Group Dynamics: Theory, Research, and Practice
2014, Vol. 18, No. 4, 302–317
© 2014 American Psychological Association
1089-2699/14/$12.00 http://dx.doi.org/10.1037/gdn0000011
Group Judgment and Advice-Taking: The Social Context
Underlying CEO Compensation Decisions
Bryan L. Bonner and Brian D. Cadman
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University of Utah
Groups are often tasked with making important organizational decisions. For example,
CEO compensation judgments are made by groups of decision makers with no specialized compensation training who receive advice from a third party with a potential
bias. This study seeks to understand the effects of this challenging context on decision
making. We explore whether and how decision makers adjust their judgments, considering the potential for advisor bias. This is an essential issue in CEO compensation
that is difficult to assess through traditional archival research based on publicly
available data. Our study explores how decision makers, in the context of compensation
committees, process and use advice provided by external parties. Our findings illustrate
that individuals adjust for known conflicts of interest in the information provided to
them. However, we do not find that individuals discount lavish advice to a greater
extent than more modest advice, and the group decision-making process fails to correct
for this tendency.
Keywords: group judgment, social decision schemes, conflict of interest, biased advice, executive compensation
Executive compensation has received substantial attention in the media for many years,
much of it negative (Crystal, 1991). Public perception is often that executives are extravagantly and unfairly compensated, whereas organizations themselves are frequently concerned
that executives are compensated too modestly
(T. Shin, 2013). Recently, both the U.S. Congress and the Security Exchange Commission
have begun questioning whether the social context in which executive compensation packages
are decided “rig the game” in favor of higher
compensation (e.g., Waxman, 2007). Whereas
some scholars believe that this issue can be
managed through already mandated reforms
(Nelson, 2006), others remain skeptical (Bazerman, Moore, Tetlock, & Tanlu, 2006).
Large companies use compensation committees, the members of which are typically on the
This article was published Online First August 11, 2014.
Bryan L. Bonner and Brian D. Cadman, David Eccles
School of Business, University of Utah.
Correspondence concerning this article should be addressed to Bryan L. Bonner, David Eccles School of Business, University of Utah, 1645 E. Campus Center Dr. #106,
Salt Lake City, UT 84112-9304. E-mail: mgtblb@business
.utah.edu
company’s board of directors, to assign compensation (typically in the form of salary, bonuses, and equity) for the Chief Executive Officer (CEO) and other top executives. These
compensation committees frequently hire expert consultants to advise them in designing
compensation schemes. However, the committee itself, the members of which are not typically selected for their expertise in compensation design, makes the ultimate judgment. Thus,
a compensation committee is a group-level
judge-advisor system (Sniezek & Buckley,
1995), in which the group receives outside advice and may act on it or not as it sees fit in
rendering its decision. Complicating matters,
many compensation consultants also provide
additional services to the firm, such as actuarial
services for pension benefits and support for the
human resources department (cf., Shockley,
1981). As a result, compensation consultants
are often subject to conflicts of interest, in that
they provide advice on compensation for the
CEO that retains them for other services (Bebchuk & Fried, 2006; Conyon, Peck, & Sadler,
2009; Morgenson, 2006).
Our goal in this research is to examine advice
taking and bias in the context of group judgment. We also aim to contribute to the thorny
302
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EXECUTIVE COMPENSATION
substantive issue of how executive compensation is determined. To these ends, we seek to
understand the effects of the context within
which executive compensation decisions are
made by examining how the preferences of
individual group members, in light of the advice
offered by the compensation consultant, affect
the final judgment of the group. Specifically, we
investigate whether mock compensation committees adjust for compensation consultant conflicts of interest when setting hypothetical CEO
pay levels, as well as the mix of pay elements
(i.e., salary, bonuses, and equity). We also examine whether any adjustment for the consultant advice is contingent on the magnitude of
the recommendation and how other information, such as hypothetical peer-group pay, influences compensation committee judgments and
their perception of the potential bias. Finally,
we consider how the a priori preferences of
individual group members translate to final
judgment of the group. We address these issues
using an experimental method in a controlled
laboratory environment in an attempt to complement the extant literature based on publically
available archival data (Cadman, Carter, &
Hillegeist, 2010; Murphy & Sandino, 2010;
J. Y. Shin & Seo, 2010).
Individual Processing of Biased Advice
The literature regarding how biased parties
may, knowingly or not, provide self-serving
recommendations is relatively well developed.
For example, Moore, Tetlock, Tanlu, and Bazerman (2006) formulated moral seduction theory as a means of understanding how people
may not be able to judge the extent to which
their decision making is affected by their conflicts of interest (cf., Diekmann, Samuels, Ross,
& Bazerman, 1997; Messick & Sentis, 1983).
Moore and colleagues invoke both psychological (e.g., escalation of commitment) and structural (financial incentives) forces contributing
to self-interested or biased behavior in the context of auditing, and provide an account of how
even auditors seeking to provide unbiased evaluations may be incapable of doing so. Work in
this area focuses on the motives and behavior of
those providing biased outputs (e.g., audits, recommendations, evaluations).
More central to the current research is the
question of how potentially biased outputs are
303
treated by those who inherit them, such as executive compensation committees (cf., Gino,
Wood, & Schweitzer, 2012). Processing potentially biased advice is problematic for a number
of reasons. Decision makers are often unaware
of the factors impacting their decision processes
and believe they are capable of discounting
irrelevant or faulty information when, in fact,
this information continues to affect their judgments (Calvert, 1985; Strack, & Mussweiler,
1997; Tversky & Kahneman, 1974). That is,
even when decision makers believe they are
making adequate adjustments for biased advice,
their lack of insight into their own decision
processes prevents them from seeing that they
are, in fact, not. This effect may be particularly
strong when decision makers actively solicit
advice from what they consider to be expert
sources (i.e., judge-advisor systems; Sniezek &
Buckley, 1995; Sniezek & Van Swol, 2001).
