THE ROLE OF INDIVIDUAL DIFFERENCES IN THE JOB CHOICE PROCESS
Shuang-Yueh Pui
A Dissertation
Submitted to the Graduate College of Bowling Green
State University in partial fulfillment of
the requirements for the degree of
DOCTOR OF PHILOSOPHY
August 2010
Committee:
Margaret E. Brooks, Advisor
Senthilkumar Muthusamy
Graduate Faculty Representative
Milton D. Hakel
Dale S. Klopfer
ii
ABSTRACT
Margaret E. Brooks, Advisor
Job choice process research focuses on how job seekers make judgments and decisions
regarding job positions. A job seeker can use one of two main types of decision strategies to
choose job(s): non-compensatory and compensatory. A non-compensatory decision strategy is
one where people choose an option using a few attribute(s). In compensatory decision strategy,
the decision-maker makes comparisons among all attributes when choosing an option. The
decision strategy a job seeker uses depends on two main factors: situation encountered and
personal characteristics. This paper examined whether choice set size (a situational
characteristic) and individual differences (a personal characteristic) affect people’s job choice
decision strategy. Results found that choice set size, and only one of the five individual
differences, need for cognition, affected decision strategy. In addition, there were interaction
effects between choice set size and two individual differences (i.e., maximizing tendency and
indecisiveness) to affect decision strategy. However, the interaction pattern for indecisiveness
was in the unexpected direction. These findings imply that job choice and decision-making
research should include individual difference variables to increase explanatory power in
understanding and predicting people’s decision strategy.
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I dedicate this dissertation to my loving husband, Adriano Sun, who have supported me through
the ups and downs of my graduate school years.
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ACKNOWLEDGMENTS
I would like to express my deepest gratitude to my advisor, Dr. Margaret E. Brooks, for
believing in me and providing guidance throughout my graduate career. She has been with me
since the beginning of my graduate school years. She was the one who always believed in me,
during times when I did not believe in myself. I am very much appreciative for her continuous
patience and encouragement, which led to the accomplishment of this dissertation.
I wish to express my sincere appreciation to my dissertation committee members, Dr.
Milton D. Hakel, Dr. Dale S. Klopfer, and Dr. Senthilkumar Muthusamy, for providing helpful
advice, whose insightful thoughts resulted in this well-thought out research project.
My gratitude is extended to my graduate colleagues in the Department of Psychology, for
their support and friendship. I am especially grateful to YoungAh Park and Michael Sliter for
reviewing final drafts of my dissertation. I am also thankful to Katherine Wolford and Shinakee
Gumber for their close friendship during my time in graduate school.
This dissertation is also dedicated to my parents, my sister, my brother, and my cousin,
Zuie. They have provided me with never-ending moral support and encouragement to achieve
my goals and overcome many obstacles. Their love, patience, and motivation are among the
most valuable assets in my life.
Last and most important, I am forever grateful to my dearest husband, Adriano Sun, for
his unconditional understanding, motivation, patience, support, love and encouragement
throughout our years together. Without him, I would not have completed my dissertation and my
doctoral degree.
.
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TABLE OF CONTENTS
Page
INTRODUCTION .................................................................................................................
1
CHAPTER I. THEORIES OF CHOICE...............................................................................
5
Expectancy Model .....................................................................................................
5
Soelberg’s Generalizable Decision Processing (GDP) Model...................................
6
Image Theory ............................................................................................................
9
CHAPTER II. INDIVIDUAL DIFFERENCES AND THE JOB CHOICE PROCESS.......
12
Need for Cognition ....................................................................................................
14
Decision-Making Style ..............................................................................................
16
Maximizing Tendency ...............................................................................................
18
Indecisiveness ............................................................................................................
20
CHAPTER III. METHOD ....................................................................................................
23
Participants
............................................................................................................
23
Measures
............................................................................................................
23
Need for Cognition ........................................................................................
23
Decision-Making Style ..................................................................................
23
Maximizing Tendency ...................................................................................
24
Indecisiveness ................................................................................................
24
Job Options
............................................................................................................
25
Choice Set Size Manipulation ....................................................................................
26
Procedure
............................................................................................................
26
Dependent Variable – Decision Strategy ...................................................................
27
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Data Analyses ............................................................................................................
28
CHAPTER IV. RESULTS ....................................................................................................
29
Intercorrelations among Study Variables...................................................................
29
Decision Strategy .......................................................................................................
29
The Effect of Choice Set Size on Decision Strategy .................................................
30
Relationships between Individual Differences and Decision Strategy ......................
30
Interactions between Choice Set Size and Individual Differences on Decision
Strategy
............................................................................................................
31
CHAPTER V. DISCUSSION ...............................................................................................
33
REFERENCES ......................................................................................................................
42
APPENDIX A1. TWELVE JOB OPTIONS ........................................................................
55
APPENDIX A2. SIX JOB OPTIONS ..................................................................................
56
APPENDIX B: INSTRUCTIONS FOR CHOICE SET CONDITIONS ...............................
57
APPENDIX C: SCREEN SHOTS OF WEB SURVEY ........................................................
58
APPENDIX D: POPULARITY OF TOP-RANKED JOBS ..................................................
66
APPENDIX E: IMPORTANCE RANKINGS OF JOB ATTRIBUTES ...............................
67
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LIST OF FIGURES/TABLES
Figure/Table
Page
Table 1
Intercorrelations among Study Variables.............................................................
Table 2
Means, Standard Deviations, and One-Way Analyses of Variance for the Effects of
Choice Set Size on Decision Strategy for Choice Task 1 and 2 ..........................
Table 3
............................................................................................................
52
Interaction between Maximizing Tendency and Choice Set Size on Decision Strategy
............................................................................................................
Figure 2
51
Moderated Linear Regression Analysis Summary for Interaction between Individual
Differences and Choice Set Size on Decision Strategy ......................................
Figure 1
50
Regression Analysis Summary for Individual Differences Predicting Decision
Strategy
Table 4
49
53
Interaction between Indecisiveness and Choice Set Size on Decision Strategy
............................................................................................................
54
1
INTRODUCTION
Job choice research focuses on how job seekers make judgments and decisions regarding
job positions. After more than three decades of research on the job choice process, there is still
very little understanding of how people actually choose jobs (Anderson, Born, & CunninghamSnell, 2002; Barber, 1998; Rynes, 1991). People’s decisions about how to evaluate job
opportunities have huge implications for their own career progress, so they would be well-served
by understanding this process better. Organizations could also benefit from an understanding of
how job seekers look for and choose a job, as this could help them tailor recruitment and
selection efforts to better attract and secure high quality candidates. An understanding of the job
choice process could also protect organizations’ human resource investments in recruiting efforts
because effective job choice research could provide data-driven recruitment direction to
organizations.
The job choice process is a well-known situation for most people; almost every adult has
had some experience looking for a job. Job seekers enter a job choice situation when they decide
to apply for a job. In the job choice process, job seekers have to make a series of decisions in a
multi-stage process. At the initial stage, job seekers start with what Barber (1998) called the
consideration set, at which point they evaluate and choose several jobs to apply to. When job
seekers are offered an interview, they decide whether or not they would like to interview for the
job. When the job seeker is offered the job, a final decision is made – to accept or reject the job
offer. As job seekers move through the stages of job choice, the size of the choice set typically
decreases. In the initial stage, job seekers face a relatively large consideration set, which is
narrowed down by both the job seeker (eliminating jobs from consideration) and the organization
2
(eliminating applicants from consideration). By the final stage, job seekers typically have a
smaller set of options.
In the job choice process situation, a job seeker faces one or more job options and
chooses job(s) using one of several decision strategies in his/her arsenal. Decision strategies can
be categorized as non-compensatory or compensatory. The non-compensatory decision strategy
is a strategy where people choose an option by focusing on one or two attribute(s) in options to
either include or exclude it from consideration. In this screening strategy, an option is screened
out when an attribute does not meet a minimum standard, and cannot be compensated by another
attribute that does meet the minimum standard. For example, a hiring manager screen out job
candidates who do not hold a Master’s degree regardless of the amount of experience they have.
Alternatively, people could use a compensatory decision strategy to make choices. In
compensatory decision strategy, the decision maker makes comparisons and trade-offs among
the attributes when choosing an option. This strategy is a ‘best’ option strategy where an option
is chosen because low levels of one attribute can be compensated with high levels of another
attribute. Using the same example as above, a hiring manager selects a job candidate because his
amount of job experience makes up for his lack of education (i.e., a Master’s degree).
The type of decision strategy a job seeker uses depends on the situation (i.e., decision
context) as well as personal characteristics. One element of the choice situation that could affect
decision strategy is the size of the job choice set. When faced with many job options, a job
seeker might first try to screen out unacceptable jobs to make the choice set more manageable
before making a comparison among the remaining jobs. Research suggests that job seekers are
more likely to use this kind of screening strategy than a ‘best’ job option strategy (i.e., strategy
that focuses on weighing all the attributes; the pros and cons of each job) when they have a large
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choice set (e.g., Osborn, 1990). A small choice set lends itself more to complex comparisons
among options. Job seekers tend to use a compensatory, ‘best’ job option strategy, rather than a
non-compensatory, screening strategy, when faced with a small choice set (e.g., Osborn, 1990).
Personal characteristics could also affect the type of decision strategy used when
evaluating and selecting a job. For example, job seekers with high need for cognition might have
a higher propensity to thoroughly process information than those with low need for cognition.
