Cooperation, Competition, and Team Performance: Toward a Contingency Approach

Abstract

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

Full text

COOPERATION, COMPETITION, AND TEAM PERFORMANCE:
TOWARD A CONTINGENCY APPROACH
BIANCA BEERSMA
University of Amsterdam
JOHN R. HOLLENBECK
STEPHEN E. HUMPHREY
Michigan State University
HENRY MOON
Emory University
DONALD E. CONLON
DANIEL R. ILGEN
Michigan State University
This study examined whether the relationship between reward structure and team
performance is contingent upon task dimension, team composition, and individual
performance level. Seventy-five four-person teams engaged in a simulated interactive
task in which reward structure was manipulated. A competitive structure enhanced
one 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 the
competitive structure. Finally, reward structure had more impact on team members
with low performance.
The degree to which organizations should em-
phasize cooperation or competition among the
members of work teams is an age-old controversy.
Competitive systems embody equity norms and
emphasize performance differences among team
members, typically rewarding individuals with
high performance and/or imposing sanctions on
those 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 will
serve the long-term needs of their organization.
Others believe that intrateam competition is de-
structive. In competing, individuals or subgroups
place their own goals above those of the larger
organization, and the gains achieved by one are
often obtained at the expense of another. For this
reason, some argue that the needs of the larger
organization are better met by employing coopera-
tive reward structures. Cooperative systems em-
body equality norms and emphasize group ac-
complishments. They emphasize minimizing
distinctions 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 and
cooperative reward structures has become particu-
larly salient in contemporary organizations because
many of these organizations are trying to make the
transition from individual-based structures to
team-based structures (Allred, Snow, & Miles,
1996). Some organizations that have transitioned
into team-based structures over the last several
years 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 author
studied at Michigan State University on a Fulbright Grad-
uate Student Scholarship, and we thank the Netherlands
America Commission for Educational Exchange grate-
fully for their support. Henry Moon was also at Michigan
State University when the research was conducted.
Grant N00014-99-1-0983 from the Cognitive and Neu-
ral Sciences Division of the Office of Naval Research
financially supported this research in part. Although
support for this work is gratefully acknowledged, the
ideas expressed herein are those of the authors and are
not necessarily endorsed by the funding agencies.
娀Academy of Management Journal
2003, Vol. 46, No. 5, 572–590.
572

port for competitive allocations when people are
working 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 supportive
behavior 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 for
members of interdependent teams should be col-
laboratively based ignores the fact that even within
interdependent groups, any single task can be bro-
ken down into subtasks that are evaluated by dif-
ferent standards. Over 100 years ago, Woodworth
documented convincingly that on a task of any
complexity, speed and accuracy are two separate—
and, in some cases, negatively related—aspects of a
single task (Woodworth, 1899). Subsequent re-
search on the speed-accuracy distinction has made
it clear that the two task dimensions have very
different antecedents (Elliott, Helsen, & Chua,
2001), and hence the simple notion that collabora-
tive rewards promote both the speed and accuracy
of teams seems unlikely to be correct.
Moreover, people have different traits and dispo-
sitions, and even within an interdependent team
context, individuals make decisions for them-
selves. The traits and dispositions of these individ-
uals will affect their own behavior in a way that
may override or run counter to the reward system
under which they work. For example, team-based
rewards are designed to promote trust and collab-
oration, but for over 70 years, psychologists have
noted consistent individual differences in the de-
gree to which people are naturally trusting and
collaborative (McDougall, 1932). Indeed, in the
best-established framework for understanding
traits, the five-factor model, two of the five factors
(agreeableness and extroversion) are devoted to
people’s interpersonal orientation (McCrae & Costa,
1997). As with the speed-accuracy distinction, the
idea that one type of reward system—a collabora-
tive one—promotes team performance regardless of
the interpersonal orientation of team members
again seems unlikely to be true.
Finally, although it is true that organizations that
change to team-based structures without changing
their reward systems encounter problems, it also
has to be recognized that even where organizations
change their reward structures to be more in line
with 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 rewards
discount these differences. Psychologists have rec-
ognized the “social loafing” phenomenon among
poor performers in groups for many years (Latane,
Williams, & Harkin, 1979), and this problem, per-
haps more than any other, underlies the reluctance
about and resistance to team-based structures that
many individuals express. Ironically, one of the
well-known prescriptions for avoiding social loaf-
ing is to identify individual contributions to a
group’s performance and reward or punish these
contributions 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 most
effective for all individuals.
With these considerations in mind, we sought in
this 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 contingency
model of reward structures that encompasses the
idea that even in interdependent team contexts, the
relationship between the reward allocation struc-
ture and team performance will be contingent upon
several variables related to the task, the composi-
tion, and the individual members of a team. More
specifically, the contingency model we developed
focuses 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, and
relative performance level as the central factor in-
fluencing a reward structure’s impact on different
team members.
Given the current state of consensus, questioning
the inherent value of collaborative reward alloca-
tions in interdependent team contexts may seem
heretical. However, even Stanne and colleagues
(1999), who meta-analytically documented the ad-
vantages of collaborative rewards, recognized the
need for more refined theorizing regarding the two
types of reward structures, noting that “more effort
needs 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 present
study 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 related
determine the way in which they interact, which in
turn affects their performance and group cohesive-
ness. Central to this theory is the categorization of
situations that create cooperative or competitive
orientations within the people involved.
When a situation is structured cooperatively,
there are positive correlations among team mem-
bers’ rewards, but when a situation is structured
competitively, there are negative correlations
among team members’ rewards. According to goal
interdependence theory, cooperatively structured
situations create perceptions of shared fate and pro-
mote supportive behavior, whereby each group
member looks out for the interests of the others. In
addition, insights and lessons learned by one mem-
ber are shared so that all can benefit vicariously
from others’ experiences. On the other hand, rather
than share information and experience, people
placed in competitive structures tend to keep valu-
able information proprietary. Moreover, rather than
supporting each other, people placed in competi-
tive reward structures may be motivated to impair
the progress of others in an effort to gain positive
advantage.
Since the formulation of Deutsch’s theory, many
studies have investigated the effects of cooperative
and competitive rewards. Most of these studies
have focused on differences between tasks and
sought to determine how to match reward struc-
tures with various types of tasks. Meta-analyses of
these studies have indicated that cooperative struc-
tures are far superior for eliciting group perfor-
mance when the means interdependence of a task
is high. “Means interdependence” is the degree to
which the task that one member of a team faces is
affected by the performance of others on the team
(and hence requires coordination). However, com-
petitive structures have been found to be slightly
superior when people work on “means-indepen-
dent” tasks, the completion of which requires little
if any coordination between team members (Stanne
et al., 1999). Thus, the theory and the empirical
data associated with goal interdependence theory
suggest that the reward structure employed in a
given context needs to match the task at hand.
Specifically, high-interdependence tasks should be
paired with cooperative rewards, and low-interde-
pendence tasks should be paired with competitive
rewards.
Dimensions of the Task: The Differing Impacts of
Reward 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 reward
structures are designed. Most complex tasks are
multidimensional and correspondingly place mul-
tiple demands on role incumbents. At the very
least, 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 still
be differentiated in terms of whether it demands
speed, or accuracy, or both speed and accuracy in
its execution.
Most complex tasks require some degree of both
speed and accuracy, but there are trade-offs that
make meeting both of these task requirements at the
same time difficult (Woodworth, 1899; Elliott et al.,
2001). That is, a manufacturing team can work
quickly to produce a large number of products, but
these products may have more defects than what
might have been the case if the team worked slowly
and carefully and produced only a small number of
products. This type of speed-accuracy trade-off is
ubiquitous in complex tasks. For example, a pit
crew in an automobile race needs to quickly repair
and maintain the car, but at the same time it has to
make sure that all the necessary repairs are per-
formed in order to avoid future mishaps. Air traffic
controllers need to work quickly with pilots and
ground controllers to keep arrivals and departures
on time, but at the same time, they also have to
ensure that safety standards are being met. Emer-
gency medical teams need to work quickly to sta-
bilize patients, but at the same time, they should
not make any errors that may endanger the future
health of the patients. Weapons directors in mili-
tary strike teams need to work quickly to exploit
windows of opportunity in the enemy’s defenses,
while at the same time minimizing “friendly fire”
casualties.
Because the trade-off between the speed of task
execution and the accuracy of task execution is
apparent in so many team tasks, it is important to
note that research concerning the performance of
individuals working alone on independent tasks
has shown that different reward structures influ-
ence speed and accuracy differently. That is, in a
meta-analysis of 39 studies examining the impact
of financial incentives on individual performance,
Jenkins, Mitra, Gupta, and Shaw (1998) showed
that financial incentives had a much stronger im-
pact on tasks in which performance was measured
in terms of speed rather than accuracy. Jenkins and
colleagues speculated that incentives influence
speed more than accuracy because speed is more
sensitive to effort and hence under the control of an
individual to a greater extent. Accuracy, on the
other hand, may require skills or abilities that the
individual simply may not possess.
574 OctoberAcademy of Management Journal

