The Effect of Indecisiveness on Consumer Choice Processes
by
Hillary N. Mellema
A dissertation proposal submitted in partial fulfillment of the requirements for the
degree of Doctor of Philosophy
(Marketing)
Kent State University
Department of Marketing & Entrepreneurship
2015
Doctoral Committee:
Associate Professor Jennifer Wiggins Johnson, Chair
Assistant Professor César Zamudio
Professor John Dunlosky (Psychology)
iii
CONTENTS
CONTENTS ........................................................................................................... iii
LIST OF FIGURES ............................................................................................... vi
LIST OF TABLES ................................................................................................ vii
Acknowledgements ................................................................................................ ix
Overview ................................................................................................................ xi
Chapter 1: The Construct of Indecisiveness ............................................................1
Indecisiveness in the Vocational Choice Literature .............................................1
Indecisiveness in the Decision Making Literature ...............................................7
Indecisiveness in the Clinical Psychology Literature ..........................................9
Indecisiveness in the Management Literature ....................................................10
Indecisiveness in the Marketing Literature ........................................................10
Antecedents of Indecisiveness ...........................................................................12
Correlates of Indecisiveness ...............................................................................16
Consequences of Indecisiveness ........................................................................18
Measurement of Indecisiveness .........................................................................25
Chapter 2: Consumer Choice Processes ................................................................32
Contingent Decision Making in the Consumer Choice Literature .....................32
Choice Goals Framework ...................................................................................34
Decision Strategies .............................................................................................38
Factors that Influence Constructed Choice Processes ........................................43
Hypothesis Development ...................................................................................53
Chapter 3: Interviews with Individuals Who Self-Identify as Indecisive ..............59
Research Methodology .......................................................................................59
Interview Guide ..................................................................................................60
Data Analysis .....................................................................................................63
Findings ..............................................................................................................63
Conclusion ..........................................................................................................77
Chapter 4: Studies................................................................................................79
Overview of Experiments...................................................................................79
Process Tracing Methods ...................................................................................79
Sample Size ........................................................................................................81
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Sample Recruitment ...........................................................................................82
Pretests ...............................................................................................................84
Study 1................................................................................................................97
Study 2..............................................................................................................113
Dependent and Control Variables ....................................................................113
Study 3..............................................................................................................126
Chapter 5: Discussion, Limitations, and Future Research ...................................136
Summary of Findings .......................................................................................136
Theoretical Implications ...................................................................................138
Methodological Implications............................................................................141
Managerial Implications ...................................................................................142
Limitations .......................................................................................................143
Future Research ................................................................................................144
References ............................................................................................................147
Figures..................................................................................................................164
Tables ...................................................................................................................174
Appendices ...........................................................................................................217
Appendix A: Interview Materials .....................................................................217
Appendix B: Scales ..........................................................................................222
Appendix C: Product Pretest Materials ............................................................226
Appendix D: Information Format Pretest Materials ........................................229
Appendix E: Process Tracing Software ...........................................................232
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LIST OF FIGURES
Figure 1. Example MouslabWEB Task ...............................................................164
Figure 2. Pretest 3: Cognitive Resource Demands Histogram ............................165
Figure 3: Study 1: Decision Latency Histogram ..................................................165
Figure 4. Study 1: Average Information Visible Histogram ................................166
Figure 5. Study 1: Click Rate Histogram .............................................................166
Figure 6. Study 1: Total Clicks Histogram ..........................................................167
Figure 7. Study 1: Total Clicks (SQRT) Histogram ............................................167
Figure 8. Study 2: Decision Latency Histogram ..................................................168
Figure 9. Study 2: Decision Latency (SQRT) Histogram ....................................168
Figure 10. Study 2: Average Information Visible Histogram ..............................169
Figure 11. Study 2: Click Rate Histogram ...........................................................169
Figure 12. Study 2: Total Clicks Histogram ........................................................170
Figure 13. Study 3: Decision Latency Histogram ................................................170
Figure 14. Study 3: Decision Latency (SQRT) Histogram ..................................171
Figure 15. Study 3: Average Information Visible Histogram ..............................171
Figure 16. Study 3: Click Rate Histogram ...........................................................172
Figure 17. Study 3: Total Clicks Histogram ........................................................172
Figure 18. Interaction Terms: Indecisiveness by Condition Format ....................173
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LIST OF TABLES
Table 1. Pretest 1: Frequency Descriptives ..........................................................174
Table 2. Pretest 1: Enjoyment Descriptives .........................................................174
Table 3. Pretest 1: Personal Involvement Item Descriptives ...............................175
Table 4. Pretest 1: Personal Involvement Descriptives .......................................175
Table 5. Pretest 1: Prompted Feature Recall and Ratings ....................................176
Table 6. Pretest 2: Frequency Descriptives ..........................................................177
Table 7. Pretest 2: Shopping Enjoyment Descriptives .........................................177
Table 8. Pretest 2: Personal Involvement Item Descriptives ...............................178
Table 9. Pretest 2: Personal Involvement Descriptives .......................................178
Table 10. Pretest 2: Prompted Feature Recall and Ratings ..................................179
Table 11. Pretest 3: Cognitive Resource Demands Descriptives .........................180
Table 12. Pretest 3: Imagery Elaboration Descriptives .......................................181
Table 13. Pretest 3: Imagery Elaboration Post Hoc Test .....................................181
Table 14. Pretest 3: Attitude (Utilitarian) Descriptives .......................................182
Table 15. Pretest 3: Attitude (Utilitarian) Post Hoc Test .....................................182
Table 16. Pretest 3: Chi-Squared Test of Total Number of Errors by Condition 183
Table 17. Pretest 3: Total Number of Errors by Condition Cross-Tab ................183
Table 18. Pretest 3: Error Present by Condition Cross-Tab.................................184
Table 19. Study 1: Descriptives ...........................................................................185
Table 20. Study 1: Manipulation Check ..............................................................185
Table 21. Study 1: Decision Latency OLS Results ..............................................186
Table 22. Study 1: Decision Latency Truncated Regression ...............................187
Table 23. Study 1: Average Information Displayed OLS Results .......................188
Table 24. Study 1: Click Rate OLS Results .........................................................189
Table 25. Study 1: Total Clicks (SQRT) OLS Results ........................................190
Table 26. Study 1: Chow Test Results .................................................................191
Table 27: Study 2: ANOVA Perceived Difficulty Manipulation Check .............191
Table 28: Study 2: Cross-Tab Performance by Difficulty Condition ..................192
Table 29: Study 2: Chi-Square Results ................................................................192
Table 30. Study 2: ANOVA Processing Fluency Manipulation Check ...............193
Table 31. Study 2: Descriptives ...........................................................................193
Table 32. Study 2: Decision Latency (SQRT) OLS Results ................................194
Table 33. Study 2: Decision Latency (SQRT) OLS Results ................................195
Table 34. Study 2: Decision Latency Truncated Regression ...............................196
Table 35. Study 2: Average Information Displayed OLS Results .......................197
Table 36. Study 2: Average Information Displayed OLS Results .......................198
Table 37. Study 2: Click Rate OLS Results .........................................................199
Table 38. Study 2: Click Rate OLS Results .........................................................200
Table 39. Study 2: Total Clicks OLS Results ......................................................201
Table 40. Study 2 Total Clicks OLS Results .......................................................202
Table 41. Study 2: Chow Test Results .................................................................203
Table 42. Study 3: Descriptives ...........................................................................204
Table 43. Study 3: Timer Descriptives (Seconds) ...............................................205
Table 45. Study 3: Post Hoc Perceived Difficulty ...............................................205
Table 46. Study 3: Cross-Tab Performance by Difficulty Condition ..................205
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Table 47. Study 3: Chi-Square Results ................................................................206
Table 50. Study 3: Post Hoc Decision Latency ...................................................206
Table 51. Study 3: Decision Latency (SQRT) OLS Results ................................207
Table 52. Study 3: Decision Latency (SQRT) OLS Results ................................208
Table 53. Study 3: Decision Latency Truncated Regression ...............................209
Table 54. Study 3: Average Information Displayed OLS Results .......................210
Table 55. Study 3: Average Information Displayed OLS Results .......................211
Table 56. Study 3: Click Rate OLS Results .........................................................212
Table 57. Study 3: Click Rate OLS Results .........................................................213
Table 58. Study 3: Total Clicks OLS Results ......................................................214
Table 59. Study 3: Total Clicks OLS Results ......................................................215
Table 60. Study 3: Chow Test Results .................................................................216
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Acknowledgements
Completion of this dissertation would not have been possible without the support
of my family, friends, and colleagues. First and foremost, I want to express my deepest
appreciation and love to my husband, Doug, for his patience during these four years
apart. I would not be the person I am without him.
I have had the good fortune to enjoy a dissertation committee that has made this
research a rewarding and fulfilling experience. I would like to thank John Dunlosky for
his outstanding seminar on cognitive psychology. His ability to reach back through time
and unpack concepts is awe-inspiring. I am grateful to César for his level-headed
approach to research and his attention to detail. His focus and dedication as a researcher,
teacher, and mentor are facets of his character to which others would do well to aspire. I
am indebted to my committee chair, Jennifer Wiggins Johnson, for her outlandish ability
to read my mind and reorganize my brain. I never would have anticipated that the
dissertation process could be such a rewarding and (mostly) painless experience. Yet,
with your mentorship and patience, I feel myself growing and learning every day.
I know that my time as a doctoral student and candidate would not have been the
same without those who have become my closest colleagues and friends. Karla, Meg,
Jamie, and Laurel—Thank you for being there for me when I was sure that I wasn’t going
to find a job (I was THIS close to starting a doggie daycare in my back yard). One of
these days I will have to find a way to get all of my favorite people in one place for a
celebratory bottle of wine.
I am thankful for the collegial and nurturing atmosphere that has been cultivated
in the Department of Marketing and Entrepreneurship. The faculty and staff go above and
ix
beyond for the doctoral students. I’m not sure exactly where I would be without Beth
(Probably locked out of my office and standing in front of the copy machine trying to
remember the code I’ve had for four years and use every day).
Finally, thank you to my family. I cannot express how much it means to me when
you come and run in a 5k that is hundreds of miles away, tag along with me when I’m
visiting somewhere new, or go thrift shopping with me just so we can wear something
wacky to dinner. I am so grateful for your support and your love.
x
Overview
This dissertation is an investigation into the decision making process of indecisive
consumers. I define indecisiveness as a “trait-related general tendency to experience
decision difficulties across a variety of situations” (Patalano, Juhasz, and Dicke 2010, p.
353). The trait of indecisiveness is different than the state of undecidedness or the state of
indecision. Being undecided or experiencing indecision are transitory events.
Indecisiveness is an enduring trait that impacts daily decision making. The goals of my
dissertation are to 1) understand the decision making processes of consumers who
experience chronic domain-general indecisiveness and 2) understand why they pursue
different processing strategies during consumer decision making than the average
individual.
The literature on consumption choice processes identifies a number of factors
about the decision making task that influence decision task difficulty (e.g. time pressure,
complexity of the decision task, unpleasant trade-offs). Decisions vary in both emotional
and cognitive difficulty. The consumer decision making literature proposes that typical
consumers are constantly balancing between the “desire to make an accurate decision and
the desire to minimize cognitive effort” (Bettman, Luce, and Payne 1998, p. 192) and,
therefore, use time- and effort-reducing strategies when making less difficult decisions.
For example, making a choice between two alternatives when there is a clearly dominant
option requires less effort than choosing a satisfactory alternative from a large set of
options with incomplete information. Consumers will also expend less effort for less
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important and low risk decisions because there is a low cost for making a wrong or suboptimal choice.
I propose that the characteristics of the decision will influence how much
difficulty decisive consumers have making the decision, but that indecisiveness
moderates this relationship. As indecisiveness increases, consumers will be less likely to
use effort reducing strategies to make decisions regardless of whether the decision is
perceived to be difficult or important.
In chapter one, I define the construct of indecisiveness and review the existing
literature on indecisiveness. I trace the development of the construct through a variety of
disciplines. The construct of indecisiveness evolved out of early literature on vocational
indecision and left its mark in the decision making literature, clinical psychology,
management, and is relatively new to the marketing literature. The topic of indecisiveness
has been of recent interest in consumer behavior literature (see Ulkumen & Malkoc 2011;
Jeong & Drolet 2011; and Fox & Barkley-Levenson 2011; Jeong and Drolet 2014). This
project adds to what we know about the construct of indecisiveness in the context of
consumer behavior and takes steps toward understanding the experiences and the decision
making process of indecisive consumers.
In chapter two, I draw from existing literature in consumer decision making to
understand the decision making and information search processes of the average
consumer. I ground my arguments in the consumer choice literature on constructive
choice processes. Drawing from the choice goals framework and the existing literature on
indecisiveness, I predict that indecisiveness will moderate the relationship between
choice task variables and amount of information processing.
xii
In chapter three, I conduct and analyze semi-structured interviews with
individuals who have identified themselves as suffering from chronic, domain-general
indecisiveness to get a better sense of the processing differences between the typical
consumer choice process and that of indecisive consumers. Through analysis of the
interviews, I discover that individuals who identify themselves as "indecisive" experience
this indecisiveness regardless of the perceived difficulty or importance of the decision.
These findings were instrumental in identifying connections in the literature and an
iterative process of analysis and literature review helped in the development of my
hypotheses in Chapter 2.
In chapter four, I conduct three experiments designed to test the search and choice
processes of indecisive consumers. The purpose of these studies is to understand the
processing differences between decisive consumers and indecisive consumers. In a
controlled environment, I investigate how importance and cognitive difficulty of the
decision making task differentially affect the amount of information processing as
indecisiveness varies. In order to collect the process-tracing data for these studies, I
developed a web-based decision software as a means of tracking decision-making process
variables.
In chapter five, I discuss the implications of my findings for consumer choice
literature, marketing, and consumer behavior. I also outline my next steps in an
experiment that serves as an extension to this current research.
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Chapter 1: The Construct of Indecisiveness
Indecisiveness has been studied in the economics, vocational choice, decision making,
clinical psychology, and consumer behavior literatures. The earliest reference to indecisiveness is
in economics. Because the inability to state a preferred alternative cannot be explained by an
assumption of perfect rationality in decision making, economists attribute this inability to
incomplete preference formation (Von Neumann & Morgenstern 1944; Ok, Ortoleva, & Riella
2012; Ok 2002). Aside than this brief mention of indecisiveness, the majority of early research
on indecisiveness occurs in the vocational choice literature between the 1960s and late 1990s.
Indecisiveness in the Vocational Choice Literature
The Construct of Vocational Indecision
One of the earliest mentions of vocational indecision suggests that students who do not
give a response or provide an ambiguous response when asked to name a vocation are
experiencing vocational indecision (Holland & Nichols 1964). Dysinger (1950) describes two
types of vocational indecision. In the first type of career indecision, young adults postpone the
decision or are considering several options. This is considered to be a wholesome and
exploratory experience. The second type of career indecision is described as an “avoidance of the
pain of decision” or the “possibility of failure” (Dysinger 1950, p. 200). According to Tyler
(1961), this second type of indecision is due to “personal problems” rather than doubts associated
with choosing a vocation.
2
In his book, Vocational Psychology, Crites (1969) defines vocational indecision as the
“inability of the individual to select, or commit himself to, a particular course of action which
will eventuate in his preparing for and entering a specific occupation” (p. 303). He identifies
three types of vocational problems that lead to career indecision. A multipotential individual may
have two or more viable choices, but cannot decide among the alternatives. An undecided
individual is not currently entertaining any viable option. An uninterested individual has at least
one choice, but does not have any stated career interests.
Many of the early vocational choice researchers treat this phenomenon very narrowly
within the career choice decision (e.g. Osipow, Carney, Barak 1976; Hawkins, Bradley, & White
1977; Kimes & Troth 1974). Within this context, indecisiveness is described as whether or not
high school or college students have settled on a future career and how confident they are in the
correctness of that choice (Osipow, Carney, Barak 1976; Hawkins, Bradley & White 1977).
Kimes and Troth (1974) define career indecisiveness as the degree to which an individual has
committed to a career choice.
Other researchers in the fields take a more broad approach to indecisiveness. As such,
they suspect that some individuals may be predisposed to being indecisive (Crites 1974; Holland
& Holland 1977). Rather than limiting the phenomenon to vocational decisions, Crites (1969)
makes a conceptual distinction between vocational indecision and general indecisiveness. He
defines indecisiveness as “difficulty in making all sorts of life decisions, whether they are of
great or little significance” (p. 305-306). This definition is based on the work of Tyler (1961)
which treats the construct as a form of domain-general indecisiveness that develops from
personal problems rather than doubts related to choosing an occupation. Crites also refers to the
work of American psychologist Forer (1953), who suggests that an individual who cannot make
3
vocational decisions or even state his or her vocational preferences is “likely to be an
emotionally maladjusted person. Often the deciding process is a threat; and wanting things,
expressing needs, and obtaining some kind of gratification are emotionally unacceptable” (p.
366). Crites (1969) agrees with other researchers that the inability to decide on a vocation may be
an indication of developmental delay (Ginzberg, Ginsberg, Axelrad, & Herma 1951), emotional
maladjustment (Forer 1953), or personal problems (Tyler 1961).
Crites (1969) states that further empirical work must be done to establish the theoretical
distinction between indecision and indecisiveness. He proposes a before-after experimental
design with experimental and control groups (See Crites 1969, pp. 599-604 for the proposed
experiment1). A career choice inventory is administered as a screener to identify vocationally
“undecided” individuals to be randomly assigned to treatment group. The proposed treatment is
an “informational experience” that provides additional information about the career choices. The
choice inventory is re-administered and compared both across and within groups. The
comparison across experimental and control groups tests the effect of treatment. Within the
treatment group, those who are able to make a choice upon retest have a problem of indecision.
Once they have the appropriate information, they are able to make a choice. Those who are not
able to make a career choice once receiving treatment and the second choice inventory are
experiencing the problem of indecisiveness. Even though they have the relevant information,
they are unable to make a choice.
1
Crites intended to conduct this research as part of his Vocational Development Project at the University of
Iowa. To my knowledge, the experiment has not been conducted. Crites published a detailed outline of the
Vocational Development Project in the Journal of Counseling Psychology in 1965.
4
In 1982, Salomone weighs in on the vocational indecision debate and establishes two
sub-concepts. He argues that there is a difference between individuals who have not decided on a
career and individuals who are chronically indecisive. Similar to Dysinger’s (1950) two types of
indecision and Crites’ (1969) distinction between vocational indecision and general
indecisiveness, Salomone elaborates on the differences between individuals who are vocationally
undecided and those who are vocationally indecisive. According to Salomone (1982), the state of
being undecided is normal. Individuals put off some decisions, including vocational choice,
because they don’t have enough information to make an informed and sound decision.
Conversely, indecisiveness is a chronic disposition in which “indecisive persons fail to make
important decisions not because they lack sufficient information but because they have personal
qualities that will not allow them to reach a decisional state of mind and take a course of action”
(Salomone 1982, p. 497). Agreeing with previous research (Tyler 1961; Forer 1953; Crites
1969), Salomone does not limit his definition of indecisiveness to the domain of career decisions,
but expects that individuals predisposed to indecisiveness in their career choices will also exhibit
indecisiveness in other aspects of their lives. In his research, Salomone (1982) focuses on the
indecisive individual’s inability to make important decisions.
Van Matre and Cooper (1984) extend Solomone’s work and conceptualize career
indecision as a “complex, multidimensional problem composed of an undecided state and an
indecisive trait” (p. 637). The state of decidedness is represented along a continuum of
decidedness to undecidedness and refers to the “transitory level of indecision that accompanies
all decision-making tasks” (p. 637). The trait of decisiveness is also conceptualized as a
continuum from decisiveness to indecisiveness and refers to the “enduring and consistent
proneness” of an individual to be indecisive when “encountered by a decision-making task” (p.
5
637). The state of indecision and the trait of indecisiveness are represented as dimensions along
two orthogonal axes (Van Matre & Cooper 1984; Salomone 1982). Slaney (1988) comments that
while the distinction between indecision and indecisiveness is useful, “very little progress has
been made thus far in demonstrating that the two constructs are valid and discriminable” (pp. 4445).
Recent work by Santos, Ferreira, and Gonçalves (2014) tests Van Matre and Cooper’s
(1984) model and find that indecisiveness and indecision can, indeed, be conceptualized as
orthogonal and that the four groups can be distinguished from one another. They find that the
low career decidedness/low indecisiveness group includes students who “lack a clear vocational
identity, but reveal low levels of anxiety and externality and high levels of self-esteem” (p. 111).
It is this group that represents the developmentally undecided group of individuals who are
exploring their career options, but have not yet made a decision.
Students in the low career decidedness/high indecisiveness group are the chronically
indecisive. These individuals have “low levels of vocational identity and self-esteem and high
levels of anxiety and externality” (p. 111). Santos and his colleagues suggest that prior to
discussing career options, career counselors may have to address underlying problems before
discussing career options.
The high career decidedness/low indecisiveness group represents individuals with a
“clear vocational identity and a psychological profile indicative of a good level op psychological
adjustment.” These individuals have a clear career path in mind and do not tend to seek help in
evaluating their career options. Interestingly, Santos and his colleagues also identified a group of
students who in the high career decidedness/high indecisiveness group. These individuals
possess a clear vocational goals, but present low levels of self-esteem, high levels of anxiety, and
6
moderate levels of externality. The authors identify these students as possessing a “dysfunctional
style of decision process” (p. 111).
The Role of Indecisiveness in Career Indecision
Jones and Chenery (1980) develop a Career Decision Profile (CDP) which treats career
indecision as a three-dimensional construct: 1) decidedness, 2) comfort, and 3) reasons.
Decidedness is viewed as an individual's self-perception of how decided they are on a choice of
occupation or career (Jones & Chenery 1980). Comfort is included as a dimension because
studies have shown that a number of college students feel relatively comfortable with being
undecided about their career choices (Jones & Chenery 1980; Holland & Holland 1977). Reasons
are the explanations individuals give for being undecided about their future career choices (Jones
& Chenery 1980).
In a revision of the CDP, Jones (1989) proposes four sub-categories of the reasons
dimension: 1) lack of self-clarity, 2) lack of educational-occupation information, 3)
indecisiveness, and 4) choice-work salience. Jones conceptualizes self-clarity as how "clearly
respondents understand their interests, ability, and personality and how they might fit with
different occupations" (p. 479). Lack of occupational-educational information is determined by
how "well informed respondents believe they are about occupations and educational programs
that will fit their interests and abilities" (Jones, 1989, p. 479). Jones defines indecisiveness as an
individual's ability to "make decisions without unnecessary delay, difficulty, or reliance on
others" (p. 479). Choice-work salience is how important an individual believes that "choosing
and working in an occupation" is at the present time (Jones 1989, p. 479). As instruments
designed to measure career indecision continued to emerge, it became common to see
7
indecisiveness as a subscale of a career decision instruments (Chartrand, Robbins, Morrill &
Boggs 1990; Gati, Krausz, & Osipow 1996).
Vocational choice research in the late 80s and early 90s also focuses on the inability of
individuals to make decisions (Chartrand, Robbins, Morrill, & Boggs 1990; Wanberg &
Muchinsky 1992), their discomfort with making decisions (Jones 1989), or their lack of an
ability to select a goal (Callanan & Greenhaus 1990, 1992). Cooper, Fuqua, & Hartman (1984),
define indecisiveness as difficulty with decisions in general. Other definitions from this time
period focus on delay in making decisions (Jones 1989; Wanberg & Muchinsky 1992),
uncertainty about decisions made (Callanan & Greenhaus 1990, 1992) , and lack of competence
in formulating decisions (Chartrand et al. 1990). Consistent with the early literature on
vocational choice (Forer 1953; Tyler 1961; Crites 1969; Salomone 1982), the more recent
vocational choice literature treats indecisiveness as a domain-general construct (Osipow 1999;
Germeijs & DeBoeck 2002; Bacanli 2000, 2006; Germeijs and Verschuren 2011; Santos,
Ferreira, and Gonçalves 2014).
Indecisiveness in the Decision Making Literature
While not immediately related to the literature on indecisiveness, there is a concurrent
stream of literature on conflict in decision making. In the decision making literature, Janis and
Mann (1977) develop the conflict theory of decision making. According to this theory, decision
makers are surrounded with uncertainty when they have to make important decisions. Decisional
conflicts are defined as “simultaneous opposing tendencies within the individual to accept and
reject a given course of action” (Janis & Mann 1977, p. 46). These opposing tendencies can lead
8
to some of the same behaviors that manifest as a result of indecisiveness, such as decisional
procrastination or preference for a no-choice action.
From this literature we see the emergence of the decisional procrastination literature
(Milgram & Tenne 2000; Ferrari & Dovidio 2001; Ferrari & Pychyl 2007; Steel 2010; Johnson
& McCown 1995). Decisional procrastination is defined as the “inability to make timely
decisions in minor matters” (Effert & Ferrari 1989). This is inconsistent with Crites' (1969)
definition, which states that indecisiveness can occur regardless of the importance of the
decision.
Cognitive psychologist Joseph R. Ferrari and his colleagues (Effert and Ferrari 1989;
Ferrari 1993; Ferrari, Johnson, and McCown 1995; Harriott, Ferrari, and Dovidio 1996; Ferrari
& Dovidio 1997, 2000, 2001; Ferrari & Pychyl 2007) use Mann’s (1982) five-item Decisional
Procrastination Scale as a proxy for indecisiveness. Although the construct of indecisiveness is
theoretically distinct from the construct of decisional procrastination, Patalano and Wengrovitz
(2007) find that the two constructs are highly correlated (r= .56 and .66).
Using decisional procrastination (DP) to represent indecisiveness, research in this specific
stream has found low DP scores to be related to low self-esteem (Effert & Ferrari 1989), high
levels of interpersonal dependency and self-defeating behaviors (Ferrari 1994), distractibility and
daydreaming (Harriott et al. 1996), a proneness to boredom (Blunt and Pychyl 1998), falsely
recalling completion of tasks (Scher and Ferrari 2000).
In a series of experiments, Ferrari and Dovido (2001) find that individuals identified as
highly indecisive (high DP scores), search for information in different ways than those who are
more decisive. Using an information board paradigm, participants were asked to select a college
course from a set of alternatives that could vary along the attributes of time of day, instructor
9
quality, and career relevance. There was no time limit and the participants could view as much
information as they desired before making a decision. Participants were randomly assigned to
one of three distractor conditions. In the first condition participants were presented with an eightdigit number prior to the task and attempted to recall the number at the end of the task. The
second condition involved counting the number of randomly presented clicks throughout the
search process. The third condition was a combination of the digit and counting tasks.
The authors find that highly indecisive individuals searched more within attribute
dimensions and searched for less information overall than did their more decisive counterparts
when under cognitive load. The authors speculate that as indecisiveness increases, individuals
“avoided information from various attributes and dimensions in their search patterns” (p. 1118).
The amount of information search by highly indecisive individuals continues to decrease as
cognitive load increases. Highly indecisive individuals were more likely to pursue a “narrowly
focused search strategy” when under high cognitive load by taking a more selective and
noncompensatory approach to evaluating alternatives. Highly indecisive individuals under high
cognitive load during the decision task were also less confident in their decisions and rated the
task as more difficult than did the more decisive individuals.
Indecisiveness in the Clinical Psychology Literature
Indecisiveness also makes an appearance in the clinical psychology literature on
obsessive compulsive behavior (Straus 1948, Walker & Beech 1969; Beech 1971; Beech 1974;
Reed 1985; Frost & Shows 1993). Indecisiveness is used as a diagnostic criterion for several
psychological disorders (e.g. depression, obsessive compulsive personality disorder, etc.). It is
referred to as “difficulty deciding” in diagnostic screening materials (APA, 2000). Reed (1985)
10
defines indecisiveness as “failure or hesitation in deciding, an inability to make up one’s mind or
come to a conclusion” (p. 171). Rassin and his colleagues (2007) define indecision as “decision
difficulties in one specific area” and indecisiveness as decision “difficulties in virtually all
possible areas” (p. 61). Spunt, Rassin, & Epstein (2009) define two types of indecisiveness:
aversive indecisiveness and avoidant indecisiveness. Aversive indecisiveness is “characterized
by negative affect and threat-oriented cognition when making and evaluating decisions” (Spunt
et al. 2009, p. 257). Avoidant indecisiveness is “characterized by avoidant preferences and
difficulties when making decisions” (Spunt et al. 2009, p. 257).
Indecisiveness in the Management Literature
In the management literature, indecisiveness is viewed as a handicap to making timely
organizational decisions. Elaydi (2006) defines indecisiveness according to two of the
dimensions from Jones’ (1989) Career Decision Profile (CDF), decidedness and comfort in the
decision situation, where being undecided suggests being “stuck in the decision-making process”
(p. 1367). Denis, Dompierre, Langley, and Rouleau (2011) evaluate organizational indecision at
the group level. They use the term “escalating indecision” to describe situations in which “people
and organizations continually make, unmake, and remake strategic decisions” (Denis et al. 2011,
p. 225).
Indecisiveness in the Marketing Literature
In the field of marketing, prior literature has addressed various topics in consumer
decision making difficulty, such as choice deferral (Dhar 1996, 1997; Dhar & Nowlis 1999,
2004), emotional trade-off difficulty (Payne, Bettman, & Johnson; Luce, Payne, & Bettman
11
1999; Drolet & Luce 2004), and difficulty caused by task complexity (Dhar 1996, 1997; Tversky
& Shafir 1992) or cognitive load (Drolet & Luce 2004). The majority of the literature focuses on
the inherent difficulty of the decision making task, rather than individual differences in decisionmaking difficulty. Very little research has addressed the topic of consumer indecisiveness.
Ulkumen and Malkoc (2011) show that when consumers make a broad category decision and
focus on attribute similarities, they are more susceptible to state indecision than when they focus
on attribute differences or make narrow category decisions. This implies that the amount of
decisional conflict and a state of indecision can brought about by features of the decision making
task.
There has been a growing interest in the marketing literature on the effect of indecisiveness
on consumer behavior. In a recent paper, Jeong and Drolet (2014) find that chronic
indecisiveness is positively associated variety-seeking behavior and that chronically indecisive
consumers feel more positively after choosing from a mix of products. Counter to existing
literature on variety-seeking, this study finds that when primed to feel negative emotions, highly
indecisive individuals increased their variety-seeking behavior. Individuals who are lower in
indecisiveness behave as expected, and their variety-seeking behavior is significantly higher
when they are in a positive mood (as compared to a negative mood). The authors suggest that the
use of variety seeking, in the case of highly indecisive individuals, is as an avoidant coping
strategy. Jeong and Drolet (2014) find that highly indecisive individuals experience more
negative emotion and engage in more variety-seeking during decision making tasks than do their
more decisive counterparts. Their findings also suggest that variety-seeking is one way that
“chronically indecisive consumers rely on to cope with negative emotion” (p. 5).
12
Using a newly developed decision behavior inventory, Fox and Barkley-Levenson
(2011), identify three types of indecisiveness: 1) neurotic/impulsive indecisiveness, 2)
perfectionistic/compulsive indecisiveness, and 3) unprepared indecisiveness. The first type,
neurotic/impulsive indecisiveness, correlates most strongly with existing measures of
indecisiveness (Frost & Shows 1993; Spunt et al. 2009). According to Fox & Barkley-Levenson,
individuals who experience this type of indecisiveness “dislike making decisions and are
distressed by them, and thus will avoid deciding by procrastinating or deferring to others, and
they avoid making tradeoffs” (p. 237). The second type, perfectionistic/compulsive
indecisiveness, is not related to measures of distress or anxiety. These individuals show a
“tendency to collect information and evaluate alternatives carefully before making a choice…but
the decision process itself does not necessarily distress them” (p. 237). The third type,
unprepared indecisive consumers, are not distressed by decision making and do not struggle with
the decision making process. But, they are disorganized and often “unprepared when the decision
moment arrives” (p. 237).
Antecedents of Indecisiveness
Indecisiveness and Individual Characteristics
Early research in vocational indecision suggests that indecisiveness may be due to
developmental immaturity (Carter 1944; Tyler 1961; Ginzberg et al. 1951), personal problems
(Tyler 1961), or maladjustment (Forer 1953). Holland and Holland (1977) conceptualize what
they call an “indecisive disposition” as the outcome of an individual’s failure to acquire
“necessary cultural involvement, self-confidence, tolerance for ambiguity, sense of identity, selfand environmental knowledge to cope with vocational decision making as well as with other
13
common problems” (p. 413). This viewpoint is grounded in Erikson’s (1963) theory of identity
and crisis. According to Erikson, there are events, or crises, that take place during adolescence
that are impact the trajectory of an individual’s identity formation. Holland and Holland (1977)
see successful identity formation as a prerequisite to the skills needed to cope with important life
decisions, such as vocational decision making.
The development of the Indecisiveness Scale by Frost and Shows (1993) allows researchers
to measure the construct and test relationships between indecisiveness and other constructs.
Studies on indecisiveness have shown conflicting findings in terms of gender differences. Some
studies were conducted exclusively with female participants (Rassin & Muris 2005b; Frost &
Shows 1993; Gayton, Clavin, Clavin, & Broida 1994). In studies that included both men and
women, some show that women are more likely to report higher levels of indecisiveness than
men (Rassin & Muris 2005a; Rassin et al. 2007 Study 1) while others find no significant gender
difference (Patalano and Wengrovitz 2006; Swami, Sinniah, Subramaniam, Pillai, Kannan, and
Chamorro-Premuzic 2008; Rassin, Muris, Booster, & Kolsloot 2008; Rassin et al. 2007 Study 2).
The effect of age on indecisiveness is also inconclusive, sometimes showing a negative
correlation between indecisiveness and age (Rassin et al. 2007 Study 1) and sometimes showing
no effect (Rassin et al. 2007 Study 2; Rassin, Muris, Booster, & Kolsloot 2008).
Studies have also produced conflicting findings in the investigation of cultural differences in
indecisiveness. Wengrovitz and Patalano (2004) find that, in the United States, Americans of
East Asian cultural backgrounds are more indecisive than Americans of European cultural
background. In a follow-up study, Patalano and Wengrovitz (2006) assess cultural differences in
indecisiveness cross-nationally. They find that Chinese participants and American participants do
not differ in their level of indecisiveness. In yet another study, researchers find that Japanese
14
participants are more indecisive than Chinese and American participants, but that Chinese and
American participants do not differ from one another (Yates, Ji, Oka, Lee, Shinotsuka, and Sieck
2010). Swami and colleagues (2008) find Chinese participants living in Malaysia to be less
indecisive than their Malay counterparts. To explore these discrepancies, Ng and Hynie (2014)
set out to explain the process by which cultural differences might manifest in chronic
indecisiveness. They explain that East Asian cultures “exhibit naïve dialecticism, a set of
worldviews that tolerates contradictions” (p. 45). As such, East Asians are more likely to possess
enduring ambivalent attitudes rather than resolving contradictions as they arise. They find that
this naïve dialecticism, and a cultural difference in a tendency to “engage in and enjoy effortful
cognitive endeavors” (NFC, Cacioppo and Petty 1982; Ng and Hynie 2014, p. 46), is what
mediates the relationship between culture and indecisiveness.
Indecisiveness and Cognitive Characteristics
Much of the early research on indecisiveness was conducted in clinical populations of
individuals diagnosed with obsessive-compulsive disorder (OCD). In his theoretical research on
decision making among obsessionals, Reed (1977) claims that within populations of individuals
clinically diagnosed with OCD, indecisiveness may be a “formal cognitive characteristic” (p.
184) caused by an inability to structure incoming information. This would suggest that
indecisiveness is due to a type of cognitive deficiency.
Spunt, Rassin, and Epstein (2009) propose three dispositional antecedents to the trait of
indecisiveness: 1) behavioral inhibition system (BIS) versus behavioral activation system (BAS)
sensitivities, 2) tendency to engage in maximization of outcomes and 3) proneness to regret.
First, the authors theorize that an individual's sensitivity to the three motivational systems in the
15
revised Reinforcement Sensitivity Theory (rRST; Gray & McNaughton 2000) determine whether
an individual might be more prone to either aversive or avoidant indecisiveness. The
Fight/Flight/Freeze System (FFFS) "mediates avoidance of punishing stimuli and produces fear"
(Spunt et al. 2009, p. 257). The Behavioral Activation System (BAS) "mediates approach
towards rewarding stimuli and produces positive affect" (p. 257). The Behavioral Inhibition
System (BIS) "mediates the detection and resolution of conflicts within or between responses
mediated by the FFFS and the BAS" and produces anxiety (p. 257). The authors predict that a
tendency to process stimuli through BIS leads to aversive indecisiveness and proneness to regret,
while greater sensitivity to the BAS leads to avoidant indecisiveness.
The second proposed dispositional antecedent is the tendency of some decision makers to
maximize the outcomes of their decisions (Spunt et al. 2009; Rassin 2007). These individuals
tend to approach decisions with "the unrealistically high expectation that they will find the best
alternative possible" (Spunt et al. 2009, p. 257). The authors find this tendency to maximize the
outcome of all decision making tasks to be positively related to aversive indecisiveness.
The third dispositional anteceded is proneness to regret. Regret is defined as “a negative,
cognitively based emotion that we experience when realizing or imagining that our present
situation would have been better, had we decided differently” (Zeelenberg 1999, p. 93).
Proneness to regret is an individual difference in how susceptible an individual is to feelings of
anticipated or felt regret (Schwartz, Ward, Monterosso, Lyubmirsky, White, & Lehman 2002). In
the literature on obsessive-compulsive behavior, Reed (1985) suggests that a possible
consequence of indecisiveness may be doubts about whether a past decision was optimal.
Although regret is an emotion that is typically conceptualized as a post-decision emotion, regret
can also affect decision makers in the form of anticipatory regret, which has been identified as a
16
proximal cause of decision avoidance (Anderson 2003). The consumer behavior literature has
shown evidence that anticipation of regret can influence consumers' purchase decisions
(Simonson 1992). This proneness to regret has been found to be associated with higher levels of
aversive indecisiveness (Spunt et al. 2009). Proneness to regret has also been shown to mediate
the positive relationship between the tendency to maximize and aversive indecisiveness (Spunt et
al. 2009).
Correlates of Indecisiveness
Indecisiveness and Obsessional Behaviors
Early theorizing by researchers in clinical psychology suggests a link between
indecisiveness and obsessive compulsive behaviors (Straus 1948; Reed 1977; APA 1987) as well
as perfectionist thinking (Guidano & Liotti 1983; McFall & Wallersheim; Salzman 1980; Straus
1948; Frost, Marten, Lahart, & Rosenblate 1990). The link between indecisiveness and obsessive
compulsive behaviors likely stems from a set of dysfunctional assumptions obsessional
individuals hold, such as “a belief in perfect solutions, excessive attempts to avoid mistakes, and
equating making a mistake with failure” (Frost & Shows 1993, p. 683; see also Frost et al. 1990).
While most people experience conflict or indecisiveness when it comes to important decisions
(Janis & Mann 1977; Crites 1969), obsessional individuals may have a lower threshold when
deciding what decisions are important (Reed 1985).
Empirical research shows correlations between the measures of indecisiveness and the
checking, rumination, and doubting subscales from the Maudsley Obsessive Compulsive
Inventory (MOCI, Rassin & Muris 2005, Frost & Shows 1993; Gayton, Clavin, Clavin & Broida
1994) as well as the impulses, checking, rumination, and precision subscales of the Padua
17
Inventory (Sanavio 1988). Frost and Shows (1993) find that indecisiveness is correlated with
measures of obsessive-compulsive symptomology and behaviors (also Rassin et al. 2007),
perfectionism (also Patalano & Wengrovitz 2007), procrastination (also Patalano & Wengrovitz
2007), and hoarding behavior. Highly indecisive individuals have been found to experience both
domain-general and domain-specific indecisiveness and to report a wider range of general
symptoms of psychopathology (Frost & Shows 1993). Individuals who report high levels of
domain-general indecisiveness are bothered by their indecisiveness, and it interferes with their
functioning to a greater degree than those who report low levels of indecisiveness (Frost &
Shows 1993).
Indecisiveness and Trait Anxiety
Anxiety is a “subjectively rather unpleasant emotional state accompanied by typical
physiological symptoms, such as increased heart rate, respiration rate, and enhanced
electrodermal reactivity” (Kirsch & Windmann 2009, p. 19). Hartley and Phelps (2012) identify
overlapping neural systems involved in anxiety and decision making. They conclude that
pathological anxiety can influence an individual’s ability to function in everyday tasks, including
decision making. Early vocational researchers find a relationship between career indecision and
anxiety (Hawkins, Bradley, & White 1977; Kimes & Troth 1974). The indecisiveness literature
also finds evidence that indecisiveness is positively related to trait anxiety, pathological worry,
and depression (Rassin et al. 2007).
One of the main indicators of Generalized Anxiety Disorder (GAD, APA 1994) is
intolerance of uncertainty. Individuals who are particularly cautious in their decision making
style tend to exhibit an intolerance of uncertainty and that increases in intolerance of uncertainty
18
are positively related to excessive worrying (Rassin & Muris 2005b) and indecisiveness (Rassin
et al. 2007). In a similar vein, indecisive individuals have been shown to resort to worst case
scenario reasoning and are more likely to interpret ambiguous situations as threatening (Rassin &
Muris 2005b). Other correlates of this type of biased reasoning and interpretation include
anxiety, depressed mood, worry, and intolerance of uncertainty (Rassin & Muris 2005b).
Individuals with heightened levels of chronic anxiety are more sensitive to risks and
potential losses and "worry enormously about making the right decisions" (Kirsch & Windmann
2009, p. 20). This heightened sensitivity to risk (Kirsch & Windmann 2009) supports findings
from early vocational researchers that suggest a between relationship career indecision and risk
avoidance. According to Kirsch and Windmann (2009), individuals with chronic heightened
anxiety experience worries about the future, are particularly sensitive to risk and danger, and
probably follow different decision making patterns.
Consequences of Indecisiveness
Indecisiveness and Information Processing
Indecisiveness has been associated with multiple stages of the decision making process. In
particular, indecisiveness has been shown to impact the amount of time spent making a decision,
how much information is gathered, and what strategies are used to evaluate alternatives.