Those with high need for cognition might be more likely to search for more jobs and more
comfortable in making comparisons among a larger set of jobs (a compensatory strategy), than
those with low need for cognition, even when faced with a large choice set. Conversely, job
seekers with low need for cognition might have a lower tendency to deliberate carefully before
deciding on a job than those with high need for cognition. They could be more likely to screen
out unacceptable options (a non-compensatory strategy), even when the choice set is small.
Most research on the job choice process has focused on situation; the role of individual
differences has received considerably less attention in the literature. Despite calls for the
inclusion of person variables (i.e., applicant characteristics, individual differences) in recruitment
and job choice research (Anderson et al., 2002; Rynes, 1991), empirical work has done little to
examine important person variables that affect job choice (e.g., Chapman, Uggerslev, Carroll,
Piasentin, & Jones, 2005). Further, the “attention to the use of individual differences to predict
decision behavior is almost nil in the judgment and decision making literature…” (Highhouse,
2001, p. 326). The bulk of job choice studies emphasize the effect of situational variables (e.g.,
job attributes, organizational characteristics, and recruiter characteristics), rather than individual
differences, on the jobs that applicants choose.
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The purpose of the present study is to examine the effect of individual differences in job
choice. In addition, this study examines how individual differences interact with choice set size,
a type of situational characteristic. The inclusion of individual differences in this investigation
fills a gap in the job choice literature. This paper begins with a description of existing theories of
choice, specifically emphasizing the effect of choice set size on decision strategy used. Then, the
paper describes and predicts the effects of several decision-making individual difference
variables on how people choose among jobs.
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CHAPTER I: THEORIES OF CHOICE
Job choice researchers seek to understand and predict people’s choices among jobs.
Critical to this endeavor are questions such as: Do job seekers choose jobs that are best on one or
two most important attributes or do they weigh many attributes to come up with the best job?
Can a favorable job attribute (e.g., friendly coworkers) make up for an unfavorable one (e.g.,
long commute)? Decision making theories are well-suited to studying these questions. A few
theoretical models of choice behavior may shed light on the job choice process: Expectancy
model (Vroom, 1964; Edwards, 1954), Soelberg’s generalizable decision processing model
(Soelberg, 1967), and Image theory (Beach & Mitchell, 1987). The next section describes and
provides empirical evidence for each model.
Expectancy Model
The most popular model of job choice is the expectancy model, which states that
decisions are a function of the probability of obtaining a job offer and the attractiveness of the
job offer. Theories that fall under the expectancy model include Expectancy theory (Vroom,
1964) and Subjective Utility theory (Edwards, 1954). The expectancy model is classified as a
compensatory model in which a job is acceptable as long as an unfavorable attribute (e.g., mean
supervisor) can be compensated by a favorable attribute (e.g., supportive coworkers).
In general, empirical studies assessing the appropriateness of expectancy models in
explaining job choice process have found support for predictions based on the model (e.g.,
Vroom, 1966). In one study, 49 graduating students identified the relative importance of 15 job
attributes (e.g., stable and secure future) and rated the likelihood that the value of these attributes
could be obtained for jobs in three organizations (Vroom, 1966). An attractiveness score was
calculated by combining the valence and instrumentality ratings. The students also rank-ordered
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and rated the attractiveness of the three jobs. Attractiveness scores derived from the expectancy
formula were strongly related to the subjective assessments of attractiveness. Additionally, the
calculated attractiveness scores effectively predicted the students’ actual job choices. Results in
this study suggested that people tend to use a compensatory strategy (i.e., a combination model
of valence and instrumentality ratings) when choice set size is small (i.e., three jobs). A review
of the usefulness of expectancy models to predict job choice concluded that expectancy models
are predictive of attraction to jobs as well as actual job choice (Wanous, Keon, & Latack, 1983).
Despite the positive support found for expectancy models, other researchers have argued
that the expectancy model is not an accurate reflection of the job choice process (e.g., Baker,
Ravichandran, & Randall, 1989; Beach & Mitchel, 1987). These researchers stated that job
seekers are not always highly rational decision makers, who weigh information on a large
number of job attributes; rather, they tend to make decisions based on only a few job attributes.
In fact, job seekers rarely generated more than two options for simultaneous consideration (e.g.,
Reynolds, 1951). In addition, job options are not always evaluated in a compensatory manner.
Many jobs predicted to be chosen by expectancy theory formulas were not even included in the
job choice set because they did not meet minimum requirements on the most important attributes
(e.g., Osborn, 1990). Thus, these studies suggest that decision makers could be using a noncompensatory strategy to make job choices. In non-compensatory models, not meeting minimum
requirements on one attribute cannot be compensated for by another favorable attribute.
Soelberg’s Generalizable Decision Processing (GDP) Model
One alternative to the expectancy model is Soelberg’s Generalizable Decision Processing
(GDP) model (Soelberg, 1967), which states that decision makers choose the first option that
meets their minimum requirements on a few criteria rather than search for an option that
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maximizes the utility of all criteria. The GDP model, like other satisficing models (e.g., March &
Simon, 1958), suggests that the decision maker looks for a minimally acceptable option instead
of an optimal option. The GDP model and satisficing models are characterized as types of noncompensatory decision-making process. This view fits with the cognitive psychology perspective
that decision makers have cognitive limitations when faced with a large amount of information
and, thus, choose options in which a few most important attributes meet minimum requirements.
In a job choice study examining the appropriateness of the GDP model, 20 business
graduates were interviewed on their job choice processes (Soelberg, 1967). Content analyses of
the interviews revealed that job seekers used non-compensatory strategies (i.e., choose options
based on one or two most important attributes), rather than compensatory strategies. A review of
the Soelberg’s GDP model noted that there are only a few empirical examinations and those
studies did not provide positive support for the model (Power & Aldag, 1985). In addition, these
studies that tested the Soelberg’s GDP model suffered from weak methodologies (Power &
Aldag, 1985).
One study compared the two general decision strategies of choice: compensatory and
non-compensatory (Sheridan, Richards, & Slocum, 1975). Specifically, the study compared the
two decision strategies in the job choice process among 49 nursing graduates in a longitudinal,
field design. The study examined whether job seekers made initial implicit favorite choices based
on many or a few attributes. Results suggested that job seekers identified initial implicit favorite
choices and were most likely to accept those implicit favorite jobs, providing support for the
non-compensatory strategy. Contrary to the non-compensatory strategy, results showed that
initial choices were made based on many, as opposed to a few attributes. Further, job seekers
continued to look for more job options after making their initial choice, which suggests that they
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were generating more options for comparison, providing support for the compensatory strategy
instead. Results did not provide any insight as to the influence choice set size might have on
decision strategy used because the study did not keep track of data related to choice set size.
Other studies have compared compensatory and non-compensatory models of decision
strategies in the larger context of the judgment and decision making literature (e.g., Billings &
Marcus, 1983; Johnson & Meyer, 1984; Mills, Meltzer, & Clark, 1977; Timmermans, 1993).
These studies were more promising in providing insights to the effect of choice set size on
decision strategy. Billings and Marcus (1983) examined the effect of information load on
decision strategy (compensatory and non-compensatory) in 48 psychology undergraduates. They
manipulated information load through the absence or presence of time pressure during an
apartment decision task and examined participants’ search behavior on an information board.
Findings indicated that participants used the compensatory strategy under low information load
and the non-compensatory strategy under high information load. Other studies operationalized
information load as choice set size (i.e., number of options) and number of attributes (e.g.,
Einhorn, 1971; Olshavsky, 1979; Payne, 1976; Timmermans, 1993). These studies found that
people tend to use non-compensatory strategies when they had a large choice set, but not
necessarily when they had a large number of attributes (Einhorn, 1971; Payne, 1976). More
interestingly, participants switched from a single-stage decision process to a two-stage decision
process as the choice set size increased (Olshavsky, 1979). These findings suggested that both
compensatory and non-compensatory strategies are processes that decision makers (e.g., job
seekers) use when deciding among options (e.g., job offers), but are used when faced with
different choice set size at different stages in the decision process.
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Image Theory
Image theory (Beach & Mitchell, 1987, 1998; Beach, 1996), a type of naturalistic
decision-making model, classifies decision-making as a two-stage process: screening and choice.
In the screening stage, options are screened based on whether they are compatible with the
decision makers’ standards. A compatibility test is conducted, in which the decision maker
screens out options that do not meet a minimum requirement on a few characteristics, a process
analogous to the non-compensatory decision making process. If only one option survives the
screening, then that last option will be the choice. If more than one option survives the screening,
the decision makers move to the second stage in which a choice is made. In the choice stage, a
profitability test is conducted, in which the best option is chosen by comparing the sum of
judgments for all available options, a process conceptually similar to the compensatory decision
making process.
Many studies provided empirical support for image theory in decision making (e.g.,
Beach, Smith, Lundell, & Mitchell, 1988; Beach & Strom, 1989; Beach & Potter, 1992; Mills,
Meltzer, & Clark, 1977; Potter & Beach, 1994; Rediker, Mitchell, Beach, & Beard, 1993). In job
choice, the studies examining image theory found similar results to those in the decision making
literature (Beach & Strom, 1989; Osborn, 1990). Beach and Strom (1989) instructed 16
undergraduate students to imagine they were seeking a job after graduating with an MBA degree.
