the decoy effect as a covert influence tactic

The Decoy Effect as a Covert Influence Tactic

JEREL E. SLAUGHTER1*, EDGAR E. KAUSEL1 and MIGUEL A. QUINONES2

1Management and Organizations, University ofArizona,Tucson, Arizona, USA2Management andOrganizations, SouthernMethodist University, Dallas,Texas, USA

ABSTRACT

The purpose of this research was to determine whether individuals could use the decoyeffect to influence others’ choices. In study 1, undergraduates (n¼ 50) and executivemaster’s of business administration (EMBA) students (n¼ 24) read an employeeselection scenario in which they were randomly assigned to prefer one of twocandidates that were equal in overall attractiveness, but that had different strengthsand weaknesses. They were then asked to choose one of three inferior candidates to addto the choice set that would make their preferred candidate more likely to be chosen byother decision makers. The ‘‘correct’’ inferior candidate was asymmetrically domi-nated—dominated by one of the two existing candidates, but not the other. Participantschose the ‘‘correct’’ decoy candidate at better than chance levels. In study 2, under-graduates and EMBA students (total n¼ 66) completed a set of four decision tasks, inwhich they were asked to choose from potential decoy alternatives that would highlighttheir preferred job candidate or the product they preferred to sell to a customer.Participants again chose the correct option at better than chance levels. Whenparticipants provided free-response reasons for their choices, these responses indicateda fairly strong recognition of the influential nature of creating a dominating relationship.Implications for understanding this effect and how it may be used by hiring managers,sales personnel, and others who attempt to influence others people’s decisions at work,are discussed. Copyright # 2010 John Wiley & Sons, Ltd.

key words decoy effect; employee selection; applied decision making influence

tactics; consumer choice

INTRODUCTION

Research on personnel selection has focused on techniques and processes that yield the most qualified

candidates for the job in question (cf. Schmitt & Chan, 1998). This literature shows that organizations can

benefit greatly from using selection tools such as cognitive ability tests, structured interviews, situational

judgment tests, and biographical data when choosing from a set of job applicants (Schmidt & Hunter, 1998).

Journal of Behavioral Decision Making

J. Behav. Dec. Making, 24: 249–266 (2011)

Published online 15 January 2010 in Wiley Online Library

(wileyonlinelibrary.com) DOI: 10.1002/bdm.687

*Correspondence to: Jerel E. Slaughter, Management and Organizations, University of Arizona, Box 210108, 1130 E. Helen, Tucson,AZ 85721, USA. E-mail: [email protected]

Copyright # 2010 John Wiley & Sons, Ltd.

All of these techniques seek to impose consistency and structure on the selection process to improve

its effectiveness. However, there is evidence that decision makers resist high levels of structure partly

because they feel constrained in their ability to influence the selection process (Posthuma, Morgeson, &

Campion, 2002). Unfortunately, there is a surprising lack of research on the tactics that individuals use to

influence the selection process. The current research seeks to fill this gap in the literature by examining the

extent to which individuals can use the decoy effect as a covert strategy for influencing the outcome of

selection decisions.

The decoy effect occurs when the addition of an inferior candidate to a choice set changes the preference

relations among the existing, superior alternatives (Huber & Puto, 1983; Huber, Payne, & Puto, 1982).

Consider the simulated employee selection scenario from Highhouse (1996), presented in Table 1. To one

half of a sample of undergraduates, Highhouse presented candidates A, B, and C1. When these candidates

were presented, two-thirds of the sample chose candidate A. However, the other half of the individuals in the

sample were each asked to choose the best candidate from a choice set that included candidates A, B, and C2.

This time, about two-thirds of the participants chose candidate B! No participants chose the decoy candidate

(candidate C) in either condition. Even though the decoy was never chosen, the manipulation of its

characteristics strongly changes the preferences for the two superior candidates, whose characteristics do not

change across conditions.

Note that, in each of these conditions, candidate C is asymmetrically dominated—that is, candidate C is

dominated by one candidate but not the other. One option dominates another when the dominating candidate

performs at least as well as the dominated candidate on all dimensions, and outperforms the dominated

candidate on at least one dimension. In the first condition, candidate C1 is asymmetrically dominated because

it is dominated by candidate A but not by candidate B. In the second condition, candidate C2 is dominated by

candidate B, but not by candidate A. In each condition, the target is the label commonly given to the option

that dominates the decoy. The terms competitor and non-target are used interchangeably to refer to the other

superior option, the one that is not targeted by the decoy.

Several explanations have been posited for the decoy effect. One is loss aversion (e.g., Tversky &

Kahneman, 1991). The loss aversion explanation suggests that the decoy is used a reference point from which

the other candidates differ in expected loss. In the example presented in Table 1, if C1 is used as a reference

point, choosing B affords the decision maker a large gain on promotability, but requires a loss on the work

sample. On the other hand, if candidate A is chosen, the decision maker earns just a small gain on

promotability, but does not take a loss on the work sample dimension. Because losses are more

psychologically painful than same-sized gains are psychologically pleasurable, most decision makers choose

candidate A.

Another explanation is based on decision makers’ use of a dominance heuristic (Simonson, 1989; Wedell,

1991). That is, the dominating relationship of the target to the decoy provides an easily available reason for

choosing the target. Moreover, choosing the dominating candidate allows an individual to justify the decision

to oneself and to others (Slaughter, Sinar, & Highhouse, 1999). Yet another explanation is based on range-

frequency theory (Huber & Puto, 1983; Huber et al., 1982). According to the range-frequency explanation,

Table 1. Candidate assessment scores from Highhouse (1996)

Candidate name Work sample score Promotability score

Candidate A 7 66Candidate B 5 80Candidate C1 7 54Candidate C2 4 80

Note: Each participant was presented with three candidates: candidate A, candidate B, and either candidate C1 or candidate C2.

Copyright # 2010 John Wiley & Sons, Ltd. Journal of Behavioral Decision Making, 24, 249–266 (2011)

DOI: 10.1002/bdm

250 Journal of Behavioral Decision Making

the decoy extends the range on the dimension on which the target is weaker than the non-target and

increases the frequency of options that outperform the non-target on the other dimension. For

example, consider the addition of C1 to a set including A and B in Table 1. The addition of candidate

C1 extends the range on promotability, the dimension on which candidate A (the target) is weaker than

candidate B. C1 also increases the frequency of those outperforming B on the work sample. Finally, context-

dependent weighting has been offered as a reason for this phenomenon (e.g., Tversky, Sattah, & Slovic,

1988). According to this explanation, the dimension on which the target and the decoy excel is given more

weight in the final decision.

