ExpertiseinGroupProblemSolvingRecognitionSocialCombinationandPerformance..pdf

Expertise in Group Problem Solving: Recognition, Social
Combination, and Performance

Bryan L. Bonner
University of Utah

This laboratory study assessed how recognition of expertise affects group decision
making and performance. Three-person groups and independent individuals solved 4
intellective problem-solving tasks in 3 experimental conditions: 4 individual tasks, 1
individual task followed by 2 group tasks followed by 1 individual task, or 1 individual
task followed by 2 group tasks (with intragroup rankings) followed by 1 individual task.
Findings indicate that (a) both groups with ranking information and groups without are
fairly well calibrated with respect to expertise, (b) group decisions were best approx-
imated by “expert-weighted” decision schemes in which the highest performing mem-
ber of the group has twice the influence of other group members, and (c) groups
performed at the level of the best of an equivalent number of individuals.

Many of the most important decisions made
in our world are arrived at not by individuals
working in isolation but by collectives working
in unison. In areas as diverse as courtroom
justice, advertising, education, and large-scale
acquisitions, groups of problem solvers are fre-
quently called upon to “put their heads to-
gether” and determine the best courses of ac-
tion. For these groups to operate as effectively
and efficiently as possible, they must coordinate
and utilize their resources to their fullest extent
(Steiner, 1972). Access to abundant intragroup
resources (e.g., member expertise) will not aid
the group if it fails to use those resources wisely
(Hackman, 1987). Because groups are typically
composed of members with variable levels of
expertise and often work to solve problems that
require them to combine their input and form
some type of aggregate product or decision,
achieving a better understanding of how groups

combine member input to reach consensus is of
great importance.

A recent study (Bonner, Baumann, & Dalal,
2002) examined the effects of performance
feedback on subsequent decision making and
performance in three-person groups working on
the logic problem Mastermind (described in
Knuth, 1976 –1977). This study found that
groups gave more weight to the input of their
highest performing members, with the group
decision-making process being best approxi-
mated by post hoc “expert-weighted” social de-
cision schemes. These weighted models attrib-
uted twice as much influence to the highest
performing group member relative to other
members when veridical expertise rankings
(based on prior performance) were made avail-
able to the group. This study also found that
groups performed at the level of the best of an
equivalent number of individuals regardless of
whether they had access to explicit performance
rankings.

The current study seeks to expand on Bonner
et al.’s (2002) findings in several areas. First,
whereas in the previous study the expert-
weighted models were derived after the fact to
fit the data, in the present study these decision
schemes were tested a priori against an inde-
pendent data set. Thus, the prior and current
studies follow a model-fitting/model-testing se-
quence in which decision schemes derived from
one set of data are tested on another (Kerr,
Stasser, & Davis, 1979). Second, the previous

This article is based on a study that was submitted in
partial fulfillment of the requirements for the degree of
doctor of philosophy in psychology in the Graduate College
of the University of Illinois at Urbana–Champaign. I would
like to thank Patrick Laughlin, David Budescu, Peter
Carnevale, Incheol Choi, and Andrea Hollingshead for their
invaluable input on this study. I would also like to thank
Michael Baumann for his comments on a draft of this
article.

Correspondence concerning this article should be
addressed to Bryan L. Bonner, David Eccles School
of , University of Utah, 1645 East Campus
Center Drive, Salt Lake City, UT 84112. E-mail:
[email protected]

Group Dynamics: Theory, Research, and Practice Copyright 2004 by the Educational Publishing Foundation
2004, Vol. 8, No. 4, 277–290 1089-2699/04/$12.00 DOI: 10.1037/1089-2699.8.4.277

277

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study constrained the problem-solving groups
to choose their collective decisions only from
the options advocated by group members (i.e.,
the groups could not generate emergent re-
sponses). In the current study this restriction
was lifted, and the expert-weighted models
were modified to account for possible emergent
group responses. Third, in the previous study,
groups solved only one collective problem over
the course of 1 hr. In the current study, groups
were given 2 hr to solve two group problems (in
addition to individual problems). This increased
level of interaction may have allowed group
members to better assess intragroup expertise.
Fourth, the Mastermind task used in the previ-
ous study allows problem solvers to make only
one type of task-related decision (i.e., they
choose a proposed solution to test against the
actual problem solution on every trial until they
are correct or they exhaust their trials). In the
current experiment, Mastermind was replaced
by the letters-to-numbers task (Laughlin &
Bonner, 1999). In the letters-to-numbers task,
problem solvers make three different decisions
on every problem trial: (a) what evidence to
gather, (b) what hypothesis to test, and (c) what
solution to propose. The greater complexity of
the problem type and the increased opportuni-
ties for problem solvers to make decisions pro-
vides a more interaction-rich task environment.
Thus, the current study seeks to assess how the
availability of information on member expertise
affects both group decision-making patterns and
subsequent group performance in the context of
the interaction-rich letters-to-numbers problem.