Jenkins and colleagues’ (1998) results make it
clear that even after the level of interdependence a
particular task requires is diagnosed (in the case of
their study, interdependence was low, with indi-
viduals working alone), the job of determining the
appropriate reward structure is still unfinished.
One still needs to consider the dimension of the
task that is most crucial (speed or accuracy). Anal-
ogously, even after determining that a task is high
in interdependence, and hence needs to be per-
formed by a team, one may still need to consider
whether one is going to emphasize speed or accu-
racy of task execution. Unfortunately, the question
of how various rewards influence the speed and
accuracy of interdependent teams could not be es-
tablished in the Jenkins et al. study because there
were no studies of teams that examined within-task
differences in the quantity versus the quality of
production.
There is some evidence that different reward
structures differently influence different aspects of
a team’s task, although the research providing this
evidence did not focus on contrasting speed and
accuracy. Beersma and De Dreu (2003) showed that
teams that worked under a cooperative structure
tended to perform better on the “convergent” as-
pects of a creative team task (for instance, generat-
ing feasible ideas), while teams that worked under
a competitive structure tended to perform better on
the “divergent” aspects of the task (such as gener-
ating original ideas). Thus, when a team’s task is
conceptualized as multifaceted, much more can be
learned about when and why different reward
structures should be used or avoided.
The same types of within-task differences seen in
the studies of individuals that were meta-analyzed
by Jenkins and coauthors (1998) and in the teams
studied by Beersma and De Dreu (2003) might be
manifested with respect to reward structures in
team contexts. For example, according to goal in-
terdependence theory, even though they are work-
ing as part of an interdependent team, people
within teams who are exposed to a competitive
reward structure may feel less cohesive. As such,
they may react like the individuals studied by Jen-
kins and colleagues and increase the speed with
which they work. Moreover, a great deal of research
documents that people working in groups generally
take longer to complete tasks than would individ-
uals working alone (Levine & Moreland, 1998), and
this time lag would only be exacerbated by reward
structures that promote discussion, collaboration,
and information sharing. If this were true, then in
interdependent teams, the speed of performance
might be higher when rewards are structured com-
petitively rather than cooperatively.
Alternatively, given that financial incentives
have 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 task
performance in teams. However, the collaborative
nature of a group’s interaction when its members
work under a cooperative reward structure would
seem to have the potential to enhance accuracy. In
many complex team tasks, accuracy is a function of
task-relevant knowledge. Under a cooperative re-
ward structure, the team member with the most
knowledge can share what he or she knows with
the team members with the least knowledge, who
can then use this knowledge to assess their own
work processes in a way that allows them to per-
form higher-quality work than they could have
managed on their own. If this were true, then the
accuracy of performance might be higher when re-
wards are structured cooperatively than when they
are structured competitively. Taken together, these
arguments lead to our first hypothesis:
Hypothesis 1. The relationship between reward
structure and performance is contingent upon
the dimension of a task: speed is enhanced by
competitive reward structures, whereas accuracy
is enhanced by cooperative reward structures.
Team Composition: The Impact of Reward
Structures Composed of Interpersonally Skilled
and Interpersonally Unskilled Members
Just as one may need to address concerns regard-
ing speed versus accuracy before choosing a reward
structure, one may also need to consider the dispo-
sitional characteristics of team members prior to
choosing a reward structure. That is, there may
need to be a fit between the traits of members and
the reward structure used with a team. The argu-
ment that there needs to be a fit between people
and their work environment is consistent with a
long line of theorizing on diverse aspects of organ-
izational behavior in general (Kristof, 1996) and on
teams in particular (Hollenbeck et al., 2002). Many
of the more recent investigations into fit have ex-
ploited the emergence of the five-factor model as a
robust, well-grounded, and culturally generalizable
conceptual framework and measurement system
for conducting research on individual differences
(McCrae & Costa, 1997). However, to date, very
little research has addressed the question of what
types of people function best under cooperative
and competitive reward structures (Wageman,
1995), and no research whatsoever has approached
this issue employing the five-factor model.
2003 575Beersma, Hollenbeck, Humphrey, Moon, Conlon, and Ilgen