In a judge-advisor system, a decision maker
receives input from another party who does not
have any formal power in the decision-making
process. The weight (if any) that the advisor’s
input has on the judge’s decision is entirely in
the judge’s hands. In the context of CEO compensation decisions, compensation committee
members jointly play the role of judge, whereas
the compensation consultant plays the role of
advisor. Although compensation consultants
have no direct decision power, their advice is
typically sought out at a cost, with the assumption that it has value and will aid in the compensation committee’s decision making (Harvey & Fischer, 1997). However, research has
also shown that judges are, in some cases at
least, capable of identifying advice as being of
poor objective quality and discounting it (Gardner & Berry, 1995; Yaniv & Kleinberger,
2000), although the extent to which advice is
actually discounted may be inadequate (Lim &
O’Connor, 1995). Specifically, when the advice
received from an advisor is suspect because of a
potential conflict of interest on the advisor’s
part, judges often inadequately adjust for the
bias in advice in their final judgments (Cain,
Loewenstein, & Moore, 2005).
Whether or not decision makers adequately
adjust for bias in advice, the literature suggests
that they do make adjustments. Thus, consistent
with the literature, we predict that decision
makers’ judgments will reflect a significant negative adjustment when they receive advice from
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BONNER AND CADMAN
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a potentially biased source. Specifically, we expect decision makers to prefer less compensation when receiving advice from a consultant
with a conflict of interest, compared with receiving the same advice from a nonconflicted
consultant. Note that this does not imply that the
negative adjustment is sufficient to offset the
effects of advisor bias.
Hypothesis 1: Decision makers receiving
potentially biased advice will prefer more
conservative judgments compared with
those receiving unbiased advice.
In addition to whether the consultant has a
conflict of interest, we must also consider the
magnitude of the advice they provide (i.e., how
much compensation they recommend). There are
two plausible means by which the magnitude of
the advisor’s recommendation could affect the
preferences of those receiving the advice. In one
scenario, consistent with an economic perspective
on decision making in a financial context (Simunic, 1984), the magnitude of the advice could be
seen as evidence of the partiality of the advisor
that should lead to discounting of the advice. This
discounting may be informed by both a judge’s
cognitive and affective reaction to the advice
(Gino & Schweitzer, 2008; McAllister, 1995).
That is, high recommendations from an advisor
with a known conflict of interested could be interpreted as evidence of manifest bias in the recommendation, whereas a more modest recommendation from the same source could suggest
that the advisor is not substantially influenced by
the conflict of interest. As such, the presence or
absence of a conflict of interest may interact with
the magnitude of the recommendation such that
judges discount high recommendations from potentially biased advisors to a greater extent than
more modest recommendations from the same
advisors.
However, for decision makers to differentially adjust for potential bias, they would have
to be sensitive to the extent to which biased
information affects their judgment, and the literature does not suggest that decision makers
exercise this level of self-awareness (Diekmann, 1997; Diekmann et al., 1997; Moore et
al., 2006). Therefore, we expect individuals to
consider consultant advice when setting compensation levels without adequately accounting
for bias in the source of the advice. Specifically,
we expect a positive relation between the magnitude of the package recommended by the consultant and the magnitude of the compensation
package decision makers prefer. This is a key
hypothesis because it predicts a departure from
informed decision making in the context of executive compensation decisions. Again, the alternative to this would be that decision makers
recognize the bias in advisors’ recommendations and adjust accordingly.
Hypothesis 2: There is a positive relation
between advisors’ recommendations and
the preferences of decision makers.
Group Processing of Biased Advice
Committees (i.e., groups) are typically the final
judges in setting executive compensation
schemes. Jointly examining how individual preferences are informed and how groups make their
final judgments is essential to providing a deeper
understanding of how compensation committees
are affected by the context underlying their decisions. Understanding how individual preferences
are combined into a group decision in this context
is also important for policymakers. For example,
if group decisions are simply the unbiased, uncorrected aggregation of individual preferences, then
any policy seeking to impact the compensation
setting process could do so either at the individual
(pregroup) stage, in which individuals form their
own preferences (perhaps in the form of training
or education), or at the interactive group stage, in
which inputs are combined (perhaps through enforcing a set of best practices for how the group
operates). However, if the group decision is not a
simple aggregation of its members’ preference,
then intervening at the individual level is unlikely
to be effective, and regulating the group process
would be prescribed.
The fact that compensation decisions are
made in a collective context complicates matters substantially relative to individual decisions. However, the group context also offers
reasons to believe that groups may correct for
biases and errors in judgment that individuals
do not. This offers the possibility of groups
producing superior judgments relative to individuals (Hinsz, Tindale, & Vollrath, 1997;
Laughlin, Bonner, & Miner, 2002; Shaw, 1932).
The social combination literature has shown
that group judgments are the product of indi-
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EXECUTIVE COMPENSATION
vidual member preferences aggregated in generally predictable ways (e.g., Bottger & Yetton,
1988; Davis, 1973; Laughlin, 2011; Lorge &
Solomon, 1955; Smoke & Zajonc, 1962;
Thomas & Fink, 1961; Yetton & Bottger,
1982). However, the nature of the decision context influences the manner in which individual
inputs combine to form group judgments. When
groups work on tasks that possess objectively
correct responses that can be demonstrated to
others (e.g., math problems), groups typically
follow a “truth wins” decision scheme, wherein
correct group members demonstrate to the
group that they have the right answer and the
group adopts that answer (Laughlin, 1980;
Laughlin & Ellis, 1986). In contrast, when
groups work on tasks that do not possess correct
responses, other factors typically govern the
aggregation of member preferences (e.g., majority preferences; Laughlin, 1999, 2011),
which may attenuate or exaggerate bias depending on the distribution of bias within the group
(cf., Dion, Baron, & Miller, 1970). However,
when group decisions are quantities (e.g., monetary compensation) rather than choices (e.g.,
job candidates), the literature on groups is less
advanced (Bonner & Baumann, 2012; Davis,
1996; Hinsz, 1999).