Recognition of Expertise

One of the greatest resources available to a
problem-solving group is the expertise of its
members (McGrath, 1984). Research in small
group behavior has repeatedly shown that a
group’s ability to accurately assess the expertise
of its members can be vital to the group’s suc-
cess (Baumann & Bonner, 2004; Bottger &
Yetton, 1988; Einhorn, Hogarth, & Klempner,
1977; Libby, Trotman, & Zimmer, 1987; Yetton
& Bottger, 1982). The literature in this area has,
however, provided somewhat mixed results
concerning the actual ability of groups to iden-
tify their best members. Whereas some studies
have found groups to be at least somewhat
proficient at expertise identification under cer-

tain conditions (Henry, Strickland, Yorges, &
Ladd, 1996; Libby et al., 1987; Yetton & Bott-
ger, 1982), others have found groups to be less
effective in this regard (Littlepage, Schmidt,
Whisler, & Frost, 1995; Miner, 1984; Trotman,
Yetton, & Zimmer, 1983).

Bonner et al. (2002) suggested that the lack
of agreement in the expertise identification lit-
erature is a function of the tasks performed by
the groups and the conditions under which the
group problem-solving experiments are con-
ducted. They defined two primary characteris-
tics that a task should possess in order for group
members to recognize and use intragroup ex-
pertise. First, group members must have access
to accurate, diagnostic information on the rela-
tive competencies, knowledge, or performance
of the group members (Henry et al., 1996;
Stasser, Stewart, & Wittenbaum, 1995). One
way that groups may assess the relative exper-
tise of group members is through the use of
explicit performance feedback (Littlepage, Ro-
bison, & Reddington, 1997). Explicit perfor-
mance feedback leads group members to under-
take a social comparison process with other
group members (O’Leary-Kelly, 1998). This al-
lows them to develop perceptions of the perfor-
mance-level rankings within the group. Alter-
natively, this ranking information, if available,
may be explicitly provided to group members.
Second, the task must have a level of difficulty
that allows for substantial variation in perfor-
mance in order for members to identify the
differences in ability levels among group mem-
bers (Baumann & Bonner, 2004; Libby et al.,
1987). If the task is too easy or difficult, with
the result that all group members perform at a
similar level (ceiling and floor effects, respec-
tively), members will have little information
with which to assess expertise. Additionally, in
such situations the value of identifying the high-
est performer in the group may be of limited
practical value.

The letters-to-numbers task used in the cur-
rent study is well suited toward satisfying the
two criteria for expertise identification. First, it
is relatively transparent in terms of perfor-
mance. That is, success in the task is demon-
strable and readily apparent to those under-
standing rudimentary mathematics and logic.
Thus intragroup performance variability may be
assessed without performance feedback. Pro-
viding veridical performance feedback should

278 BONNER

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reinforce these accurate perceptions of member
expertise. Second, this task has an established
level of difficulty that promotes substantial vari-
ance in performance (Laughlin & Bonner, 1999;
Laughlin, Bonner, & Miner, 2002; Laughlin,
Zander, Knievel, & Tiong, 2003). These points
lead to the first two hypotheses of the current
study:

Hypothesis 1: Group members will be well
calibrated in their perceptions of intra-
group expertise (i.e., actual and perceived
expertise rankings will be positively
correlated).

Hypothesis 2: Members of groups pro-
vided with performance rankings will be
better calibrated with respect to intragroup
expertise than members of groups lacking
this information.

Social Permutations: Expert-Weighted
Models

Identifying expertise is only the first step in
utilizing this valuable resource. The next step
involves how groups use the information. Spe-
cifically, this is a question of how groups weigh
the input of expert group members relative to
other members of the group. This weighting
procedure can be framed in the context of the
social combination approach to studying group
interaction. This approach has played an impor-
tant role in the study of group behavior for
decades. The social combination method con-
ceptualizes the processes of cooperative prob-
lem solving in the form of social decision
schemes (e.g., Davis, 1973; Lorge & Solomon,
1955; Smoke & Zajonc, 1962; Thomas & Fink,
1961). Given a set of mutually exclusive and
exhaustive response alternatives, group mem-
bers may initially prefer different alternatives.
The task of the group is to map this distribution
of preferences to a collective decision. This
mapping process is tested against models drawn
from theoretical expectations as to what would
occur given certain assumptions about the pro-
cesses underlying group decision making.

