COOPERATION, COMPETITION, AND TEAM PERFORMANCE: … Hollenbeck Humphrey Moon... · COOPERATION, COMPETITION, AND TEAM PERFORMANCE: TOWARD A CONTINGENCY APPROACH BIANCA BEERSMA University

COOPERATION, COMPETITION, AND TEAM PERFORMANCE:TOWARD A CONTINGENCY APPROACH

BIANCA BEERSMAUniversity of Amsterdam

JOHN R. HOLLENBECKSTEPHEN E. HUMPHREYMichigan State University

HENRY MOONEmory University

DONALD E. CONLONDANIEL R. ILGEN

Michigan State University

This study examined whether the relationship between reward structure and teamperformance is contingent upon task dimension, team composition, and individualperformance level. Seventy-five four-person teams engaged in a simulated interactivetask in which reward structure was manipulated. A competitive structure enhancedone task dimension, speed, whereas a cooperative structure enhanced accuracy.Teams with extroverted and agreeable members performed better under the coopera-tive structure, whereas teams low on these orientations performed better under thecompetitive structure. Finally, reward structure had more impact on team memberswith low performance.

The degree to which organizations should em-phasize cooperation or competition among themembers of work teams is an age-old controversy.Competitive systems embody equity norms andemphasize performance differences among teammembers, typically rewarding individuals withhigh performance and/or imposing sanctions onthose with low performance. Therefore, some be-lieve that competition promotes efficiency and in-novation because it stimulates individuals to out-perform each other by working faster, or “smarter,”or cheaper, and the belief is that this activity willserve the long-term needs of their organization.Others believe that intrateam competition is de-

structive. In competing, individuals or subgroupsplace their own goals above those of the largerorganization, and the gains achieved by one areoften obtained at the expense of another. For thisreason, some argue that the needs of the largerorganization are better met by employing coopera-tive reward structures. Cooperative systems em-body equality norms and emphasize group ac-complishments. They emphasize minimizingdistinctions among group members (that is, distinc-tions based on performance) because these distinc-tions may impede teamwork, information sharing,and helping.

The inherent tension between competitive andcooperative reward structures has become particu-larly salient in contemporary organizations becausemany of these organizations are trying to make thetransition from individual-based structures toteam-based structures (Allred, Snow, & Miles,1996). Some organizations that have transitionedinto team-based structures over the last severalyears have left their reward structures unchanged.This lack of change resulted in a failure of team-based work to result in the supposed benefits(Hackman, 1998). Indeed, the consensus in the sci-entific literature regarding competitive and collab-orative rewards structures is quite clear in its sup-

This research was conducted while the first authorstudied at Michigan State University on a Fulbright Grad-uate Student Scholarship, and we thank the NetherlandsAmerica Commission for Educational Exchange grate-fully for their support. Henry Moon was also at MichiganState University when the research was conducted.

Grant N00014-99-1-0983 from the Cognitive and Neu-ral Sciences Division of the Office of Naval Researchfinancially supported this research in part. Althoughsupport for this work is gratefully acknowledged, theideas expressed herein are those of the authors and arenot necessarily endorsed by the funding agencies.

� Academy of Management Journal2003, Vol. 46, No. 5, 572–590.

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port for competitive allocations when people areworking independently, but collaborative alloca-tions when people are interdependent (Deutsch,1949; Miller & Hamblin, 1963; Rosenbaum et al.,1980; Stanne, Johnson, & Johnson, 1999; Wageman,1995). The general logic underlying this prescrip-tion is that collaborative reward allocations pro-mote trust, cohesiveness, and mutually supportivebehavior among team members, which in turn pro-mote performance, and this general theme is reiter-ated in almost all organizational behavior textbooks(cf. Ivancevich & Matteson, 1999).

However, the simple notion that rewards formembers of interdependent teams should be col-laboratively based ignores the fact that even withininterdependent groups, any single task can be bro-ken down into subtasks that are evaluated by dif-ferent standards. Over 100 years ago, Woodworthdocumented convincingly that on a task of anycomplexity, speed and accuracy are two separate—and, in some cases, negatively related—aspects of asingle task (Woodworth, 1899). Subsequent re-search on the speed-accuracy distinction has madeit clear that the two task dimensions have verydifferent antecedents (Elliott, Helsen, & Chua,2001), and hence the simple notion that collabora-tive rewards promote both the speed and accuracyof teams seems unlikely to be correct.

Moreover, people have different traits and dispo-sitions, and even within an interdependent teamcontext, individuals make decisions for them-selves. The traits and dispositions of these individ-uals will affect their own behavior in a way thatmay override or run counter to the reward systemunder which they work. For example, team-basedrewards are designed to promote trust and collab-oration, but for over 70 years, psychologists havenoted consistent individual differences in the de-gree to which people are naturally trusting andcollaborative (McDougall, 1932). Indeed, in thebest-established framework for understandingtraits, the five-factor model, two of the five factors(agreeableness and extroversion) are devoted topeople’s interpersonal orientation (McCrae & Costa,1997). As with the speed-accuracy distinction, theidea that one type of reward system—a collabora-tive one—promotes team performance regardless ofthe interpersonal orientation of team membersagain seems unlikely to be true.

Finally, although it is true that organizations thatchange to team-based structures without changingtheir reward systems encounter problems, it alsohas to be recognized that even where organizationschange their reward structures to be more in linewith team-based work, new problems often arise(Ezzamel & Willmot, 1998). In particular, in most

groups, the performance levels of individual mem-bers vary to some degree, and collaborative rewardsdiscount these differences. Psychologists have rec-ognized the “social loafing” phenomenon amongpoor performers in groups for many years (Latane,Williams, & Harkin, 1979), and this problem, per-haps more than any other, underlies the reluctanceabout and resistance to team-based structures thatmany individuals express. Ironically, one of thewell-known prescriptions for avoiding social loaf-ing is to identify individual contributions to agroup’s performance and reward or punish thesecontributions accordingly (Miles & Greenberg,1993). If this prescription is valid, it calls into ques-tion the generic idea that in interdependent teams,collaborative reward structures are going to be mosteffective for all individuals.

With these considerations in mind, we sought inthis study to test the generalizability of the tradi-tionally accepted recommendation to use collabo-rative reward structures in interdependent teams.Specifically, our goal was to develop a contingencymodel of reward structures that encompasses theidea that even in interdependent team contexts, therelationship between the reward allocation struc-ture and team performance will be contingent uponseveral variables related to the task, the composi-tion, and the individual members of a team. Morespecifically, the contingency model we developedfocuses on speed versus accuracy as a crucial dis-tinction when one considers the nature of a task,interpersonal orientation as being an important fac-tor when one considers a team’s composition, andrelative performance level as the central factor in-fluencing a reward structure’s impact on differentteam members.

Given the current state of consensus, questioningthe inherent value of collaborative reward alloca-tions in interdependent team contexts may seemheretical. However, even Stanne and colleagues(1999), who meta-analytically documented the ad-vantages of collaborative rewards, recognized theneed for more refined theorizing regarding the twotypes of reward structures, noting that “more effortneeds to be focused on conceptualizing the essen-tial elements of competition and clarifying the con-ditions under which competition may be effec-tively used” (Stanne et al., 1999: 148). The presentstudy is clearly in line with this recommendation.

THEORY AND HYPOTHESES

Defining Cooperative and Competitive Situations

In his goal interdependence theory of coopera-tion and competition, Deutsch (1949) argued that

2003 573Beersma, Hollenbeck, Humphrey, Moon, Conlon, and Ilgen

people’s beliefs about how their goals are relateddetermine the way in which they interact, which inturn affects their performance and group cohesive-ness. Central to this theory is the categorization ofsituations that create cooperative or competitiveorientations within the people involved.

