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Representational Gaps, Information Processing, and Conflict in Functionally Diverse Teams


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Functional diversity in teams, while potentially beneficial, increases the likelihood that individual team members will perceive the team's task differently, leading to gaps between teammates' interpretations of what is needed for the team to be successful. These representational gaps are likely to create conflict as teammates try to solve what are essentially incompatible problems. Understanding how these general mechanisms work should deepen our understanding of information processing and conflict in diverse teams.
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Matthew A. Cronin
School of Management
George Mason University
Fairfax, VA 22030
Voice 703.993.1783
Fax 703.993.1870
Laurie R. Weingart
David A. Tepper School of Business
Carnegie Mellon University
Pittsburgh, PA 15213
Voice 412.268.7585
Fax 412.268.6920
In Press, Academy of Management Review
The authors would like to thank Susan Brodt, Michael Prietula, Don Moore, Ray Reagans,
Catherine Cramton, Michelle Marks, Rich Klimoski, as well as two anonymous reviewers for
their insightful comments on the development of this manuscript. We would also like to
acknowledge Jonathan Cagan, Craig Vogel, and Hillary Carey as invaluable collaborators in the
field research that inspired the ideas put forth in this manuscript.
Functional diversity in teams, while potentially beneficial, increases the likelihood that individual
team members will perceive the team’s task differently, leading to gaps between teammates
interpretations of what is needed for the team to be successful. These representational gaps are
likely to create conflict as teammates try to solve what are essentially incompatible problems.
Understanding how these general mechanisms work should deepen our understanding of
information processing and conflict in diverse teams.
Many have argued that diversity in knowledge and experience in teams should lead to
better and more creative solutions (e.g., Kanter, 1988). Yet such gains have not been consistently
found (Bettenhausen, 1991; Hambrick, Cho, & Chen, 1996; Milliken & Martins, 1996; Simons,
Pelled, & Smith, 1999; Williams & O'Reilly, 1998). These mixed findings have often been
explained as information sharing problems, including unwillingness to share information
(Bunderson & Sutcliffe, 2002; Lovelace, Shapiro, & Weingart, 2001), the degree to which people
feel safe to express their viewpoint (Edmonson, 1999), and the tendency of groups to focus on
common knowledge (Stasser, 1999; Wittenbaum & Stasser, 1996). Implicit in this literature is
that a) people are capable of integrating others’ information with little misunderstanding, and b)
they would choose to do so. We will argue that representational gaps, inconsistencies between
individual’s definition of the team’s problem, limit both of these processes making it more
difficult for team members to integrate one another’s information and increasing the likelihood of
An example of a representational gap comes from a cross-functional product development
team at an auto manufacturer. The team is given a mandate to make a “tough truck.” A designer,
thinking in terms of styling, conceptualizes “tough” as “powerful looking.” The designer then
sketches a vehicle with a large grille and large tires creating a very powerful stance. When seeing
this mock-up, an engineer, thinking in terms of functionality and seeing tough as implying
durability, is unhappy with the design because it compromises the vehicle’s power. To maintain
hauling capacity with large tires implies a need for greater torque output from the engine, adding
expense and difficulty to the engineer’s part of the problem. When the engineer suggests 16 rather
than 20 inch wheels, the designer balks, claiming it makes the vehicle look cartoonish rather than
The representational gap illustrated here is in the conceptualization of “tough.” Although
the two conceptualizations can coexist (a vehicle can be both powerful looking and durable),
resources are often limited and accommodations between alternative perspectives are usually
required if the truck is to be built within specifications. Such accommodations, we will argue, can
be difficult to achieve. In the best of situations, a designer may want to help solve the engineer’s
torque problem by offering solutions that maintain the vehicle theme. However, lacking training
in engineering principles, it will be difficult for the designer to propose feasible solutions, as
much as it will be difficult for the engineer to know what features will and will not convey the
“tough” theme (a design concept). For the engineer to accept the use of 20 inch wheels, she would
have to justify the resulting cost of increased torque. Even if cost and benefit could be calculated
accurately (how does one quantify the utility of 20 inch wheels?), the designer’s and engineer’s
assessments of the tradeoff will likely differ in line with their functional values. If styling (a la
design) is assessed to be the primary driver of the purchase of a truck, then the benefit of the 20
inch wheels would be greater than if functionality (a la engineering) is the primary driver.
Fundamentally, a representational gap reflects differences between team members’
problem definitions that will ultimately affect group problem solving. In this paper, we examine
representational gaps in detail – their causes, how they affect information processing, how they
create conflict, and what can be done to bridge them. We do this by using information processing
psychology (Newell & Simon, 1972) to build on the research on shared mental models (Cannon-
Bowers, Salas, & Converse, 1993; Klimoski & Mohammed, 1994) in accordance with the
suggestion by Klimoski and Mohammed (1994) that the potential of understanding shared
cognition “can best be realized if the notion of a group mind is examined relative to what we
know about mental representations and cognitive architecture at the level of the individual.” (p.
404). The SMM literature demonstrates the importance for team performance of team members
holding similar views (Marks, Sabella, & Burke, 2002; Mathieu, Heffner, Goodwin, Salas, &
Cannon-Bowers, 2000). We hope to advance the theory of shared mental models by using the
notion of the representational gap to shift the focus from what is shared among teammates to
what is inconsistent among them. We will argue that inconsistencies in individual cognitive
representations are at the heart of many decision making problems in diverse teams.
Representational gaps are a process loss phenomenon (Steiner, 1972) in diverse teams that
influence each of the three core processes in group functioning identified by McGrath and Argote
(2001): information processing, coordination, and conflict management. Representational gaps
degrade information processing by leading to misunderstanding and potential misuse of
information. Representational gaps make coordination difficult by creating contradictions in how
teammates believe the problem should be solved, leading members to take actions that contradict
each other. Finally, when team members interpret the same information differently and view how
the problem should be solved differently, the team is likely to experience conflict. In explicating
representational gaps we focus on its influence on information processing; we also consider its
subsequent effects on team coordination and conflict.
We will also argue that our theory of representational gaps is very generalizable.
Representational gaps come from the different knowledge and values that people hold. Although
we focus primarily on functional diversity, most types of diversity lead people to have different
knowledge and values (Williams & O’Reilly, 1998). Norms and values can vary across local
conditions (Cramton, 2001), culture (Hofstede, 1980), and as a result of individual personality
difference (Rokeach, 1979). Thus representational gaps can even occur in what appear to be
homogenous teams.