The decoy effect has been demonstrated in multiple applied domains. Likely because the phenomenon

was first described by consumer psychologists, a great deal of work has shown its prominence in choices

among consumer products (Heath & Chatterjee, 1995). Other research has shown that the decoy effect holds

for choices among apartments (Simonson, 1989) and gambles (Wedell, 1991). The decoy effect has also been

an especially popular topic in the job-finalist choice domain. In addition to the Highhouse (1996) study

described above, Slaughter et al. (1999) showed that the decoy effect held even when participants were

presented with only video-based samples of candidate performance (i.e., no numerical information was

presented to decision makers). More recently, Slaughter, Bagger, and Li (2006) showed that the decoy effect

occurred for group decision making as well as individual decision making among job finalists. In fact, this

research showed the effect was actually strongest when groups rather than individuals were making selection

decisions, and when participants were accountable for both their decision processes and decision outcomes.

Mounting evidence, therefore, suggests that the decoy effect is robust to manipulations designed to weaken it,

and in fact may be even stronger when the conditions of the study are designed to approximate ‘‘real-world’’

choice conditions (e.g., making participants accountable for their decisions; Simonson, 1989; Slaughter et al.,

2006).

One important question, given the robust nature of this effect, is whether managers are able to use the

decoy effect to their advantage when attempting to influence their peers when selecting from among job

candidates. This issue has not been studied empirically, but it appears likely that such strategies may be used

by individuals to increase preferences for one alternative over another. For example, an article in the

Washington Post suggested that presidential candidates could potentially use the decoy effect to their

advantage, by drawing voters’ attention to qualities of a third candidate that make their own qualities appear

superior (Vedantam, 2007). Pan, O’Curry, and Pitts (1995) presented evidence that suggested that the decoy

effect may have played a part in voter choices in two political elections—the 1994 Illinois state primary

elections and the 1992 presidential election. Simonson and Tversky (1992) suggested that retailers might go

so far as to introduce new and inferior products in order to increase sales of weakly selling existing products.

For example, Ariely (2008) showed how marketers at The Economist were able to increase sales of an

expensive print-and-internet subscription ($125) over a cheaper internet-only subscription ($69), by also

offering a third, decoy option: A print subscription for $125.

In the present investigation, wewere interested in whether decision makers whowere put in the position of

favoring one strong candidate over another candidate that is similar in overall attractiveness could choose the

correct third option to add to the choice set—the one that would target their preferred candidate. More

specifically, this study examined whether participants were able to recognize the dominating relationship

among a set of candidates and use this relationship, in a covert way, to their advantage.

Although there is an abundant literature on managerial influence tactics (e.g., Fu & Yukl, 2000; Kipnis,

Schmidt, & Wilkinson, 1980; Yukl & Tracey, 1992), the tactics studied tend to be fairly overt and to be clear

attempts by the agent to gain compliance or commitment from the target (e.g., Yukl, Chavez, & Seifert,

2005). There is virtually no organizational research on employees’ usage of more indirect, covert tactics, such

as deception, trickery, or ‘‘outsmarting’’ other employees. In Experiment 1, we intended to take a first step

toward filling this gap in the literature by examining the extent to which decision-makers could use the decoy

effect to make their preferred candidate more attractive.


DOI: 10.1002/bdm

J. E. Slaughter et al. Covert Influence Tactics 251

EXPERIMENT 1

MethodParticipants

This study used two separate samples of participants. Sample 1 (n¼ 50) was comprised of junior-level

undergraduate students enrolled in a management course at large public university in the Southwestern

United States. Sample 2 (n¼ 24) was comprised of master’s of business administration (EMBA) students in a

program at the same university. Although demographic information was not collected from these samples, the

overall executive MBA program consists of individuals with 10 or more years of work experience with an

average age of 39 years, and 20% are members of underrepresented minority groups (e.g., African-American,

non-White Hispanic, Native American). Participation was voluntary for both samples.

ProcedureParticipants were given a short printed scenario titled, ‘‘Exercise Your Influence.’’ They were asked to

imagine themselves in an executive position for an organization named Handelman Industries that was

seeking to hire a new plant manager. Participants were told that a search firm had, through a series of

interviews and assessment exercises, narrowed down the pool of acceptable candidates to a list of five

finalists, who were presumed to be equal on all but two dimensions: Years of relevant work experience, and

the overall score produced by the search firm consultants. The materials for one of the conditions are

presented in the Appendix.

The materials presented to the participants indicated that the consultants recommended two finalists to the

firm: A. Johnson, who had 7 years of relevant experience and an overall assessment score of 66, and L. Smith,

with 5 years of relevant work experience and an assessment score of 80.1 Because the firm typically brings in

three candidates, the participant was asked to decide which of the remaining three candidates should be

brought in for further interviews by firm executives.

The scenario was written such that either A. Johnson or L. Smith was the participant’s preferred candidate

because the participant and the candidate attended the same undergraduate institution and shared some of the

same interests. Thus, the task for the participants was to choose one of the three inferior candidates that

should be brought in to make A. Johnson or L. Smith ‘‘look better,’’ depending upon the experimental

condition. The three inferior candidates included S. Bass, a decoy that would target A. Johnson; G. Frank, a

decoy that would target L. Smith, and M. Ellis, a candidate inferior to both superior candidates on both

dimensions and therefore would not target either candidate. Thus, if decision makers can use the decoy effect

to their advantage, those told that A. Johnson was their preferred candidate should choose to bring in S. Bass,

and those told that L. Smith was their preferred candidate should choose to bring in G. Frank. The only

information that differed across the two conditions was the candidate that was supposedly favored by the

participant. We counterbalanced both the order of presentation of superior candidates and order of

presentation of inferior candidates. It is important to make clear that the participants were not given any

information about the decoy effect or how they might use it in this situation. The study was aimed at

determining the extent to which participants would recognize the fact that the third candidate they choose to

add to the choices set could influence the likelihood of their preferred candidate being chosen.