Research shows that indecisive individuals spend more time making decisions than those who
are not indecisive (Frost & Shows 1993; Rassin & Muris 2005a; Gayton, Clavin, Clavin, &
Broida 1994; Rassin et al. 2008). Frost and Shows (1993) identify this amount of time from the
onset of the decision making task to the time a decision is made as decision latency.
19
Rassin, Muris, Franken, Smit, and Wong (2007) find indecisiveness to be positively
associated with amount of information gathered before reaching a decision (see also Reed 1985).
Rassin and colleagues (2007) show that indecisive individuals require more information before
making a decision in identification and inference tasks, even when controlling for intolerance of
uncertainty and age. For indecisive individuals, this processing tends to be negatively biased
(Rassin & Muris 2005b) and focused on the item they will ultimately choose (Rassin et al. 2007,
2008). Indecisive individuals have also been found to divide the time spent during a decision
making task among a greater number of attributes of the alternative they eventually select
(Patalano, Juhasz, & Dicke 2010).
Findings indicate that indecisive individuals may unable to efficiently organize stimuli in
their environment, resulting in over-structuring or over-categorizing of information (Rassin &
Muris 2005a, b). Because this additional categorizing and structuring takes time and effort, it
may lead to increased decision latency (Tallis 1997), which is consistent with the finding that
individuals who are highly indecisive require more time to make decisions (Frost and Shows
1993).
Indecisive individuals also differ in their strategic approach to decision making. In a
paper by Patalano and Wengrovitz (2007), decisive and indecisive individuals participated in two
decision making studies with sequential dynamic choice sets using a variation of the information
board paradigm commonly used in process-tracing research (Payne 1976). In the first of the two
studies, the decision scenario involves a set of alternatives from which the participant can choose
to delay making a choice, gaining a chance to add more alternatives to the choice set. If the
individual delays until the next decision round, additional alternatives will be added to the choice
set. In the "risk" condition, some of the original alternatives may become unavailable. In the "no-
20
risk" condition, alternatives are only added, so there is no penalty for delaying the decision.
Information about the value of each alternative on each attribute starts out in a hidden state and
the decision maker can choose to display additional information. The instructions were to view
only as much information as possible to make their decision.
The authors find that decisive individuals modulate the number of choice cycles they use
to deliberate in response to the presence (versus absence) risk in dynamic decision-making
situations while indecisive individuals do not. Decisive individuals in the no risk condition and
indecisive individuals in both conditions delayed their decision a statistically equal number of
rounds. Only decisive individuals in the risk condition changed their behavior to make the
decision in fewer rounds. Highly indecisive individuals delay their choice despite the risk of
losing the optimal alternative from the choice set. The authors suggest that a "high need for
certainty and a desire to minimize errors" might be contributing to a "desire to make sure that no
better alternatives exist before committing to an inferior choice alternative" (p. 421). The authors
further speculate that indecisive individuals weight anticipated regret for missing a future
opportunity more highly than regret for passing up something that they have already had a
chance to observe.
In addition to differences in patterns of delaying a decision, highly decisive individuals
have also been shown to process information differently. In the same study, Patalano and
Wengrovitz (2007) find that indecisive individuals make a greater percentage of alternativebased shifts than decisive individuals and decisive individuals use more attribute-based shifts
during decision making tasks. Decision making literature has found that information search
strategies that rely on alternative-based shifts tend to be more effortful than those that rely
primarily on attribute-based shifts (Payne, Bettman, and Johnson 1988). The authors also find
21
evidence of an interaction effect between indecisiveness and decision making risk on decision
latency. In the risk condition, indecisive individuals take longer to proceed to the next round than
their decisive counterparts. They spend more time on average within each round (measured in
seconds) than they do in the no-risk condition while decisive individuals do not differ by risk
condition. In terms of performance, both indecisive and decisive individuals were equally likely
to select the optimal choice in the no-risk condition. However, in the risk condition, the
indecisive individuals waited until the optimal choice was no longer available, losing the
opportunity to choose it. Preference ratings did not differ by indecisiveness group or risk
condition.
In their second study, Patalano and Wengrovitz (2007) make a few adjustments to their
study procedure. First, rather than leaving information visible after clicking on it, when the
participant is finished with each cell of information, it reverts to its original state and is no longer
displayed (i.e. hidden information method). In this experiment the second-best alternative is
available at the beginning of the experiment (in Round 1). The optimal alternative is not
available until Round 3 and becomes unavailable in Round 4. Therefore, there is a limited
window of opportunity in which the participants can select the optimal alternative.
As in the first study, the authors find that decisive individuals delay fewer days in the risk
condition than in the no-risk condition. Indecisive individuals, again, do not modulate their delay
behavior in response to the risk of losing access to alternatives in subsequent rounds of the
decision task. However, they do look at more information and take longer before proceeding to
the next round. Decisive individuals do not modulate their information search behavior.
Indecisive individuals in both risk conditions were equally likely to choose the optimal
alternative. Decisive individuals were as likely as indecisive individuals to select the optimal
22
alternative in the no risk condition but were less likely to choose it in the risk condition. This
suggests that indecisive individuals are able to outperform decisive individuals in a dynamic
choice set condition. However, in this particular study, there was a low rate of choice for the
optimal course (48%) and the results are not reliable.
The authors also find no group differences in the number of subjects opting to delay
choice. However, they do find that indecisive individuals pay attention to different information
during different stages in a decision making task than do decisive individuals. During the first
half of a decision task, indecisive individuals make more alternative-based shifts in their
attention than decisive individuals. During the second half of the decision task, there is no
difference in the shift patterns between decisive and indecisive individuals. Both indecisive and
decisive individuals made a greater number of attribute-based shifts in the second half of the
decision task. The authors suggest that one explanation for this difference is that indecisive
individuals want to collect as much information as possible and eventually have to switch
strategies to evaluate the large decision set they have generated in the information gathering
process (Rassin et al. 2008).
The findings also suggest that indecisive individuals use a compensatory decision making
strategy in the first half of a decision making task, then switch to a noncompensatory decision
making strategy for the second half of the decision task (Patalano et al. 2010). The authors
suggest that the use of compensatory decision making strategies can be explained by the
indecisive individual's desire to maximize the outcome of the decision making task, resulting in a
large set of alternatives. A need to narrow down the set of viable alternatives prevents the
continued use of compensatory strategies and forces the indecisive individual to use
noncompensatory strategies as the decision task progresses (Patalano et al. 2010).
23
Patalano and colleagues (2010) find evidence that indecisive individuals spend time
looking at different information than their more decisive counterparts. This study shows that
highly indecisive individuals spend a greater amount of time looking at empty space than
decisive individuals. The authors suggest that the time spent looking away from information cells
may illustrate an attempt to delay the task, as a way to deliberate alternatives without the
distraction of visual information, or as a way to peripherally refresh information that has already
been viewed. In addition, more decisive individuals focused more exclusively on a single
attribute (time of day course is offered) while highly indecisive individuals looked relatively
equally at all three attributes that required a tradeoff. The authors posit that “decisive individuals
might more quickly resolve conflict on the basis of a single dimension while indecisive
individuals spend more time deliberating about all dimensions of conflict” (363).
Indecisiveness and Negative Affect
The literature on indecisiveness suggests that individuals who experience chronic
indecisiveness are bothered by their indecisiveness and that it interferes with their daily
functioning (Frost & Shows 1993). Discomfort with the decision making process “reflects the
negative concurrent emotions” that these individuals experience (Elaydi 2006, p. 1367).
Indecisiveness has been linked to anxious and depressive emotional states and negative affect
(Elaydi 2006; Rassin & Muris 2005a, b; Rassin et al. 2007; Jeong & Drolet 2014). Indecisiveness
positively correlated with feelings of panic when facing a difficult decision and negatively
correlated with self-confidence in decision making (Rassin et al. 2007). These negative emotions
seem to be pervasive, as indecisive individuals report lower levels of life satisfaction (Rassin and
Muris 2005a) and more frequent diagnoses of anxiety (Kirsch & Windmann 2009; Harley &
24
Phelps 2012; Hawkins, Bradley & White 1977; Kimes & Troth 1974; Ferrari & Dovidio 2001)
depression (Di Schiena, Luminet, Chang, Philippot 2013; APA 2000).
Indecisiveness and Coping Mechanisms
Indecisive individuals tend to cope with perceived decision making difficulty and
negative affect in a variety of ways. Indecisiveness has been linked to decision avoidance
(Anderson 2003; Rassin et al. 2007; Patalano and Wengrovitz 2007; Rassin & Muris 2005a,b),
the "tendency to avoid making a choice by postponing it or by seeking an easy way out that
involves no action or change" (Anderson 2003, p. 139). Decision avoidance can manifest in
several ways. Avoidance of the decision making process may involve resorting to the following
tactics: delaying the decision until another time (Dhar 1996; Dhar & Nowlis 1999; Patalano &
Wengrovitz 2007; Tversky & Shafir 1992; Ferrari & Dovidio 2001; Ferrari & Pychyl 2007),
resorting to a default no-action/choosing not to choose (Dinner, Johnson, Goldstein, & Liu 2011;
Dhar 1997; Anderson 2003), choosing status quo/no-change options (Luce 1998; Anderson
2003; Dinner, Johnson, Goldstein, & Liu 2011), or relying on someone else to make the decision
(Janis & Mann 1977). Recent consumer behavior literature has also found that variety-seeking
behavior may be a mechanism by which highly indecisive individuals cope with negative
emotions associated with decision making tasks (Jeong and Drolet 2014).
While decisional procrastination is better conceptualized as a behavioral outcome of
indecisiveness, some researchers treat decisional procrastination as an indicator of indecisiveness
(Ferrari & Pychyl 2007; Ferrari & Dovidio 2001). Within this stream of literature, decisional
procrastination has been linked to greater levels of state anxiety and lower self-confidence when
making decisions in cognitively demanding situations (Ferrari & Dovidio 2001). Ferrari and
25
Pychyl (2007) suggest that decision making tasks are more cognitively demanding for
individuals who are prone to decisional procrastination. Thus, these individuals are susceptible to
decisional fatigue, which leads to a decrease in self-regulation strength. An individual might
decide to defer a choice for the purpose of obtaining more information on existing alternatives or
to find information on new alternatives (Corbin 1980) in an effort to avoid making difficult
trade-offs (Tversky & Shafir 1992; Luce 1998; Dhar 1997).
Measurement of Indecisiveness
The definition of indecisiveness possesses some continuity in the literature across time
and across domains. The construct developed out of the career indecision literature to clarify the
difference between the state of indecision or undecidedness and the trait of indecisiveness.
Research has also made a distinction between domain-general and domain-specific
indecisiveness. Measurement instruments for the construct of indecisiveness have undergone a
similar transformation. There is evidence of a continued effort in the vocational literature to
develop valid and reliable measurement of the domain-specific phenomenon of career indecision
(Osipow & Carney 1975; Holland & Holland 1977). The earliest instruments are based on how
committed an individual is to a particular vocational choice (Kimes & Troth 1974; Hawkins,
Bradley, & White 1977). Later developments are multidimensional, often including a domaingeneral subscale of indecisiveness (Jones 1989; Chartrand, Robbins, Morril & Boggs 1990; Gati,
Krausz, & Osipow 1996). Once the domain-general trait indecisiveness is identified as a separate
construct from the domain-specific state of career indecision, researchers in clinical psychology
(Frost & Shows 1993; Rassin, Muris, Franken, Smit, & Wong 2007), vocational decision making
(Germeijs & DeBoeck 2002; Bacanli 2000, 2006), and management decision making (Elaydi
2006) attempt to develop a measure of trait indecisiveness.
26
Development of Career Indecision Measurement Instruments
Career indecision is a domain-specific state of undecidedness. In 1964, Holland and
Nichols develop a 15-item Indecision Scale. They explore the connection between vocational
indecision and a variety of personal attributes. They propose a list of activities, hobbies, sports
and school subjects that are more or less correlated with a crude measure of career decidedness.
Career decidedness is measured through a fill-in-the-blank response to the following item: "My
present career choice is: [blank]." Students are identified as undecided if they responded
"undecided," "don't know," or "?." To identify discriminating items, the authors begin with an
analysis of 273 activities, hobbies, sports, and school subjects rated by a sample of high aptitude
high school students. The 15 most discriminating items are selected for males and another 15 for
females to test the concurrent validity of the Indecision Scale. Holland and Nichols find low
reliability for their Indecision Scale (K-R 20Males=0.60, K-R 20Females=0.40)2.
Kimes and Troth (1974) measure career decisiveness as a single-item self-report measure.
The authors ask college students to state the level of their career decisiveness as either a)
definitely decided, b) tentatively decided, c) moving toward a decision but have not made a
tentative decision, d) have a career in mind but not moving toward a decision, or e) completely
undecided. They find that 79.98% of the students surveyed responded that they had either
definitely or tentatively decided on a career choice, 10.25% were moving toward a decision but
had not made a tentative decision, 6.51% had a career in mind but were still undecided, and
3.26% were completely undecided.
It is also common for vocational researchers to ask about the level of comfort or satisfaction
an individual has with their current career choice (see Kimes & Troth 1974; Holland & Holland
2
A measure of internal consistency for scales that contain dichotomous items (Kuder & Richardson 1937)
27
1977; Jones 1989 for examples). In an approach introduced by Holland and Holland (1977),
subjects are asked to respond to a multiple choice question about job/career choice satisfaction.
Individuals who respond with dissatisfaction or uncertainty are asked to respond to additional
questions about decision making maturity, knowledge about occupations, and individual
characteristics. The greater number of items a student identified as reasons for their
indecisiveness is used to indicate a greater degree of vocational indecision.
Osipow, Carney, and Barak (1976) test a Scale of Vocational Indecision that reliably
discriminates between college students who have decided on a career and those who have not.
The 16-item questionnaire is based on interviews with clients experiencing educational and/or
vocational indecision. A factor analysis of the results reveals four factors that explain 81.3% of
total variance. The first factor, avoidance of choice, is made up of nine items that indicate a lack
of structure and confidence in vocational decision making, presumably resulting in a desire to
avoid making a vocational choice. The five items representing the second factor suggest a
perception of external barriers to the student’s preferred choice and/or a need for additional
career alternatives. The third factor, identified as positive choice conflict, includes two items that
suggest the student is having difficulty deciding from among a number of attractive alternatives.
The two items in the fourth factor refer to some type of personal conflict about how to make a
choice.
As instruments designed to measure career indecision continue to emerge, it is common
to see indecisiveness as a subscale of a career decision instrument (Jones 1989; Chartrand,
Robbins, Morril & Boggs 1990; Gati, Krausz, & Osipow 1996). Chartrand, Robbins, Morrill, and
Boggs (1987) developed a revised version of the Career Factors Inventory (CFI; Robbins,
Morrill, & Boggs 1987). Analysis revealed four factors: 1) Career Choice Anxiety, 2)
28
Generalized Indecisiveness, 3) Need for Career Information, and 4) Need for Self-Knowledge.
Jones (1989) developed a revision of his Vocational Decision Scale (Jones 1977), renaming it the
Career Decision Profile (CDP). The CDP assesses level of decidedness (two items) and comfort
(one item) in the vocational decision making process. Twenty-four additional items are used to
identify reasons the subject remains undecided. The items load onto four factors: 1) lack of selfclarity, 2) lack of educational-occupational information, 3) indecisiveness, and 4) choice-work
salience. The three items from the indecisiveness subscale refer to domain-general difficulty in
making decisions and a feeling of relief if someone else takes over responsibility for the
decision. The authors report a high test-retest correlation for their composite indecisiveness
score, but do not report internal reliability of the scale items.
Development of Trait Indecisiveness Measurement Instruments
Another approach to developing a measurement of career indecisiveness is to evaluate
indecisiveness as an enduring individual characteristic of individuals struggling with a state of
career indecision. Cooper, Fuqua, and Hartman (1984) develop the Trait Indecisiveness Scale.
The instrument is based on eight statements developed by Salomone (1982). Subjects complete
the Trait Indecisiveness Scale by responding to a variety of questions in a dichotomous yes-no
format. The authors find that the Trait Indecisiveness Scale does identify vocational uncertainty.
The scale correlates with a self-report measure of career indecision, correctly classifying 66% of
the cases. The authors also find a relationship between their scale and the interpersonal
characteristics of self-criticalness, submissiveness, passivity, and need for approval.
While the Trait Indecisiveness Scale does show predictive power, the name of the scale is a
bit misleading and lacks face validity. While the scale is intended to measure trait indecisiveness,
29
the scale items represent career undecidedness ("I feel a lot of frustration and uncertainty in
making personal-vocational choices"), locus of control ("I often ask my parents for help when I
have an emotional problem", "I like to get specific suggestions from others but usually do not act
on this advice", "Situations and people exercise a great deal of influence over my life"), feelings
of helplessness ("I often feel helpless in dealing with unpleasant situations"), depression ("I seem
not to feel good about myself much of the time"), and desire for change ("I would like to change
some of my patterns of behavior but find it difficult to do so"). Only one of items is related to
indecisiveness as defined by the literature ("I find it difficult to make a decision even after
collecting information and talking to others").
In their seminal work on indecisiveness, Frost and Shows (1993) devise a 15-item Likerttype questionnaire to measure compulsive indecisiveness. The items in the scale accurately
capture various components of indecisiveness that include chronically prolonged decision
latency, procrastination, strategic waiting, preference uncertainty, doubt, worry, difficulty
planning and making decisions, and an absence of desire to be in a decision making role (See
Appendix C, p. 178). Frost and Shows’ (1993) approach to indecisiveness is one of the most
widely used instruments to measure indecisiveness, but is criticized for including both domainspecific and domain-general items in measuring a domain-general phenomenon (Germeijs & De
Boeck 2002; Rassin et al. 2007).
As a solution, Germeijs and De Boeck (2002) propose a 22-item Likert-type questionnaire
based on eleven features they identify as being components of indecisiveness: difficulty, don’t
know how, feeling uncertain, takes a long time, delaying, avoidance, leaving to others,
reconsideration, worrying, regretting, and calling oneself indecisive. The authors also formulate a
parallel scale for domain-specific indecision by adapting the wording of the items to apply to a
30
specific situation, career indecision. An exploratory factor analysis of the 22-item domaingeneral Indecisiveness Scale results in a single factor solution with acceptable, although low,
internal reliability. When compared to the measure of domain-specific career indecisiveness,
domain-general indecisiveness shows discriminant validity (Germeijs & De Boeck 2002). This
scale is written in Dutch and has not been tested with English-speaking subjects.
Bacanli (2000, 2006) developed the Personal Indecisiveness Scale. The scale consists of
29 items that load on two subscales, which she calls Exploratory Indecisiveness (20 items) and
Impetuous Indecisiveness (9 items). Although the scale demonstrates acceptable reliability and
validity, the scale Bacanli developed is in Turkish and has not been translated and validated for
use with English-speaking subjects.
Rassin and colleagues (2007) propose a revised version of the Frost and Shows (1993)
Indecisiveness Scale that excludes the four domain-specific items. The remaining domaingeneral items show good validity and reliability (Rassin et al. 2007; Rassin et al. 2008). The
instrument also shows good test-retest reliability (Rassin et al. 2007). This scale revision is
widely used in current research (Rassin et. al 2008; Spunt et al. 2009; Patalano et al. 2010;
Patalano & LeClair 2011).
Patalano and Wengrovitz (2007) compared validity and reliability of the Frost & Shows
(1993) Indecisiveness Scale and the Mann (1982) Decisional Procrastination scale. In their study,
the Frost and Shows (1993) scale has better internal reliability (alpha = .85) than the Mann
(1982) scale (alpha = .81). The authors find a positive correlation (r (74) =.66, p=0.001) between
the scales. However, the magnitude of the correlation would suggest that indecisiveness and
decisional procrastination are distinct constructs (Campbell & Fiske 1959).
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In a working paper, Barkley-Levenson and Fox, introduce the Decision Behaviors
Inventory, an 18-item scale that represents three types of indecisiveness: neurotic, perfectionistic,
and lackadaisical. In addition to the indecisiveness items, the scale is accompanied by four
impulsive behavior items. The paper does not indicate reliability or validity of the scale.
While there continue to be promising advances in behavioral decision making and
psychology that have addressed the effect of indecisiveness on decision making processes, there
has been limited discussion on how indecisiveness might specifically affect the consumer
decision making process. Not only do indecisive individuals experience difficulty making
consumption decisions, they have been shown to exhibit differences in their decision making
processes and strategies, which may lead to suboptimal consumption decisions as a result of their
indecisiveness. The goal of this dissertation is to add to the existing knowledge of how
indecisiveness impacts the decision making processes of consumers as the context of the decision
task changes.
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Chapter 2: Consumer Choice Processes
The first step in understanding the consumption choice processes of indecisive
individuals is to understand how decisive individuals make consumption choices. Rational
choice theory assumes that consumer preferences are well-formed and stable with a calculable
utility. The option with the optimal utility will be selected and is not contingent on how the
options are presented or the process selected to elicit a preference. It is also assumed that the
consumer has the cognitive ability and skill to calculate and select the option with the highest
utility (Bettman, Luce, & Payne 1998). Consumer researchers disagree with this account of
consumer decision making. The information-processing approach assumes that consumers make
decisions under conditions of bounded rationality and have “limitations on their capacity for
processing information” (Bettman, Luce, & Payne 1998, p. 187; Simon 1957). Consumers,
therefore, lack the cognitive resources to generate and store well-defined and stable preferences
for every situation the encounter. Research on consumption choice has been heavily influenced
by Bettman, Luce and Payne’s (1998) model of constructive consumer choice processes.
Contingent Decision Making in the Consumer Choice Literature
Consumers may have well-formed and stable preferences when they are experienced and
familiar with the object under consideration and simply retrieve these “previously formed
evaluations from memory and select the option with the highest evaluation” (Bettman et al. 1998,
p. 188). However, while some preferences may be stored in memory, many are constructed in the
moment of the decision making task (Bettman, Luce, & Payne 1998, 2008). Many consumer
choice researchers expect that “preferences for options of any complexity or novelty are often
constructed, not merely revealed, in making a decision” (Bettman, Luce, & Payne 1998, p. 188;
33
Bettman 1979; Bettman & Park 1980; Payne, Bettman, & Johnson 1992; Slovic 1995; Tversky,
Sattath, & Slovic 1988; Bettman, Luce, & Payne 2008). Constructed preferences are developed
at the time of choice using fragments or elements of rules stored in memory (Bettman 1979;
Bettman & Park 1980a, b). Rather than storing complete heuristics in memory, consumers store
these fragments to retrieve at the time of a decisions to construct their decision making process.
The elements stored in memory may include beliefs about specific alternatives, evaluations, and
simple rules of thumb involving a subset of beliefs, rules for integrating beliefs, rules for
assigning weights, and computational rules (Bettman, Johnson, & Payne 1998). In addition to
constructing specific preferences, there is evidence that consumers also construct their
processing strategies rather than retrieving them directly from memory (Bettman, Luce, & Payne
1998, 2008; Simonson 1989, 1990; Nowlis & Simonson 1997; Dhar & Simonson 2003; Drolet
2002).
Research has shown that the same individual may use a "variety of different strategies
when making decisions" (Bettman et al. 1998, p. 189; Drolet 2002; Abelson & Levi 1985; Payne,
Bettman, & Johnson 1988). Decision strategy selection can be influenced by emotional or
cognitive difficulty experienced during the decision making task, as well as characteristics of the
decision problem, decision maker, and social context (Luce, Payne, & Bettman 2001; Bettman et
al. 2008). It has also been shown that consumers do not always follow the same processing
strategy for making choices, even when context and decision making task factors are identical
(Drolet 2002).
Constructed choice processes are also influenced by the goals of the decision maker. A
goal might be "described in terms of the desirable state of affairs that people intend to attain
through action" and the impact of a goal on choice “depends on its activation" (van Osselaer et
34
al. 2005, p. 335-336). In decision making, goals can be either outcome-based or process-based
(van Osselaer et al. 2005). This multi-class, goal-based view of consumer choice assumes that
goals are cognitively represented in memory and can be primed. Outcome based goals are
context dependent and the perceived value of a product or service is "tied to its ability to satisfy
consumption goals" (p. 336). When making decisions, consumers may search for the outcome
that gives them the most consumption pleasure (van Osselaer et al. 2005), is easily justifiable to
others (Simonson 1989; Bettman, Luce, & Payne 1998), enhances various aspects of selfpresentation (Puntoni & Tavassoli 2004); increases anticipated satisfaction (Shiv & Huber 2000),
or gathers information (Ariely & Levav 2000).
Consumers can also attempt to satisfy process goals which are independent of what
option is ultimately selected (van Osselaer et al. 2005). Common process goals include
maximizing accuracy (Bettman et al. 1998), making a decision quickly or reducing effort
(Bettman et al. 1998), avoiding negative emotions and difficult trade-offs (Luce et al. 2001), and
justifying the decision to others (Bettman et al. 1998). Other possible process goals include
enjoying the decision process (Cziksentmihalyi & Nakamura 1999) or making sure the choice
process is an efficient way to process the information (Russo, Meloy, & Medvec 1998; Simon,
Snow, & Read 2004).
Choice Goals Framework
Bettman and his colleagues (1998) propose the choice goals framework to guide
understanding of contingent choice processes. First, they posit that during the construction of a
decision making strategy, consumers spend some of their time responding to perceptual aspects
of the decision making task, which involves noticing “aspects or characteristics of the choice set”
35
(p. 65). Slight differences in how the decision problem is framed or structured can impact the
outcome of a decision task (Tversky & Kahneman 1979, 1981). Because consumers have limited
processing capacity they "generally cannot process all of the available information in a particular
situation" (Bettman et al. 1998, p.193).
Because of this limited ability to process multiple stimuli (Broadbent 1958; Deutsch &
Deutsch 1963; Treisman 1969; Johnston & Heinz 1978) and the required mental effort to process
incoming information (Payne et al. 1988), selective attention to information is inevitable (Simon
1955; Bettman et al. 1998). Two major influences on selective attention are voluntary and
involuntary attention (Kahneman 1973; Bettman et al. 1998). Voluntary attention is goal-driven
while involuntary attention can be captured by "surprising, novel, threatening, unexpected, or
otherwise perceptually salient aspects of the choice environment" (Bettman et al. 1998, p. 193).
These factors contribute to differences in perceptual interpretation of "focal aspects of the
environment" (p. 194).
Second, consumers also determine how they are going to exploit the aspects and
characteristics of the decision task through an assessment of the costs and benefits associated
with different decision strategies. The proposed framework outlines process goals and explains
how consumers assess perceptual information to construct various simplified decision strategies,
or heuristics, to meet these goals. The four goals identified as the most important in consumer
decision making are: minimizing cognitive effort, maximizing the accuracy of the choice,
minimizing the experience of negative emotion while making the choice, and maximizing the
ease of justifying the decision. The authors suggest that this limited set of goals captures “many
of the most important motivational aspects relevant to decision making” (Bettman et al. 1998, p.
193).
36
Given the differences in perception, interpretation, and goals in a given decision situation,
a consumer can select "different approaches in different situations as their goals, the constraints
of the situation, and/or their knowledge change" (p. 194). Research conducted in consumer
decision making processes suggests that any decision strategy can be decomposed into a measure
of cognitive effort required to enact the strategy known as elementary information processes
(EIPs; Bettman, Luce, & Payne 1998; Payne, Bettman, & Johnson 1988; Johnson & Payne
1985). Because decision makers have competing process goals when making a decision, they
must decide how much cognitive effort they are willing to expend during the decision making
task. Cognitive effort is defined as a "function of the number and types of EIPs needed to
complete a task" (Bettman, Luce, & Payne 1998, p. 195).
The decision to dedicate more mental effort to processing during a decision making task
may impact the ability of the decision maker to meet competing process goals. The amount of
cognitive effort available for processing impacts the selection of which decision processing
strategy is constructed and implemented to make a choice. As perceptions and existing
knowledge are updated, the information used to construct the decision choice process may also
change.
For consumer choices that require little emotional involvement or need to justify,
Bettman and colleagues (1998) argue that the two dominant choice goals are to maximize
accuracy and minimize cognitive effort to reach a decision (see also Beach & Mitchel 1978;
Hogarth 1987; Payne Bettman, & Johnson 1993; Shugan 1980). The competing process goals of
maximizing accuracy and minimizing effort are derived from the cost-benefit (accuracy-effort)
framework. The first goal, accuracy, is based on the assumption from rational choice theory that
individuals are capable of calculating the exact utility of options and will make the optimal
37
selection. The second goal, minimization of cognitive effort, addresses the realization that
humans have a limited capacity for processing (Simon 1955). Decision makers are required to
make trade-offs between accuracy and effort as task difficulty increases (Bettman, Luce, &
Payne 1998; Payne, Bettman & Johnson 1988). This results in the use of different processing
strategies which yield differing levels of accuracy for a given level of effort depending on the
decision context (Payne, Bettman, & Johnson 1988).
Sometimes consumers are faced with a choice between alternatives that results in choice
conflicts. They are forced to give up attainment of one outcome-based goal to gain achieve a
different outcome-based goal. In cases where these competing goals are both important to the
consumer, the trade-offs that are required to make a choice are emotion-laden. An example of an
emotion-laden trade-off might include trading off the goal of safety of an automobile against
financial concerns. These trade-offs can lead to negative emotion. The dominant goal of
consumers when facing emotion-laden trade-offs is to minimize the experience of negative
emotion (Bettman et al. 1998, 197).
Consumer decisions are also made within a social context and are subject to evaluation
by "others to whom one is accountable or by oneself" (Bettman, Luce, & Payne 1998, p. 197).
When individuals feel accountable to others for their decisions, this leads them to weight the goal
to maximize ease of justification more heavily. When this goal is given priority, decision makers
will "search for good reasons to use as justifications" (Bettman, Luce, & Payne 1998, p. 198;
Simonson 1989). Justifications can be explained at either an outcome level or a process level. An
outcome level explanation involves a justification of the chosen option based on its superiority to
the other options. Identification of an outcome level justification requires an assessment of the
relationships between attributes and alternatives. While this is not a problem for simple
38
problems, meeting this goal may become more of a challenge as task difficulty increases
(Bettman et al. 1998). A process level explanation might describe a preference for one type of
trade-off over another. Process level explanations are difficult to verbalize and are not always
complete or accurate representations of the decision making process (Ericsson & Simon 1980;
Nisbett & Wilson 1977). Bettman and colleagues (1998) also suggest that outcomes may be more
salient than the process used to make the decision and, thus, provide better reasons (p. 198).
As outlined in the choice goals framework, the relative weight placed on each of these
four goals is influenced by differences in problem characteristics such as, "importance and
irreversibility of the decision and the timeliness and ambiguity of the feedback available on
performance relative to each goal" (Bettman, Luce, & Payne 1998, p. 193). When a decision is
important and irreversible, the goal of maximizing accuracy might outweigh the goal of
minimizing effort. If a consumption choice is under public scrutiny, goals of maximizing
accuracy and ease of justification might be weighted more heavily. If the consumer is purchasing
a gift for a loved one at the last minute, all four goals may be activated. The features of the
constructed decision making strategies differ in how effectively they meet each of these goals
(Bettman et al. 1998, p. 194).
Decision Strategies
Characteristics of Decision Strategies
Consumer choice research shows that consumers use a variety of strategies during the
decision making process (Payne, Bettman, & Johnson 1988; Bettman, Luce, & Payne 1998;
Abelson & Levi 1985). Decision strategies vary by whether they are compensatory or
noncompensatory. In a compensatory strategy, high values on an attribute can compensate for
39
low values on others. Thus, compensatory strategies require more cognitive effort and resolution
of "explicit trade-offs among attributes" (Bettman et al. 1998, p. 190; Bettman, Johnson, &
Payne 1998). A noncompensatory strategy does not allow for trade-offs between good and poor
attributes.
Decision making strategies vary in the amount of information the consumer processes
throughout the decision task. The amount of information processed can range from a very limited
amount to a detailed consideration of all available information. The approach to information
processing can be either consistent or selective. In a consistent strategy, the same amount of
information is examined for each alternative or attribute. In a selective strategy, the decision
maker processes different amounts of information for each attribute or alternative. Decision
strategies differ in the degree of quantitative versus qualitative reasoning they require. Some
heuristics involve multiplying and summing weighted attribute values while others involve
simple comparisons of values (Bettman et al. 1998). Decision strategies also “differ in terms of
whether or not an evaluation for each alternative is formed” (Bettman et al. 1998, p. 61). In some
strategies, the decision maker will calculate a score for each alternative that represents an overall
evaluation. Following this calculation, the alternatives will be compared based on their overall
score. In contrast, some strategies do not require an assessment of overall performance of each
alternative on a set of attributes.
A decision maker can choose to gather information using an alternative-based approach
or an attribute-based approach. In alternative-based processing, "multiple attributes of a single
option are processed before another option is considered" (Bettman et al. 1998, p. 189). In
attribute-based processing, "values of several alternatives on a single attribute are examined
before information on another attribute is considered" (p. 189). Research shows that attribute-
40
based processing requires less cognitive effort than alternative-based processing (Russo &
Dosher 1983; Payne et al. 1988).
Specific Decision Strategies
In the literature on constructive choice processes, researchers have compiled and
described several specific decision strategies. The constructive process presupposes that these
strategies are not stored in memory as fully constructed strategies. Rather, they are built and
constructed based on the characteristics of the problem, person, and social context. There are
several common approaches to decision making that have been identified in the literature which
vary in terms of the characteristics outlined above.
A weighted adding (WADD, Bettman et al. 1998) strategy involves consideration of one
alternative at a time. Each attribute for that option will be assigned an importance and a value.
Processing for this strategy is alternative-based. Multiplication of the subjective value and
importance of each attribute results in a score for each attribute. The sum of these scores results
in the overall score of the alternative. This is repeated for each alternative and the alternative
with the highest value is chosen. This strategy is compensatory, consistent and evaluative. While
accurate, this strategy requires a great deal of effort and extensive processing and quantitative
reasoning. The equal weight (EQW, Dawes 1979; Bettman et al. 1998) strategy is a simplified
version of the weighted adding strategy, but still requires extensive processing and quantitative
reasoning. In this strategy, alternatives are evaluated on all attribute values, but importance
weights are ignored. Processing is still alternative-based, compensatory, consistent, and
evaluative.
41
A lexicographic (LEX, Bettman et al. 1998; Payne et al. 1988) strategy requires limited
and selective processing. In this case, the alternative with a superior value on the most important
attribute is selected. This strategy is noncompensatory, attribute-based, and non-evaluative. If
there is a tie, the second most important attribute is evaluated. This continues until the tie is
broken. Lexicographic semi-order (LEXSEMI; Tversky 1969; Payne et al. 1988) is a variation of
the LEX strategy and involves the notion of just-noticeable differences (JND). In this variation,
if an alternative (or multiple alternatives) are within a JND of the "best alternative on the most
important attribute, they are considered to be tied” (Payne et al. 1988, p. 356).
In satisficing (SAT, Simon 1955), alternatives are considered one at a time, in the order
they occur in the choice set, and each attribute is assigned a cutoff value. If any attribute falls
below the cutoff, the alternative is rejected and the decision maker moves to the next alternative.
The first alternative that meets the cutoff for all attributes is selected. The amount of processing
requires depends on the cutoff values and how different they are from the actual attribute values.
This strategy is noncompensatory, selective, and non-evaluative. Elimination-by-aspects (EBA,
Bettman et al. 1998) is a combination of satisficing and a lexicographic strategy. An individual
using this strategy will eliminate options that do not meet a cutoff value on the most important
attribute. The process continues for each attribute, in order of importance until only one option
remains. Elimination-by-aspects is noncompensatory, selective, attribute-based, and nonevaluative.
A strategy that seeks a majority of confirming dimensions (MCD, Russo & Dosher 1983;
Bettman et al. 1998) also requires extensive information processing, but follows a different set of
rules. Alternatives are processed in pairs. The two alternatives are compared on each attribute
and the alternative with the higher number of superior attributes is selected. The winning
42
alternative is then compared to the next alternative in the choice set. This process continues until
the last pair of alternatives has been compared. This strategy is compensatory, consistent,
attribute-based, and evaluative.
Another strategy is to count the number of good and bad features of each alternative and
choose the alternative that has the highest number of good attributes (Frequency of good/bad
features, FRQ; Bettman et al. 1998). The amount of processing required will depend on how the
decision maker determines whether an attribute has a "good" or "bad" value. This strategy is
compensatory, consistent, alternative-based, and evaluative.
The componential context model (CCM) allows for the calculation of "relative
advantages and disadvantages of an option to those of other options" (Bettman et al. 1998, p.
191; Simonson & Tversky 1992). The advantage of this strategy is that some of the relative
advantages and disadvantages can be recognized perceptually with little effort (Bettman et al.
1998). However, with larger problems, this may not be feasible. This strategy is consistent,
compensatory, and both alternative- and attribute-based.
Because choice strategies are constructed, it is no surprise that strategies or parts of
strategies might be combined. Payne, Bettman, and Johnson (1988) evaluated two of these
possible combinations in a simulation that investigated the effectiveness of ten decision
strategies. The first combination was an elimination-by-aspects plus weighted additive
(EBA+WADD, Payne et al. 1988; Bettman & Park 1980) rule. In this combination, EBA is used
to reduce the number of alternatives until a WADD could be used to select from the remaining
options. The second combination makes use of elimination-by-aspects plus majority of
confirming dimensions (EBA+MCD, Payne et al. 1988; Bettman & Park 1980) to minimize the
choice set with EBA, then pick from the remaining options using an MCD approach.
43
The random (RAN, Payne et al. 1980) choice strategy "chooses an alternative at random
with no search of the available information" (p. 536). In simulation studies, the WADD strategy
is considered to be the most cognitively effortful strategy and resulting in the highest possible
accuracy of all of the decision making strategies. In contrast, the RAN strategy requires the least
cognitive effort and is the least accurate. The remaining strategies fall between these two
baselines in the effectiveness of managing the trade-off between accuracy and effort.
The effectiveness of the strategy constructed will depend on the characteristics of the
decision making task (Payne et al. 1988; Bettman et al. 1998). For a decision task that is not
important for financial, social, or personal reasons, there may be little reason to spend a lot of
time or energy making the decision. In such a case, minimizing effort at the cost of making a
suboptimal choice would actually be preferable to expending effort to maximize accuracy.
Because consumers are subject to cognitive limitations, they do not always select a decision
strategy that most effectively meets their processing and outcome goals.
Factors that Influence Constructed Choice Processes
Bettman, Johnson, & Payne (1998) conceptualize three primary categories of factors that
influence which strategy a decision maker will use in constructing preferences. The first
category, characteristics of the social context, acknowledges that consumption decisions are
often made in the presence of or on behalf of other individuals. If an individual feels accountable
to others, this may lead to the use of choice strategies that “focus on the how easy a decision is to
justify to others” (Bettman, Johnson, & Payne 1998).
The second category, characteristics of the decision making problem, includes features of
the decision making task and context that impact decision making difficulty. As the difficulty of
44
a decision task increases, decision makers attempt to simplify the decision making process
through the use of choice heuristics (Bettman, Johnson, & Payne 1998). The literature on
consumer decision making predicts that changes in the difficulty of the decision making task lead
to changes in how individuals process information, what information they process, and what
strategies they use to eliminate alternatives. These predictions are based the assumption that
consumers make decisions while maintaining simultaneous and competing goals of minimizing
effort and maximizing accuracy (Beach & Mitchell 1978; Hogarth 1987; Payne, Bettman, &
Johnson 1993; Shugan 1980; Bettman, Luce, & Payne 1998). The context of the decision also
impacts the decision making process. Context variables such as similarity of alternatives (Restl
1961; Tversky 1972; Bettman, Johnson, & Payne 1998) and the presence of dominated
alternatives (Huber, Payne, Puto 1982; Bettman, Johnson, & Payne 1998) can impact both the
construction and the effectiveness of decision strategies (Bettman et al. 1993; Johnson & Payne
1985).
The third category, characteristics of the person, takes into consideration the decision
maker's cognitive ability and prior knowledge to evaluate the various aspects of the decision
making task. Decision makers also vary in how much relevant prior knowledge they possess with
respect to a given decision task. Various characteristics of an individual influence how they
form their own processes and preferences.
Cognitive Difficulty of the Decision Making Task.
There is an abundance of literature on how changes to the inherent difficulty of the
decision task can and do impact the constructive choice process of consumers (Bettman et al.
1998, 2008 a, b; Luce 1998; Luce et al. 2001; Luce et al. 1999). Difficulty of the decision
45
making task can vary as a result of changes to several characteristics of the decision task. Choice
task difficulty can be increased through missing information, uncertainty, technological changes,
content or structure of the information, importance of the decision making task, a large number
of alternatives in the decision making set, or undesirable value trade-offs (Bettman, Johnson, &
Payne 1998; Luce, Bettman, & Payne 2001). Decision makers have limited cognitive capacity
and as the complexity of the decision making task increases, consumers are forced to use
heuristics that allow them to process limited amounts of perceptual information to make a choice.
Cognitive decision difficulty is impacted by factors that influence "comprehension and
manipulation of decision information" (Luce, Bettman, & Payne 2001, p. 4). Bettman, Luce, and
Payne (1998) identify what they consider to be the six main characteristics of the decision
making task that increase cognitive decision making difficulty: problem size (i.e. number of
alternatives), time pressure, completeness of information, information format, comparable versus
noncomparable choice, and attribute correlation.
Size of the Decision Set. The literature on consumer decision making predicts an increase in
decision task difficulty associated with an increase in the size of the decision set. According to
Bettman, Luce, and Payne (1998), an increase in the number of alternatives leads the decision
maker to use a decision making strategy that eliminates options more readily based on a single
attribute or through the use of another type of heuristic. Decision sets with a large number of
alternatives tends to result in "greater use of noncompensatory strategies that eliminate
alternatives" (Johnson & Meyer 1984; Payne 1976). As the number of alternatives increases, the
relative efficiency of decision strategies other than WADD increases. The more alternatives
included in the decision set, the more processing a WADD strategy requires. The effort required
46
to "implement other heuristics increases much less rapidly with problem size than the effort
required for weighted adding" (Bettman et al. 1998, p. 199). An increase in the number of
attributes does not lead as quickly to strategy changes, but increases selectivity (Olshavsky 1979;
Payne 1976). Research on choice set size (Scheibehenne, Greifendeder, & Todd 2010) also
suggests that extremely large choice sets can lead consumers to the decision not to choose
(Iyengar, Huberman, & Jiang 2004; Iyengar & Lepper 2000), a decrease in preference and
satisfaction with the choice (Chernev 2003; Iyengar & Lepper 2000), and an increase in negative
emotions (Schwartz 2000).
