intrateam coordination likewise facilitate team

performance. Similar to personality, the extent of benefit varies across teams, with increased

autonomy being more beneficial forteamsdoingphysical work andhighercoordinationbeing

more beneficial for teams doing knowledge tasks. Effective leadership, particularly

transformational and empowering forms of leadership, also improves team performance. The

meta-analyses as a whole thus show that proper design is critical for high-performing teams.Both research andpracticecanadvance by payingincreasedattention to issuesassociatedwith

group composition, task design, and leadership.




21/28

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