Results and discussionTo determine whether the preferred candidate influenced a participant’s choice of a third candidate to be

added to the choice set, we first conducted a 2 (target)� 3 (choice) x2 analysis. This test was significant, x2

1Note that we used the same dimension scores as Highhouse (1996, study 2), largely because of the strength of the observed decoy effect.


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(2, n¼ 74)¼ 8.07, p¼ .018, f2¼ .11, indicating that the change in target applicant influenced the choice of

decoy candidate.2 Choice percentages are presented in Table 2. Inspection of Table 2 reveals that when

Johnson was targeted, Bass (the decoy that would target Johnson) was chosen 47% of the time, more than

either Frank or Ellis and significantly higher than the 33% expected by chance alone. Table 2 also shows that

when Smith was targeted as the preferred candidate, Frank (the decoy that would target Smith) was chosen

42% of the time, more than Bass, but with the same frequency as Ellis.

Moreover, Table 2 shows that Ellis, the fully dominated candidate that would target neither of the superior

options, was chosen 42% of the time when Smith was the preferred candidate, but only 24% of the time when

Johnson was the preferred candidate. To determine whether this difference was driving the significance of the

omnibus x2 analysis, we performed a second x2 analysis, eliminating those individuals who chose Ellis. This

test was also significant, x2 (1, n¼ 50)¼ 5.48, p¼ .017, f2¼ .11, indicating that decision makers were more

likely to bring in the candidate that targeted their preferred option (66% of participants) than the candidate

that would target their non-preferred option (34% of participants).

To determine whether undergraduates and working managers differed in their ability to select the

appropriate inferior option that would make their preferred candidate more attractive, we first converted

participant choices to a dichotomous option, coded as being correct (choose the correct decoy) or incorrect

(choose one of the incorrect decoys). A 2 (sample)� 2 (correct choice) x2 test was not significant, x2 (1,

n¼ 74)¼ 0.02, p> .05. This suggests that working managers and undergraduate business students do not

differ in their ability to use the decoy effect to influence candidate choices. Percentages are presented in

Table 3. Inspection of Table 3 reveals that 44% of the undergraduates chose the correct decoy, whereas 46%

Table 2. Decoy choice percentages by target condition, combined samples, study 1

Target

Choice

Bass Frank Ellis

Johnson (targeted by Bass) 47% (n¼ 18) 29% (n¼ 11) 24% (n¼ 9)Smith (targeted by Frank) 17% (n¼ 6) 42% (n¼ 15) 42% (n¼ 15)

Note: x2 (2, n¼ 74)¼ 8.07, p¼ .018. Bold values represent expected choices, given the target condition. Choice percentages do not sumto 100 because of rounding.

Table 3. Correct choice by sample, study 1

SampleCorrect or incorrect choice

Correct Incorrect

Undergraduate 44% (n¼ 22) 56% (n¼ 28)EMBA 46% (n¼ 11) 54% (n¼ 13)

Note: x2 (2, n¼ 74)¼ 0.02, p< .05. Correct¼ chose the decoy alternative that targets the participant’s preferred candidate; incor-rect¼ chose either (a) the decoy alternative that targets the participant’s non-preferred candidate or (b) the fully dominated candidate thattargets neither the preferred or non-preferred candidate.

2When the degrees of freedom for the x2 statistic are greater than 1, we use Cramer’s f to estimate the percentage of variance accountedfor by the effect. Cramer’s f is calculated as the square root of (x2)/[N(k� 1)], where k is equal to the smaller of the number of rows orcolumns. The f is interpreted as being similar to a correlation coefficient, and thus it is squared to reflect a variance-accounted-for effectsize.


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of the EMBA sample made the correct choice. To examine further whether classifying the choice of the fully

dominated option as an incorrect decision influenced this analysis, we eliminated those individuals who

chose this option. This limited-sample x2 was not significant, x2 (1, n¼ 50)¼ 0.08, p> .05. Of those who did

not choose the fully dominated option (which is technically incorrect, but which would not likely serve to

increase votes for the non-preferred candidate), 65% of undergraduates and 69% of the EMBA sample made

the correct choice.

Experiment 1 is the first study of which we are aware that suggests that individuals can use the decoy effect

as a covert influence tactic. This is an important finding, because it shows that a situation that has been shown

to influence job-finalist choice decisions (e.g., Highhouse, 1996; Slaughter et al., 1999, 2006) can actually be

created by those interested in influencing the choices of others. However, the conclusions that can be drawn

on the basis of the results of Experiment 1 alone are limited by some of this study’s weaknesses and its

relatively simplicity. First, the fully dominated option we used as a distracter seems to have made the results

less clear. As 32% of participants chose the distracter option, we may have overestimated or underestimated

the likelihood that laypersons can use the decoy effect as a covert influence tactic. Second, Experiment 1 was

based on a single decision task. Third, we also questioned whether the results of Experiment 1 may have been

influenced by participants’ ethical concerns. That is, we wondered whether participants may have felt it was

unethical to manipulate the job-candidate choice sets and ‘‘trick’’ their colleagues into choosing their friend,

and for this reason, they did not exert enough effort to determine the correct candidate that would make

salient the advantages of their ‘‘preferred’’ candidate. Thus, Experiment 1 provided the foundation for

Experiment 2, in which we sought to increase validity and generalizability by improving upon some the

weaknesses of Experiment 1.

EXPERIMENT 2

MethodIn Experiment 2, we presented to participants scenarios that included just two decoy options. One option was

clearly correct, because it would target their preferred option; and one was clearly incorrect, in that it targeted

their non-preferred option. Participants also completed four separate decisions tasks. We made this change in

order to increase reliability and bolster our confidence that the Experiment 1 results were not due to a single

scenario. Much previous work on the decoy effect has utilized multiple scenarios to demonstrate that the

effect is powerful enough to be found in a variety of contexts (e.g., Ariely & Wallsten, 1995; Pettibone &

Wedell, 2000; Wedell & Pettibone, 1996). Thus, we also added sales decision making tasks, in which

participants chose a third, decoy product to add to a choice set to influence buyers to choose the product they

prefer to sell. As the decoy effect originated in the consumer behavior literature (Huber et al., 1982), and it is

often invoked as reason why companies successfully increase sales of a product by introducing inferior

models (Simonson & Tversky, 1992), we felt it was important to study whether those who had not received

formal training in sales could also use the decoy effect in a covert manner, in order to increase sales

productivity.