The early literature on choice overload shows conflicting findings. Early research on the
topic (Jacoby, Speller, & Kohn 1974a, b) suggests that consumers make poorer decisions when
given more information. In a debate that follows, some researchers argue that the decrease in
decision making accuracy does not occur if the amount of attribute information is increased
(Russo 1974; Summers 1974; Wilkie 1974) while others claim that attribute information can
cause decreases in accuracy (Keller & Staelin 1987; Malhotra 1982). Bettman, Luce, & Payne
(1998) suggest that as long as consumers select a "subset of the information that reflects their
values," then there is no decrease in accuracy, but when consumers disregard their values in
selecting information to process, then information load can decrease accuracy. The literature
seems to agree that a large set of alternatives is detrimental to decision making accuracy, but
there is not a consensus on the effect of additional attribute information.
Time Pressure. Consumers have been shown to adjust their decision making processing when
faced with varying levels of time pressure. Under a moderate level of time pressure, individuals
will attempt to process information about alternatives more quickly and become more selective
47
in the information they choose to process (Bettman, Luce, & Payne 1998). When under severe
time pressure, decision makers tend to switch to attribute based processing (Bettman, Luce, &
Payne 1998), become more selective in the information they process (Lurie 2004), and place
more emphasis on processing the negative information about the options under consideration
(Wright 1974).
Completeness of Information. Consumer decisions also vary in the completeness of information
available at the time of the decision making task. Consumers often have to make decisions
between alternatives (brands) that are missing attribute information. Consumers can attempt to
infer the missing value based on information available about other brands on that same attribute
(other-brand information, Ford & Smith 1987) or values on other attributes within the given
option (same-brand information, Johnson & Levin 1985). According to the choice goals
framework, the choice to use same-brand or other-brand attributes to infer missing information
depends on the "relative accessibility and diagnosticity of each type of information" (Bettman et
al. 1998, p. 202).
Information Format. Bettman, Luce, & Payne (1998) propose that not only does information
format have an impact on choices (Russo 1977), but also the processing strategy selected by the
decision maker (also Biehal & Chakravarti 1982). The structure of a problem can be affected by
changes to problem characteristics such as number and complexity of inputs, noise inherent in
inputs, ease of selecting and evaluating inputs, ease with which the problem can be decomposed,
clarity of goals, and number of acceptable goal states (Spence & Brucks 1997). The format in
which information is presented can make it easier or more difficult to process (Bettman, Payne,
48
& Staelin 1986) and different formats may facilitate the use of certain decision strategies while
making others more difficult to implement (Bettman et al. 1998). Information structure also has
an impact on whether a decision maker experiences information overload (Lurie 2004).
When individuals are presented with a display of information, they tend to "use it in the
form it is displayed, without transforming it" (Bettman, Luce, & Payne 1998, p. 202; Slovic
1972; Bettman & Kakkar 1977; Jarvenpaa 1989). This effect is known as the concreteness
principle. A format that is easier to process can lead to better choices (Russo 1977). Consumers
are more likely to deviate from the given format if the costs of making a suboptimal decision are
sufficient to justify the additional cognitive effort required to restructure the information.
Comparability. While many consumer choices may involve a selection among alternatives of the
same product class, some choices may involve alternatives that are not easily comparable
because the attributes used to evaluate the options differ across the options. Processing differs
between comparable and non-comparable choices. As alternatives become less comparable,
consumers respond by considering attributes a more abstract level (Johnson 1984) until they are
able to develop enough of an overall evaluation of each alternative to make abstract comparisons.
Interattribute Correlation. Individuals adapt their decision processing for different levels of
interattribute correlation. The correlational relationships among attributes represent "the extent to
which one has to give up something of value in order to get something else of value" (Bettman,
Johnson, Luce, & Payne 1993, p. 932) When negative interattribute correlation is present in a
choice set, and individuals are forced to sacrifice on one attribute to gain on another (Einhorn &
Hogarth 1981; Bettman, Luce, & Payne 1998; Bettman, Johnson, Luce, & Payne 1993). This trade-
49
off between attributes is related to the trade-off between processing goals. If the consumer is
concerned with decision making accuracy, negative interattribute correlation leads to "more
extensive, less selective, and more alternative-based processing" (Bettman, Luce, & Payne 1998,
p. 201; Bettman et al. 1993).
Emotional Difficulty of the Decision Making Task
There is evidence that decisions vary not only in cognitive difficulty, but in emotional
difficulty as well (Luce 1998; Luce et al. 2001; Luce et al. 1999). Emotional decision difficulty
"reflects the implications of decision consequences for attainment of the decision maker's valued
goals" (Luce, Payne, & Bettman, p. 4). When a decision task requires the consumer to make
difficult trade-offs between attributes "linked to highly valued goals" (Luce 1998, p. 409), they
are forced to accept a loss on one attribute to gain on another. Emotional trade-off difficulty is
defined as the "level of subjective threat a decision maker associates with making an explicit
trade-off between two attributes" (Luce, Payne, & Bettman 1999, p. 144). The perception of
trade-off difficulty in decision making has been shown to lead to the experience of negative
emotion and subsequent coping behavior (Luce et al. 1999; Luce et al. 2001).
For many consumer choices "there is little emotional involvement or need to justify"
(Bettman et al. 1998, p. 194) so consumers can use effort-reducing strategies to save time and
energy. Some decisions require trade-offs that are either emotionally or morally difficult (Luce et
al. 1999). One example of an emotion-laden trade-off involves the conflicting goals of a desire
for a high value on car safety and a desire to be environmentally conscious. Depending on the
interattribute correlations of the alternatives available in the choice set, these goals may be
mutually exclusive. An example of a trade-off that is not emotion-laden might be a mutually
50
exclusive trade-off between the desired exterior color of the car and the availability of cruise
control.
With emotion-laden decisions, reducing the experience of negative affect becomes a
prevalent processing goal. To reduce negative affect, consumers construct strategies to cope with
the difficulty inherent in the decision making task. According to Luce, Bettman, and Payne
(2001), these coping strategies are either problem-focused or emotion-focused. Problem-focused
coping involves following a careful, analytical decision processing strategy. The decision maker
will work harder to make an accurate decision, resorting to an extensive, selective, attributebased processing decision strategy. This allows the decision maker to avoid the negative
emotions elicited by difficult trade-offs (Luce, Bettman, & Payne 1997) but leads to increased
processing times (Luce et al. 2001).
Emotion-focused coping encourages simplified, heuristic decision processing that avoids
recognition of the difficult and unpleasant trade-offs (Luce 1998; Luce et al. 1999; Luce et al.
2001). One example would be to select an option based on the attribute that is highly emotion
laden, such as quality or safety, rather than calculating importance weights for one attribute
compared to another (Luce et al. 1997). Kahn and Baron (1995) find that while consumers are
likely to use a “noncompensatory, lexicographic rule when making their own high-stakes
decisions, but that consumers [want] advisors to make decisions for them using compensatory
rules” (Bettman et al. 1998, p. 205). Increases in negative emotion and trade-off difficulty have
also been shown to lead to increased choice avoidance through selection of a status quo option,
selection of an asymmetrically dominating alternative, or putting of the decision until another
time (Luce 1998).
51
Individual Differences
According to the consumer decision making literature on choice processes (Bettman,
Luce, & Payne 1998; Bettman, Johnson, & Payne 1998) there are individual differences that can
contribute to differences in the constructive choice process. Some examples include cognitive
ability and prior knowledge (Bettman, Johnson, & Payne 1998). For example, an individual
difference in math ability may preclude some individuals from using highly computational
heuristics when constructing their decision making process and preferences (Bettman, Johnson &
Payne 1990, 1998). Consequently, while a cognitively demanding and extensive processing
strategy such as WADD may be the optimal decision strategy for the decision task at hand, an
individual with limited cognitive abilities may not be capable of the complex calculations the
strategy requires.
Limited working memory capacity makes selective attention to information inevitable
(Simon 1955; Bettman et al. 1998). Because individuals are limited in their ability to process
multiple stimuli (Broadbent 1958; Deutsch & Deutsch 1963; Treisman 1969; Johnston & Heinz
1978) and because processing expends mental effort (Payne et al. 1988), working memory
capacity is a valuable resource. Because decision makers have competing process goals when
making a decision, they must decide how much effort they are willing to expend during the
decision making task. The decision to dedicate more mental effort to processing during a
decision making task may impact the ability of the decision maker to meet competing process
goals. The amount of cognitive capacity available for processing impacts the selection of which
decision processing strategy is constructed and implemented to make a choice.
The research on consumer decision making proposes that any decision strategy can be
decomposed into a measure of cognitive effort required to enact the strategy known as
52
elementary information processes (EIPs; Bettman, Luce, & Payne 1998; Payne, Bettman, &
Johnson 1988; Johnson & Payne 1985). Cognitive effort is defined as a "function of the number
and types of EIPs needed to complete a task" (Bettman, Luce, & Payne 1998, p. 195). Research
shows that cognitive load increases the use of attribute trade-offs in choices and "decreases
emotion-based trade off avoidance "(Drolet & Luce 2004, p. 75). One explanation for this
phenomenon is that cognitive load disrupts the ability of consumers to engage in imagery
processing when pursuing anticipated-satisfaction task goals which leads to a decreased impact
of "certain quality attributes in the construction of preferences under load (Drolet & Luce 2004,
p. 71; Shiv & Huber 2000). Drolet and Luce (2004) find, however, that the effects of emotional
trade-off difficulty are distinguishable from imagery effects. Rather, it seems that the "reduction
of cognitive resources through increased load can result in more normative decision behavior" (p.
64).
A consumer's prior knowledge and beliefs about a given choice also impacts their
approach to the decision making process (Bettman, Johnson, & Payne 1998; Spence & Brucks
1997; Brucks 1985; Cowley & Mitchel 2003; Moorman, Diehl, Brinberg, & Kidwell 2004;
Peraccio & Tybout 1996; Sujan 1985). Research finds that experts and novices exhibit
differences in their selection of problem-solving strategies (Spence & Brucks 1997. While
experts tend to categorize problems on the "basis of solution procedures or underlying concepts,"
and use more efficient top-down strategies, novices tend to approach problems on the "basis of
surface features" and work their way up (Spence & Brucks 1997, p. 234; Chi, Feltovich, &
Glaser 1981; Larkin et al. 1980).
What is missing in the consumer choice process literature is a discussion of the effect of
individual differences in indecisiveness on constructed choice processes. Indecisive individuals
53
have been shown to respond differently to various aspects of the decision making tasks in
psychology and decision making literature. However, no one has tested how indecisiveness
interacts with changes in decision making task difficulty and/or importance to affect consumer
decision making processes. I expect that indecisiveness will be associated with higher amounts
of information processing, regardless of the cognitive or emotional decision task difficulty or the
importance of the decision task. The rationale for this prediction is discussed in the following
section.
Hypothesis Development
Choice Goals and Decision Importance
Bettman, Luce, and Payne (1998) propose that activation of different choice goals leads
to the construction of decision strategies with different characteristics. Most decisions are made
with the two predominant goals of maximizing accuracy and minimizing cognitive effort
(Bettman et al. 1998; Beach & Mitchell 1978; Hogarth 1987; Payne et al. 1993; Shugan 1980).
However, Bettman, Luce, and Payne (1998) propose that the “relative weight placed on various
goals will be influenced by a variety of problem characteristics” (p. 193). As importance or
irreversibility of the decision increases, an individual may be more motivated to ensure accuracy.
If the decision involves undesirable trade-offs, a goal to minimize negative affect may become
more salient. Decisions that are made in a social context may activate a goal to increase ease of
justifying the decision to others. In low-probability high-consequence decision contexts, Bettman
and colleagues (2009) suspect that all four process goals are likely to be activated. The type and
number of process goals activated within a decision scenario differentially impact the
constructive choice process.
54
The literature on indecisiveness finds that highly indecisive individuals use decision
strategies that are quite different than those used by decisive individuals. When compared to
decisive more individuals, highly indecisive individuals have been shown to take longer to reach
a decision (Frost & Shows 1993; Rassin & Muris 2005a; Rassin et al. 2008) and to require more
information when making decisions (Rassin et al. 2007). Indecisive individuals are also more
likely to use compensatory (Patalano & Wengrovitz 2007) and alternative-based processing
(versus attribute-based processing) than decisive individuals (Patalano et al. 2010). In particular,
indecisive individuals were found to use alternative-based processing during the first half of the
decision making task before switching to an attribute-based approach during the second half of
the task (Patalano et al. 2010). When given a similar decision making task, decisive individuals
use an attribute-based approach throughout the entirety of the decision making task (Patalano et
al. 2010). Indecisive individuals have also been found to focus more extensively on the attributes
of the alternative they eventually select (Patalano et al. 2010; Rassin et al. 2008). Patalano and
colleagues (2010) do not believe that indecisive individuals are ignoring all of the other
alternatives, but returning to process the attributes of a preferred alternative more than the
competing alternatives. These processing differences suggest that indecisive individuals are
engaging in more extensive processing during the decision making task than decisive individuals.
Based on these differences in strategy characteristics, it would follow that highly
indecisive individuals may weight their choice process goals differently than more decisive
individuals. Because indecisive individuals engage in more effortful processing than decisive
individuals (Rassin et al. 2007; Patalano & Wengrovitz 2007), it would appear that they tend to
place lower weight on the choice goal of minimizing cognitive effort. Likewise, the use of
compensatory strategies and preference for additional information suggest that highly indecisive
55
individuals are weighting maximization of accuracy goals more highly than more decisive
individuals (Rassin et al. 2007).
In the indecisiveness literature, researchers also find that indecisiveness is positively
associated with the experience of negative emotions (Spunt et al. 2009; Frost & Shows 1993;
Rassin & Muris 2005a, b; Rassin et al. 2007; Elaydi 2006; Jeong & Drolet 2014). If they are
experiencing more negative affect, indecisive individuals may be motivated to weight the process
goal of minimizing negative affect more highly than their decisive counterparts. The literature on
indecisiveness does not give any indication on whether indecisive and decisive individuals differ
in weights assigned to the process goal of maximizing ease of justification.
If indecisive individuals are weighting a greater number of their process goals more
highly than decisive individuals, they will have more active goals during decision making tasks
than decisive individuals. I expect the opposite to be true for individuals who are low in
indecisiveness, and therefore highly decisive. Those who are at the extreme low end of
indecisiveness will likely have fewer active processing goals.
For the average consumer, Bettman and colleagues (1998) expect that more choice goals
are likely to be activated when the decision is important and/or irreversible. They predict that
when more process goals are activated, “individuals will devote more effort to examining
information they believe will help them attain whichever goals are more heavily weighted in that
situation” (Bettman et al. 1998, p. 193; Feldman & Lynch 1988). If highly indecisive individuals
have more simultaneously active goals than the average consumer, regardless of the decision
importance, this would explain why they engage in more information processing in general and
would predict that they would engage in this extensive processing regardless of the importance
of the decision. If a highly decisive consumer only has one active process goal and is not making
56
tradeoffs between effort and accuracy, he or she will likely be able to make a quick decision
using a non-compensatory, selective, non-evaluative decision strategy. This allows them to
expend very little energy, regardless of decision importance.
H1: Indecisiveness will moderate the effect of importance of the decision making task on
information processing, such that at low levels of indecisiveness information
processing will be low for both low and high importance decisions, at moderate
levels of indecisiveness, high importance decisions will result in more information
processing than low importance decisions, and as indecisiveness increases from
moderate to high, both high and low importance decisions will lead to increases in
information processing.
Responses to Cognitive Difficulty
The literature on consumer choice processes agrees that the average consumer will vary
their choice goals and strategies based on the cognitive difficulty of the decision task (Bettman,
Luce, & Payne 1998; Bettman, Johnson, & Payne 1998; Bettman, Luce, & Payne 2008; Payne,
Bettman, & Johnson 1988). The literature also outlines how particular aspects of cognitive
difficulty such as choice set size and severity of time pressure affect processing strategies
(Bettman, Luce, & Payne 1998, 2008). The average consumer will respond to changes in
cognitive difficulty by choosing decision strategies that attempt to maintain a balance between
the process goals of maximizing accuracy while minimizing cognitive effort (Bettman et al.
1998). Because highly indecisive and highly decisive individuals may not be balancing this
trade-off between accuracy and effort in the same way, changes to the cognitive difficulty of the
decision task should affect them differently than the average consumer.
Size of the Decision Set. When choice sets are small, the average consumer is more likely to use
compensatory processing strategies (Bettman et al. 1998), but as the number of alternatives
57
increases they will resort to “greater use of noncompensatory choice strategies that eliminate
alternatives” (Bettman, Luce, & Payne 1998, p.199; Johnson & Meyer 1984; Payne 1976). As
the number of alternatives increases, the relative accuracy of effort-saving heuristics is “fairly
robust” (Bettman et al. 1998, p. 199; Payne, Bettman, & Johnson 1993) which allows the
decision maker to maintain both effort minimization and accuracy maximization goals.
The indecisiveness literature shows that highly indecisive individuals use greater
compensatory processing (Patalano & Wengrovitz 2007) and actively seek more information
before making a decision (Rassin et al. 2007) than more decisive individuals. Because for highly
indecisive individuals the process goal of minimizing cognitive effort is not weighted as highly
as the other process goals, it is possible that an increase in the number of alternatives in the
choice set will be less likely to reduce the effort that highly indecisive individuals expend on
decision making tasks. Similarly, because highly decisive individuals are already focused on
minimizing effort at the expense of accuracy, an increase in the number of alternatives is not
likely to change their processing behavior.
H2: Indecisiveness will moderate the relationship between choice set size and amount of
information processing, such that when indecisiveness is low, information
processing will be low for all levels of choice set size, at moderate levels of
indecisiveness, choice set size and information processing will be negatively
correlated, and as indecisiveness increases from moderate to high, the effect of
choice set size on amount of information processing will decrease.
Time Pressure. If all other aspects of a decision making task are held constant, time pressure
should result in a fixed amount of information that can be processed in a given amount of time.
Once the time limit has passed, no more effort can be spent on the decision making. Therefore,
the goal of minimizing effort is held to a fixed point. When under severe time pressure, the
average consumer is forced to resort to attribute-based heuristics that allow them to quickly
58
examine some information about each alternative (Bettman et al. 1998). Because they are unable
to process all of the available information within the limited allotment of time, the average
consumer will also use more selective processing strategies, placing a greater “emphasis on
negative information about options” (Bettman et al. 1998, p. 2000; Wright 1974).
Highly decisive individuals, however, do not need adjust their processing strategies in
response to the limitations imposed by time pressure. Because they are already minimizing their
effort, time pressure should not affect them. Highly indecisive individuals also fail to adjust their
processing strategies in response to risks related to time pressure (Patalano & Wengrovitz 2007).
They seem to ignore the time limit and continue to process information as if the time limit were
not present. Patalano and Wengrovitz (2007) speculate that this failure to adjust processing
strategies could be because indecisive individuals “value an ideal alternative so highly that no
degree of risk can offset the value” (p. 419). In light of this evidence, it is possible that indecisive
individuals approach all decisions, both difficult and easy, using the same amount of information
processing.
H3: Indecisiveness will moderate the relationship between time pressure and amount of
information processing, such that when indecisiveness is low, information
processing will be low for all levels of time pressure, at moderate levels of
indecisiveness, time pressure and amount of information processing will be
negatively correlated, and as indecisiveness increases from moderate to high, the
effect of time pressure on amount of information processing will decrease.
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Chapter 3: Interviews with Individuals Who Self-Identify as Indecisive
There has been limited research on the effect of indecisiveness on information processing
variables (see Patalano and Wengrovitz 2007; Patalano et al. 2010; Patalano and LeClair 2011
for exceptions). There has also been very little work on indecisiveness in the context of
consumer behavior (see Jeong and Drolet 2014 for exception). Because the goal of my
dissertation is to understand and help indecisive consumers when they are faced with a
consumption choice, I take a step in addressing this gap in the research by conducting
exploratory in-depth interviews with individuals who self-identify as highly indecisive.
This study examines how consumers make decisions in general as well as specific scenarios
of deciding what to wear and choosing a meal at a restaurant, as well as coping mechanisms used
to simplify the decision making process. The definition of indecisiveness in this research
suggests that indecisive individuals experience difficulty making decisions of all kinds, even
routine decisions with minimal consequence. Informants were asked to share their experiences
making various types of everyday decisions. The specific scenarios listed above were chosen
because they are situations in which a typical consumer might find themselves in on a daily
basis.
Research Methodology
I conducted eleven semi-structured interviews with individuals in Michigan, Ohio, and
South Carolina who self-identify as experiencing chronic, domain-general indecisiveness. I also
conducted interviews with the significant-others of two of my subjects to provide an outside
perspective on the indecisiveness of a loved-one. The informants for this study were identified
through a snowball sampling procedure (Creswell 2013, p. 158). Individuals who identified
60
themselves as chronically indecisive in a range of domains were asked if they would like to
participate in an interview for my research. Subjects were recruited in person and through the
online social network Facebook.
Table 1 provides a summary of the informants. My sample contains more females than
males. With previous research as a guide, this is not entirely unexpected (Rassin et al. 2007;
Rassin & Muris 2005). While there are not consistent gender effects of indecisiveness, women
may be more willing to openly admit to and talk about their indecisiveness than men. Ten of the
informants are full time professional employees. Of the remaining three informants, two are full
time students and one is a stay-at-home mother. Six of the informants are in their mid to late
twenties and six are in their thirties. One subject is a recent high school graduate pursing an
undergraduate degree in a healthcare profession. One subject is in her early forties. Two
respondents are single and two cohabiting, but the majority of the sample is married.
Interview Guide
Construction of the interview guide was informed by immersion in the indecisiveness
literature from other fields (Chapter 1) as well as the decision making literature in consumer
behavior (Chapter 2). This approach has been used in previous studies (Bernard 2002;
McCracken 1988; Bardhi & Eckhardt 2012). The interview guide is available in its entirety in
Appendix B (p. 169).
The interview guide was developed to encourage informants to share details about their
personal experiences with decision making. In early interviews, I began by asking informants
what decision task attributes were likely to make a decision experience more difficult for them.
After a few interviews, it was clear that this type of question did not provide sufficient context to
begin a good conversation. In later interviews, I asked informants to explain why, when they
61
heard about my research on indecisiveness in consumer behavior, they believed they were a good
fit for my study. Following this icebreaker, the interviews generally followed a discussion format
of 1) general decision making, 2) specific decision making, 3) coping mechanisms, and 4)
feelings while experiencing indecisiveness. Informants were encouraged to discuss anything
related to their decision making behavior and indecisiveness, even if it was unrelated to my
current question. They were especially encouraged to share stories and anecdotes about specific
decision making experiences that came to mind.
Informants were asked to list attributes about decisions, in general, that make them more
difficult to make. I also asked each of my informants to try to think of a specific decision they
had struggled with recently. If they could think of one, I allowed the narrative to continue, while
asking follow-up questions for clarification.
Because I expect everyday decisions to be more difficult for indecisive individuals than
for those who are relatively more decisive, informants were asked to think about what it is like
for them to make everyday decisions, such as what to wear, what to eat, where to go shopping, or
what route to take to a destination. Sometimes informants responded with a story related to one
of the prompts, and other times, informants claimed they did not struggle with one or more of
those sorts of decisions. To get a clearer sense of the process involved in their daily decision
making experiences, I asked each subject to think back to a recent purchase of any kind. I asked
them to explain what prompted the need for the purchase, their decision making process, their
final decision, and what they would have done if the store did not have the item they needed.
To ensure some homogeneity in the stories, I was prepared with two decision making
scenarios. The first scenario involved choosing a meal from an unfamiliar menu. I asked
informants to look at an electronic copy of an unfamiliar menu from a bar and grill in an
62
unfamiliar town (see Appendix A). Informants were asked to use a speak-aloud protocol while
reviewing the menu as if they were going to order something. I allowed each individual to
choose the context of the decision. They could make the decision as if they were in the restaurant
alone for lunch, out for dinner with their significant other, or out with family. Several of the
informants completed one version of the scenario and then repeated the exercise for a second
restaurant scenario. This menu was chosen because it involves a familiar decision making
context with unfamiliar options. It represents a plausible consumer consumption scenario that the
informants could easily visualize and narrate a feasible account of their decision making process.
For the second scenario, I asked each informant to imagine they were going to an outdoor
wedding for someone they knew. They were asked to walk me through how they would decide
what to wear. This scenario takes the decision making process of getting dressed—a decision
task that the informants encounter every day—and places it in a less familiar context. This was
done to avoid a recounting of decisions made on the basis of habituation, heuristics, and/or
established coping mechanisms.
Following questions that prompted stories about general and specific decision making,
informants were asked to think of ways they deal with their indecisiveness. I asked if they have
any tricks that they use to help them make decisions. If they had mentioned examples earlier in
the interview, I asked them to elaborate. These questions were expected to generate examples of
the coping mechanisms identified by Janis and Mann (1977).
For the final phase of the interview, I provided a list of pleasant feelings followed by a
list of difficult/unpleasant feelings (see Appendix A). I asked each informant to identify any of
the feelings that represented how they feel when they experience difficulty deciding. Informants
were expected to identify mainly difficult and unpleasant feelings based on previous findings that
63
indecisiveness is positively correlated with negative affect (Elaydi 2006; Rassin & Muris 2005a,
b; Jeong and Drolet 2014).
Data Analysis
The interviews were recorded and transcribed. Analysis of the interviews was conducted
according to the iterative process outlined by Spiggle (1994). Therefore, the unit of analysis was
not defined, rather, relevant data was categorized into chunks based on “coherent meaning”
(Spiggle 1994, p. 493). The first phase of data analysis involved reading and listening to the
interviews two or three times while taking notes and writing memos. This was done to get a
sense of the context of each interviewee’s responses (Corbin & Strauss 2008). Next, I read the
interview transcripts with a goal to identify relationships identified by the existing literature on
indecisiveness as well as recurring themes within and across the interviews. As multiple themes
emerged, I began to group them into more abstract categories (Spiggle 1994). The software
NVivo 10 was used to systematically code the themes and categories. Use of the software allows
me to trace common themes within and across the interviews.
Findings
Failure to Adjust to Changes in Task Characteristics.
Bettman, Luce, and Payne (1998, 2008) identify a number of task characteristics that
affect the cognitive difficulty of decision making tasks, including problem size, time pressure,
attribute correlation, completeness of information, information format, and attribute
comparability. The average consumer will adjust their processing strategy to accommodate these
changes in the decision making context. For example, extant literature would predict that
64
decision task difficulty is positively associated with increases in choice set size (Bettman, Luce,
and Payne 1998; Iyengar and Lepper 2000), social pressure (Bettman, Luce, and Payne 1998),
and time pressure (Bettman, Luce, and Payne 1998). These predictions were supported by the
data, and informants in this research reported increased cognitive difficulty due to the increasing
difficulty of task features. However, informants reported that they experience decision making
difficulty with a wider range of decisions and at a greater intensity than less decisive individuals,
and they seem to be unable or unwilling to adjust their decision strategies in response to changes
in the features of the decision task.
When describing their decision making experiences, informants describe decision making
as a long, exhausting process. They recognize that it takes them longer to make decisions than
their friends and other people around them. This is consistent with previous indecisiveness
literature which finds that increases in indecisiveness are positively associated with increases in
decision latency (Patalano and Wengrovitz 2007; Rassin et al. 2008; Frost and Shows 1993) and
the amount of information gathered before making a decision (Rassin et al. 2007; Rassin et al.
2008). Furthermore, informants report experiencing difficulty making routine, everyday
decisions. For example, informants reported that making dinner plans, shopping for shoes or
clothing, deciding what to wear, and grocery shopping can be can be stressful and difficult. As
Ben shares,
Well, it could be as small as what to do for dinner. Whether to go
to this place or another place. Or should I make food at home or
should I go out? That’s a small decision that can really just be
very painful and very difficult…You could take a small decision
and over the matter of a couple dollars: The cost of should I make
food at home or should I go out and get it? I can waste a half hour
trying to decide what I should do… And that will seem like it’s a
big deal in my mind—a much larger deal than a couple dollars
should be.
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Informants report spending extensive amounts of time and energy regardless of how
personally involving or important the decision task is perceived to be. When asked what
decisions she has struggled with lately, Melissa responds with a list of decisions that range from
mundane to potentially life-altering.
Whether or not I wanted to keep my job. I had to weigh a bunch of
pros and cons. What’s another decision? The restaurant the other day.
Took us, what, a good forty-five minutes to pick a restaurant out.
What to wear took a long time the other day. What to do today has
taken us all morning.
Highly indecisive individuals also have a difficult time adjusting to other task
characteristics. Payne and colleagues (1988) find that under moderate time pressure, individuals
will process information more rapidly and become “somewhat more selective” (Bettman, Luce,
and Payne 1998, p. 200). Under severe time pressure, the average individual will accelerate
processing, become more selective, and change their decision making strategy (Bettman, Luce,
and Payne 1998). A response from Melissa indicates that she attempts to process the available
information in much the same way as when there is no time pressure is present.
I hate being under pressure by time, but yeah, it affects me. It just
makes that process quicker. It’s the same process, but just quicker.
I feel like I got bullied into a decision.
The presence of time pressure not only affects the mood and processing ability of these
individuals, it also affects the outcome of their purchase decisions. As Shelby recounts, she may
have narrowed down her options to two competing alternatives, but if she has not decided
between the two, she will fall back on a default option.
If I’m debating between a couple different things and then it’s last
minute, I normally don’t even end up with what I’m debating on. I
go towards what I already know that’s good and I’m safe with it. I
still haven’t figured out exactly what I want.
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Highly indecisive individuals also have a difficult time adjusting to changes in choice set
size. Informants reported that they purposefully selected outlets which offer a wide variety of
options and their respective features. This results in an extensive repertoire of possible
combinations.
I think I tend to find a place that I know has a broad menu. So that,
hopefully, everyone’s pleased. (Heather)
I do lots of options. Because I want to explore. I don’t limit myself
to one thing. There might be something else out there that I like
better. So, I want to explore as many options as possible. (JoAnne)
But rather than limiting their processing once they encounter their intentionally large
choice set, they set out to process as much of it as they can.
I decided one day after work. I set aside the time. I’m going to go do
this. And I had some place in particular that had a lot of different
shoes that I planned on going to and I did end up buying them there.
Which is…that’s pretty impressive. A lot of times I’ll go to four
different stores before I decide, make my decision. (JoAnne)
As this suggests, despite seeking out a wide range of alternatives, individuals who are
highly indecisive appear to have a hard time adjusting their processing strategies to efficiently
deal with their large choice sets. While having a lot of options from which to choose may
increase the odds that an available option will fit a customer’s preferences, too many options and
too much information can have a negative impact on customer satisfaction (Iyengar and Lepper
2000). As Annette illustrates in her recounting of her search for a movie to enjoy on a rare
evening alone, she feels obligated to evaluate several potential options before making a final
choice:
I couldn’t just grab off the shelf. I had to look at them and think about
what the movie was about… and ‘did I really feel like it?...did I really
want to watch that one? Well, maybe I didn’t want to watch that one.’
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So, as it’s getting later, I kept telling myself ‘Oh my gosh, you have
to put the movie in. It’s going to get later. You have to just pick one.’
So, it would seem that highly indecisive individuals not only recognize that they are
processing more and taking longer to make decisions than the average individual, they are—
perhaps—doing so on purpose. Highly indecisive individuals are going out of their way to pick
large choice sets and setting aside large amounts of time to process them. It is therefore possible
that this may actually be a preference for a certain type of processing that requires extensive
information search.
Specific Decision Strategies
Rather than relying on heuristics to decrease the amount of processing required to make a
decision, informants in this research engage in what seem to be counterintuitive processing
strategies. They either pursue decision making strategies that require greater amounts of
processing or resort to coping mechanisms that let them escape from the decision making task
altogether. For example, rather than switching to a less effortful processing strategy when
importance of the decision is low or when time pressure is severe, informants reported that they
persist in extensive processing for unimportant and/or urgent decisions. The attention that highly
indecisive individuals give to the small, perhaps insignificant, decisions seems to disrupt their
daily decision making.
To get a sense of how each would approach decision making within a homogeneous
context, informants were asked to walk though how they would order from an unfamiliar menu
using a speak-aloud protocol. I find that, in general, informants began the decision task by
looking at the abstract category levels (for example: Appetizers, Salads, Sandwiches, Burgers,
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Favorites, etc.). Then, based on the context of the situation and how they are feeling, they begin
to eliminate categories that did not meet their current preferences and drilled down into
categories that looked appealing. For example, Kayla outlines her approach to ordering from an
unfamiliar menu.
Usually, if I’m feeling healthy, I’ll look at salads, but I don’t feel
healthy today. So, I look at the burgers and then, sometimes, I’ll
get the normal…mini cheeseburgers because I know that I like
that. And then, if I have more time, I’ll look through the different,
the other types of burgers and see if I’ll actually want to get those.
But if I’m not feeling burgers, I’ll look at appetizers because I
know that I like mozzarella sticks and chicken tenders. I normally
don’t get soups, just because I can make it at home…I don’t really
get chicken sandwiches, either. Or sides, I usually just get—if I’m
healthy, a Caesar salad. But, normally, I’m not so I get fries.
Sometimes I do look at the ‘Favorites.’ The quesadilla does look
good, too.
Like, Kayla, nearly every subject mentioned that they would look for a default option on
the menu and hold that one thing in the back of their mind, just in case. But rather than
satisficing once they have identified an acceptable option, highly indecisive informants stated
that if they have time, they will keep looking for other options. As illustrated by the example
above, even once Kayla has identified a meal that sufficiently matches her preferences, she
continues to peruse the menu. With a default menu item identified, Kayla was able to return to
the abstract level of menu categories and identify other preferences while excluding whole
categories from consideration.
JoAnne followed a similar strategy while shopping for athletic shoes. Because she is not
highly familiar with the product category, she chose a store with a wide selection. As she entered
the aisle of athletic shoes, she took an abstract approach, looking for the brand she had purchased
last time and for other shoes that were pleasing to the eye. She grabbed six boxes of shoes from
the shelf and began to try them on.
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And I start trying them all on and of course I’m looking at them
and feeling them and some of them are just too tight…and so I
have to go get a different pair. So I try them all on and I’m kind of
going back and forth a little bit and deciding. And I try to tell
myself “do I like this one better than the last pair I tried on?” If I
don’t, then that one goes aside. So, I went through six pair pretty
quickly. And I think actually the first ones I was attracted to, I set
aside, “I kind of like those.” So, then I went. I looked for six more
pair.
As she was working through these six pair of shoes, JoAnne was evaluating the specific
attributes of each as compared to the last pair she had on her feet. This strategy has been
identified in the consumer decision making literature as majority of confirming dimensions
(MCD; Payne, Bettman, and Johnson 1988; Bettman et al. 1998). However, rather than
purchasing the pair that she liked the best out of that set, she went back to the shelf and found six
more, repeating the pairwise comparison. She kept trying on shoes for two hours, and finally
narrowed her consideration set down to two pairs. After asking the sales person which of the two
shoes he would recommend, JoAnne settled on the first pair of shoes she had tried on.
Informants were typically able to identify a default or safe choice early in the decision
making process. This process was completed in preparation for the eventuality that they be
pressured to make a decision before they are truly ready. If this happened, they would at least
have a backup plan.
Usually if I decide something, it’s based on where my friends
want to go to and I just find something there. But, I don’t know,
if I’m under pressure, it’s usually the normal—chicken fingers
and fries or a side salad. (Kayla)
While the idea of a default choice may sound simple, there is still the potential that it is
the product of extensive processing. While Amanda had identified a default choice while looking
at the menu, her default choice was still contingent on the choices of others that may be in her
party.
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I'd probably make someone else choose the appetizer and then I'd
pick something depending on what they picked...if it's different
enough than what they picked. So, if they picked something with
salmon in the appetizer. I wouldn't get the salmon. But I'd get
salad. And then, if they didn't pick the salmon, then I'd still have
to decide between the salad and the salmon. (Amanda)
Mark also settled on a default choice, but it is evident from his description below of his
usual meal that even his default choice may not remain consistent from one moment to the next.
I usually go with the salad. Any regular salad with the chicken.
With no cheese and no dressing and a glass of water with no ice.
But water with some lemon. Looks like there is no pasta.
Because I need more carbohydrates these days. So, but I’m going
to stick with my regular sandwich, chicken sandwich with no
cheese on it.
The quotes from Amanda and Mark suggest that highly indecisive individuals do have a
set of stable preferences, but that these preferences are contingent and somewhat complex. But at
the same time that they are trying to form a backup plan, they are engaging in variety-seeking
behavior. This particular finding is consistent with recent findings from Jeong and Drolet (2014)
that highly indecisive individuals engage in more variety seeking behavior than their less
decisive counterparts. They engage in effortful processing to identify an acceptable default,
while simultaneously trying to maximize their utility by identifying something on the menu that
they have never tried that they might enjoy more than the items they have tried in the past.
I probably would just think that I should have gone with the
normal instead of trying new things. But I think if I would have
looked back, even if I didn’t like it, I still would have tried
something new. Just to try to get out of the comfort shell and
trying something. (Kayla)
Informants also report a tendency to deal with decision making difficulty by deferring the
decision to another time. In the case of ordering from a menu, the individual might ask the
waitress for more time. They may worried that the choice they are about to make will be
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incorrect or one that they might regret. There seems to be a sense of anticipated regret for the
opportunities that are lost as a result of making a final choice. This is true across multiple
contexts and types of decisions. Amanda shares her concerns with selecting a small item—an
inexpensive accessory for an electronic device.
Interviewer: So, had you come to a point where you had found
one that you were considering?
Amanda: Um, not quite that far yet. I've been actively looking.
I haven't narrowed it down yet.
Interviewer: How long have you been actively looking?
Amanda: Since Monday, probably. It's Wednesday now. Two
days.
Interviewer: How long do these types of decisions usually take
you to make?
Amanda: Um, a while. Especially if it's going to be my own
money that's paying for it. Because if it's not, then I can just
have just one on a "wish list." Otherwise, I have to decide,
finally, to actually pay for something,
In another example, Shelby was shopping for a pair of shoes with her husband, but ran
out of time before she could reach a decision. So, rather than making a decision, she and her
husband left the store without buying anything. A week after the incident, she still was not sure if
she could justify the expense of a new pair of shoes.
Shelby: So for trying to find a pair of tennis shoes. I’ve been
debating on for a week now, just to find if I want to spend that
much money on just shoes.
Interviewer: So, have you decided on a pair of shoes?
Shelby: Nope. They’re still at the store. We have to go back for
them. And we didn’t go back for them.
Interviewer: So, how long did you spend in the store when you
went?
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Shelby: Oh gosh, we were there over an hour. I mean, just trying
on different kinds and then deciding on this one and…
Interviewer: When you left the store, why did you leave, instead
of buying something?
Shelby: We had to go run another errand… I had to go to work
and [the baby] had to take a nap. So, we just ran the other errand
and I was like “Okay, well, we’ll be back” and then it was, like
“Oh, it’s too close to the time and I didn’t know.” I was kind of
thinking I could find them cheaper online. So, we just didn’t go
back and get them.
Another strategy indecisive individuals seem to prefer is deferral of the decision to
another party. Informants achieved this in a variety of ways: asking for advice once the decision
set has been narrowed down to a few options, asking someone to share a meal, or openly forcing
the choice on someone else. In the following example, Amanda shares an experience from a
recent trip to a restaurant with family. In the end, when she could not decide between two
desirable meals, she found a way to leave the decision to someone else, and therefore avoiding
the pain of making the decision herself.
Well, if it's a pre-ordained lunch, or something, like someone
already decided that we're going there and if I can find a menu
online, I try and look at the menu ahead of time and sort of
decide way in advance, so that I don't have the stress around me,
of needing to get it done in a certain amount of time. That helps.
But if we just decided to go there and we are there, that doesn't
work. But in that case, if I've been there, I have to decide
between doing the easy and getting what I've gotten and
knowing I like it versus trying something new and expanding
my repertoire at that restaurant of what I've found out if I like or
not. And I can ask if someone—ask the waitress or waiter their
opinion. Or I can arrange with someone else at the table to share
between the two.
Like, on Sunday, when we went to Cheeseburger in Paradise.
[My sister] couldn't decide between a burger and salmon and I
had ultimately decided on a burger, but I still wanted the
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salmon. So, we decided to trade some bites. So, that helped.
And I kind of wanted a bit of fish and chips, but I didn't want
the whole thing. So, [my husband] got the fish and chips for
me—for himself to share with me. So, that really helps me,
when other people are willing to share and trade and let
everybody have more than one choice.
Otherwise, a lot of times I just end up settling on trying to
choose the first thing that I start to lean towards.
These behaviors are consistent with the literature on choice deferral to another time (Dhar
1996; Dhar & Nowlis 1999; Dhar & Simonson 1999), deferral to another person (buck-passing,
Mann, Radford, Burnett, Ford, Bond, Leung et al. 1998), default options (Dinner, Johnson,
Goldstein, & Liu 2011; Luce 1998), or the choice not to choose (Dinner et al. 2011; Dhar 1997).
While the strategies manifested in different ways, one commonality across the avoidant strategies
is that each one removes the responsibility for the outcome of the choice from the decision
maker.
Deferring a decision is not necessarily a maladaptive strategy. There are strategic reasons
an individual may choose not to decide, or to delay a decision. A consumer may forgo a purchase
decision because none of the alternatives meet the minimum criteria on important attributes. A
decision maker may delay a decision because they do not possess enough information about the
alternatives or they may seek additional alternatives. An individual may also be uncertain or
ambivalent about which alternative they prefer. This uncertainty can be due to cognitive
difficulty of the decision task (Dhar 1997) or a desire to avoid emotionally difficult trade-offs
(Tversky & Shafir 1992; Luce 1998). Delaying a decision can, however, result in lost
opportunities and suboptimal outcomes (Anderson 2003).