When a situation is structured cooperatively,there are positive correlations among team mem-bers’ rewards, but when a situation is structuredcompetitively, there are negative correlationsamong team members’ rewards. According to goalinterdependence theory, cooperatively structuredsituations create perceptions of shared fate and pro-mote supportive behavior, whereby each groupmember looks out for the interests of the others. Inaddition, insights and lessons learned by one mem-ber are shared so that all can benefit vicariouslyfrom others’ experiences. On the other hand, ratherthan share information and experience, peopleplaced in competitive structures tend to keep valu-able information proprietary. Moreover, rather thansupporting each other, people placed in competi-tive reward structures may be motivated to impairthe progress of others in an effort to gain positiveadvantage.

Since the formulation of Deutsch’s theory, manystudies have investigated the effects of cooperativeand competitive rewards. Most of these studieshave focused on differences between tasks andsought to determine how to match reward struc-tures with various types of tasks. Meta-analyses ofthese studies have indicated that cooperative struc-tures are far superior for eliciting group perfor-mance when the means interdependence of a taskis high. “Means interdependence” is the degree towhich the task that one member of a team faces isaffected by the performance of others on the team(and hence requires coordination). However, com-petitive structures have been found to be slightlysuperior when people work on “means-indepen-dent” tasks, the completion of which requires littleif any coordination between team members (Stanneet al., 1999). Thus, the theory and the empiricaldata associated with goal interdependence theorysuggest that the reward structure employed in agiven context needs to match the task at hand.Specifically, high-interdependence tasks should bepaired with cooperative rewards, and low-interde-pendence tasks should be paired with competitiverewards.

Dimensions of the Task: The Differing Impacts ofReward Structures on Speed and Accuracy

Although documenting interaction effects be-tween tasks is important, there are also within-task

issues that need to be considered when rewardstructures are designed. Most complex tasks aremultidimensional and correspondingly place mul-tiple demands on role incumbents. At the veryleast, even after one has classified a task as requir-ing a high or low level of interdependence (a be-tween-tasks consideration), the task itself can stillbe differentiated in terms of whether it demandsspeed, or accuracy, or both speed and accuracy inits execution.

Most complex tasks require some degree of bothspeed and accuracy, but there are trade-offs thatmake meeting both of these task requirements at thesame time difficult (Woodworth, 1899; Elliott et al.,2001). That is, a manufacturing team can workquickly to produce a large number of products, butthese products may have more defects than whatmight have been the case if the team worked slowlyand carefully and produced only a small number ofproducts. This type of speed-accuracy trade-off isubiquitous in complex tasks. For example, a pitcrew in an automobile race needs to quickly repairand maintain the car, but at the same time it has tomake sure that all the necessary repairs are per-formed in order to avoid future mishaps. Air trafficcontrollers need to work quickly with pilots andground controllers to keep arrivals and departureson time, but at the same time, they also have toensure that safety standards are being met. Emer-gency medical teams need to work quickly to sta-bilize patients, but at the same time, they shouldnot make any errors that may endanger the futurehealth of the patients. Weapons directors in mili-tary strike teams need to work quickly to exploitwindows of opportunity in the enemy’s defenses,while at the same time minimizing “friendly fire”casualties.

Because the trade-off between the speed of taskexecution and the accuracy of task execution isapparent in so many team tasks, it is important tonote that research concerning the performance ofindividuals working alone on independent taskshas shown that different reward structures influ-ence speed and accuracy differently. That is, in ameta-analysis of 39 studies examining the impactof financial incentives on individual performance,Jenkins, Mitra, Gupta, and Shaw (1998) showedthat financial incentives had a much stronger im-pact on tasks in which performance was measuredin terms of speed rather than accuracy. Jenkins andcolleagues speculated that incentives influencespeed more than accuracy because speed is moresensitive to effort and hence under the control of anindividual to a greater extent. Accuracy, on theother hand, may require skills or abilities that theindividual simply may not possess.

574 OctoberAcademy of Management Journal

Jenkins and colleagues’ (1998) results make itclear that even after the level of interdependence aparticular task requires is diagnosed (in the case oftheir study, interdependence was low, with indi-viduals working alone), the job of determining theappropriate reward structure is still unfinished.One still needs to consider the dimension of thetask that is most crucial (speed or accuracy). Anal-ogously, even after determining that a task is highin interdependence, and hence needs to be per-formed by a team, one may still need to considerwhether one is going to emphasize speed or accu-racy of task execution. Unfortunately, the questionof how various rewards influence the speed andaccuracy of interdependent teams could not be es-tablished in the Jenkins et al. study because therewere no studies of teams that examined within-taskdifferences in the quantity versus the quality ofproduction.

There is some evidence that different rewardstructures differently influence different aspects ofa team’s task, although the research providing thisevidence did not focus on contrasting speed andaccuracy. Beersma and De Dreu (2003) showed thatteams that worked under a cooperative structuretended to perform better on the “convergent” as-pects of a creative team task (for instance, generat-ing feasible ideas), while teams that worked undera competitive structure tended to perform better onthe “divergent” aspects of the task (such as gener-ating original ideas). Thus, when a team’s task isconceptualized as multifaceted, much more can belearned about when and why different rewardstructures should be used or avoided.

The same types of within-task differences seen inthe studies of individuals that were meta-analyzedby Jenkins and coauthors (1998) and in the teamsstudied by Beersma and De Dreu (2003) might bemanifested with respect to reward structures inteam contexts. For example, according to goal in-terdependence theory, even though they are work-ing as part of an interdependent team, peoplewithin teams who are exposed to a competitivereward structure may feel less cohesive. As such,they may react like the individuals studied by Jen-kins and colleagues and increase the speed withwhich they work. Moreover, a great deal of researchdocuments that people working in groups generallytake longer to complete tasks than would individ-uals working alone (Levine & Moreland, 1998), andthis time lag would only be exacerbated by rewardstructures that promote discussion, collaboration,and information sharing. If this were true, then ininterdependent teams, the speed of performancemight be higher when rewards are structured com-petitively rather than cooperatively.

Alternatively, given that financial incentiveshave not been shown to have an effect on the accu-racy of performance when individuals work alone(Jenkins et al., 1998), it seems unlikely that com-petitive rewards would promote this aspect of taskperformance in teams. However, the collaborativenature of a group’s interaction when its memberswork under a cooperative reward structure wouldseem to have the potential to enhance accuracy. Inmany complex team tasks, accuracy is a function oftask-relevant knowledge. Under a cooperative re-ward structure, the team member with the mostknowledge can share what he or she knows withthe team members with the least knowledge, whocan then use this knowledge to assess their ownwork processes in a way that allows them to per-form higher-quality work than they could havemanaged on their own. If this were true, then theaccuracy of performance might be higher when re-wards are structured cooperatively than when theyare structured competitively. Taken together, thesearguments lead to our first hypothesis:

Hypothesis 1. The relationship between rewardstructure and performance is contingent uponthe dimension of a task: speed is enhanced bycompetitive reward structures, whereas accuracyis enhanced by cooperative reward structures.

