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\title{What does it mean to maximize? “Decision difficulty,” indecisiveness, and the jingle-jangle fallacies in the measurement of maximizing}

\author{Nathan N. Cheek\thanks{Princeton University. Email: nncheek@princeton.edu.}
\and
Jacob Goebel\thanks{Princeton University.}
}

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  \jdate \hfill Decision difficulty, indecisiveness, and maximizing \qquad}
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For two decades, researchers have investigated the correlates and
consequences of individual differences in \textit{maximizing}, the
tendency to pursue the goal of making the best possible choice by
extensively seeking out and comparing alternatives. In this time, many
different conceptualizations of maximizing have been proposed, including
several that incorporate a construct called “decision difficulty.” We
propose that including decision difficulty in measures of maximizing is
problematic because the tendency to experience difficulty when making
decisions is a separate individual difference construct already studied
independently of maximizing — namely, \textit{indecisiveness}. Across two
studies (total \textit{N} = 639), we find that scales measuring decision
difficulty and indecisiveness are strongly correlated (\textit{r}’s
${\geq}$ .85), load on the same component in a principal component
analysis, and show a very similar pattern of correlations with related
variables. Moreover, decision difficulty and indecisiveness scales both
show a divergent pattern of correlations when compared to measures of
maximizing. We argue that decision difficulty scales are best interpreted
as tapping the same underlying tendency as indecisiveness scales, and
conclude that the tendency to experience difficulty in decision making is
best conceptualized not as a component of maximizing, but rather a cause
or consequence of it.

\smallskip
\noindent
Keywords: maximizing, decision difficulty, indecisiveness, jingle fallacy, jangle fallacy

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\footnotetext{

Copyright: \copyright\ 2020.
The authors license this article under the terms of the
\href{http://creativecommons.org/licenses/by/3.0/}{Creative Commons
  Attribution 3.0 License.}
}}

\saythanks

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\section{Introduction}
Who seeks the best when making a choice? Traditional economic theories
assumed that people were rational choosers who attempted to
\textit{maximize} by making the best possible choice after fully
considering all available information and alternatives. In contrast, Simon
(1955, 1956) argued that cognitive and logistical constraints made it
impossible to maximize; he proposed that people \textit{satisfice} by
striving to make a “good enough” choice instead of an optimal one. Nearly
two decades ago, Schwartz et al.\ (2002) integrated these perspectives with
modern research on individual differences in decision making to suggest
that people vary in the extent to which they seek the best during
choice — some people are more likely to try to maximize, while others are
content to satisfice.

A large body of research has examined the correlates and consequences
of individual differences in maximizing using the scale developed by
Schwartz et al.\ (2002) — the Maximization Scale. For example, researchers
have argued that, relative to satisficers, maximizers are more likely to
seek out additional alternatives during decision making, to expend more
effort and time making decisions, to experience regret and dissatisfaction
after choice, and to achieve objectively better decision outcomes (e.g.,
Cheek \& Ward, 2019; Dar-Nimrod, Rawn, Lehman \& Schwartz, 2009; Iyengar,
Wells \& Schwartz, 2006; Misuraca \& Teuscher, 2013; Polman, 2010;
Schwartz et al., 2002). Many researchers have also suggested that
maximizing is related to a broad range of potentially maladaptive traits
and tendencies, such as perfectionism, neuroticism, unhappiness,
depression, attention deficit/hyperactivity disorder, and suicidality
(e.g., Bruine de Bruin, Dombrovski, Parker \& Szanto, 2016; Chang et al.,
2011; Schepman, Weyandt, Schlect \& Swentosky, 2012; Schwartz et al.,
2002). Claims that maximizing is related to negative outcomes are
controversial, however; indeed, there is substantial disagreement about
how (mal)adaptive maximizing actually is (e.g., Cheek \& Schwartz, 2016;
Dalal, Diab, Zhu \& Hwang, 2015; Diab, Gillespie \& Highhouse, 2008;
Schwartz et al., 2002). This disagreement largely revolves around the
meaning and measurement of maximizing, because conclusions about its
correlates largely depend on how researchers define and operationalize
maximizing. In the present research, we address one aspect of
disagreement — namely, whether or not decision difficulty should be
considered part of maximizing.