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Negative Affective State
In the interviews, the presence of negative emotion during the decision making process
was overwhelmingly present. Informants described the decision making process with words like
stressful, frustrating, and overwhelming. They also described their past emotional states during
decision making tasks as anxious, worried, annoyed, hesitant, and uncertain. Findings in the
indecisiveness literature show a positive relationship between the trait of indecisiveness and the
trait variables of anxiety and depression (Spunt et al. 2009; Frost and Shows 1993; Rassin and
Muris 2005a, b; Rassin et al. 2007; Elaydi 2006; Jeong and Drolet 2014).
While it is difficult to be certain what is causing the negative affect during decision
making tasks, choice overload and time pressure seem to be major stressors to highly indecisive
individuals during decision the decision making process. Rather than engaging in effort-reducing
and time-saving heuristics, highly indecisive individuals panic when faced with limited time in
which to make a decision. When asked how time pressure affects her decision making, Amanda
responds:
Amanda: I'd get even more flustered. And I'd have a harder
time. And I'd ultimately just have to choose something. And
then have to settle with it.
Interviewer: So, if a waitress was at your table now. Everyone
else has ordered. Now you've narrowed it down.
Amanda: My blood pressure would go up. My pulse would
probably go up, too. I'd get nice and flustered.
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Other informants also share how time limitations affect their mood state and their ability
to make decisions.
It’s more stressful. I go into panic mode. I think it’s like anxiety.
Yeah, because I don’t have time to process. To weigh the pros
and cons. (Heather)
That usually make things much worse. When I feel like there's time
pressure, I just kind of feel like I will tend to lock up. And, to a
certain extent, I'll freak out a little bit. Because then I realize, in my
mind think "Oh, my gosh. I have to make the absolute best
decision here and now I feel like I don’t have enough time to make
that best decision." And so then, it's not good. Usually that doesn't
help the decision making process. Usually that just causes me to
freeze up. (Ben)
When asked what she does when she just cannot decide within a given time frame,
Amanda responds:
I guess I ask someone else to decide. Or I just, sort of, for a little
bit, kind of have a little anxiety attack. Just, sort of, internal and
my whole body is kind of stops. I just, I can't do anything for a
minute and I need it quiet. And I need nobody to talk to me for a
minute while I calm back down.
For Jane, time pressure leads to a more enduring consequence. When she is pressured to
make a decision, she feels that she is destined to make a poor choice. These feelings of regret
continue to nag her, long after the decision has been made and the consequences have passed.
Interviewer: If you were being pressured to make a decision
quickly, does that make it worse?
Jane: Yes. I always choose least optimal one. And I always
think that later…I think like I’ll regret, “Why did I choose this
one?” at that moment. And later I think I regret and say, I really
say should have chosen the other one.
Interviewer: So once you’ve made a decision and you felt
pressured into it, do you usually look back at the decision and
try to make sure you made the best decision? Or do you just
leave it behind you?
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Jane: It bothers me a lot, for a while. So I can’t leave it alone. I
cannot leave it alone. It bothers me, coming back. While I’m
sleeping it will show up in my dream.
The negative affect experienced during decision making is not limited to instances in
which the informants feel as if they are under time pressure. As Heather shares, when she is
outside of her comfort zone or does not know whether her choices are the right choices, she
experiences a variety of negative emotions.
Heather: See, because I like clothes that you can, like, wear this
pair of pants with this many tops and these tops with this. I like
to make my outfits go a long ways. So I just walked out with a
pair of red pants. So right now I’m freaking out that I don’t have
very many shirts to wear with red pants. So I feel like I need to
make some more decisions and find some more shirts.
Interviewer: And is that kind of a daunting prospect for you?
That you’re going to have to go make those decisions?
Heather: Yes. I’m terrified. Because I feel like I still need
someone else’s opinion on what is in style right now. Because I
feel like I have goggles on. I only go to where I always go. So,
it’s nice to have someone else help me make a decision. And it’s
nice to know when they say “does this look good, yes or no”
and it’s not just me making a decision myself.
Although highly indecisive individuals may insist on large choice sets, the cognitive
effort that processing large amounts of information requires actually contributes to the stress and
negative affect that these individuals experience during the decision making process.
I had this huge open slate and when I have an open slate, it stresses
me out because I’m like, “I don’t know what I’m going to do with
this. How am I going to figure it out?” (JoAnne)
It is important to note that the decision making scenarios in most of these examples
would be perceived by most individuals as relatively trivial, and not a source of extreme negative
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affect. However, informants share that both important and unimportant decisions cause them
some level of distress. Shelby reveals that even when trying to make a small decision with her
husband, and neither one claims to have a preference, she feels responsibility for making the
optimal decision.
Well, even just small [decisions]…like what to eat, where to go,
what to do for the night. What to watch on TV. Well, I mean,
unless I really know what I want to do. I also really don’t care. So,
with two people going “oh, I don’t care what we do,” then it’s up
to me to make the decision. Then I have to try and please two
people. (Shelby)
Even when the highly indecisive individual claims to have no preference in the
outcome of a decision, the task can still be a source of negative affect. Highly indecisive
individuals are expending so much effort when making decisions that even the little things are a
drain on their cognitive capacity and limited energy.
Conclusion
In summary, highly indecisive individuals seem to prefer decision sets that give them
ample opportunity to find a default alternative they know they will find acceptable. They also
prefer to have enough time to process the decision set to an extent that they might be able to find
an alternative that will meet their needs better than the satisficing alternative. In evaluating sets
of alternatives, they seem to be processing information using compensatory strategies and
evaluating tradeoffs between different options. While this approach would be efficient in the
case of an important decision that must be made quickly, informants acknowledge that the
decisions they are making are not “that important.” It is as if a decision is “final,” then it must be
important in some way.
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Also evident in the findings from this study is that as difficulty and importance of the
decision task increase, indecisive individuals experience a drastic increase in the amount of
negative affect they experience. Research has shown that indecisiveness is positively associated
with negative affect in decision making (Jeong and Drolet 2014), but little is understood about
why highly indecisive individuals experience a more negative state during decision making than
their less indecisive counterparts.
Low levels of inherent difficulty or importance of the task seem to result in effortful
information processing for highly indecisive individuals, as predicted in Chapter 2. However, it
is possible that as importance and difficulty increase, rather than failing to adjust to increases in
difficulty and importance indecisiveness will have additive effect on the amount of information
processing. Because the interviews were limited to highly indecisive informants, I cannot
compare the decision making process of highly indecisive individuals to those of less indecisive
individuals. My observations are limited to comparisons to behavior we would expect to see
from the average consumer. The next steps in my research is to test my hypotheses in an
experimental setting.
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Chapter 4: Studies
Overview of Experiments
I conduct three experiments designed to test the search and choice processes of indecisive
consumers. The purpose of these studies is to understand how indecisiveness affects the
relationship between decision task characteristics and amount of information processing. In
Study 1, I manipulate product category importance to explore how indecisiveness differentially
impacts information processing strategies at high versus low levels of importance. Studies 2 and
3 test how indecisiveness affects the amount of information processing at different levels of
cognitive task difficulty. In Study 2, cognitive task difficulty is manipulated through varying the
choice set size, and in Study 3, it is manipulated through different levels of time pressure.
Process Tracing Methods
All three experiments share a common procedure adapted from previous research studies
on information acquisition processes known as the Information Board Paradigm. In the 1970s,
information acquisition processes were studied using a combination of information display
boards (Bettman & Kakkar 1977; Payne 1976), eye-tracking from videotapes (Russo & Rosen
1975), and verbal protocols (Payne 1976). Information boards or file folders were set up with an
envelope for each alternative and attribute combination attached to a poster board in a matrix
format with a corresponding note card with the value of the alternative on each attribute (Payne
1976; Bettman & Kakkar 1977). This approach could be combined with an early version of eye
tracking (Russo & Rosen 1975; Russo & LeClerc 1994) which involved video recording the
subject during the decision task and manually coding eye movements for location and duration.
Researchers could combine both of these approaches with verbal protocols (Payne 1976) which
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involve asking subject to “think aloud” or asking them what they are doing during various stages
of the decision task. More current methods use graphical user interfaces that mimic the
information board setup (Patalano & Wengrovitz 2007) and automated eye-tracking software
(Patalano, Juhasz, & Dicke 2010).
In a procedure used by Patalano and Wengrovitz (2007), participants are shown an
information grid on a computer screen. The columns are labeled with various alternatives and the
rows were labeled with attributes. The grid cells contain hidden values for each dimension. To
display the information hidden within each cell, the participant clicks on the cell. This procedure
allows the researchers to infer whether the participants are using an alternative-based (versus
attribute-based) approach to acquiring information. The process tracing method used in this
research is specifically based on the procedure used by Patalano and Wengroviz (2007).
The decision task portion of my studies were conducted using a similar procedure to a
graphical representation of the information board paradigm (see Figure 18), but rather than a
matrix format, information was displayed using a graphical chart. The products represented were
different alternatives from the same product category. The user interface has been designed to
allow the participants to request additional information or to hide information by clicking on a
buttons next to the column chart that represent alternative and attribute information (see Figure
30). The horizontal axis represents clusters of the activated attributes for each alternative. The
vertical axis represents ratings for each attribute/brand combination ranging from 0-100, with 0
representing the worst value possible and 100 representing the best value possible. This
standardized attribute scoring has been used in prior research (Bettman et al. 1998; Luce, Payne,
& Bettman 1999) and allows for a side-by-side comparison of unrelated attributes. To end the
decision task, participants select from a dropdown menu that includes options to select one of the
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alternatives, or to select none of the above. Unless a countdown timer is activated, subjects are
allowed to view and customize the information layout for as long as they wish before making a
choice.
Participants were instructed to complete the decision making task as if they were going to
make a purchase. The alternatives were labeled in a generic manner (i.e. Laptop 1, Laptop 2).
Subjects were instructed that decision making task would be displayed in an interactive visual
format and could be customized. They were also shown a tutorial on how to interact with the
information they would see on the screen during the decision task. They were specifically shown
how to turn alternatives and attributes on/off and how to change the display order of the
information displayed on the screen. After viewing informed consent, subjects were instructed to
make the decision as if they were going to make a purchase. Following the decision making task,
subjects were asked to respond to a questionnaire.
Sample Size
According to Cohen (1992), the sample size required for an effect size calculated by
using Cohen’s d can range from small (d ≈ .20) to medium (d ≈ .50) or large (d ≈ .80). A
between subjects ANOVA with two treatment groups, an effect size of d=.60, 1-β =.80, and α
=.05, requires 45 subjects per treatment group (Cohen 1988). Many of the findings in the
indecisiveness literature are based on scales rather than experimental design. Patalano and
Wengrovitz (2007) and Patalano and LeClair (2011) are the only studies to have used
experimental design to evaluate the effect of indecisiveness on decision making processes and
outcomes. The effect of indecisiveness on decision latency has at least a medium effect size (d =
.65, Patalano & Wengrovitz 2007; Cohen 1992). In order to detect a difference in decision
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latency for decisive versus indecisive individuals experimental studies will require
approximately 45 subjects per treatment group.
The effect of indecisiveness on total information viewed is just below the cutoff for what
is considered to be a medium effect size (d = .44). An effect size of d = .40 (1-β = .80, α = .05),
the required number of subjects per treatment group is 99 (Cohen 1988). Because I am interested
in both decision latency and total information viewed as measures of amount of information
processing, the preferred sample size for each treatment group in the following studies is 99 per
cell. While a larger sample size would be preferred, Patalano and Wengrovitz (2007) find
significant group differences between decisive and indecisive individuals (based on a median
split) when faced with risk in both decision latency and amount of information processing with a
much lower sample size (15 decisives; 22 indecisives). In both of these cases, the effect size was
around d = .70. For this effect size, a sample size of 45 subjects per treatment group should be
sufficient.
Sample Recruitment
Subjects were recruited to participate in the studies through the Amazon Mechanical Turk
system, which gives researchers access to panels of survey-takers for a 10% service fee (Blair &
Roese 2013). Amazon Mechanical Turk (MTurk) is an “online crowdsourcing service where
anonymous online workers complete web-based tasks for small sums of money” (Crump,
McDonnell, & Gureckis 2013). Use of the Mechanical Turk platform simplifies recruitment and
payment of incentives to research participants. Because there is a large pool of MTurk users, it is
not difficult to quickly find a sufficient number of participants to take part in a 15-30 minute
study for a small incentive (Crump et al. 2013).
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There are also disadvantages to conducting experimental studies through an online
resource, like MTurk, rather than in a laboratory. First, researchers have virtually no control
over the environment. Workers could be completing other tasks while participating in the
experiment or not paying attention to information that is critical to the study (Crump et al. 2013).
Second, as part of MTurk’s terms of service, all workers are anonymous (Crump 2013). A
researcher is not able to verify the truthfulness of any information participants provide. This is no
more of a problem than gathering data from undergraduate students in an anonymous survey.
Third, researchers must assume that the speed of computer systems owned and used by the
workers varies widely across participants. As a result, there is likely to be error when measuring
reaction times or relying on precise timing of stimuli (Crump 2013). Finally, there is also the
chance of subversive behavior by human or non-human workers (Crump et al. 2013). Individuals
may try to work quickly through the experiment by responding randomly to measurement items
or questions, paying no attention to instructions.
Some of these issues can be addressed by using screening or training questions to ensure
that the subjects are paying attention. If the subject fails the screening questions, the researcher
has the option, through MTurk, to refuse payment to that individual. Quality control questions
were included in this research to verify that the subjects were paying attention to the
experimental tasks. Because the tools provided by MTurk are not sufficient to create the complex
design required for the procedure described above, the custom design was hosted through a
custom web-page with a link to a Qualtrics survey that captured network latencies to control for
variation in the speed of computer systems and network connections of participants. To ensure
that I received responses from MTurk workers who followed instructions and were highly
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involved in the decision task, I limited my request for participants to those who had approval
ratings greater than 95%.
Pretests
The products for the three studies were determined through a pretest. To control for
familiarity, task difficulty, and involvement with the product category, I pretested seven different
products at varying levels of product category importance. The ideal low importance product
category would be something consumers purchase somewhat regularly, but not daily. Buying a
product from this category should not be especially involving or a difficult purchase decision, so
there would be very little risk in making a suboptimal decision. The ideal high importance
product category would be something that consumers do not purchase regularly, yet a category
with which they are somewhat familiar. A product from a high importance category should
involve more risk because these products are typically valued more highly and a suboptimal
decision could be more costly. All product categories, of both high and low importance, needed
to have a reasonable number of salient attributes to take advantage of the process tracing
software design.
Pretest 1: Product Category Low Importance
Methodology. The pretest involved three potential low importance product categories
(toothpaste, multi-purpose cleaning wipes (e.g. Clorox Wipes), and reusable water bottles). The
survey was administered using randomized blocks in Qualtrics (See Appendix C) Using evenly
presented elements, each subject was randomly shown one of the three low importance product
categories and asked to respond to a series of items. Frequency for the product categories was
measured on a six item Likert scale (Item: How often do you purchase a [product]; Responses: 1-
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Never, 2-Every few years, 3-Once a year, 4-Every few months, 5-Once a month, 6-More than
once a month).
Subjects were also asked to list and rate any features of the product category that came
easily to mind. Enjoyment and difficulty involved in making a purchase within the product
category were each measured with a 7-point single-item statements labeled strongly disagree to
strongly agree. Personal involvement with the product category was measured using
Zaichkowsky’s 10 item semantic differential Personal Involvement Inventory (1994). Subjects
were also asked to rate the importance, from 1-Not at all important to 7-Extremely important, of
a list of attributes specific to the product category they were shown. Finally, subjects were asked
how important it would be to make the right choice in the given product category.
Sample Description. Subjects for the low importance product category pretest were recruited
from a single Principles of Marketing course offered by the Kent State University Department of
Marketing and Entrepreneurship. Of the 595 students enrolled in the course, 349 completed the
survey (106 males and 175 females, 68 unknown). The participants were evenly split across the
product categories (Water bottle: n = 119, Cleaning Wipes: n = 117, Toothpaste: n = 113).
Criteria and Results. For the low importance product category, I was looking for product that is
purchased somewhat infrequently to avoid high levels of habituation or established preferences. I
was also looking for a product that does not inspire a great deal of shopping enjoyment or
personal involvement. I was looking for a product that shoppers do not really think about when
they make a purchase and for which they have not formed strong, easily accessible preferences.
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The ideal product category is one that is purchased less than once a month and is neither highly
involving nor enjoyable as a shopping item.
The mean purchase frequency for all three products combined was Mall=3.68 (SE=.075).
The distributions for the products overall (Skewall = .906, Kurtall = .319) and individually were
reasonably normally distributed according to an absolute cutoff of 2 for skewness and 7 for
kurtosis (West, Finch, and Curran 1995) as well as a visual inspection (See Table 2). In a series
of one sample t-tests, the means for purchase frequencies for individual product categories were
all significantly lower than a value of 5-once a month, (Mwaterbottle=2.77, SE=.128, t(118) = 17.367, p<.001; Mwipes=3.91, SE=.133, t(116) = -8.228, p<.001; Mtoothpaste=4.42, SE=.065, t(112)
= -8.987, p<.001) and greater than a value of 2-every few years (Water bottle: t(118) = 6.029,
p<.001; Wipes: t(116) = 14.334, p<.001; Toothpaste: t(112) = 37.175, p<.001). Toothpaste was
identified as an item that most individuals purchase every few months (64 out of 113). For the
most part, all three product categories tend to be purchased at least once a year and less than once
per month. Therefore, in terms of purchase frequencies, any of the three products would be a
good fit for this research.
The average shopping enjoyment reported by the subjects across all product categories
was moderate (Mall= 3.76, SE=.071) and normally distributed (See Table 2). In a One-Way
ANOVA, the three product categories showed no significant differences in amount of shopping
enjoyment (F (2, 339) = .132, p=.876). In a series of one sample t-tests, all product categories
were either lower than, or not significantly different from, a midpoint value of 4-neutral on a
seven-point scale (Mwaterbottle=3.80 SE=.136, t(115) = -1.46, p=.147; Mwipes=3.71, SE=.121, t(114)
= -2.37, p=.019; Mtoothpaste=3.77, SE=.111, t(110) = -2.11, p=.037), suggesting that the process of
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purchasing from the three product categories is not a prospect that is overly enjoyable. Therefore,
any of the three products should work for my studies.
To ensure that none of the product categories are overly involving, a composite measure
of Zaichkowsky’s Personal Involvement Inventory (PII; 1994) was calculated by taking the
average of the 10 items after adjusting for reverse coding (see Appendix B for items). The
internal reliability of the PII was high with a Cronbach’s Alpha of .873 (see Table 3). The
distribution of the PII composite measure across all three product categories is normal (Skewall=.553; Kurtall=.814). The distribution of the PII composite measure meets the assumptions of
normality for each of the individual product categories (See Table 4). The means for the PII
composite measure fall between values of four and five on a seven point scale (Mwaterbottle=4.46,
SE=.100; Mwipes=4.08, SE=.100; Mtoothpaste=4.76, SE=.081).
A One-Way ANOVA does show a significant difference in the personal involvement composite
measure between the product categories (F (2,344) =12.848, p<.001). To assess the degree to
which the product categories are personally involving, values on the PII were compared to a
value of 4-neutral and a value of 5-somewhat high. Even for products that possessed a mean PII
score that was greater than neutral, all of the product categories scored significantly lower than a
value of 5 on a 7 point scale (Water bottle: t(118) = -5.439, p<.001; Wipes: t(114) = -9.177,
p<.001; Toothpaste: t(112) = -2.965, p=.004). This suggests that the product categories are, on
average, either neutral or only somewhat personally involving.
Based on an analysis of the pretest results, it was decided that all three product categories
are acceptable for this research. None of the three are highly involving, a source of high
shopping enjoyment, or overly frequent purchases for the average consumer. Yet, all three of the
product categories are items that the average consumer does buy on a semi-regular basis. These
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are familiar purchases that may not be part of a routine shopping trip. As such, selection of a
product from one of these product categories is not likely to be made out of habit or brand
loyalty. For the purposes of this study, I selected toothpaste as the low importance product
category. Toothpaste is something that consumers purchase on a fairly regular basis (every few
months to once a month) and does not require much involvement during decision making (M=
4.76, SE= .081, SD= .859).
The next step was to determine which product category features to include in the decision
making task portion of the studies. Survey subjects were asked to list, from memory, any features
they could think of and to rate their importance. In order to establish the importance of a
homogeneous set of features, subjects were also asked to rate the importance of ten product
category features provided by the researcher (Table 5. Pretest 1: Prompted Feature Recall and
Ratings). The top three most commonly mentioned and highly rated features were 1) Longlasting fresh breath (n=38, M=6.66, S= .97), Whitening Power (n= 73, M= 6.07, SD= 1.23), and
3) Cavity Prevention (n= 23, M= 6.51, SD= .99). To allow for comparison between high and
low importance categories, the feature of Price (n= 32, M= 5.31, SD= 1.28) will be held constant
and some unit of measurement such as Quantity/Size (n= 14, M= 4.9, SD= 1.38) will be
included.
Pretest 2: Product Category High Importance
Methodology. The second pretest involved four potential high importance product categories
(Car, Laptop, College Course, Cell Phone). The survey was administered using randomized
blocks in Qualtrics (see Appendix D). Using evenly presented elements, each subject was
randomly shown one of the four high importance product categories and asked to respond to a
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series of items. Because the high involvement/importance product categories are more expensive
and require less frequent replacement, frequency for the high importance product categories was
measured on a slightly different six item Likert scale than in Pretest 1 (Item: How often do you
purchase a [product]; Responses: 1-Never, 2-Every 6+ years, 3-Every 4-5 years, 4-Every 2-3
years, 5-Once a year, 6-More than once a year). Aside from purchase frequency, subjects
completed the same scale items as in Pretest 1.
Sample Description. Subjects for the high importance product category pretest were recruited
from a variety of undergraduate business courses offered by the Kent State University College of
Business. I received 183 complete responses (74 males and 72 females, 37 unknown). The
participants were evenly split across the product categories (Laptop: n = 47, College Course: n =
44, Car: n = 46, Cell Phone: n = 46).
Criteria and Results. For the high importance product category, I was looking for a product that
is purchased somewhat infrequently to avoid high levels of habituation or established
preferences. I was also looking for a product that results in greater than neutral levels of
shopping enjoyment and personal involvement. I was looking for a product that shoppers tend to
think about before they make a purchase, yet do not make often enough to have formed strong,
easily accessible preferences when making a purchase decision. The ideal product category is
one that is purchased less than once a year and is potentially highly involving and enjoyable as a
shopping item.
The mean purchase frequency for all three products combined was Mall = 3.74 (SE=.116). The
distributions for the products overall (Skewall = .124, Kurtall = -1.066) were distributed normally
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(see Table 6) according to an absolute cutoff of 2 for skewness and 7 for kurtosis (West, Finch,
and Curran 1995). College courses and cell phones were purchased more frequently than laptops
and cars. For this research, a product that falls within a range of a purchase that takes place every
1-4 years would be with which subjects will be familiar, yet might not have habituated
preferences.
The average enjoyment reported by the subjects across all product categories appeared to
greater than neutral (Mall= 4.67, SE=.116) and normally distributed (see Table 7). A One-Way
ANOVA reveals significant difference between the different product categories in amount of
shopping enjoyment (F (3,177) = 3.454, p=.018). In a series of one sample t-tests, two of the
product categories (Laptop and Cell Phone) were significantly higher than a shopping enjoyment
value of 4-neutral (Mlaptop = 4.66 SE = .226, t(46) = 2.17, p = .005; MCellPhone = 5.26, SE = .200,
t(46) = 6.30, p < .001) yet lower than a value of 5-somewhat high (Mlaptop = 4.66 SE = .226, t(46)
= -1.51, p = .139; MCellPhone = 5.26, SE = .200, t(46) = 1.304, p = .199). Two of the product
categories (College Course and Car) were no different than a neutral value of 4 on a seven-point
scale. It appears as if subjects enjoy shopping for a cell phones and laptops more than college
courses and cars. Higher levels of shopping enjoyment indicate a higher level of personal
involvement in either the product category or the shopping experience associated with the
product category. Therefore the product categories of laptop and cell phone would be preferable
to those of college course or car.
To ensure the product categories are more involving the low importance product
category, a composite measure of Zaichkowsky’s Personal Involvement Inventory (PII; 1994)
was calculated by taking the average of the 10 items after adjusting for reverse coding. The
internal reliability of the PII was high with a Cronbach’s Alpha of .81. The distribution of the PII
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composite measure across all three product categories is normal (Skewall = -1.152; Kurtall =
2.250). The distribution of the PII composite measure also meets the assumptions of normality
for each of the individual product categories (See Table 9). The means for the PII composite
measure fall above a value of 5-somewhat high on a seven point scale (MLaptop = 5.44, SE = .128;
MCCourse = 5.02, SE = .010; MCar = 5.67, SE = .106, MCellPhone = 5.65, SE=.087). Independent
sample T-tests show that the product categories of laptop, car, and cell phones are at least
somewhat high according to the PII, and can be considered high involvement products for the
purposes of this study.
Product categories from this pretest were to be selected for higher levels of importance,
shopping enjoyment and personal involvement with the product category. The two product
categories that best meet the criteria of high importance and greater than neutral shopping
enjoyment and personal involvement are cell phones and laptops.
Pretest 3: Information Format
Generally, decision process research presents brand and attribute information using a
version of the information board paradigm that has been adapted to work on a computer screen
(see Figure 1). Information about attribute/brand combinations is displayed in a tabular format
that participants can click on to reveal or hide. Because the information for my studies will be
displayed in a visual format that is not common in consumer decision process literature, I tested a
variety of information formats for perceived processing difficulty, vividness, and utility.
Methodology. Identical product and attribute information was displayed to each subject in one of
three information formats (column chart, line graph, tabular grid). The column chart and line
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graph were both shown in either color or black and white (see Appendix D for stimuli). The
tabular grid (or matrix) format was shown with brand information by row and the attribute
information by column, or in the inverse. The survey was administered using randomized blocks
in Qualtrics (see Appendix D). Each subject was randomly shown one of the six information
format combinations (Black & White Column Chart, Color Column Chart, Black & White Line
Graph, Color Line Graph, Feature-Based Tabular Format, and Brand-Based Tabular Format) and
asked to respond to a series of survey items.
Cognitive processing difficulty was measured using a seven-point, three-item semantic
differential scale from White and Peloza (2009) intended to measure cognitive resource demands
(See Appendix B for all scales). Subject were asked to rate the information format of the graph
they were shown as Difficult to Process/Easy to Process, Difficult to Understand/Easy to
Understand, Difficult to Comprehend/Easy to Comprehend. Therefore, the higher the rating on
the semantic differential, the easier the perceived processing of the information format.
Vividness was measured using a three-item, seven-point semantic differential scale for
Imagery Elaboration (Unnava & Burnkrant 1991). Sample items include Provokes Imagery/Does
not Provoke Imagery, Vivid/Dull, Interesting/Boring. Therefore, higher scores reflect lower
levels of vividness.
Subjects were asked questions focused on their attitudes toward the utilitarian nature of
the of the information format (Voss, Spangenberg, and Grohmann 2003). This scale was
originally developed as a measure of attitude toward the product/brand and includes five, sevenpoint semantic differential combinations (Effective/Not Effective, Helpful/Not Helpful,
Functional/Not Functional, Necessary/Not Necessary, and Practical/Not Practical).
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Finally, there were four quality control questions included in the survey that allow for a
direct measure of performance. Because all of the information formats contained identical
attribute/alternative combinations, subjects were asked which of the brands possessed the highest
value on each of the four features. Performance was calculated by counting the number of
subjects in each condition that made a mistake in identifying the correct brand.
Sample Description. The same students who completed Pretest 1 also participated in Pretest 3
immediately following their completion of Pretest 1. Of the 595 students enrolled in the course,
343 completed the survey in Qualtrics. Random assignment of condition was achieved through
the use of Qualtrics’ quota system as subjects entered the survey interface. The participants were
evenly split across the information format conditions (NColumnB&W=57, NColumnColor=58,
NLineB&W=56, NLineColor=55, NTabularBrand=59, NTabularAttribute=58).
Criteria and Results. Because I am manipulating processing difficulty in the main studies, the
ideal baseline information format should be something that is not highly difficult to process.
Because tabular displays of information in most process tracing research do not show product
information in color, I also wanted to know whether showing the information formats in color
would be perceived as more vivid than those presented in black and white. An increase in
vividness that accompanies a change in information format from tabular to a graphical display is
to be expected. However, it was important to know whether there would be a significant
difference between a black and white version of the information format and a color version of the
same format. In an effort to control for the effect of vividness, the ideal information format
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should not be highly vivid. The ideal format would also be is easy to process and does not
distract from systematic processing of the information contained in the display.
The three-item reverse coded cognitive resource demand scale (White and Peloza 2009) had high
internal reliability (α = .96). All results for processing difficulty are reported using a composite
measure created by taking the average across all three items. Higher scores reflect greater ease of
processing while lower scores reflect more difficulty processing. The mean processing difficulty
for all of the information formats combined was 5.97 (SE=.079; Table 11). The distributions for
the information formats overall according to an absolute cutoff criteria (Skewall = -1.39, Kurtall =
1.08). However, a visual inspection (see Figure 2) would indicate that the results for all
conditions are negatively skewed with the majority of subjects stating that the information format
they viewed was easy to process. This negative skew results in high composite scores for all six
information formats (MColumnB&W= 6.07, MColumnColor= 6.21, MLineB&W= 5.71, MLineColor= 6.22,
MTabularBrand=5.78, MTabularAttribute= 5.82). Because the stimuli for this pretest were developed to be
easy to process, it is possible that what we are seeing is a ceiling effect because all six
information formats are easy to process. For the purpose of this research, all of the information
formats are sufficiently easy to process and therefore meet the criteria for my studies.
The three-item reverse-coded measure of vividness (Unnava and Burnkrant 1991) had
high internal reliability (α = .84) and was converted into a composite measure by taking the
average of all three items. The scale is constructed in such a way that high values reflect a lower
levels of vividness while low values represent high levels of vividness. The mean vividness for
all six information formats combined was Mall=3.68 (SE=.075; see Table 12). The distributions
for the information formats overall (Skewall = -.179, Kurtall = -.824) and individually were
reasonably normally distributed according to an absolute cutoff of 2 for skewness and 7 for
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kurtosis (West, Finch, and Curran 1995) as well as a visual inspection. None of the product
categories were highly vivid. Three were all rated with average scores greater than a value of 4neutral (meaning lower levels of vividness) and the remaining three were not significantly
different from an average value of 4-neutral.
A one-way ANOVA of the six information format groups on the imagery composite
score was significant (F (5,335) =5.358, p<.001). A Tukey Honest Significant Difference posthoc test shows that both tabular formats are significantly less vivid than the color versions of
both the column and line charts (see Table 13). Perceived vividness of the brand-focused tabular
format (M = 4.99) was less vivid than in the color conditions for the line format (M = 3.98, p =
.008) and column format (M = 4.07, p = .019). Similarly, the attribute-focused tabular format (M
= 5.14) was rated as less vivid than both color conditions (MLineColor = 3.98, p = .001;
MColumnColor= 4.07, p = .004). The tabular formats did not differ from the black and white versions
of the column and line formats. Whether the column or line formats are shown in black & white
or color did not result in differing perceptions of vividness. It also did not matter whether the
tabular format was brand-focused or attribute-focused. Subjects rated both tabular formats as
equally vivid.
The internal reliability for the five-item utilitarian attitude measure (Voss et al. 2003) was
high (α = .92). The five items were converted into a composite measure using the average of the
items. A higher value on the composite measure indicates lower perceived functionality and
usefulness. The mean utilitarian attitude toward the usefulness of the information formats
combined was Mall = 3.54 (SE=.080). The distributions for the information formats overall
(Skewall = .397, Kurtall = -.260) and individually were reasonably normally distributed according
to an absolute cutoff of 2 for skewness and 7 for kurtosis (West, Finch, and Curran 1995) as well
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as a visual inspection (see Table 14). In a series of one sample t-tests, utilitarian attitude toward
each information format was either equal to a value of 4-neutral (MMatrixB = 3.84, SE = .190, t(58)
= -.86, p = .394; MMatrixF = 3.99, SE = .199, t(57) = -.069, p = .945) or more negative than a value
of 4- neutral (MColumnBW = 3.08, SE = .185, t(56) = -4.995, p < .001; MLineC = 3.32, SE = .189,
t(54) = -3.59, p = .004; MColumnC = 3.53, SE = .214, t(55) = -2.205, p = .032; MLineBW = 3.44, SE =
.184, t(55) = -3.04, p = .004), suggesting a slightly positive utilitarian attitude toward the
usefulness of the graphical information formats.
A one-way ANOVA of the six information format groups on the utilitarian attitude composite
score was significant (F (5,335) = 3.021, p = .011). A Tukey Honest Significant Difference posthoc test (see Table 15) shows that the feature-focused version of the tabular information format
is considered to be less useful (M = 3.99) than the black & white version of the column chart (M
= 3.08; p = 0.12). The remaining information formats do not differ significantly from one
another. Because all of the graphical formats are perceived as being either neutral in usefulness
or better, any of them will work for the purpose of my study.
The direct measure of performance shed some additional light on the difficulty of the
information formats. Of the 57 subjects in the black and white column chart condition, only 4
(7%) made an error (see Table 17). Only 5.2% subjects made an error in the color column chart
condition, 12.5% in the black and white line graph condition, and 3.6% in the color line graph
condition. The performance in the tabular formats was quite a bit lower than in the graphical
formats. In the brand-based tabular format, 49.2% of the subjects made and error and in the
feature-based tabular format, 29.3% made an error. So, while subjects rated all of the information
formats as extremely easy to process, it seems that they were unaware that they were making
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more errors in the tabular formats. This performance measure is a more direct measure of the
processing difficulty experienced across the different information format conditions.
A chi-squared test reveals no significant differences in the number of errors made
between color and black and white formats of the same type (see Table 16). The number of
errors made within the two column chart conditions does not vary based on whether or not the
chart is in color or black and white (Pearson χ2(1)=.171, p = .679). The number of errors made in
the two line graph conditions is marginally affected by color condition (Pearson χ2 (1) = 2.926, p
= .087). This pretest is intended to identify an information format that is easy to process, so
decision task difficulty manipulated without contamination by how the information is displayed.
Therefore, the graphical formats seem to be preferable to the tabular formats. In addition, the
results from the column chart are cleaner and results in less variation in performance across
different graphical adjustments in color. The information format that best meets the criteria for
my studies is the column chart. Because the decision platform is easier to process in color than in
black and white, the experiments will be run in color.
Study 1
The primary goal of study 1 is to test the hypothesis that indecisiveness positively
moderates the effect of product category importance on amount of information processing (H1).
This study examines the effects of indecisiveness on amount of information processing under
conditions of high and low importance of the decision task. Importance of the decision task was
manipulated through the importance of the product category. The results of Pretest 1 suggest that
the product category of toothpaste best meets the criteria for this present study. The results of
Pretest 2 suggest that either product category, laptop or cell phone, will suffice as the relatively
more important product category.
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Independent Variables
Decision Importance. Decision importance was manipulated through product category
importance. The low importance product category was toothpaste, as determined by the product
category pretest. The high importance product category was a laptop, also selected based on the
results of the product category pretest. Effectiveness of the manipulation was checked using the
10-item Personal Involvement Inventory (Zaichkowsky 1994) and purchase frequency.
Indecisiveness. The proposed moderator, indecisiveness, was measured using the 11-item
reduced version of the Indecisiveness Scale (Rassin et al. 2007). Past research has typically been
designed using a median split (Patalano & LeClair 2011; Patalano & Wengrovitz) or with a
selective sampling from the highest and lowest quartiles (Frost & Shows 1993) to categorize
participants as decisive versus indecisive. In this research, indecisiveness was used as a
continuous variable. Scores on the indecisiveness scale were multiplied by a dummy variable for
each of the experimental conditions to allow for a calculation of the interaction effect of
indecisiveness and condition. The magnitude of the difference of slopes for any significant
interactions was calculated using a Chow test (Chow 1960).
Dependent Variables
Amount of Information Processing. The amount of information processing was measured in
multiple ways, following operationalizations that have been used in previous research. The first
method is to track decision latency by tracking the amount of time it takes each decision maker
to complete a decision making task (Patalano & Wengrovitz 2007; Rassin et al. 2008; Patalano &
LeClair 2011; Patalano, Juhasz, & Dicke 2010). A timer began as soon as the participant could
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see the graph containing the alternative and attribute information. The timer ended when the
participant selected one of options from the drop down menu. This approach was applied through
the custom decision making platform. The Decision Latency variable was calculated as the sum
of milliseconds elapsed in the decision making platform. The software tracks time, in
milliseconds, from the point the subject enters the decision making task to the time that they
make their decision and exit the platform.
Process-tracing research also represents amount of information processing by keeping
track of what information is viewed and how many times the decision maker returns to that
particular information (Bettman and Kakkar 1977, Rassin, Muris, Booster, and Kolsloot 2008;
Ferrari and Dovidio 2000) as well as the total amount of information viewed and the proportion
of the information shifts that are dimension-based (Patalano & LeClair 2011; Patalano et al.
2010; Ferrari and Dovidio 2001; Bettman & Kakkar 1977). In this study, the custom web
platform recorded how many alternative/attribute combinations were visible at any given time.
Using the interactive capabilities of the decision making platform, subjects were able to add and
remove products from the information display. The number of product/attribute combinations
visible was averaged across the decision making activities of each subject to assess the average
amount of information each subject had visible during the duration of the decision making task.
This variable is identified in the analysis models as Average Information Visible.
The custom decision making platform is set up to identify when a subject checks or
unchecks a product or feature, when they move a product or feature up or down, and when they
select a product from the drop down menu. The Total Clicks variable was calculated as the sum
of these actions made by each individual within the decision making task. The Click Rate
variable was calculated as the number of actions (clicks) per minute.
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Control Variables
There are a number of variables that can be plausibly expected to affect information
processing outcomes and were included in the analysis models as covariates (See Appendix C for
scales).
Frequency of Purchase. Because frequency of purchase will vary quite drastically between the
two product categories selected, it was measured on two separate scales. Subjects only saw the
scale that was relevant to the product category to which they were assigned. The low importance
condition was measured on a categorical 6-point scale (1 = Never, 2 = Every few years, 3 = Once
a year, 4 = Every few months, 5 = Once a month, 6 = More than once a month). The high
importance condition was measured on a more long-term, but similar scale (1 = Never, 2 = Every
6+ years, 3 = Every 4-5 years, 4 = Every 2-3 years, 5 = Once a year, 6 = More than once a
year). For analysis, the two scales were combined to create a single, 8-point scale.
Information Format and Tutorial Difficulty. Information format and tutorial difficulty were
measured using the cognitive resource demands scale (White & Peloza 2009; White,
MacDonnell, & Dahl 2011). The three, seven-point semantic differential items are intended to
measure the perceived difficulty a person has experienced while processing information. Higher
scores reflect greater ease of processing (1 = difficult to process; 7 = easy to process). The scale
has good internal reliability (α = .85, White & Peloza 2009; α = .94, White, MacDonnell, & Dahl
2011).
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Impulsiveness. Impulsiveness was measured using the Impulsivity Subscale from the BarkleyLevenson and Fox (2014) Decision Behaviors Inventory. This subscale assess a consumer’s
proneness to make impulsive purchases. The scale has not yet been published so reliability and
validity are unknown.
Choice Confusion. This scale measures the extent to which a consumer reports having
experienced difficulty making a recent decision. This measure was referred to by Diehl and
Poynor (2010) as overload. This scale is made up of three, 7-point items as subjects are asked to
indicate the extent they felt overwhelmed and confused or experienced difficulty during a recent
decision (1 = Strongly Disagree; 7 = Strongly Agree). The scale has good internal reliability (α =
.85, Diehl & Poynor 2010).
Task Difficulty. Difficulty of the task (Argo, Popa, & Smith 2010) is measured with three, 7point unipolar statements (e.g. To what extent do you agree that the decision task you just
completed was difficult?) Higher scores represent more agreement with each statement (1 =
Strongly Disagree; 7 = Strongly Agree). This scale measures how challenging a task or process is
perceived to be. The scale has been reported to have high internal consistency (α = .85, Argo et
al. 2010).
Task Involvement. The task involvement scale (Wilcox, Kramer, & Sen 2011) consists of three
items that address involvement, interest, and engagement with a decision task. The items are
answered on a 7-point scale (1 = not at all involved at all; 7 = very involved). Higher scores
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reflect a greater level of involvement with the decision making task. The scale has a high level of
internal consistency (α = .85, .90 as reported by Wilcox et al. 2011).
Personal Involvement Inventory. The Personal Involvement Inventory (Zaichkowsky 1994)
consists of ten items that address how personally involving, exciting, or important an individual
might find an object or task. The items are answered on a 7-point semantic differential (1 =
Boring; 7 = Interesting) and some of the items are reverse-coded (1 = Involving; 7 =
Uninvolving).The scale possesses high levels of internal consistency (α = .90) and good testretest reliability (r = .77, .84, .73; Zaichkowsky 1994).
Presence of a Dominating Alternative. A score is calculated for each alternative based on the
randomized values of the alternative/attribute combinations and the self-reported weights using a
weighted averaging model (Fishbein & Ajzen 1972). The value of the second-best alternative is
subtracted from the optimal alternative. The higher the resulting number, the greater the
dominance of the optimal alternative.
Network Latency. Network latency was calculated using a portion of JavaScript code from the
QRTEngine which is intended to response times in reaction time experiments (Barnhoon,
Haasnoot, Bocanegra, & van Steenbergen 2014).
Number of Combinations Visible at Start. Because the number and combination of alternatives
and attributes that were activated/deactivated at the beginning of the decision task, I control for
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the number of combinations that were visible at the beginning of the task for each individual.
This information was available in the data downloaded from the decision making website.
Discomfort with Technology. Discomfort with technology is measured using a technological
anxiety scale (Collier and Sherrell 2010). The scale uses four statements to measure the degree to
which a consumer is fears and avoids the use of technology (i.e. “I feel apprehensive about using
technology”). The items are measured on a 7-point scale (1 = Strongly Disagree; 7 = Strongly
Agree) and have good internal consistency (α = .93).