Because goal interdependence theory focuses on
how a reward structure influences the interaction
patterns between team members, traits within the
five-factor model that relate to interpersonal orien-
tation are especially relevant in this context. That
is, the five traits that are identified by this approach
can be conceived as composing a “circumplex” (a
circular configuration) in which certain pairs of
traits are more theoretically proximal to each other
than are others. Costa and McCrae (1992) defined
the interpersonal plane of the circumplex as being
comprised of extroversion and agreeableness. Be-
cause these traits influence interaction patterns be-
tween people, they seem ideally suited to being
integrated with goal interdependence theory in an
effort to understand how people are likely to react
to reward structures.
More specifically, Costa and McCrae (1992) de-
scribed extroverts as “liking people and working in
groups.” In contrast, introverts are “reserved and
independent” and “tend to dislike and avoid social
stimulation” (Costa & McCrae, 1992: 15). It is easy
to see how this dispositional characteristic could
support 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 better
than they fit the style of extroverts.
Agreeableness is the second trait that Costa and
McCrae placed in the “interpersonal plane” of the
five-factor model. Those high in agreeableness are
described as “fundamentally altruistic, sympa-
thetic to others, eager to help and be helped in
return. 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 but
work against those of people who are low in agree-
ableness, and vice versa for competitive reward
structures.
Thus, both of these traits associated with the
interpersonal plane of the five-factor model should
be relevant for creating a fit between a reward struc-
ture and a team’s members. More specifically, we
propose
Hypothesis 2a. The relationship between re-
ward structure and performance is contingent
upon the extroversion of team members: the
performance of teams whose members are high
on 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 structures
are competitive.
Hypothesis 2b. The relationship between re-
ward structure and performance is contingent
upon the agreeableness of team members: the
performance of teams whose members are high
on agreeableness is higher when reward struc-
tures are cooperative, whereas the perfor-
mance of teams whose members are low in
agreeableness is higher when reward structures
are competitive.
The Relative Standing of Team Members: The
Impact of Reward Structures on the Best and
Worst Performers
In any team, it is unlikely that all members are
exactly equal in their ability or willingness to con-
tribute to the team’s overall performance level. Re-
cent theoretical work has explicitly addressed how
the member with the lowest performance influ-
ences a team (LePine & Van Dyne, 2001). Coopera-
tive and competitive reward structures are both
posited to influence this issue. The first, positive
proposition is that the member with the most
knowledge will share that knowledge with the least
knowledgeable person and thereby raise the latter’s
performance higher than it would have been had
that individual been working alone. This phenom-
enon is a “training response,” in the words of
LePine and Van Dyne (2001). The second, negative
proposition is that, under a cooperative structure,
members, who have the opportunity to free ride on
the accomplishments of the other team members,
will engage in social loafing (Latane et al., 1979).
Social loafing can create animosity among team
members and restrict group output.
Interestingly, the traditional “cure” for social
loafing is to isolate individual contributions to a
group’s performance and reward or punish people
on the basis of these contributions (LePine and Van
Dyne [2001] called this a “motivate response”). Of
course, rewarding and punishing individual contri-
butions implies having used a competitive reward
structure aimed at increasing the performance of
the potentially poorest performers. The ironic part
of this analysis, of course, is that each of the two
mutually exclusive reward structures is seen as the
solution to enhancing the performance of group
members viewed as likely to be the worst performers.
One way to perhaps resolve this discrepancy is to
revisit the distinction between speed and accuracy.
When contributors to the social loafing literature
have focused on individuals with the lowest per-
formance in a group, the presumption has been that
576 OctoberAcademy of Management Journal

they are failing to put forth effort. When authors
advocating 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 if
competitive reward structures work to increase the
speed (effort) of team members, but cooperative
reward structures work to increase their accuracy
(knowledge). This formulation would be consistent
with Jenkins, Mitra, Gupta, and Shaw’s (1998) ar-
gument that an individual may have more control
over the speed of task performance, which is
largely a function of effort, than over the accuracy
of task performance, which is more closely related
to skill.
Moreover, the impact of the two different reward
structures on the poorest performer in a group
(when performance is individually assessed) is
likely to be stronger than the structures’ impact on
the group’s best performer for several reasons. First,
the group decision making literature makes it very
clear that the accuracy of groups’ decisions tends to
be much higher than that of their average members
but is rarely better than that of their best members
(Levine & Moreland, 1998). Thus, from a goal inter-
dependence perspective, the member of a team
who has the lowest amount of knowledge has a
great deal more to gain from collaboration the mem-
ber with the most knowledge. Similarly, from a
social 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 member
has to fear from having this ability made public.
These arguments suggest that reward structure will
be observed to have more strongly influenced the
performance of the members of a team who have
the lowest individual performance. Therefore, we
propose
Hypothesis 3. The impact of a team’s reward
structure on the speed and accuracy of the
team’s poorest performer is stronger (in terms
of explained variance) than the impact of the
reward structure on the speed and accuracy of
the best performer.
METHODS
Research Participants
Three hundred business students at Michigan
State University were arrayed into 75 four-person
work teams. Sixty percent of our sample members
were male, and approximately 90 percent were
Caucasian. In return for their participation, partic-
ipants earned class credit and were eligible for cash
prizes ($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 task
used here was developed for teams of two to five
members with little or no military experience.
The geographical space and mission. Figure 1
depicts the grid used in MSU-DDD. This grid was
partitioned in several ways. First, four geographic
quadrants of equal area (NW, NE, SW, SE) were
defined, and each area was assigned to one team
member, who was called a “decision maker” (hence
the abbreviation “DM” in Figure 1). The grid was
also divided into three zones that varied on the
extent of protection from penetration by unfriendly
forces 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 center
of the restricted zone). The team’s mission was to
monitor this air and ground space, keeping un-
friendly forces from moving into the restricted ar-
eas, while at the same time allowing friendly forces
to move about freely. Radar representations of these
forces 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 grid
units to use in monitoring the geographic space.
The decision maker could detect the presence or
absence of any track within this detection ring.
Each base also had an identification ring (base IR in
Figure 1) radius of roughly four grid units. A team
member could discern whether a track was friendly
or unfriendly once it was within this range. Any
track outside the DR was invisible to the team
member from the base. A team member who
wanted to determine the nature of a track outside
the identification ring had two options: ask team-
mates to share that information, or launch a vehicle
and move it near the track. Since each vehicle had
its own detection and identification rings and
could 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 to
engage tracks outside of one’s personal region.
Vehicles. Each team member had control of four
vehicles that could be launched and moved to dif-
ferent areas of the screen. These vehicles could
automatically perform certain functions (follow
2003 577Beersma, Hollenbeck, Humphrey, Moon, Conlon, and Ilgen