A representational gap is a group level phenomenon that arises as a function of the
cognition of individuals working together to solve a problem. It is rooted in the individual-level
problem representation – the framework that guides the way an individual solves a particular
problem. The representation is “a cognitive structure that corresponds to a given problem…
constructed by the solver on the basis of domain-related knowledge and its organization” (Chi,
Feltovich, & Glaser, 1981, p. 131). A representation can be distinguished from a frame (Putnam
& Holmer, 1992) or schema (Fiske & Linville, 1980) in that representations are constructed for
specific problems, while frames and schemata are generalized templates used to make sense of
large classes of problems. Representations are often created using the perceptual schemata that
people hold.
Groups have what we call a joint representation: a group level cognitive structure that
corresponds to the group’s problem. Like a shared mental model, a joint representation is seen
only by considering the aggregate of individual representations (Cannon-Bowers et al., 1993). It
is necessary to view the joint representation as the union of all individual representations for two
reasons. First, representations arise from perception, and people can only “see though their own
eyes.” In other words, each individual only has his/her own perception, and other’s perceptions
(and hence problem representations) are not actually experienced. This is not to say that one
person cannot affect another’s representation, just that any outside influence is going to have to be
encoded through one’s own perceptual schemata. Second, since individuals will create
representations that they think accurately model the team’s task, any parts of that representation,
shared or unshared, will affect the individual’s problem solving. Thus, we use the union as the
aggregating function because people cannot fully know others’ cognitions, and their personal
representations of the team’s problem will affect their actions even if their representation differs
from other team members’ representations.
Because individuals are limited to perceiving the problem from their own vantage point,
internal consistency of the joint representation is difficult to obtain. An individual representation,
which is contained in one person, allows the person to inspect his or her thoughts for consistency
and to change them as he or she sees fit. In contrast, the mechanism for comparing representations
across team members is more difficult to implement and less reliable. The default beliefs of
others are difficult to inspect because they are often not verbalized, as much of the process of
creating a problem representations is based on the automatic application of pattern recognition
and matching within a cognitive system that is built over time (Bargh & Ferguson, 2000). Thus
any inconsistencies across these tacit or non-verbalized beliefs can easily go undetected and
One may ask why people on the same team and presumably working toward the same
objectives would differ in their problem representations in the first place. Variation among
representations is possible because the representation is created from a subset of the task
environment (Hinsley, Hayes, & Simon, 1977; Newell & Simon, 1972). The resulting simplified
model of the problem provides a framework one can use to guide problem solving without
overtaxing one’s limited cognitive capacity (Simon, 1979). Yet this simplification process can
introduce variation among the cognitive representations of people who look at the same situation,
but attend to different aspects simply due to individual differences in the knowledge and values
that they use to encode the problem. Representation gaps occur when the different encodings lead
people to create representations that cannot be integrated.
Different Knowledge and Values Create Different Problem Representations
Given a problem, people will look for a way to solve it that capitalizes on the knowledge
they already possess. Thus people will tend to represent a problem in accordance with the
knowledge they hold (Dougherty, 1992; Weingart, Cronin, Houser, Cagan, & Vogel, 2005).
Knowledge can therefore drive how people categorize a problem and then formulate their
problem representation. Dearborn and Simon (1958) demonstrated this when they showed how
executives viewed organizational problems from the perspective of their own departments (e.g.,
finance managers saw a decrease in revenue as stemming from mismanagement of money, while
marketing managers saw the same decrease in revenue as stemming from poor marketing of the
product). Since people rely on their knowledge to function in the world, it logically follows that to
solve problems they will represent problems in ways that capitalize on the knowledge they hold.
In addition to knowledge, beliefs about what is beneficial or desirable (i.e., values, see
Rokeach, 1973) will also influence how a problem is represented. In contrast to knowledge, which
informs what can be done; values dictate what should be done. For example, Sarasvathy (2001)
demonstrated a difference in the value placed on risk between bankers and entrepreneurs. Bankers
viewed risk negatively, and therefore risk minimization was an important aspect of any endeavor
in which they engaged. Entrepreneurs, on the other hand, were not as concerned with risk
minimization per se. Instead, they focused on controlling the market (an aspect that bankers
valued much less). Value-based beliefs are likely to be functional in that they support the
achievement of one’s objectives; in our example, minimizing risk was useful for increasing return
on investment. However, they are also idiosyncratic to a domain, as values arise in part from
acculturation via training, rewards, and socialization. Again, in our example, entrepreneurs
thought that the level at which risk was shunned by bankers was irrational, reflecting their values
in addition to objective standards.
Thus even when working on the same problem, for example, to design a “quality”
component, the representations constructed can be quite different. The knowledge that defines
what quality is as well as what one can change to improve quality is a function of the skills and
experience of the individual performing this task. Different values will also lead people to have
differing perceptions of why quality is important and how one should rank the aspects of quality
in terms of importance. Two people tasked with building a quality component may have very
different representations of their collective task when their background knowledge and values
differ. These differences become liabilities when they lead the individuals to work against each
other in solving their joint problem.
Problem solving involves processing information relevant to the current situation (the
problem state) in order to create and select among actions taken to change the situation (moves)
so that it looks more like the desired endpoint (i.e., the goal state). Information is interpreted and
evaluated using the problem representation. Interpreting information means determining which
implications of the information are relevant for the problem (Bransford & Johnson, 1973; Daft &
Weick, 1984; Duimering & Wensley, 2001; Lindsay & Norman, 1977; Nonaka, 1994). For
example, information that a bumper design will cost $200 per part can be interpreted as: 1) the
bumper is too expensive, 2) that there is only $450 left for other front fascia components, 3) that
the bumper will be high quality, or some combination thereof. In contrast, evaluation is a process
of determining how useful information will be for satisfying problem objectives (Ohlsson, 1992;
Streufert & Nogami, 1992). In the above example, both the information that the bumper will cost
$200 and the interpretation that such a cost leaves $450 would be judged of little value for solving
the problem of how to make the bumper cheaper. Thus, one’s representation guides both the
interpretation and evaluation of information. That information, once processed, is used to decide
what move to make next. However, when the joint representation has gaps, information
processing and subsequent move selection can result in coordination problems and conflict.