The decisions of interest in a compensation
scenario are judgmental in nature and involve
quantities as opposed to choices. Prior research
suggests two competing predictions for such
tasks: centrality and extremity. Centrality models
predict that in judgmental tasks involving quantities, group decisions will be most affected by
members whose preferences are closest to the
central tendency of the group (e.g., the median),
which is analogous to a “majority wins” decision
in the context of choices (Davis, 1996; Davis et
al., 1997; Hinsz, 1999; Nadler et al., 2001; Ohtsubo, Masuchi, & Nakanishi, 2002). Other research demonstrates that, in some situations,
members with extreme positions may dominate
the group (Van Swol, 2009). This process can lead
groups to become more extreme in their judgments relative to average member preferences,
similar to what is found in the classic research on
group polarization (Dion et al., 1970; Moscovici
& Zavalloni, 1969). Specifically, recent research
suggests that this tendency toward extremity in the
context of a compensation paradigm could be
expected to take the form of “lowballing,” or
taking an aggressive, hawkish position with re-
305
spect to the payee (Aaldering & De Dreu, 2012;
Bonner, Okhuysen, & Sondak, 2011; Steinel, De
Dreu, Ouwehand, & Ramírez-Marín, 2009). In a
compensation scenario, a lowballing scheme
would lead those determining the compensation
package to offer a relatively low amount to the
payee.
Both of these tendencies (i.e., shifts to centrality or lowballing) have support in the groupdecision-making literature. Further, because
there is relatively little work in this specific
area, neither is clearly better suited to the specific situation of interest in the current research
(i.e., compensation decisions). As such, we set
up these two predictions as competing hypotheses to be tested against our data.
Hypothesis 3a: Group judgments will
closely mirror the preference of the most
central member.
Hypothesis 3b: Group judgments will
closely mirror the preference of the most
conservative (“lowball”) member.
These last hypotheses address an important
but overlooked scenario in the processing of
potentially biased information (e.g., information
received from a party with a conflict of interest),
namely, the degree to which the dynamic processes involved in group judgment may or may
not affect how such information is treated in
forming a group judgment.
Method
Participants and Design
Participants were 315 master of business administration (MBA) students enrolled in
courses at a large western U.S. university. Of
these participants, 90 took part in pilot work
used to calibrate the materials of our final experiment, and the remaining 225 took part in the
study proper. We employed a vignette (i.e.,
hypothetical situation/role-playing) experiment.
Our procedure involved exposing participants
to a hypothetical situation in which they played
the role of a member of a board in the process
of determining the proper compensation package for a new CEO (cf., Elliott, Hodge, Kennedy, & Pronk, 2007).
Our experiment used a between-subjects design with two independent variables, each with
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306
BONNER AND CADMAN
two levels: consultant conflict of interest (CoI),
with “conflict of interest” versus “no conflict of
interest”; and magnitude of compensation package
recommended by the consultant (MAGNITUDE),
with “in-bounds” (i.e., within the bounds of
provided comparison packages) versus “out-ofbounds” (i.e., higher than any comparable
scheme provided). In addition, the study included a control condition with no consultant
recommendation (i.e., a five-cell design). All
participants first completed the task individually, and then once again in the same experimental condition as a member of a three-person
group. We used three-person groups to mimic
the size of typical compensation committees.
This individual– group design allowed us to assess how individual preferences aggregated to the
group level. The primary dependent variables of
interest were the perceptions of the individuals in
terms of what was guiding their preferences, the
overall value of the compensation package preferred by the individuals (preinteraction), the final
judgment of the group (postinteraction), and the
differential distribution of the package value
across its subcomponents (i.e., salary, performance bonus, and equity), both for preinteraction
individual preferences and postinteraction group
judgments.
Materials and Procedure
Participants were presented with a hypothetical case in which they played the role of a
board member who was part of the decisionmaking body tasked with determining the compensation package for a new CEO. Participants
first read a description of their charge:
As a member of the board of directors, your task is to
determine the annual compensation package for the
new CEO of your company. This individual is a leader
in his field, the best qualified candidate, and is about to
be hired by the firm. Note that this CEO’s previous
employment was at a privately held firm that is not
required to file financial statements with the Securities
Exchange Commission. As a result, the compensation
package he received from his previous employer is not
known.
Participants were then told that they would
receive information to help them in their decision making that would include the CEO’s resume and information on comparable compensation from a sample of publicly traded firms
of similar size that operate in the same standard
industrial classification code as the participant’s
company. All participants except those in the
control condition were informed that the board
had contracted a compensation consulting firm
to recommend an annual compensation package
for the CEO. Those in the “conflict of interest”
condition were told,
This compensation consultant was hired by you and
performs other work for the firm. This additional work
includes actuarial service for the firm’s broad based
pension plan, consulting for the human resources department, and also compensation advice for other senior managers in the firm. In the previous year, the
additional work constituted 85% of the consulting
firm’s revenues earned for services to this firm. These
services ultimately fall under the umbrella of the CEO,
and will remain so under the incoming CEO.
Those in the “no conflict of interest” condition were instead told, “This compensation consultant was hired by you and does not perform
other work for the firm.”
Participants were then provided with an exceptional CEO candidate resume (see Appendix
A), a list of comparison realized compensation
packages (kept constant across cells; see Appendix B), and, except in the control condition,
the advice of the compensation consultant. This
advice contained the recommendation that the
total compensation package should be divided
into 5% salary, 15% performance-contingent
bonus, and 80% equity (kept constant across
cells). This advice also manipulated both of the
independent variables of the study, reinforcing
the conflict of interest or lack thereof introduced
in the earlier materials, and also providing advice that the CEO’s total package value should
either be high, but within the range of the packages provided in the comparisons (i.e., “inbounds” ⫽ $20,000,000) or outside of this
range (i.e., “out-of-bounds” ⫽ $30,000,000).