designated tracks, return to base to refuel, and so
forth), and hence each team member was the man-
ager of semi-intelligent agents. Each team member
had one AWACS plane, one tank, one helicopter,
and one jet. These vehicles varied in their capaci-
ties on four dimensions: range of vision, speed of
movement, duration of operability, and weapons
capacity.
An asset that was high on one dimension tended
to be low on another, meaning each asset had its
own unique advantages and disadvantages. For ex-
ample, the tank had high weapons capacity but a
short range of vision, whereas the AWACS had low
weapons capacity but a wide range of vision. Thus,
the various vehicles constituted a complex set of
assets that ranged widely in their capacities. A
symbol for each vehicle appears in Figure 1, along
with the ranges of vision that characterized each
vehicle (depicted by the largest ring surrounding
each vehicle). A team member could operate any or
FIGURE 1
The DDD-MSU Grid
578 OctoberAcademy of Management Journal

all of the vehicles concurrently, but it took more
effort to simultaneously operate multiple vehicles.
For example, when a track appeared, a person
could simply launch one vehicle and move it to
engage the incoming track. Alternatively, the same
person could work quickly to launch all four vehi-
cles, move them to various areas of the geographic
space, in anticipation of incoming tracks, and in-
tercept them as soon as they crossed over into the
restricted zone. Thus, the speed with which tracks
were engaged was a function of how hard a person
or team was working. Because of the variation in
the four vehicles’ capacities, it required a great deal
of cognitive effort to effectively have all four vehi-
cles out at once and then use them efficiently, but
doing this did increase the speed with which tracks
were engaged.
Identifying and engaging tracks. All tracks orig-
inated from the edge of the screen and proceeded
inward. It was important for team members to iden-
tify tracks quickly and differentiate them along two
dimensions: (1) friendly versus unfriendly and (2)
standard versus novel. When a track was close
enough to be detected but not close enough to be
identified, it was represented by a question mark
followed by a unique identification number set
above a diamond (see the bottom right portion of
Figure 1 for an example). Once the track came
within the identification range of either the base or
a vehicle, the team member could identify it. Once
identified, the symbol representing the track
changed from a diamond to a rectangle with a
letter-number combination in it (see the middle of
Figure 1). The letter indicated whether the track
was 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 power
needed to disable it. The team member who made
the identification was the only one who could see
this information, although he or she could share
this information with other team members.
If a track within the restricted zones was identi-
fied as being unfriendly, team members needed to
disable it. There were two requirements for suc-
cessful disabling. First, the track had to be close
enough, meaning that it had to be within the attack
ring of the vehicle engaging it. Second, the vehicle
needed to have as much power as the track (as
indicated by the number in the rectangle), or more
power. If a team member attempted to engage a
track that was too far away or for which he or she
had insufficient power, the track continued on un-
impeded. If the track was successfully engaged, it
disappeared from the screen. The attacking vehicle
then 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 session
prior to the start of the task. There were also four
types of “novel tracks” that were not encountered
during training. Thus, team members did not know
whether the novel tracks were air-based or ground-
based, or friendly or unfriendly, or powerful or not
powerful. Trial-and-error experience gained from
the simulation was the only source of this knowl-
edge. Thus, determining the nature of the novel
tracks was a complex deductive exercise in which
some 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, and
thus the performance of teams could be evaluated
not just in terms of their speed, but also in terms of
their accuracy. Thus, a team’s objective was to dis-
able enemy tracks as fast as possible while not
disabling any friendly tracks (that is, making
friendly fire errors) or wasting resources by engag-
ing enemy tracks with more power than was
needed.
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 the
teams that had the best overall team performance
would receive a reward of $40, which would be
split evenly among the team members, regardless of
how well they performed as individuals. Partici-
pants under the competitive condition were in-
formed that the top-performing individuals would
each receive a reward of $10, regardless of how
well their teams performed as a whole.
Because the number of tracks was fixed at a rel-
atively low number (19 tracks per quadrant), to
obtain a score that was high enough to warrant
winning the individual bonus in the competitive
condition, an individual had to venture outside of
his or her quadrant during the simulation. That is,
he or she had to detect, identify, and attack tracks
in the other team members’ quadrants, thus limit-
ing the potential score of the other team members.
Thus, it was impossible for two members of one
team to both qualify for the bonus in the competi-
tive condition.
We used a four-item competitive orientation
scale 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 orientation
was “While I was playing the DDD game, I was
2003 579Beersma, Hollenbeck, Humphrey, Moon, Conlon, and Ilgen