Representational Gaps Derail Team Information Processing
If a team member is to use another’s information, there must be a minimum degree of
shared understanding (e.g., at the very least the receiver’s interpretation should not contradict the
sender’s intended meaning). Representational gaps can degrade this process. Sometimes a concept
in one domain has no equivalent in another (e.g., “vehicle theme” for design has no parallel in
engineering). Thus any sophisticated use of that concept by the recipient is unlikely. Other times
the words used may be the same but the nuance may be different (e.g., “trust” as used by
economists vs. sociologists, “risk and uncertainty” as used by behavioral decision scholars vs.
managers). In this case it is likely that there will be information distortion. The meaning and value
intended by the sender may be distorted as the information is assimilated to fit the receiver’s
problem representation. Note that the representation is unlikely to change to accommodate the
new information because the representation is the framework with which to make sense of new
information (Hayes & Simon, 1974).
Information integration is difficult because although all teammates may be highly skilled,
intelligent, and familiar with each other, they may not be fluent in each other’s domains. When
trying to make decisions that require tradeoffs across functional areas, these differences in domain
expertise could become a major impediment to joint problem solving. Experts and novices have
been shown to solve problems differently (Larkin, McDermott, Simon, & Simon, 1980). Novices
pay attention to surface features of the problem and try to solve the problem in those terms (e.g.,
the role of cars and hills when trying to solve a physics problem). In contrast, experts pay
attention to deep structural relationships in the problem and try to solve the problem by applying
their trained principles (e.g., force on an inclined plane when solving the same physics problem).
Similarly, an engineer is likely to see a problem in terms of deep engineering principles while a
designer sees the engineering surface features, and vice versa. These differences limit the
effectiveness of cross functional communication, as the novices’ lack of depth will lower their
understanding of the nuance and interrelations between the structural features which the expert is
trying to describe (or may simply believe to be understood).
Experts can educate novices, and over time people from one functional area can become
facile in other areas. In this way, an engineer can develop a more than casual understanding of
design principles and concerns. However, it would be overly optimistic to think that a designer
could easily educate an engineer to understand design with the same level of sophistication that
the designer has gained after years of training and experience. First, this training would occur as
people work together, which is an ad hoc, potentially incomplete, kind of training. Moreover,
people have their own functions to perform in these teams, so any attention for learning would be
leftover from what was devoted to one’s primary task. Learning from others also requires
motivation to do so, and when reward structures are in place that do not support an activity, it is
unlikely to occur (for example, designers have no incentive to stay under budget when that
parameter is unrelated to their performance review). Finally, there may be reasons to avoid cross-
functional education, as it might homogenize the diverse perspectives of the team, potentially
interfering with creativity and innovation.
Shared value systems can aid in the integration of diverse perspectives. However, not all
functions or backgrounds are perceived with the same level of legitimacy by others, and so all
information is not given equal weighting. Value-driven preferences (rather than objective criteria)
may lead people to view some information as more useful than others. Consider how economists
value mathematical modeling whereas psychologists value causal modeling in developing theory.
Both approaches provide information regarding the ways human behavior might occur, but the
two disciplines value their own approach more highly and infrequently attend to work in the
opposing field. Egocentric bias may play a central role in this process (Kruger, 1999). People give
importance to information in proportion to its relevance to their own knowledge and values.
In sum, we think about representational gaps as places where information processing
between teammates breaks down. For one team member to successfully use others’ information,
the person must have the tools to process the information. Across a representational gap, the
means to properly interpret and evaluate the message may be lacking. For instance, an engineer
may hear that “maintaining the vehicle theme is our most important priority” from the designers,
but what “maintaining the vehicle theme” means, exactly, is not obvious. Engineers certainly do
not understand “theme” with the nuance of people trained or experienced with design. Themes are
not in the lexicon of engineering, and so what this implies for the engineer is not clear. Further,
even if the engineer understands that “theme” means that components share similar shapes, lines,
colors, and materials, the engineer (who does not deal in those terms) will have almost no ability
to evaluate how this concern should be ranked in comparison to maintaining weight, cost,
durability, and ease of manufacturing. What’s more, if the engineer were to want to make the
tradeoff, we should expect the engineer’s weightings of importance of the factors to be different
from the designer’s.
Delineating incompatibilities in representational gaps. Klimoski and Mohammed
(1994) observed that the notion of what is meant by “shared” amongst mental models required
further development, as expecting teammates to have no unique information was probably too
restrictive. In contrast to the shared mental model literature, which has focused on the sharedness
(or similarity) between team members’ mental models (Cannon-Bowers et al., 1993; Marks,
Zaccaro, & Mathieu, 2000; Mathieu et al., 2000), we focus on the notion of “incompatible”
representations to address Klimoski and Mohammed’s concern. These constructs differ in that
representations can be different (not shared) without being incompatible (or conflicting).
The existence of unshared but compatible representations can be seen in research on
specialized teams. Successful teams are often purposely specialized in their knowledge, implying
minimal overlap in teammates’ representations. For instance, Hutchins (1990) showed how large
ships’ navigational teams partial their perception as they try to dock the ship. Levesque, Wilson
and Wholey (2001) found that successful software development teams’ mental models diverged
over time. More generally, transactive memory research (Argote, Gruenfeld, & Naquin, 2001;
Liang, Moreland, & Argote, 1995; Moreland, Argote, & Krishnan, 1998; Wegner, 1987) has
shown that teams with specialization of knowledge, where there is less common knowledge, were
superior in their performance to those without such differentiated knowledge. Although team
members were highly differentiated in their representations of the problem, team success implies
that their representations were compatible.
To advance theory, we must go further than merely shifting the focus from similarity to
compatibility. We must consider the specific ways in which representations can be
(in)compatible. To do this we further develop the definition of a representation and identify
specific incompatibilities and their implications for teams.
For a particular problem, the representation (joint or individual) is comprised of a goal
hierarchy, assumptions, elements, and operators (GAEO) (Hayes & Simon, 1974; Newell &
Simon, 1972). The goal hierarchy specifies the objectives that need to be met to solve the
problem, as well as their precedence (e.g., the car needs to be affordable but stylish, and the
former is most important). The assumptions are the general restrictions (e.g., a due date) and
preferences (e.g., cheaper is better) taken as “given” (i.e., fundamentally true, requiring no
justification). The elements are the components of the problem that are changeable (e.g., vehicle
theme, profit margin). The operators are the rules for how to transform the elements (e.g.,
changing vendors enables one to get a component cheaper, reducing overhead).