Traditional social combination approaches
typically treat group members as being inter-
changeable and indistinguishable from one an-
other and are therefore not suited to dealing
with questions involving individual differences
within groups. A method of social combination

termed social permutation (Bonner, 2000) ex-
pands on the traditional approach by treating
individual group members as consistent entities
across trials. This method is amenable to deci-
sion schemes capable of predicting unequal in-
fluence between group members on the basis of
known individual differences. This allows for
the development of models that differentially
weight member influence on the basis of such
factors as extroversion (Bonner, 2000) or exper-
tise (Bonner et al., 2002).

Recent research involving social permutation
analysis of groups working on the Mastermind
task found that expert-weighted social decision
schemes, where the highest performing member
of the group wields twice as much influence as
other group members, provided a very good a
posteriori fit to the obtained data (Bonner et al.,
2002). The model that was found to have the
best fit was majority, otherwise weighted pro-
portionality, with experts receiving 2/(N � 1)
proportion of group influence and all other
group members receiving 1/(N � 1) proportion
of influence (in this and all following social
decision schemes, N represents the number of
group members). Although the model fit well
with the data in that case, it is limited in that it
cannot, in its current form, account for emergent
group responses.

Laughlin and Hollingshead (1995) have
shown that taking emergent responses into ac-
count substantially improves model fit for col-
lective induction problems. They found the best
fitting model to be majority, otherwise propor-
tionality with a 1/(N � 1) possibility of emer-
gent responses. Incorporating a proportionate
possibility of an emergent group response into
the expert-weighted model yields majority, oth-
erwise a weighted proportionality in which the
expert receives a 2/(N � 2) proportion of the
influence and all other group members receive a
1/(N � 2) proportion of influence, with a
1/(N � 2) possibility of an emergent response.
This leads to the third hypothesis of this study:

Hypothesis 3: Group choices will be best
approximated by the decision scheme ma-
jority, otherwise a weighted proportional-
ity in which the expert receives a 2/(N � 2)
proportion of the influence and all other
group members receive a 1/(N � 2) pro-
portion of influence, with a 1/(N � 2)
possibility of an emergent response.

279EXPERTISE IN GROUP PROBLEM SOLVING

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Group Performance Relative to
Individuals

If group members are able to recognize ex-
pertise and this knowledge influences their de-
cision-making processes, the next question
deals with how performance will be affected. A
growing body of literature suggests that on in-
tellective tasks (i.e., tasks possessing demon-
strably correct answers), groups tend to outper-
form the average individual (Hastie, 1986; Hill,
1982; Kelly & Thibaut, 1969) and to perform at
the level of the best of an equivalent number of
individuals (Bonner et al., 2002; Laughlin, Bon-
ner, & Altermatt, 1998; Laughlin, VanderStoep,
& Hollingshead, 1991, Experiment 2). In two
recent experiments, groups were found to out-
perform even the best comparison individuals
(Laughlin et al., 2002, 2003). Superior group
performance has been attributed to groups’ su-
perior processing ability on information-rich
problems (Laughlin et al., 1998), an interpreta-
tion consistent with the emerging notion of
groups as information processors (Hinsz, Tin-
dale, & Vollrath, 1997).

The current experiment assessed group ver-
sus individual performance on the letters-to-
numbers task. As no previous studies involving
tasks of this type have taken group decision
processes or member expertise into account, the
current study is in a unique position to frame
performance differences in the context of both
of these potentially influential factors. Thus, the
final hypothesis of the study:

Hypothesis 4: Groups will perform at the
level of the best of an equivalent number of
independent individuals and better than
other comparison individuals.

Method

Participants

The participants were 162 students enrolled
in introductory psychology courses at the Uni-
versity of Illinois at Urbana–Champaign who
received course credit for their participation in
this 2-hr experiment.

The Letters-to-Numbers Task

The goal of the letters-to-numbers task
(Laughlin & Bonner, 1999) is to decode a series

of numbers (0 –9) that have been randomly
coded, without replacement, into a series of
letters (A–J). Problem solvers go through a se-
quence of 10 trials. On each trial, they first ask
for information about the series of letters in the
form of an addition or subtraction equation. The
experimenter then solves their equation in letter
form. Problem solvers then propose a hypothe-
sis as to the letter-to-number mapping of one of
the characters. The experimenter then labels the
hypothesis as correct or incorrect. Finally the
problem solvers attempt to solve the entire se-
quence by providing the complete code (the
solution to the problem). The instructions and
examples given to participants are provided in
the Appendix.