Team Composition: The Impact of RewardStructures Composed of Interpersonally Skilledand Interpersonally Unskilled Members

Just as one may need to address concerns regard-ing speed versus accuracy before choosing a rewardstructure, one may also need to consider the dispo-sitional characteristics of team members prior tochoosing a reward structure. That is, there mayneed to be a fit between the traits of members andthe reward structure used with a team. The argu-ment that there needs to be a fit between peopleand their work environment is consistent with along line of theorizing on diverse aspects of organ-izational behavior in general (Kristof, 1996) and onteams in particular (Hollenbeck et al., 2002). Manyof the more recent investigations into fit have ex-ploited the emergence of the five-factor model as arobust, well-grounded, and culturally generalizableconceptual framework and measurement systemfor conducting research on individual differences(McCrae & Costa, 1997). However, to date, verylittle research has addressed the question of whattypes of people function best under cooperativeand competitive reward structures (Wageman,1995), and no research whatsoever has approachedthis issue employing the five-factor model.


Because goal interdependence theory focuses onhow a reward structure influences the interactionpatterns between team members, traits within thefive-factor model that relate to interpersonal orien-tation are especially relevant in this context. Thatis, the five traits that are identified by this approachcan be conceived as composing a “circumplex” (acircular configuration) in which certain pairs oftraits are more theoretically proximal to each otherthan are others. Costa and McCrae (1992) definedthe interpersonal plane of the circumplex as beingcomprised of extroversion and agreeableness. Be-cause these traits influence interaction patterns be-tween people, they seem ideally suited to beingintegrated with goal interdependence theory in aneffort to understand how people are likely to reactto reward structures.

More specifically, Costa and McCrae (1992) de-scribed extroverts as “liking people and working ingroups.” In contrast, introverts are “reserved andindependent” and “tend to dislike and avoid socialstimulation” (Costa & McCrae, 1992: 15). It is easyto see how this dispositional characteristic couldsupport or contradict an existing reward structure.Cooperative reward structures reinforce the pro-clivities of extroverts but work against the procliv-ities of introverts. In contrast, competitive struc-tures fit the interpersonal style of introverts betterthan they fit the style of extroverts.

Agreeableness is the second trait that Costa andMcCrae placed in the “interpersonal plane” of thefive-factor model. Those high in agreeableness aredescribed as “fundamentally altruistic, sympa-thetic to others, eager to help and be helped inreturn. By contrast, the disagreeable person is ego-centric, skeptical of others’ intentions, and compet-itive rather than cooperative (Costa & McCrae,1992: 15). Clearly, as in the case of extroversion,cooperative reward structures reinforce the dispo-sitional tendencies of highly agreeable people butwork against those of people who are low in agree-ableness, and vice versa for competitive rewardstructures.

Thus, both of these traits associated with theinterpersonal plane of the five-factor model shouldbe relevant for creating a fit between a reward struc-ture and a team’s members. More specifically, wepropose

Hypothesis 2a. The relationship between re-ward structure and performance is contingentupon the extroversion of team members: theperformance of teams whose members are highon extroversion is higher when reward struc-tures are cooperative, whereas the perfor-mance of teams whose members are low in

extroversion is higher when reward structuresare competitive.

Hypothesis 2b. The relationship between re-ward structure and performance is contingentupon the agreeableness of team members: theperformance of teams whose members are highon agreeableness is higher when reward struc-tures are cooperative, whereas the perfor-mance of teams whose members are low inagreeableness is higher when reward structuresare competitive.

The Relative Standing of Team Members: TheImpact of Reward Structures on the Best andWorst Performers

In any team, it is unlikely that all members areexactly equal in their ability or willingness to con-tribute to the team’s overall performance level. Re-cent theoretical work has explicitly addressed howthe member with the lowest performance influ-ences a team (LePine & Van Dyne, 2001). Coopera-tive and competitive reward structures are bothposited to influence this issue. The first, positiveproposition is that the member with the mostknowledge will share that knowledge with the leastknowledgeable person and thereby raise the latter’sperformance higher than it would have been hadthat individual been working alone. This phenom-enon is a “training response,” in the words ofLePine and Van Dyne (2001). The second, negativeproposition is that, under a cooperative structure,members, who have the opportunity to free ride onthe accomplishments of the other team members,will engage in social loafing (Latane et al., 1979).Social loafing can create animosity among teammembers and restrict group output.

Interestingly, the traditional “cure” for socialloafing is to isolate individual contributions to agroup’s performance and reward or punish peopleon the basis of these contributions (LePine and VanDyne [2001] called this a “motivate response”). Ofcourse, rewarding and punishing individual contri-butions implies having used a competitive rewardstructure aimed at increasing the performance ofthe potentially poorest performers. The ironic partof this analysis, of course, is that each of the twomutually exclusive reward structures is seen as thesolution to enhancing the performance of groupmembers viewed as likely to be the worst performers.

One way to perhaps resolve this discrepancy is torevisit the distinction between speed and accuracy.When contributors to the social loafing literaturehave focused on individuals with the lowest per-formance in a group, the presumption has been that


they are failing to put forth effort. When authorsadvocating interdependence have focused on indi-viduals with the lowest performance, the presump-tion seems to be that they lack knowledge or in-formation. Both approaches could be valid ifcompetitive reward structures work to increase thespeed (effort) of team members, but cooperativereward structures work to increase their accuracy(knowledge). This formulation would be consistentwith Jenkins, Mitra, Gupta, and Shaw’s (1998) ar-gument that an individual may have more controlover the speed of task performance, which islargely a function of effort, than over the accuracyof task performance, which is more closely relatedto skill.

Moreover, the impact of the two different rewardstructures on the poorest performer in a group(when performance is individually assessed) islikely to be stronger than the structures’ impact onthe group’s best performer for several reasons. First,the group decision making literature makes it veryclear that the accuracy of groups’ decisions tends tobe much higher than that of their average membersbut is rarely better than that of their best members(Levine & Moreland, 1998). Thus, from a goal inter-dependence perspective, the member of a teamwho has the lowest amount of knowledge has agreat deal more to gain from collaboration the mem-ber with the most knowledge. Similarly, from asocial loafing perspective, the team’s slowest mem-ber has much more to fear from having this inca-pacity made public than the team’s fastest memberhas to fear from having this ability made public.These arguments suggest that reward structure willbe observed to have more strongly influenced theperformance of the members of a team who havethe lowest individual performance. Therefore, wepropose

Hypothesis 3. The impact of a team’s rewardstructure on the speed and accuracy of theteam’s poorest performer is stronger (in termsof explained variance) than the impact of thereward structure on the speed and accuracy ofthe best performer.

METHODS

Research Participants

Three hundred business students at MichiganState University were arrayed into 75 four-personwork teams. Sixty percent of our sample memberswere male, and approximately 90 percent wereCaucasian. In return for their participation, partic-ipants earned class credit and were eligible for cashprizes ($10 per student) based upon their perfor-

mance (see “Reward structure” under “Manipula-tions and Measures,” below).

Task and Objectives

Participants engaged in a dynamic and net-worked computer simulation. The task was a mod-ified version of a simulation developed for the U.S.Department of Defense for research and training,Michigan State University Distributed Dynamic De-cision Making (MSU-DDD). The version of the taskused here was developed for teams of two to fivemembers with little or no military experience.

The geographical space and mission. Figure 1depicts the grid used in MSU-DDD. This grid waspartitioned in several ways. First, four geographicquadrants of equal area (NW, NE, SW, SE) weredefined, and each area was assigned to one teammember, who was called a “decision maker” (hencethe abbreviation “DM” in Figure 1). The grid wasalso divided into three zones that varied on theextent of protection from penetration by unfriendlyforces they needed. The regions were labeled “neu-tral,” “restricted” (a 12-by-12 grid in the center),and “highly restricted” (a 4-by-4 grid in the centerof the restricted zone). The team’s mission was tomonitor this air and ground space, keeping un-friendly forces from moving into the restricted ar-eas, while at the same time allowing friendly forcesto move about freely. Radar representations of theseforces moving through the geographic space moni-tored by the team were known as “tracks.”