After reviewing these materials, participants
generated an individual preference for the
CEO’s total compensation package and how it
should be divided between salary, performance
contingent bonus, and equity. After generating
this preference, participants were asked to indicate, on 7-point scales, the extent to which their
decision was influenced by the CEO’s resume,
the comparison realized compensation packages, and (except in the case of the control
condition) the advice of the compensation consultant. After completing this individual section
of the task, participants were assigned to groups
of three members (all of whom shared the same
EXECUTIVE COMPENSATION
Time 1, i.e., individual-level – cell assignment),
to generate a collective judgment as to the
CEO’s total compensation package and how it
should be divided between salary, performance
contingent bonus, and equity.
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Results
Individual Perceptions of
Influence on Preferences
To provide context to our hypotheses, we
assessed participants’ perceptions of the extent
to which their judgments were influenced by the
provided CEO resume (held constant across
cells), the comparison compensation packages
we provided (held constant across cells), and
the advice of the compensation consultant
(through which we varied both conflict of interest and package magnitude). As discussed, prior
research suggests that participants receiving a
CEO compensation recommendation from a
consultant with a conflict of interest would perceive themselves to be less influenced by the
recommendation than participants receiving
recommendations from a consultant without a
conflict of interest. To verify this, we conducted
a 2 (CoI: conflict of interest vs. no conflict of
interest) ⫻ 2 (MAGNITUDE: out-of-bounds vs.
in-bounds) multivariate analysis of variance
(MANOVA), with ratings of resume influence,
comparison package influence, and consultant
influence as the dependent variables. The multivariable main effect of CoI was significant,
F(3, 174) ⫽ 4.129, p ⬍ .01, Wilks ␭ ⫽ .934.
Turning to each of the variables potentially affected by CoI, univariate (ANOVA) results suggest that consultant conflict of interest did not
have a significant effect on perceived resume
influence, F(1, 176) ⬍ 1, p ⬎ .05. At the same
time, consultant conflict of interest significantly
related to the perceived influence of both the
comparison package and the consultant recommendation, F(1, 176) ⫽ 6.377, p ⬍ .05, ␩2p ⫽
.035, and F(1, 176) ⫽ 8.751, p ⬍ .01, ␩2p ⫽
.047, respectively. The multivariate main effects of MAGNITUDE and the two-way interaction between CoI and MAGNITUDE were
both nonsignificant, F(3, 174) ⬍ 1.108, p ⬎ .05,
and F(3, 174) ⬍ 1, p ⬎ .05, respectively.
Although the mean perceived influence of the
resume was not significantly different when receiving a recommendation from a conflicted
307
consultant as opposed to a consultant without a
conflict of interest (4.02 [SD ⫽ 1.34] vs. 3.93
[SD ⫽ 1.40]), the mean perceived influence of
the comparator group is significantly greater
when receiving a recommendation from a consultant with a conflict of interest as opposed to
a consultant without a conflict of interest (5.41
[SD ⫽ .90] vs. 5.74 [SD ⫽ .87]). Furthermore,
the average perceived influence of the consultant recommendation is significantly lower
when the consultant has a conflict of interest
(4.52 [SD ⫽ 1.23] vs. 3.69 [SD ⫽ 1.62]). Simultaneously, the perceived weight placed on
the comparator group increases.
Overall Individual Package Preferences
To provide evidence on the actual (as opposed to perceived) influence of compensation
consultant conflicts of interest and the magnitude of the consultant recommendation on individual preferences, we conducted a 2 (CoI: conflict of interest vs. no conflict of interest) ⫻ 2
(MAGNITUDE: out-of-bounds vs. in-bounds)
ANOVA, with overall compensation package in
dollars as the dependent variable. The estimated
marginal means for CoI/out-of-bounds, CoI/inbounds, no CoI/out-of-bounds, and no CoI/inbounds were $18,741,004 (SD ⫽ $6,694,424),
$14,385,892 (SD ⫽ $3,466,937), $21,860,020
(SD ⫽ $5,839,158), and $16,002,551(SD ⫽
$3,490,609), respectively. This test found significant main effects on the influence of the
consultant conflict of interest, F(1, 176) ⫽
7.199, p ⬍ .01, ␩2p ⫽ .039, and the magnitude of
the recommendation, F(1, 176) ⫽ 39.706, p ⬍
.01, ␩2p ⫽ .184. There was not a significant
interaction between these variables, F(1, 176) ⬍
1, p ⬎ .05. These findings support Hypotheses 1
and 2. Consistent with individuals adjusting for
recommendations from a consultant with a
known bias, as predicted in the first hypothesis,
individuals prefer lower compensation when
advised by a consultant with a conflict of interest. In addition, consistent with Hypothesis 2,
results indicate that individuals assign higher
value compensation packages when the advice
of the consultant was higher. Most telling, the
lack of a significant interaction between conflict
of interest and the magnitude of the recommendation suggests that individuals do not jointly
consider the potential bias and the magnitude of
the recommendation. In fact, judgments appear
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BONNER AND CADMAN
to be influenced by the consultant’s advice and
then uniformly lowered by about 10% if the
advice came from a consultant with a conflict of
interest, regardless of the magnitude of the recommendation. This may reflect an anchoring
and adjustment process on the part of the participants (Strack, & Mussweiler, 1997; Tversky
& Kahneman, 1974).
To provide more insight into these findings,
we also compared the control condition in
which participants did not receive a recommendation from a consultant directly (M ⫽
$14,193,905, SD ⫽ $4,662,212) to both of the
CoI cells of the design (i.e., in-bounds and
out-of-bounds). If participants completely discount the advice from consultants with a conflict of interest, regardless of the content of the
advice, these comparisons should be nonsignificant. Although there was not a significant difference between the control condition and the
CoI/In-bounds cell, t(88) ⫽ .22, p ⬎ .05, the
CoI/out-of-bounds cell significantly differed
from the control condition, t(88) ⫽ 3.74, p ⬍
.01, Cohen’s d ⫽ .79. Similarly, the CoI/Inbounds cell differed significantly from the CoI/
out-of-bounds cell, t(88) ⫽ 3.88, p ⬍ .01, Cohen’s d ⫽ .82. Together these findings indicate
that the preferences of participants who received advice from a consultant with a conflict
of interest were, at least in the case of lavish
recommendations, influenced by that advice.