Representational gaps manifest as incompatibilities among the GAEO of a joint
representation. Some gaps may be very large, for example, when the difference relates to the
overall categorization of the problem (e.g., a sales problem vs. a cost reduction problem). Such
large scale differences are likely to encompass many incompatible GAEO (e.g., whether increased
sales or reduced overhead is the ultimate goal, whether or not to assume that costs are fixed,
whether to use marketing operators to increase sales, whether one should change internal
operational elements). Representational gaps will be much smaller when the different perceptions
are about a specific issue (e.g., the meaning of market data). Small gaps may encompass just one
GAE or O (e.g., designers assume customers do not know what they want until design shows
them, marketers assume the customer knows best).
Incompatible GAEO are problematic because they lead to incompatible interpretation and
evaluation of information in relation to the problem, which can in turn lead people to select
different moves (actions taken to solve the problem) than what their teammates would choose,
potentially resulting in coordination problems and conflict. For example, people who hold
different goals will at some point want to move in contrary directions (e.g., one may want to put
more resources to marketing, while another may think resources are better applied to improving
technology). People may make assumptions that others do not, and can be surprised when others’
moves violate their own assumptions (e.g., ignoring a deadline). Finally, people may
unintentionally make others’ jobs more difficult by applying operators that change the elements
of the problem so that it is now harder to solve (e.g., often designers draw things that engineers
believe to be infeasible).
Although incompatibilities within a joint representation pose problems for team
coordination and conflict, not all differences among the parts of a representation result in
incompatibilities, and only some aspects of representations must be shared for teams to function
effectively. Figure 1 presents a minimal overlap configuration for a joint representation. While
teams may experience more similarity in their joint representation than presented in Figure 1
(depending on task demands and levels of specialization), it is informative to explicate minimal
conditions for effective teamwork. We use the GAEO distinction as the foundation of this
The goal hierarchy specifies what the end state of solving the problem should look like. If
goal hierarchies differ among team members, it means that teammates are working toward
different desired end states. This implies that to be effective, all teammates should have the same
set of team goals in the same order of precedence (Figure 1, “G”). When interdependent team
members have the same goal state in mind, it is easier to agree on the relevance and importance of
information. For example, team members will agree that information about consumer preferences
for a competitor’s product is important if they concur that the goal of gaining market share
dominates the goal of increasing return on investment. Additionally, when interpretations and
evaluations of information are different for other reasons (i.e., different AEO, see below), people
are more willing to synthesize their disparate knowledge when they are looking to move toward
the same end state.
Assumptions serve as the background information taken as “given” for the problem.
Assumptions, therefore, need to be aligned for the same reason goals do – so that people make
consistent interpretations and evaluations of information. Yet unlike goals, assumptions may not
affect all teammates; many assumptions are only relevant to a subset of the team’s problem
(Figure 1, “A”). For example, the assumption of the importance of a deadline is only relevant to
those whose tasks relate to that deadline.
The elements name the aspects of the problem that people can change to get to a solution.
Unlike goals and assumptions, elements do not need to be shared at all (Figure 1, “E”). One team
member can see the problem in terms of gears and leverage while another sees it in terms of lines
and theme without any inherent conflict because elements are cognitive symbols for objects or
concepts in the world that rarely preclude other symbol systems (e.g., parsing a task environment
in terms of cost per unit does not preclude parsing it in terms of labor rate or defect rate).
Elements function much like language, providing a coherent system with which to describe
components of the task and imply relationships among them. Thus unshared elements across team
members present the same problem as unshared language – interference with the transmission of
meaning both explicitly and implicitly. Many have studied the influence of language on thought
and understanding (see Gentner & Goldin-Meadow, 2003) using national language to illustrate
the effects on thinking. Yet different systems of elements should operate similarly because they
operate using the same cognitive mechanisms to derive meaning. For example, when a top
management team comprised of diverse specialists discusses corporate strategy, a member from
marketing may evaluate a strategy stated in terms of financial elements as weak because the
elements imply connections and nuance that the marketing person does not see.
The operators are the rules for how to transform the elements in order to solve the
problem; they are “if-then” rules that take information as the “if” input, and extract the “then”
implication based on one’s knowledge of how the rule works. Much like elements, operators
cannot themselves be in conflict (engineering operators are neither consistent nor inconsistent
with design operators), so they also do not need to be shared (Figure 1, “O”). Yet since operators
are the rules one uses to decide what information means and how to use it, when teammates apply
different operators to the same information, the interpretation and evaluation that results will also
be different (and potentially in conflict).
Figure 1 illustrates the minimum overlap of GAEO needed for effective group
functioning. First, all group members need to hold the same goal hierarchies. Second, group
members with the power to violate each others’ assumptions need to hold similar assumptions.
Finally, group members are free to hold their own elements and operators, and if the assumptions
and goals are aligned when people execute operators and reconfigure elements, these moves
should not create problems in coordination and conflict.
What qualifies as “incompatible” can change across GAEO. With goal hiearchies,
anything less than identical will likely be incompatible, but as you move from assumptions to
operators and elements, incompatibilities can become further removed from the specific AEO.
That is, assumptions can conflict indirectly (designers assume that only designers can create style
trends which implies an assumption that focus groups are not useful for creating style trends; this
is incompatible with marketers who assume that focus groups are useful in this capacity).
Operators and elements may conflict only after they have been applied to the state of the problem
(e.g., using an operator breaks apart elements in a way that goes against one’s assumptions). The
complexity of where and how incompatibility exists among representations suggests that we need
to develop theory that allows us to consider how incompatibilities among the GAEO differentially
affect team information processing, coordination, and conflict.