competing with the others on my team.” A sample
item 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 a
team.” The four competitive items formed a reliable
scale (
␣
⫽.93), as did the three cooperative items
(
␣
⫽.93).
Extroversion and agreeableness. Extroversion
and agreeableness were each measured with a 12-
item scale taken from the short form of the Revised
NEO Personality Inventory (NEO-PI-R-short). This
is the most widely used instrument for measuring
the five-factor model, and Costa and McCrae (1992)
have provided ample historical evidence on the
reliability and construct validity of these measures.
Coefficient alpha estimates of reliabilities for extro-
version and agreeableness in this specific study
were .79 and .76, respectively.
If team members’ extroversion and agreeableness
are likely to be relevant for how teams react to
reward structures, the question becomes how to
aggregate these personality variables to the team
level. In a recent review of empirical research on
this 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, and
Hedlund (1997), Barrick, Stewart, Neubert, and
Mount (1998), and Neuman and Wright (1998),
who all noted that when interdependence among
task members is low, an additive model (using av-
erages) is most appropriate, but when interdepen-
dence is high, a conjunctive model (using lowest
scores) 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 a
disproportionate impact on a team as a whole. Be-
cause the level of interdependence among team
members 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 the
average amount of time it took to disable unfriendly
tracks. Accuracy was success in avoiding two types
of errors, both of which were automatically re-
corded by MSU-DDD. A friendly fire error occurred
when a team member engaged a friendly track, and
a rules of engagement error occurred when anyone
engaged a track outside the restricted zone. The
scores for each type of error were summed into an
accuracy score.
Accuracy was then recoded so that a high value
reflected high performance, and the values were
then standardized to make the measures of the two
task dimensions comparable. Speed and accuracy
scores were assessed at both the team and individ-
ual level. Overall performance was measured as the
sum 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’ extroversion
and agreeableness. Then, each participant was ran-
domly assigned to a four-person team, and then the
teams were randomly assigned to reward structure
conditions. The teams were trained together for
approximately 90 minutes. Because rewards can
only work if people have feedback and knowledge
of results, we focused the team members on the
relevant scores (individual scores in the competi-
tive condition and team scores in the cooperative
condition) throughout the training.
The first 30 minutes of training were devoted to
familiarizing the participants with the object of the
simulation, its scoring, and the capabilities and
characteristics 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 final
30 minutes of training provided the participants
with an opportunity to practice their new skills in
an environment similar to the environment in
which they would later perform. During this pe-
riod, participants were allowed to ask their trainers
questions as they practiced. In addition, the trainer
could 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, the
task was identical for all the teams. A total of 76
tracks appeared during the experimental session,
and each participant experienced 19 tracks that
originated in his or her quadrant. The tracks never
stayed within the quadrant they originated in; in-
stead, they crossed from one team member’s area to
another. It is also important to stress that the team
members were not restricted to operating vehicles
within their own quadrants, but instead could
move their vehicles into other quadrants. Thus,
even though a track may have originated in the SE
quadrant, the team member from the NW quadrant
could be the first person to engage it. Because both
tracks and vehicles were free to roam quadrants, all
teams in this simulation experienced a great deal of
580 OctoberAcademy of Management Journal

means interdependence. That is, what one person
needed to do (or could do) was strongly influenced
by what others were doing. For a team in the coop-
erative condition, if one team member was working
slowly or ineffectively and failed to engage tracks
that originated in his or her quadrant, all the other
players had to “clean up after” this person. For a
team in the competitive condition, if one person
was aggressively “hogging all the tracks,” the num-
ber of opportunities for other members of the team
went down. Regardless of the team members’ own
perceptions of outcome interdependence, from a
managerial perspective, the goal of each team was
the same: defend the geographic space with as
much speed and accuracy as possible.
Data Analysis
The research design employed in this study had
both 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 two
scores, one for speed and one for accuracy. Given
this 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 Cohen
and Cohen [1983: 428–451] for an extensive treat-
ment). In general, this type of analysis decomposes
variance 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 of
the speed-accuracy distinction), and variability
within teams (within a single team, there is vari-
ance in performance depending on whether speed
or accuracy is the task dimension).
The criteria are then regressed on the predictors
and matched to their levels. A between-teams ma-
nipulation like reward structure is used to try to
explain 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-coded
speed-accuracy distinction) is used to attempt to
predict variance within teams (within any one
team, performance varies depending upon whether
one is looking at speed of task execution or accu-
racy of task execution). The technique also allows
one to examine interactions among within- and
between-teams measures and to directly test if the
effect of a between-teams manipulation like reward
structure has different influences on different task
dimensions. Such a difference in effects is one of
the core ideas underlying the contingency model
we are testing.
The test of this within-between interaction is
identical to what would be obtained if one were to
treat speed and accuracy as separate dependent
variables, separately regress reward structure on
each, and then test for the differences in unstand-
ardized regression coefficients. The repeated-
measures approach, however, provides a direct
measure of the statistical significance of the differ-
ence in unstandardized regression weights, as well
as a direct measure of the effect size for the inter-
action (that is, the variance explained), the latter of
which cannot be obtained when speed and accu-
racy are treated as separate dependent variables.
Summarizing the above, we would note that if
speed and accuracy were treated as separate depen-
dent variables and analyzed in separate regression
analyses, and if these analyses were followed up by
a test for the differences in unstandardized regres-
sion coefficients, the conclusions drawn from our
data would be identical to our present conclusions.
However, we chose the repeated-measures regres-
sion approach because it was more parsimonious
and direct (for more information on this topic, we
refer 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. As
might be expected, there was a slightly negative
correlation between performance levels on the two
dimensions 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 the
interpersonal plane of the five-factor model cir-
cumplex, there was a slight positive correlation
between these two variables (r⫽.22, p⫽.06).
Analysis of variance (ANOVA) of cooperative
versus 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 competitive
reward 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 competitive
orientation (mean ⫽3.51, s.d. ⫽0.52) than teams in
the cooperative reward structure (mean ⫽2.24, s.d.
⫽0.53, F[1, 73] ⫽104.74, p⬍.01).
2003 581Beersma, Hollenbeck, Humphrey, Moon, Conlon, and Ilgen

Tests of Hypotheses
Hypothesis 1. Table 2 shows the results of a
repeated-measures regression analysis designed to
test Hypothesis 1. This regression is based upon
150 observations: 75 teams were observed on two
task aspects, speed and accuracy. As noted above,
team-level variance in these 150 observations is
either within-teams, based on the task dimension
(speed versus accuracy) or between-teams, based
on overall performance.
As shown in Table 2, 61 percent of the total
variance in the 150 observations was attributable to
within-teams variance, whereas the remaining 39
percent was attributable to between-teams vari-
ance. The first row of this table shows that there
was no effect for speed versus accuracy (a natural
result of standardizing the variables), and the sec-
ond row shows that there was no “main effect” for
reward structure (that is, no one structure was bet-
ter irrespective of the speed-accuracy distinction).
The third row of this table shows that a statistically
significant interaction between the reward struc-
ture and the nature of the task explained 21 percent
of the within-teams variance. This interaction is
plotted 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 on
speed. As noted above, if speed and accuracy were
treated as separate dependent variables and ana-
lyzed in separate regression analyses, the conclu-
sions drawn from our data would be identical to
our present conclusions. Specifically, the coeffi-
cients associated with the separate regressions on
speed and accuracy were ⫺.26 and .47, respectively
(p⫽.03 and .01, respectively).
An additional analysis supported the idea that
cooperative structures worked well because they
promote diffusion of knowledge throughout a team.
We analyzed accuracy scores separately for the
standard and the novel tracks. (Recall that standard
tracks were those covered in the training, and the
novel tracks were not presented in training.) If the
cooperative structure worked because it promoted
diffusion of knowledge, then the effect this reward
structure had on accuracy should be stronger for
the 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 1
Descriptive Statistics for the Between-Team Variables
a
Variable Mean s.d. 1 2 3 4 5
1. Reward structure 0.40 0.49
2. Agreeableness 3.10 0.47 ⫺.04
3. Extroversion 3.04 0.39 .17 .22
4. Speed 0.00 1.00 ⫺.26* ⫺.04 ⫺.03
5. Accuracy 0.00 1.00 .47* ⫺.07 .22 ⫺.22
6. 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 2
Results of Repeated-Measures Regression Analysis for Performance on Task Dimension and
Reward Structure
a
Step Independent Variable
␤
Total R
2
⌬R
2
Incremental
Variance
within Teams
b
Incremental
Variance
between teams
c
1 Task dimension ⫺0.01 .00 .00 .00
2 Reward structure ⫺0.10 .01 .01 .03
3 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
582 OctoberAcademy of Management Journal