Overall Individual Equity Preferences
Prior research suggests a specific relation between the magnitude of the recommendation
and the form of compensation. Agency theory
suggests that firms must compensate managers
for the uncertainty imposed on them by risky
compensation elements, such as equity, which
leads to higher pay (e.g., Aggarwal & Samwick,
1999; Jensen & Meckling, 1976; Lambert &
Larcker, 1987; Smith & Watts, 1992). To the
extent that participants understand this relationship and the overall paradigm of executive compensation, their recommendations should reflect
this. As such, we would expect to observe a
positive relation between the proportion of
compensation in the form of equity grants and
the magnitude of the compensation package.
To test this conjecture, we investigate the
form of the compensation in relation to the
conflict of interest and magnitude of the recom-
mendation using a 2 (CoI: conflict of interest vs.
no conflict of interest) ⫻ 2 (MAGNITUDE:
out-of-bounds vs. in-bounds) ANOVA with the
percentage of the compensation package to be
given as equity as the dependent variable. We
found no main effect for CoI, F(1, 176) ⫽
1.194, p ⬎ .05, and a significant effect for
MAGNITUDE, F(1, 176) ⫽ 7.140, p ⬍ .01,
␩2p ⫽ .039. There was not a significant interaction between these variables, F(1, 176) ⬍ 1,
p ⬎ .05. When the recommendation was outof-bounds, individuals preferred to assign more
of the package as equity than when the recommendation was in-bounds. This finding suggests
that the proportion of the compensation in the
form of equity is positively related to the magnitude of the compensation, consistent with economic theory that the firm must compensate the
agent for the risk of equity compensation. At the
same time, the characteristics of the consultant
or the magnitude of the recommendation did not
significantly influence this relationship.
Group Judgment
As we have discussed, in situations in which
a group judgment is called for, it cannot be
assumed that the judgment reflects an unbiased
aggregation of member preferences. Therefore,
it was necessary to map the final group judgment to the pregroup preferences of group
members to determine how group judgments
were generated. We modeled group judgments
as a function of initial member preferences to
assess whether group judgments, relative to individual preferences, reflected lowballing (i.e.,
were indistinguishable from the lowest individual preference), centrality (i.e., were indistinguishable from the median individual preference), or were, for the sake of comparison,
liberal (i.e., were indistinguishable from the
highest individual preference). We also tested
two parametric models of centrality: a simple
arithmetic mean model as well as a weighted
centrality model based on the assumption that as
individuals’ preferences deviated more from the
mean they would have exponentially less influence on group judgment (see Appendix C; Bonner, Gonzalez, & Sommer, 2004; Davis, 1996).
We compared the fit of the obtained data (i.e.,
group judgments in the context of member preferences) to each model. That is, each of the five
models described was used to generate predic-
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EXECUTIVE COMPENSATION
tions as to what the groups would decide at
Time 2 (i.e., the interactive group phase) based
on the preferences of group members at Time 1
(i.e., the pregroup phase). The fit of these predictions to the actual obtained group estimates
was assessed using Kolmogorov–Smirnov onesample tests. An alpha level of .20 is the standard for Kolmogorov–Smirnov tests involving
group decision making and constitutes a conservative test of fit (e.g., Davis, 1996).
The results for the model fit tests for the
control, CoI/out-of-bounds, CoI/in-bounds, no
CoI/out-of-bounds, no CoI/in-bounds cells are
presented in Table 1. For these tests, given
sample size and alpha level, models that exceeded a discrepancy of 0.2763 (i.e., the Dcrit)
from the obtained cumulative relative data were
rejected as implausible. The median model
closely corresponded to the obtained group decisions and could not be rejected in any cell of
the design, showing that group choices resembled the preference of the most central group
member. This finding supports Hypothesis 3a
and fails to support Hypothesis 3b. Essentially,
when selecting an overall package level, we find
that group choices resemble the preference of
their median member in all cells of the experiment. It should also be noted that for two of the
cells of the design (CoI/out-of-bounds and no
CoI/in-bounds), both the simple arithmetic
mean and weighted centrality models also provided adequate fit to the data. In the control
condition, models assuming median and lowest
member choices fit adequately (i.e., could not
be distinguished).
An additional lens for examining the combination of quantitative member preferences to a group
judgment involves assessing the extent to which
the preferences of different members are predic-
309
tive of the final group judgment, using a general
linear model (GLM) approach (i.e., multiple regression). This approach, though not typical in the
group-decision-making literature, adds context to
the question of how member preferences map to
group judgments, by showing which member
preferences, or combinations thereof, are most
predictive of the group outcome independent of
theoretical assumptions beyond those inherent to
regression. This constitutes a type of model-fitting
procedure (Kerr, Stasser, & Davis, 1979) that
complements the more classic a priori modeltesting approach. Note that a classic model-testing
approach that focuses on the fit of each member’s
preferences to the group judgment and a GLM
approach examine the issue of how member preferences map to group judgments from different
perspectives. The former tests whether the group
judgment is systematically proximal to the predictions of various models. The latter assesses which
preferences or combinations of preferences are
causal agents of the group judgment. For example,
it may be that if the preference of the most conservative (lowest) member is relatively high, the
final group judgment is much higher in general,
even though that judgment is not numerically
close to that of the driving member. Said differently, the model-testing method assesses the fit of
member preferences to the group judgment,
whereas the GLM model-fitting method assesses
the variance accounted for by the preferences of
the different members or the interactions of those
preferences.
We therefore supplement our a priori modeltesting approach with a model-fitting method.