Participants screened a large choice set of 14 available jobs and decided which jobs were
acceptable and unacceptable. Each job description had 16 job attributes, presented on 16
sequential pages of a booklet; one booklet was provided for each job. The job attributes
presented either violated (has a negative statement) or did not violate (has a positive statement)
the job seeker’s 16 standards. Results showed that job seekers rejected a job after examining an
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average of four violated job attributes, suggesting that four violations is the rejection threshold.
Results also suggested that non-violations played no role at all in screening job options; that is,
non-violations of job attributes did not balance out the violations when evaluating the
acceptability of job options. These results supported image theory’s first step, in which the
screening process predominantly relies on violations of standards. Results also suggested that
people tend to use a non-compensatory (i.e., screening) decision strategy when faced with a large
choice set (i.e., 14 jobs).
A second study (Osborn, 1990) that provided further empirical support for image theory
predictions was conducted on 96 graduating students seeking for jobs in a longitudinal, field
design. They completed several surveys throughout their job search process. They provided their
minimum standards for job attributes and indicated the importance of each job attribute in
evaluating jobs. For each interview that the students had, they stated whether the job met their
standards, whether the job was acceptable (yes/no), and rated the acceptability of the job (on a
scale of 1 = not acceptable to 7 = acceptable). After one month, students were presented with all
the jobs they interviewed for and asked to rank order those jobs. They were also asked to choose
one acceptable job. Results showed that in all of the 200 jobs that were rejected during the
screening process, one or more attributes had violated minimum standards, consistent with the
screening process of image theory predictions. In addition, Osborn tested whether a
compensatory strategy could explain the job seekers’ choice by summing the products of
importance ratings and ratings of acceptability across all job attributes. Results showed that for
97% of students, jobs that the compensatory strategy predicted as attractive were rated as
unacceptable, failing to meet minimum standards on one or more attributes. This finding was
also consistent with the screening process of image theory. Osborn also examined whether
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importance of job attributes changes throughout the job choice process. Image theory, in the twostage decision process, predicts that information used in the screening stage should not impact
decision making in the choice stage. Findings indicated that the most important job attributes in
the screening stage had no impact when job seekers made their final job choice, supporting the
image theory prediction.
This study adopted image theory’s notion of the two-stage decision process to explain
how job seekers choose among job options. Specifically, it examined the decision strategies (i.e.,
non-compensatory or compensatory) job seekers use when faced with different choice set size:
large versus small. In the initial stage, job seekers typically have a large choice set of jobs to
evaluate and choose to interview for. Previous literature on information/cognitive load suggested
that people tend to use non-compensatory decision strategies when making a decision in a large
choice set (e.g., Einhorn, 1971; Mills, Meltzer, & Clark, 1977; Payne, 1976; Timmermans,
1993). Hence, this research predicted that people use a non-compensatory decision strategy when
choice set size is large. On the contrary, job seekers typically have a small set of job offers to
consider and accept at the final stage of the job choice process. In situations with a small choice
set, people tend to use compensatory decision strategies (e.g., Mills, Meltzer, & Clark, 1977;
Payne, 1976; Timmermans, 1993). Hence, people use a compensatory decision strategy when
choice set size is small. Therefore, the following hypothesis is proposed:
Hypothesis 1: There is a relationship between choice set size and decision strategy, such
that people are more likely to use a non-compensatory decision strategy than a
compensatory decision strategy when choice set size is large, and people are more likely
to use a compensatory decision strategy than a non-compensatory decision strategy when
choice set size is small.
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CHAPTER II: INDIVIDUAL DIFFERENCES AND THE JOB CHOICE PROCESS
This section describes several individual differences that may play a role in the decision
strategies people adopt when choosing among jobs. Individual differences have been largely
ignored in the job choice literature. The few studies that examined individual differences in the
job choice literature focused mainly on what, rather than how, information was used in making a
job choice (Lancaster, Colarelli, King, & Beehr, 2001; Olian, 1981; Saks, Weisner, & Summers,
1994).
The scarcity of studies examining the role of individual differences in the job choice
process stems, in part, from the emphasis theories of judgment and decision making place on
situational determinants (as opposed to individual differences). Major theories of decision
making, such as subjective expected utility theory (Savage, 1954) and prospect theory
(Kahneman & Tversky, 1979), tend to leave out individual difference variables; that is, variance
attributed to individual differences is considered error. In subjective expected utility theory
(Savage, 1954), decision makers have subjective values of a choice option, but the theory ignores
individual differences as a variable influencing the subjective values people hold and ultimately
the decisions that people make. Similarly, prospect theory (Kahneman & Tversky, 1979)
explains preferences people have between risky decision options, but the theory also ignores
individual difference variables affecting people’s preferences for a decision option.
Most theoretical frameworks relevant to job choice are theories of decision making;
Expectancy model, Soelberg’s GDP model, and Image theory generally ignore individual
difference as a contributing variable. Expectancy model, the most commonly used theory in job
choice, predicts that job choices are a function of the probability of obtaining a job offer and the
value or attractiveness of the job offer. Although the expectancy model takes into account the
13
differences job seekers have in subjective value ratings, it does not include individual difference
variables (e.g., need for cognition) that may also affect job choice. Soelberg’s GDP model
predicts that all job seekers use minimum requirements when choosing among jobs. Although
different job seekers have different minimum requirements, no individual difference variables
were included in the theory. As an extension of the expectancy model and the GDP model, image
theory also ignores individual differences as important variables in job choice. In image theory,
all job seekers employ the two-stage decision process, by first screening their job options and
then choosing from the surviving job options. Decision strategy selection is seen as a result of
situational characteristics, ignoring any effect individual difference variables may have on job
choice. These theories discounted individual differences as potentially useful variables in
predicting job choice. Theories of choice need to begin incorporating individual difference
variables to first examine whether these variables are important predictors of job choice.
Decision making depends on both the situation encountered (e.g., time pressure) and
person characteristics (e.g., personality traits) (Einhorn, 1970). The vast majority of studies on
decision-making and job choice have emphasized the first factor, whereas person characteristics
have gone largely unattended. Indeed, it has been suggested that individual differences (e.g.,
cognitive ability) could explain discrepant findings in classic decision theories (e.g., Stanovich,
1999). Therefore, the next major step in advancing understanding of job choice, as well as
theories of decision-making, is to integrate person characteristics (i.e., individual differences)
when examining people’s decision making. In the present study, I focus specifically on
examining individual differences related to the decision-making process because these variables
are most likely to affect people’s decision-making. This study will not only examine the
influence of decision-making individual differences on decision strategy, it will also examine
14
whether these individual differences interact with a situational variable, choice set size, to impact
decision strategy. The next subsections review four decision-making individual difference
variables that may affect a job seeker’s job choice process: (1) Need for cognition, (2) Decisionmaking style, (3) Maximizing tendency, and (4) Indecisiveness.
Need for Cognition
Need for cognition is defined as a tendency in people to engage in and enjoy effortful
thinking (Cacioppo & Petty, 1982). Different from cognitive ability, need for cognition is
considered a cognitive style rather than ability. People with high intrinsic motivation to process
information and engage in effortful cognitive activities are known as having high need for
cognition, while those with low intrinsic motivation to expend effort for cognitive tasks have low
need for cognition. Although need for cognition is a concept typically examined in the
persuasion and attitude change literature, the concept has recently begun to appear in the
decision making literature (e.g., Smith & Levin, 1996). Of the studies that have examined the
influence of need for cognition on decision making, most have focused on framing effects, with
less emphasis on information search behavior, choice, and biases. Unfortunately, no studies were
found examining the relationship between need for cognition and decision making strategy.
People with high levels of need for cognition expend more relevant thoughts and process
more information in decision making tasks than do those with low levels of need for cognition
(e.g., Verplanken, Hazenberg, & Palenewen, 1992). Making a choice using a compensatory
decision strategy requires more cognitive effort compared to a non-compensatory decision
strategy. People high in need for cognition might be more likely to use a decision strategy that
requires more cognitive effort because they enjoy engaging in effortful thinking. Therefore, it is
expected that:
15
Hypothesis 2: Need for cognition is related to decision strategy, such that people with
high scores on need for cognition are more likely to use a compensatory decision strategy
than are those with low scores on need for cognition.
This study also examined the role individual differences play in the relationship between
choice set size and decision strategy. Specifically, does need for cognition moderate the effect of
choice set size on decision strategy? When choice set size is large, choosing an option would
require more cognitive effort as compared to choosing an option in a small choice set. In
addition, using a compensatory decision strategy would also require a lot of cognitive effort.
People with high need for cognition would expend more effort to process information and
compare more information among jobs when choosing a job in a large choice set as compared to
those with low need for cognition. As such, it is expected that people with high need for
cognition would be more likely to use a compensatory decision strategy when choosing a job in a
large choice set than those with low need for cognition. However, this difference in decision
strategies would not be expected in a small choice set because both people with high and low
need for cognition are expected to use a compensatory decision strategy. In small choice set,
even a compensatory decision strategy does not require much cognitive effort because the
number the options are smaller than in a large choice set. Hence, the following hypothesis is
proposed:
Hypothesis 3: There is an interaction between scores on need for cognition and choice
set size on decision strategy, such that people with high scores on need for cognition are
more likely to use a compensatory decision strategy than are those with low scores on
need for cognition in a large choice set, and both people with high and low scores on
16
need for cognition are more likely to use a compensatory decision strategy in a small
choice set.