more novel-track errors than standard-track errors,
but the cooperative teams only made 2.27 more
novel-track errors than standard-track errors. This
difference 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 of
sharing information and diffusing knowledge about
novel 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 to
overall team performance aggregated across the two
dimensions of the task. The second row of this table
indicates that there was no main effect for extrover-
sion. The third row of this table, however, indicates
that the interaction between extroversion and the
reward structure explained 5 percent of the vari-
ance. Figure 3, the plot of this interaction, shows
that extroverts respond positively to cooperative
reward structures, whereas introverts respond neg-
atively to this type of reward structure. This pattern
of findings supports Hypothesis 2a.
In Table 3b, results for the same equation com-
puted for agreeableness mirror those in Table 3a in
that there was no main effect for agreeableness, but
a statistically significant interaction between agree-
ableness and reward structure. Figure 4 graphically
shows that only agreeable individuals respond pos-
itively to cooperative structures. Thus, Hypothesis
2b was supported.
The results documented in these tables are based
upon a conjunctive measurement of team composi-
tion that was chosen because of the high level of
means interdependence. Because the task could
also be argued to have some additive elements, we
repeated these regressions employing an additive
model, and there were virtually no differences in
the parameter estimates obtained from the alterna-
tive analysis.
In addition to evaluating results in terms of sta-
tistical significance, it is worthwhile to examine
the sum of these effects in a practical sense, by
looking at the raw number of decision-making er-
rors made by different types of teams under differ-
ent reward conditions. For the teams above the
mean on extroversion and agreeableness and work-
ing under the cooperative reward structure, the av-
erage number of errors was 3.8. In contrast, under
the competitive reward structure, teams above the
mean on extroversion and agreeableness averaged
6.7 errors.
Hypothesis 3. Table 4 shows the results of two
repeated-measures regression analyses designed to
test Hypothesis 3. These regressions are replica-
tions of those presented in Table 2, with the excep-
tion that, rather than team-level performance, they
examine the best and worst individual performance
in each team. With respect to the poorest performer
in each team, as shown in the top half of Table 4, 28
percent of the within-person variance in perfor-
mance can be attributed to the interaction between
reward structure and the task dimension. In con-
trast, as indicated in the bottom half of this table,
FIGURE 2
Interaction of Task Dimension and Reward Structure
TABLE 3a
Results of Regression Analysis of Performance on
Reward Structure and Extroversion
a
Step Independent Variable
␤
Total R
2
⌬R
2
1 Reward structure 0.16 .02 .02
2 Extroversion ⫺0.12 .04 .02
3 Extroversion ⫻reward structure 1.75* .09* .05*
a
n⫽75 (one observation per 75 teams; df ⫽75 ⫺k⫺1).
*p⬍.05, one-tailed test
2003 583Beersma, Hollenbeck, Humphrey, Moon, Conlon, and Ilgen

among the best performers this interaction explains
only 9 percent of the variance. Figure 5 depicts
these interactions; consistent with Hypothesis 3,
although the interactions are similar for the best
and the worst performers, they are clearly greater
for the worst performers.
DISCUSSION
The current consensus regarding reward struc-
tures suggests that competitive structures should be
used when people are working independently,
whereas cooperative reward structures should be
used 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 in
this study were working within means-interdepen-
dent teams, this study does not speak to how one
should design reward structures when people are
working alone. However, with respect to people
who are working interdependently, the results from
the 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 results
validate the conventional recommendations. That
is, when it came to decision-making errors, a team
with the appropriate interpersonal orientation just
described made almost twice as many errors when
placed in a competitive reward structure than the
same type of team placed in a cooperative reward
structure.
Reward Structures and Speed of Performance
Few teams have an unlimited amount of time in
which to complete their work and, in line with past
research (Elliott et al., 2001; Woodworth, 1899),
this study showed that speed and accuracy are
separable aspects of a task (r⫽⫺.22). Moreover,
when it came to decomposing variance in team
performance, most of the variance was attributable
to within-team differences on the two different
aspects 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 in
interdependent teams needs some qualification.
Cooperative reward structures had a negative ef-
fect on the teams’ speed. The measurable impact of
this interaction far exceeded what one would ex-
pect just given the moderate negative relationship
between the two performance dimensions. That is,
although accuracy alone accounted for only 5 per-
cent of the variance in speed, the interaction of
reward structure and task dimension accounted for
21 percent of the variance. This seems to suggest
that there was something slowing teams down in
the cooperative structure other than simply the
greater attention they were giving to accuracy.
The interaction plotted in Figure 5 suggests that
this finding may be attributable to social loafing on
the part of the worst performer. As is apparent in
this figure, the reward structure had virtually no
TABLE 3b
Results of Regression Analysis of Performance on
Reward Structure and Agreeableness
a
Step Independent Variable
␤
Total R
2
⌬R
2
1 Reward structure 0.16 .02 .02
2 Agreeableness 0.08 .03 .01
3 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 3
Interaction of Extroversion and Reward Structure
584 OctoberAcademy of Management Journal