Design

Participants were randomly assigned to one
of three possible conditions: group not given
explicit ranking feedback, group given explicit
ranking feedback, or individual. Of the 162
participants, 108 were assigned to one of the
two group conditions. Fifty-four of these partic-
ipants were assigned to the no-feedback condi-
tion and 54 to the feedback condition. All par-
ticipants in the group conditions completed the
letters-to-numbers task first individually, twice
more as part of the same three-person group,
and once more individually. The remaining 54
participants were assigned to the individual
condition and completed the letters-to-numbers
task four times individually.

This study used a randomized block design
with session as the blocking variable. Partici-
pants came to the laboratory in groups of 9 on
one of 18 sessions and were randomly assigned
to one of the three experimental conditions.
Each set of 9 participants solved the same set of
four random letters-to-numbers codes; a differ-
ent set of four codes was used for each of the 18
sessions. Groups of 9 participants were obtained
by overbooking participants for the sessions and
randomly selecting 9 participants from those
who attended. Participants not chosen to partic-
ipate in this study participated in a questionnaire
study instead.

Group members were ranked from first (high-
est performance on the first individual adminis-
tration of the task) to third based on trials to
solution. Ties were broken by summing the
number of correct mappings of the problem

280 BONNER

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solvers’ codes across trials, with the participant
having the higher sum winning the tie (Laughlin
et al., 2002). Groups were assembled immedi-
ately after the initial individual administration
of the task was scored. In the no-feedback con-
dition, the ranking information was not commu-
nicated to the participants. In the feedback con-
dition, the rankings of all group members were
verbally communicated to the assembled groups
by the experimenter immediately prior to the
group administrations of the task.

On every trial of the group administrations of
the task, the members of the group first, without
discussion, recorded the equation that they
wished the experimenter to solve. The group
then engaged in free discussion and proposed
one equation (e.g., A � B � __), which the
experimenter solved for them (e.g., A � B �
G). Next, each member of the group, without
discussion, recorded the hypothesis that they
wished the experimenter to answer. The group
then engaged in free discussion and selected one
hypothesis (e.g., A � 2), which the experi-
menter identified as being true or false (e.g.,
A � 2 is true). Then the group members, again
without discussion, recorded their proposed so-
lutions to the letters-to-numbers problem (i.e.,
the complete code). The group again engaged in
free discussion and proposed one solution. If the
group’s proposed solution was correct, the
group was told this and the problem ended. If
the proposed solution was not correct, then the
group continued on to the next trial and repeated
the process. This continued for 10 trials or until
the group solved the problem. Groups were not
constrained to adopt the equations, hypotheses,
or codes proposed by group members (i.e.,
emergent responses were allowed). In the group
conditions, after the final group problem was
solved (the third administration of the task,
overall), group members were separated and
each group member ranked all members, in-
cluding themselves, in terms of expertise on the
letters-to-numbers task.

Participants assigned to the individual condi-
tion completed the task individually four times,
proposing equations, hypotheses, and codes in a
manner similar to that of participants in the
group condition, with the exception that no col-
laboration was called for. The primary proce-
dural distinction between group and individual
conditions was that individuals were able to

move at their own pace because they did not
have to wait for others to generate responses.

Results

Recognition of Expertise

Recognition of expertise was evaluated by
comparing the participants’ actual obtained
rankings to their perceived rankings. Actual ob-
tained ranks were based on the relative perfor-
mance of problem solvers on the initial (indi-
vidual) administration of the task. Perceived
rankings were obtained by aggregating all
member-generated rankings of a given group
member across the group (i.e., rankings were
based on the mean of the three rankings as-
signed to each member including the self-rank-
ing). Ties in perceived rankings were broken
randomly. Because recognition of expertise was
expected to vary as a consequence of the pres-
ence or absence of explicit ranking information,
analyses were computed separately for both
conditions. Figure 1 shows the proportion of
actual obtained ranks by perceived ranks as-
signed by the group for both feedback condi-
tions. Chi-square tests indicated a lack of inde-
pendence between actual and perceived ranks
in both the no-explicit-feedback condition
and the explicit feedback condition, �2(4, N
� 51) � 15.52, p � .01, and �2(4, N �
54) � 32.67, p � .01, respectively. Correlations
between actual and perceived rankings also in-
dicated that group members were significantly
calibrated in both the no-explicit-feedback con-
dition, rs(51) � .53, p � .05, and the explicit
feedback condition, rs(54) � .64, p � .01.
These results support Hypothesis 1.