Each decision maker’s base had a detection ring(base DR in Figure 1) radius of roughly six gridunits to use in monitoring the geographic space.The decision maker could detect the presence orabsence of any track within this detection ring.Each base also had an identification ring (base IR inFigure 1) radius of roughly four grid units. A teammember could discern whether a track was friendlyor unfriendly once it was within this range. Anytrack outside the DR was invisible to the teammember from the base. A team member whowanted to determine the nature of a track outsidethe identification ring had two options: ask team-mates to share that information, or launch a vehicleand move it near the track. Since each vehicle hadits own detection and identification rings andcould be moved anywhere on the screen, all partic-ipants could detect and identify any track any-where on the screen, but it took more effort toengage tracks outside of one’s personal region.

Vehicles. Each team member had control of fourvehicles that could be launched and moved to dif-ferent areas of the screen. These vehicles couldautomatically perform certain functions (follow


designated tracks, return to base to refuel, and soforth), and hence each team member was the man-ager of semi-intelligent agents. Each team memberhad one AWACS plane, one tank, one helicopter,and one jet. These vehicles varied in their capaci-ties on four dimensions: range of vision, speed ofmovement, duration of operability, and weaponscapacity.

An asset that was high on one dimension tendedto be low on another, meaning each asset had its

own unique advantages and disadvantages. For ex-ample, the tank had high weapons capacity but ashort range of vision, whereas the AWACS had lowweapons capacity but a wide range of vision. Thus,the various vehicles constituted a complex set ofassets that ranged widely in their capacities. Asymbol for each vehicle appears in Figure 1, alongwith the ranges of vision that characterized eachvehicle (depicted by the largest ring surroundingeach vehicle). A team member could operate any or

FIGURE 1The DDD-MSU Grid


all of the vehicles concurrently, but it took moreeffort to simultaneously operate multiple vehicles.For example, when a track appeared, a personcould simply launch one vehicle and move it toengage the incoming track. Alternatively, the sameperson could work quickly to launch all four vehi-cles, move them to various areas of the geographicspace, in anticipation of incoming tracks, and in-tercept them as soon as they crossed over into therestricted zone. Thus, the speed with which trackswere engaged was a function of how hard a personor team was working. Because of the variation inthe four vehicles’ capacities, it required a great dealof cognitive effort to effectively have all four vehi-cles out at once and then use them efficiently, butdoing this did increase the speed with which trackswere engaged.

Identifying and engaging tracks. All tracks orig-inated from the edge of the screen and proceededinward. It was important for team members to iden-tify tracks quickly and differentiate them along twodimensions: (1) friendly versus unfriendly and (2)standard versus novel. When a track was closeenough to be detected but not close enough to beidentified, it was represented by a question markfollowed by a unique identification number setabove a diamond (see the bottom right portion ofFigure 1 for an example). Once the track camewithin the identification range of either the base ora vehicle, the team member could identify it. Onceidentified, the symbol representing the trackchanged from a diamond to a rectangle with aletter-number combination in it (see the middle ofFigure 1). The letter indicated whether the trackwas in the air or on the ground. The number indi-cated whether the track was friendly or unfriendly,and if it was unfriendly, the amount of powerneeded to disable it. The team member who madethe identification was the only one who could seethis information, although he or she could sharethis information with other team members.

If a track within the restricted zones was identi-fied as being unfriendly, team members needed todisable it. There were two requirements for suc-cessful disabling. First, the track had to be closeenough, meaning that it had to be within the attackring of the vehicle engaging it. Second, the vehicleneeded to have as much power as the track (asindicated by the number in the rectangle), or morepower. If a team member attempted to engage atrack that was too far away or for which he or shehad insufficient power, the track continued on un-impeded. If the track was successfully engaged, itdisappeared from the screen. The attacking vehiclethen had to return to base to reload and refuel.

There were eight types of “standard tracks” that

were known a priori to have specific characteris-tics, and these were taught in the training sessionprior to the start of the task. There were also fourtypes of “novel tracks” that were not encounteredduring training. Thus, team members did not knowwhether the novel tracks were air-based or ground-based, or friendly or unfriendly, or powerful or notpowerful. Trial-and-error experience gained fromthe simulation was the only source of this knowl-edge. Thus, determining the nature of the noveltracks was a complex deductive exercise in whichsome behaviors were more diagnostic than others(better for supporting or refuting specific hypothe-ses about a track). This complexity created an op-portunity for decision-making errors to occur, andthus the performance of teams could be evaluatednot just in terms of their speed, but also in terms oftheir accuracy. Thus, a team’s objective was to dis-able enemy tracks as fast as possible while notdisabling any friendly tracks (that is, makingfriendly fire errors) or wasting resources by engag-ing enemy tracks with more power than wasneeded.

Manipulations and Measures

Reward structure. Teams were randomly as-signed to either a cooperative or a competitive re-ward structure. Participants assigned to the coop-erative condition were informed that each of theteams that had the best overall team performancewould receive a reward of $40, which would besplit evenly among the team members, regardless ofhow well they performed as individuals. Partici-pants under the competitive condition were in-formed that the top-performing individuals wouldeach receive a reward of $10, regardless of howwell their teams performed as a whole.

Because the number of tracks was fixed at a rel-atively low number (19 tracks per quadrant), toobtain a score that was high enough to warrantwinning the individual bonus in the competitivecondition, an individual had to venture outside ofhis or her quadrant during the simulation. That is,he or she had to detect, identify, and attack tracksin the other team members’ quadrants, thus limit-ing the potential score of the other team members.Thus, it was impossible for two members of oneteam to both qualify for the bonus in the competi-tive condition.

We used a four-item competitive orientationscale and a three-item cooperative orientation scale(1 � “disagree strongly” and 5 � “agree strongly”)to check the adequacy of the manipulation. A sam-ple item used to measure competitive orientationwas “While I was playing the DDD game, I was


competing with the others on my team.” A sampleitem used to measure cooperative orientation was“While I was playing the DDD game, it was impor-tant to achieve as many points as possible as ateam.” The four competitive items formed a reliablescale (� � .93), as did the three cooperative items(� � .93).

Extroversion and agreeableness. Extroversionand agreeableness were each measured with a 12-item scale taken from the short form of the RevisedNEO Personality Inventory (NEO-PI-R-short). Thisis the most widely used instrument for measuringthe five-factor model, and Costa and McCrae (1992)have provided ample historical evidence on thereliability and construct validity of these measures.Coefficient alpha estimates of reliabilities for extro-version and agreeableness in this specific studywere .79 and .76, respectively.

If team members’ extroversion and agreeablenessare likely to be relevant for how teams react toreward structures, the question becomes how toaggregate these personality variables to the teamlevel. In a recent review of empirical research onthis issue, Moynihan and Peterson (2001) con-cluded that the best method of aggregating disposi-tional scores depends upon the nature of the task,particularly the degree of task interdependence.This conclusion is consistent with earlier argu-ments put forth by LePine, Hollenbeck, Ilgen, andHedlund (1997), Barrick, Stewart, Neubert, andMount (1998), and Neuman and Wright (1998),who all noted that when interdependence amongtask members is low, an additive model (using av-erages) is most appropriate, but when interdepen-dence is high, a conjunctive model (using lowestscores) is most appropriate. According to Moyni-han and Peterson, a conjunctive model better cap-tures highly interdependent teams because it re-flects the fact that one team member can have adisproportionate impact on a team as a whole. Be-cause the level of interdependence among teammembers in MSU-DDD is clearly very high (see“Procedures”), we measured team composition us-ing a conjunctive model.