effect on the speed of the best performer, but a
discernable effect on the speed of the slowest team
member. If the effect of cooperative reward struc-
tures on speed simply reflected more time spent on
discussion, information sharing, and a concern for
accuracy, this timing effect should have been in
evidence 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, only
the worst performer seemed to slow down when a
free-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 the
speed problem in these teams.
Although few organizations would be willing to
forego all quality considerations in a full-blown
effort to speed workers up, it is nevertheless true
that many organizations compete on speed. In fact,
in many negotiations, delivery time is a deciding
factor in successfully winning contracts (road con-
struction teams are an example). In addition, in
certain contexts (such as seasonal promotions in a
retail unit), unless the product can be delivered on
time, its quality will not matter. Speed of opera-
tions is also a well-known force multiplier: if the
production of given number of workers in a manu-
FIGURE 4
Interaction of Agreeableness and Reward Structure
TABLE 4
Results of Repeated-Measures Regression Analysis of Performance on Task Dimension and Reward
Structure for the Worst and Best Performers
Step
Independent
Variable
␤
Total
R
2
⌬R
2
Incremental
Variance within
Teams
a
Incremental
Variance
between Teams
b
Worst performers
1 Task dimension 0.04 .00 .00 .00
2 Reward
structure
⫺0.20* .04 .04 .09*
3 Task dimension
⫻reward
structure
⫺0.71* .19* .15* .28*
Best performers
1 Task dimension ⫺0.09 .01 .01 .02
2 Reward
structure
⫺0.14 .03 .02 .04
3 Task dimension
⫻reward
structure
⫺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
2003 585Beersma, Hollenbeck, Humphrey, Moon, Conlon, and Ilgen

facturing team can be doubled, the size of the team
can be cut in half. Thus, speed of operations is
critical to organizations that are competing on the
basis of cost rather than differentiation.
Finally, in product development teams, a typical
product development cycle often requires a focus
on quality rather than speed in the beginning. In
the initial stages of product development, quality
may be critical to creating a small niche market for
a 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 the
product matures and becomes standardized, more
competitors may enter the market, and the speed of
production becomes more critical. Thus, the
changes that take place over product life cycles
may require an evolution from collaborative reward
structures that place little premium on speed in the
beginning to more competitive schemes, in which
speed and cost are the central driving factors, at the
end of the cycle.
In addition to these types of external consider-
ations, the need to focus on speed is also critical for
internal 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 a
matter of time before perceptions of inequity create
negative interaction patterns that could threaten
the team’s viability (Ezzamel & Willmot, 1998). For
all these reasons, while recognizing the virtues of
cooperative structures with respect to promoting
quality, we believe that recognizing the liabilities
of these structures when it comes to speed is also
essential.
Reward Structures and Interpersonal Orientation
Just as no one reward structure is best for both
dimensions of team tasks, there is no one best way
to design reward structures irrespective of team
composition. Past studies that have searched for
interactions 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 individual
difference measures of unknown validity. In this
study, we used the well-accepted five-factor model
framework and the extensively documented NEO-PI
instrument (Costa & McCrae, 1992) to characterize
team composition and found significant interaction
effects between reward structure and personality
variables.
Specifically, we found that both aspects of the
interpersonal orientation domain of the five-factor
model circumplex were relevant for predicting how
teams composed of different types of people re-
acted to various reward structures. Goal interde-
pendence theory is based upon the presumption
that cooperative reward structures promote collab-
oration and trust, but the five-factor model clearly
denotes that there are stable individual differences
in the degree to which people are naturally collab-
orative (that is, extroverted) and trusting (that is,
agreeable). Our study is the first research attempt to
see how people react when placed in a reward
structure that either reinforced or contradicted
their natural proclivities.
The results indicated that the conventional rec-
ommendation derived from goal interdependence
FIGURE 5
Interaction of Task Dimension, Reward Structure, and Level of Performance
586 OctoberAcademy of Management Journal

theory regarding the use of cooperative reward
structures in interdependent teams was validated
in extroverted and agreeable teams. However, this
same reward structure did not promote perfor-
mance when it contradicted the natural tendencies
of teams (that is, when teams were low on agree-
ableness and extroversion). A close examination of
Figures 3 and 4, however, indicates that although
teams comprised of introverted and disagreeable
members clearly did not respond positively to co-
operative reward structures, they did not in fact
respond all that well to competitive structures ei-
ther. Apparently, the value in creating a good fit
between the people and the reward structure was
much greater for agreeable extroverts than it was for
disagreeable introverts.
The disagreeable introverts in this study clearly
took exception to the cooperative reward structure
that yoked their outcomes to those of others. How-
ever, they may have also found both the high level
of means interdependence inherent in this task and
the negative outcome relationship created by the
competitive reward structure somewhat objection-
able. In other words, disagreeable introverts may
not want to work in teams at all, and rather than
being put in competition with others, would rather
just be left alone.
One might be tempted to design work for these
types of people as independent jobs. However, set-
ting tasks up as series of noninteracting individual
jobs is also problematic. As is the case with many
complex team tasks, uncertainty was inherent in
the workload distribution in the simulation used in
this study. At any one time, a certain team member
might be flooded with tracks, but at other times,
there might be very little activity in this person’s
section. The reason many organizations use teams
in 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 and
help 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 her
region, overall performance would be harmed be-
cause dynamic workload adjustment would be pre-
vented. Therefore, future research needs to explore
how to best structure work and rewards for people
who are low in agreeableness and extroversion.
Another interesting direction for future research
would be to investigate the impact of structures in
which multiple rewards are used, some of which
are allocated cooperatively whereas others are allo-
cated competitively. When there is only one single
reward, cooperative and competitive structures are
mutually exclusive ends of a continuum, but when
there are multiple rewards, one can take a cooper-
ative approach with one type of reward (such as an
end-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 questions
like these are beyond the scope of the current
study, it would be interesting to see how the com-
bination of the two different reward systems would
play out (for instance, does one trump the other, do
they each neutralize each other, or do individual
differences take over?). Future experiments com-
paring cooperative and competitive reward struc-
tures could be efforts to answer these questions by
adding a condition in which the two reward struc-
tures are combined (by rewarding participants with
both an individual base pay and a collaborative
team-based bonus). It might also be interesting to
study reward structures that change from one type
to the other, to see how the dynamics of changing
reward structures evolve over time.
Practical Implications
Many complex tasks embody a speed-accuracy
trade-off; thus, these findings, which suggest that
cooperative rewards promote accuracy, whereas com-
petitive rewards promote speed, have important im-
plications for practice. Managers may not be able to
jointly maximize both aspects of task performance via
a single reward structure, and thus they should con-
sider which aspect of a task they want to prioritize
before designing the reward structure.
Second, our findings have important practical
implications for two types of questions regarding
team composition. Organizations may pose the
question of which people should be selected to
compose a team. Our findings show that if cooper-
ative reward structures are in place, teams perform
much better when composed of people who are
high on extroversion and agreeableness. Organiza-
tions also may pose the question of which reward
structure works best with a given team. That is, if a
team’s members have already been selected and are
in place, what reward structure should be applied?
Our findings show that when teams are composed
of 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 selected
from any definable population, but rather were col-
lege students. One disadvantage that this procedure
entails is that the sample of subjects that partici-
2003 587Beersma, Hollenbeck, Humphrey, Moon, Conlon, and Ilgen