Research also needs to consider how the task and organizational environments influence
the “optimal” level of overlap of GAEO within the joint representation. Teams that require their
members to be interchangeable, for example infantrymen in a particular unit of the military,
should have complete similarity (i.e., all team members share the same GAEO). In these teams,
any individual may be called upon to do any of the jobs of the other individuals, thus they must
all have similar skills (operators). Because group members must be able to adapt quickly and in
concert to changes in the environment, they must also have similar representations (i.e., see the
same elements, have the same goals, make the same assumptions), that lead them to similar
At a minimum, our theory suggests that incompatibilities in GAEO (i.e., a representational
gap) make it difficult for functionally diverse teams to capitalize on each other’s unique
knowledge. However, when people process information differently, they will also want to solve
the problem differently. The function of processed information is to guide the moves (actions
taken to change the state of the problem so it looks more like the goal state) people make.
Inconsistently processed information can mean that teammates will work against each other and
misunderstand each other, which in turn implies that there is also likely to be conflict over what
the team does, as well as what information implies.
Representational gaps cause problems for people working together because 1) they
increase the misunderstanding and misuse of information, 2) they decrease coordination both
explicitly and implicitly, and 3) they lead to conflict that can be more detrimental than positive.
As discussed above, representational gaps interfere with team information processing, making it
much more difficult to develop the shared understanding that allows groups to communicate
efficiently and effectively. These gaps result in problems of coordination and conflict.
Coordination problems can be distinguished from conflict by focusing on action versus
disagreement. Coordination problems occur when one team member’s actions/moves work
against, contradict, or interfere with another’s moves. Conflict focuses on disagreements about
what actions should be taken. These disagreements could be about how to interpret information
(information conflict) or what moves should be taken (move conflict). We discuss the
implications of representational gaps for each.
Coordination. In building our theory, we have alluded to problems with coordination.
Coordination errors can occur when team members have misunderstandings as a function of
holding different GAEO. Different interpretations of information can cause people to make
moves that work against others’ actions. For example, information that customer satisfaction
ratings are down could be interpreted in terms of problems with product quality or customer
service – each implying the need for different remedies. Even when people interpret information
similarly, they may value it differently, resulting in people making moves that undo others’
efforts or make the problem harder for their teammates. Imagine the situation where one team
member cares that a deadline might not been met, whereas another does not. Differential pacing
of work across the two team members could result in substantial coordination problems.
Coordination problems are especially likely when people have different goal hierarchies, as they
will be looking to move toward different end states.
Conflict. Differentiating between information conflict and move conflict recognizes that
conflicts can occur at different levels of abstraction. Information conflict being more abstract as
team members are presenting their different perspectives on the meaning of information; move
conflict being more concrete relating to what actions should be taken by members of the teams.
Either type of conflict can be driven by a representation gap, and when it is, the conflict can be
difficult to identify and resolve.
When a representational gap is the cause of information conflict, it reflects a difference in
the evaluation and interpretation of the information itself. The conflict will be about what the
information means and how useful that information is for solving the problem. The information
conflict can be specific (e.g., conflict over the implications of an assumption about wear and tear),
more general to the problem solving process (e.g., debate over the usefulness of a schedule for
keeping the team on track), or can relate to the GAEO themselves (e.g., is price more important
than functionality in purchase behavior?). Regardless of the generality, the source of the
information conflict in this example is a representational gap over what drives vehicle purchase
(price vs. features).
When a representational gap is the cause of move conflict, it usually reflects an underlying
difference in the evaluation and interpretation of the information supporting that move (i.e.,
underlying information conflict). For example, two may disagree whether adding a feature to the
vehicle is the right move, but this disagreement is a result of responding to different information
as relevant (what adding the feature does to price vs. ease of use) and/or evaluating the
information differently (whether it is more important to keep costs low or add functionality).
Since the interpretation of information informs the moves team members make, it is the
interpretation of the information that is the cause of the conflict, the move is merely symptomatic.
Note that move conflict does not necessarily imply a representational gap. Two people might
view the problem similarly, as well as the supporting information, but favor different strategies or
tactics for solving the problem.
In that information conflict is expected to be at the root of move conflict and coordination
problems in teams with representational gaps, the source of the conflict (incompatible GAEO) is
often difficult to diagnose because it is relatively unobservable. It may not occur to a person to
ask if other team members are making the same assumptions. As Dougherty (1992) notes in her
research on the problems working across functional areas, people do not generate conflict
maliciously or because they ignore the needs of others, rather they “gloss over the concerns of
others and tend not to appreciate their complexities” (p.189). People can also mistakenly attribute
the move conflict to their teammate’s insufficient concern for their perspective rather than a
fundamental difference in problem definition as a result of functional diversity, this would simply
be an instance of an attribution error (Baron, 1985; Kelley & Michela, 1980).
In that conflict stemming from representational gaps is difficult to identify and resolve, it
may not be surprising that task conflict (conflict over what the team must do to perform a task or
solve a problem) (Jehn, 1995) has been found to be negatively related to team performance across
a variety of studies (De Dreu & Weingart, 2003). In some studies, task conflict has been shown to
be beneficial to teams because it allows them to explore and integrate multiple perspectives (e.g.,
Jehn, 1995; Amason & Schweiger, 1997). Representational gaps derail the beneficial effect of
task conflict and make resolving it difficult because the mechanism for both exploration and
resolution, sharing information to increase understanding, is derailed. Subgroups can voice their
issues and concerns, but the receivers are limited in their ability to respond to this information.
Diverse team members may have some understanding of what others ask for, but this is often the
understanding of a novice. Moreover, people may simply not value what others offer as much as
they value their own perspective, and so the motivation to use others’ information may be lower
than the motivation to use one’s own. Over time, as people share information but only encounter
more conflict over the use of that information, it can build frustration and turn into relationship
conflict (De Dreu & Weingart, 2003; Simons & Peterson, 2000).
Closing Representational Gaps
Assuming representational gaps cause problems raises the question of how to close or
bridge a gap. Central to bridging the gap is creating some degree of shared understanding so that
people can translate between their knowledge bases; this problem is primarily cognitive.
Expanding people’s representations so that they include the capabilities and concerns of others
(i.e., increasing similarity) may just be a matter of education; teaching enough functional
knowledge to those with different backgrounds to allow for shared understanding. However,
when representations are incompatible, integrating knowledge bases becomes trickier as a
teammate may need to change their GAEOs. The research on schemas (e.g., Fiske & Linville,
1980) may provide insight into the cognitive process of accommodation (changing one’s
framework for interpreting the environment in response to novel information). Weick (1996) has
also discussed this problem in terms of getting people to eschew their standard practices and
approach so they can adaptively respond in contexts that cannot be handled by people’s standard
operating procedures. Finally, in that insight is associated with changes in cognitive
representations (Ohlsson, 1992), the literature on insight may be helpful as well. Certainly, the
goal would not be to homogenize team members’ representations. The goal should not be to
reduce the diversity of perspectives that these teams were created to capitalize on, but rather to
engender enough understanding to facilitate communication and accurate interpretation. The
question of how to bridge gaps without decreasing divergent thinking will be challenging.