Decision-making Style
Decision-making style is a difference in people’s learned response patterns when faced
with a decision (Scott & Bruce, 1995). Of the five distinct decision-making styles these
researchers measure, Rational and Intuitive decision-making styles are most relevant to the
decision making process; these two decision-making styles are also most closely related to
modes of cognitive processing (i.e., Type I and Type II systems; Hammond, Hamm, Grassia, &
Pearson, 1997). Rational decision-making style is the logical and analytical thinking process
people use when making a decision, while Intuitive decision-making style is the process that uses
hunches and feelings. Rational and Intuitive decision-making style are orthogonal with each
other, and a person can have high (or low) levels of both types of decision-making style. People
with rational decision-making style tend to approach rather than avoid decision situations,
engaging in more logical, step-by-step, calculative cognitive processing. Those with high rational
decision-making style are more likely to utilize an analytical strategy of comparing all options
when making a decision (i.e., a compensatory decision strategy). It is expected that people with
high levels of rational decision-making style would be more likely to choose an option by using a
compensatory decision strategy than a non-compensatory one.
On the other hand, people with intuitive decision-making style tend to make “gut
feeling,” heuristic-based decisions. Non-compensatory decision making is fundamentally a
heuristic-based decision making process in that decision makers only focus on a small number of
attributes as heuristics in order to make quicker choices. As such, it is expected that people with
high levels of intuitive decision-making style would be more likely choose an option using a
17
non-compensatory decision strategy than would those with low levels of intuitive decisionmaking style. Thus, the following hypotheses are offered:
Hypothesis 4: Rational decision-making style is related to decision strategy, such that
people with high scores on rational decision-making style are more likely to use a
compensatory decision strategy than are those with low scores on rational decisionmaking style.
Hypothesis 5: Intuitive decision-making style is related to decision strategy, such that
people with high scores on intuitive decision-making style are more likely to use a noncompensatory decision strategy than are those with low scores on intuitive decisionmaking style.
Decision-making style could moderate the influence choice set size has on the job choice
process. Although a few studies have examined the influence of decision-making style on
framing effects (Shiloh, Salton, & Sharabi, 2002) and decision confidence (Phillips, Pazienza, &
Ferrin, 1984), no studies were found relating decision-making style to the decision making
process.
In a large choice set, people with high levels of rational decision-making style would tend
to use an analytical, compensatory decision strategy compared to those with low levels of
rational decision-making style. This more effortful, compensatory decision strategy is adopted
because an analytical decision style is the learned response pattern for people with high levels of
rational decision-making style. Similar to the hypothesis about need for cognition, people with
both high and low levels of rational decision-making style are expected to use a compensatory
decision strategy when choice set size is small. For intuitive decision-making style, the reverse
finding is expected; people with high levels of intuitive decision-making style would be more
18
likely than those with low levels to use a heuristic-based, non-compensatory decision strategy
when choice set size is small. This decision strategy is adopted because a heuristic-based
decision strategy is the preferred mode of those with high intuitive decision-making style. In a
large choice set, people with both high and low levels of intuitive decision-making style would
use a non-compensatory decision strategy. Therefore, it is hypothesized that:
Hypothesis 6: There is an interaction between scores on rational decision-making style
and choice set size on decision strategy, such that people with high scores on rational
decision-making style are more likely to use a compensatory decision strategy than are
those with low scores on rational decision-making style in a large choice set, and both
people with high and low scores on rational decision-making style are more likely to use
a compensatory decision strategy in a small choice set.
Hypothesis 7: There is an interaction between scores on intuitive decision-making style
and choice set size on decision strategy, such that both people with high and low scores
on intuitive decision-making style are more likely to use a non-compensatory decision
strategy in a large choice set, and people with high scores on intuitive decision-making
style are more likely to use a non-compensatory decision strategy than are those with low
scores on intuitive decision-making style in a small choice set.
Maximizing Tendency
The term “satisfice,” coined by Herbert Simon (1955), describes a decision-making
strategy that strives for adequacy, rather than maximizing utility. People generally satisfice, due
to limitations in the human information processing capacity, by evaluating options until they
encounter one that exceeds their minimally acceptable standard. More recently, satisficing
(maximizing) has been conceptualized as an individual difference or trait (Schwartz, Ward,
19
Monterosso, Lyubomirsky, White, & Lehman, 2002). Maximizing tendency refers to differences
in people to seek optimality. Low scores on the maximizing tendency scale would reflect
people’s tendency to satisfice. A maximizing tendency scale validation study, consisting of 401
college students, found that higher scores on maximizing tendency related to more product
comparisons, more social comparisons, and considered more products when purchasing a
product (Schwartz et al., 2002).
Given that this individual difference variable was rooted in the decision making
literature, it is expected that maximizing tendency could influence differences in people’s
decision making, specifically job choices. People with high maximizing tendency would be more
likely to evaluate all their options in order to optimize their choices. The compensatory decision
strategy is a strategy that emphasizes comparison of options in order to optimize one’s options.
On the contrary, the non-compensatory decision strategy reflects the satisficing of options,
choosing the first option that meets minimum standards. Hence, it is expected that people with
high levels of maximizing tendency would be more likely to use a compensatory decision
strategy regardless of the number of choices presented to them. The following main effect
hypothesis is proposed:
Hypothesis 8: Maximizing tendency is related to decision strategy, such that people with
high scores on maximizing tendency are more likely to use a compensatory decision
strategy than are those with low scores on maximizing tendency.
Maximizing tendency could moderate the effect of choice set size on decision strategy. It
is expected that people with high levels of maximizing tendency would be more likely to
evaluate all jobs and use a compensatory decision strategy to choose a job in a large choice set
compared to those with low maximizing tendency. In a small choice set, this difference in
20
decision strategies would not be expected because both people with high and low maximizing
tendency are expected to use a compensatory decision strategy. Therefore, the following
hypothesis is proposed:
Hypothesis 9: There is an interaction between scores on maximizing tendency and
choice set size on decision strategy, such that people with high scores on maximizing
tendency are more likely to use a compensatory decision strategy than are those with low
scores on maximizing tendency in a large choice set, and both people with high and low
scores on maximizing are more likely to use a compensatory decision strategy in a small
choice set.
Indecisiveness
Indecisiveness is the general tendency to experience difficulties in making decisions
(Germeijs & De Boeck, 2002). Researchers differentiate indecisiveness from indecision, stating
that indecision is the difficulty in choosing an option in a specific situation (e.g., career
indecision), whereas indecisiveness is the difficulty in choosing or making decisions in more
than one situation - implying that indecisiveness is an individual difference variable. When faced
with difficult decisions, people high in indecisiveness took longer to make decisions compared to
those low in indecisiveness (e.g., Ferrari & Dovidio, 2000; Frost & Shows, 1993). When making
a difficult decision, indecisives are more likely to search for more information as a way to
overcome their indecision. Indeed, when a sample of 130 college students were asked to search
information about college courses on an information board, those with high levels of
indecisiveness searched for more information on the chosen option and made more withinattribute comparisons than did those with low levels of indecisiveness (Ferrari & Dovidio, 2000).
Although indecisive people took longer to make a decision (Ferrari & Dovidio, 2000), they were
21
more likely to choose options by focusing on one attribute at a time, similar to a noncompensatory decision strategy. It is expected that people high in indecisiveness are more likely
to use a non-compensatory decision strategy than those with low levels of indecisiveness. The
hypothesis for indecisiveness is as follows:
Hypothesis 10: Indecisiveness is related to decision strategy, such that people with high
scores on indecisiveness are more likely to use a non-compensatory decision strategy
than are those with low scores on indecisiveness.
The larger the number of options in a choice set, the higher the difficulty in evaluating
that decision, and this in turns increases the cognitive load. A large choice set has higher decision
difficulty, and higher cognitive load, than a small choice set. People with high indecisiveness
should find making a choice in a large choice set to be more difficult than should people with
low indecisiveness. Under high cognitive load conditions (operationalized as distracter tasks),
people high on indecisiveness searched less overall information (Study 1, N=58) and were more
likely to make within-attribute comparisons compared to those with low indecisiveness (Study 2,
N=100; Ferrari & Dovidio, 2001). This suggests that when people high in indecisiveness face
high cognitive load conditions, such as in a large choice set, they adopt a non-compensatory
decision strategy. Because people in general are expected to use a non-compensatory strategy in
a large choice set, people both high and low in indecisiveness are expected to use a noncompensatory strategy. Under low cognitive load conditions, such as when choice set size is
small, people high in indecisiveness would be expected to continue using a non-compensatory
decision strategy compared to those low in indecisiveness. The following hypothesis is
presented:
22
Hypothesis 11: There is an interaction between scores on indecisiveness and choice set
size on decision strategy, such that both people with high and low scores on
indecisiveness are more likely to use a non-compensatory decision strategy in a large
choice set, and people with high scores on indecisiveness are more likely to use a noncompensatory decision strategy than those with low scores on indecisiveness in a small
choice set.
23
CHAPTER III: METHOD
Participants
Undergraduate students (N= 309; 67% females) from a Midwestern university
participated in this study, which was part of a larger study. Students were recruited from
introductory psychology courses and given extra credit for their participation. Participants were
between 18 and 40 years of age, with a mean age of 19.26 (SD=1.86). Among the students who
participated, majority of them were Caucasian (85.8%), with the remaining identifying
themselves as African Americans (8.1%), Hispanic Americans (2.3%), Asian Americans (.6%),
and ‘Other’ designations (2.9%). In this sample, majority of the students were freshmen (66.7%),
with the remaining sample identified as sophomores (19.4%), juniors (7.4%), and seniors (5.5%).