Speed and accuracy. Speed was defined as theaverage amount of time it took to disable unfriendlytracks. Accuracy was success in avoiding two typesof errors, both of which were automatically re-corded by MSU-DDD. A friendly fire error occurredwhen a team member engaged a friendly track, anda rules of engagement error occurred when anyoneengaged a track outside the restricted zone. Thescores for each type of error were summed into anaccuracy score.

Accuracy was then recoded so that a high valuereflected high performance, and the values were

then standardized to make the measures of the twotask dimensions comparable. Speed and accuracyscores were assessed at both the team and individ-ual level. Overall performance was measured as thesum of the standardized scores for speed and accu-racy, also for both teams and individuals.

Procedures

We first administered the Revised NEO Person-ality Inventory to assess participants’ extroversionand agreeableness. Then, each participant was ran-domly assigned to a four-person team, and then theteams were randomly assigned to reward structureconditions. The teams were trained together forapproximately 90 minutes. Because rewards canonly work if people have feedback and knowledgeof results, we focused the team members on therelevant scores (individual scores in the competi-tive condition and team scores in the cooperativecondition) throughout the training.

The first 30 minutes of training were devoted tofamiliarizing the participants with the object of thesimulation, its scoring, and the capabilities andcharacteristics of the vehicles employed in the sim-ulation. The next 30 minutes of training concen-trated on how to manipulate the vehicles: launch-ing them, moving them around the screen,identifying targets, and disabling targets. The final30 minutes of training provided the participantswith an opportunity to practice their new skills inan environment similar to the environment inwhich they would later perform. During this pe-riod, participants were allowed to ask their trainersquestions as they practiced. In addition, the trainercould help those who seemed to be having diffi-culty with the task.

The teams then performed the task for the exper-imental session, during which each team, regard-less of condition, experienced the same number,nature, timing, and sequence of tracks. Thus, thetask was identical for all the teams. A total of 76tracks appeared during the experimental session,and each participant experienced 19 tracks thatoriginated in his or her quadrant. The tracks neverstayed within the quadrant they originated in; in-stead, they crossed from one team member’s area toanother. It is also important to stress that the teammembers were not restricted to operating vehicleswithin their own quadrants, but instead couldmove their vehicles into other quadrants. Thus,even though a track may have originated in the SEquadrant, the team member from the NW quadrantcould be the first person to engage it. Because bothtracks and vehicles were free to roam quadrants, allteams in this simulation experienced a great deal of


means interdependence. That is, what one personneeded to do (or could do) was strongly influencedby what others were doing. For a team in the coop-erative condition, if one team member was workingslowly or ineffectively and failed to engage tracksthat originated in his or her quadrant, all the otherplayers had to “clean up after” this person. For ateam in the competitive condition, if one personwas aggressively “hogging all the tracks,” the num-ber of opportunities for other members of the teamwent down. Regardless of the team members’ ownperceptions of outcome interdependence, from amanagerial perspective, the goal of each team wasthe same: defend the geographic space with asmuch speed and accuracy as possible.

Data Analysis

The research design employed in this study hadboth between-teams and within-teams elements.Reward structure was a between-teams measure,because each team obtained only one score (0 or 1)for this variable. The nature of the task is a within-teams measure, since each team obtained twoscores, one for speed and one for accuracy. Giventhis mixed-level design, we used repeated-measures regression analysis to analyze the data.

A full description of repeated-measures regres-sion is beyond the scope of this article (see Cohenand Cohen [1983: 428–451] for an extensive treat-ment). In general, this type of analysis decomposesvariance in the dependent variable (overall perfor-mance in this case) into two orthogonal sources;here, these are variability between teams (that is,some teams perform better than others regardless ofthe speed-accuracy distinction), and variabilitywithin teams (within a single team, there is vari-ance in performance depending on whether speedor accuracy is the task dimension).

The criteria are then regressed on the predictorsand matched to their levels. A between-teams ma-nipulation like reward structure is used to try toexplain variance between teams (some teams per-form better than others regardless of the speed-accuracy distinction), whereas a within-teams mea-sure like the nature of the task (the dummy-codedspeed-accuracy distinction) is used to attempt topredict variance within teams (within any oneteam, performance varies depending upon whetherone is looking at speed of task execution or accu-racy of task execution). The technique also allowsone to examine interactions among within- andbetween-teams measures and to directly test if theeffect of a between-teams manipulation like rewardstructure has different influences on different taskdimensions. Such a difference in effects is one of

the core ideas underlying the contingency modelwe are testing.

The test of this within-between interaction isidentical to what would be obtained if one were totreat speed and accuracy as separate dependentvariables, separately regress reward structure oneach, and then test for the differences in unstand-ardized regression coefficients. The repeated-measures approach, however, provides a directmeasure of the statistical significance of the differ-ence in unstandardized regression weights, as wellas a direct measure of the effect size for the inter-action (that is, the variance explained), the latter ofwhich cannot be obtained when speed and accu-racy are treated as separate dependent variables.Summarizing the above, we would note that ifspeed and accuracy were treated as separate depen-dent variables and analyzed in separate regressionanalyses, and if these analyses were followed up bya test for the differences in unstandardized regres-sion coefficients, the conclusions drawn from ourdata would be identical to our present conclusions.However, we chose the repeated-measures regres-sion approach because it was more parsimoniousand direct (for more information on this topic, werefer the reader to Hollenbeck, Ilgen, and Sego[1994]).

RESULTS

Descriptive Statistics and Manipulation Checks

Table 1 presents the means, standard deviations,and correlations for the variables of interest. Asmight be expected, there was a slightly negativecorrelation between performance levels on the twodimensions of the task, speed and accuracy (r ��.22, p � .06), suggesting some degree of trade-off.Also, as might be expected from the fact that extro-version and agreeableness are both parts of theinterpersonal plane of the five-factor model cir-cumplex, there was a slight positive correlationbetween these two variables (r � .22, p � .06).

Analysis of variance (ANOVA) of cooperativeversus competitive orientation showed that the ma-nipulation of reward structure was successful.Teams working with the cooperative reward struc-ture had a more cooperative orientation (mean �4.26, s.d. � 0.46) than teams in the competitivereward structure (mean � 2.65, s.d. � 0.53, F [1,73] � 184.77, p � .01). Also, teams with the com-petitive reward structure had a more competitiveorientation (mean � 3.51, s.d. � 0.52) than teams inthe cooperative reward structure (mean � 2.24, s.d.� 0.53, F [1, 73] � 104.74, p � .01).


Tests of Hypotheses

Hypothesis 1. Table 2 shows the results of arepeated-measures regression analysis designed totest Hypothesis 1. This regression is based upon150 observations: 75 teams were observed on twotask aspects, speed and accuracy. As noted above,team-level variance in these 150 observations iseither within-teams, based on the task dimension(speed versus accuracy) or between-teams, basedon overall performance.

As shown in Table 2, 61 percent of the totalvariance in the 150 observations was attributable towithin-teams variance, whereas the remaining 39percent was attributable to between-teams vari-ance. The first row of this table shows that therewas no effect for speed versus accuracy (a naturalresult of standardizing the variables), and the sec-ond row shows that there was no “main effect” forreward structure (that is, no one structure was bet-ter irrespective of the speed-accuracy distinction).The third row of this table shows that a statisticallysignificant interaction between the reward struc-ture and the nature of the task explained 21 percentof the within-teams variance. This interaction isplotted in Figure 2. Consistent with Hypothesis 1,cooperative reward structures had a positive effect

on accuracy, but a negative effect on speed,whereas competitive reward structures had a neg-ative effect on accuracy, but a positive effect onspeed. As noted above, if speed and accuracy weretreated as separate dependent variables and ana-lyzed in separate regression analyses, the conclu-sions drawn from our data would be identical toour present conclusions. Specifically, the coeffi-cients associated with the separate regressions onspeed and accuracy were �.26 and .47, respectively(p � .03 and .01, respectively).