To do this, we regressed the preferences of the
ranked members (ranked by the value of their
preference for the total compensation package),
as well as the interaction terms of these prefer-
Table 1
Group Decision Making: Model Testing
Decision model
Experimental condition
Control
CoI/In-bounds
CoI/Out-of-bounds
No CoI/In-bounds
No CoI/Out-of-bounds
Highest member
Median member
0.6667
0.5333
0.4000
0.4667
0.6000
Note. N ⫽ 15 groups per cell. Dcrit ⫽ 0.2763.
a
Fail to reject at ␣ ⫽ .20.
a
0.2667
0.1333a
0.2000a
0.1333a
0.2667a
Lowest member
0.2667
0.4667
0.6000
0.3333
0.4000
a
Arithmetic mean
Weighted centrality
0.5333
0.3333
0.2667a
0.2667a
0.3333
0.4667
0.3333
0.2667a
0.2667a
0.3333
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310
BONNER AND CADMAN
ences, on the final group total compensation
judgment. We used a stepwise regression, initially entering the highest member preference,
the median member preference, the lowest
member preference, the three two-way interactions, and the three-way interaction. As with the
model-testing approach, tests were conducted
separately in each cell of the design. As Table 2
indicates, the group judgments within each cell
were predicted by either a single predictor or a
single interaction between predictors in each
case. As the table indicates, in both of the experimental cells in which there was no apparent
conflict of interest, it was the interaction between the median and lowest member preferences that predicted the final group judgment.
This is similar to a “trimmed” model in which
the highest member preferences are ignored in
generating a group judgment and only the interaction between the lower two members has
an impact. In contrast, in both cases in which a
conflict of interest is suggested, the highest
member preference influences the final group
judgment either alone, in the case of an inbounds recommendation, or in a three-way interaction with the preferences of the other group
members, in the case of the out-of-bounds recommendation.
pensation preferences at the group level with final
group judgments as the dependent measure. To
assess overall group judgments with regard to
CEO compensation, we performed a 2 (CoI: conflict of interest vs. no conflict of interest) by 2
(MAGNITUDE: out-of-bounds vs. in-bounds)
ANOVA, with the groups’ judgment on overall
compensation package in dollars as the dependent
variable. This test found main effects for both
independent variables, F(1, 56) ⫽ 5.276, p ⬍ .05,
␩2p ⫽ .086, and F(1, 56) ⫽ 22.925, p ⬍ .01, ␩2p ⫽
.290, respectively. There was no interaction between these variables, F(1, 56) ⬍ 1, p ⬎ .05. The
estimated marginal means for CoI/out-of-bounds,
CoI/in-bounds, no CoI/out-of-bounds, and no CoI/
in-bounds were $15,843,333 (SD ⫽ 2,469,880),
$14,766,667 (SD ⫽ 2,658,320), $21,517,061
(SD ⫽ 4,859,254), and $18,216,667 (SD ⫽
4,205,509), respectively. Groups chose to provide
higher compensation when the advice of the consultant was out of bounds (high) and also when the
consultant did not have a conflict of interest. Thus,
judgments were influenced by the consultant’s
advice and lowered if the advice was biased.
Overall, group judgments regarding the overall
compensation packaged mirrored those of the median member.
Overall Group Equity Judgments
Overall Group Package Judgments
Our examination of member preferences shows
that group judgments closely correspond to the
median of member preferences. That is, the judgments of groups reflects the preferences of average group members. It follows then that group
judgments should adhere to the same pattern as
the preferences of individuals, albeit with less
statistical power. To verify this, we repeated our
individual-level test pertaining to member com-
To parallel our individual-level analysis, we
also assessed group judgments with regard to
CEO equity compensation. We performed a 2
(CoI: conflict of interest vs. no conflict of interest) by 2 (MAGNITUDE: out-of-bounds vs.
in-bounds) ANOVA with the percentage of the
compensation package to be given as equity as
the dependent variable. We found no main effect for CoI, F(1, 56) ⫽ 1.490, p ⬎ .05, and a
significant effect for MAGNITUDE, F(1, 56) ⫽
Table 2
Group Decision Making: Model Fitting
Regression results
Experimental condition
Variables retained in stepwise regression
␤
t
Control
CoI/In-bounds
CoI/Out-of-bounds
No CoI/In-bounds
No CoI/Out-of-bounds
Median/Highest interaction
Highest
Lowest/Median/Highest interaction
Lowest/Median interaction
Lowest/Median interaction
.79
.82
.76
.84
.77
4.60
5.17
4.23
5.58
3.61
Note. N ⫽ 15 groups per cell. All tests significant at ␣ ⬍ .01.
EXECUTIVE COMPENSATION
7.857, p ⬍ .01, ␩2p ⫽ .123. There was no interaction between these variables, F(1, 56) ⬍ 1,
p ⬎ .05. When the recommendation was out of
bounds, groups wanted to assign more of the
package as equity, consistent with what we
found on the individual level.
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Discussion
Group judgment is used for a wide variety of
purposes in organizations. Importantly, groups are
typically the vehicles for determining how executive officers (e.g., CEOs) are compensated. In the
current study, we provide evidence on how the
group decision-making context shapes executive
compensation and, theoretically, similar decisions.
We first examined participants’ self-reports of the
extent to which they were influenced by different
information about a potential CEO hire. We found
that participants self-reported that a compensation
consultant’s conflict of interest influenced how
they used that person’s advice. Participants in
experimental cells in which advice came from
consultants with a conflict of interest believed that
they were influenced by the consultant recommendation less than comparison participants in unconflicted conditions. In addition, participants receiving advice from conflicted compensation
consultants self-reported that objective information about peer compensation packages (identical
for all groups) influenced their recommendations
more than those in the unconflicted conditions.
Our results indicate that a conflict of interest
in a hypothetical compensation consultant scenario influences the pay preferences of individual decision makers as well as the group’s final
judgment. We do not find that decision makers
adequately adjust for potential bias. Specifically, participants in conditions in which the
consultant recommended higher pay and also
had a conflict of interest compensated the CEO
significantly more than participants without a
consultant. Thus, there is evidence that the advice received anchored CEO pay to be higher
despite the advisor’s potential for bias. Interestingly, participants self-reported that they considered other factors, such as peer CEO compensation, more, and placed less weight on the
consultant recommendations when the consultant had a conflict of interest. Together, these
findings suggest that compensation committee
members may perceive their ability to screen
out potentially biased information to be greater
311
than it actually is when biased sources provide
extravagant recommendations (Cain et al.,
2005). This is, of course, precisely the problem
identified in CEO compensation (Waxman,
2007)—that biased consultants inflate their recommendations to curry favor with incoming
executives, and compensation committees are
not correcting for it.