Click Rate. For the decision latency and average amount of information visible models, click rate
is included as a covariate.
Educational Background. Subjects were asked to report the highest degree or level of school they
had completed. This was measured with a range of categories (1 = Grade School, 2 = Some High
School, 3 = High School Graduate or GED, 4 = Some College, 5 = Associate Degree, 6 =
Bachelor Degree, 7 = Graduate Degree). For analysis, categories that did not have enough
responses (<15% of total) were combined with the next closest education category.
Age. Subjects reported what year they were born in the questionnaire.
Gender. Subjects reported their gender in the questionnaire.
Income. Subjects were asked to report the range that best described their household income. This
was measured with a range of categories (1 = Less than $10,000, 2 = $10,000 to $39,999, 3
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=$40,000 to $59,999, 4 = $60,000 to $79,999, 5 = $80,000 to $129,999, 6 = $130,000 or more).
For analysis, categories that did not have enough responses (<15% of total) were combined with
the next closest salary range.
Procedure
Subjects were randomly assigned to a treatment group in which they were shown a graph
comparison between brands from a low (high) importance product category. Participants were
given the chance to view and interact with combinations from five attributes and three brands in
the interactive decision making software before making a choice. Following the completion of
the decision task, participants completed a series of scales (see Appendix C).
Sample Description
This study was conducted on Amazon Mechanical Turk with subjects with a Human
Intelligence Task (HIT) approval rate of greater than or equal to 95% and limited to workers in
the United States. Participation in the experiment took an average of 20.8 minutes. The subjects
were paid as workers through the MTurk system. The average effective hourly rate per subject
was $2.91.
I received 193 valid responses that matched between process-tracing software and the
Qualtrics survey. Eight responses were excluded because they were completed using a mobile
device, 1 subject was excluded because he/she was colorblind, 9 subjects were excluded from
analysis because they had network latencies greater than 2SD from the mean (M = 1212.30; SD
= 554.16; Range: 2332.38 – 5865.88ms), and 1 subject was excluded for most of the analysis
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because he/she did not report his/her age. This left a valid sample of 174 subjects (NMale = 89,
NFemale =85).
There was a good distribution of educational attainment. Nineteen subjects had not
attended college (10.9%), 42 had attended some college (24.1%), 30 had earned an associate’s
degree (17.2%), 68 had earned a bachelor’s degree (39.1%), and 15 had earned a graduate degree
(8.6%). The most commonly reported household income was less than $40,000 (N=76). Fortyfour subjects reported a household income within the $40,000-59,999 range, 24 reported a
household income between $60,000-79,999, while 30 reported income of greater than $80,000.
The average age of subjects participating in this study was 36 years old (SD = 11.41, Min = 20,
Max = 76, Median = 33, Mode = 29). The number of subjects was balanced across experimental
conditions. There were 88 subjects in the high importance condition and 86 in the low
importance condition.
Manipulation Check
In this study, I manipulated product category importance. To assess the extent to which
subjects considered the product category to be an important purchase, I measured personal
involvement with the product category using the Personal Involvement Inventory (PII,
Zaichkowsky 1994). The High Importance Condition should be rated as more personally
involving than the Low Importance condition. I also wanted to make sure that task involvement
and task difficulty were held constant across experimental conditions. This is to ensure that it is
not the task itself that is the source of involvement, but the product category. Because the
features of the decision task were held constant, task difficulty should not have varied across
conditions.
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The low importance condition (n = 86) had an average PII score of 4.82 (SD =1.05) and
the high importance condition (n =89) had an average score of 5.68 (SD = 0.95). This difference
was statistically significant (t (173) = -5.673, p < .001). So, as expected, subjects were more
highly involved with the high importance product category (laptop) than the low importance
category (toothpaste). Task involvement and task difficulty ratings were not significantly
different across conditions (see Table 20).
Results
Decision Latency. Decision latency is measured as the number of milliseconds that elapsed
during each subject’s decision making task. On average, subjects (n=174) took 63,524.35
milliseconds (SD = 43,441.12, Min = 8330, Max = 282,167) before exiting the decision making
platform. This is equivalent to 63.52 seconds. The distribution is sufficiently normal (Skewness =
1.837, SE = .184, Kurtosis = 4.772, SE = .366) to justify the use of OLS multiple regression
(Absolute Cutoffs: Skewness = 2, Kurtosis = 7). However, because the distribution has the
characteristics of a truncated normal distribution, robustness of the OLS regression was assessed
using a truncated regression.
When I control for covariates and experimental condition using OLS multiple regression
(See Table 21, Model 1), I find a main effect of indecisiveness on decision latency (Adjusted R2
= 0.22, F (23, 150) = 3.12, p < .001). As indecisiveness increases, decision latency also increases
(b = 7936.95 MS (SE = 3411.29), t = 2.30, p = .023). I did not find a main effect of importance
condition on decision latency (p = .203). In the interaction model (Model 2: Adjusted R2 = 0.22,
F (23, 150) = 3.08, p < .001) I find a significant effect of indecisiveness in the high importance
condition (b = 9809.56 MS SE = 3984.21, t (23) = 2.46, p = .015), but not in the low importance
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condition (p = .11). These findings suggests a moderating effect of indecisiveness on the
relationship between product category importance and decision latency. The more indecisive an
individual is, the more time it takes to decide. This increase in decision latency is further
amplified by higher importance of the decision.
These findings are robust when the model is run as a truncated regression (lower bound=
8330 MS; see Table 22). I still find a main effect of indecisiveness (LL = -2023.82, Wald χ2 (23)
= 66.99, p < .001, b = 12,503.29, Robust SE =6105.04, z = 2.05, p = .04) and no effect of
importance condition (p = .342). The interaction findings are also robust. I find a significant
effect of indecisiveness in the high importance condition (LL= -2024.36, Wald χ2 (23) = 58.86, p
< .001, b = 10,005.08, Robust SE = 6641.62, z = 2.07, p = .04), but not in the low importance
condition (p = .13).
There were a number of significant covariates that remained consistent across both the
main effects model and the interaction models. The coefficients reported are from the interaction
model of the OLS multiple regression (Table 21: Model 2). Click rate was negatively related to
decision latency (b = -1945.24, SE = 803.63, t (23) = -2.42, p = .017). Impulsivity shows a
negative relationship with decision latency (b = -6944.87, SE = 2506.948, t (23) = -2.77, p =
.006). When compared to those with an education level of No College (n = 19), those with in the
Graduate Degree group (n = 15) also showed almost a 30 second decrease in decision latency (b
= -29,492.20, SE = 14,401.18, t (23) = -2.05, p =.042). Two of the socio-economic status groups
showed differences when compared to individuals who make less than 40 thousand dollars a
year. There was also a significant positive relationship between age and how long the individual
took to make their decision (b = 1032.34, SE = 294.47, t (23) = 3.51, p = .001).
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Average Information Visible. The amount of information visible at each point in the decision
making task was averaged for each individual. At any given moment, the average subject (n =
174) would have been viewing 7.88 product/attribute combinations (SD = 3.06, Min = 1.43, Max
= 13.75; See Table 19). The distribution is sufficiently normal (Skewness = -.342, SE = .184,
Kurtosis = -.887, SE = .366) to justify the use of OLS multiple regression (Absolute Cutoffs:
Skewness = 2, Kurtosis = 7).
When I control for covariates and experimental condition (See Table 23), I find a main
effect of indecisiveness on the average amount of information displayed at one time during the
duration of the decision making task (Model 1: Adjusted R2 = 0.34, F(23, 150) = 4.83, p < .001).
As indecisiveness increases, the average amount of information displayed at one time also
increases (b = 0.77, SE=0.22, t (23) = 3.47, p = .001). I do not find a main effect of importance
condition on information displayed (p = .20).
While the overall model is significant (Model 2: Adjusted R2 = 0.33, F (23, 150) = 4.74, p
< .001), I do not find an interaction effect of indecisiveness by importance. The effect of
indecisiveness in the high importance condition (b = .67 (SE = 0.26), t = 2.59, p = .01) is
significant and is in the same direction as effect of indecisiveness in the low importance
condition (b = .86 (SE = 0.25), t = 3.46, p = .001). Because the nature of the relationship does not
change (Chow Test: F(23, 128) = 1.51, p = .08; See Table 26), there is no evidence of an
interaction effect between indecisiveness and importance condition The more indecisive the
individual, the more information they chose to display at one time throughout the duration of the
decision making task. This was true regardless of condition importance.
There were a number of significant covariates that remained consistent across both the
main effects model and the interaction models. The coefficients reported are from the interaction
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model of the OLS multiple regression (Table 23: Model 2). Average information visible is
positively associated with combinations visible at start (b = .40, SE = .09, t (23) = 4.65, p < .001),
task difficulty (b = .51, SE = .19, t (23) = 2.72, p = .007), and tutorial difficulty (b = .43, SE = .15,
t (23) = 2.92, p = .003). Average information visible is negatively associated with choice
confusion (b = -.78, SE = .21, t (23) = -3.76, p < .001), discomfort with technology (b = -.43, SE
= .19, t (23) = -2.32, p = .02), and impulsivity (b = -.37, SE = .16, t (23) = -2.29, p = .02). When
compared to those who make less than $40,000 per year in household income, those who have
high household incomes (>$80,000) tend to have more information visible on average (b = 1.80,
SE = .59, t (23) = 3.04, p = .002).
Click Rate. Click Rate represents each subject’s number of clicks per minute during the decision
making task. On average, subjects (n = 174) made 6.55 clicks per minute during the decision task
(SD = 4.20, Min = .40, Max = 22.87; see Table 19) before exiting the decision making platform.
The distribution is sufficiently normal (Skewness = .896, SE = .184, Kurtosis = .793, SE = .366)
to justify the use of OLS multiple regression (Absolute Cutoffs: Skewness = 2, Kurtosis = 7).
When I control for covariates and experimental condition using OLS multiple regression
(See Table 24), I find a main effect of indecisiveness on click rate (Model 1: Adjusted R2 = 0.15
F (22, 151) = 2.34, p = .001). As indecisiveness increases, the number of clicks per minute also
increases (b = 0.77 (SE = 0.34), t = 2.27, p = .02). I do not find a main effect of importance
condition on click rate (p = .10).
I find an interaction effect of indecisiveness by importance condition (Model 2: Adjusted R2 =
0.14, F (22, 151) = 2.3, p = .002). I find a significant effect of indecisiveness in the low
importance condition (b = 1.03 (SE = 0.38), t = 2.73, p = .007), but not in the high importance
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condition (p = .23). While the slopes of the two lines not significantly different from one another
(Chow: F (21,132) = 1.047, p = .413; see Table 26), the slope of indecisiveness in the low
importance condition is significantly different from zero, while that of the high importance
condition is not. This suggests a moderating effect of indecisiveness on the relationship between
product category importance and how quickly individuals interact with information during an
interactive decision making task. The more indecisive an individual is, the more quickly they
interact with the decision making platform. This effect is amplified for the low importance
decision making task.
There were three covariates that had a significant effect on click rate. The coefficients
reported are from the interaction model of the OLS multiple regression (Table 24: Model 2). The
number of combinations visible at the start of the decision task had a significant negative effect
on click rate (b = -.41, SE = .13, t (23) = -3.19, p = .002) as did tutorial difficulty (b =.-.47, SE =
.23, t (23) = -2.10, p = .04). Network latency had a significant negative effect (t (23) = -2.54, p =
.01), however the practical effect was very small (b = -.003, SE = .001).
Total Clicks (Transformed). The average number of clicks per subject (n = 174) was 6.32 (SD =
5.63, Min = 1, Max = 47; see Table 19). The total clicks dependent measure failed the
assumption of normality (Skewness = 3.15, SE = .18, Kurtosis = 17.68, SE = .37), so it was
transformed using a square root transformation to justify the use of OLS multiple regression. The
transformed version of the total clicks measure had an average value of 2.31 (SD = .99, Min = 1,
Max = 6.86, Skewness = .80, SE = .18, Kurtosis = 2.13, SE = .37).
When I control for covariates and experimental condition (see Table 25), I find a main
effect of indecisiveness on the number of total clicks before making a decision (Model 1:
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Adjusted R2 = 0.18, F (22, 151) = 2.72, p < .001). As indecisiveness increases, the number of
total clicks also increases (b = 0.24 (SE=0.08), t = 3.00, p = .003). I do not find a main effect of
importance condition on the number of total clicks (p = .32).
While the overall interaction model is significant (Model 2: Adjusted R2 = 0.18, F (22, 151) =
2.67, p < .001), I do not find an interaction effect of indecisiveness by importance. The effect of
indecisiveness in the high importance condition (b = .26 (SE = 0.09), t = 2.95, p = .003) is
significant and is in the same direction as effect of indecisiveness in the low importance
condition (b = .21 (SE = 0.09), t = 2.32, p = .02). Because the nature of the relationship does not
change (Chow Test: F (22, 130) = 1.51, p = .08; see Table 26), there is no evidence of an
interaction effect between indecisiveness and importance condition. The more indecisive the
individual, the more information they chose to display at one time throughout the duration of the
decision making task. This was true regardless of condition importance.
There were three covariates that had a significant effect on number of total clicks. The
coefficients reported are from the interaction model of the OLS multiple regression (Table 25:
Model 2). Personal involvement had a significant negative effect on click rate (b = .20, SE = .08,
t (22) = 2.41, p = .02) as did high socio-economic status (b = .43 SE = .21, t (22) = 2.01, p = .05).
The number of total clicks was negatively associated with the number of combinations visible at
the start (b = -.07 SE = .03, t (22) = -2.37, p = .02), discomfort with technology (b = -.16 SE =
.07, t (22) = -2.38, p = .02), and impulsivity (b = -.16 SE = .06, t (22) = -2.78, p = .006).
Discussion
In Study 1, I find evidence that increases in indecisiveness lead to greater amounts of
information processing regardless of product category importance. More indecisive individuals
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spent more time making a decision overall and interacted more with the decision task. This is
consistent with prior literature on indecisiveness.
I also find an interaction between indecisiveness and product category importance on
amount of information processing. For high importance decisions, the increase in decision
latencies that accompany increases in indecisiveness seems much stronger. While it does not
appear that higher levels of indecisiveness lead to slower processing for low importance
decisions, there is evidence that increases in indecisiveness do lead to other strategy adaptations
as indecisiveness increases.
In the high importance condition, an increase in indecisiveness led to increased
processing times without an increase in the speed of adding, removing, and rearranging
information. This suggests a longer, more deliberate processing of static information when
considering a purchase of a high importance product. Conversely, in the low importance
condition, increases in indecisiveness seem to lead to quicker processing of interactive
information as subjects increases their click rate. We can see from the average information
displayed, click rate, and number of total clicks that individuals who are higher in indecisiveness
are processing low importance decisions more quickly. So, even though an increase in
indecisiveness does not result in an increase in decision latency, greater interaction with the
decision task in an equal amount of time suggests that highly indecisive individuals are engaging
in more information processing for low importance decisions than their more decisive
counterparts. It also seems that highly decisive individuals are not as sensitive to differences in
product category importance as are those who are relatively more indecisive.
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Study 2
The primary goal of Study 2 is to demonstrate that indecisive individuals are likely to
engage in extensive information processing regardless of the cognitive difficulty of the decision
making task (H2). The second study examines the effects of indecisiveness on the amount of
information processing under conditions of high, moderate, and low choice set size.
Independent Variables
Cognitive Difficulty—Choice Set Size. Previous research has manipulated cognitive difficulty
through choice set size by showing participants a small set of alternatives in the low cognitive
difficulty condition and a large set of alternatives in the high cognitive difficulty condition
(Iyengar & Lepper 2000; Luce et al. 2001, p.48). In this study, cognitive difficulty of the
decision task was manipulated by showing subjects 3 brands in the low cognitive difficulty
condition, 6 brands in the moderate cognitive difficulty condition, and 10 brands in the high
cognitive difficulty condition.
Indecisiveness. Indecisiveness was measured as in Study 1.
Dependent and Control Variables
The dependent and control variables for study two are the same as for Study 1. Study 2
also includes product category as a dummy variable because the study was conducted with two
product categories (Laptop and Cell Phone). Both products were pretested prior to Study 1 and
found to be acceptable high importance product categories. The product categories will be tested
for differences in task involvement, product category involvement, and task difficulty.
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Procedure
The procedure of study 2 was the same as in study 1. Subjects were randomly assigned to
a treatment group in which they were shown a graphical comparison of two, six, or ten brands.
Subjects participated in the experiment following the procedure as previously explained.
Following the completion of the decision task, participants completed the control variable scales
and quality control items. As a manipulation check, subjects also rated the cognitive difficulty of
the decision task using a three item measure of task difficulty (Argo, Popa, & Smith 2010).
Sample Description
This study was conducted on Amazon Mechanical Turk with subjects with a Human
Intelligence Task (HIT) approval rate of greater than or equal to 95% and limited to workers in
the United States, and those who had not participated in Study 1. Participation in the experiment
took subjects an average of 12.97 minutes. The subjects were paid as workers through the MTurk
system. The average effective hourly rate per subject was $4.62.
I received 294 valid responses that matched between the process-tracing software and the
Qualtrics survey. Three responses were excluded because they were completed using a mobile
device, 3 subjects were excluded because they were colorblind, 2 subjects were excluded because
they had network latencies greater than 2SD from the mean (M = 1265.44; SD = 1530.2; Range:
601.27 – 22,799.88ms), and 11 subjects were excluded for missing data. This left a valid sample
of 275 subjects (NMale = 157, NFemale =118).
There was a good distribution of educational attainment. Twenty-six subjects had not
attended college (9.5%), 91 had attended some college (33.1%), 24 had earned an associate’s
degree (8.7%), 114 had earned a bachelor’s degree (41.5%), and 20 had earned a graduate degree
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(7.3%). The most commonly reported household income was less than $40,000 (N=125). Sixtynine subjects reported a household income within the $40,000-59,999 range, 28 reported a
household income between $60,000-79,999, while 53 reported income of greater than $80,000.
The average age of subjects participating in this study was 32.57 years old (SD = 9.71, Min = 20,
Max = 64, Median = 33, Mode = 25). The experimental conditions remained balanced. I was left
with 94 subjects in the Low Cognitive Difficulty condition (Cell Phone: n = 42, Laptop: n = 52),
88 in the Moderate Cognitive Difficulty condition (Cell Phone: n = 39, Laptop: n = 49), and 93
in the High Cognitive Difficulty condition (Cell Phone: n = 48, Laptop: n = 45).
Manipulation Check
In this study, I manipulated cognitive task difficulty of the decision task through choice
set size. The high cognitive task difficulty condition (50 alternative/attribute combinations)
should be rated as more difficult than the moderate cognitive difficulty condition (30
combinations) and the both should be rated as more difficult than the low cognitive difficulty
condition (15 combinations). To assess perceived task difficulty, I measured difficulty of the task
(Argo et al. 2010). In addition to the self-report measure, I use a direct measure of performance. I
also wanted to make sure that processing fluency of the information format was held constant
across experimental conditions. This is measured using a three item scale (White & Peloza 2009)
for information format of both the decision task and the tutorial instructions.
The self-report ratings of cognitive difficulty of the decision task are all quite low (See
Table 27). The average difficulty rating was 2.74 (SD = 1.51) in the low difficulty condition,
2.78 (SD = 1.60) in the moderate difficulty condition, and 2.88 (SD = 1.51) in the high difficulty
condition. The difference in these self-report ratings does not approach significance (F (2,272) =
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.22, p = .81). However, the direct measure of performance indicates that while participants did
not perceive increases in decision task difficulty as the number of combinations increased, their
performance suffered considerably when they were placed in the moderate and high difficulty
conditions (See Table 28). In the low cognitive difficulty condition, 54.3% of subjects selected
the alternative that best met their preferences. In the moderate and high cognitive difficulty
conditions the percentage decreased to only 33% and 28%, respectively. Performance is
significantly higher in the low cognitive difficulty than in either the moderate (χ2 (1) = 8.37, p =
.005), or high cognitive difficulty (χ2 (1) = 13.35, p < .001) conditions (See Table 29). However,
I do not find a significant difference between the high and moderate cognitive difficulty
conditions (p =.29). It is possible that there was not enough of an increase in decision task
difficulty when increasing from 30 to 50 alternative/attribute combinations. It is also possible
that 30 combinations is sufficiently high that further increases in difficulty have diminishing
returns.
The scale used to measure processing fluency of the task and the tutorial is coded such that
higher values indicate greater ease of processing and lower values indicate greater processing
difficulty. The low difficulty condition (n = 94) had an average score of 5.87 (SD =1.39) for task
information format and 5.58 (SD = 1.51) for tutorial information format (See Table 30). The
moderate difficulty condition (n = 88) had an average score of 5.81 (SD = 1.420) for task
information format and 5.50 (SD = 1.52) for tutorial format. The high difficulty condition (n =
93) had an average score of 5.76 (SD = 1.44) for task information format and 5.58 (SD = 1.52)
for tutorial format. There were no significant differences in perceived difficulty for processing
the information format of the task (F (2, 272) = .15, p = .86) or the tutorial (F (2,272) = .20, p =
.92).
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In a One-Way ANOVA, I find no differences between the two product categories of
laptop and cell phone on task involvement (p = .70), product category involvement (p =.156), or
task difficulty (p = .742). Results for this study will be collapsed across the two product
categories and the model will include a dummy variable covariate to control for any remaining
variance due to product category.
Results
Decision Latency. On average, subjects (n=275) took 82,694.36 milliseconds (SD = 56,881.73,
Min = 8736, Max = 455,532; see Table 31) before exiting the decision making platform. The
average decision latency is equivalent to 82.69 seconds. In this study, the distribution of decision
latency violated assumptions of normality (Skewness = 2.15 SE = .15; Kurtosis = 7.75, SE = .29)
and the data was transformed by taking the square root of the observations (Absolute Cutoffs:
Skewness = 2, Kurtosis = 7). After the transformation the distribution is sufficiently normal
(Skewness = .95, SE = .15, Kurtosis = 1.50, SE = .29) to justify the use of OLS multiple
regression. However, because the distribution has the characteristics of a truncated normal
distribution, robustness of the OLS regression was assessed using a truncated regression on the
untransformed version of the decision latency variable.
When I control for covariates and experimental condition using OLS multiple regression
(See Table 32), I find a main effect of indecisiveness on the transformed decision latency
measure (Model 1: Adjusted R2 = 0.19, F (27, 247) = 3.35, p < .001). As indecisiveness
increases, decision latency also increases (b = 12.80 (SE = 4.84), t = 2.64, p = .008). I find a
positive main effect of moderate difficulty on decision latency when compared to the low
cognitive difficulty (b = 35.47, SE = 16.34, t (27) = 2.17, p = .03; see Table 32), but not high
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cognitive difficulty (p = .07; see Table 33). I also find no effect of high cognitive difficulty when
compared to low cognitive difficulty (p = .42). This inverted-U shaped effect of increasing
difficulty is consistent with extant literature on choice overload (Iyengar & Lepper 2000).
I find an interaction effect of indecisiveness by importance condition (Model 2: Adjusted
R2 = 0.19, F (27, 247) = 3.42, p < .001). I find a significant effect of indecisiveness in the high
cognitive difficulty condition (b = 18.70 (SE = 5.36), t (27) = 3.49, p < .001), and in the moderate
cognitive difficulty condition (b = 10.37 (SE = 5.19), t (27) = 2.00, p = .05), but not in the low
importance condition (p = .11). The slopes of the moderate and high cognitive difficulty
conditions are significantly different from one another (Chow: F (26, 129) =4.37, p < .001; see
Table 41).
These findings suggest a moderating effect of indecisiveness on the relationship between
decision making task difficulty and decision latency. The more indecisive an individual is, the
more time it takes to decide. This increase in decision latency is further amplified by increasing
the difficulty of the decision.
The indecisiveness findings are robust when the model is run as a truncated regression
(lower bound= 8736 MS; see Table 34). I still find a main effect of indecisiveness (LL = 3298.74, Wald 2(27) = 47.00, p = .01, b = 18,185.09, Robust SE = 7617.54, z = 2.39, p = .02).
However, I no longer see an effect of high difficulty (p = .12). The interaction findings are also
robust. I find a significant effect of indecisiveness in the high and moderate difficulty conditions
(LL= -3298.28, Wald χ2 (27) = 44.93, p = .02; High: b = 23,647.64, Robust SE = 8909.07, z =
2.65, p = .008; Moderate: b = 15,042.08, Robust SE = 6668.88, z = 2.26, p = .02), but not in the
low difficulty condition (p = .11).
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There were three significant covariates in the main effects model and the interaction
models. The coefficients reported are from the interaction model of the OLS multiple regression
(Table 32: Model 2). Increases in discomfort with technology led to shorter decision latencies (b
= -15.32, SE = 5.66, t (27) = -2.71, p = .007). As perceived task difficulty and age increased,
decision latencies also increased (b = 11.09, SE = 5.19, t (27) = -2.14, p = .03; Age: b = 1.83, SE
= .57, t (27) = 3.19, p = .001).
Average Information Visible. Over time the average participants (n = 275) would have been
viewing an average of 14.56 product/attribute combinations (SD = 8.21, Min = 2, Max = 41.56;
Table 31). The distribution is sufficiently normal (Skewness = 1.13, SE = .15, Kurtosis = .91, SE =
.29) to justify the use of OLS multiple regression (Absolute Cutoffs: Skewness = 2, Kurtosis = 7).
When I control for covariates and experimental condition (See Table 35), I find no effect
of indecisiveness on the average amount of information displayed at one time during the duration
of the decision making task (Model 1: Adjusted R2 = 0.40, F (27, 247) = 7.83, p < .001). As
indecisiveness increases, the average amount of information displayed remains the same across
experimental conditions (p = .23).
One might expect a main effect of condition on average amount of information displayed
simply because there are more combinations available in the moderate (30 combinations) and
high (50 combinations) difficulty conditions than in the low difficulty condition (15
combinations). I find a main effect of high cognitive difficulty when compared to the low
difficulty condition (High: b = 3.44, SE= 1.06, t (27) = 3.25, p = .001; Low: b = -3.44 SE= 1.06, t
(27) = -3.253.25, p = .001; see Table 35 and Table 36). I also find a main effect of the moderate
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difficulty on average amount of information displayed when compared to high (b = -3.44, SE=
1.06, t (27) = -3.25, p = .001) or low difficulty (b = 8.52, SE= 1.31, t (27) = 6.51, p < .001).
The interaction model is significant (Model 2: Adjusted R2 = 0.40, F (27, 247) = 7.61, p <
.001). The effect of indecisiveness has a significant positive effect on amount of information
visible in the high cognitive difficulty condition (Average Information Visible Model 2: b = 1.58
(SE = 0.43), t = 3.65, p < .001) and no significant effect in the low and moderate cognitive
difficulty conditions (p = .16 and p = .52, respectively). The more indecisive the individual, the
more information they chose to display at one time throughout the duration of the decision
making task. This effect was strong in the high cognitive difficulty condition and absent in the
low and moderate difficulty conditions.
There were three significant covariates in the main effects model and the interaction
model. The coefficients reported are from the interaction model of the OLS multiple regression
(Table 35: Model 2). Because the increase in difficulty was achieved through an increase in the
number of alternatives, the amount of information available to the subjects was higher as
difficulty increased. Because the number of combinations visible at the start of the decision task
was randomly generated, it is possible that a subject would end up with a high or low number of
alternative/attribute combinations when they started the task. To control for subjects who
preferred to view information in its original state, rather than interacting with the decision
making website, I included a covariate to account for the number of combinations visible at the
start of the decision task. As expected, this variable is positively associated with the average
amount of information visible (b = .39, SE = .08, t (27) = 4.69, p < .001). Discomfort with
technology is negatively associated with amount of information visible (b = -1.15, SE = .46, t
(27) = -2.51, p = .01) as is age of the participant (b = -.13, SE = .05, t (27) = -2.90, p = .004).
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Click Rate. On average, subjects (n = 275) made 7.78 clicks per minute during the decision task
(SD = 4.92, Min = .67, Max = 24.91; see Table 31) before exiting the decision making platform.
The distribution is sufficiently normal (Skewness = 1.04, SE = .15, Kurtosis = .854, SE = .29) to
justify the use of OLS multiple regression (Absolute Cutoffs: Skewness = 2, Kurtosis = 7).
When I control for covariates and experimental condition using OLS multiple regression
(Model 1: Adjusted R2 = 0.14, F (26, 248) = 2.74, p = .002; see Table 37), I find no main effect
of indecisiveness on click rate (p = .10). I do find a main effect of the high cognitive difficulty
condition on click rate (b = 1.95, SE = 0.93, t (26) = 2.10, p = .04; see Table 37) when compared
to the low cognitive difficulty condition (b = -1.19, SE = 0.76, t (26) = -1.56, p = .12; see Table
38). I find no effect of the moderate difficulty condition when compared to high (p = .33) or low
difficulty (p = .12).
I find an interaction effect of indecisiveness by importance condition (Model 2: Adjusted
R2 = 0.13, F (26, 248) = 2.76, p < .001) on click rate. I find a significant negative effect of
indecisiveness in the low cognitive difficulty condition (b = -.76 (SE = 0.31), t (26) = -2.43, p =
.02), but not in the high or moderate difficulty conditions (p = .52 and p = .12, respectively). This
suggests a moderating effect of indecisiveness on the relationship between cognitive task
difficulties and how quickly individuals interact with information during an interactive decision
making task. The subjects interacted the more quickly with the decision making platform when
cognitive task difficulty was high, compared to when it was low or moderate. In the low
difficulty condition, increases in indecisiveness led to slower, more deliberate processing.
There were five covariates that had a significant effect on click rate in both the main
effects model and the interaction model. The coefficients reported are from the interaction model
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of the OLS multiple regression (Click Rate Model 2). The number of combinations visible at the
start of the decision task had a significant negative effect on click rate (b = -.15, SE = .06, t(26) =
-2.52, p = .01) as did discomfort with technology (b = -.94, SE = .32, t(26) = -2.92, p = .03),
gender (b = -1.52, SE = .60, t(26) = -2.54, p = .01), and age (b = -.11, SE = .03, t(26) = -3.35, p =
.001). Network latency had a significant negative effect (t (26) = -2.78, p = .006), however the
practical effect was very small (b = -.002, SE < .001).
Total Clicks. The average number of clicks per subject (n = 275) was 10.49 (SD = 9.42, Min = 1,
Max = 56). The total clicks measure was within accepted bounds of normality (Skewness = 3.15,
SE = .18, Kurtosis = 17.68, SE = .37). When I control for covariates and experimental condition
(See Table 39), the overall model is significant (Model 1: Adjusted R2 = 0.16, F (26, 248) = 3.01,
p < .001). I find no effect of indecisiveness on the number of total clicks before making a
decision (p = .39). I do find a main effect of cognitive difficulty condition on number of total
clicks. Number of clicks in the moderate and high cognitive difficulty conditions (Moderate: b =
2.84, p = .05; High: b = 6.98, p < .001; see Table 39) are greater than in the low cognitive
difficulty condition (Low: b = -2.84, p = .05; see Table 40). This is likely because there is a
greater availability of information as the conditions increase in difficulty.
The interaction model is also significant (Model 2: Adjusted R2 = 0.17, F (26, 248) =
3.15, p < .001). I find an interaction effect of indecisiveness by difficulty condition. The effect of
indecisiveness in the high importance condition is significant and positive (b = 1.50 (SE = 0.58), t
= 2.57, p = .01). Indecisiveness has no effect on total clicks in the moderate and low cognitive
difficulty conditions (p = .70 and p = .39, respectively). The number of total clicks increases as
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the amount of information increases. The only condition in which indecisiveness contributes to a
greater amount of dynamic processing of information is the high cognitive difficulty condition.
There were three covariates that had a significant effect on number of total clicks in both
the main effects model and the interaction effect model. The coefficients reported are from the
interaction model of the OLS multiple regression (Total Clicks Model 2). Perceived task
difficulty had a significant positive effect on total clicks (b = 1.57 SE = .56, t (26) = 2.79, p =
.005). Discomfort with technology had a significant negative effect on total clicks (b = -1.80 SE
= .60, t (26) = -2.98, p = .003). Network latency also had a significant negative effect (t (26) = 2.76, p = .006), however the practical effect was very small (b = -.003, SE = .001).
Discussion
There is a significant main effect of indecisiveness on how long subjects spent making a
decision in the interactive decision making tool. This is consistent with the findings from Study
1. The more indecisive the individual, the longer they took before selecting and confirming an
alternative. When difficulty is high or moderate, highly indecisive individuals take longer to
make a decision than their less indecisive counterparts. Increases in indecisiveness in the high
difficulty condition were associated with significantly greater increases in decision latency than
in the moderate difficulty condition. Indecisiveness did not seem to impact decision latency in
the low difficulty condition.
Unlike in Study 1, there does not seem to be a main effect of indecisiveness on click rate.
Because I do see an increase in decision latency that is not paired with an increase in click rate,
this would indicate that as indecisiveness increases, more time is being spent processing static
information rather than interacting with the decision making platform.
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I do find an interaction effect of indecisiveness and low task difficulty on click rate. In
the condition that has three alternatives and five features, there is a lesser rate of interaction with
the information as indecisiveness increases. Literature suggests that it is possible for an
individual to be able to process faster in a low difficulty situation. This, paired with the finding
that indecisiveness is associated with a slower click rate in the low cognitive difficulty condition,
suggests that high levels of indecisiveness may still be linked to greater effort in processing. In a
low difficulty condition, maintaining a consistent decision latency requires minimal effort
because there are fewer combinations available to process. Therefore, highly indecisive
individuals seem to over adjusting their processing strategies in response to changes in the
decision making task. Individuals on the low end of the indecisiveness spectrum, however, seem
to be making little or no adjustment to changes in decision difficulty at all, regardless of
difficulty condition.
Subjects took more time in the moderate difficulty condition than in either the high or
low difficulty conditions. There was no difference in decision latency between the low and high
difficulty conditions. This is consistent with existing decision-making literature and suggests the
use of heuristics or less time consuming decision strategies as decision difficulty exceeds a
certain threshold (Anand & Sternthal 1990). I find that subjects in the high difficulty condition
made significantly more clicks in the decision task than did those in the low difficulty condition.
This makes sense because in the high difficulty condition, there is more opportunity for clicking
(50 alternative/attribute combinations). However, this does not mean that the subjects were
required to click on the information and we control for the number of combinations visible when
the task starts. I find no significant difference in click rate between the low and moderate
difficulty conditions. This suggests that when there is a large amount of information and
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individuals presumably have a the dual goals of maximizing accuracy and minimizing effort,
they will make an effort to speed up their processing in order to view the information necessary
to make an informed decision. However, since I do not see this increase in decision latency in the
high difficulty condition, it is likely that the processing strategy in this condition is more
selective and noncompensatory than the strategies used in the other difficulty conditions.
As in Study 1, there is a main effect of indecisiveness on the average amount of
information subjects had displayed on the screen at one time during the duration of the decision
making task. This effect remains in the high difficulty condition, but is not present in the low and
moderate difficulty conditions. This suggests that while lower levels of indecisiveness might
indicate an ability to hide or ignore non-diagnostic or unimportant attributes, as both
indecisiveness and difficulty increase, an individual chooses to show more information on the
screen.
In the high difficulty condition, I see a greater number of attribute/alternative
combinations turned on at a time throughout the decision task than in the low difficulty
condition. I find no difference between the low and moderate difficulty conditions. This may be
because there are more combinations available in the high difficulty condition (50 combinations)
compared to the low difficulty (15 combinations) and the moderate difficulty (30 combinations)
conditions. As a result of the manipulation, there is a greater likelihood that there will be more
combinations displayed when the decision task begins in the high difficulty condition. I have
controlled for this number, however, and still see an effect of both high cognitive difficulty and
indecisiveness. This suggests that highly indecisive individuals will prefer to process a greater
proportion of available information, regardless of the consequences of choice overload.
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Study 3
Study 3 was designed to replicate the findings of Study 2 with an alternate manipulation
of cognitive difficulty, time pressure (H3). Study 3 examines the effects of indecisiveness on
amount of information processing under conditions of low cognitive difficulty (time pressure
absent), moderate cognitive difficulty (90 second time limit), and high cognitive difficulty (20
second time limit).
Independent Variables
Cognitive Difficulty—Time Pressure. Cognitive difficulty was manipulated through the absence
or presence of moderate to severe time pressure. Time pressure was made salient though the
presence of a countdown timer on the visual display above the comparison graph of the products
under evaluation. In the moderate cognitive difficulty condition, the amount of time for the
countdown timer was determined by taking an amount of time from the previous two studies that
would be enough time for the majority of the subjects to finish making a decision from a 25
combination set (5 alternatives, 5 attributes) without being in a hurry (90 seconds). This was
intended to ensure that the subjects do, in fact, have enough time to make the decision. However,
the presence of a countdown timer will make the time pressure salient, increasing the perceived
cognitive difficulty of the task. In the high cognitive difficulty condition, an amount of time was
identified from the previous two studies that would only be enough time for the most decisive
individuals to make a selection from a 25 combination set (5 alternatives, 5 attributes; 20
seconds). The amount of perceived cognitive difficulty for different amounts of time pressure
were confirmed through a manipulation check. In the control condition (low cognitive difficulty
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condition-time pressure absent), subjects were allowed to make their decision without a time
limit and no countdown timer, as in the previous two studies.
Indecisiveness. Indecisiveness was measured as in Studies 1 and 2.
Dependent and Control Variables
The dependent and control variables for Study 3 are the same as Study 2.
Procedure
This study followed the same procedure as the previous two studies. Participants were
randomly assigned to conditions with no, moderate, or severe time pressure. Following the
completion of the decision task, participants responded to the control variable scales and quality
control items. As a manipulation check, subjects rated the cognitive difficulty of the decision
task.
Sample Description
This study was conducted on Amazon Mechanical Turk with subjects with a Human
Intelligence Task (HIT) approval rate of greater than or equal to 95%, and limited to workers in
the United States, and those who had not participated in Study 1 or Study 2. Participation in the
experiment took an average of 13 minutes. The subjects were paid as workers through the MTurk
system. The subjects were paid as workers through the MTurk system. The average effective
hourly rate per subject was $4.62.
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I received 285 valid responses that matched between the CDP platform and the Qualtrics
survey. Fifteen responses were excluded because they were completed using a mobile device, 8
subjects were excluded because they were colorblind, 4 subjects were excluded because they had
network latencies greater than 2SD from the mean (M = 1292.70; SD = 1261.21; Range: 573.95
– 18,142 MS), and 6 others were excluded for missing data. After removing subjects for the
reasons enumerated above, this left a valid sample of 252 subjects (NMale = 125, NFemale =127).
There was a good distribution of educational attainment. Twenty-one (21) subjects had
not attended college (7.9%), 76 had attended some college (30.2%), 15 had earned an associate’s
degree (6.0%), 110 had earned a bachelor’s degree (43.7%), and 30 had earned a graduate degree
(11.9%). The most commonly reported household income was less than $40,000 (N=104). Fiftyseven subjects reported a household income within the $40,000-59,999 range, 48 reported a
household income between $60,000-79,999, while 43 reported income of greater than $80,000.
The average age of subjects participating in this study was 33.98 years old (SD = 10.90, Min =
19, Max = 70, Median = 33). The number of subjects in each experimental condition was
balanced. I was left with 83 subjects in the Time Pressure Absent condition, 86 in the Time
Pressure Moderate condition, and 83 in the Time Pressure High condition.
Manipulation Check
To assess whether the time pressure manipulation resulted in greater cognitive difficulty,
I administered a three item scale (Argo et al. 2010). I also compared the number of seconds
remaining on the countdown timer and decision latency (seconds) for all three conditions.
If the manipulation worked as it should, I should see higher perceived difficulty in the
moderate cognitive difficulty condition than in the low cognitive difficulty. I should also see
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higher perceived difficulty in the high cognitive difficulty than either of the other conditions. I
should see more time remaining on the countdown timer in the moderate cognitive difficulty
condition than the high cognitive difficulty condition because most subjects should have had
time to finish the decision task in the moderate difficulty condition before the timer ran out. The
high cognitive difficulty condition was set up to allow only the quickest decision makers to make
a decision with time to spare. Because performance suffered in Study 2 as a result of increasing
difficulty, I also expected to see accuracy decrease as cognitive difficulty of the decision task
increased.
Results
Decision Latency. On average, subjects (n = 252) took 62,621.47 milliseconds (SD = 47,711.24,
Min = 9890, Max = 335,580; see Table 42) before exiting the decision making platform. The
average is equivalent to 62.62 seconds. In the low cognitive difficulty (Time Pressure: Absent)
condition, the average decision latency was 87.66 seconds (See Table 43). In the moderate
cognitive difficulty (Time Pressure: 90 seconds), the average decision latency was 65.93
seconds. Seventy-one of the 86 subjects were able to complete the decision-making task before
the timer expired. In the high cognitive difficulty (Time Pressure: 20 seconds), the average
decision latency was 34.15 seconds and the minimum decision latency in this condition was
10.45 seconds. Five subjects were able to finish the decision-making task before the timer
expired.
The distribution fails the assumption of normality (Skewness = 2.83, SE = .15, Kurtosis =
10.42, SE = .31) and was transformed using the square root of the variable to justify the use of
OLS multiple regression (Absolute Cutoffs: Skewness = 2, Kurtosis = 7). The average value of
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decision latency transformed was 237.70 (SD = 78.38, Min = 99.45, Max = 3579.29). Because
the distribution has the characteristics of a truncated normal distribution, robustness of the OLS
regression was assessed using a truncated regression on the untransformed version of the
decision latency variable.
When I control for covariates and experimental condition using OLS multiple regression
(Model 1: Adjusted R2 = 0.37, F (26, 225) = 6.54, p < .001; see Table 48), I find a marginal
directional effect of indecisiveness on decision latency (b = 6.98, SE = 10.64, t (26) = -3.63; p =
.10). Because there was a 20-second time limit in the high cognitive difficulty condition, I find a
significant negative effect of high cognitive difficulty on decision latency when compared to low
(b = -111.83, SE = 11.64, t(26)= -9.61, p < .001; see Table 48) and moderate (b = -75.09, SE =
11.11, t(26)= -6.76, p < .001; see Table 49) cognitive difficulty. There was also a significant
main effect of the moderate cognitive difficulty condition on decision latency when compared to
low cognitive difficulty (b = -36.74, SE = 10.11, t (26) = -3.63, p < .001; see Table 48).