Because of our unease over the potential for participants’ ethical concerns to influence the results, we also

provided opportunities for participants to indicate any ethical concerns they had about manipulating others’

decisions. Finally, we asked participants to indicate their reasons for selecting the candidate they did, hoping

to shed some light on potential theoretical explanations for why participants are able to choose the correct

decoy. Slaughter et al. (2006) found that, when presented with a choice set that included a decoy job

candidate, decision makers in groups mentioned attribute weights most often (supporting a context-

dependent weighting explanation). However, it will be important to understand whether this finding holds

when decision makers are attempting to influence the decisions of others. That is, wewanted to study whether


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those who could successfully influence the decisions of others in Experiment 2 also perceived that they were

doing so by influencing others’ perceptions of the relative importance of attribute weights.

Participants

Participants (n¼ 66) were EMBA students (n¼ 18) from a small, private university and undergraduate

students (n¼ 48) enrolled in an introductory management course at a large, public university. Both

universities are located in the southwestern United States. The EMBA sample was 83% male, and 78%

White, 6% Black, and 16% Asian. The mean age was 32.3 years (SD¼ 7.4). They had worked for their

employers an average of 4.2 years and in their current jobs for 2.9 years. They held a range of different job

titles (e.g., engineering manager, attorney, software consultant, president) and worked in a variety of different

industries (e.g., defense contractor, legal services, financial services, manufacturing). The undergraduate

sample was 71% male, with a mean age of 22.4 (SD¼ 3.2). The majority of the sample was White (81%),

while 8% were Asian, 8% were Hispanic, and 3% did not indicate race. The majority (73%) were

management majors, with the others majoring in management information systems (4%), accounting (2%),

finance (13%), consumer sciences (4%), marketing (2%), and journalism (2%).

Materials, procedure, and measures

Participants received a packet that described the study as one in which they were to exercise their influence

and persuade other people to make decisions that were in their own best interest. Participants were asked to

perform the task during class time at the end of one day of class. As noted above, participants made four

separate decisions, each of which involved choosing one of two decoy options to add to a choice set, in order

to target their preferred option. As we also noted above, participants made two decisions involving employee

selection, and two decisions involving sales scenarios. The options and dimension values are presented in

Table 4. Participants were randomly assigned to one of two conditions, in which they either preferred one of

the two superior options in each choice set. We also counterbalanced (a) the order of the presentation of

scenarios; (b) the order of presentation of the superior options within each scenario, and (c) the order of the

presentation of the decoy options within each scenario. As such, there were 16 different forms of the survey.

We computed a ‘‘percentage correct’’ variable for each participant, representing the number of times the

participants chose the correct response divided by 4 (for four different scenarios). The form of the survey did

not affect participants’ performance on the task, F (15, 49)¼ 0.75, p> .05.

After participants made each decision, they were asked to explain why they made the decision they did.

Specifically, they were asked:

Think about how you made the choice about which candidate to bring into the pool of candidates (orwhich

computer to show or which printer to show). In the space provided below, we would like you to explain or

justify why you selected the option you did. That is, we want to know your thought process as you

evaluated the information and why you ended up selecting that specific option.

As discussed above, we also wanted to determine whether participants felt it was unethical to trick

consumers, clients, or coworkers by bringing in options to make their preferred option look better. However,

we were concerned that using the term ‘‘unethical’’ might prime them to think differently about the scenarios

that followed that question in the packet. Therefore, we used an open-ended question on each scenario: ‘‘Do

you have any other comments about this scenario? If so, please write them below.’’

After completing the four scenarios and related questions, participants completed a number of

demographic questions and questions that related to previous experiences. In addition to the questions noted

above, undergraduates were asked about whether they previously had experience with promotion and hiring

decisions (yes/no) and retail sales (yes/no). EMBA participants were asked to rate their experiences with


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previous personnel decisions similar to the ones found in these scenarios and previous, similar sales decisions

(1¼ no experience; 5¼ a great deal of experience).

ResultsParticipant choice

First, we coded each of the 263 choices made by the 66 participants (1 of the 66 participants had missing data

for one of the choices) as either correct or incorrect (i.e., they either chose the correct decoy or they did not).

Overall, 182 of the 263 choices were correct (69.2%). We had converted each participant’s set of choices to a

single score that ranged from 0 to 1.00 and that reflected the percentage of the time the participant chose the

correct decoy. A one-sample t-test showed the average score was significantly different from .50, t

(65)¼ 4.75, p< .001. This suggests that participants chose the correct decoy at better than chance levels. We

Table 4. Options and dimension values for study 2

Interview rating Work sample

Scenario 1Superior optionsC. Connolly 7 66F. Bradley 5 80

Decoy optionsS. Hill 7 54E. Donovan 4 80

Knowledge of the law Clerical knowledge

Scenario 2Superior optionsJ. Levin 4 42C. Smith 6 28

Decoy optionsT. Barnes 3 42D. Anderson 6 21

Memory RAM (GB) Size of hard drive (GB)

Scenario 3Superior optionsD-Byte 4 320Irata 3 440

Decoy optionsWORP 4 260Pantel 2.5 440

Print speed (ppm) Print quality (dpi)

Scenario 4Superior optionsPrintimax 25 900SXP 35 600

Decoy optionsBaxley 20 900Pendix 35 450


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then examined participant choices for individual scenarios to determine whether the decoy effect occurred in

each one. The results of this analysis are presented in Table 5, where we present choice percentages and x2

tests for independence. Inspection of Table 5 reveals that the decoy effect was not significant in scenario 1, x2

(1, n¼ 65)¼ 2.13, p¼ .145, but was significant in scenario 2, x2 (1, n¼ 66)¼ 15.52, p< .001, f2¼ .24;

scenario 3, x2 (1, n¼ 66)¼ 14.79, p< .001, f2¼ .22; and scenario 4, x2 (1, n¼ 66)¼ 8.69, p¼ .003,

f2¼ .13.

As in Experiment 1, we conducted a follow-up analysis by subsample to determine whether these two

groups with different levels of experience showed performance differences on these tasks. On average,

EMBAs and undergraduates did not differ in terms of average number of correct choices, F (1, 64)¼ 2.06.

p¼ .16. Undergraduate chose correctly an average of 71.4% of the time (SD¼ 29.1%), while EMBA students

chose correctly an average of 59.2% of the time (SD¼ 34.2%). Again, however, choosing at random would

lead to being correct an average of 50% of the time, as there were only two available options in each scenario.