Measures
Need for cognition. The Need for Cognition scale (NFC; Cacioppo, Petty, & Kao, 1984)
measured the tendency in people to engage in and enjoy thinking. The short-form NFC scale has
18 items with adequate internal consistencies in this study (alpha = .86). A sample item from this
scale is: “I would prefer complex to simple problems.” Responses were made on a 5-point Likert
scale ranging from 1 (strongly disagree) to 5 (strongly agree), with higher scores reflecting
higher levels of need for cognition. Need for cognition mean item scores ranged from 1.83 to
4.56.
Decision-making Style. The Rational (4 items) and Intuitive (5 items) subscales of the
Decision-Making Style (DMS; Scott & Bruce, 1995) were used to assess analytic and intuitive
decision-making style, respectively. Participants were asked to respond on the degree to which
they agree with statements describing how they make important decisions. Responses were made
on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). A sample item
24
for each subscale is as follows: “I make decisions in a logical and systematic way” for Rational;
“When making decisions, I rely upon my instincts” for Intuitive. Rational DMS mean item
scores ranged from 2.25 to 5 and Intuitive DMS mean item scores ranged from 2 to 5. The
internal consistencies (coefficient alpha) in this study were .74 for the Rational subscale and .77
for the Intuitive subscale. The Rational and Intuitive DMS subscales had a non-significant
correlation (r = .05, n.s.). Further, factor analysis using maximum likelihood extraction with
Oblimin rotation was conducted. The results showed that two factors were extracted, accounting
for 55.51% of variance in the responses. The factor loadings of the rotated matrix showed that all
the Intuitive DMS items loaded on the first factor while all the Rational DMS items loaded on
the second factor.
Maximizing Tendency. The Maximizing Tendency scale (Diab, Gillespie, & Highhouse,
2008) was used to measure people’s tendency to maximize. This 9-item scale (“No matter what it
takes, I always try to choose the best thing”) was responded on a 5-point Likert-type scale (1 =
strongly disagree to 5 = strongly agree), with higher scores reflecting higher levels of
maximizing tendency. Maximizing tendency mean item scores ranged from 2 to 5. Adequate
internal consistencies of .73 were found for the Maximizing Tendency scale in this study.
Indecisiveness. The General Indecisiveness scale (Germeijs & De Boeck, 2002) was
used to measure people’s tendency to be indecisive when making a decision. This 22-item scale
was responded on a 5-point Likert-type scale (1 = strongly disagree to 5 = strongly agree), with
higher scores reflecting higher levels of indecisiveness. Indecisiveness mean item scores ranged
from 1.23 to 4.64. A sample item is: “It’s hard for me to come to a decision.” The internal
consistency of the General Indecisiveness scale in this study was .90.
25
Job Options
Twelve job options were created for this study, each including statements about four job
attributes: a statement about interaction with others, a statement about opportunities to learn, a
statement about the salary and benefits of the job, and a statement about supervising others. For
each job attribute, three levels of statements were used (i.e., high, medium, low). All twelve job
options (4 job attributes X 3 levels) are shown in Appendix A1. Six job options were designated,
in an a priori manner, to be compensatory jobs, and six other job options were designated to be
non-compensatory jobs. The six compensatory jobs were created to have one low-level attribute,
one high-level attribute, and two medium-level attributes. These jobs were created based on the
idea that people using a compensatory strategy would choose these jobs because a low-level
attribute could be compensated by a high-level attribute. Jobs A to F in Appendix A1 are the
designated compensatory jobs. The six non-compensatory jobs were created to have two lowlevel attributes and two high-level attributes. These jobs were created on the notion that people
who use a non-compensatory strategy choose jobs by focusing on 1 or 2 attributes. That is, they
would choose or eliminate jobs with extreme values on only 1 or 2 attributes. Jobs G to L in
Appendix A1 are the designated non-compensatory jobs. Job options were created such that the
valence of the job across the four job attributes was equal for all jobs. This was done so that
when attribute importance is held constant, no job option dominated any other job option in the
set. This ensures that choices among job options could be due only to differences found in
participants’ subjective attribute importance. For the small choice set, six jobs out of the twelve
created were randomly selected such that three jobs (Jobs A, D, and E) were compensatory jobs
and three jobs (Jobs H, I, and K) were non-compensatory jobs (see Appendix A2).
26
Choice Set Size Manipulation
Choice set size (large=12 jobs; small=6 jobs) was manipulated in a within-subject
manner, such that all participants were presented with both large and small choice sets. The order
in which the choice set is presented was manipulated in a between-subjects manner, such that
half of the participants were presented with the large choice set first (large choice set, then small
choice set) and the other half were presented with the small choice set first (small choice set,
then large choice set). Instructions for the Large-to-Small condition and the Small-to-Large
condition are shown in Appendix B.
Procedure
Participants were recruited from a pool of undergraduate psychology students using a
university-based online recruiting system. Students interested in participating in the study clicked
on a web link and were directed to a web-based survey (see Appendix C). This study was
published online using the web-based survey software, Perseus.
Once participants were directed to the web-based survey, they were asked to provide
consent for participating in this study. As part of the larger study, participants rated the four job
attributes (i.e., interaction with others, opportunities to learn, salary and benefits, and,
supervising others) on the importance of each job attribute when choosing a job (from 1 = not at
all important to 5 = extremely important). Participants also rank ordered the four job attributes
on the importance of each attribute, with 1 being the most important and 4 the least important
attribute. Then, participants responded to several individual difference measures: Need for
Cognition, Decision-making Style, Maximizing Tendency, and Indecisiveness. The order of
administration was counterbalanced, such that half of the participants responded to the Need for
Cognition measure first (i.e., Need for Cognition, Decision-making Style, Maximizing
27
Tendency, and Indecisiveness), and the other half of the participants responded in the reverse
order (i.e., Indecisiveness, Maximizing Tendency, Decision-making Style, Need for Cognition).
Participants also responded to the 50-item International Item Personality Pool as a measure of the
Big Five personality traits as part of a larger study.
At this point, participants were randomly assigned to one of the two conditions: (1)
Large-to-Small choice set, or (2) Small-to-Large choice set. In these conditions, participants
imagined that they were in the last semester of college and they were seeking a job to begin after
they graduated. In all cases, they chose five jobs and rank ordered them. Then, participants
responded to an open-ended question about the decision strategies they used to make their
choices. As part of a larger study, participants also responded to a Decision Strategy Scale
(Zakay, 1990). Finally, participants were asked to provide information about themselves, such as
sex, ethnicity, age, year in college, and major. As part of a larger study, participants also reported
their ACT scores and cumulative college GPA, and provided their email address to be contacted
in 2 weeks for a follow-up data collection.
Dependent Variable –Decision Strategy
To determine whether each participant was using a compensatory or non-compensatory
strategy, their top three choices in both the large and small choice set were examined. When the
top three choices were all non-compensatory jobs, their decision strategy was coded a 1; when
two of the top three choices were non-compensatory jobs, the decision strategy was coded a 2;
when two of the top three choices were compensatory jobs the decision strategy was coded a 3;
and when all top three choices were compensatory jobs, the decision strategy was coded a 4.
Thus, higher scores on this variable indicated a more compensatory decision strategy, and lower
scores indicated a more non-compensatory decision strategy.
28
Data Analyses
To test for Hypothesis 1, a one-way Analysis of Variance (ANOVA) was conducted to
examine the effect of choice set size (large, small) on decision strategy. Two decision strategy
variables were created: one for the first choice task (either large or small) presented and one for
the second choice task (large or small) presented. Therefore, two one-way ANOVAs conducted
for the two choices people made.
To test for the main effect hypotheses (Hypothesis 2, 4, 5, 8, 10), multiple linear
regressions were conducted to test the relationship between scores on individual difference
scores (Need for Cognition/Decision-making Style/Maximizing Tendency/Indecisiveness) and
decision strategy.
To test for the interaction hypotheses (Hypothesis 3, 6, 7, 9, 11), moderated linear
regressions were conducted to test the interaction between choice set size (large, small) and
scores on individual difference scores (Need for Cognition/Decision-making Style/Maximizing
Tendency/Indecisiveness) on decision strategy. Specifically, choice set size and the individual
difference variables were entered in Step 1, and the interaction terms between the choice set size
and individual difference variables were entered in Step 2.
29
CHAPTER IV: RESULTS
Intercorrelations among Study Variables
The intercorrelations among all the study variables, including variables that are part of
the larger study, are presented in Table 1. As shown in Table 1, need for cognition was found to
be significantly related to all the individual differences in this study: Rational decision-making
style (r = .24), Intuitive decision-making style (r = -.12), Maximizing tendency (r = .25), and
Indecisiveness (r = -.20). In addition, Rational decision-making style significantly positively
correlated with Maximizing tendency (r = .40), and Intuitive decision-making style had a
significant negative correlation with Indecisiveness (r = -.21). Decision strategy in the first
choice task had a significantly positive correlation with need for cognition (r = .12).
Unexpectedly, decision strategy in the first choice task did not significantly correlate with
Decision strategy in the second choice set (r = .06).
Decision Strategy
For Decision Strategy, data were excluded if participants did not provide all top three
choices; that is, missing information was indicated for those who only provided zero, one, or two
choices. Of the 309 participants in this study, one person was excluded in the first choice task for
only providing one job and two other people were excluded in the second choice task for not
providing any choices at all.