I find an interaction effect of indecisiveness by difficulty condition (Table 48: Model 2:
Adjusted R2 = 0.33, F (26, 225) = 5.71, p < .001). I find a significant positive effect of
indecisiveness on decision latency in the low and moderate cognitive difficulty conditions (Low:
b = 21.77 (SE = 4.48), t (26) = 4.85, p < .001; Moderate: b = 11.87 (SE = 4.58), t (26) = 2.59, p =
.01. The slopes of these lines are significantly different (Chow: F (25, 119) = 5.75, p < .001),
suggesting that the positive effect of indecisiveness on decision latency is amplified by increases
in task difficulty. The effect disappears in the high cognitive difficulty condition because only
five subjects were able to complete the decision task before the countdown timer expired.
The main effect findings are robust when the model is run as a truncated regression
(lower bound= 9890 MS; LL = -2910.96, Wald χ2 (26) = 49.68, p = .003; see Table 50). I find, no
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effect of indecisiveness (p = .16). The significance of interaction results is slightly different but
follows the same pattern as the OLS results. The OLS results showed that the impact of
indecisiveness on decision latency lessened as time pressure became more severe. The low and
moderate difficulty conditions both showed a significant effect of indecisiveness, while the high
difficulty condition did not. In the truncated regression with robust standard errors (LL= 2917.29, Wald χ2(26) = 38.77, p = .05), I find a significant negative effect of indecisiveness in
the low cognitive difficulty condition (b = -25,690.06, Robust SE = 9215.14, z = -2.79, p = .005),
a positive effect in the high cognitive difficulty condition (b = 21,164.44, Robust SE = 6940.34,
z = 3.05, p = .002), and a marginally significant positive effect in the moderate cognitive
difficulty condition (p = .10).
There were four significant covariates in the main effects model and the interaction
model. The coefficients reported are from the interaction model of the OLS multiple regression
(Table 48: Model 2). Decision Latency was positively influenced by task involvement (b = 15.42,
SE = 5.36, t(26) = 2.88, p = .004), perceived task difficulty (b = 10.94, SE = 3.96, t(26) = 2.96, p
= .003), network latency (b = .03, SE = .01, t(26) = 2.73, p = .006), and reported age (b = .98, SE
= .45, t(26) = 2.168, p = .03).
Average Information Visible. The average information visible was 12.13 product/attribute
combinations (SD = 5.21, Min = 2, Max = 24.17; see Table 42). The distribution is sufficiently
normal (Skewness = .007, SE = .15, Kurtosis = -.97, SE = .31) to justify the use of OLS multiple
regression (Absolute Cutoffs: Skewness = 2, Kurtosis = 7).
When I control for covariates and experimental condition (See Table 51, Model 1:
Adjusted R2 = 0.33, F(26, 225) = 5.70, p < .001), I find a significant negative effect of high
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cognitive difficulty on amount of information displayed compared to low (b = -3.43, SE = 0.80, t
(26)= -4.31, p = .001; see Table 51) and moderate (b =-2.62, SE = 0.76, t(26)= -3.45, p = .001;
see Table 52) cognitive difficulty. I find no significant effect of moderate difficulty as compared
to low cognitive difficulty (p = .24). The effect of indecisiveness is not significant (p = .85).
The overall interaction model is significant (Model 2: Adjusted R2 = 0.40, F (26, 225) =
5.61, p < .001). However, none of the interaction terms are significant and I find no interaction
effect of indecisiveness by cognitive difficulty. There were four significant covariates that
remained consistent across both the main effect model and the interaction models. The
coefficients reported are from the interaction model of the OLS multiple regression (Table 51:
Model 2). Average information visible is positively influenced by the number of combinations
visible at the start (b = .53, SE = .07, t (26) = 7.59, p < .001) and click rate (b = .29, SE = .07, t
(26) = 4.22, p < .001). Average information visible decreases with increases in reported
frequency of purchase (b = -.82, SE = .37, t (26) = -2.19, p = .03). Females view fewer
combinations at a time than males (b = -1.17, SE = .58, t (23) = -2.03, p = .04).
Click Rate. On average, subjects (n = 252) made 7.39 clicks per minute during the decision task
(SD = 4.49, Min = .48, Max = 27.44; see Table 42) before exiting the decision making platform.
The distribution is sufficiently normal (Skewness = 1.16, SE = .15, Kurtosis = 1.88, SE = .31) to
justify the use of OLS multiple regression (Absolute Cutoffs: Skewness = 2, Kurtosis = 7).
When I control for covariates and experimental condition using OLS multiple regression
(Table 53, Model 1: Adjusted R2 = 0.15 F(22, 151) = 2.34, p = .001), I find a significant positive
main effect of high cognitive difficulty on click rate when compared to low (b = 2.60, SE = 0.65),
t (25)= 3.53, p = .007; see Table 53) and moderate cognitive difficulty (b = 1.71, SE = 0.71,
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t(25)= 3.53, p = .02; see Table 54). There is no effect of moderate cognitive difficulty (p = .17)
or indecisiveness (p = .46). Although the overall model that includes covariates and the
interaction effect on click rate is significant condition (Model 2: Adjusted R2 = 0.17, F (25, 226)
= 3.11, p < .001; see Table 53), the interaction effect is not significant.
There were four covariates that had a significant effect on click rate. The coefficients
reported are from the interaction model of the OLS multiple regression (Table 53: Model 2). The
number of combinations visible at the start of the decision task had a significant negative effect
on click rate (b = -.16, SE = .07, t(25) = -2.43, p = .02) as did network latency (b = -.002, SE =
.001, t(25) = -3.26, p = .001), discomfort with technology (b = -.49, SE = .25, t(25) = -1.98, p =
.05), and age (b = -.11, SE = .03, t(25) = -4.06, p < .001) .
Total Clicks. The average number of clicks per subject (n = 252) was 6.95 (SD = 5.07, Min = 1,
Max = 37; see Table 42). The total clicks measure satisfied the assumption of normality
(Skewness = 1.64, SE = .15, Kurtosis = 5.38, SE = .31). When I control for covariates and
experimental condition (Model 1: Adjusted R2 = 0.17, F (25, 226) = 2.99, p < .001; see Table
55), I find a main effect of indecisiveness on the number of total clicks before making a decision.
As indecisiveness increases, the number of total clicks also increases (b = 0.67 (SE=0.32), t (25)
= 2.13, p = .03).
I also find a significant negative effect of high cognitive difficulty on number of total
clicks compared to low (b = -4.11 (SE=0.84), t (25) = -4.88, p < .001; see Table 55) and
moderate (b = -3.55, SE = 0.81, t (25) = -4.36, p < .001; see Table 56) cognitive difficulty. This
is likely because there was such a limited time period (20 seconds), there was not enough time
for individuals to continue to search for information before the countdown timer expired. This
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limiting factor was not present in the moderate cognitive difficulty condition when compared to
the other two conditions (p = .46).
The interaction model is significant (Table 55, Model 2: Adjusted R2 = 0.16, F (25, 226)
= 2.94, p < .001). I find an increase in total clicks in both the low (b = 1.23 (SE = 0.32), t (25) =
3.79, p < .001) and moderate (b = .92 (SE = 0.32), t (25) = 2.78, p = .005) cognitive difficulty
conditions as indecisiveness increases. According to a Chow Test, these two slopes are
significantly different from one another (F (24, 121) = 3.20, p < .001) as well as the high
cognitive difficulty condition (see Table 57). Therefore as indecisiveness increases and difficulty
decreases, number of total clicks increases.
There were five covariates that had a significant effect on number of total clicks in both
the main effects model and the interaction model. The coefficients reported are from the
interaction model of the OLS multiple regression (Table 55: Model 2). Task involvement rate (b
= 1.08, SE = .39, t (25) = 2.78, p = .005) and reported frequency of purchase rate (b = .84, SE =
.40, t (25) = 2.10, p = .04) had a positive effect on total clicks. The number of combinations
visible at the start (b = -.15, SE = .07, t(25) = -2.03, p = .04), personal involvement (b = -.85, SE
= .40, t(25) = -2.10, p = .04), and a limited college education (compared to no college education,
b = -2.42, SE = 1.21, t(22) = -1.99, p = .05) had a negative effect on total number of clicks.
Discussion
As one might expect, introducing extreme time pressure led to a negative effect of high
cognitive difficulty on decision latency. When subjects only had 20 seconds to process the
available information (25 combinations), decision latencies fell drastically. In the moderate
cognitive difficulty condition, subjects had a minute and a half to process the information before
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making a decision. There was enough time available that there is no significant difference of
moderate difficulty on decision latency when compared to the low cognitive difficulty condition.
Somewhat unexpectedly, there is a negative main effect of indecisiveness on decision latency. It
is possible that the presence of a time limitation was enough to encourage selection of an
alternative strategy. If this is the case, then it would appear that higher levels of indecisiveness
lead to the selection of more selective, noncompensatory decision making strategies than their
more decisive counterparts. This is the opposite of what I predicted based on a study of the
literature. However, it is also possible that as indecisiveness increases, individuals are more
likely to avoid the decision by using the avoidant strategy of just picking an alternative at
random.
I also find an interaction effect between indecisiveness and the cognitive difficulty
conditions on decision latency and total number of clicks. As indecisiveness increases in the low
and moderate difficulty conditions, individuals take longer to select from the list of alternatives,
and they also interact with the system to a greater extent. Because 20 seconds is such a short time
to make a decision with the amount of information the subjects were expected to process, there is
no difference in any of the processing variables. In fact, in this study, I find no difference in
average information displayed or click rate, regardless of cognitive difficulty condition. It is
possible that with a choice set size of five attributes and five alternatives for a high importance
product, both high and low indecisiveness manifest similarly to what we have come to expect
from the average consumer. We do see an increase in click rate and a decrease in information
displayed as all subjects try to adjust to the presence of severe time pressure.
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Chapter 5: Discussion, Limitations, and Future Research
Summary of Findings
In Study 1, I find that higher levels of chronic indecisiveness lead to greater decision
latencies, increased information search, and faster search patterns. When making an important
decision, higher levels of indecisiveness leads to increased processing times without an increase
in the speed of adding, removing, and rearranging information. This suggests a longer, more
deliberate processing of static information. In the low importance condition, increases in
indecisiveness seem to lead to quicker, but still effortful, processing of interactive information.
Thus, I find support for Hypothesis 1.
Study 2 provides evidence that the more indecisive the individual, the longer they take
before selecting and confirming an alternative. This is consistent with the existing literature on
indecisiveness (Frost & Shows 1993; Rassin & Muris 2005a; Gayton et al. 1994; Rassin et al.
2008). This study adds to extant literature by showing that as cognitive difficulty of the decision
task increases, highly indecisive individuals take increasingly longer to make a decision than
their less indecisive counterparts.
While my findings do not reflect an exact demonstration of my hypothesized moderating
relationship between indecisiveness and cognitive difficulty on information processing (H2), I
still find an interaction effect. Indecisiveness does not seem to impact decision latency when
there is only a limited amount of information available. Literature suggests that it is possible for
individuals speed up information processing under conditions of low cognitive difficulty
(Bettman et al. 1998). This, paired with the findings from Study 2, suggests that under conditions
of low difficulty, an increase in indecisiveness does not impact the amount of time an individual
takes to make a decision, but leads them to slow down how quickly they interact with the
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information in a graphical display. This suggests an attempt to process information
systematically, yet quickly enough to avoid delay in making a decision. This is consistent with
the tradeoff between the process goals of maximizing accuracy and minimizing effort. So,
consistent with the literature on consumer choice processes (Bettman et al. 1998, 2008), it
appears that when in a low difficulty situation, with a limited number of options, a highly
indecisive individual is able to sufficiently meet the goal of maximization without sacrificing too
much effort.
Under conditions of moderate difficulty, I find a positive effect of indecisiveness on
decision latency that is not paired with an increase in click rate, amount of information displayed,
or total clicks. This would indicate that as indecisiveness increases, more time is being spent
processing static information rather than interacting with the decision making platform. The
speed of processing remains the same across varying levels of indecisiveness, yet highly
indecisive individuals do not become more selective in the amount of information they process.
Therefore, the trade-off between effort minimization and accuracy maximization becomes
skewed as highly indecisive individuals place a higher priority on choosing the best alternative.
As cognitive difficulty increases to high, I find a positive effect of indecisiveness on
decision latency, amount of information displayed, and total clicks, while click rate remains
steady. This again suggests a consistent objective to maximize accuracy when choosing an
alternative from a large choice set.
While I did not find my hypothesized relationship (H3), Study 3 replicated the finding
that highly indecisive individuals, when able, prefer to spend more time evaluating their options
before making a decision. There is an increase in information processing up to a certain point of
cognitive difficulty (time pressure) that is due to increases in indecisiveness. In the low and
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moderate difficulty conditions, high levels of indecisiveness led to increases in decision latency
while average information visible, click rate, total number of clicks remained stable. This, again,
suggests that when the context of the decision task allows, increases in indecisiveness lead to
more extensive information processing.
Under extreme time pressure, all subjects were forced to use an effort-reducing strategy
to identify an acceptable alternative in Study 3. This eliminated the effect of indecisiveness on
any of the information processing variables.
Theoretical Implications
Together, these studies suggest that as indecisiveness increases, the weight placed on the
process goals of effort minimization and accuracy maximization changes. At low levels of
indecisiveness, individuals will place more weight on effort minimization and less on accuracy
maximization. At high levels of indecisiveness, the priority of the weights reverses. However, as
cognitive difficulty of a task increases to a point that makes high levels of accuracy
maximization impossible, the effect of indecisiveness on amount of information processing is
reduced and the process goal of effort minimization becomes more of a priority.
Although my hypothesized relationships were not reflected by the findings of these three
studies, it is clear that the changes in decision making processes due to individual differences in
indecisiveness are much more complicated and nuanced than can be predicted by current
literature on indecisiveness or consumer choice processes. The semi-structured interviews
provide a deeper look into the decision making processes of highly indecisive individuals.
Consistent with the findings from Study 1, informants confirm that even routine,
everyday decisions might take them longer or require more thought to make than other people
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they know. They perceive differences in decision importance, yet persist in evaluating the pros
and cons for relatively unimportant decisions.
In Study 2, I find that indecisiveness had no impact on the amount of information
processing in the low difficulty condition. If indecisiveness is defined as a “trait-related general
tendency to experience decision difficulties across a variety of situations” (Patalano et al. 2010),
one might suspect that high levels of indecisiveness would lead individuals to seek less difficult
decision contexts. This would lead them to experience less decision-making difficulty, as well as
save time and effort. However, high levels of indecisiveness lead to just the opposite. Highly
indecisive individuals seek out large choice sets and, if they are able, process as much of it as
they can.
Both the experimental studies and the interviews suggest a systematic approach to
processing choice set information. In all three studies, it seems that the main mechanism for
maintaining accuracy without sacrificing additional processing time is to increase processing
speed. As indecisiveness increases, I see a different approach to dealing with changes in task
importance, amount of information available, and time limitations. The literature on consumer
choice processes would predict a more selective, non-compensatory approach to information
processing for less important or more difficult decisions. Increases in indecisiveness lead to an
attempt to process information using the same consistent, compensatory, and effortful processing
one might use for an important or less difficult decision, just faster.
Findings from the interviews also suggest that high levels of indecisiveness also lead to
the use of a satisficing strategy to identify a backup plan, or default option, before continuing to
process the remaining options in more detail. This may explain the increase in number of total
clicks during the decision task in Studies 1 and 2.
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Another effortful processing strategy identified in the interviews is a pairwise comparison
approach, similar to the majority of confirming dimensions. This further supports the findings
from Studies 1 - 3 and suggests that, when time allows, and when the decision is important or
there are several alternatives to consider, a highly indecisive individual prefers to take their time
and process as much information as they feel is necessary to make the best decision. Consistent
with previous findings in consumer behavior literature on indecisiveness (Jeong & Drolet 2014),
informants report that they like to seek variety when making consumption decisions. Therefore,
rather than sticking with an alternative they have chosen in the past, or even using the same
decision-making criteria as before, highly indecisive individuals like to explore their options. It is
as if they are perpetually looking for something they might like better than what they have
previously experienced.
One finding that is not captured in the experimental studies, but is evident in the
interviews is the presence of negative affect that permeates the decision making process for
highly indecisive individuals. Findings from the qualitative interviews suggest that high levels of
indecisiveness increase a focus on the goal of minimizing or managing negative affect associated
with decision making. Informants reported that, to them, decision making is stressful, frustrating,
and overwhelming. They identify choice overload and time pressure as two task characteristics
that make them feel anxious, worried, annoyed, hesitant, and uncertain. So, even though
increases in indecisiveness lead to a selection of the same decision processes under severe time
pressure, highly indecisive individuals are experiencing the decision task differently than those
who are less indecisive.
In Study 3, I find similar processing strategies across all levels of indecisiveness in the
severe time pressure condition. What the process tracing software does not reveal is the negative
141
emotion that highly indecisive individuals associate with feeling rushed into making a decision.
Informants report that time pressure, choice overload, and unfamiliar situations can be a source
of stress or anxiety. My findings also suggest that highly indecisive individuals are very
concerned with the social and interpersonal implications of their decisions and like to have a
justifiable reason for their decision outcomes. If they cannot come up with an acceptable
justification, they may leave without purchasing or experience buyers’ remorse more readily than
the average consumer. To avoid the negative consequences they experience when they feel
rushed or pressured to make a decision, highly indecisive individuals might defer the decision to
another individual or to another time.
Methodological Implications
Up until this point, the indecisiveness literature has focused mainly on comparing the behavior of
individuals with high levels of indecisiveness to that of relatively less indecisive individuals by
conducting median splits on a scale measuring indecisiveness (Frost & Shows 1993). I contribute
to the literature on indecisiveness by conducting semi-structured interviews with individuals who
self-identify as being high in chronic indecisiveness. I also contribute to a greater understanding
of indecisiveness by analyzing the full indecisiveness scale rather than performing a median split
or spotlight analysis. By creating modified dummy variables for each experimental condition
(See Figure 18), I was able to assess the impact of each individual’s indecisiveness score on the
dependent variables using multiple regression. This is, to my knowledge, the first process-tracing
research on indecisiveness to analyze the impact of the full spectrum of indecisiveness on
information processing.
142
The development of my process-tracing software is a major methodological contribution
to decision making research. In the past, researchers have had to rely on outdated graphical
software or expensive eye-tracking technology to conduct process-tracing studies. The
development of a new software that is highly flexible and affordable will allow researchers to
conduct higher volumes of process-tracing research. While other software allow for the same
types of data collection, none provide the level of customizability in a graphical information
format.
Managerial Implications
The desire to be sure they are making the right decision leads highly indecisive
individuals to spend longer in the information search and alternative evaluation stages of the
decision making process. This can be a time consuming and exhausting process that can stretch
for minutes, days, or even weeks. Time pressure or self-consciousness may lead a highly
indecisive individual to delay or avoid selecting an alternative. Marketers and online retailers
who have access to click data may be the best equipped to identify and serve this segment of
consumers. If retailers can identify how much information is viewed over time before making a
purchase as well as shopping cart or wish list behavior, it may be possible to cater to high levels
of indecisiveness in an online environment. A web-retailer with access to customer search
behavior may be able to accommodate a highly indecisive consumer’s preference for more
information by offering an interactive comparison tool. Conversely, a less decisive individual
will want to see less information and will want to make the decision more quickly. A low-hassle
shopping experience that allows them to “get in and get out” would appeal more to their decision
making style.
143
Front-line sales associates can also be trained to identify an individual’s level of
indecisiveness. An individual who is relatively low in indecisiveness will likely walk into a store
and walk back out with the first item that meets their criteria. As indecisiveness increases,
individuals will likely appreciate more time, options, and suggestions while they build a
consideration set. A salesperson who can appreciate that a highly indecisive individual may want
to leave and come back before making a decision will have a less flustered and more confident
consumer. Once a highly indecisive individual comes to a decision, they may be more satisfied
with the product and the customer service.
Limitations
One of the main limitations of the semi-structured interviews is that I only interviewed
eleven highly indecisive individuals. This may not be enough to have reached theoretical
saturation. I also only interviewed those who self-identified as highly indecisive. I would have a
much clearer picture of how indecisiveness impacts choice processes and strategy selection if I
had collected interviews from individuals spanning all levels of indecisiveness. This is an
opportunity for further study.
A weakness of the process-tracing software is that it is impossible to tell what the
subjects are doing or processing during times when they are not interacting with the system.
Because the studies were conducted using MTurk, it is difficult to tell whether subjects were
giving the decision task their full attention or if they were doing other things in the background.
While quality control questions were included in the survey, other than pairing the software with
eye-tracking software in a lab, there is no foolproof way to ensure that the process-tracing
software is capturing a subject’s exact process. Such a test of the process-tracing software would
144
provide valuable insight to the reliability of the software and method used to collect data for this
research.
Future Research
The findings from this research suggest that further study into the emotional implications
of indecisiveness is needed. Decision tasks can vary not only in cognitive difficulty, but in
emotional difficulty as well. Negative interattribute correlation requires decision makers to make
trade-offs. The literature in consumer decision making indicates that decision makers will vary
their decision strategies based on the degree to which these trade-offs are emotion-laden (Luce et
al. 2001). Trade-offs are considered to be emotion-laden when consumers are required to make
sacrifices between features or outcome goals that are emotionally or morally important to them
(Bettman et al. 1998). The literature predicts that the average consumer will adapt to emotionladen decision tasks using coping strategies.
To cope with difficult, emotion-laden trade-offs, more decisive individuals use problemfocused or emotion-focused coping strategies to reduce or avoid the experience of negative affect
(Luce et al. 2001). Behaviorally, a problem-focused strategy results in extensive but selective
processing of information while avoiding information related to the difficult trade-offs. An
emotion-focused strategy involves avoidance of the emotionally difficult trade-offs through the
use of a simplified decision strategy. This approach involves the use of heuristics that allow
selective and limited information processing. If individuals at different levels of indecisiveness
are pursuing different processing goals, the emotional difficulty inherent in a decision task may
lead to different amounts of information processing at varying levels of emotional difficulty. It is
also possible that greater levels of emotional difficulty may ameliorate the effects of
145
indecisiveness, as those who are less indecisive adapt their processing goals to grant more weight
to maximizing accuracy and minimizing negative affect.
Also of interest is the possible effect of indecisiveness on self-control. Current decision
making research finds that when individuals make choices, this impairs self-control in
subsequent decision tasks (Vohs, Baumeister, Schmeichel, Twenge, Nelson, & Tice 2008). One
major topic in the literature on self-control is the ego-depletion explanation for self-control
failures. The early explanations of this model are based on a self-regulatory strength model
(Baumeister, Bratlavsky, Muraven, & Tice 1998). The more of these ego resources that an
individual spends on subsequent self-regulation events, the less he or she has left over for later
events. These resources eventually run out unless they are replenished through sleep
(Baumeister, Sparks; Stillman, & Vohs 2008).
Findings suggest that making decisions seems to reduce the same resource that is used for
self-control (Twenge, Baumeister, Tice, & Schmeichel 2001). This is important in the context of
consumer behavior because everyone must make multiple decisions every day. Highly indecisive
individuals may be depleting their resource of energy more quickly by approaching more of their
decisions with greater information search and a goal of maximizing accuracy. If this is the case,
they may also be more susceptible to decisional fatigue and temptation.
Further research into these topics will add to what we know about the effect of
indecisiveness on information processing in consumer behavior. Knowing more about how
different levels of indecisiveness impact constructed choice processes will benefit researchers
and marketers to develop ways to better identify and target consumers based on individual
differences in how they search for and process information. Research in this area will also
146
benefit consumers. Knowing more about how they process information can further inform
consumers how to make better decisions.
147
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Figures
Figure 1. Example MouslabWEB Task
165
Cognitive Resource Demands Score
200
180
160
140
120
100
80
60
40
20
0
1.00
1.67
3.00
4.00
5.00
6.00
7.00
Figure 2. Pretest 3: Cognitive Resource Demands Histogram
Values 1 (difficult) -7 (easy)
Figure 3: Study 1: Decision Latency Histogram
166
Figure 4. Study 1: Average Information Visible Histogram
Figure 5. Study 1: Click Rate Histogram
167
Figure 6. Study 1: Total Clicks Histogram
Figure 7. Study 1: Total Clicks (SQRT) Histogram
168
Figure 8. Study 2: Decision Latency Histogram
Figure 9. Study 2: Decision Latency (SQRT) Histogram
169
Figure 10. Study 2: Average Information Visible Histogram
Figure 11. Study 2: Click Rate Histogram
170
Figure 12. Study 2: Total Clicks Histogram
Figure 13. Study 3: Decision Latency Histogram
171
Figure 14. Study 3: Decision Latency (SQRT) Histogram
Figure 15. Study 3: Average Information Visible Histogram
172
Figure 16. Study 3: Click Rate Histogram
Figure 17. Study 3: Total Clicks Histogram
173
Figure 18. Interaction Terms: Indecisiveness by Condition Format
174
Tables
Table 1. Pretest 1: Frequency Descriptives
Mean
(SE)
Median
Var.
Min
Max
Skewness
(SE)
Kurtosis
(SE)
Water bottle
(n=119)
2.77
(.128)
2
1.957
1
6
.906
(.222)
.319
(.440)
Cleaning Wipes
(n=117)
3.91
(.133)
4
2.069
1
6
-.328
(.224)
-.596
(.444)
4.42
(.065)
4
.477
3
6
.556
(.227)
.085
(.451)
3.68
(.075)
4
1.945
1
6
-.211
(.131)
-.697
(.260)
Toothpaste
(n=113)
All
(n=349)
Table 2. Pretest 1: Enjoyment Descriptives
Skewness
(SE)
Kurtosis
(SE)
7
-.157
(.225)
-.485
(.446)
1
7
-.284
(.226)
.149
(.447)
1.363
1
7
-.263
(.229)
.535
(.455)
1.725
1
7
-.214
(.132)
.024
(.263)
Mean (SE)
Median
Var
Min
Max
Water bottle
(n=116)
3.80
(.136)
4
2.143
1
Cleaning Wipes
(N=115)
3.71
(.121)
4
1.680
3.77
(.111)
4
3.76
(.071)
4
Toothpaste
(N=111)
All
(N=342)
175
Table 3. Pretest 1: Personal Involvement Item Descriptives
Items
N
Mean
SD
Unimportant/Important
346
5.41
1.736
Boring/Interesting
342
3.49
1.552
Irrelevant/Relevant
345
5.25
1.630
Unexciting/Exciting
344
3.19
1.515
Means Nothing/Means a Lot
346
4.14
1.581
Unappealing/Appealing
344
4.49
1.463
Mundane/Fascinating
344
3.42
1.416
Worthless/Valuable
344
5.17
1.481
Uninvolving/Involving
345
4.14
1.428
Not Needed/Needed
345
5.56
1.598
Valid
338
Alpha
.873
Table 4. Pretest 1: Personal Involvement Descriptives
Mean
SE)
Median
Var
Min
Max
Skewness
(SE)
Kurtosis
(SE)
Water bottle
(n=119)
4.4558
(.100)
4.6
1.191
1.60
7
-.274
(.222)
.008
(.440)
Cleaning Wipes
(n=115)
4.0795
(.100)
4.2
1.157
1
6.40
-.829
(.226)
1.044
(.447)
4.7603
(.081)
4.7
.739
1.90
7
-.235
(.227)
.546
(.451)
4.4303
(.056)
4.5
1.103
1
7
-.553
(.131)
.814
(.261)
Toothpaste
(n=113)
All
(n=347)
176
Table 5. Pretest 1: Prompted Feature Recall and Ratings
Toothpaste
Feature
Whitening
Tartar prevention/Control
Stain Fighting/Removal
Smell
Sensitive teeth/gums
Quantity/Size
Professional Recommendation
Price
Plaque prevention
Packaging
Oral Health
Gum Protection
Fluoride
Flavor
Enamel Protection
Effectiveness
Color
Cleaning
Cavity prevention
Brand
Brand
Bad Breath/Freshens Breath
Mean
6.068
5.444
7.000
6.333
5.529
4.929
5.556
5.313
6.429
4.556
6.625
6.667
5.750
5.898
6.714
6.133
4.000
6.600
6.522
5.000
5.346
6.658
SD
1.228
1.130
NA
1.118
1.231
1.385
1.424
1.281
0.756
1.236
0.744
0.707
1.500
1.279
0.756
1.457
2.374
0.843
0.994
2.160
1.413
0.966
Count
73
9
2
9
17
14
9
32
14
9
8
9
4
49
7
15
12
10
23
7
26
38
177
Table 6. Pretest 2: Frequency Descriptives
Mean
(SE)
Median
Var
Min
Max
Skewness
(SE)
Kurtosis
(SE)
Laptop
(n=47)
2.96
(.129)
3
.781
1
5
-.112
(.347)
-.320
(.681)
College Course
(n=44)
5.89
(.067)
6
1.96
4
6
-3.945
(.357)
14.824
(.702)
2.15
(.124)
2
.710
1
4
.632
(.350)
.149
(.688)
Cell Phone
(n=46)
4.07
(.100)
4
.462
3
6
1.205
(.350)
2.974
(.688)
All
(n=183)
3.74
(.116)
4
2.469
1
6
.124
(.180)
-1.066
(.357)
Car
(n=46)
Table 7. Pretest 2: Shopping Enjoyment Descriptives
Skewness
(SE)
Kurtosis
(SE)
7
-.093
(.347)
-.917
(.681)
2
7
-.090
(.361)
-.822
(.709)
2.786
1
7
-.452
(.354)
-.515
(.695)
6
1.842
2
7
-1.057
(.350)
.402
(.688)
5
2.445
1
7
-.414
(.181)
-.677
(.359)
Mean (SE)
Median
Var
Min
Max
Laptop
(n=47)
4.66
(.226)
5
2.403
2
College Course
(n=43)
4.35
(.235)
5
2.375
Car
(n=45)
4.38
(.249)
5
Cell Phone
(n=46)
5.26
(.200)
All
(n=178)
4.67
(.116)
178
Table 8. Pretest 2: Personal Involvement Item Descriptives
All Products
Items
N
Mean
SD
Unimportant/Important
183
6.49
.907
Boring/Interesting
183
5.49
1.354
Irrelevant/Relevant
183
5.96
1.220
Unexciting/Exciting
183
5.30
1.350
Means Nothing/Means a Lot
183
5.57
1.323
Unappealing/Appealing
183
5.49
1.317
Mundane/Fascinating
183
2.74
1.304
Worthless/Valuable
183
6.00
1.129
Uninvolving/Involving
183
5.33
1.396
Not Needed/Needed
183
6.15
1.229
Valid
183
Alpha
.807
Table 9. Pretest 2: Personal Involvement Descriptives
Skewness
(SE)
Kurtosis
(SE)
6.70
-1.800
(.347)
4.477
(.681)
3.20
6.40
-.222
(.357)
.400
(.702)
.522
2.90
6.40
-1.644
(.350)
3.978
(.688)
5.80
.345
4
6.60
-.666
(.350)
.184
(.688)
5.50
.581
2.20
6.70
-1.152
(.180)
2.250
(.357)
Mean SE)
Median
Var
Min
Max
Laptop
(n=47)
5.44
(.128)
5.60
.771
2.20
College Course
(n=44)
5.02
(.010)
5.05
.436
Car
(n=46)
5.67
(.106)
5.80
Cell Phone
(n=46)
5.65
(.087)
All
(n=183)
5.45
(.056)
179
Table 10. Pretest 2: Prompted Feature Recall and Ratings
Laptop
Feature
Size/Weight
Processor Speed
Brand/OS
Hard Drive Space
Battery Life
RAM
Graphics
Price
Design
Ease Of Use/Interface
Color
Software
Durability
Touchscreen
Camera capabilities
Ports
Quality
Internet capability
Number Keypad
Keyboard Illumination
Security
Features
Mean
5.610
6.091
5.857
5.737
6.000
6.063
5.357
5.643
6.100
5.333
4.750
5.833
5.600
4.400
5.000
5.250
6.667
7.000
5.333
6.667
6.000
6.500
SD
1.181
0.921
0.910
1.284
1.225
1.181
1.823
1.082
0.738
1.581
1.669
0.753
0.894
1.342
0.000
2.872
0.577
0.000
0.577
0.577
0.000
0.707
Count
41
22
21
19
17
16
14
14
10
9
8
6
5
5
4
4
3
3
3
3
2
2
180
Table 11. Pretest 3: Cognitive Resource Demands Descriptives
Cognitive Resource Demands Composite (AVE of Items)
Mean
(SE)
Median
Var
Min
Max
Skewness
(SE)
Kurtosis
(SE)
Column (B&W)
(n=57)
6.0702
(.179)
7
1.828
2.33
7
-1.196
(.316)
.179
(.623)
Column (Color)
(n=56)
6.2143
(.193)
7
2.078
1
7
-2.163
(.316)
4.645
(.628)
Line (B&W)
(n=56)
5.7083
(.222)
6.5
2.768
1
7
-1.150
(.319)
2.87
(.628)
Line (Color)
(n=55)
6.2182
(.156)
7
1.342
3
7
-1.557
(.322_
1.503
(.634)
Matrix (Brand)
(n=59)
5.7797
(.195)
6
2.244
1.33
7
-1.118
(.311)
.210
(.613)
Matrix (Feature)
(n=58)
5.8247
(.201)
6.5
2.33
1.5
7
-1.279
(3.14)
.685
(.618)
All
(n=341)
5.9663
(.079)
7
2.116
1
7
-1.392
(.132)
1.083
(.263)
181
Table 12. Pretest 3: Imagery Elaboration Descriptives
Mean
(SE)
Median
Var
Min
Max
Skewness
(SE)
Kurtosis
(SE)
Column (B&W)
(n=57)
4.4883
(.214)
4.67
2.613
1
7
-.249
(.316)
-.835
(.623)
Column (Color)
(n=56)
4.0685
(.222)
4.00
2.765
1
7
-.025
(.319)
-.923
(.628)
Line (B&W)
(n=56)
4.3333
(.228)
4.17
2.905
1
7
.007
(.319)
-.847
(.628)
Line (Color)
(n=55)
3.9818
(.197)
4.00
2.138
1
7
.124
(.322)
-.761
(.634)
Matrix (Brand)
(n=59)
4.9944
(.203)
5.00
2.423
1
7
-.499
(.311)
-.399
(.613)
Matrix (Feature)
(n=58)
5.1437
(.171)
5.33
1.696
2.67
7
-.152
(.314)
-1.030
(.618)
All
(n=341)
4.5112
(.087)
4.67
2.577
1
7
-.179
(.132)
-.824
(.263)
Table 13. Pretest 3: Imagery Elaboration Post Hoc Test
Tukey HSD
Means (SE)
Column
(Black &
White)
Column
(Color)
Column
(Color)
Column
(Color)
Column (Color)
.4199
(.293)
Line
(Black & White)
.1550
(.293)
-.2649
(.294)
Line (Color)
.5065
(.294)
.0866
(.295)
.3515
(.295)
Tabular (Brand)
-.5061
(.289)
-.9259*
(.290)
-.661
(.290)
-1.0125**
(.292)
Tabular (Feature)
-.6554
(.290)
-1.075***
(.291)
-.810
(.291)
-1.162***
(.293)
* The mean difference is significant at the 0.05 level.
** The mean difference is significant at the .01 level.
*** The mean difference is significant at the .001 level.
Column
(Color)
-.1493
(.288)
182
Table 14. Pretest 3: Attitude (Utilitarian) Descriptives
Attitude Composite (AVE of Items)
Mean
(SE)
Median
Var
Min
Max
Skewness
(SE)
Kurtosis
(SE)
Column (B&W)
(n=57)
3.0772
(.185)
3.2
1.945
1
6
.070
(.316)
-1.054
(.623)
Column (Color)
(n=56)
3.5277
(.214)
3.2
2.571
1
7
.682
(.319)
-.170
(.628)
Line (B&W)
(n=56)
3.4411
(.184)
3.4
1.889
1
7
.500
(.319)
-.160
(.628)
Line (Color)
(n=55)
3.3200
(.189)
3.4
1.971
1
7
.468
(.322)
.185
(.634)
Matrix (Brand)
(n=59)
3.8373
(.190)
3.8
2.119
1
7
.175
(.311)
-.382
(.613)
Matrix (Feature)
(n=58)
3.9862
(.199)
3.8
2.286
1
7
.396
(.314)
.221
(.618)
All
(n=341)
3.5362
(.080)
3.4
2.194
1
7
.397
(.132)
-.260
(.263)
Table 15. Pretest 3: Attitude (Utilitarian) Post Hoc Test
Tukey HSD
Means (SE)
Column (Color)
Line
(Black & White)
Line (Color)
Tabular (Brand)
Tabular (Feature)
Column
(Black &
White)
-.4505
(.275)
-.3639
(.275)
-.2428
(.276)
-.7601
(.271)
.9090*
(.272)
Column
(Color)
Column
(Color)
.0866
(.276)
.2077
(.277)
-.3096
(.272)
-.4585
(.273)
.1211
(.277)
-.3962
(.272)
-.5451
(.273)
* The mean difference is significant at the 0.05 level.
** The mean difference is significant at the .01 level.
*** The mean difference is significant at the .001 level.
Colum
n
(Color)
-.5173
(.274)
-.6662
(.275)
Column
(Color)
-.1489
(.270)
183
Table 16. Pretest 3: Chi-Squared Test of Total Number of Errors by Condition
Column and Line Formats Collapsed
Type of Chart
Column Graph (n=115) versus Line Graph (n=111)
Value
df
Asymp. Sig. (2-sided)
(n=226)
Pearson χ2
2.635
4
.621
Likelihood Ratio
3.414
4
.491
Linear-by-Linear Association
.243
1
.622
Graph (n=226) versus Matrix (n=59)
(n=285)
Pearson χ2
79.884
4
.000
Likelihood Ratio
63.925
4
.000
Linear-by-Linear Association
21.987
1
.001
Graph (n=226) versus Inverted Matrix (n=58)
2
(n=284)
Pearson χ
25.412
4
.000
Likelihood Ratio
20.466
4
.000
Linear-by-Linear Association
10.418
1
.020
Table 17. Pretest 3: Total Number of Errors by Condition Cross-Tab
Information Format
Column Black
& White
Column
Color
Line Black
& White
No error
53
55
49
53
30
41
281
Made error
4
3
7
2
29
17
41
57
58
56
55
59
58
343
Total
Line
Color
Matrix
Brand
Matrix
Feature
Total
184
Table 18. Pretest 3: Error Present by Condition Cross-Tab
Test of Visual Format: B&W versus Color and Matrix Style
Column
(n=115)
Type of Chart
Black & White (n=58) versus Color (n=57)
Asymp. Sig.
Value
df
(2-sided)
2
Pearson χ
.171
1
.679
Continuity Correction
.001
1
.981
Likelihood Ratio
.172
1
.679
Fisher’s Exact Test
Linear-by-Linear Association
.170
1
.680
2
Pearson χ
Line
(n=111)
Exact Sig.
(2-sided)
.717
Black & White (n=55) versus Color (n=56)
.087
2.926
1
Continuity Correction
1.857
1
.173
Likelihood Ratio
3.090
1
.079
Fisher’s Exact Test
Linear-by-Linear Association
.162
2.900
1
.089
Matrix (n=58) versus Inverted Matrix (n=59)
Matrix
(n-117)
Pearson χ2
4.826
1
.028
Continuity Correction
4.031
1
.045
Likelihood Ratio
4.870
1
.027
Fisher’s Exact Test
Linear-by-Linear Association
.037
4.785
1
.029
185
Table 19. Study 1: Descriptives
Mean
Decision
Latency
S.D.
Min
Max
Skewness
(SE)
Kurtosis
(SE)
1.837
(.18)
4.772
(.37)
63524.35 43441.124 8330 282167
Ave Information
Displayed
7.88
3.06
1.43
13.75
-0.34
(.18)
-0.89
(.37)
Click Rate
6.55
4.20
.40
22.87
0.90
(.18)
0.79
(.37)
Total Clicks
6.32
5.63
1
47
3.15
(.18)
17.68
(.37)
Total Clicks
(Transformed)
2.31
0.99
1.00
6.86
0.80
(.18)
2.13
(.37)
Table 20. Study 1: Manipulation Check
Personal Involvement
Inventory
(Zaichkowsky 1994)
Task Involvement
(Wilcox et al. 2011)
Task Difficulty
(Argo et al. 2010)
Mean
(SE)
t
df
Sig (2tailed)
Mean
Difference
Low
Importance
4.82
(.11)
-5.67
173
.000
-.86
High
Importance
5.68
(.10)
Low
Importance
6.124
(.11)
-1.27
173
.206
-.18
High
Importance
6.307
(.10)
Low
Importance
2.349
(.17)
-1.39
173
.167
-.31
High
Importance
2.667
(.16)
186
Table 21. Study 1: Decision Latency OLS Results
Model 1
Covariates + Main Effects
Standardized
Unstandardized
Coefficients
Coefficients
B
Std. Error
Beta
(Constant) -59206.18
Importance(Hi/Lo) 20294.78
Indecisiveness 7836.95
Importance High*Indecisivness
Importance Low*Indecisiveness
45606.38
15889.12
3411.29
Combinations Visible at Start
Task Involvement
Personal Involvement Inventory
Task Difficulty
Tutorial Difficulty
Dominating Alternative
Frequency of Purchase
Choice Confusion
Network Latency
Click Rate
Discomfort with Technology
Impulsivity
EDU (No College): Some College
Associate Degree
Bachelor Degree
Graduate Degree
SES (<40k) 40k-59,999
60k-79,999
More than 80k
Age
Gender
1307.59
3731.39
3680.96
2821.22
2325.22
123.08
3993.27
3153.05
9.77
803.51
2854.47
2534.51
11198.06
11964.10
10893.69
14386.43
7632.42
9951.10
9028.13
295.29
6444.42
R-Square
Adjusted R-Square
ANOVA
N
K
-850.67
4007.11
4704.04
334.32
1392.45
9.87
4086.94
1894.95
9.28
-1901.36
-1729.18
-7295.54
9578.06
-19815.92
-16607.58
-31195.43
14914.78
6730.92
17931.34
1058.14
2608.16
Model 2
Covariates + Interaction
t
-1.30
0.23 1.28
0.21 2.30
-0.05
0.09
0.12
0.01
0.05
0.01
0.18
0.06
0.07
-0.18
-0.05
-0.24
0.09
-0.17
-0.19
-0.20
0.15
0.05
0.16
0.28
0.03
-0.65
1.07
1.28
0.12
0.60
0.08
1.02
0.60
0.95
-2.37
-0.61
-2.88
0.86
-1.66
-1.52
-2.17
1.95
0.68
1.99
3.58
0.40
Sig.