Thus, we created a variable that represented whether a participant chose correctly more than 50% of the time

(i.e., they chose correctly in either 3 or 4 of the 4 scenarios) or 50% of the time or less (i.e., they chose

correctly in 0, 1, or 2 of the scenarios). Here, we found a stronger difference: 73% of the undergraduates were

Table 5. Decoy choice percentages by target condition, study 2

Target

Choice

Hill Donovan

Scenario 1Connolly (targeted by Hill) 60% (n¼ 22) 40% (n¼ 15)Bradley (targeted by Donovan) 41% (n¼ 12) 59% (n¼ 17)

Target

Choice

Barnes Frank

Scenario 2Levin (targeted by Barnes) 73% (n¼ 27) 27% (n¼ 10)Smith (targeted by Anderson) 24% (n¼ 7) 76% (n¼ 22)

Target

Choice

WORP Pantel

Scenario 3

D-Byte (targeted by WORP) 81% (n¼ 30) 19% (n¼ 7)Irata (targeted by Pantel) 35% (n¼ 10) 65% (n¼ 19)

Target

Choice

Baxley Pendix

Scenario 4Printimax (targeted by Baxley) 68% (n¼ 25) 32% (n¼ 12)SXP (targeted by Pendix) 31% (n¼ 9) 69% (n¼ 20)

Note: For scenario 1, x2 (1, n¼ 65)¼ 2.13, p¼ .145; for scenario 2, x2 (1, n¼ 66)¼ 15.52, p< .001, f2¼ .24; for scenario 3, x2 (1,n¼ 66)¼ 14.79, p< .001, f2¼ .22; for scenario 4, x2 (1, n¼ 66)¼ 8.69, p< .01, f2¼ .13. Bold values represent expected choices, giventhe target condition. Choice percentages do not sum to 100 because of rounding.


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correct at greater than chance levels, while only 50% of the EMBAs fell into this category, x2 (1,

n¼ 65)¼ 3.09, p¼ .079, f2¼ .05.

We also checked for a potential learning effect, whereby participants’ average performance would have

improved on successive decisions, irrespective of the order in which participants received scenarios. To do so,

we performed a 4 (choice order)� 2 (choice performance) x2 analysis. Performance did not differ by scenario

order, x2 (3, n¼ 264)¼ 0.19, p¼ .979. On the first scenario presented, 70% of participants selected the

correct decoy; corresponding percentages were 70%, 68%, and 67% for the second, third, and fourth

scenarios presented.

Analysis of written comments

Next, we turned our attention to the written responses to questions about why participants chose the option

they did. Specifically, we sought to analyze the degree to which reasons provided by participants for correct

decisions (i.e., decisions in which the correct decoy was chosen) reflected one of the theoretical arguments

posited for the decoy effect: Loss aversion (Tversky & Kahneman, 1991), the use of a dominance heuristic

(Simonson, 1989; Wedell, 1991), range-frequency theory (Huber & Puto, 1983; Huber et al., 1982), and

context-dependent weighting (Tversky et al., 1988). As noted above, of the 263 choices made, 182 of these

were correct, and 82 were incorrect. We coded both incorrect and correct responses; correct responses are

discussed first below. The first and second authors coded the responses independently; interrater agreement

was 93.6%. Disagreements were resolved by discussion.

Of the 182 correct choices, 1 participant did not write a response to the question that followed, and 7

provided a response that did not fit with one of the above explanations. Of the 174 remaining correct

responses, none of the explanations could be classified as indicating loss aversion. This is consistent with

some research that suggests that loss aversion is not a strong explanation for the asymmetrically dominated

decoy effect (e.g., Pettibone & Wedell, 2000), but inconsistent with the findings of other studies (e.g.,

Bonaccio & Reeve, 2006; Highhouse, 1996, Slaughter et al., 2006). We return to this issue in the Discussion

Section below.

The theoretical argument that was invoked most often in participants’ explanations was the use of a

dominance heuristic, with 123 of the 174 explanations reflecting this reasoning. An example of such an

explanation was the following, relating to scenario 2 in Table 4, in which Smith is targeted, and Anderson is

the correct decoy: ‘‘I picked Anderson because (1) he and Smith both had a common law knowledge, and (2)

though that category is the same for both, Smith had a higher number of clerical knowledge (Smith¼ 28,

while Anderson 21).’’

Explanations reflecting range-frequency theory were invoked in just 23 of the 174 reasons provided. An

example explanation reflecting this theoretical perspectivewas: ‘‘I picked Barnes to demonstrate that Levin is

middle of the road in law knowledge but superior in clerical skills to Smith.’’ This explanation was provided

by a participant who correctly chose Levin to target Barnes in scenario 2 in Table 4.

Context-dependent weighting explanations were present in 28 of the 174 responses. An example

explanation reflecting context-dependent weighting was, ‘‘I think law knowledge is more important in this

case and I expect the manager to pick the applicant who is the highest in law knowledge. Clerical knowledge

is easier to comprehend.’’ This was provided by a participant who correctly indicated that adding Anderson

would make it more likely that Smith is chosen in scenario 2 in Table 4.

We also analyzed the explanations associated with the 81 incorrect choices. Eight of them were absent or

did not fit any of the four explanations provided above. All of the 73 remaining explanations were classified as

indicating a dimension-weighting argument. For example, a participant assigned to try get C. Connolly hired

in scenario 1 incorrectly chose Donovan, and provided as an explanation, ‘‘Donovan has the worst interview

rating and only equaled Bradley in work sample rating. Here I’d pitch personality fit over work product (sic)

and argue that work skills can be taught but personality traits cannot.’’


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Finally, we analyzed the written responses to the open-ended question that asked whether participants

wished to express any other issues or concerns, in hopes of determining whether participants had concerns

about the ethicality of covertly influencing others’ decisions. Generally speaking, we did not find this to be the

case. Relatively few participants provided comments; of those that did, only five comments could be

considered a concern over ethics (two of these were made by the same participant). Examples include ‘‘In the

end, you really need to do the right thing for the business before you look at assisting a friend. There are other

ways in which you can help a friend such as forwarding his resume to a company he may be better able to

assist based on his skill level’’ and ‘‘Since this is for an educational institution and she is on a fixed budget I

would not be so concerned with the higher commission but rather her (sic) truly find the best option.’’ Most of

the comments provided by participants seemed to be an effort to further clarify their choices; for example:

‘‘The print speed was a factor due to it being used for educational purposes. If it were for a Photoshop my

decision may have differed.’’ Overall, then, it did not appear from these comments that many participants had

ethical concerns.