Beyond understanding the pure cognitive difficulties of getting people to align their
GAEO, research needs to determine the role of motivation to learn and/or change. Team leaders/
organizations must motivate teammates to try to understand each other. Without adequate
motivation, information will be rejected if the source is viewed negatively (Fox & Irwin, 1998;
Hovland & Weiss, 1951). Much of the work on minority influence would be relevant to
understanding how dissent, especially when coming from a subset or low power group, changes
the dominant viewpoint (e.g., Forgas & Williams, 2001; Levine & Russo, 1987; Maass, West, &
Cialdini, 1987). Also relevant is the work on persuasion that shows that one’s attitude toward a
source of an argument will influence the persuasiveness of that argument (Petty, Schumann,
Richman, & Strathman, 1993; Sharma, 1999). Factors like psychological safety within the team
(Edmonson, 1999) or willingness to engage in learning behaviors (Bunderson & Sutcliffe, 2003)
will also play an important role. The common theme among these ideas is that the thoughts and
feelings one has about another teammate will bias the view of the information coming from that
teammate. One could easily apply much of the work on social cognition, but it would be the other
person’s ideas or perspectives instead of that person him or herself that is the object of assessment
(Bodenhausen, Macrae, & Hugenberg, 2003; Chatman & von Hippel, 2001; Cohen, 2003).
The mechanisms and paths through which representational gaps work should inform
theory on diversity, conflict, and shared cognition, as well as addressing some specific
conundrums in each domain. In particular, discussing how team members’ task representations
can differ will address the question of how much knowledge needs to be shared among team
members in order for the team to be functional, a question relevant to the research on group
cognition and shared mental models (Cannon-Bowers et al., 1993; Klimoski & Mohammed,
1994). Understanding how peoples’ representations affect their ability to use the unique
information of others will illuminate a reason why diverse teams fail to optimally use the team’s
knowledge, a puzzle to many researchers who study diversity (Dahlin, Weingart, & Hinds, 2005;
Milliken & Martins, 1996; Williams & O'Reilly, 1998). Finally, understanding the nature of the
conflict created by representational gaps will provide reasons why task conflict (which is what
representational gaps create) is generally not helpful for teams (De Dreu & Weingart, 2003),
despite the widely held belief that it should be (e.g., Amason & Schweiger, 1997; Simons &
Peterson, 2000; Van de Vliert & De Dreu, 1994).
We present this theory of representational gaps in the context of multi-functional teams.
However, representational gap-based conflict can happen whenever people with different
backgrounds come together to solve a problem. All kinds of diversity can lead people to have
different knowledge and values. A large body of literature on demographic diversity supports this
conclusion (Williams & O’Reilly, 1998). Age group and culture (both organizational and
national) are likely to affect one’s knowledge and values. For example, someone from IBM where
the culture is well structured and conservative will approach software development very
differently than someone from Microsoft, known for having a more free-flowing and open
environment. Similarly, we would expect the Japanese to conceive of social interaction very
differently than Americans. Yet there may be also smaller groupings that produce similar
discrepancies between people’s knowledge and values. Cramton (2001) has described how local
conditions of people in geographically distributed teams can beget different perceptions of norms
(e.g., people take public transportation) and day to day functioning (e.g., it was snowing in
Finland so team members were late to work).
Representational gaps may also occur in what appear to be homogenous teams. People
often have different fundamental beliefs because they have different personalities (e.g., cognitive
style, see Tetlock, 2000) and life experiences. The mechanisms that create representational gaps
are general to people (all people draw upon their knowledge and values to make sense of the
world and solve the problems they are given). However, representational gaps based on
personality may be even harder to find than those that occur between people from different social
categories because people within similar social categories have more reason to believe they
should think similarly (Williams, Mannix, Neale, & Gruenfeld, 2004).
Representational gaps create process losses in teams that can both undermine creativity
but also basic effectiveness. Research needs to be done on how to either close or bridge these
gaps in such a way that preserves the diversity of knowledge for which teams were created, but
makes integration among these perspectives possible. In understanding how to deal with
representational gaps, we are likely to advance our understanding of information processing and
conflict in diverse teams. Although we have focused on functional diversity, many of the issues
we have highlighted should generalize to other types of diversity as well (e.g., cultural). Since
teams are often the basic work unit, and since people have to work with others from different
backgrounds, understanding representational gaps should help teams work better together as well.
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Dissimilar but Compatible Joint-Representation
Note: Ovals represent each individual’s representation. Operators (O) and elements (E) are unique
to individuals, assumptions (A) are shared between those for whom they matter, goal hierarchy
(G) is shared by all.
... Well-being Chemistry: Positive feelings and attitudes based on mutual liking, trust, respect (Cronin & Weingart, 2007) and compatibility between leader and follower (affective). ...
... Building on and extending research on team chemistry (Gershgoren et al., 2013), we define shared leader-follower chemistry as positive feelings and attitudes based on mutual liking, trust, respect (Cronin & Weingart, 2007) and compatibility between leader and follower. In this regard, chemistry is focused mainly on affective attributes with relevant adjacent constructs in a recent review of the team emergent states literature (Rapp et al., 2021) including team interpersonal attraction, team affective trust, social cohesion (Kozlowski & Chao, 2012), and task cohesion (a motivational attribute) (Rapp et al., 2021). ...
... Differences in dyadic interactions in this scenario may increase over time as leaders and followers invest more effort and attention in joint work with similar others, and show less respect and trust towards dissimilar others who may be viewed as lower in status. Thus, we expect that over time teams with compilation processes will experience increased variability in proximal emergent states within the team than those with composition processes as status differences are amplified rather than minimized (Edmondson, 2003) and norms of mutual respect, trust and liking fail to diffuse throughout the team (Bergman et al., 2012;Cronin & Weingart, 2007). Therefore, for emergent processes of compilation we propose: ...