This paper also examined whether people’s top choices from their first choice task were
presented in the choice set of the second choice task. In the Large-to-Small condition (n = 156),
40 (26%) people were presented with their top choices from their first choice task in the second
choice task. Of those 40 participants, 27 (69%) of them chose the same job in both choice tasks.
30
In the Small-to-Large condition (n = 153), everyone was presented with their top choices. Of
those 153 participants, 26 (17%) of them chose the same job in both choice tasks.
The Effect of Choice Set Size on Decision Strategy
Two one-way ANOVAs were conducted to examine the relationship between choice set
size (large, small) and decision strategy rating, for the first choice task and second choice task.
There were significant effects of choice set size on decision strategy for the first choice task, F(1,
306) = 6.45, p < .05, d = .28, and second choice task, F(1, 305) = 23.86, p < .001, d = .60.
Specifically, in both choices, people had a higher likelihood of using compensatory strategies
when presented with a small choice set than with a large choice set. Table 2 shows the mean
levels of decision strategy for large and small choice set size. Hypothesis 1 was supported.
This study also tested whether there was an order effect on decision strategy. A pairedsample t-test found no significant mean differences in decision strategy between the Large-toSmall condition and Small-to-Large condition, t(305) = .81, p = .42.
Relationships between Individual Differences and Decision Strategy
Multiple linear regressions were conducted to test the relationship between scores on
individual difference measures (Need for Cognition/Decision-making Style/Maximizing
Tendency/Indecisiveness) and decision strategy for the first and second choice tasks presented.
The results for the first and second choice tasks were inconsistent. In addition, the correlations
between decision strategy in the first choice task and second choice task were non-significant.
This suggests that making the first choice may affect the results found in the second choice.
Therefore, from this point forward, this paper reports results only for the first choice task.
The overall F -test found a non-significant relationship between scores on the five
individual differences measures and decision strategy, R2 = .03, F(5, 299) = 1.68, p = .14. The
31
individual regression coefficients showed that need for cognition was the only significant
predictor of decision strategy, β = .13, p < .05 (see Table 3). The regression coefficient indicated
that people with higher scores on need for cognition were more likely to use a compensatory
decision strategy than those with lower scores on need for cognition. Therefore, there was
support for Hypothesis 2, but no support for Hypotheses 4, 5, 8, and 10.
Interactions between Choice Set Size and Individual Differences on Decision Strategy
A moderated linear regression was conducted to test the interaction between choice set
size (large, small) and scores on individual difference measures (Need for Cognition/Decisionmaking Style/Maximizing Tendency/Indecisiveness) on decision strategy. The overall F-test was
significant when entering the interaction terms into the regression, ΔR2 = .04, F(11, 293) = 2.57,
p < .01. The regression coefficients showed that there were significant interactions for
maximizing tendency (β = 1.47, p < .01) and indecisiveness (β = -76, p < .05) on decision
strategy (Table 4). Figures 1 and 2, respectively, show the pattern of interactions for maximizing
tendency and indecisiveness. As expected, the pattern of interaction for maximizing tendency
showed that people with high scores on maximizing tendency were more likely to use a
compensatory decision strategy than are those with low scores on maximizing tendency in a
large choice set, and both people with high and low scores on maximizing tendency are more
likely to use a compensatory decision strategy in a small choice set. The simple slope for the
large choice set was significant (β = .23, t(304) = 2.96, p < .01), and the simple slope for the
small choice set was non-significant (β = -.08, t(304) = -.96, p = .34). The pattern of interaction
for indecisiveness was not as expected. It was expected that in a small choice set, people with
high scores on indecisiveness would be more likely to use a non-compensatory decision strategy
than those with low scores on indecisiveness, and both people with high and low scores on
32
indecisiveness were more likely to use a non-compensatory decision strategy in a large choice
set. Figure 2 showed that people with high scores on indecisiveness were more likely to use a
compensatory decision strategy than are those with low scores on indecisiveness in a small
choice set, and both people with high and low scores on indecisiveness were more likely to use a
compensatory decision strategy in a large choice set. The simple slope for the small choice set
was significant (β = .19, t(306) = -2.61, p < .01), and the simple slope for the large choice set
was non-significant (β = -.07, t(306) = -.88, p = .38). Therefore, there was support for Hypothesis
9, but no support for Hypotheses 3, 6, 7, and 11.
33
CHAPTER V: DISCUSSION
The purpose of this study was to examine whether individual differences play a role in
people’s job choice process. Decision behavior is influenced by two main factors: the situation
encountered and personal characteristics (Einhorn, 1970). Most research on the job choice
process has focused on situation (e.g., Beach & Strom, 1989; Soelberg, 1967; Vroom, 1966); that
is, the role of individual differences has received considerably less attention in the literature. It is,
therefore, an important next step for the present study to empirically investigate whether
individual differences play an important role in predicting job choice behavior. This study first
examined whether a situation characteristic, choice set size, has an effect on decision strategy.
Next, the study examined whether decision-making individual differences were related to
decision strategies people used to choose among jobs, and whether these individual differences
interacted with choice set size, to impact decision strategy.
Despite the focus on individual differences, an important part of this study is to first
examine the effect of choice set size on decision strategy in the job choice context. The typical
study on decision strategy uses process tracing methods (e.g., think aloud technique) to
determine the type of decision strategy people use when making choices. This study utilized a
new and different way of measuring decision strategy; that is, by designating jobs, in an a priori
way, as either a compensatory job or non-compensatory job based on the job seekers’ underlying
cognitive processes. Using this designation method, choice set size did have an effect on decision
strategy, such that people presented with a large choice set were more likely to use a noncompensatory strategy than a compensatory strategy, and those presented with a small choice set
were more likely to use a compensatory strategy than a non-compensatory strategy. This finding
is consistent with previous studies in the literature (Beach & Strom, 1989; Einhorn, 1971; Mills,
34
Meltzer, & Clark, 1977; Olshavsky, 1979; Osborn, 1990; Payne, 1976; Timmermans, 1993). For
example, studies in the cognitive literature found that information load, operationalized as choice
set size, affects whether people used a non-compensatory or compensatory decision strategy
(Einhorn, 1971; Mills, Meltzer, & Clark, 1977; Payne, 1976; Timmermans, 1993). This result
suggests that the a priori designation approach is a promising avenue for observing decision
strategies in future research.
This study adopted image theory’s notion of the two-stage decision process for how job
seekers chose among job options. Specifically, image theory predicts that job seekers switch
from a single-stage decision process to a two-stage decision process as the choice set size
increases. In the initial stage, job seekers typically have a large choice set of jobs to evaluate and
choose to interview for. Indeed, results from this study as well as previous studies found that
people do tend to use non-compensatory decision strategies when making a decision in a large
choice set (e.g., Einhorn, 1971; Mills, Meltzer, & Clark, 1977; Payne, 1976; Timmermans,
1993). In the final stage of the job choice process, job seekers typically have a small set of job
offers to consider and accept. In situations with a small choice set, people tend to use
compensatory decision strategies (e.g., Mills, Meltzer, & Clark, 1977; Payne, 1976;
Timmermans, 1993). To examine whether or not results in this study is consistent with image
theory’s notion that people use a two-stage process to make choices, this study examined
whether or not people switched strategies to match the choice set size presented to them. In this
sample, 54% of people switched their decision strategies as a function of the order in which
choice set size was presented to them. Out of the people who did switch their decision strategies,
91% of them switched their decision strategies according to the size of choice sets presented to
them. That is, these people switched from a non-compensatory decision strategy to a
35
compensatory decision strategy when presented with a large choice set followed by a small
choice set. They also switched from a compensatory to a non-compensatory decision strategy
when presented with a small choice set first. These findings provide some support for image
theory that is consistent with previous literature (Beach & Strom, 1989; Einhorn, 1971; Mills,
Meltzer, & Clark, 1977; Olshavsky, 1979; Osborn, 1990; Payne, 1976; Timmermans, 1993).
Do individual differences play a role in decision strategy? The main purpose of this study
is to examine this research question. Of the five individual differences examined, only need for
cognition significantly predicted people’s decision strategy. Specifically, people with high scores
on need for cognition were more likely to use a compensatory decision strategy than a noncompensatory decision strategy. This result suggests that need for cognition may be one of a few
individual differences that affect people’s decision strategy. Researchers could examine other
individual differences, possibly even variables unrelated to decision-making, in future studies
examining decision strategy.
This study also tested the exploratory interaction effect between the five decision-making
individual differences and choice set size on decision strategy. Although there were no main
effects found for maximizing tendency and indecisiveness on decision strategy, interaction
effects were found. This suggests that the effect of some individual differences may only emerge
when they interact with a situational characteristic. In general, the pattern of interaction for
maximizing tendency was in the expected direction, but the pattern of interaction for
indecisiveness was not. It was expected that high maximizing people would be more likely to
optimize their options and use a compensatory decision strategy, regardless of the choice set size.
This study found support for this hypothesis. In other words, choice set size does not seem to
matter for high maximizing people.
36
For the indecisiveness interaction effect, it was expected that high indecisive people
would be more likely to use a non-compensatory decision strategy, regardless of choice set size.
However, it was found that high indecisive people were more likely to use a compensatory
decision strategy, regardless of choice set size. This finding was quite puzzling because the
literature on indecisiveness assumes that high indecisive people experience more difficulty than
low indecisive people when making a decision (Germeijs & DeBoeck, 2002). Regardless of the
choice set size, high indecisive people should experience much more difficulty making a
decision, and would want to reduce that difficulty by using a screening strategy. It could be that
decision strategy is not the consequence, but is a determinant of high indecisive people
experiencing difficulty. That is, high indecisive people may be more likely to use a
compensatory strategy to make choice, which in turn lead them to experience difficulty with the
decision at hand.