%Table 1: 
\begin{table*}[t]
  \caption{Summary of constructs in maximizing definitions and scales. X’s
    indicate the presence — whether explicit or implicit — of a particular
    construct in a given definition/operationalization.}
\begin{tabular}{lC{.6in}C{.6in}C{.6in}C{.6in}}\toprule
Definition/operationalization & High standards & Alternative search & Decision difficulty & Regret\\\midrule
Maximization Scale (Schwartz et al., 2002) & X & X & X & \\
Short Form Maximization Scale (Nenkov et al., 2008) & X & X & X & \\
Maximization Tendency Scale (Diab et al., 2008) & X & X &  & \\
Modified Maximizing Scale (Lai, 2010) & X & X &  & \\
Maximization Inventory (Turner et al., 2012) &  & X & X & \\
Revised Short Form Maximization Scale (Weinhardt et al., 2012) & X & X & X & \\
Revised Maximizing Tendency Scale (Weindhardt et al., 2012) & X &  &  & \\
Relational Maximization Scale (Mikkelson \& Pauley, 2013) & X & X & X & \\
Refined Maximization Scale (Richardson et al., 2014) & X & X & X & X\\
Maximizing mind-set (Ma \& Roese, 2014) & X & X &  & \\
Maximizing Tendency Scale-7 (Dalal et al., 2015) & X &  &  & \\
Decision Making Tendency Inventory (Misuraca et al., 2015) & X & X & X & \\
Relational Maximization Scale (friendship version; Newman et al., 2018) & X & X & X & \\
Career Maximizing Scale (Voss et al., 2019) & X & X &  & \\\bottomrule
\end{tabular}
\end{table*}

\subsection{What does it mean to maximize?}
Since Schwartz et al.’s (2002) original development of the Maximization
Scale, the literature has seen over a dozen new measures and definitions
of maximizing. In an attempt to clarify the confusing measurement
literature, Cheek and Schwartz (2016) reviewed and analyzed 11 existing
scales, as well as the conceptualization of maximizing used in Ma and
Roese’s (2014) maximizing mind-set priming procedure. Their review
illuminated four constructs that are specifically defined by at least one
measure to be part of maximizing.\footnote{ Two additional constructs,
satisficing and minimizing, are typically assumed by proponents to be
separate theoretical constructs (e.g., distinct decision strategies or
goals) rather than part of maximizing (see Cheek \& Schwartz, 2016); thus,
we do not discuss them further in the present research.} Table 1
summarizes the different definitions of maximizing and outlines which
definitions include each of four relevant maximizing constructs, including
the two maximizing scales of which we are aware that were published after
Cheek and Schwartz’s review (Newman, Schug, Yuki, Yamada \& Nezlek, 2018;
Voss, Lake \& Chelvin-Thiele, 2019). 

Two of the most common constructs are a goal construct (often labeled “high
standards”) that captures the goal of making the best possible choice and
a strategy construct (often labeled “alternative search”) that captures
the strategy of extensively seeking out and comparing alternatives. Cheek
and Schwartz (2016) proposed a two-component theoretical model of
maximizing that comprises these two constructs, arguing that maximization
can be best understood as the pursuit of the maximization goal of wanting
the best\footnote{ Cheek and Schwartz (2016) argued that the label “high
standards” for the maximization goal is inappropriate and confusing
because satisficers may also have high standards; what distinguishes
maximizers is that they seek the \textit{very best} choice, rather than a
choice that may meet high standards but be suboptimal.} through the
maximization strategy of alternative search. The two other constructs that
have been included in maximizing definitions are decision difficulty and
regret.

“Decision difficulty” was the label applied by Nenkov et al.\
(2008) to one of the factors that emerged from factor analyses of Schwartz
et al.’s (2002) original Maximization Scale. This factor included four
items that broadly described the tendency to find decisions challenging
(e.g., “I often find it difficult to shop for a gift for a friend”).
Schwartz et al.\ included these items because they theorized that
maximizers would tend to find decisions more difficult than satisficers,
which is a reasonable claim given that maximizers have a more challenging
goal of choosing the very best alternative. Because of the presence of
decision difficulty items in the original maximizing measure, and the
formal establishment of decision difficulty as a component of maximizing
in the Short Form Maximization Scale (Nenkov et al., 2008), the majority
of definitions and measures of maximizing have included decision
difficulty (see Table 1).