However, this may have been because of participant fatigue, or because they did not make a connection

between their concerns about ethics and our general question, ‘‘Do you have any other comments about this

scenario?’’ Thus, we also wanted to know whether decision makers would indicate ethical concerns if

specifically asked about whether using the decoy effect as a covert tactic was unethical. To determinewhether

this was true, we recruited an additional sample of participants (n¼ 98, 63% male, 68% White, 65% fourth-

year and above, mean age¼ 22.2) from one of two undergraduate business courses. For one of the groups

(n¼ 56), we used a between-subjects design in which participants viewed either a selection scenario (the

second scenario in Table 4) or a sales scenario (the fourth scenario in Table 4). In the other group (n¼ 42), we

used a within-subjects design in which participants viewed both a selection scenario and a sales scenario, and

the order of scenario presentation was counterbalanced.

After the presentation of each scenario, we asked participants to indicate the degree to which the task was

unethical, on a 5-point scale (1¼ not at all unethical; 3¼ somewhat unethical; 5¼ very unethical). When

presented with these items, participants were more forthcoming with their concerns. In the between-subjects

design, we found that the selection scenario was viewed as more unethical (M¼ 3.23, SD¼ 0.76) than the

sales scenario (M¼ 1.96, SD¼ 0.76), t¼ (51)¼ 5.11, p< .01, d¼ 1.42. Thus, on average, participants

viewed the selection scenario as ‘‘somewhat unethical,’’ whereas the sales scenario was slightly closer to ‘‘not

at all unethical.’’ In the within-subjects design, however, we found a weaker, albeit statistically significant

difference between the two scenarios, Mselection¼ 3.35 (SD¼ 1.08), Msales¼ 2.50 (SD¼ 1.18),

t¼ (39)¼ 3.87, p< .01, d¼ .76. Within this condition, participants’ perceptions of both scenarios were

closest to ‘‘somewhat unethical.’’

DiscussionWhen considered in combination with the results of Experiment 1, the results of Experiment 2 increase

confidence in our conclusion that lay decision makers can utilize the decoy effect in a convert manner to

influence others’ decisions in a variety of contexts. Whereas in Experiment 1, we used a single scenario that

focused on employee selection, Experiment 2 utilized four different scenarios, two of which included the

manipulation of products to be sold to customers. Thus, we can now be confident that the result observed in

Experiment 1 is not limited to the employee selection context or the specific scenario used in the first study.

The original, and primary, goal of this study was to fill a gap in the employee selection literature, because

research shows that managers tend to resist structure in employee selection systems because of their

perceived inability to influence the selection process (Posthuma et al., 2002), and yet there is little research on

tactics managers might use to exercise such influence, or published information about training managers

might receive on covert influence tactics. With the addition of sales scenarios in Experiment 2, it would

appear that these results serve to fill an entirely new gap in the literature, as the results demonstrate that


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laypersons without training on covert influence tactics can use such tactics to influence product choice.

Whereas it seems likely that sales personnel receive training on persuasion, methods of influence, and covert

tactics (e.g., Blair & Sisakhti, 2007; Miller, Percy, & Mullen, 1993), the majority (52%) of undergraduate

participants in Experiment 2 had never worked in sales. Moreover, those who had no previous sales

experience performed as well as those who had not, F (1, 46)¼ 0.15, p> .05. Now that it has been established

that relative novices can perform well on these tasks, future research might focus on issues such as the age at

which individuals acquire the skills necessary to use asymmetrical dominance to influence choice.

It is also interesting to note that, despite the fact that participants in study 2 are unlikely to have been aware

of the concept of asymmetrical dominance, reasons reflecting an asymmetrical-dominance explanation were

by far the most common type of explanation provided by those who responded with the correct decoy. This

stands in contrast to the results of Slaughter et al. (2006). They found that, when groups made decisions from

choice sets in which decoy options were presented, the most common discussion points related to the

relative importance of the attribute weights, reflecting a context-dependent weighting explanation for

the decoy effect (e.g., Tversky et al., 1988). This may help to explain the paradox implied by findings that

(a) decision makers can recognize that an asymmetrically dominated candidate will cause a target candidate

to be preferred, and yet (b) the decoy effect is a very robust phenomenon observed across a variety of contexts. If

people are reasonably proficient at recognizing asymmetrical dominance in creating a choice set, why do they

fail to realize it when they are asked to choose from that same set of options? The likely answer is that, whereas

individuals are adept at recognizing that the creation of asymmetrical dominance will influence the choices of

others, when they themselves are presented with such a situation, they are less likely to recognize the dominating

relationship. Rather, they are simply convinced (by the distribution of dimension scores across alternatives)

that the dimension on which the target and decoy excel is the more important dimension.

A similar inconsistency between our findings and previous research was the fact that not a single

explanation provided by decision makers reflected loss aversion. Pettibone and Wedell (2000) reported no

support for loss aversion as an explanation of the decoy effect, consistent with our findings here. However,

Highhouse (1996), Bonaccio and Reeve (2006), and Slaughter et al. (2006) all report support for the loss-

aversion explanation. Particularly relevant are (a) the Bonaccio and Reeve (2006) study, in which participants

wrote reasons for their choices; and (b) the Slaughter et al. (2006) study, in which participants’ group

discussions were audiotaped. Bonaccio and Reeve found that 16% of the participants wrote reason related to

loss aversion when explaining their choices, while Slaughter et al. reported that 39% of the groups discussed

reasoning related to loss aversion. One possible explanation of the inconsistency across studies is that the

concept of creating reference points in order to capitalize on loss aversion is not intuitive, and therefore is not

a strategy that is part of the lay decision maker’s available repertoire. Thus, decision makers in this study did

not use this reasoning when deciding which decoy to add to the choice set in the present investigation.

However, when presented with a decoy candidate, and options that differ from the decoy candidate in

expected loss, decision makers anticipate the powerful negative consequences of expected loss (Camerer,

2000; Kahneman & Tversky, 1979), such anticipation influences their decisions.