When examining compensation preferences,
we found that participants preferred greater CEO
pay levels when the compensation consultant recommended higher pay. However, participants in
the conflicted consultant condition preferred lower
pay than participants in the nonconflicted condition. More specifically, regardless of the level of
pay advised by the consultant, participants that
receive advice from a conflicted consultant preferred pay levels that are approximately 10% less
than participants that received the same advice
from a consultant without a conflict of interest.
Participants adjusted their preferences to the same
degree regardless of the magnitude of the advice.
That is, participants preferred to compensate the
CEO more lavishly when the consultant recommended greater pay, regardless of the consultant’s
potential conflict of interest, when compared with
both the control (no consultant recommendation)
and when the consultant recommends less pay.
This finding has broad implications and suggests
that, at least in certain situations, negative perceptions of an advisor and the fairness of the advisor’s
advice are processed additively rather than multiplicatively.
We also considered the form of pay. A fundamental premise of agency theory is that payees
demand a risk premium for compensation in the
form of equity and other risky payments. We held
the consultant advice with regard to the form of
pay constant across all groups to 5% of the total
compensation in the form of salary, 15% as performance-contingent bonus, and 80% in the form
of equity. This distribution of compensation mix is
consistent with the mean distributions of the peer
group provided in the materials. Despite this uniform distribution across all groups, we found the
proportion of total compensation in the form of
equity increased when our participants prefer to
pay the CEO more. This is consistent with decision makers compensating more highly when imposing greater risk on the manager, and individuals discounting risky payments (e.g., Hodge,
Rajgopal, & Shevlin, 2010), and can be taken as
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312
BONNER AND CADMAN
evidence that our participants were relatively well
informed with respect to compensation dynamics.
Finally, we examined how member preferences were aggregated into group judgments.
Our results indicate that the deficits in the decision making of groups are directly carried
over from the biased preferences of individual
group members. Member biases were neither
remedied nor exaggerated through the group
decision-making process. This implies that interventions and attempts to improve the ability
of compensation committees and similar bodies
to render unbiased preferences should be managed at the individual level and would then be
expected to propagate to the group.
A model-fitting procedure showed that in the
absence of bias, the highest of member preferences were functionally ignored in generating a
final group judgment. This may imply that in
generating their judgment in an unbiased circumstance, problem solvers looked to the lower
group estimates to determine how best to form
their final judgment. However, when bias was
apparent, the highest of member preferences
were influential in informing the group judgment, likely by setting limits to what might be
considered reasonable by the group. Also interesting is that when bias was apparent and the
received recommendation was also out of the
bounds of the provided comparisons, the factor
that predicted the group judgment was the full
three-way interaction of all members’ preferences. This may indicate that this condition
spurred group members to collaborate such that
all member inputs were respected and the final
judgment reflected the thoughtful synthesis of
all member preferences.
In interpreting our research, a number of limitations must be acknowledged. First, the current research used an unusual approach for investigating the well-researched phenomenon of
executive compensation by choosing an experimental method. We see this as a strength of the
research, in that it allowed us to get at parts of
the question of how executive compensation is
decided that prior work using archival methods
could not easily assess. For all its many
strengths, archival research can only infer the
relation between compensation consultant conflicts of interest and their recommendation from
observed CEO compensation schemes. That is,
archival searches for biased recommendations
are based on realized pay levels because the
recommendation provided by the consultant is
not publicly available. This results in multiple,
uncertain interpretations of the situation.
For example, one possibility is that conflicted
compensation consultants recommend that
firms compensate the CEO more richly, and
compensation committees adjust for the consultants’ conflicts of interest. Such an adjustment
might involve discounting the advice, taking the
advice and adjusting based on its content (e.g.,
discounting more lavish recommendations at a
steeper rate relative to more modest recommendations), or taking the advice and then adjusting
for the possible bias without respect to its content (e.g., recommendations from a potentially
biased source are assumed to be too high, so the
ultimate decision as to compensation should be
uniformly lower). Importantly, extant research
does not offer insight into how executive compensation committees might manage the advice
they receive from compensation consultants
(cf., Gino et al., 2012). Further, because groups,
not individuals, make compensation decisions,
archival research cannot rule out the possibility
that the group consensus process itself introduces other biases into the process (e.g., group
polarization; Moscovici & Zavalloni, 1969), or
improves upon the contributions of individuals
by correcting individuals’ misperceptions or errors (Hinsz et al., 1997; Shaw, 1932). As noted,
the current research addresses these issues and
offers evidence that potential conflicts of interest on the part of a consultant do affect both
individual preference and group judgment, but
independent of the magnitude of the recommendation. In addition, groups do not appear to
correct for this possible oversight.
Our goal with regard to our methodological
choice was to maximize internal validity (i.e., the
ability to infer causality) and examine variables
that might not be feasible using standard archival
methods (specifically, the relationship between
consultant advice and group judgments). We see
the current research as a complement to the extant
archival research on executive compensation,
which, though high in generalizability, is not designed to assess a number of process issues. Because we used an artificial laboratory approach,
along with a hypothetical choice scenario without
real-world consequences, the generalizability of
our findings is questionable. Further work with an
appropriate field sample is called for to support or
refute our findings. Similarly, our choice to use
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EXECUTIVE COMPENSATION
MBA students rather than to select participants
specifically for their executive compensation experience may likewise be a limiting factor (although it should be noted that board members
who make executive compensation decisions do
not receive special training). Importantly, although we believe the type of information we
provided to decision makers in the current study to
be typical of those provided for the purpose of
compensation decisions, real committees making
compensation decisions in actual organizations
could gather more or different information on
executive candidates than we provided.