Yet, that maximizing is often related to experiencing decision difficulty
does not mean that decision difficulty is inherently \textit{part} of
maximizing. In fact, maximizers need not always experience increased
decision difficulty — for example, if there are only a few options, it may
be relatively easy to identify the best one, and thus both maximizers and
satisficers will have an easy decision process. Cheek and Schwartz (2016)
argued that decision difficulty is more appropriately conceptualized as
either a consequence of maximizing, such as when maximizers face greater
cognitive load in complex decisions, or a cause of maximizing, such as
when difficulty makes decisions seem more important and drives people to
maximize (Sela \& Berger, 2012). In addition to undermining construct
validity, including decision difficulty in measures of maximizing is
problematic because it is impossible to accurately study the relation
between maximizing and decision difficulty when the latter is included in
the measurement of the former. Moreover, it builds a negative outcome into
measurement, which can then affect what maximizing appears to be related
to (e.g., by making maximizing seem more problematic than it may actually
be; Cheek \& Schwartz, 2016; Dalal et al., 2015; Diab et al., 2008; Hughes
\& Scholer, 2017; Kim \& Miller, 2017; Lai, 2010). 

Although the inclusion of decision difficulty as part of the first measure
of maximizing explains why it originally became a prominent component in
many definitions and operationalizations, it is striking that decision
difficulty \textit{continues} to be so frequently considered part of
maximizing despite several prominent calls to the contrary (e.g., Cheek \&
Schwartz, 2016; Dalal et al., 2015; Lai, 2010). Indeed, many recent
studies of maximizing have measured decision difficulty as part of
maximizing (e.g., French \& Meltzer, 2019; Hsieh \& Yalch, 2019; Luan \&
Li, 2017a, 2017b, 2019; Smallman \& Becker, 2017; Oren, Dar \& Liberman,
2018). What is also striking is how often researchers measure decision
difficulty as part of maximizing compared to how rarely they measure
regret as part of maximizing. To our knowledge, only one scale includes
regret as part of maximizing (Richardson et al., 2014), and a December
2019 search on Google Scholar did not yield any articles except the
original scale construction article that used that scale. Given arguments
that neither decision difficulty nor regret should be considered part of
maximizing, why is one inappropriately included so much more often than
the other?

One essential difference between decision difficulty and regret may be that
the latter is widely accepted to be its own, distinct construct. There is
a large body of theory and research on regret independent of maximizing
(e.g., Connolly \& Zeelenberg, 2002; Gilovich \& Medvec, 1995; Landman,
1993; Zeelenberg \& Pieters, 2007), and even in their original article,
Schwartz et al.\ (2002) constructed their Regret Scale separately from
their Maximization Scale. Thus, using items that measure regret while
instead calling them maximizing is a relatively obvious example of the
\textit{jingle fallacy} — conflating distinct constructs by measuring them
with scales of the same name (Kelley, 1927; Thorndike, 1904). Because
regret is well-known and well-studied outside of the maximizing
literature, the fallacy in this case is relatively straightforward. 

When it comes to decision difficulty, on the other hand, the problem may be
obscured because “decision difficulty” is not a common term for an
individual difference, except when studied as a component of maximizing.
In the present paper, we aim to demonstrate that measuring decision
difficulty as part of maximizing is an equally problematic instance of the
jingle fallacy as measuring regret as part of maximizing. Specifically, we
argue that, like regret, decision difficulty is \textit{already} an
individual difference that has been and continues to be studied outside of
the maximizing literature. This fact, however, may not be obvious because
individual differences in decision difficulty are typically referred to in
the literature by another name — \textit{indecisiveness}. Thus, measuring
decision difficulty as part of maximizing is an instance of the jingle
fallacy because it conflates maximizing and indecisiveness, whereas
measuring decision difficulty and indecisiveness separately is instead an
instance of the \textit{jangle fallacy} — assuming two constructs are
different because they are measured by scales with different names
(Kelley, 1927; Thorndike, 1904). 