One question that had been left open by study 2 was whether decision makers viewed the practice of using

covert influence tactics as unethical. Although we provided an opportunity for participants to indicate such

concerns, by soliciting responses to an open-ended question, only a handful of participants wrote comments

that related to potential unethical nature of the scenarios. In the follow-up study in which we asked

participants specific questions about the ethicality of using these tactics, participants admitted that they were

not entirely comfortable with the idea of using covert tactics to deceive others so that they would act in the

participants’ best interests. However, it should be noted that the scenarios in this study described two

situations that reflected the clear manipulation of others’ choices to serve one’s own interest (helping out a

friend so that the friend is motivated to help you in the future; selling a product that earns you a larger

commission). In fact, the decoy effect could be used covertly to influence others for a variety of different

reasons, including a covert attempt to increase the representation of women or members of minority groups


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when such groups are underrepresented due to a history of discrimination within a firm. Moreover, one issue

that is not clear from these results is whether participants viewed these particular scenarios as being so

unethical that they would not pursue such a course of action if it were available to them in a real-world

selection scenario. This seems unlikely, given that only 10 of the 134 ratings of ethicality reflected the

‘‘highly unethical’’ rating. However, this is an empirical question, and one that would also be an interesting

avenue for future research.

GENERAL DISCUSSION

Although considerable research has shown that choices can be influenced by the manipulation of a decoy

option, this was the first study to show that decision makers can choose the decoy that would target a

particular option. As we noted in the introduction, organizational research has not given much attention to the

idea of trickery or covert deception, instead choosing to focus on more overt influence tactics, such as

ingratiation, persuasion, and rational appeals (e.g., Cable & Judge, 2003). The few articles we were able to

locate on this topic dealt with outright lying (e.g., Grover, 1993) or theft (e.g., Greenberg, 1990, 1993), or

were based on McCormack’s (1992) information manipulation theory (IMT). IMT is derived from

interpersonal communication theories, and classifies deception behaviors into categories such as withholding

messages, distortion, changing the subject, and strategic ambiguity (e.g., Hubbell, Chory-Assad, & Medved,

2005). Thus, there appears to be an important gap in the literature, and filling this gap can provide an

important missing link from the judgment/decision making (JDM) perspective to organizational literature:

Managers’ use of JDM phenomena to influence others’ decisions.

Interestingly, and perhaps somewhat surprisingly, results showed that highly experienced EMBA students

(executives with more than 10 years of experience) and inexperienced undergraduate students (junior-level

college students) were equally adept at creating an asymmetrical dominance situation in study 1, and that the

undergraduates actually outperformed EMBA students in study 2. This suggests that perhaps it is not the

amount of experience, but rather the type of experience that affords individuals this covert influence skill.

Although EMBA students clearly have more and more varied work experience than do undergraduates, it is

possible that some of them do not have a great deal of experience with employee recruitment and selection.

Moreover, even if they do have such experience, it is possible that their experience has not been helpful in

instructing them how to influence others’ choices. In fact, we found that among EMBA students, self-reported

previous experience with these types of selection scenarios was actually negatively related to overall

performance in Experiment 2 (r¼�.52, p< .05). This is consistent with research that shows that experts do

not necessarily outperform novices in decision-making tasks (Einhorn, 1974; Gaeth & Shanteau, 1984;

Sullivan & Kida, 1995). However, it is difficult to speculate about why experience was negatively related to

performance; perhaps, such experience led to overconfidence in the experimental task.

We conducted post-hoc analyses with demographic and previous-experience variables as predictors, in

order to gain a better understanding of what might cause some participants to perform better than others on

these tasks. We did not find that gender, race, experience with selection decisions or sales (for undergraduate

students), or job or organizational tenure (for EMBA students) related significantly to the percentage of

correct decoys chosen. Given that direct experience with similar scenarios did not positively influence

accuracy, an interesting possibility for future research would be to study other, psychologically oriented

variables related to performance in these tasks. Some worth considering are general quantitative ability, need

for cognition, and Machiavellianism. It would also be worthwhile to sample HR professionals or recruiters

who have experience specifically with building sets of job finalists. Perhaps even more interesting, specific to

employee selection situations, would be to determine who has the ability to ‘‘pull off’’ the sort of trickery that

is involved with creating sets of options in order to influence the choices of others. That is, it would be

interesting to go beyond the question of what types of individuals are able to choose the correct decoy, and on


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to the questions of what types of individuals are able to convince peers to bring in specific decoy candidates,

without revealing their biases.

It is notable that the job-finalist choice scenario used by Highhouse (1996), which we also used as the

scenario in Experiment 1 (see Appendix), did not produce a significant effect in Experiment 2. That is,

although 60% of the participants in Experiment 2 presented with this scenario chose the correct decoy, a one-

sample t-test showed that this was not significantly different from 50%, or the percentage that would be

correct by chance alone, t (64)¼ 1.63, p¼ .11. As we discussed previously, we used this scenario specifically

because it was found by Highhouse to produce strong decoy effects, and thus it made sense to determine

whether decision makers could detect the inferior candidate that had been shown to produce the strong effect.

However, as one reviewer noted, perhaps one reason why this scenario did not produce a significant effect in

Experiment 2 is that one of the dimension values did not conform to those that would be produced by the

formula that is typically used to construct choice sets with asymmetrically dominated decoys (e.g., Pettibone

&Wedell, 2000, p. 307; Wedell & Pettibone, 1996). If this formula had been used, the work sample score for

Hill would have been 59, and not 54 (see Table 4). Thus, it is possible that using the decoy effect as a covert

influence tactic requires specific attribute values that conform to these formulae. We are reluctant to conclude

this on the basis of the findings from one choice set, especially because these same values produced a

significant effect in Experiment 1. However, in future work on the use of the decoy effect as an influence

tactic, it would be interesting to study whether deviations from attribute values produced by this formula

leads to poorer performance by decision makers.