Standardized
Unstandardized
Coefficients
Coefficients
B
Std. Error
Beta
t
-39708.54
40534.45
-0.98
9809.57
6149.66
3984.22
3802.11
0.40 2.46
0.26 1.62
-827.85
4362.96
5212.76
187.51
869.75
-2.06
2095.09
1899.28
8.36
-1945.24
-1987.62
-6944.87
9459.33
-19934.30
-16475.93
-29492.20
14650.27
7126.88
17726.89
1032.34
2692.59
1312.17
3753.01
3641.40
2864.16
2263.68
123.03
3161.06
3193.16
9.78
803.63
2848.07
2506.97
11222.26
11990.00
10916.20
14401.18
7671.78
9961.12
9073.06
294.47
6457.68
Sig.
*
*
**
*
*
***
0.32
0.22
F(23, 150)=3.118, p<.001
-0.05
0.10
0.13
0.01
0.03
0.00
0.09
0.06
0.06
-0.19
-0.06
-0.23
0.09
-0.17
-0.19
-0.19
0.15
0.06
0.15
0.27
0.03
0.32
0.22
F(23, 150)=3.076, p<.001
174
24
-0.63
1.16
1.43
0.07
0.38
-0.02
0.66
0.59
0.85
-2.42
-0.70
-2.77
0.84
-1.66
-1.51
-2.05
1.91
0.72
1.95
3.51
0.42
*
*
**
*
***
187
Table 22. Study 1: Decision Latency Truncated Regression
Model 1
Covariates + Main Effects
Model 2
Covariates + Interaction
Unstandardized Coefficients
B
(Constant) -195144.30
Importance(Hi/Lo)
39376.32
Indecisiveness
12503.29
Importance High*Indecisivness
Importance Low*Indecisiveness
Combinations Visible at Start
Task Involvement
Personal Involvement Inventory
Task Difficulty
Tutorial Difficulty
Dominating Alternative
Frequency of Purchase
Choice Confusion
Network Latency
Click Rate
Discomfort with Technology
Impulsivity
EDU (No College): Some College
Associate Degree
Bachelor Degree
Graduate Degree
SES (<40k) 40k-59,999
60k-79,999
More than 80k
Age
Gender
Lower Bound
Upper Bound
Log Pseudolikelihood
Wald
N
-1678.58
10304.19
8343.57
308.57
2270.11
50.82
8770.22
4069.21
15.47
-3844.22
-5432.11
-13737.66
17165.64
-38865.97
-27777.41
-53011.72
26549.33
12205.35
34457.50
1712.57
11852.88
Unstandardized
Robust
Std. Error
z
114249.00
41445.66
6105.04
-1.71
0.95
2.05
2500.17
6802.25
6670.28
4397.15
4177.91
239.24
10998.79
5721.53
18.41
1665.85
5243.79
5618.82
18007.80
24570.67
18982.69
28189.52
13104.27
23100.94
15418.51
526.08
12857.45
8330
+inf
-2023.82
χ^2(23) = 66.99, p<.001
173
-0.67
1.51
1.25
0.07
0.54
0.21
0.80
0.71
0.84
-2.31
-1.04
-2.44
0.95
-1.58
-1.46
-1.88
2.03
0.53
2.23
3.26
0.92
B
Robust
Std. Error
-152551.50
84675.78
-1.80
15968.16
10005.08
7719.14
6641.62
2.07
1.51
-1697.26
10621.67
9587.09
-202.37
984.77
22.28
3916.56
3912.96
14.75
-3981.21
-6078.49
-12955.31
17209.01
-37913.74
-27546.48
-49972.94
26652.08
14290.56
35217.52
1700.38
11942.46
2530.86
6887.56
6285.20
4715.26
4018.59
231.42
6320.78
5748.47
18.51
1752.92
5660.17
5028.30
18677.55
24835.90
19664.66
27764.21
13671.96
23618.55
15894.09
537.05
13083.78
-0.67
1.54
1.53
-0.04
0.25
0.10
0.62
0.68
0.80
-2.27
-1.07
-2.58
0.92
-1.53
-1.40
-1.80
1.95
0.61
2.22
3.17
0.91
z
*
*
*
*
*
**
8330.00
+inf
-2024.36
χ^2(23) = 58.86, p<.001
173
*
*
*
*
**
188
Table 23. Study 1: Average Information Displayed OLS Results
Model 1
Covariates + Main Effects
Standardized
Coefficients
Unstandardized
Coefficients
B
Std.
Error
(Constant)
Importance(Hi/Lo)
Indecisiveness
Importance High*Indecisivness
Importance Low*Indecisiveness
9.49
-1.36
0.77
2.97
1.03
0.22
Combinations Visible at Start
Task Involvement
Personal Involvement Inventory
Task Difficulty
Tutorial Difficulty
Dominating Alternative
Frequency of Purchase
Choice Confusion
Network Latency
Click Rate
Discomfort with Technology
Impulsivity
EDU (No College): Some College
Associate Degree
Bachelor Degree
Graduate Degree
SES (<40k) 40k-59,999
60k-79,999
More than 80k
Age
Gender
0.40
-0.40
0.12
0.51
0.39
0.00
-0.35
-0.79
0.00
0.09
-0.45
-0.35
-0.12
-0.66
-0.87
0.90
0.10
-0.09
1.80
-0.01
-0.60
R-Square
Adjusted R-Square
ANOVA
N
K
Model 2
Covariates + Interaction
0.09
0.24
0.24
0.18
0.15
0.01
0.26
0.21
0.00
0.05
0.19
0.16
0.73
0.78
0.71
0.94
0.50
0.65
0.59
0.02
0.42
Beta
t
3.20
-0.22 -1.32
0.29 3.47
0.31
-0.13
0.04
0.25
0.21
0.00
-0.22
-0.37
-0.09
0.12
-0.18
-0.16
-0.02
-0.08
-0.14
0.08
0.01
-0.01
0.22
-0.04
-0.10
Standardized
Coefficients
Unstandardized
Coefficients
4.68
-1.66
0.52
2.76
2.59
-0.01
-1.34
-3.87
-1.37
1.73
-2.42
-2.10
-0.17
-0.84
-1.22
0.96
0.19
-0.14
3.06
-0.53
-1.43
Sig.
**
B
Std.
Error
8.00
2.64
3.03
0.67
0.86
0.26
0.25
0.39 2.59
0.51 3.46
0.40
-0.42
0.07
0.51
0.43
0.00
-0.17
-0.78
0.00
0.09
-0.43
-0.37
-0.11
-0.65
-0.88
0.80
0.10
-0.13
1.80
-0.01
-0.61
0.09
0.24
0.24
0.19
0.15
0.01
0.21
0.21
0.00
0.05
0.19
0.16
0.73
0.78
0.71
0.94
0.50
0.65
0.59
0.02
0.42
Beta
t
Sig.
**
***
***
**
*
***
*
*
**
0.43
0.34
F(23, 150)=4.828, p<.001
0.31
-0.13
0.03
0.25
0.23
0.01
-0.11
-0.37
-0.09
0.13
-0.17
-0.17
-0.02
-0.08
-0.14
0.07
0.01
-0.01
0.22
-0.03
-0.10
0.42
0.33
F(23, 150)=4.742, p<.001
174
24
4.65
-1.72
0.31
2.72
2.92
0.09
-0.83
-3.76
-1.28
1.81
-2.32
-2.29
-0.16
-0.83
-1.24
0.85
0.21
-0.20
3.04
-0.42
-1.44
*
***
***
**
**
***
*
*
**
189
Table 24. Study 1: Click Rate OLS Results
Model 1
Covariates + Main Effects
Standardized
Coefficients
Unstandardized
Coefficients
B
Std.
Error
(Constant)
Importance(Hi/Lo)
Indecisiveness
Importance High*Indecisivness
Importance Low*Indecisiveness
13.29
-2.65
0.77
4.49
1.59
0.34
Combinations Visible at Start
Task Involvement
Personal Involvement Inventory
Task Difficulty
Tutorial Difficulty
Dominating Alternative
Frequency of Purchase
Choice Confusion
Network Latency
Discomfort with Technology
Impulsivity
EDU (No College): Some College
Associate Degree
Bachelor Degree
Graduate Degree
SES (<40k) 40k-59,999
60k-79,999
More than 80k
Age
Gender
-0.40
0.38
0.69
-0.25
-0.54
0.01
-0.49
0.00
0.00
-0.49
0.06
-1.12
-0.86
-0.36
-0.10
-0.19
-1.50
0.65
-0.05
-0.85
R-Square
Adjusted R-Square
ANOVA
N
K
Model 2
Covariates + Interaction
0.13
0.38
0.37
0.29
0.23
0.01
0.40
0.32
0.00
0.29
0.26
1.13
1.21
1.10
1.46
0.77
1.00
0.91
0.03
0.65
Beta
t
2.96
-0.32 -1.66
0.21 2.27
-0.23
0.09
0.18
-0.09
-0.21
0.07
-0.23
0.00
-0.20
-0.14
0.02
-0.11
-0.08
-0.04
-0.01
-0.02
-0.12
0.06
-0.14
-0.10
Standardized
Coefficients
Unstandardized
Coefficients
-3.15
1.01
1.88
-0.87
-2.32
0.97
-1.22
0.01
-2.67
-1.72
0.24
-0.99
-0.71
-0.32
-0.07
-0.25
-1.50
0.71
-1.79
-1.30
Sig.
**
B
Std.
Error
10.98
4.01
2.74
**
0.48
1.03
0.40
0.38
0.20 1.19
0.45 2.73
**
-0.41
0.33
0.65
-0.22
-0.47
0.01
-0.28
-0.01
0.00
-0.46
0.02
-1.11
-0.84
-0.37
-0.34
-0.14
-1.54
0.69
-0.05
-0.86
0.13
0.38
0.36
0.29
0.23
0.01
0.32
0.32
0.00
0.29
0.25
1.13
1.21
1.11
1.46
0.78
1.00
0.92
0.03
0.65
Beta
t
Sig.
*
**
*
**
0.25
0.15
F(22, 151)=2.341, p=.001
-0.23
0.07
0.17
-0.08
-0.18
0.08
-0.13
0.00
-0.19
-0.14
0.01
-0.11
-0.08
-0.04
-0.02
-0.01
-0.13
0.06
-0.14
-0.10
0.25
0.14
F(22, 151)=2.303, .002
174
23
-3.19
0.87
1.78
-0.76
-2.10
1.10
-0.87
-0.03
-2.54
-1.62
0.08
-0.98
-0.70
-0.34
-0.23
-0.19
-1.54
0.76
-1.70
-1.32
**
*
*
190
Table 25. Study 1: Total Clicks (SQRT) OLS Results
Model 1
Covariates + Main Effects
Standardized
Coefficients
Unstandardized
Coefficients
B
Std.
Error
(Constant)
Importance(Hi/Lo)
Indecisiveness
Importance High*Indecisivness
Importance Low*Indecisiveness
2.00
-0.36
0.24
1.03
0.37
0.08
Combinations Visible at Start
Task Involvement
Personal Involvement Inventory
Task Difficulty
Tutorial Difficulty
Dominating Alternative
Frequency of Purchase
Choice Confusion
Network Latency
Discomfort with Technology
Impulsivity
EDU (No College): Some College
Associate Degree
Bachelor Degree
Graduate Degree
SES (<40k) 40k-59,999
60k-79,999
More than 80k
Age
Gender
-0.07
0.11
0.22
0.01
-0.10
0.00
-0.09
-0.02
0.00
-0.16
-0.15
0.05
-0.23
-0.09
-0.20
0.18
-0.18
0.43
0.01
0.06
R-Square
Adjusted R-Square
ANOVA
N
K
Model 2
Covariates + Interaction
0.03
0.09
0.08
0.07
0.05
0.00
0.09
0.07
0.00
0.07
0.06
0.26
0.28
0.25
0.34
0.18
0.23
0.21
0.01
0.15
Beta
t
1.94
-0.18 -0.99
0.27 3.00
-0.17
0.11
0.24
0.01
-0.16
0.05
-0.17
-0.03
-0.11
-0.20
-0.22
0.02
-0.09
-0.04
-0.06
0.08
-0.06
0.16
0.07
0.03
Standardized
Unstandardized Coefficients Coefficients
Std.
Error
Sig.
B
Beta
-2.36
1.31
2.57
0.13
-1.79
0.74
-0.93
-0.29
-1.55
-2.45
-2.61
0.17
-0.83
-0.35
-0.60
1.00
-0.78
2.03
0.89
0.42
t
Sig.
1.59 0.92
1.73
3.03
0.21 0.09 0.38
0.26 0.09 0.47
2.32
2.95
2.59
3.46
*
-0.17
0.11
0.22
0.01
-0.14
0.06
-0.07
-0.02
-0.11
-0.20
-0.23
0.02
-0.09
-0.05
-0.07
0.08
-0.07
0.16
0.08
0.03
-2.37
1.25
2.41
0.11
-1.63
0.82
-0.45
-0.22
-1.49
-2.38
-2.78
0.18
-0.82
-0.37
-0.68
0.99
-0.84
2.01
0.97
0.40
*
**
*
-0.07
0.11
0.20
0.01
-0.08
0.00
-0.03
-0.02
0.00
-0.16
-0.16
0.05
-0.23
-0.09
-0.23
0.18
-0.19
0.43
0.01
0.06
*
*
**
*
0.28
0.18
F(22, 151)=2.717, p<.001
0.03
0.09
0.08
0.07
0.05
0.00
0.07
0.07
0.00
0.07
0.06
0.26
0.28
0.25
0.34
0.18
0.23
0.21
0.01
0.15
0.28
0.18
F(22, 151)=2.669, p<.001
174
23
**
*
*
**
*
191
Table 26. Study 1: Chow Test Results
Chow
Statistic
df1
df2
Sig.
Decision Latency
High vs. Low
Importance
1.44
23
128
.10
Average Information
Displayed
High vs. Low
Importance
1.51
23
128
.08
Click Rate
High vs. Low
Importance
1.19
22
130
.26
Total Clicks (SQRT)
High vs. Low
Importance
1.36
22
130
.14
Table 27: Study 2: ANOVA Perceived Difficulty Manipulation Check
Mean
(S.D)
Difficulty of the Task
(Argo et al. 2010)
SS
df
MS
F
Sig.
.216
.806
Low
Difficulty
2.74
(1.51)
1.021
2
.510
Moderate Difficulty
2.79
(1.60)
643.765
272
2.367
High
Difficulty
2.89
(1.51)
644.785
274
192
Table 28: Study 2: Cross-Tab Performance by Difficulty Condition
Performance
Low
Difficulty
Moderate
Difficulty
High
Difficulty
Total
Error
43
(45.7%)
59
(67.0%)
67
(72.0%)
169
No Error
51
(54.3%)
29
(33.0%)
26
(28.0%)
106
94
88
93
275
Total
Table 29: Study 2: Chi-Square Results
Performance
Conditions
Low vs. Mod
Chi-Square
Value
8.37
Low vs. High
13.35
Moderate vs. High
.534
df
Fisher’s Exact
Sig.
1
.005
1
.000
1
.285
193
Table 30. Study 2: ANOVA Processing Fluency Manipulation Check
Mean
(S.E)
Cognitive Demands of
Information Format
(White & Peloza 2009)
Cognitive Demands of
Tutorial Format
(White & Peloza 2009)
SS
df
MS
F
Sig.
.147
.863
.085
.918
Low
Difficulty
5.87
(.14)
.592
2
.296
Moderate Difficulty
5.81
(.15)
547.629
272
2.013
High
Difficulty
5.76
(.15)
548.221
274
Low
Difficulty
5.58
(.16)
.394
2
.197
Moderate Difficulty
5.50
(.16)
628.631
272
2.311
High
Difficulty
5.58
(.16)
629.025
274
Table 31. Study 2: Descriptives
Mean
S.D.
Min
Max
Skewnes
s
(SE)
Kurtosi
s
(SE)
Decision
Latency
82694.3
6
56881.73
2
8736
45553
2
2.153
(.15)
7.750
(.29)
Decision
Latency
(Transformed)
273.820
9
88.00358
93.4
7
674.93
.953
(.15)
1.502
(.29)
Ave
Information
Displayed
14.5628
8.20841
2.00
41.56
1.128
(.15)
.906
(.29)
Click Rate
7.7939
4.92618
.67
24.91
1.038
(.15)
.854
(.29)
Total Clicks
10.49
9.423
1
56
1.872
(.15)
4.259
(.29)
194
Table 32. Study 2: Decision Latency (SQRT) OLS Results
Comparison Group = Low
Model 1
Covariates + Main Effects
Standardized
Unstandardized
Coefficients
Coefficients
B
Std. Error
Beta
(Constant)
Difficulty Moderate (Low)
Difficulty High (Low)
Indecisiveness
Difficulty High*Indecisivness
Difficulty Moderate*Indecisiveness
Difficulty Low*Indecisiveness
128.80
10.66
35.47
12.80
Combinations Visible at Start
Product Category
Task Involvement
Product Category Involvement
Personal Involvement Inventory
Task Difficulty
Information Format Difficulty
Tutorial Difficulty
Dominating Alternative
Frequency of Purchase
Choice Confusion
Network Latency
Click Rate
Discomfort with Technology
Impulsivity
EDU (No College): Some College
Associate Degree
Bachelor Degree
Graduate Degree
SES (<40k) 40k-59,999
60k-79,999
More than 80k
Gender
Age
0.78
-10.02
13.01
-14.69
3.80
10.89
4.87
3.53
-0.31
-8.52
2.17
0.00
-0.09
-14.94
-7.65
35.40
8.83
10.95
-4.39
11.73
11.84
-4.73
16.17
1.85
R-Square
Adjusted R-Square
ANOVA
N
K
78.29
13.24
16.34
4.84
1.11
11.10
7.31
9.20
7.18
5.21
5.12
3.64
0.25
5.03
6.61
0.01
1.10
5.66
4.12
18.53
23.54
18.25
25.16
12.56
17.68
13.62
10.50
0.58
Model 2
Covariates + Interaction
t
Sig.
1.65
0.06 0.80
0.19 2.17 *
0.17 2.64 **
0.05
-0.06
0.13
-0.12
0.04
0.19
0.08
0.06
-0.07
-0.11
0.04
-0.02
0.00
-0.18
-0.12
0.19
0.03
0.06
-0.01
0.06
0.04
-0.02
0.09
0.20
0.70
-0.90
1.78
-1.60
0.53
2.09 *
0.95
0.97
-1.22
-1.69
0.33
-0.40
-0.08
-2.64 **
-1.85
1.91
0.38
0.60
-0.17
0.93
0.67
-0.35
1.54
3.21 **
0.27
0.19
F(27, 247)=3.353, p<.001
Standardized
Unstandardized
Coefficients
Coefficients
B
Std. Error
Beta
146.43
78.26
18.70
10.37
8.88
5.36
5.19
5.49
0.92
-8.60
13.36
-14.85
2.74
11.09
5.01
3.59
-0.33
-8.31
2.18
0.00
-0.13
-15.32
-7.41
35.75
8.42
12.22
-2.57
10.32
12.29
-7.05
16.67
1.83
1.04
11.07
7.28
9.17
7.23
5.19
5.11
3.63
0.25
5.02
6.59
0.01
1.10
5.66
4.09
18.49
23.49
18.17
25.05
12.57
17.62
13.61
10.49
0.57
1.87
0.37 3.49 ***
0.20 2.00 *
0.17 1.62
0.06
-0.05
0.13
-0.12
0.03
0.19
0.08
0.06
-0.08
-0.11
0.04
-0.02
-0.01
-0.19
-0.11
0.19
0.03
0.07
-0.01
0.05
0.04
-0.03
0.09
0.20
0.27
0.19
F(27, 247)=3.415, p<.001
275
28
t
0.88
-0.78
1.83
-1.62
0.38
2.14 *
0.98
0.99
-1.33
-1.65
0.33
-0.42
-0.12
-2.71 **
-1.81
1.93
0.36
0.67
-0.10
0.82
0.70
-0.52
1.59
3.19 **
Sig.
195
Table 33. Study 2: Decision Latency (SQRT) OLS Results
Comparison Group = Moderate
Model 3
Covariates + Main Effects
Standardized
Unstandardized
Coefficients
Coefficients
B
Std. Error
Beta
t
(Constant)
Difficulty Low (Moderate)
Difficulty High (Moderate)
Indecisiveness
139.46
-10.66
24.81
12.80
78.53
13.24
13.84
4.84
1.78
-0.06 -0.80
0.13 1.79
0.17 2.64
Combinations Visible at Start
Product Category
Task Involvement
Product Category Involvement
Personal Involvement Inventory
Task Difficulty
Information Format Difficulty
Tutorial Difficulty
Dominating Alternative
Frequency of Purchase
Choice Confusion
Network Latency
Click Rate
Discomfort with Technology
Impulsivity
EDU (No College): Some College
Associate Degree
Bachelor Degree
Graduate Degree
SES (<40k) 40k-59,999
60k-79,999
More than 80k
Gender
Age
0.78
-10.02
13.01
-14.69
3.80
10.89
4.87
3.53
-0.31
-8.52
2.17
0.00
-0.09
-14.94
-7.65
35.40
8.83
10.95
-4.39
11.73
11.84
-4.73
16.17
1.85
1.11
11.10
7.31
9.20
7.18
5.21
5.12
3.64
0.25
5.03
6.61
0.01
1.10
5.66
4.12
18.53
23.54
18.25
25.16
12.56
17.68
13.62
10.50
0.58
0.05
-0.06
0.13
-0.12
0.04
0.19
0.08
0.06
-0.07
-0.11
0.04
-0.02
0.00
-0.18
-0.12
0.19
0.03
0.06
-0.01
0.06
0.04
-0.02
0.09
0.20
0.70
-0.90
1.78
-1.60
0.53
2.09
0.95
0.97
-1.22
-1.69
0.33
-0.40
-0.08
-2.64
-1.85
1.91
0.38
0.60
-0.17
0.93
0.67
-0.35
1.54
3.21
Sig.
**
*
**
**
196
Table 34. Study 2: Decision Latency Truncated Regression
Model 1
Covariates + Main Effects
Model 2
Covariates + Interaction
Unstandardized
B
(Constant) -145591.20
Difficulty Moderate (Low)
9073.19
Difficulty High (Low) 35402.19
Indecisiveness 18185.09
Difficulty High*Indecisivness
Difficulty Moderate*Indecisiveness
Difficulty Low*Indecisiveness
Combinations Visible at Start
Product Category
Task Involvement
Product Category Involvement
Personal Involvement Inventory
Task Difficulty
Information Format Difficulty
Tutorial Difficulty
Dominating Alternative
Frequency of Purchase
Choice Confusion
Network Latency
Click Rate
Discomfort with Technology
Impulsivity
EDU (No College): Some College
Associate Degree
Bachelor Degree
Graduate Degree
SES (<40k) 40k-59,999
60k-79,999
More than 80k
Age
Gender
Lower Bound
Upper Bound
Log Pseudolikelihood
Wald
N
1471.62
-10514.61
19788.60
-27639.62
8527.45
10573.98
6048.27
5481.32
-330.95
-10114.61
5315.61
-9.38
-569.11
-15864.23
-9324.08
50469.29
39458.93
20411.86
3963.29
19332.85
20082.19
-2254.18
2255.96
17662.51
Robust
Std. Error
105966.10
17367.97
21886.84
7617.54
1423.12
13930.39
15346.08
17201.47
8996.57
6675.18
6717.89
4288.01
392.88
6352.24
7541.64
13.42
1481.15
10448.46
5495.29
26763.66
47264.82
22725.87
29699.05
19945.17
20610.79
17234.07
854.35
12471.69
8736
+inf
-3298.74
χ^2(27) = 47.00, p = .01
274
Unstandardized
z
-1.37
0.52
1.62
2.39 *
1.03
-0.75
1.29
-1.61
0.95
1.58
0.90
1.28
-0.84
-1.59
0.70
-0.70
-0.38
-1.52
-1.70
1.89
0.83
0.90
0.13
0.97
0.97
-0.13
2.64 **
1.42
B
Robust
Std. Error
z
-125175.80
105508.50
-1.19
23647.64
15042.08
13853.39
8909.07
6668.88
8748.43
2.65 **
2.26 *
1.58
1519.17
-9176.70
19991.67
-27494.78
7490.68
10813.86
6083.26
5703.24
-368.73
-9833.19
5325.31
-9.63
-671.74
-16329.53
-9261.48
50626.62
37698.24
21327.86
5571.93
17572.28
19731.51
-4900.26
2230.43
18001.32
1326.31
13909.38
15162.17
16805.28
9016.67
6532.71
6581.27
4281.08
396.64
6236.96
7559.79
13.35
1475.86
10469.27
5519.44
26491.34
46935.90
22345.49
29048.41
19945.60
20443.27
17177.04
853.40
12502.50
1.15
-0.66
1.32
-1.64
0.83
1.66
0.92
1.33
-0.93
-1.58
0.70
-0.72
-0.46
-1.56
-1.68
1.91
0.80
0.95
0.19
0.88
0.97
-0.29
2.61 **
1.44
8736.00
+inf
-3298.28
χ^2(27) = 44.93, p<.017
274
197
Table 35. Study 2: Average Information Displayed OLS Results
Comparison Group = Low
Model 1
Covariates + Main Effects
Standardized
Coefficients
Unstandardized
Coefficients
B
Std.
Error
(Constant)
Difficulty Moderate (Low)
Difficulty High (Low)
Indecisiveness
Difficulty High*Indecisivness
Difficulty Moderate*Indecisiveness
Difficulty Low*Indecisiveness
9.28
3.44
8.52
0.46
6.27
1.06
1.31
0.39
Combinations Visible at Start
Product Category
Task Involvement
Product Category Involvement
Personal Involvement Inventory
Task Difficulty
Information Format Difficulty
Tutorial Difficulty
Dominating Alternative
Frequency of Purchase
Choice Confusion
Network Latency
Click Rate
Discomfort with Technology
Impulsivity
EDU (No College): Some College
Associate Degree
Bachelor Degree
Graduate Degree
SES (<40k) 40k-59,999
60k-79,999
More than 80k
Gender
Age
0.33
-0.80
-0.06
0.06
0.03
0.27
0.37
0.31
0.02
-0.17
0.47
0.00
-0.12
-1.19
-0.32
-1.70
-2.89
-1.51
-0.97
0.15
-0.35
1.31
-0.21
-0.12
R-Square
Adjusted R-Square
ANOVA
N
K
Model 2
Covariates + Interaction
0.09
0.89
0.59
0.74
0.57
0.42
0.41
0.29
0.02
0.40
0.53
0.00
0.09
0.45
0.33
1.48
1.88
1.46
2.01
1.01
1.41
1.09
0.84
0.05
Beta
t
1.48
0.20 3.25
0.49 6.51
0.07 1.20
0.24
-0.05
-0.01
0.01
0.00
0.05
0.06
0.06
0.05
-0.02
0.08
0.05
-0.07
-0.16
-0.05
-0.10
-0.10
-0.09
-0.03
0.01
-0.01
0.06
-0.01
-0.15
Standardized
Coefficients
Unstandardized
Coefficients
3.68
-0.90
-0.11
0.09
0.06
0.64
0.89
1.06
1.04
-0.41
0.89
0.94
-1.40
-2.62
-0.98
-1.15
-1.54
-1.04
-0.48
0.15
-0.25
1.20
-0.25
-2.69
Sig.
B
Std.
Error
13.32
6.32
2.11
1.58
0.27
-0.62
0.43
0.42
0.44
0.33 3.65
0.06 0.64
-0.13 -1.39
***
0.39
-0.55
0.00
-0.04
-0.02
0.32
0.38
0.33
0.02
-0.18
0.44
0.00
-0.13
-1.15
-0.33
-1.65
-2.87
-1.24
-0.42
-0.03
-0.18
0.81
-0.12
-0.13
0.08
0.89
0.59
0.74
0.58
0.42
0.41
0.29
0.02
0.41
0.53
0.00
0.09
0.46
0.33
1.49
1.90
1.47
2.02
1.01
1.42
1.10
0.85
0.05
0.29
-0.03
0.00
0.00
0.00
0.06
0.07
0.06
0.04
-0.02
0.08
0.05
-0.08
-0.15
-0.05
-0.09
-0.10
-0.07
-0.01
0.00
-0.01
0.04
-0.01
-0.16
***
Beta
t
Sig.
*
**
***
***
**
**
0.46
0.40
F(27, 247)=7.827, p<.001
0.45
0.40
F(27, 247)=7.614, p<.001
275
28
4.69
-0.61
0.01
-0.05
-0.04
0.77
0.92
1.12
0.83
-0.44
0.83
0.93
-1.43
-2.51
-0.99
-1.10
-1.51
-0.85
-0.21
-0.03
-0.12
0.74
-0.14
-2.90
*
**
198
Table 36. Study 2: Average Information Displayed OLS Results
Comparison Group = Moderate
Model 3
Covariates + Main Effects
Standardized
Coefficients
Unstandardized
Coefficients
B
Std.
Error
(Constant)
Difficulty Low (Moderate)
Difficulty High (Moderate)
Indecisiveness
12.72
-3.44
5.08
0.46
6.29
1.06
1.11
0.39
2.02
-0.20 -3.25
0.29 4.59
0.07 1.20
*
Combinations Visible at Start
Product Category
Task Involvement
Product Category Involvement
Personal Involvement Inventory
Task Difficulty
Information Format Difficulty
Tutorial Difficulty
Dominating Alternative
Frequency of Purchase
Choice Confusion
Network Latency
Click Rate
Discomfort with Technology
Impulsivity
EDU (No College): Some College
Associate Degree
Bachelor Degree
Graduate Degree
SES (<40k) 40k-59,999
60k-79,999
More than 80k
Gender
Age
0.33
-0.80
-0.06
0.06
0.03
0.27
0.37
0.31
0.02
-0.17
0.47
0.00
-0.12
-1.19
-0.32
-1.70
-2.89
-1.51
-0.97
0.15
-0.35
1.31
-0.21
-0.12
0.09
0.89
0.59
0.74
0.57
0.42
0.41
0.29
0.02
0.40
0.53
0.00
0.09
0.45
0.33
1.48
1.88
1.46
2.01
1.01
1.41
1.09
0.84
0.05
0.24
-0.05
-0.01
0.01
0.00
0.05
0.06
0.06
0.05
-0.02
0.08
0.05
-0.07
-0.16
-0.05
-0.10
-0.10
-0.09
-0.03
0.01
-0.01
0.06
-0.01
-0.15
***
Beta
t
3.68
-0.90
-0.11
0.09
0.06
0.64
0.89
1.06
1.04
-0.41
0.89
0.94
-1.40
-2.62
-0.98
-1.15
-1.54
-1.04
-0.48
0.15
-0.25
1.20
-0.25
-2.69
Sig.
**
***
**
**
199
Table 37. Study 2: Click Rate OLS Results
Comparison Group = Low
Model 1
Covariates + Main Effects
Standardized
Coefficients
Unstandardized
Coefficients
B
Std.
Error
(Constant)
Difficulty Moderate (Low)
Difficulty High (Low)
Indecisiveness
Difficulty High*Indecisivness
Difficulty Moderate*Indecisiveness
Difficulty Low*Indecisiveness
12.02
1.19
1.95
-0.46
4.44
0.76
0.93
0.28
Combinations Visible at Start
Product Category
Task Involvement
Product Category Involvement
Personal Involvement Inventory
Task Difficulty
Information Format Difficulty
Tutorial Difficulty
Dominating Alternative
Frequency of Purchase
Choice Confusion
Network Latency
Discomfort with Technology
Impulsivity
EDU (No College): Some College
Associate Degree
Bachelor Degree
Graduate Degree
SES (<40k) 40k-59,999
60k-79,999
More than 80k
Gender
Age
-0.16
-0.03
0.43
-0.01
-0.31
0.38
0.20
0.21
0.01
0.16
0.35
0.00
-0.94
0.07
-0.54
-1.10
-0.94
0.52
-0.19
-0.98
0.31
-1.55
-0.11
R-Square
Adjusted R-Square
ANOVA
N
K
Model 2
Covariates + Interaction
0.06
0.64
0.42
0.53
0.41
0.30
0.29
0.21
0.01
0.29
0.38
0.00
0.32
0.24
1.07
1.35
1.05
1.45
0.72
1.02
0.78
0.60
0.03
Beta
t
Sig.
2.71 **
0.11 1.56
0.19 2.10 *
-0.11 -1.65
-0.19
0.00
0.08
0.00
-0.06
0.12
0.06
0.07
0.03
0.04
0.10
-0.16
-0.21
0.02
-0.05
-0.06
-0.09
0.03
-0.02
-0.06
0.02
-0.16
-0.21
Standardized
Coefficients
Unstandardized
Coefficients
-2.47
-0.05
1.02
-0.02
-0.74
1.27
0.68
1.01
0.53
0.54
0.91
-2.74
-2.93
0.31
-0.51
-0.81
-0.90
0.36
-0.26
-0.97
0.40
-2.61
-3.31
*
**
**
**
**
0.22
0.14
F(26, 248)=2.735, p<.002
B
Std.
Error
13.18
4.43
-0.20
-0.46
-0.76
0.31
0.30
0.31
-0.07 -0.64
-0.16 -1.56
-0.26 -2.43 *
-0.15
0.06
0.44
-0.04
-0.33
0.39
0.20
0.21
0.01
0.16
0.34
0.00
-0.94
0.09
-0.55
-1.15
-0.89
0.63
-0.25
-0.93
0.21
-1.52
-0.11
0.06
0.64
0.42
0.53
0.42
0.30
0.29
0.21
0.01
0.29
0.38
0.00
0.32
0.24
1.07
1.35
1.05
1.44
0.72
1.01
0.78
0.60
0.03
-0.18
0.01
0.08
-0.01
-0.06
0.12
0.06
0.07
0.03
0.04
0.10
-0.16
-0.21
0.02
-0.05
-0.07
-0.09
0.03
-0.02
-0.06
0.02
-0.15
-0.21
Beta
Sig.
2.97 **
0.22
0.13
F(26, 248)=2.760, p<.001
275
27
t
-2.52
0.09
1.06
-0.07
-0.79
1.32
0.68
1.02
0.49
0.55
0.88
-2.78
-2.92
0.36
-0.52
-0.85
-0.85
0.44
-0.34
-0.92
0.26
-2.54
-3.35
*
**
**
*
***
200
Table 38. Study 2: Click Rate OLS Results
Comparison Group = Moderate
Model 3
Covariates + Main Effects
Standardized
Coefficients
Unstandardized
Coefficients
B
Std.
Error
(Constant)
Difficulty Low (Moderate)
Difficulty High (Moderate)
Indecisiveness
13.21
-1.19
0.77
-0.46
4.44
0.76
0.80
0.28
2.97
-0.11 -1.56
0.07 0.96
-0.11 -1.65
**
Combinations Visible at Start
Product Category
Task Involvement
Product Category Involvement
Personal Involvement Inventory
Task Difficulty
Information Format Difficulty
Tutorial Difficulty
Dominating Alternative
Frequency of Purchase
Choice Confusion
Network Latency
Discomfort with Technology
Impulsivity
EDU (No College): Some College
Associate Degree
Bachelor Degree
Graduate Degree
SES (<40k) 40k-59,999
60k-79,999
More than 80k
Gender
Age
-0.16
-0.03
0.43
-0.01
-0.31
0.38
0.20
0.21
0.01
0.16
0.35
0.00
-0.94
0.07
-0.54
-1.10
-0.94
0.52
-0.19
-0.98
0.31
-1.55
-0.11
0.06
0.64
0.42
0.53
0.41
0.30
0.29
0.21
0.01
0.29
0.38
0.00
0.32
0.24
1.07
1.35
1.05
1.45
0.72
1.02
0.78
0.60
0.03
-0.19
0.00
0.08
0.00
-0.06
0.12
0.06
0.07
0.03
0.04
0.10
-0.16
-0.21
0.02
-0.05
-0.06
-0.09
0.03
-0.02
-0.06
0.02
-0.16
-0.21
*
Beta
t
-2.47
-0.05
1.02
-0.02
-0.74
1.27
0.68
1.01
0.53
0.54
0.91
-2.74
-2.93
0.31
-0.51
-0.81
-0.90
0.36
-0.26
-0.97
0.40
-2.61
-3.31
Sig.
**
**
**
**
201
Table 39. Study 2: Total Clicks OLS Results
Comparison Group = Low
Model 1
Covariates + Main Effects
Standardized
Coefficients
Unstandardized
Coefficients
B
Std.
Error
(Constant)
Difficulty Moderate (Low)
Difficulty High (Low)
Indecisiveness
Difficulty High*Indecisivness
Difficulty Moderate*Indecisiveness
Difficulty Low*Indecisiveness
1.90
2.84
6.98
0.45
8.40
1.43
1.76
0.52
Combinations Visible at Start
Product Category
Task Involvement
Product Category Involvement
Personal Involvement Inventory
Task Difficulty
Information Format Difficulty
Tutorial Difficulty
Dominating Alternative
Frequency of Purchase
Choice Confusion
Network Latency
Discomfort with Technology
Impulsivity
EDU (No College): Some College
Associate Degree
Bachelor Degree
Graduate Degree
SES (<40k) 40k-59,999
60k-79,999
More than 80k
Gender
Age
-0.09
-0.09
1.29
-0.88
-0.42
1.52
0.79
0.53
-0.02
-0.03
0.29
0.00
-1.79
-0.53
2.48
0.10
0.30
1.04
0.16
-1.56
-1.30
-1.44
0.03
R-Square
Adjusted R-Square
ANOVA
N
K
Model 2
Covariates + Interaction
0.12
1.21
0.79
1.00
0.78
0.57
0.56
0.40
0.03
0.55
0.72
0.00
0.61
0.45
2.02
2.56
1.98
2.74
1.37
1.92
1.48
1.13
0.06
Beta
t
0.23
0.14 1.98
0.35 3.96
0.06 0.85
-0.06
0.00
0.12
-0.07
-0.04
0.25
0.12
0.08
-0.04
0.00
0.04
-0.16
-0.21
-0.08
0.12
0.00
0.02
0.03
0.01
-0.05
-0.05
-0.08
0.04
Standardized
Unstandardized Coefficients Coefficients
Std.
Error
Sig.
B
Beta
-0.77
-0.08
1.63
-0.88
-0.53
2.69
1.41
1.33
-0.73
-0.06
0.40
-2.73
-2.95
-1.17
1.23
0.04
0.15
0.38
0.12
-0.81
-0.88
-1.28
0.56
t
5.49
8.35
0.66
1.50
0.22
-0.51
0.58
0.56
0.59
0.28 2.57
0.04 0.39
-0.09 -0.86
-0.07
0.17
1.33
-0.95
-0.51
1.57
0.80
0.54
-0.02
-0.01
0.28
0.00
-1.80
-0.51
2.48
-0.02
0.49
1.43
-0.06
-1.43
-1.76
-1.31
0.03
0.11
1.20
0.79
1.00
0.78
0.56
0.55
0.39
0.03
0.54
0.71
0.00
0.60
0.44
2.01
2.55
1.97
2.72
1.36
1.91
1.48
1.12
0.06
-0.04
0.01
0.12
-0.07
-0.05
0.25
0.12
0.09
-0.05
0.00
0.04
-0.16
-0.21
-0.07
0.12
0.00
0.03
0.04
0.00
-0.05
-0.07
-0.07
0.03
Sig.
*
***
**
**
**
0.24
0.16
F(26, 248)=3.013, p<.001
0.25
0.17
F(26, 248)=3.153, p<.001
275
27
-0.60
0.15
1.69
-0.95
-0.66
2.79
1.45
1.36
-0.80
-0.03
0.39
-2.76
-2.98
-1.16
1.23
-0.01
0.25
0.53
-0.05
-0.75
-1.19
-1.17
0.50
*
**
**
**
202
Table 40. Study 2 Total Clicks OLS Results
Comparison Group = Moderate
Model 3
Covariates + Main Effects
Standardized
Coefficients
Unstandardized
Coefficients
B
Std.
Error
(Constant)
Difficulty Low (Moderate)
Difficulty High (Moderate)
Indecisiveness
4.74
-2.84
4.14
0.45
8.40
1.43
1.50
0.52
0.56
-0.14 -1.98
0.21 2.75
0.06 0.85
Combinations Visible at Start
Product Category
Task Involvement
Product Category Involvement
Personal Involvement Inventory
Task Difficulty
Information Format Difficulty
Tutorial Difficulty
Dominating Alternative
Frequency of Purchase
Choice Confusion
Network Latency
Discomfort with Technology
Impulsivity
EDU (No College): Some College
Associate Degree
Bachelor Degree
Graduate Degree
SES (<40k) 40k-59,999
60k-79,999
More than 80k
Gender
Age
-0.09
-0.09
1.29
-0.88
-0.42
1.52
0.79
0.53
-0.02
-0.03
0.29
0.00
-1.79
-0.53
2.48
0.10
0.30
1.04
0.16
-1.56
-1.30
-1.44
0.03
0.12
1.21
0.79
1.00
0.78
0.57
0.56
0.40
0.03
0.55
0.72
0.00
0.61
0.45
2.02
2.56
1.98
2.74
1.37
1.92
1.48
1.13
0.06
-0.06
0.00
0.12
-0.07
-0.04
0.25
0.12
0.08
-0.04
0.00
0.04
-0.16
-0.21
-0.08
0.12
0.00
0.02
0.03
0.01
-0.05
-0.05
-0.08
0.04
Beta
t
-0.77
-0.08
1.63
-0.88
-0.53
2.69
1.41
1.33
-0.73
-0.06
0.40
-2.73
-2.95
-1.17
1.23
0.04
0.15
0.38
0.12
-0.81
-0.88
-1.28
0.56
Sig.