An additional analysis supported the idea thatcooperative structures worked well because theypromote diffusion of knowledge throughout a team.We analyzed accuracy scores separately for thestandard and the novel tracks. (Recall that standardtracks were those covered in the training, and thenovel tracks were not presented in training.) If thecooperative structure worked because it promoteddiffusion of knowledge, then the effect this rewardstructure had on accuracy should be stronger forthe novel tracks than for the standard tracks.

We tested this idea by creating a difference mea-sure (novel-track errors minus standard-track er-rors) and comparing the scores between conditions.We found that the competitive teams made 3.58

TABLE 1Descriptive Statistics for the Between-Team Variablesa

Variable Mean s.d. 1 2 3 4 5

1. Reward structure 0.40 0.492. Agreeableness 3.10 0.47 �.043. Extroversion 3.04 0.39 .17 .224. Speed 0.00 1.00 �.26* �.04 �.035. Accuracy 0.00 1.00 .47* �.07 .22 �.226. Average performance 0.00 0.63 .16 �.09 .15 .65* .61*

a n � 75. Reward structure was dummy-coded; 0 � competition and 1 � cooperation.* p � .05

TABLE 2Results of Repeated-Measures Regression Analysis for Performance on Task Dimension and

Reward Structurea

Step Independent Variable � Total R2 �R2

IncrementalVariance

within Teamsb

IncrementalVariance

between teamsc

1 Task dimension �0.01 .00 .00 .002 Reward structure �0.10 .01 .01 .033 Task dimension � reward

structure�1.29* .14* .13* .21*

a For task dimension, speed � 0 and accuracy � 1. For reward structure, competition � 0 and cooperation � 1.b 61 percent. n � 150 (two observations per 75 teams; df � 150 � 75 � k � 1).c 39 percent. n � 75 (one observation per 75 teams; df � 75 � k � 1).

* p � .05, one-tailed test


more novel-track errors than standard-track errors,but the cooperative teams only made 2.27 morenovel-track errors than standard-track errors. Thisdifference was significant (t [73] � 3.26, p � .01).Thus, our observations are consistent with goal in-terdependence theory: people working in coopera-tive reward structures seemed to do a better job ofsharing information and diffusing knowledge aboutnovel tracks that had to be gained on the job.

Hypotheses 2a and 2b. Table 3a shows the re-sults of a regression equation designed to test Hy-pothesis 2a. The first row again shows that no sin-gle reward structure was best with respect tooverall team performance aggregated across the twodimensions of the task. The second row of this tableindicates that there was no main effect for extrover-sion. The third row of this table, however, indicatesthat the interaction between extroversion and thereward structure explained 5 percent of the vari-ance. Figure 3, the plot of this interaction, showsthat extroverts respond positively to cooperativereward structures, whereas introverts respond neg-atively to this type of reward structure. This patternof findings supports Hypothesis 2a.

In Table 3b, results for the same equation com-puted for agreeableness mirror those in Table 3a inthat there was no main effect for agreeableness, but

a statistically significant interaction between agree-ableness and reward structure. Figure 4 graphicallyshows that only agreeable individuals respond pos-itively to cooperative structures. Thus, Hypothesis2b was supported.

The results documented in these tables are basedupon a conjunctive measurement of team composi-tion that was chosen because of the high level ofmeans interdependence. Because the task couldalso be argued to have some additive elements, werepeated these regressions employing an additivemodel, and there were virtually no differences inthe parameter estimates obtained from the alterna-tive analysis.

In addition to evaluating results in terms of sta-tistical significance, it is worthwhile to examinethe sum of these effects in a practical sense, bylooking at the raw number of decision-making er-rors made by different types of teams under differ-ent reward conditions. For the teams above themean on extroversion and agreeableness and work-ing under the cooperative reward structure, the av-erage number of errors was 3.8. In contrast, underthe competitive reward structure, teams above themean on extroversion and agreeableness averaged6.7 errors.

Hypothesis 3. Table 4 shows the results of tworepeated-measures regression analyses designed totest Hypothesis 3. These regressions are replica-tions of those presented in Table 2, with the excep-tion that, rather than team-level performance, theyexamine the best and worst individual performancein each team. With respect to the poorest performerin each team, as shown in the top half of Table 4, 28percent of the within-person variance in perfor-mance can be attributed to the interaction betweenreward structure and the task dimension. In con-trast, as indicated in the bottom half of this table,

FIGURE 2Interaction of Task Dimension and Reward Structure

TABLE 3aResults of Regression Analysis of Performance on

Reward Structure and Extroversiona


1 Reward structure 0.16 .02 .022 Extroversion �0.12 .04 .023 Extroversion � reward structure 1.75* .09* .05*

a n � 75 (one observation per 75 teams; df � 75 � k � 1).* p � .05, one-tailed test


among the best performers this interaction explainsonly 9 percent of the variance. Figure 5 depictsthese interactions; consistent with Hypothesis 3,although the interactions are similar for the bestand the worst performers, they are clearly greaterfor the worst performers.

DISCUSSION

The current consensus regarding reward struc-tures suggests that competitive structures should beused when people are working independently,whereas cooperative reward structures should beused when people are working interdependently(Deutsch, 1949; Miller & Hamblin, 1963; Rosen-baum et al., 1980; Stanne et al., 1999; Wageman,1995). Because all of the research participants inthis study were working within means-interdepen-dent teams, this study does not speak to how oneshould design reward structures when people areworking alone. However, with respect to peoplewho are working interdependently, the results fromthe current study both support and qualify the va-lidity of the conventional recommendations.

Reward Structures and Accuracy of Performance

If a team is primarily concerned with the accu-racy of performance, and if this team is composed

of extroverted and agreeable members, our resultsvalidate the conventional recommendations. Thatis, when it came to decision-making errors, a teamwith the appropriate interpersonal orientation justdescribed made almost twice as many errors whenplaced in a competitive reward structure than thesame type of team placed in a cooperative rewardstructure.

Reward Structures and Speed of Performance

Few teams have an unlimited amount of time inwhich to complete their work and, in line with pastresearch (Elliott et al., 2001; Woodworth, 1899),this study showed that speed and accuracy areseparable aspects of a task (r � �.22). Moreover,when it came to decomposing variance in teamperformance, most of the variance was attributableto within-team differences on the two differentaspects of the task (61%), not to between-team dif-ferences in overall performance (39%). If one rec-ognizes this distinction, the standard recommenda-tion regarding the use of cooperative rewards ininterdependent teams needs some qualification.

Cooperative reward structures had a negative ef-fect on the teams’ speed. The measurable impact ofthis interaction far exceeded what one would ex-pect just given the moderate negative relationshipbetween the two performance dimensions. That is,although accuracy alone accounted for only 5 per-cent of the variance in speed, the interaction ofreward structure and task dimension accounted for21 percent of the variance. This seems to suggestthat there was something slowing teams down inthe cooperative structure other than simply thegreater attention they were giving to accuracy.