It was predicted that group members would
be significantly better calibrated in the explicit
feedback condition than in the no-explicit-feed-
back condition. A Fisher’s r-to-z test was used
to compare the magnitudes of the two correla-
tions in the previous analysis. The difference
between the correlations was not significant
(z � .84, p � .05). Similarly, a test comparing
the proportions of correct perceived rankings
between the no-explicit-feedback condition (in
which 54.90% of group-assigned rankings were
accurate) and the explicit feedback condition
(in which 62.96% of group-assigned rank-
ings were accurate) was also nonsignificant,
t(102) � 0.84, p � .05. These findings failed to

281EXPERTISE IN GROUP PROBLEM SOLVING

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support Hypothesis 2.1 As explicit feedback did
not significantly affect recognition of expertise,
these two conditions were combined for the
analysis of social combination patterns.

Group Decision Models

For the purpose of this analysis, each group is
represented by a series of three letters. The first
(highest performing) member is represented by
the leftmost letter, the second (median) member
is represented by the middle letter, and the third
(lowest performing) member is represented by
the rightmost letter. Correct responses are la-
beled as C, and noncorrect responses are labeled
as A, B, or D. Emergent group responses are
labeled as E. Different letters denote different
individual responses. For example, if codes
were being examined, the label “CAB” would
represent a member distribution in which the

1 Another method of evaluating the accuracy of expertise
assessment involves comparing actual rankings to self-rank-
ings only. Self-rankings and aggregated group rankings
correlated very highly in both the no-feedback and feedback
conditions, rs(51) � .75, p � .01, and rs(54) � .80, p � .01,
respectively. In a three-person group there is only 1 degree
of freedom remaining in ranking the entire group after a
self-assessment is made. Thus, it is not surprising that the
two methods provided very similar results. Tests indicated a
lack of independence between actual and self-ranks in both
the no-feedback condition and the feedback condition, �2(4,
N � 51) � 8.06, p � .05, and �2(4, N � 54) � 28.06, p �
.01, respectively. Correlations between actual and self-rank-
ings were also significant in both conditions, rs(51) � .34,
p � .05, and rs(54) � .57, p � .01, respectively. The
difference between the correlations was not significant,
Z � 1.44, p � .05. A comparison of the proportions of
correct self-rankings between the no-feedback condition
(50.98% accurate) and the feedback condition (59.30% ac-
curate) was also nonsignificant, t(102) � 0.86, p � .05.

Figure 1. Actual versus perceived rankings for no-feedback and feedback conditions. 1st
Gn � first group member, no-feedback condition; 1st Gf � first group member, feedback
condition; 2nd Gn � second group member, no-feedback condition; 2nd Gf � second group
member, feedback condition; 3rd Gn � third group member, no-feedback condition; 3rd Gf �
third group member, feedback condition.

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first member selected the correct code and the
second and third members selected two differ-
ent noncorrect codes.

Note that correct responses (C) apply only to
accurate problem solutions (i.e., correct codes),
not to information gathered toward the solution
of the problem (i.e., equations and hypotheses).
For codes, a correct choice represents the true
solution to the puzzle. Equations, however, are
neither correct nor incorrect. They are simply
questions asked by group members about math-
ematical relationships between coded letters.
Hypotheses may be used positively (i.e., with
the expectation that the hypothesis will be con-
firmed) or negatively (i.e., with the expectation
that the hypothesis will not be confirmed) to
gain information about the problem (Klayman
& Ha, 1987, 1989; Laughlin, Bonner, & Alter-
matt, 1999; Wason, 1960). Because hypotheses
may be used strategically in this way, it would
be inappropriate to treat them as meaningfully
correct for the purposes of this study. Thus, for
equations and hypotheses, only responses A, B,
and D are appropriate, whereas for codes, all
four response alternatives (i.e., C, A, B, and D)
are applicable. This results in 5 member distri-
butions for equations and hypotheses (AAA,
AAB, ABA, ABB, and ABD) and 15 member
distributions for codes (CCC, CCA, CAC,
ACC, CAA, ACA, AAC, CAB, ACB, ABC,
AAA, AAB, ABA, ABB, and ABD).

Row labels indicate the distribution of mem-
ber preferences prior to the group discussion.
Column labels indicate the group decision. An
“x” indicates an impossible group decision for a
given member distribution. To illustrate, con-
sider the following example trial. The first
member chooses the equation, “I � J � ?” (i.e.,
“What letter represents the sum of I and J?”) as
her individual selection, and the second and
third members both choose the equation, “J �
J � ?” as their individual selections. This group
preference distribution would be represented as
“ABB” (row). If this group chooses “I � J � ?”
as its response, then the group choice would be
an “A” (column). If this group chooses “J � J …