*
**
**
**
**
203
Table 41. Study 2: Chow Test Results
Decision Latency
(SQRT)
Average Information
Displayed
Click Rate
Total Clicks
Chow
Statistic
df1
df2
Sig.
High vs. Moderate
Difficulty
4.37
26
129
.000
Moderate vs. Low
Difficulty
6.66
26
130
.000
High vs. Low
Difficulty
5.11
26
135
.000
High vs. Moderate
Difficulty
2.462
26
129
.000
Moderate vs. Low
Difficulty
22.28
26
130
.000
High vs. Low
Difficulty
4.18
26
135
.000
High vs. Moderate
Difficulty
4.75
25
131
.000
Moderate vs. Low
Difficulty
5.24
25
132
.000
High vs. Low
Difficulty
6.39
25
137
.000
High vs. Moderate
Difficulty
3.02
25
131
.000
Moderate vs. Low
Difficulty
13.62
25
132
.000
High vs. Low
Difficulty
5.30
25
137
.000
204
Table 42. Study 3: Descriptives
Mean
S.D.
Min
Max
Skewnes
s
(SE)
62,621.468
3
47,711.2408
0
9890.0
0
335,580.0
0
2.833
(.15)
237.7037
78.37610
99.45
579.29
1.508
(.15)
Ave
Information
Displayed
12.1314
5.20741
2.00
24.17
.007
(.15)
Click Rate
7.3939
4.48700
.48
27.44
1.156
(.15)
Total Clicks
6.9484
5.07879
1.00
37.00
1.640
(.15)
Decision
Latency
Decision
Latency
(Transformed)
205
Table 43. Study 3: Timer Descriptives (Seconds)
# expired
Time Pressure-Absent
N/A
N
Min
Max
Mean (S.E.)
S.D.
83 14.46 335.58 87.66 (7.17) 65.33
Time Pressure-Moderate
15
86
9.89
Time Pressure-High
78
83 10.45
253.37 65.93 (3.56) 33.02
75.93
34.15 (1.11) 10.13
Table 44. Study 3: Post Hoc Perceived Difficulty
Absolute Mean Differences
(S.E)
Low
Difficulty
Moderate
Difficulty
High
Difficulty
.17
(.25)
1.62***
(.25)
.14***
(.25)
Moderate
Difficulty
Table 45. Study 3: Cross-Tab Performance by Difficulty Condition
Performance
Low
Difficulty
Moderate
Difficulty
High
Difficulty
Total
Error
54
(65.1%)
58
(67.4%)
54
(65.1%)
166
No Error
29
(34.9%)
28
(32.6%)
29
(34.9%)
86
83
86
83
252
Total
206
Table 46. Study 3: Chi-Square Results
Performance
Conditions
Chi-Square Value
df
Fisher’s Exact Sig.
Low vs. Moderate
.107
1
.748
Low vs. High
.000
1
1.00
Moderate vs. High
.107
1
.748
Table 47. Study 3: Post Hoc Decision Latency
Absolute Mean Differences
(S.E)
Low
Difficulty
Moderate
Difficulty
Moderate
Difficulty
High
Difficulty
21.74**
(6.5)
53.51***
(6.6)
31.77***
(6.5)
207
Table 48. Study 3: Decision Latency (SQRT) OLS Results
Comparison Group = Low
Model 1
Covariates + Main Effects
Unstandardized
Coefficients
B
Std. Error
(Constant)
Difficulty Moderate (Low)
Difficulty High (Low)
Indecisiveness
Difficulty High*Indecisivness
Difficulty Moderate*Indecisiveness
Difficulty Low*Indecisiveness
71.74
-36.74
-111.83
6.98
62.18
10.11
11.64
4.25
Combinations Visible at Start
Task Involvement
Product Category Involvment
Personal Involvement Inventory
Task Difficulty
Information Format Difficulty
Tutorial Difficulty
Dominating Alternative
Frequency of Purchase
Choice Confusion
Network Latency
Click Rate
Discomfort with Technology
Impulsivity
EDU (No College): Some College
Associate Degree
Bachelor Degree
Graduate Degree
SES (<40k) 40k-59,999
60k-79,999
More than 80k
Age
Gender
-0.35
16.41
8.82
-10.13
11.81
2.72
0.60
0.12
3.13
-1.40
0.03
-0.20
-1.68
-4.69
-8.19
17.90
-12.85
3.94
-7.01
4.18
-1.52
0.86
-9.30
R-Square
Adjusted R-Square
ANOVA
N
K
1.01
5.21
8.48
5.46
3.60
3.31
2.89
0.23
5.44
4.28
0.01
1.03
3.86
3.83
16.41
22.21
16.02
19.18
11.05
11.52
12.48
0.44
8.43
Model 2
Covariates + Interaction
Standardized
Coefficients
Beta
Unstandardized
Coefficients
t
1.15
-0.22 -3.63
-0.67 -9.61
0.10 1.64
-0.02
0.21
0.07
-0.12
0.27
0.06
0.01
0.03
0.03
-0.03
0.15
-0.01
-0.03
-0.08
-0.05
0.05
-0.08
0.02
-0.04
0.02
-0.01
0.12
-0.06
-0.35
3.15
1.04
-1.86
3.28
0.82
0.21
0.53
0.58
-0.33
2.73
-0.20
-0.44
-1.23
-0.50
0.81
-0.80
0.21
-0.63
0.36
-0.12
1.95
-1.10
Sig.
Standardized
Coefficients
B
Std. Error
Beta
t
37.10
63.98
0.58
-7.05
11.87
21.77
5.02
4.58
4.48
-0.15 -1.40
0.26 2.59
0.48 4.85
-0.67
15.42
7.93
-8.81
10.94
2.08
0.41
0.01
2.65
-2.63
0.03
-0.67
-2.95
-3.97
-8.06
10.95
-15.05
7.44
-1.73
4.28
-5.28
0.98
-10.61
1.04
5.36
8.74
5.62
3.69
3.41
2.97
0.24
5.59
4.39
0.01
1.05
3.96
3.93
16.85
22.86
16.40
19.72
11.35
11.86
12.85
0.45
8.66
Sig.
***
***
**
**
**
0.43
0.37
F(26, 225)=6.543, p<.001
-0.04
0.19
0.06
-0.10
0.25
0.05
0.01
0.00
0.03
-0.06
0.15
-0.04
-0.05
-0.07
-0.05
0.03
-0.10
0.03
-0.01
0.02
-0.03
0.14
-0.07
0.40
0.33
F(26, 225)=5.710, p<.001
252
27
-0.65
2.88
0.91
-1.57
2.96
0.61
0.14
0.03
0.47
-0.60
2.73
-0.64
-0.74
-1.01
-0.48
0.48
-0.92
0.38
-0.15
0.36
-0.41
2.16
-1.22
*
***
**
**
**
*
208
Table 49. Study 3: Decision Latency (SQRT) OLS Results
Comparison Group = Moderate
Unstandardized
Coefficients
Standardized
Coefficients
B
Std.
Error
Beta
(Constant)
Difficulty Low (Moderate)
Difficulty High (Moderate)
Indecisiveness
35.00
36.74
-75.09
6.98
62.05
10.11
11.11
4.25
0.56
0.22 3.63
-0.45 -6.76
0.10 1.64
Combinations Visible at Start
Task Involvement
Product Category Involvement
Personal Involvement Inventory
Task Difficulty
Information Format Difficulty
Tutorial Difficulty
Dominating Alternative
Frequency of Purchase
Choice Confusion
Network Latency
Click Rate
Discomfort with Technology
Impulsivity
EDU (No College): Some College
Associate Degree
Bachelor Degree
Graduate Degree
SES (<40k) 40k-59,999
60k-79,999
More than 80k
Age
Gender
-0.35
16.41
8.82
-10.13
11.81
2.72
0.60
0.12
3.13
-1.40
0.03
-0.20
-1.68
-4.69
-8.19
17.90
-12.85
3.94
-7.01
4.18
-1.52
0.86
-9.30
1.01
5.21
8.48
5.46
3.60
3.31
2.89
0.23
5.44
4.28
0.01
1.03
3.86
3.83
16.41
22.21
16.02
19.18
11.05
11.52
12.48
0.44
8.43
-0.02
0.21
0.07
-0.12
0.27
0.06
0.01
0.03
0.03
-0.03
0.15
-0.01
-0.03
-0.08
-0.05
0.05
-0.08
0.02
-0.04
0.02
-0.01
0.12
-0.06
t
-0.35
3.15
1.04
-1.86
3.28
0.82
0.21
0.53
0.58
-0.33
2.73
-0.20
-0.44
-1.23
-0.50
0.81
-0.80
0.21
-0.63
0.36
-0.12
1.95
-1.10
***
***
**
**
**
209
Table 50. Study 3: Decision Latency Truncated Regression
Model 1
Covariates + Main Effects
Unstandardized
Robust
Std. Error
B
(Constant) -232513.10
Difficulty Moderate (Low) -41641.76
Difficulty High (Low) -169542.20
Indecisiveness
7637.30
Difficulty High*Indecisivness
Difficulty Moderate*Indecisiveness
Difficulty Low*Indecisiveness
114091.60
12539.54
34988.67
5449.93
Combinations Visible at Start
-187.68
Task Involvement 23793.37
Product Category Involvment 12036.06
Personal Involvement Inventory -15362.22
Task Difficulty 15733.63
Information Format Difficulty
4859.23
Tutorial Difficulty
2852.91
Dominating Alternative
111.05
Frequency of Purchase
1802.98
Choice Confusion
554.32
Network Latency
37.97
Click Rate
-1000.82
Discomfort with Technology
-4433.69
Impulsivity
-5027.68
EDU (No College): Some College
-915.57
Associate Degree 57235.61
Bachelor Degree
-5632.99
Graduate Degree
21705.66
SES (<40k) 40k-59,999 -14090.52
60k-79,999
-3704.37
More than 80k
-7669.21
Age
1145.90
Gender -13246.34
1192.73
8233.06
10132.96
6788.96
5471.30
4799.86
3503.35
276.38
7289.28
5410.14
16.28
1310.20
4617.30
4444.08
15061.82
33194.98
14953.08
18792.50
13487.81
14141.81
15698.00
586.90
10759.66
Lower Bound
Upper Bound
Log Pseudolikelihood
Wald
N
9890
+inf
-2910.96
χ^2(26) = 49.68, p = .003
251
Model 2
Covariates + Interaction
Unstandardized
Robust
Std. Error
B
z
-2.04 *
-3.32 **
-4.85 ***
1.40
-0.16
2.89
1.19
-2.26
2.88
1.01
0.81
0.40
0.25
0.10
2.33
-0.76
-0.96
-1.13
-0.06
1.72
-0.38
1.16
-1.04
-0.26
-0.49
1.95
-1.23
**
*
**
*
z
-259286.50
121742.60
-2.13
*
-25690.06
9417.13
21164.44
9215.14
5702.31
6940.34
-2.79
1.65
3.05
**
-738.46
24445.38
10424.64
-14119.47
15042.47
3430.60
2554.32
-43.26
2665.24
-848.90
37.04
-1855.13
-5349.95
-5086.43
-1011.51
49892.07
-7658.71
27139.63
-9542.45
-3117.38
-13428.48
1245.06
-16194.91
1244.93
8977.77
10994.24
6819.92
5669.62
4966.55
3770.53
316.07
7595.03
5624.90
17.10
1471.53
5099.63
4795.15
16602.84
35059.45
16144.12
20737.25
14402.78
15225.51
16474.65
625.79
11919.35
-0.59
2.72
0.95
-2.07
2.65
0.69
0.68
-0.14
0.35
-0.15
2.17
-1.26
-1.05
-1.06
-0.06
1.42
-0.47
1.31
-0.66
-0.20
-0.82
1.99
-1.36
9890.00
+inf
-2917.29
χ^2(26) = 38.77, p = .05
251
**
**
*
**
*
*
210
Table 51. Study 3: Average Information Displayed OLS Results
Comparison Group = Low
Model 1
Covariates + Main Effects
Standardized
Coefficients
Unstandardized
Coefficients
B
Std.
Error
(Constant)
Difficulty Moderate (Low)
Difficulty High (Low)
Indecisiveness
Difficulty High*Indecisivness
Difficulty Moderate*Indecisiveness
Difficulty Low*Indecisiveness
-0.05
-0.81
-3.43
-0.05
4.25
0.69
0.80
0.29
Combinations Visible at Start
Task Involvement
Product Category Involvment
Personal Involvement Inventory
Task Difficulty
Information Format Difficulty
Tutorial Difficulty
Dominating Alternative
Frequency of Purchase
Choice Confusion
Network Latency
Click Rate
Discomfort with Technology
Impulsivity
EDU (No College): Some College
Associate Degree
Bachelor Degree
Graduate Degree
SES (<40k) 40k-59,999
60k-79,999
More than 80k
Age
Gender
0.54
-0.24
1.54
-0.01
0.25
0.05
0.32
-0.02
-0.80
0.17
0.00
0.31
-0.27
-0.39
1.29
1.14
1.57
1.40
0.12
0.11
-0.45
-0.02
-1.14
R-Square
Adjusted R-Square
ANOVA
N
K
Model 2
Covariates + Interaction
0.07
0.36
0.58
0.37
0.25
0.23
0.20
0.02
0.37
0.29
0.00
0.07
0.26
0.26
1.12
1.52
1.09
1.31
0.76
0.79
0.85
0.03
0.58
Beta
t
Sig.
-0.01
-0.07 -1.17
-0.31 -4.31 ***
-0.01 -0.19
0.43
-0.04
0.18
0.00
0.09
0.02
0.11
-0.06
-0.13
0.06
0.00
0.26
-0.07
-0.10
0.11
0.05
0.15
0.09
0.01
0.01
-0.03
-0.03
-0.11
Standardized
Coefficients
Unstandardized
Coefficients
7.77
-0.67
2.66
-0.04
1.03
0.22
1.61
-1.14
-2.16
0.58
-0.01
4.36
-1.02
-1.50
1.15
0.75
1.44
1.07
0.16
0.14
-0.53
-0.52
-1.98
***
*
***
*
0.40
0.33
F(26, 225)=5.704, p<.001
B
Std.
Error
-1.15
4.27
-0.27
-0.54
0.16
0.38
0.33
0.31
0.30
-0.17 -1.60
0.05 0.54
0.13 1.26
0.53
-0.27
1.52
0.02
0.23
0.04
0.32
-0.02
-0.82
0.14
0.00
0.29
-0.31
-0.37
1.28
0.92
1.49
1.50
0.28
0.10
-0.57
-0.01
-1.17
0.07
0.36
0.58
0.37
0.25
0.23
0.20
0.02
0.37
0.29
0.00
0.07
0.26
0.26
1.12
1.52
1.09
1.31
0.76
0.79
0.86
0.03
0.58
0.42
-0.05
0.17
0.00
0.08
0.01
0.11
-0.08
-0.13
0.05
0.00
0.25
-0.08
-0.09
0.11
0.04
0.14
0.09
0.02
0.01
-0.04
-0.02
-0.11
Beta
0.40
0.32
F(26, 225)=5.608, p<.001
252
27
t
7.59
-0.76
2.60
0.06
0.94
0.16
1.60
-1.36
-2.19
0.47
0.08
4.22
-1.17
-1.40
1.14
0.61
1.36
1.14
0.38
0.13
-0.66
-0.38
-2.03
Sig.
***
*
***
*
211
Table 52. Study 3: Average Information Displayed OLS Results
Comparison Group = Moderate
Standardized
Coefficients
Unstandardized
Coefficients
B
Std.
Error
(Constant)
Difficulty Low (Moderate)
Difficulty High (Moderate)
Indecisiveness
-0.86
0.81
-2.62
-0.05
4.24
0.69
0.76
0.29
-0.20
0.07 1.17
-0.24 -3.45
-0.01 -0.19
Combinations Visible at Start
Product Category
Task Involvement
Product Category Involvement
Personal Involvement Inventory
Task Difficulty
Information Format Difficulty
Tutorial Difficulty
Dominating Alternative
Frequency of Purchase
Choice Confusion
Network Latency
Discomfort with Technology
Impulsivity
EDU (No College): Some College
Associate Degree
Bachelor Degree
Graduate Degree
SES (<40k) 40k-59,999
60k-79,999
More than 80k
Age
Gender
0.54
-0.24
1.54
-0.01
0.25
0.05
0.32
-0.02
-0.80
0.17
0.00
0.31
-0.27
-0.39
1.29
1.14
1.57
1.40
0.12
0.11
-0.45
-0.02
-1.14
0.07
0.36
0.58
0.37
0.25
0.23
0.20
0.02
0.37
0.29
0.00
0.07
0.26
0.26
1.12
1.52
1.09
1.31
0.76
0.79
0.85
0.03
0.58
0.43
-0.04
0.18
0.00
0.09
0.02
0.11
-0.06
-0.13
0.06
0.00
0.26
-0.07
-0.10
0.11
0.05
0.15
0.09
0.01
0.01
-0.03
-0.03
-0.11
Beta
t
7.77
-0.67
2.66
-0.04
1.03
0.22
1.61
-1.14
-2.16
0.58
-0.01
4.36
-1.02
-1.50
1.15
0.75
1.44
1.07
0.16
0.14
-0.53
-0.52
-1.98
***
***
**
*
***
*
212
Table 53. Study 3: Click Rate OLS Results
Comparison Group = Low
Model 1
Covariates + Main Effects
Standardized
Coefficients
Unstandardized
Coefficients
B
Std.
Error
(Constant)
Difficulty Moderate (Low)
Difficulty High (Low)
Indecisiveness
Difficulty High*Indecisivness
Difficulty Moderate*Indecisiveness
Difficulty Low*Indecisiveness
10.83
0.89
2.60
0.20
3.97
0.65
0.73
0.28
Combinations Visible at Start
Task Involvement
Product Category Involvment
Personal Involvement Inventory
Task Difficulty
Information Format Difficulty
Tutorial Difficulty
Dominating Alternative
Frequency of Purchase
Choice Confusion
Network Latency
Discomfort with Technology
Impulsivity
EDU (No College): Some College
Associate Degree
Bachelor Degree
Graduate Degree
SES (<40k) 40k-59,999
60k-79,999
More than 80k
Age
Gender
-0.16
0.34
0.47
-0.50
-0.13
0.01
0.00
-0.01
0.59
0.43
0.00
-0.53
-0.25
-1.40
-1.95
-1.13
-0.88
0.06
0.21
-1.17
-0.11
-0.40
R-Square
Adjusted R-Square
ANOVA
N
K
Model 2
Covariates + Interaction
0.06
0.34
0.55
0.35
0.23
0.21
0.19
0.02
0.35
0.28
0.00
0.25
0.25
1.06
1.43
1.04
1.24
0.72
0.75
0.81
0.03
0.55
Beta
t
Sig.
2.73 **
0.09 1.37
0.27 3.53 ***
0.05 0.74
-0.15
0.07
0.06
-0.10
-0.05
0.01
0.00
-0.03
0.11
0.18
-0.20
-0.16
-0.07
-0.14
-0.10
-0.12
-0.06
0.01
0.02
-0.10
-0.26
-0.04
Standardized
Coefficients
Unstandardized
Coefficients
-2.46
1.00
0.85
-1.41
-0.55
0.07
-0.01
-0.43
1.70
1.57
-3.23
-2.13
-0.99
-1.32
-1.36
-1.09
-0.71
0.09
0.27
-1.45
-3.85
-0.74
*
**
*
***
0.27
0.19
F(25,226)=3.269, p<.001
B
Std.
Error
11.94
3.98
0.52
0.03
-0.09
0.32
0.29
0.28
0.20 1.65
0.01 0.09
-0.03 -0.31
-0.16
0.39
0.46
-0.52
-0.11
0.03
0.01
0.00
0.59
0.48
0.00
-0.49
-0.27
-1.44
-1.84
-1.14
-0.98
-0.05
0.21
-1.10
-0.11
-0.40
0.07
0.34
0.55
0.35
0.23
0.22
0.19
0.02
0.35
0.28
0.00
0.25
0.25
1.06
1.45
1.04
1.25
0.72
0.75
0.81
0.03
0.55
-0.15
0.09
0.06
-0.11
-0.04
0.01
0.00
-0.02
0.11
0.19
-0.20
-0.15
-0.08
-0.15
-0.10
-0.13
-0.07
0.00
0.02
-0.09
-0.27
-0.04
Beta
Sig.
3.00 **
0.26
0.17
F(25, 226)=3.114, p<.001
252
26
t
-2.43
1.16
0.83
-1.47
-0.46
0.12
0.03
-0.27
1.69
1.74
-3.26
-1.98
-1.10
-1.35
-1.28
-1.09
-0.79
-0.07
0.28
-1.35
-4.06
-0.73
*
**
*
***
213
Table 54. Study 3: Click Rate OLS Results
Comparison Group = Moderate
Standardized
Coefficients
Unstandardized
Coefficients
B
Std.
Error
(Constant)
Difficulty Low (Moderate)
Difficulty High (Moderate)
Indecisiveness
11.72
-0.89
1.71
0.20
3.95
0.65
0.71
0.28
2.97
-0.09 -1.37
0.18 2.40
0.05 0.74
**
Combinations Visible at Start
Task Involvement
Product Category Involvement
Personal Involvement Inventory
Task Difficulty
Information Format Difficulty
Tutorial Difficulty
Dominating Alternative
Frequency of Purchase
Choice Confusion
Network Latency
Discomfort with Technology
Impulsivity
EDU (No College): Some College
Associate Degree
Bachelor Degree
Graduate Degree
SES (<40k) 40k-59,999
60k-79,999
More than 80k
Age
Gender
-0.16
0.34
0.47
-0.50
-0.13
0.01
0.00
-0.01
0.59
0.43
0.00
-0.53
-0.25
-1.40
-1.95
-1.13
-0.88
0.06
0.21
-1.17
-0.11
-0.40
0.06
0.34
0.55
0.35
0.23
0.21
0.19
0.02
0.35
0.28
0.00
0.25
0.25
1.06
1.43
1.04
1.24
0.72
0.75
0.81
0.03
0.55
-0.15
0.07
0.06
-0.10
-0.05
0.01
0.00
-0.03
0.11
0.18
-0.20
-0.16
-0.07
-0.14
-0.10
-0.12
-0.06
0.01
0.02
-0.10
-0.26
-0.04
*
Beta
t
-2.46
1.00
0.85
-1.41
-0.55
0.07
-0.01
-0.43
1.70
1.57
-3.23
-2.13
-0.99
-1.32
-1.36
-1.09
-0.71
0.09
0.27
-1.45
-3.85
-0.74
*
**
*
***
0
0
0
0
0
0
0
0
1
1
1
1
0
0
0
0
0
0
0
0
0
1
1
0
0
0
214
Table 55. Study 3: Total Clicks OLS Results
Comparison Group = Low
Model 1
Covariates + Main Effects
Standardized
Coefficients
Unstandardized
Coefficients
B
Std.
Error
(Constant)
Difficulty Moderate (Low)
Difficulty High (Low)
Indecisiveness
Difficulty High*Indecisivness
Difficulty Moderate*Indecisiveness
Difficulty Low*Indecisiveness
2.63
-0.55
-4.11
0.67
4.54
0.75
0.84
0.32
Combinations Visible at Start
Task Involvement
Product Category Involvment
Personal Involvement Inventory
Task Difficulty
Information Format Difficulty
Tutorial Difficulty
Dominating Alternative
Frequency of Purchase
Choice Confusion
Network Latency
Discomfort with Technology
Impulsivity
EDU (No College): Some College
Associate Degree
Bachelor Degree
Graduate Degree
SES (<40k) 40k-59,999
60k-79,999
More than 80k
Age
Gender
-0.13
1.09
0.82
-0.91
0.38
0.14
-0.06
0.00
0.89
0.52
0.00
-0.42
-0.49
-2.37
0.23
-2.13
-1.16
-0.05
0.05
-0.38
-0.06
-1.00
R-Square
Adjusted R-Square
ANOVA
N
K
Model 2
Covariates + Interaction
0.07
0.39
0.63
0.40
0.27
0.25
0.21
0.02
0.40
0.32
0.00
0.28
0.28
1.21
1.64
1.19
1.42
0.82
0.86
0.92
0.03
0.63
Beta
Standardized
Unstandardized Coefficients Coefficients
Std.
Error
Sig.
B
Beta
t
0.58
-0.05 -0.74
-0.38 -4.88 ***
0.15 2.13 *
-0.11
0.21
0.10
-0.16
0.13
0.05
-0.02
-0.02
0.15
0.18
-0.12
-0.11
-0.12
-0.21
0.01
-0.21
-0.07
0.00
0.00
-0.03
-0.13
-0.10
-1.81
2.81
1.30
-2.25
1.41
0.59
-0.28
-0.26
2.22
1.64
-2.01
-1.50
-1.72
-1.95
0.14
-1.79
-0.82
-0.06
0.06
-0.41
-1.93
-1.60
**
*
*
*
0.25
0.17
F(25,226)=2.992, p<.001
1.46
4.54
0.05
0.92
1.23
0.36
0.33
0.32
-0.15
1.08
0.75
-0.85
0.35
0.12
-0.05
-0.01
0.84
0.49
0.00
-0.46
-0.46
-2.42
-0.05
-2.30
-1.08
0.17
0.05
-0.50
-0.06
-1.06
0.07
0.39
0.63
0.40
0.27
0.25
0.22
0.02
0.40
0.32
0.00
0.28
0.28
1.21
1.65
1.18
1.43
0.82
0.86
0.93
0.03
0.63
Sig.
0.32
0.02 0.15
0.32 2.78 **
0.42 3.79 ***
-0.12
0.21
0.09
-0.15
0.12
0.04
-0.02
-0.03
0.14
0.17
-0.11
-0.12
-0.12
-0.22
0.00
-0.23
-0.07
0.01
0.00
-0.04
-0.12
-0.10
0.25
0.16
F(25,226)=2.939, p<.001
252
26
t
-2.03
2.78
1.18
-2.10
1.31
0.50
-0.24
-0.52
2.10
1.54
-1.82
-1.61
-1.63
-1.99
-0.03
-1.94
-0.76
0.21
0.06
-0.54
-1.82
-1.70
*
**
*
*
*
215
Table 56. Study 3: Total Clicks OLS Results
Comparison Group = Moderate
Standardized
Coefficients
Unstandardized
Coefficients
B
Std.
Error
(Constant)
Difficulty Low (Moderate)
Difficulty High (Moderate)
Indecisiveness
2.08
0.55
-3.55
0.67
4.52
0.75
0.81
0.32
0.46
0.05 0.74
-0.33 -4.36
0.15 2.13
Combinations Visible at Start
Task Involvement
Product Category Involvement
Personal Involvement Inventory
Task Difficulty
Information Format Difficulty
Tutorial Difficulty
Dominating Alternative
Frequency of Purchase
Choice Confusion
Network Latency
Discomfort with Technology
Impulsivity
EDU (No College): Some College
Associate Degree
Bachelor Degree
Graduate Degree
SES (<40k) 40k-59,999
60k-79,999
More than 80k
Age
Gender
-0.13
1.09
0.82
-0.91
0.38
0.14
-0.06
0.00
0.89
0.52
0.00
-0.42
-0.49
-2.37
0.23
-2.13
-1.16
-0.05
0.05
-0.38
-0.06
-1.00
0.07
0.39
0.63
0.40
0.27
0.25
0.21
0.02
0.40
0.32
0.00
0.28
0.28
1.21
1.64
1.19
1.42
0.82
0.86
0.92
0.03
0.63
-0.11
0.21
0.10
-0.16
0.13
0.05
-0.02
-0.02
0.15
0.18
-0.12
-0.11
-0.12
-0.21
0.01
-0.21
-0.07
0.00
0.00
-0.03
-0.13
-0.10
Beta
t
-1.81
2.81
1.30
-2.25
1.41
0.59
-0.28
-0.26
2.22
1.64
-2.01
-1.50
-1.72
-1.95
0.14
-1.79
-0.82
-0.06
0.06
-0.41
-1.93
-1.60
***
*
**
*
*
*
0.646004
0.459816
1.94E-05
0.03411
0.071345
0.005398
0.194957
0.025564
0.158951
0.557783
0.783279
0.798233
0.027571
0.102456
0.045409
0.135632
0.085947
0.052198
0.891067
0.074618
0.414493
0.955907
0.950631
0.684046
0.054382
0.111915
216
Table 57. Study 3: Chow Test Results
Decision Latency
(SQRT)
Total Clicks
Chow
Statistic
df1
df2
Sig.
High vs. Moderate
Difficulty
23.40
25
119
.000
Moderate vs. Low
Difficulty
5.75
25
119
.000
High vs. Low
Difficulty
8.83
25
116
.000
High vs. Moderate
Difficulty
8.50
24
121
.000
Moderate vs. Low
Difficulty
3.20
24
121
.000
High vs. Low
Difficulty
6.40
24
118
.000
217
Appendices
Appendix A: Interview Materials
Interview Discussion Guide
Thank you for agreeing to participate in my research study. Today I want to talk with you about
what makes decisions difficult. I am interested in learning a few things from you including the
types of decisions you find difficult to make. I am also interested in learning about your decisionmaking process. As you answer the following questions, please feel free to tell me everything
you are thinking, even if it seems silly or unrelated. As you can see, I am making an audio
recording of this conversation. This is being done so that we can talk without me writing down
what you are saying. Are you okay with this conversation being recorded?
General Decision Making
Q1: What makes a decision difficult for you?
Prompt: Can you list some things about a decision that make it harder to make?
Q2: Please describe some types of decisions that you struggle to make? Can you tell me about a
decision you have struggled with lately?
Specific Decision Making
Now I want to talk about the decisions you make every day (such as what to wear, what to eat,
where to go shopping, what route to take to get there, and so on)
Q3: So, can you tell me what it is like it is like for you to make these types of everyday decisions.
Q4: Think back to a recent purchase. How did you decide what to get? What prompted the
purchase? Talk me through your decision making process. Why did you choose the item you
chose? What if the store had not had what you were looking for?
Q5: Walk me through your decision-making process if I were to ask you to order off of this
unfamiliar menu. [Note: Will use an actual menu. The proposed menu is attached]
Q6: Walk me through your decision-making process if you were deciding what to wear to a
wedding. It is a wedding for someone that you know. Imagine that this is an outdoor wedding in
June. The weather is expected to be mild, but nice.
Note: The above questions will possibly be followed by probing questions such as:
1)
Tell me more about X.
2)
What did you mean when you said X?
3)
Could you please elaborate on X?
Coping Mechanisms and Affect
Q7: What kinds of things do you do to make it easier to make decisions?
Prompt: Do you have any tricks you use to help you in your decision making?
Q8: What do you do if you just can’t decide?
Q9: How do you feel when you can’t decide?
Prompt: Here is a list of feelings, can you point out a few that you feel when you cannot decide?
218
Unfamiliar Menu
219
List of Feelings
Pleasant Feelings
OPEN
HAPPY
ALIVE
GOOD
understanding
confident
reliable
Easy
amazed
Free
sympathetic
interested
satisfied
receptive
accepting
Kind
Great
Gay
joyous
lucky
fortunate
delighted
overjoyed
gleeful
thankful
important
festive
ecstatic
satisfied
glad
playful
courageous
energetic
liberated
optimistic
provocative
impulsive
free
frisky
animated
spirited
thrilled
wonderful
calm
peaceful
at ease
comfortable
pleased
encouraged
clever
surprised
content
quiet
certain
relaxed
serene
free and easy
cheerful
sunny
bright
blessed
merry
reassured
elated
jubilant
LOVE
INTERESTED
POSITIVE
STRONG
loving
considerate
affectionate
sensitive
tender
devoted
attracted
passionate
admiration
warm
touched
concerned
affected
fascinated
intrigued
absorbed
inquisitive
nosy
snoopy
engrossed
curious
eager
keen
earnest
intent
anxious
inspired
determined
excited
enthusiastic
bold
brave
impulsive
free
sure
certain
rebellious
unique
dynamic
tenacious
hardy
secure
sympathy
daring
close
loved
challenged
optimistic
comforted
re-enforced
drawn toward
confident
hopeful
220
Difficult/Unpleasant Feelings
ANGRY
DEPRESSED
CONFUSED
HELPLESS
irritated
enraged
hostile
insulting
sore
annoyed
upset
hateful
unpleasant
offensive
bitter
aggressive
resentful
inflamed
provoked
incensed
infuriated
cross
worked up
boiling
lousy
disappointed
discouraged
ashamed
powerless
diminished
guilty
dissatisfied
miserable
detestable
repugnant
despicable
disgusting
abominable
terrible
in despair
sulky
bad
a sense of loss
upset
doubtful
uncertain
indecisive
perplexed
embarrassed
hesitant
shy
stupefied
disillusioned
unbelieving
skeptical
distrustful
misgiving
lost
unsure
uneasy
pessimistic
tense
incapable
alone
paralyzed
fatigued
useless
inferior
vulnerable
empty
forced
hesitant
despair
frustrated
distressed
woeful
pathetic
tragic
in a stew
dominated
INDIFFERENT
AFRAID
HURT
SAD
insensitive
dull
nonchalant
neutral
reserved
weary
bored
preoccupied
cold
disinterested
lifeless
fearful
terrified
suspicious
anxious
alarmed
panic
nervous
scared
worried
frightened
timid
shaky
restless
crushed
tormented
deprived
pained
tortured
dejected
rejected
injured
offended
afflicted
aching
victimized
heartbroken
tearful
sorrowful
pained
grief
anguish
desolate
desperate
pessimistic
unhappy
lonely
grieved
mournful
dismayed
doubtful
threatened
agonized
appalled
cowardly
humiliated
quaking
menaced
wronged
alienated
fuming
indignant
wary
221
Informant Demographic Characteristics
Name*
Age
Gender
Level of Education
Profession
Amanda
Mid-20s
Female
Associates
Mark
Mid-20s
Male
Graduate
Shelby
Late-20s
Female
Unknown
Home
Information
Systems
Healthcare and
Information
Systems
Education
Graphic Design
Student
Healthcare
Student
Healthcare
Education
Event Planning
Retail and Labor
Sales and Retail
Heather
Early-30s Female
Graduate
Joanne
Early-40s Female
Unknown
Ben
Early-30s Male
Post-Graduate
Helen
Early-30s Female
Undergraduate
Kayla
18
Female
High School
Annette
Early-30s Female
Undergraduate
Jane
Mid-30s
Female
Post-Graduate
Melissa
Late-20s
Female
Undergraduate
Jake
Late-20s
Male
Unknown
Caleb
Mid-30s
Male
Undergraduate
*Names have been changed to protect privacy
Relationsh
ip Status
Married
SelfReport
Yes
Single
Yes
Married
Yes
Married
Married
Married
Married
Single
Married
Married
Cohabiting
Cohabiting
Married
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
222
Appendix B: Scales
1
Used in main studies
Used in Pretest
*Reverse Coded
2
ATTITUDE TOWARD THE PRODUCT/BRAND (UTILITARIAN)2
α= 0.95 & 0.92 (Voss, Spangenberg, & Grohmann 2003)
Directions: For each statement below, place a check mark closer to the adjective that you
believe best describes your feelings about the product. The more appropriate the adjective
seems, the closer you should place your mark to it.
Effective/Not Effective
Helpful/Not Helpful
Functional/Not Functional
Necessary/Not Necessary
Practical/Not Practical
ATTITUDE TOWARD THE COMPANY (GENERAL)2
α=0.97 (Homer 1995)
Please express your attitudes toward ___________.
Negative/Positive
Unpleasant/Pleasant
Disagreeable/Agreeable
Worthless/Valuable
Bad/Good
Foolish/Wise
Unfavorable/Favorable
Dislike a lot/Like a lot
Useless/Useful
COGNITIVE RESOURCE DEMANDS 1,2
α=0.85 (White & Peloza 2009), α=0.94 (White, MacDonnell, & Dahl 2011)
Difficult to Process/Easy to Process
Difficult to Understand/Easy to Understand
Difficult to Comprehend/Easy to Comprehend
223
CHOICE CONFUSION1
α=0.85 (Diehl & Poynor 2010)
Please indicate the extent to which…
You felt overwhelmed.
You felt confused by the decision process.
It was difficult for you to decide which ____ to choose.
DISCOMFORT WITH TECHNOLOGY
α=0.93 (Meuter, Bitner, Ostrom, and Brown 2005)
I feel apprehensive about using technology.
Technical terms sound like confusing jargon to me.
I have avoided technology because it is unfamiliar to me.
I hesitate to use most forms of technology for fear of making mistakes I cannot correct.
DIFFICULTY OF THE TASK1
α=0.85 (Argo, Popa, and Smith 2010)
Difficult
Hard
Easy*
IMAGERY ELABORATION2
α=0.81 Unnava & Burnkrant (1991)
Provokes/ Does not provoke imagery
Vivid/Dull
Interesting/Boring
224
IMPULSIVENESS SUBSCALE1
(Barkley-Levenson & Fox 2014)
When I go to a mall I buy things “on impulse” and later regret having bought them.
When I go shopping for food I end up buying things I hadn’t planned to buy because they look good.
When I go shopping for clothing I end up bringing home at least one item I never wear.
When I go on vacation I come back home with some souvenirs and gifts that I later throw away.
INDECISIVENESS SCALE1
α=0.88 (Spunt et al. 2009; Rassin et al. 2007)
I try to put off making decisions.
I always know exactly what I want.*
I find it easy to make decisions.*
I like to be in a position to make decisions.*
Once I make a decision, I feel fairly confident that it is a good one.*
I usually make decisions quickly.*
Once I make a decision, I stop worrying about it.*
I become anxious when making a decision.
I often worry about making the wrong choice.
After I have chosen or decided something, I often believe I’ve made the wrong choice or decision.
It seems that deciding on the most trivial thing takes me a long time.
225
PERSONAL INVOLVEMENT INVENTORY1, 2
α=0.90 (Zaichkowsky 1994)
Important/Unimportant*
Boring/Interesting
Relevant/Irrelevant*
Exciting/Unexciting*
Means nothing/Means a lot to me
Appealing/Unappealing*
Fascinating/Mundane*
Worthless/Valuable
Involving/Uninvolving*
Not needed/Needed
TASK INVOLVEMENT
α=0.90 (Wilcox, Kramer, & Sen 2011)
During the task you were not/very involved (at all).
During the task you were not/very interested (at all).
During the task you were not/very engaged (at all).
226
Appendix C: Product Pretest Materials
Low Importance Stimuli
Subjects were shown one of the following:
High Importance Stimuli
Subjects were shown one of the following:
227
Survey Instrument
1. How often do you purchase a [product]? [Low Importance Product]
o Never
o Every few years
o Once a year
o Every few months
o Once a month
o More than once a month
1. How often do you purchase a [product?] [High Importance Product]
o Never
o Every 2-3 years
o Every 6+ years
o Once a year
o Every 4-5 years
o More than once a year
2. Imagine you are shopping for a [product]. List any features of [product] that come to mind.
3. To what extent do you agree with the following statements?
(7-point Scale: Strongly Disagree to Strongly Agree)


I enjoy shopping for and purchasing [product].
It is difficult to decide which type of [product] to buy.
228
4. To me, [product] is:
(7-point Scale: Semantic Differential)





Important/Unimportant
Boring/Interesting
Relevant/Irrelevant
Exciting/Unexciting
Means nothing/Means a lot to me





Appealing/Unappealing
Fascinating/Mundane
Worthless/Valuable
Involving/Uninvolving
Not needed/Needed
5. Imagine you are thinking about purchasing a reusable water bottle. How important are each of
the following features/benefits?
(7-point Scale: Not Important-Extremely Important)
[Reusable Water Bottle]
 Capacity (in ounces)
 Hours of Insulation
 Percentage of Content Made of
Recycled Materials
 Warranty
 Percentage of Proceeds Donated
to Charity
[Multi-Purpose Cleaning Wipes]
 Number of Wipes
 Size of Wipes
 Disinfecting Power
 Thickness of Wipes
 Natural Ingredients
Toothpaste
 Effectiveness Against:
o Stains
o Plaque
o Gingivitis
o Cavities
o Bad Breath





Dishwasher Safe
Leak-Proof
Ergonomic Shape
Non-Slip Grip
Price




Effectiveness of Dispensing
Mechanism
Effectiveness Against Grease
Effectiveness on Glass
Price




Whitening Power
Strengthening/Hardening
Gum Protection
Size (in ounces)
229
Appendix D: Information Format Pretest Materials
Stimuli
Subjects were shown one of the following information formats:
230
231
Survey Instrument
1. As a quality/difficulty check, subjects were asked to identify which brand scored best on each
of the features.
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Which of the brands has the highest value on Feature 4?
Which of the brands has the highest value on Feature 2?
Which of the brands has the highest value on Feature 1?
Which of the brands has the highest value on Feature 3?
2. For each statement below, select a response closer to the statement you believe best describes the
information format of the graph. The more appropriate the statement seems, the closer you should place
your mark to it. The information format of the graph you saw on the previous page...
(7-point Semantic Differential)
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provokes imagery/does not provoke
imagery
is vivid/is dull
is interesting/is boring
is effective/is not effective
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is helpful/is not helpful
is functional/is not functional
is necessary/is not necessary
is practical/is not practical
3. For each statement below, select a response closer to the adjective you believe best describes your
attitude toward the information format of the graph. The more appropriate the adjective seems, the closer
you should place your mark to it.
(7-point Semantic Differential)
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Negative/Positive
Unpleasant/Pleasant
Disagreeable/Agreeable
Worthless/Valuable
Bad/Good
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Foolish/Wise
Unfavorable/Favorable
Dislike a lot/Like a lot
Useless/Useful
232
Appendix E: Process Tracing Software
Welcome Pages
233
Informed Consent
234
Instructions
235
Tutorial
236
237
238
Example Decision Task
Follow this link to view Process Tracing Software:
http://www.choiceresearch.org/CDP/Default.aspx?StudyId=5dbyj5Wcp15mv0db919%2f%2bw%3d%3d
Completion Page