While research on leadership has acknowledged the joint efforts of leaders and followers, advancements in conceptualizing the outcomes of such joint efforts have stagnated. Integrating leadership theory and research with multilevel theories on emergent states, we develop and propose a new framework of shared leader-follower outcomes (SLFOs). We reconceptualize SLFOs as four emergent states (presence, synergy, professionalism and chemistry) that are proximal outcomes in teams. Each SLFO is an early indicator of corresponding key traditional outcomes (absenteeism, productivity, quality and satisfaction) that focus on the leader, followers, or the team as a whole. We also discuss the dynamics of emergence of SLFOs from the dyadic leader-follower level to the team level through composition (i.e. convergent) and compilation (i.e., divergent) processes. Our framework extends leadership theories by accounting for emergent states as shared leader-follower outcomes that temporally unfold over multiple levels of analysis. We develop propositions to guide future empirical research on leader-follower proximal outcomes of presence, synergy, professionalism, and chemistry.
... Bantel & Jackson, 1989;Amason et al., 2006;Hambrick, 2007;Talke et al., 2010;Boone & Hendriks, 2009), crossfunctional teams within organizations (e.g. Mohrman et al., 1995;Finegold & Wagner, 1998;Mathieu et al., 2000;Cronin and Weingart, 2007), employee teams more generally (Hambrick et al.,1998;Ely et al., 2012;Youtie et al., 2012), and founding teams (e.g. Ucbasaran et al., 2003;Huang et al., 2012;Kaiser & Muller, 2013;Visintin & Pittino, 2014;Kristinsson et al., 2016;Protogerou et al., 2017). ...
... As often underlined in the literature, if the "mental models" are too distinct, team members may not share the same venture goals and may hold diverse views on how their business should develop in the future. These differences create a lack of common ground, resulting in problems of information exchange and interpretation, and eventually lead to further misunderstandings and distrust (Cronin and Weingart, 2007). Traditionally, the literature has looked at (and operationalized) knowledge diversity in new ventures in two somewhat separate ways. ...
... Scholars have underlined that when diversity is the result of the combination of team members with different knowledge background, it benefits the development of innovations, since it is associated with increased levels of information, cognitive diversity and greater variance in decision-making alternatives and overall more constructive task conflicts that facilitate strategic decisions to focus on innovation fields (Dahlin et al., 2005;Talke et al., 2010;Kristinsson, 2016). Even if some scholars have underlined the risks of having too diverse knowledge backgrounds and experiences in a team, which might generate problems of information exchange, communication and eventually undermine the performance of the projects/businesses (Mathieu et al., 2000;Cronin and Weingart, 2007), most of the existing works agree that on the fact that teams that are more diverse tend to be more creative and to engage in explorative strategic behaviour as compared with teams with shared common experiences (Mohrman et al., 1995;Finegold and Wagner, 1998;Beckman, 2006). Knowledge diversity helps the recognition of opportunities, the identification of potential barriers to innovation and a more fruitful discussion of all the differ-ent aspects related to the implementation of innovations -technology, market and firm-specific factors (Talke et al., 2010). ...
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This paper studies the relationship between founding team knowledge diversity and firms’ innovative performance. We posit that knowledge diversity entails two dimensions: a team dimension and an individual dimension. In particular, we argue that founding team knowledge diversity can derive both from the presence of founders with different knowledge backgrounds, and from the presence of similar jack-of-all-trades (JOTs). We suggest that knowledge diversity is positively associated with innovation, especially when diversity comes from founders with different knowledge backgrounds, instead of coming from many JOTs. Furthermore, it matters more for firms whose knowledge base is oriented towards technical and scientific applications, as opposed to firms with a generalist, business-oriented knowledge base. We provide support to these propositions relying on a study of 1,800 newly established firms in Europe.
... Research suggests that leaders of creative teams must work to facilitate both the individual member's problem construction process, and the team's consensus on problem representations. While team members may individually construct accurate problem representations, such conceptualizations can become disjointed between individuals on a team (Cronin & Weingart, 2007). Empirical research on team problem construction suggests that teams who are unable to integrate individual problem representations into joint understandings experience increased levels of relationship conflict and overall lower creative performance at work (Gish & Clausen, 2013;Weingart et al., 2008). ...
... Empirical research on team problem construction suggests that teams who are unable to integrate individual problem representations into joint understandings experience increased levels of relationship conflict and overall lower creative performance at work (Gish & Clausen, 2013;Weingart et al., 2008). Cronin and Weingart (2007) refer to such representational disagreements as representational gaps (rGaps). rGaps arise from contradictions between members' representations or the lack of a shared understanding regarding problem parameters and products. ...
... But the clashing viewpoints and opinions can demarcate boundaries between several sub-groups of actors which may hamper their collaboration. Boundaries can be defined, for instance, by separation (e.g. in interests, beliefs, values, norms, attitudes), by variety (e.g. in professional, institutional, or cultural backgrounds), or by disparity (e.g. in power, authority, prestige, or income) (Cronin and Weingart, 2007;Edmondson and Harvey, 2018;Harrison and Klein, 2007). Due to our thematic focus on ES governance, we concentrate our analysis on boundaries by separation due to divergent interests regarding which ES should be given priority. ...
In this study we explore the concept of boundary organizations in the context of collaborative network initiatives which aim to achieve collective action to improve ecosystem services governance at landscape scale. We are specifically interested to analyze how the boundary, which exists between actors with mainly economic interests, related to producing provisioning ecosystem services, and actors with mainly ecological interests, related to preserving regulating, cultural, or supporting ecosystem services, can be overcome for collective action. The analysis is based on empirical research conducted in a case study area from Eastern Germany, the Biosphere reserve Spreewald, where two collaborative governance approaches are compared: a citizen foundation and a water management board. For data collection we use face-to-face interviews based on the participatory Net-Map method. For data analysis we combine social network analysis and qualitative content analysis of the interview transcripts. Results show that both governance networks display the typical structural and procedural characteristics of boundary organizations. We further discuss how single design characteristics can potentially affect ecosystem services provision as a collective benefit of the collaboration. Overall, the concept of boundary organizations provides a useful lens to study collaborative governance arrangements.
... Refs. [78,79] emphasize that the more diverse the disciplines are, as presented by team members with little common experience, the greater the differences between their tasks may be. Each team member, who represents a different specialization of knowledge, comes to the team with a different "world of thought" and understands problems, critical elements and problem-solving steps differently than other team members [77,80]. ...