The interaction plotted in Figure 5 suggests thatthis finding may be attributable to social loafing onthe part of the worst performer. As is apparent inthis figure, the reward structure had virtually no

TABLE 3bResults of Regression Analysis of Performance on

Reward Structure and Agreeablenessa


1 Reward structure 0.16 .02 .022 Agreeableness 0.08 .03 .013 Agreeableness � reward structure 1.63* .08* .05*

a n � 75 (one observation per 75 teams; df � 75 � k � 1).* p � .05, one-tailed test

FIGURE 3Interaction of Extroversion and Reward Structure


effect on the speed of the best performer, but adiscernable effect on the speed of the slowest teammember. If the effect of cooperative reward struc-tures on speed simply reflected more time spent ondiscussion, information sharing, and a concern foraccuracy, this timing effect should have been inevidence for both the best and worst performers.After all, according to goal interdependence theory,these two people should both be talking—in fact,they should be talking to each other. Instead, onlythe worst performer seemed to slow down when afree-riding opportunity was created by the cooper-ative reward structure. As the social loafing re-search would suggest, switching from a cooperative

to a competitive reward structure largely solved thespeed problem in these teams.

Although few organizations would be willing toforego all quality considerations in a full-blowneffort to speed workers up, it is nevertheless truethat many organizations compete on speed. In fact,in many negotiations, delivery time is a decidingfactor in successfully winning contracts (road con-struction teams are an example). In addition, incertain contexts (such as seasonal promotions in aretail unit), unless the product can be delivered ontime, its quality will not matter. Speed of opera-tions is also a well-known force multiplier: if theproduction of given number of workers in a manu-

FIGURE 4Interaction of Agreeableness and Reward Structure

TABLE 4Results of Repeated-Measures Regression Analysis of Performance on Task Dimension and Reward

Structure for the Worst and Best Performers

StepIndependent

Variable �Total

R2 �R2

IncrementalVariance within

Teamsa

IncrementalVariance

between Teamsb

Worst performers1 Task dimension 0.04 .00 .00 .002 Reward

structure�0.20* .04 .04 .09*

3 Task dimension� rewardstructure

�0.71* .19* .15* .28*

Best performers1 Task dimension �0.09 .01 .01 .022 Reward

structure�0.14 .03 .02 .04

3 Task dimension� rewardstructure

�0.40* .07* .05* .09*

a n � 150 (two observations per 75 teams; df � 150 � 75 � k � 1).b n � 75 (one observation per 75 teams; df � 75 � k � 1). Between-teams variance is 46 percent for the worst performers and 45 percent

for the best performers.*p � .05, one-tailed test


facturing team can be doubled, the size of the teamcan be cut in half. Thus, speed of operations iscritical to organizations that are competing on thebasis of cost rather than differentiation.

Finally, in product development teams, a typicalproduct development cycle often requires a focuson quality rather than speed in the beginning. Inthe initial stages of product development, qualitymay be critical to creating a small niche market fora new product, and the lack of rival products pro-vides the development team the luxury of not hav-ing to worry about speed or cost. However, as theproduct matures and becomes standardized, morecompetitors may enter the market, and the speed ofproduction becomes more critical. Thus, thechanges that take place over product life cyclesmay require an evolution from collaborative rewardstructures that place little premium on speed in thebeginning to more competitive schemes, in whichspeed and cost are the central driving factors, at theend of the cycle.

In addition to these types of external consider-ations, the need to focus on speed is also critical forinternal team dynamics. If some workers are work-ing feverishly toward accomplishing a team’s mis-sion while others are taking it easy, it is only amatter of time before perceptions of inequity createnegative interaction patterns that could threatenthe team’s viability (Ezzamel & Willmot, 1998). Forall these reasons, while recognizing the virtues ofcooperative structures with respect to promotingquality, we believe that recognizing the liabilitiesof these structures when it comes to speed is alsoessential.

Reward Structures and Interpersonal Orientation

Just as no one reward structure is best for bothdimensions of team tasks, there is no one best wayto design reward structures irrespective of teamcomposition. Past studies that have searched forinteractions between individual differences and re-ward structures have met with little success (Wage-man, 1995), but their lack of success may be attrib-utable to the use of locally developed individualdifference measures of unknown validity. In thisstudy, we used the well-accepted five-factor modelframework and the extensively documented NEO-PIinstrument (Costa & McCrae, 1992) to characterizeteam composition and found significant interactioneffects between reward structure and personalityvariables.

Specifically, we found that both aspects of theinterpersonal orientation domain of the five-factormodel circumplex were relevant for predicting howteams composed of different types of people re-acted to various reward structures. Goal interde-pendence theory is based upon the presumptionthat cooperative reward structures promote collab-oration and trust, but the five-factor model clearlydenotes that there are stable individual differencesin the degree to which people are naturally collab-orative (that is, extroverted) and trusting (that is,agreeable). Our study is the first research attempt tosee how people react when placed in a rewardstructure that either reinforced or contradictedtheir natural proclivities.

The results indicated that the conventional rec-ommendation derived from goal interdependence

FIGURE 5Interaction of Task Dimension, Reward Structure, and Level of Performance


theory regarding the use of cooperative rewardstructures in interdependent teams was validatedin extroverted and agreeable teams. However, thissame reward structure did not promote perfor-mance when it contradicted the natural tendenciesof teams (that is, when teams were low on agree-ableness and extroversion). A close examination ofFigures 3 and 4, however, indicates that althoughteams comprised of introverted and disagreeablemembers clearly did not respond positively to co-operative reward structures, they did not in factrespond all that well to competitive structures ei-ther. Apparently, the value in creating a good fitbetween the people and the reward structure wasmuch greater for agreeable extroverts than it was fordisagreeable introverts.

The disagreeable introverts in this study clearlytook exception to the cooperative reward structurethat yoked their outcomes to those of others. How-ever, they may have also found both the high levelof means interdependence inherent in this task andthe negative outcome relationship created by thecompetitive reward structure somewhat objection-able. In other words, disagreeable introverts maynot want to work in teams at all, and rather thanbeing put in competition with others, would ratherjust be left alone.

One might be tempted to design work for thesetypes of people as independent jobs. However, set-ting tasks up as series of noninteracting individualjobs is also problematic. As is the case with manycomplex team tasks, uncertainty was inherent inthe workload distribution in the simulation used inthis study. At any one time, a certain team membermight be flooded with tracks, but at other times,there might be very little activity in this person’ssection. The reason many organizations use teamsin the first place is that team-based structures en-able dynamic workload adjustment. Team mem-bers who have few tasks on their hands can go andhelp others who are busy. If one were to “deteam”the task used in the current study by, for example,saying that no team member could leave his or herregion, overall performance would be harmed be-cause dynamic workload adjustment would be pre-vented. Therefore, future research needs to explorehow to best structure work and rewards for peoplewho are low in agreeableness and extroversion.

Another interesting direction for future researchwould be to investigate the impact of structures inwhich multiple rewards are used, some of whichare allocated cooperatively whereas others are allo-cated competitively. When there is only one singlereward, cooperative and competitive structures aremutually exclusive ends of a continuum, but whenthere are multiple rewards, one can take a cooper-

ative approach with one type of reward (such as anend-of-the-year team bonus), but a competitive ap-proach to another type of reward (such as an end-of-the-year merit pay raise). Although questionslike these are beyond the scope of the currentstudy, it would be interesting to see how the com-bination of the two different reward systems wouldplay out (for instance, does one trump the other, dothey each neutralize each other, or do individualdifferences take over?). Future experiments com-paring cooperative and competitive reward struc-tures could be efforts to answer these questions byadding a condition in which the two reward struc-tures are combined (by rewarding participants withboth an individual base pay and a collaborativeteam-based bonus). It might also be interesting tostudy reward structures that change from one typeto the other, to see how the dynamics of changingreward structures evolve over time.

Practical Implications

Many complex tasks embody a speed-accuracytrade-off; thus, these findings, which suggest thatcooperative rewards promote accuracy, whereas com-petitive rewards promote speed, have important im-plications for practice. Managers may not be able tojointly maximize both aspects of task performance viaa single reward structure, and thus they should con-sider which aspect of a task they want to prioritizebefore designing the reward structure.