A number of additional important research questions arise as a result of this study. For example,

participants in the present investigation were able to select for inclusion a decoy candidate that targeted their

preferred candidate, but it is not clear whether this would occur among managers responsible for selection

decisions in actual organizations. On the one hand, it could be argued that the artificial nature of the scenario

survey detracts from the study’s generalizability, and thus the effects in real-world organizations would be

much weaker. On the other hand, it could be argued that in an actual selection decision situation, an

employee’s motivation to influence peers to select the candidate that he or she preferred would naturally be

stronger than could be manipulated in the laboratory. Therefore, it is possible that in field settings, the effect

would be stronger. In fact, research shows that accountability conditions often lead to stronger effects for JDM

phenomena (e.g., Lerner & Tetlock, 1999; Tetlock & Boettger, 1989). Still, it should also be noted that we have

shown here that a significant proportion of the individuals can choose the correct decoy when the candidate

scores are quantified, explicit, and presented in a matrix format. It would be interesting to knowwhether decision

makers can still recognize this relationshipwhen the scores aremore ambiguous or less explicitly quantified (e.g.,

Slaughter et al., 1999), as information about options is likely to be more ambiguous in field settings.

It should also be noted that a number of employee selection situations involve choosing from a small set of

final candidates and can lend themselves to the sort of candidate selection manipulation found in this study.

For example, search committees for university professors often are given the task of narrowing the long list of

applicants to three finalists, who are then asked to participate in on-campus interviews. Thus, even though this

is the first study to document the use of the decoy effect by decision makers to manipulate the candidate

selection process, it is possible that this scenario occurs quite frequently in practice. Future research might

examine this issue by obtaining records of faculty hires when three candidates were brought in for campus

interviews, and the choice set included either two females and one male, or two males and one female.

Highhouse (1997) presented hypothetical scenarios in which, for example, a choice set that included two

males and one female would make it more likely that the male with the higher research productivity would be

chosen disproportionately more often—even if the lone female had higher research productivity than the

highest-performing male. Other dimensions that might be attainable for investigating the decoy effect in

faculty selection could be prestige of doctoral-granting institution or reputation of the candidate’s advisor.

In sum, the current study shows that the decoy effect provides one covert mechanism by which individuals

can influence selection decisions. This may explain why individuals resist highly structured selection


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processes. These results potentially open up an entirely new line of research into how individuals can

influence employee selection, and shed new light on why organizations often have difficulty implementing

more ‘‘rational’’ selection processes (e.g., Highhouse, 2008). Furthermore, we hope that this study stimulates

further investigation into of the broader question of how JDM phenomena are used by managers.

ACKNOWLEDGMENTS

We are grateful to Scott Highhouse for his helpful comments on an earlier version of this manuscript.

Portions of these data were presented at the Annual Conference of the Society for Industrial and

Organizational Psychology, April, 2007, New York, NY.

APPENDIX

Exercise Your Influence

Imagine that the organization for which you work, Handelman United Industries, is a multinational

conglomerate with a wide variety of holdings, from autos and auto finance to sugar and cement.

You are an executive in the automobile division. Your division has numerous locations throughout the

United States and Asia, including Detroit, Michigan; Pittsburgh, Pennsylvania; Birmingham, Alabama;

Omaha, Nebraska; and Osaka, Japan. Your group has been charged with the duty of selecting a plant manager

(PM) for the Omaha plant, which has recently been a trouble spot. Over the last 3 years, production costs have

been extremely high and there has been a good deal of labor strife (e.g., numerous work slowdowns, an

excessive number of grievances filed). The most recent Omaha PM was terminated, although by mutual

agreement the company stated that she left for a better job with another company.

Although Handelman United often promotes individuals from within the organization and does not have a

policy against accepting lateral transfers (e.g., a PM transferring to a different plant as a PM), there were no

internal applicants for the Omaha PM position. Thus, all of the candidates for the Omaha PM previously had

been working in a different organization.

An executive search firm was given the task of putting together a group of five viable candidates based on

amount and type of previous experience, degree of responsibility in current job, and recent career success.

The firm was able to narrow a rather large pool of candidates, whowere interviewed by the search firm. Based

on the exercises and interviews conducted by the search firm staff, they were able to produce overall scores

that included all of the areas of work beyond the amount of relevant experience. Therefore, the five remaining

candidates were deemed to be equal on all potential qualifications, with the exception of two: Years of

relevant experience, and the overall score on the battery delivered by the outside consultants. Below are the

years of relevant experience and the overall score assigned by outside consultants for the top two candidates

they recommend:

Candidate name Years of relevant experienceOverall score by outside

consultants (can range from 1–100)

A. Johnson 7 66L. Smith 5 80

Of the two candidates available, you have a strong preference for A. Johnson. You and A. Johnson went

actually to the same undergraduate university and shared some of the same interests outside of work. Johnson


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was a few years behind you, but you were friendly and you always liked each other. You always like to do a

favor for a friend if possible, because many of the opportunities you have gotten over the years have been

because of personal connections. You have always subscribed to the belief that what goes around, comes

around.

You and your colleagues have always decided to bring in three candidates for on-site interviews, and this

time is no exception. There are two clear superior candidates, and your goal is to convince the other

executives on your team to bring in a third candidate that makes A. Johnson look best. You do not want to

‘‘let on’’ that you know A. Johnson, because the other members of the selection committee would suspect

your bias. Thus, your task is to make A. Johnson look better by bringing in a third candidate that makes A.

Johnson look better than L. Smith.

If the following three candidates were also in the pool, and the numbers below represented their years of

experience and overall scores assigned by the search firm, which candidate would you try to convince your

colleagues to bring to the site for an interview: S. Bass, G. Frank, or M. Ellis?

Candidate name Years of relevant experienceOverall score by outside

consultants (can range from 1–100)

S. Bass 7 54G. Frank 4 80M. Ellis 4 54

Please mark and ‘‘X’’ on the line before the candidate you would bring in for an interview to make A.

Johnson seem like the best candidate.

_____ S. Bass _____ G. Frank _____ M. Ellis

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Authors’ biographies:

Jerel E. Slaughter is an Associate Professor of Management & Organizations in the Eller College of Management atUniversity of Arizona. He earned his Ph.D. in Industrial-Organizational Psychology at Bowling Green State University in


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2000. His research interests include recruitment and applicant attraction, personality and individual differences, andmanagerial decision making.

Edgar E. Kausel is a doctoral candidate in Department of Management & Organizations Department in the Eller Collegeof Management at University of Arizona. His research interests fall include judgment and decision making, and employeerecruitment and selection.

Miguel A. Quinones is the O. Paul Corley Distinguished Chair in Organizational Behavior at the Cox School of Businessat Southern Methodist University and is a Fellow of the Society for Industrial and Organizational Psychology and theAmerican Psychological Association. His research examines various aspects of individual and organizational effec-tiveness.


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the decoy effect as a covert influence tactic

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