In closing, our findings contribute to a broad
stream of literature on how individuals and groups
use information provided by a potentially biased
source. At the same time, our results are consistent
with economic predictions such as Akerlof (1976)
and Stein (1989), in which information providers
recognize that users will adjust for the perceived
bias such that the provider must supply a biased
recommendation to achieve the “correct” outcome. This signal-jamming framework reconciles
our findings with those from the archival work
that do not find a relation between compensation
consultant conflicts of interest and CEO compensation levels (Cadman et al., 2010; Murphy &
Sandino, 2010). That is, consultants either (a) do
not act strategically, or (b) recommend higher pay
and committees rationally adjust sufficiently (consistent with a signal jamming model as in Stein,
1989). Regardless of the mechanism, our results
suggest that decision makers, when informed of a
conflict of interest, may adjust for this perception
when considering the recommendation of the advisor, even if insufficiently.
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BONNER AND CADMAN
Appendix A
Abridged CEO Resume
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
Geddy Neilson
98 Limerick Dr. Syracuse, NY 15065
Office: (315) 123– 4567/Fax: (315) 123–
4568
Cell Phone: 315–123-4569
E-Mail: Neilsongd@yahoo.com
EDUCATION:
1984 –1986 Columbia University, New York,
NY
Master of Business Administration
1976-1980, Boston College, Chestnut Hill,
MA
Bachelor of Science Degree
Majored in Economics, Summa cum Laude
WORK EXPERIENCE:
2004-present President and Chief Executive Officer, Turnbuckle Technologies, International; President, Turnbuckle Technologies, Inc.; President, SecureFit Automation,
President, Turnbuckle Technologies, Ltd and
Silgel Packaging Co. Ltd.; President, Eagle
Chemical Company
Turnbuckle International is a holding company consisting of manufacturing operations
in Syracuse, New York; Ithaca, New York;
Mobile, Alabama and Warrington, U.K. and
Telford, U.K. Turnbuckle is a world leader in
the design, development production and marketing of high quality packaged and fabricated sorbents for controlling constrained environments. Products are sold and distributed
world-wide and are utilized in the automotive,
pharmaceutical, electronics, medical diagnostic, spill control, general packaging, food
packaging, plastics and resins and consumer
products. Annual sales are $320 million, with
4,100 employees worldwide.
2001–2004 President, Communication Solutions, LLC, Communication Solutions is a specialty consulting and Internet Technology Development firm with a concentration in the
health care industry. Engaged in business development strategies, strategic planning, workplace redesign, risk management, quality improvement and corporate reengineering.
Success factors include increasing corporate clients’ market penetration and market share, resource utilization, and internal operating efficiencies. Former clients include: Massachusetts
General Hospital; Long Island Eye Ear and
Throat Hospital; Triplex Technologies; Turnbuckle Technologies; The American Rehabilitation Hospital; SoloCraft Corporation; and
Johnson Screw Products, Inc.
1998 –2001 President/CEO, TFC Consulting,
Responsible for overseeing and directing all of
the activities of the corporation including, TFC
Group, Inc., TFC Foundation, Inc.; TFC Services Corporation and Moonflight, Inc. TFC
Consulting is the regional Consulting center for
Upstate New York. TFC offers a full range of
consulting services.
1986 –1998 President/CEO, Epiphone Technologies Responsible for overseeing and directing all of the activities of the Epiphone Technologies. Epiphone Technologies consists of
800 employees with an annual operating budget
of 96 million dollars.
Honors and Awards
• Brady Fellowship – 2007
Honoree at the Davis Foundation Award
Night for Superior Management – 2006
Honorary Doctorate From William’s College
– 2005
Fortune 500 top Rising Stars in the Corporate
World – 2002
EXECUTIVE COMPENSATION
317
Appendix B
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
Comparison Realized Compensation Packages
Firm
Salary
Bonus/Nonequity
A
B
C
D
E
F
G
H
I
J
K
$ 1,225,000
$ 1,375,000
$ 1,500,000
$ 945,000
$ 1,210,000
$ 1,100,000
$ 1,132,000
$ 818,000
$ 1,500,000
$ 1,125,000
$ 1,224,000
$ 250,000
$ 1,689,000
$ 1,875,000
$ 2,232,000
$ 5,390,000
$ 3,267,000
$ 3,987,000
$ 1,562,000
$ 3,016,000
$ 2,887,000
$ 943,000
Equity
$ 9,816,000
$ 16,232,000
$ 8,037,000
$ 9,068,000
$ 2,429,000
$0
$ 3,386,000
$ 3,639,000
$0
$ 21,775,000
$ 6,626,000
Total
$ 11,291,000
$ 19,296,000
$ 11,412,000
$ 12,245,000
$ 9,029,000
$ 4,367,000
$ 8,505,000
$ 6,019,000
$ 4,516,000
$ 25,787,000
$ 8,793,000
Note. These data represent realized compensation packages over the prior year from other publicly traded firms of
comparable sales revenue within the same two-digit standard industrial classification code. Granted compensation packages
in the previous year amounted to the values provided in the table. Equity grant values are based on the fair market value
at the time of the grant.
Appendix C
Social Judgment Models
Assume that group decisions (Gi) are
weighted sums of r individual preferences on i
problems, denoted xij. Further, let cij denote the
weight of the jth member on the ith problem,
with the sum of all cij totaling 1, or, Gi ⫽
ci1xi1 ⫹ ci2xi2 ⫹ . . . cirxir. The centrality equation (Bonner et al., 2004) is defined as
cij ⫽
(e⫺ⱍZijⱍ)
r
e
兺
j⫽1
⫺ⱍZijⱍ
where Zij is a standardized value that reflects
distance from the intragroup average, ([xij –
intragroup mean]/ intragroup standard deviation) and e represents the natural log e.
Received July 19, 2013
Revision received March 26, 2014
Accepted May 8, 2014 䡲