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Contemporary project teams are increasingly used to solve problems that are at the crossroads of many disciplines and areas dedicated to Industry 4.0, which is a watershed in the implementation of Sustainable Development Goals (SDGs). Industry 4.0 can serve as a platform for the alignment of SDGs with the ongoing digital transformation. This involves specific challenges for teams, but also allows perspectives that may create innovative and high-quality results. In order to meet these challenges while taking advantage of the opportunities offered by interdisciplinary cooperation, project teams, including the team leader, should have specific competencies. With this in mind, the aim of this article is to identify the challenges and perspectives related to working in interdisciplinary Sustainable Industry 4.0 project teams and to define the competencies necessary to act as a member and leader of these teams. Implementation of this aim will be possible by answering two research questions: (1) What requirements and opportunities are involved with interdisciplinary work amongst members of Sustainable Industry 4.0 project teams; and (2) What are the competencies necessary of members and leaders of such teams to meet these requirements and take advantage of the opportunities for such cooperation? An exploratory case study was conducted among members of interdisciplinary project teams at one of the leading technical universities in Poland. Qualitative data were obtained from many sources: interviews, internal documentation of analyzed projects and managerial notes. The obtained results allow us to state that the most important challenges and perspectives related to the work of interdisciplinary Sustainable Industry 4.0 teams include coordination of individual parts of the project, integrative leadership, establishing a common language, broad views on the issues raised and building a team consisting of specialists with the required competencies. The competencies of the project team that are important for working in the analyzed environment include strategic perspective, communication skills and persuasion, while for leaders, competencies must include the ability to coordinate work, resource management, empowering and motivation.
Purpose The research on shared mental models (SMMs) focuses on the importance of all team members holding similar mental models to realize team performance. However, for a perceived decomposable task, it is not required for all team members to have similar mental models to achieve team performance. Moreover, unnecessary overlapping mental models among team members may engender information overloading, translating into suboptimal team performance. Absent from the current literature is an understanding of the factors that determine the minimal overlapping mental models required across specific members for team performance. The purpose of this study is to yield an understanding of these issues. Design/methodology/approach This study highlights that the requirement to hold similar mental models across specific team members depends on the task decomposition mechanisms used: task complexity and decomposability, subtask assigned and layer, task modularity, workflow interdependence type and tool attributes. Findings Unlike much prior research which measured the relationship between SMMs and team performance at the team level, our conceptualization suggests that the measurement of SMMs and team performance needs to be conducted across a team and subsets of the team or individuals depending on task complexity and decomposability. This current research offers an important viewpoint regarding when team members need to hold similar mental models to realize task performance. Originality/value By suggesting new insights into when mental models should be similar across specific team members, this research also provides understanding of why some empirical SMMs studies do not yield positive relationships between similar SMMs and team effectiveness while others do.
The performance of agile development is variable, and the literature has not yet determined the mechanisms behind this behaviour. Agile teams may function as a sociotechnical system and these sociotechnical mechanisms may cause the performance variation. A configurational analysis determined that the presence of both agile technical and agile social subsystems was required for successful work outcomes, indicating that agile does operate as a balanced sociotechnical system. Individual agile components were also found to interact with and compensate for the absence of other agile components, confirming that the theory of constrained sociotechnical systems applies to agile development.
Organizational teams increasingly engage in inter-team coordination activities. Though past literature suggests that inter-team coordination benefits team performance, recent studies reveal that teams may in fact fail to reap such performance benefits. To investigate the boundary condition as well as the process underpinning the relationship between inter-team coordination and team performance, we draw on the knowledge integration model and develop a mediated moderation model. In particular, we propose that team information elaboration mediates the interaction effect of inter-team coordination and knowledge integration capability on team performance such that the indirect relationship is stronger for teams with a high knowledge integration capability. Two field survey studies support our hypothetical model. Based on these findings, we further discuss the theoretical and practical implications of this research.
Purpose The purpose of this study is to test a contingency model in which the relationship between task conflict and team performance depends on the extent to which team members differ in their levels of expertise and functional backgrounds. Design/methodology/approach Data were obtained from 71 student teams that completed a semester-long entrepreneurial project. Findings The results support the moderating role of expertise disparity in the process through which task conflict contributes to team performance. Task conflict had a curvilinear effect (inverted-U) on team performance in teams with high expertise disparity. In contrast, in teams with low expertise disparity, the relationship between task conflict and team performance was found to be linear and positive. The moderating role of functional background diversity was not supported. Research limitations/implications This paper shows that the relationship between task conflict and team performance can exist in both a linear and a curvilinear fashion, and that what determines the form of the relationship has to do with a team’s diversity characteristics. The focus of future conflict research should be whether and how teams can realize the possible beneficial effects of task conflict, not whether task conflict is simply good or bad. Practical implications Managers may deliberately consider the differences in expertness among members when creating teams or assigning members to a team. Further, they may want to avoid extensive task conflict when a team’s expertise levels are unevenly distributed to lessen expected performance loss. Originality/value This study’s examination of the roles of two moderators in catalyzing the processes through which potential effects of task conflict are realized enhances the understanding of equivocal results in conflict research. The empirical evidence that this study provides informs a long-standing debate in the conflict literature – whether task conflict is functional or dysfunctional for teams – in a new, insightful way.
The effects of conflict on strategic decision making effectiveness and organizational performance Most scholars and practitioners agree that the complexities and pressures of strategic decision making can often produce conflict (Carter, 1971; Hickson, Butler, Cray, Mallory & Wilson, 1986; Mintzberg, Raisinghani & Theoret, 1976). The effect this conflict has on the decision making process, however, is unclear (Eisenhardt & Zbaracki, 1992). Traditional wisdom holds that conflict hinders decision making. Conflict can disrupt the exchange of information among the team of decision makers, reducing decision quality. Conflict can undermine the commitment that is needed to get the decision properly implemented. Finally, conflict can reduce satisfaction and affective acceptance among the team members, threatening cohesion and the prospects for future decisions (Schweiger, Sandberg & Ragan, 1986; Schweiger & Sandberg, 1991). However, there is also reason to believe that conflict improves decision making. Conflict can enhance decision quality by encouraging thorough evaluation of ...