Although this study did find support for a few of the individual difference hypotheses,
some plausible explanations for the lack of support for most of the hypotheses may be offered. It
is worth noting that while all participants in the Small-to-Large condition were presented with
their top choices from their first choice task, approximately one-fourth of the participants the
Large-to-Small condition were presented with their top choices. When all participants in the
Small-to-Large condition were presented with their top choice in the second choice task, only 17
percent of them chose the same job. For the Large-to-Small condition, a large percentage (67%)
of those participants chose the same job in both choice tasks. These findings suggest that for the
Large-to-Small condition, people may not be using any decision strategy in the second choice
task (with a smaller choice set), but rather remember their top choice in the first choice task and
reusing it as the top choice in the second choice task. On the other hand, people in the Small-to-
37
Large condition may see the second choice task as a new choice task because they were
presented in with more options (in a larger choice set), and thus people may be using a decision
strategy in the second choice task. Future research could present all participants in a Large-toSmall condition with their top choice in the small choice set to examine whether similar results
emerge.
Another plausible explanation is that some jobs presented to participants were more
popular than other jobs, which could relate to the popularity of the job attributes. Appendix D
shows the popularity of each of the twelve jobs; the number of people who chose each job as
their top-ranked choice. The tables showed that when presented with all twelve jobs, Jobs J and
L were the two most popular jobs, both assigned to be non-compensatory jobs. These are the two
jobs that were high on salary and benefits, and low on supervising others. In addition, the three
least popular jobs were Jobs I, K, and H, all designated non-compensatory jobs. Jobs I, K, and H
had high levels of supervising others. This finding was worth noting because jobs in this study
were created such that no one job dominated any other job. That is, any preference for a job
could only be attributed to differences in participants’ subjective attribute importance. There
could be a strong effect of particular attributes inherent in the most popular jobs, which makes it
harder to find any effects of individual difference variables on decision strategy.
Preference for particular attribute in a job could have also affected the findings. Appendix
E shows the popularity of the four job attributes; the number of people who ranked each job
attribute as most important (rank 1) to least important (rank 4) when making a choice among
jobs. In general, Supervising Others was ranked the least important attribute while Salary &
Benefits was ranked the most important, suggesting that participants in this study may have
screened out jobs that were high on supervising others. This explains why Jobs I, K, and H were
38
the least popular jobs in the large choice set. It is worth noting that these were also the three noncompensatory jobs presented to participants in the small choice set, which ultimately made the
compensatory jobs in the small choice set seem more attractive to participants. To have the noncompensatory jobs in the small choice set so undesirable could have weaken the mean
differences found in Hypothesis 1. The effect of choice set size on decision strategy could
possibly be smaller if attribute preference were taken into consideration. To test whether the
effect of choice set size on decision strategy would still be significant if the study controlled for
attribute preference, a regression analysis with participants’ ranking of attributes as control
variables were conducted. Results showed that after controlling for attribute preference, choice
set size was still a significant predictor of decision strategy ((β = -.13, t(306) = -2.28, p < .05).
Therefore, the finding supporting the effect of choice set size on decision strategy still holds.
Finally, the strength of the situation (choice set size) presented to people could have
affected the individual difference results in this study. Specifically, the manipulated situation in
this study may have limited the freedom for individual differences to influence behavior. Mischel
(1973) stated that individual differences are most likely to directly affect behavior when situation
is weak and ambiguously structured. In fact, Weiss and Adler (1984) suggested that laboratory
experiments posit strong situations, which can mute the effects of individual differences in
laboratory settings. Therefore, future research should examine the effects of individual
differences on job choice behavior in a setting designed to create a level of situational strength
that is most appropriate for the phenomena of interest.
There are useful theoretical implications for the findings on the effect of choice set size
on decision strategy. The findings of this study suggest that the stage of job choice affect
people’s decisions strategy. For theories of job choice process as well as theories of choice,
39
predictions could be made for the type of decision strategies people use when they are at
different stages of the choice process. In the initial (choose-to-apply) stage of job choice, people
typically have a large choice set to consider, and the choice set is generally smaller at the final
(choose-to-accept) stage of job choice. Based on the results of this study, it is expected that
people would be more likely to use a non-compensatory strategy when choosing to apply to jobs
and people may switch to a compensatory strategy when choosing to accept a job offer. Future
studies should examine whether there is an effect of stage of job choice on decision strategy.
This study also found a few individual differences that play a role in people’s decision
strategy. Specifically, need for cognition had a main effect on decision strategy, and maximizing
tendency and indecisiveness had an interaction effect on decision strategy. Results found for
these individual differences suggest that individual differences should not be ignored in decisionmaking. In fact, researchers should begin to include individual differences when formulating
theories of choice, and empirically examining decision-making phenomena.
One practical implication for job seekers is that they should be more aware of the type of
decision strategy they use when choosing among jobs. A better understanding of their own
decision strategy could improve the quality of their decision-making. For example, when using a
non-compensatory strategy to screen out jobs on one attribute to narrow down a large choice set,
job seekers could have eliminated an otherwise attractive job from their consideration. If job
seekers are aware that this may result from using a purely non-compensatory strategy, they may
want to change their decision strategies to incorporate some compensatory strategies when faced
with a large choice set.
This study also has practical implications for organizations. If organizations seek to hire
applicants with a particular individual difference, they could design job descriptions that attract
40
those applicants. Based on this study, people high on need for cognition are more likely to use
compensatory decision strategies than those with low levels of need for cognition. If an
organization is interested in hiring people high on need for cognition, then the organization could
design a job description that emphasizes a trade-off strategy between job attributes. The job
description should present both positive and negative attributes, also taking into consideration
attribute importance.
There are practical implications for decisions other than job choice in organizations.
Decisions in organizations are made by people. An understanding of the effect choice set size
and individual differences have on decision strategy could help decision makers make better
choices for organizations. For example, organizations typically want day-to-day operational
decisions to be standardized across people, which means that organizations should reduce any
effect individual difference have on those decisions. To make decision-making standardized,
organizations could train decision makers to use the same decision strategy when making these
day-to-day decisions. In addition, organizations could train decision makers to use decision aids
to improve the quality of decision-making. On the other hand, organizations may prefer more
creative decision-making tailored to different situations from their top-level decision makers.
Individual differences can relate to creative decision-making. In this scenario, organizations
would want the context of the decision making and the decision makers’ individual differences to
affect their decision making. Organizations could create a climate where decision makers can
share various decision strategies they use to make a difficult decision.
A limitation of the current study is the lack of realism in the hypothetical situation
presented to participants. There may be questions about the generalizability of findings in this
study applied to an actual job search and choice setting. People in an actual setting may have
41
higher motivation to choose the best-suited job for themselves. It is likely that with more salience
of consequences associated in an actual choice setting, stronger results would be expected.
Another limitation is the use of student sample, mostly freshmen, in examining a topic
more relevant for senior students. Although a student sample is appropriate in examining a basic
decision-making phenomenon, freshmen students may be further removed from the job search
process to be aware of the type of job they would likely choose. Senior students may be closer to
the job search process to have a higher awareness of jobs that fit them best. Future research
should examine whether graduating students with different levels of individual differences are
more likely to influence their decision strategies.
In conclusion, the size of the job choice set affects the type of decision strategy people
use to choose among jobs. Individual differences did influence decision strategies in this study,
suggesting that researchers should begin included individual differences when investigating
decision-making. Some influence of individual differences only emerged when examined with a
situational characteristic, consistent with the notion that behavior is a combination of individual
differences and situations (e.g., Einhorn, 1970; Mischel, 1999; Weiss & Adler, 1984). By
including individual differences into research on decision strategy, this study took a step toward
advancing research and theory on the understanding job choice. A better understanding of the job
choice process benefits both job seekers and recruiting organizations alike. This study seeks to
encourage further research aimed at understanding and predicting job choice, by including
individual differences to the theories on job choice.
42
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49
Table 1
Intercorrelations among Study Variables
Variables
1
2
3
4
5
6
7
8
9
10
11
12
13
14
1. NFC
2. Rat. DMS
3. Int. DMS
4. Maxi.
5. Indec.
6. 1st DS
7. 2nd DS
8. DSS
9. Extrav.
10. Agree.
11. Consc.
12. Neur.
13. Open.
14. ACT
.24**
-.12*
.25**
-.20**
.12*
-.06
.21**
.12*
.16**
.19**
-.11
.52**
.25**
.05
.40**
-.05
.03
.05
.49**
.01
.23**
.28**
-.02
.26**
.11
.11
-.21**
.03
-.08
-.11
.14*
.15**
.04
-.09
.04
-.09
-.11
.08
.01
-.11
.10
.22**
.24**
-.04
.29**
.11
.05
.15*
.04
-.32**
.01
-.22**
.38**
-.22**
.06
.06
.09
0
.04
.04
0
-.03
-.03
.08
.15*
0
.06
.12*
.05
-.02
-.12*
.34**
.16**
0
.20**
.15*
.22**
.05
-.25**
.15**
-.08
.19**
-.06
.28**
.06
-.08
.08
.03
-.12*
.04
.35**
-
Note. *p<.05, **p<.01. NFC = Need for cognition, DMS = Decision-making style, Rat. = Rational, Int. = Intuitive, Maxi. =
Maximizing tendency, Indec. = Indecisiveness, DS = Decision strategy, DSS = Decision strategy scale, Extrav. = Extraversion,
Agree. = Agreeableness, Consc. = Conscientiousness, Neur. = Neuroticism, Open. = Openness.