\subsection{Decision difficulty as indecisiveness }
Indecisiveness is an individual difference that describes the general
tendency to experience difficulty during decision making. This tendency
includes specific features such as finding decisions challenging, taking a
long time to make decisions, attempting to delay or avoid making
decisions, frequently changing one’s mind before a final decision has been
made, and ruminating and worrying about decisions once they have been made
(Crites, 1969; Frost \& Shows, 1993; Germeijs \& De Boeck, 2002; Rassin,
2007; Salomone, 1982; Van Matre \& Cooper, 1984).\footnote{ Following the
convention in the literature, we use \textit{indecisiveness} to refer to
individual differences in the general tendency to find decisions
difficult, whereas \textit{indecision} describes one instance of decision
difficulty.} Indecisiveness tends to be positively related to maladaptive
traits, lower well-being, and psychopathology, including neuroticism,
impatience, perfectionism, worrying, dysfunctional impulsivity, stress,
anxiety, depression, obsessive compulsive disorder (OCD), lower quality of
life, lower life satisfaction, and lower self-esteem (Barkley-Levenson \&
Fox, 2016; Bavolar, 2018; Di Fabio, Palazzeschi, Asulin-Peretz \& Gati,
2013; Effert \& Ferrari, 1989; Frost \& Shows, 1993; Gayton, Clavin,
Clavin \& Broida, 1994; Germeijs \& Verscheuren, 2011a; Rassin \& Muris,
2005a, 2005b; Rassin, Muris, Franken, Smit \& Wong, 2007; Taillefer, Liu,
Ornstein \& Vickers, 2016). Indecisiveness also has a wide variety of
consequences for decision making, such as greater information seeking and
tunnel vision during choice; attribute-based comparison and compensatory
decision making; delaying and avoiding decision making; greater
distraction during choice; greater effort expense and working memory load
during choice; lower choice commitment; less decision stability; lower
decision confidence; more regret; and worse decision outcomes (Bavolar,
2018; Ferrari \& Dovidio, 2000, 2001; Ferrari \& Pychul, 2007; Frost \&
Shows, 1993; Germeijs \& Verschueren, 2011b; Germeijs, Verschueren \&
Soenens, 2006; Patalano, Juhasz \& Dicke, 2010; Patalano \& LeClair,
2011; Patalano \& Wengrovitz, 2007; Rassin \& Muris, 2005a; Rassin et al.,
2007; Rassin, Muris, Booster \& Kolsloot, 2008). As this summary shows,
there is already an established and growing literature on indecisiveness
as an individual difference.

%Table 2: 
\begin{table*}[t]
\caption{Comparison of Decision Difficulty and Indecisiveness Scale Items}
\begin{tabular}{@{}lL{3.3in}L{1.9in}@{}}\toprule
Specific Feature & Decision Difficulty Example Items  & Indecisiveness Example Items\\\midrule
Difficulty & “I usually have a hard time making even simple decisions.” &
“It’s hard for me to come to a decision.”\\
Uncertainty & “I am usually worried about making the wrong decision.” &
“While making a decision, I feel uncertain.”\\
Delay & “I often put off making a difficult decision until a deadline.” &
“I delay deciding.”\\
Mind changing & “I often change my mind several times before making a
decision.” & “I often reconsider my decision.”\\
Post-decision rumination & “I often think about changing my mind after I
have already made my decision.” & “After making a decision, I can’t get it
out of my mind.”\\\bottomrule
\end{tabular}
\end{table*}

We propose that indecisiveness and decision difficulty are two names for
what is essentially the same construct. Indeed, both describe individual
differences in the tendency to experience decision difficulty, and scales
that seek to measure both constructs have extremely similar items. Table 2
presents a comparison of items from the Indecisiveness Scale developed by
Germeijs and De Boeck (2002) and the Decision Difficulty Scale developed
by Turner et al.\ (2012). Both scales not only share similar items in
general, but specifically tap a very similar set of particular features,
such as difficulty, delaying, and uncertainty. To our knowledge, there is
no existing theory that specifically advances an argument for any
conceptual distinctions between indecisiveness and decision difficulty.
Rather, they have simply been assumed to be distinct because decision
difficulty has been defined as part of maximizing, whereas indecisiveness
has been separately studied as its own unique construct.