Second, our findings have important practicalimplications for two types of questions regardingteam composition. Organizations may pose thequestion of which people should be selected tocompose a team. Our findings show that if cooper-ative reward structures are in place, teams performmuch better when composed of people who arehigh on extroversion and agreeableness. Organiza-tions also may pose the question of which rewardstructure works best with a given team. That is, if ateam’s members have already been selected and arein place, what reward structure should be applied?Our findings show that when teams are composedof extroverted and agreeable members, a coopera-tive reward structure is a very effective choice.

Limitations

The fact that this study was conducted in a lab-oratory context may evoke the usual questions re-garding the external validity of the findings. Partic-ipants in this study were not randomly selectedfrom any definable population, but rather were col-lege students. One disadvantage that this procedureentails is that the sample of subjects that partici-


pated in our study was culturally quite homoge-neous and, therefore, we cannot be certain that ourfindings generalize to different populations. Exam-ining the impact of cultural factors on how rewardstructures influence team performance would be aninteresting question for future research, because thepossibility exists that reward structures interactwith cultural factors.

A second limitation that also concerns the exter-nal validity of our findings relates to the task usedin the current study. Although we believe that thistask is representative of many kinds of means-interdependent team tasks that have a speed-accu-racy trade-off (including the tasks that manufactur-ing teams, emergency medical teams, pit crews, airtraffic controllers, and weapons directors need toperform), we technically cannot generalize the pa-rameter estimates found in this study to all othertasks, because we did not randomly select the taskfrom the entire population of team tasks. However,prior research has shown that participants who en-gage in the task we used in the current study dofind it psychologically engaging. Moreover, theywere aware of the financial bonuses that could beachieved by performing well on the task and weregenuinely interested in winning the bonus money.Indeed, if anything, the meta-analytic evidence re-garding incentives suggests that in almost all cases,the findings from laboratory contexts provide con-servative estimates of what is found in field settingsusing the analogous interventions (Jenkins et al.,1998).

Moreover, the primary purpose of this study wasto test the boundary conditions of goal interdepen-dence theory and, for the most part, research onthis theory is based upon similarly structured stud-ies. Nothing inherent in the theory implies that itwould not work in the current context, suggestingthat this context is a viable one in which to test thistheory. Indeed, it would be very difficult to rigor-ously test many of the ideas tested here in a fieldsetting. Given what is known about the attraction-selection-retention cycle, a field study that allowednatural gravitation of people to teams, rewardstructures, and tasks would be highly confounded.Moreover, subjective supervisory judgments re-garding speed and accuracy of performance arenotoriously unreliable and low in discriminantvalidity. Thus, in order to draw rigorous causalinferences in this context, it is crucial to (1) ran-domly assign people to teams, (2) randomly assignteams to conditions, (3) create objectively identicaltask demands, and (4) obtain objective measures ofaccuracy and speed. Nevertheless, a field study thatcould overcome these traditional difficulties couldshed some light on the nuances that make the ap-

plication of either of these two types of rewardstructures more difficult in practice than it mightseem in theory it would be.

A third limitation here is that the experimentersconveyed the manipulations and thus were notblind to experimental conditions. Although wetook extreme care to write protocols that stated theexact words an experimenter had to say to partici-pants at various points during the experiment, wecannot exclude the possibility that the experiment-ers influenced the data in some subtle way, throughtheir nonverbal behavior, for example. Future re-search could address this limitation of the currentstudy by employing alternative means of providingthe information regarding reward structures.

A final aspect of the current study that may beviewed as a limitation is that the manipulation ofreward structure was reinforced; team members inthe competitive reward structure condition weretold that during the task, that they should pay at-tention to their own individual scores, whereasteam members in the cooperative reward structurecondition were told that they should pay attentionto their team’s score. We focused the team memberson the relevant scores throughout the training tomake sure they understood the reward structureunder which they worked, because rewards canonly work if people have feedback on and knowl-edge of results. In most real-life situations, rewardstructures co-occur with a performance monitoringsystem that matches the reward structure (for ex-ample, it would not make sense to tell a team’smembers they will be rewarded for their individualperformance and then measure the performance ofthe team as a whole). The same was true for ourstudy. Therefore, focusing the subjects on the“right” scores was an important part of our manip-ulation. However, critical readers may ask whetherit was our manipulation of reward structure per se,or the participants’ focus on the relevant scores, orthe combination of both that caused the effectsreported here. Future research should address thisissue by separately manipulating reward structureand the scores participants focus on during anexperiment.

Directions for Future Research

The current study focused on moderators of theeffects of reward structures, not mediators of theeffects of reward structures. We focused first onthese moderating variables because, from an ap-plied perspective, establishing the factors that in-fluence the relationship between reward structureand performance is key in answering questionsabout which structure to use in which contexts


with which people. However, this focus on moder-ation limits our ability to answer the mediationquestion that might be of interest from an academicperspective. Teams working under a cooperativereward structure may have outperformed competi-tive teams on accuracy for a number of reasons, andthe same can be said for the effects of competitivestructure on speed. This study cannot pin down theprecise mechanisms underlying all of the effectsdetected here. While admitting this, we note thatwe hope that researchers, armed with the knowl-edge provided by this study (for instance, that dif-ferent reward structures work best under differentconditions), may be able to extend this work byisolating the precise reason for these effects in amore direct way. This latter type of effort will onlybe forthcoming, however, if future researchers re-alize that there are interesting interactions betweenreward structures and conditions regarding the di-mensions of a task, the personality composition ofteams, and individual performance levels withinteams, that need to be explained.

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Bianca Beersma ([email protected]) is a postdoctoralfellow in organizational psychology at the University ofAmsterdam. She received her doctorate from the Univer-sity of Amsterdam. Her research interests include teamperformance, social motives in teams, group negotia-tions, and conflict management.

John R. Hollenbeck received his Ph.D. in managementfrom New York University in 1984, and he is currentlythe Eli Broad Professor of Management at the Eli BroadGraduate School of Business Administration at Michigan

State University. He cofounded the Michigan State Uni-versity Team Effectiveness Research Laboratory; the mul-tilevel theory of team decision making grew out of theprogram of research there. Professor Hollenbeck is alsothe codeveloper of the most highly cited model and mea-sure of goal commitment, one of the central features of allself-regulation theories of motivation.

Stephen E. Humphrey is a doctoral candidate in organi-zational behavior at the Eli Broad Graduate School ofManagement at Michigan State University. His researchinterests include teams and the group context, decisionmaking, and dispositions.

Henry Moon is an assistant professor at Emory Univer-sity. He received his Ph.D. at Michigan State University.He studies decision making, teams, and personality.

Donald E. Conlon is a professor of management at the EliBroad Graduate School of Management, Michigan StateUniversity. He received his Ph.D. in business adminis-tration from the organizational behavior group at theUniversity of Illinois. His current research interests in-clude organizational justice theory, negotiation and con-flict management, and managerial decision making.

Daniel R. Ilgen is the John A. Hannah DistinguishedProfessor of Psychology and Management at MichiganState University. He received his Ph.D. from the Univer-sity of Illinois. The cofounder of the Michigan StateUniversity Team Effectiveness Research Laboratory, hehas written extensively on work motivation and teamdecision making.


COOPERATION, COMPETITION, AND TEAM PERFORMANCE: … Hollenbeck Humphrey Moon... · COOPERATION, COMPETITION, AND TEAM PERFORMANCE: TOWARD A CONTINGENCY APPROACH BIANCA BEERSMA University

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