pated in our study was culturally quite homoge-
neous and, therefore, we cannot be certain that our
findings generalize to different populations. Exam-
ining the impact of cultural factors on how reward
structures influence team performance would be an
interesting question for future research, because the
possibility exists that reward structures interact
with cultural factors.
A second limitation that also concerns the exter-
nal validity of our findings relates to the task used
in the current study. Although we believe that this
task 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, air
traffic controllers, and weapons directors need to
perform), we technically cannot generalize the pa-
rameter estimates found in this study to all other
tasks, because we did not randomly select the task
from 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 do
find it psychologically engaging. Moreover, they
were aware of the financial bonuses that could be
achieved by performing well on the task and were
genuinely 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 settings
using the analogous interventions (Jenkins et al.,
1998).
Moreover, the primary purpose of this study was
to test the boundary conditions of goal interdepen-
dence theory and, for the most part, research on
this theory is based upon similarly structured stud-
ies. Nothing inherent in the theory implies that it
would not work in the current context, suggesting
that this context is a viable one in which to test this
theory. Indeed, it would be very difficult to rigor-
ously test many of the ideas tested here in a field
setting. Given what is known about the attraction-
selection-retention cycle, a field study that allowed
natural gravitation of people to teams, reward
structures, and tasks would be highly confounded.
Moreover, subjective supervisory judgments re-
garding speed and accuracy of performance are
notoriously unreliable and low in discriminant
validity. Thus, in order to draw rigorous causal
inferences in this context, it is crucial to (1) ran-
domly assign people to teams, (2) randomly assign
teams to conditions, (3) create objectively identical
task demands, and (4) obtain objective measures of
accuracy and speed. Nevertheless, a field study that
could overcome these traditional difficulties could
shed some light on the nuances that make the ap-
plication of either of these two types of reward
structures more difficult in practice than it might
seem in theory it would be.
A third limitation here is that the experimenters
conveyed the manipulations and thus were not
blind to experimental conditions. Although we
took extreme care to write protocols that stated the
exact words an experimenter had to say to partici-
pants at various points during the experiment, we
cannot exclude the possibility that the experiment-
ers influenced the data in some subtle way, through
their nonverbal behavior, for example. Future re-
search could address this limitation of the current
study by employing alternative means of providing
the information regarding reward structures.
A final aspect of the current study that may be
viewed as a limitation is that the manipulation of
reward structure was reinforced; team members in
the competitive reward structure condition were
told that during the task, that they should pay at-
tention to their own individual scores, whereas
team members in the cooperative reward structure
condition were told that they should pay attention
to their team’s score. We focused the team members
on the relevant scores throughout the training to
make sure they understood the reward structure
under which they worked, because rewards can
only work if people have feedback on and knowl-
edge of results. In most real-life situations, reward
structures co-occur with a performance monitoring
system that matches the reward structure (for ex-
ample, it would not make sense to tell a team’s
members they will be rewarded for their individual
performance and then measure the performance of
the team as a whole). The same was true for our
study. Therefore, focusing the subjects on the
“right” scores was an important part of our manip-
ulation. However, critical readers may ask whether
it was our manipulation of reward structure per se,
or the participants’ focus on the relevant scores, or
the combination of both that caused the effects
reported here. Future research should address this
issue by separately manipulating reward structure
and the scores participants focus on during an
experiment.
Directions for Future Research
The current study focused on moderators of the
effects of reward structures, not mediators of the
effects of reward structures. We focused first on
these moderating variables because, from an ap-
plied perspective, establishing the factors that in-
fluence the relationship between reward structure
and performance is key in answering questions
about which structure to use in which contexts
588 OctoberAcademy of Management Journal

with which people. However, this focus on moder-
ation limits our ability to answer the mediation
question that might be of interest from an academic
perspective. Teams working under a cooperative
reward structure may have outperformed competi-
tive teams on accuracy for a number of reasons, and
the same can be said for the effects of competitive
structure on speed. This study cannot pin down the
precise mechanisms underlying all of the effects
detected here. While admitting this, we note that
we hope that researchers, armed with the knowl-
edge provided by this study (for instance, that dif-
ferent reward structures work best under different
conditions), may be able to extend this work by
isolating the precise reason for these effects in a
more direct way. This latter type of effort will only
be forthcoming, however, if future researchers re-
alize that there are interesting interactions between
reward structures and conditions regarding the di-
mensions of a task, the personality composition of
teams, and individual performance levels within
teams, that need to be explained.
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Bianca Beersma (b.beersma@uva.nl) is a postdoctoral
fellow in organizational psychology at the University of
Amsterdam. She received her doctorate from the Univer-
sity of Amsterdam. Her research interests include team
performance, social motives in teams, group negotia-
tions, and conflict management.
John R. Hollenbeck received his Ph.D. in management
from New York University in 1984, and he is currently
the Eli Broad Professor of Management at the Eli Broad
Graduate 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 the
program of research there. Professor Hollenbeck is also
the codeveloper of the most highly cited model and mea-
sure of goal commitment, one of the central features of all
self-regulation theories of motivation.
Stephen E. Humphrey is a doctoral candidate in organi-
zational behavior at the Eli Broad Graduate School of
Management at Michigan State University. His research
interests include teams and the group context, decision
making, 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 Eli
Broad Graduate School of Management, Michigan State
University. He received his Ph.D. in business adminis-
tration from the organizational behavior group at the
University 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 Distinguished
Professor of Psychology and Management at Michigan
State University. He received his Ph.D. from the Univer-
sity of Illinois. The cofounder of the Michigan State
University Team Effectiveness Research Laboratory, he
has written extensively on work motivation and team
decision making.
590 OctoberAcademy of Management Journal