50
Table 2
Means, Standard Deviations, and One-Way Analyses of Variance for the Effects of
Choice Set Size on Decision Strategy for Choice Task 1 and 2
Variable
Decision
Strategy 1
Decision
Strategy 2
Large Choice
Set
M
SD
Small Choice
Set
M
SD
2.93
0.68
3.90
2.80
0.69
3.14
F
p
d
0.53
6.45
0.01
0.28
0.52
23.86
0.00
0.60
51
Table 3
Regression Analysis Summary for Individual Differences Predicting Decision Strategy
Individual difference
Need for cognition
Rational DMS
Intuitive DMS
Maximizing tendency
Indecisiveness
B
SE β
β
t
p
0.17
-0.03
0.06
0.08
0.11
0.08
0.07
0.06
0.08
0.07
0.13
-0.02
0.06
0.06
0.10
2.17
-0.35
1.02
0.95
1.65
0.03
0.73
0.31
0.34
0.10
52
Table 4
Moderated Linear Regression Analysis Summary for Interaction between Individual
Differences and Choice Set Size on Decision Strategy
Step and predictor variable
Step 1:
Need for cognition
Rational DMS
Intuitive DMS
Maximizing tendency
Indecisiveness
Choice set size
Step 2:
NFC x Choice size
Rational DMS x Choice size
Intuitive DMS x Choice size
Maximizing x Choice size
Indecisiveness x Choice size
* p < .05, ** p < .01.
B
SE β
β
R2
ΔR2
0.04*
0.15
-0.02
0.06
0.08
0.12
-0.16
0.08
0.07
0.06
0.08
0.07
0.07
0.12*
0.04
0.09
0.08
0.11
-0.13*
0.03
-0.16
-0.07
0.44
-0.28
0.15
0.14
0.12
0.16
0.14
0.08
-0.54
-0.25
1.44**
-0.76*
0.09**
0.04*
53
Figure 1. Interaction between maximizing tendency and choice set size on decision strategy.
54
Figure 2. Interaction between indecisiveness and choice set size on decision strategy.
55
APPENDIX A1: TWELVE JOB OPTIONS
Job D
Job A
Job I
Inter actions with
other s
Low
Inter actions with
other s
High
Inter actions with
other s
High
Oppor tunities to
lear n
Medium
Oppor tunities to
lear n
Low
Oppor tunities to
lear n
Low
Salar y & benefits
High
Salar y & benefits
Medium
Salar y & benefits
Low
Super vising other s
Medium
Super vising other s
Medium
Super vising other s
High
Job L
Job J
Job H
Inter actions with
other s
High
Inter actions with
other s
Low
Inter actions with
other s
Low
Oppor tunities to
lear n
Low
Oppor tunities to
lear n
High
Oppor tunities to
lear n
Low
Salar y & benefits
High
Salar y & benefits
High
Salar y & benefits
High
Super vising other s
Low
Super vising other s
Low
Super vising other s
High
Job F
Job B
Job E
Inter actions with
other s
Medium
Inter actions with
other s
Low
Inter actions with
other s
Medium
Oppor tunities to
lear n
Low
Oppor tunities to
lear n
High
Oppor tunities to
lear n
High
Salar y & benefits
High
Salar y & benefits
Medium
Salar y & benefits
Low
Super vising other s
Medium
Super vising other s
Medium
Super vising other s
Medium
Job G
Job K
Job C
Inter actions with
other s
High
Inter actions with
other s
Low
Inter actions with
other s
High
Oppor tunities to
lear n
High
Oppor tunities to
lear n
High
Oppor tunities to
lear n
Medium
Salar y & benefits
Low
Salar y & benefits
Low
Salar y & benefits
Low
Super vising other s
Low
Super vising other s
High
Super vising other s
Medium
Note: Jobs A-F = Compensatory jobs; Jobs G-L = Non-compensatory jobs
56
APPENDIX A2: SIX JOB OPTIONS
J ob H
J ob D
J ob K
Inter actions with
other s
Low
Inter actions with
other s
Low
Inter actions with
other s
Low
Oppor tunities to
lear n
Low
Oppor tunities to
lear n
Medium
Oppor tunities to
lear n
High
Salar y & benefits
High
Salar y & benefits
High
Salar y & benefits
Low
Super vising other s
High
Super vising other s
Medium
Super vising other s
High
J ob A
J ob I
J ob E
Inter actions with
other s
High
Inter actions with
other s
High
Inter actions with
other s
Medium
Oppor tunities to
lear n
Low
Oppor tunities to
lear n
Low
Oppor tunities to
lear n
High
Salar y & benefits
Medium
Salar y & benefits
Low
Salar y & benefits
Low
Super vising other s
Medium
Super vising other s
High
Super vising other s
Medium
Note: Jobs A, D, E = Compensatory jobs; Jobs H, I, K = Non-compensatory jobs
57
APPENDIX B: INSTRUCTIONS FOR CHOICE SET CONDITIONS
Lar ge-to-Small Condition
Large Choice Set Instructions
You are probably aware of the job search website “Monster.com”. On the website, job seekers
are able to search for jobs, upload their application materials, and apply for jobs online.
Imagine that you are in the last semester of college and you are seeking a job to begin after you
graduate. You have searched and narrowed down your options to twelve job positions. You have
taken notes about each job from the job descriptions. Below is the list of twelve jobs with your
notes about them. You plan to interview for five of the twelve jobs. Please list and rank order the
five jobs you would like to interview for, with 1 as your preferred job and 5 as your least
preferred job.
Small Choice Set Instructions
Now, imagine that you have interviewed for all five jobs in the previous scenario and you were
offered one of the five jobs. The company that offered you the job has six similar positions
across the many divisions around the country. You now have to indicate your preferred jobs for
the company to place you. Again, you have taken notes about each position from the job
descriptions the company gave you. Below is the list of six jobs with your notes about them.
Please rank order five of the six jobs you would like the company to place you, with 1 as your
preferred job and 5 as your least preferred job.
Small-to-Lar ge Condition
Small Choice Set Instructions
You are probably aware of the job search website “Monster.com”. On the website, job seekers
are able to search for jobs, upload their application materials, and apply for jobs online.
Imagine that you are in the last semester of college and you are seeking a job to begin after you
graduate. You have searched and narrowed down your options to six job positions. You have
taken notes about each job from the job descriptions. Below is the list of six jobs with your notes
about them. You plan to interview for five of the six jobs. Please rank order the five jobs you
would like to interview for, with 1 as your most preferred job and 5 as your least preferred job.
Large Choice Set Instructions
Now, imagine that you have interviewed for all five jobs in the previous scenario and you were
offered one of the five jobs. The company that offered you the job has twelve similar positions
across the many divisions around the country. You now have to choose five out of the twelve
positions for the company to place you. Again, you have taken notes about each position from
the job descriptions the company gave you. Below is the list of twelve jobs with your notes about
them. Please list and rank order the five jobs you would like the company to place you, with 1 as
your preferred job and 5 as your least preferred job.
58
APPENDIX C: SCREEN SHOTS OF WEB SURVEY
59
60
61
62
63
64
65
66
Choice Size
Large
Small
Total
APPENDIX D: POPULATIRY OF TOP-RANKED JOBS
Job A
Job G
Order Given
Order Given
First Second Total
Choice Size
First Second
11
10
21
Large
8
12
62
56
118
Small
N/A
N/A
73
66
139
Total
8
12
Total
20
N/A
20
Choice Size
Large
Small
Total
Job B
Order Given
First Second
8
5
N/A
N/A
8
5
Choice Size
Large
Small
Total
Job H
Order Given
First Second
4
4
10
13
14
17
Total
8
23
31
Choice Size
Large
Small
Total
Job C
Order Given
First Second
6
7
N/A
N/A
6
7
Choice Size
Large
Small
Total
Job I
Order Given
First Second
0
0
3
4
3
4
Total
0
7
7
Choice Size
Large
Small
Total
Job D
Order Given
First Second
15
15
60
61
75
76
Choice Size
Large
Small
Total
Job J
Order Given
First Second
29
29
N/A
N/A
29
29
Total
58
N/A
58
Choice Size
Large
Small
Total
Job E
Order Given
First Second
7
5
18
19
25
24
Choice Size
Large
Small
Total
Job K
Order Given
First Second
0
2
0
2
0
4
Total
2
2
4
Job L
Order Given
First Second
40
41
N/A
N/A
40
41
Total
81
N/A
81
Total
13
N/A
13
Total
13
N/A
13
Total
30
121
151
Total
12
37
49
Job F
Order Given
Choice Size
First Second Total
Choice Size
Large
28
22
50
Large
Small
N/A
N/A
N/A
Small
Total
28
22
50
Total
Note. N/A = unavailable options in small choice set.
67
APPENDIX E: IMPORTANT RANKINGS OF JOB ATTRIBUTES
Ranking
Job Attribute
Interacting with
others
Opportunity to learn
Salary & Benefits
Supervising others
1
2
3
Overall
Rank
4
n
%
n
%
n
%
n
%
93
57
139
18
30
18
45
6
100
108
88
14
32
35
28
5
85
112
55
56
28
36
18
18
31
32
27
221
10
10
9
71
2
3
1
4