We are not the first to consider decision difficulty in relation to
indecisiveness. Some authors (e.g., Barkley-Levenson \& Fox, 2016; Dalal
et al., 2015) have explicitly linked decision difficulty and
indecisiveness as very related constructs, and others have also shown that
indecisiveness is correlated with either decision difficulty specifically
or with maximizing measures that include decision difficulty (Diab et al.,
2008; Oren et al., 2018; Patalano \& LeClair, 2011; Spunt et al., 2009;
Weinhardt et al., 2012). However, this previous research has typically
found only moderate correlations between decision difficulty and
indecisiveness, including correlations of .40 in both Patalano and LeClair
(2011) and Dalal et al.\ (2015), and .48 and .51 (depending on the scale)
in Weinhardt et al.\ (2012). To our knowledge, the highest correlation that
has been found between a decision difficulty scale and an indecisiveness
scale is .63 in Barkley-Levenson and Fox (2016). Although these
correlations suggest a meaningful relationship between indecisiveness and
decision difficulty, they do not necessarily suggest that scales measuring
each construct are really measuring the same underlying individual
difference.

Why might decision difficulty and indecisiveness be only moderately
correlated in previous studies? We suggest that previous findings have
largely been limited by relying on the Decision Difficulty subscale from
the Short Form of the Maximization Scale (Nenkov et al., 2008; Schwartz et
al., 2002). The scale has low reliability and relatively few items; was
originally developed to measure maximizing broadly instead of decision
difficulty specifically; has outdated items (e.g., “Renting videos is
really difficult. I’m always struggling to pick the best one.”); and has
domain-specific items in limited contexts. Each of these limitations may
explain why previous research has not found larger relations between
decision difficulty and indecisiveness. In the present research, we used
Turner et al.’s (2012) Decision Difficulty Scale, which is domain-general
and has better psychometric properties (Cheek \& Schwartz, 2016; Turner et
al., 2012). We hypothesized that correlations between decision difficulty
measured with this scale and indecisiveness would be substantially higher
than in previous research, as would be expected if decision difficulty and
indecisiveness are really two names for the same individual difference.

\subsection{The present research}
We tested the relation between decision difficulty and indecisiveness
in two studies. In Study 1, participants completed measures of the
maximization goal of wanting the best and maximization strategy of
alternative search, as well as measures of decision difficulty and
indecisiveness. We expected that decision difficulty and indecisiveness
would be very strongly correlated, both to an absolute degree and relative
to their correlations with the two maximizing measures. We also conducted
a principal component analysis to explore whether decision difficulty
items would load on the same component as indecisiveness items, supporting
our argument that they tap the same underlying dimension. In Study 2,
participants completed these same measures as well as several other
related measures to test the hypothesis that decision difficulty and
indecisiveness would show a robust pattern of convergent correlations with
related variables. Participants in Study 2 also made decisions and
reported their decision difficulty to allow us to compare how decision
difficulty and indecisiveness measures related to actual reported
difficulty after making decisions. In both studies, we report all
measures, manipulations, and exclusions, as well as how sample size was
determined. Data, materials, and analysis code are available as online
supplemental material.

\section{Study 1}
 In the first study, participants completed measures of decision
difficulty and indecisiveness, as well as measures of the maximizing goal
of wanting the best and the maximizing strategy of alternative search. We
expected that there would be a high correlation between decision
difficulty and indecisiveness, and also explored whether they would load
on the same component in a principal component analysis.

\subsection{Method}
\subsubsection{Participants}
We used TurkPrime (Litman, Robinson \& Abberbock, 2017) to recruit 350
participants through Amazon’s Mechanical Turk (MTurk). We recruited the
largest sample possible given available funding, with the restriction that
there be at least 250 participants, the sample size at which correlations
stabilize (Schönbrodt \& Perugini, 2013). To be included in analyses,
participants had to pass two instructional manipulation checks
(Oppenheimer, Meyvis \& Davidenko, 2009) and indicate that they did not
respond randomly. In total, 336 participants met these criteria and were
included in analyses. The average age of participants was 37.46
(\textit{SD} = 12.13) and there were 145 women, 189 men, and 2 who did not
identify as men or women.\footnote{ Additional demographic information
(e.g., income, ethnicity) was collected in both studies and is available
in the data in supplemental material; for brevity we report only age and
gender. In Study 1, one missing decision difficulty item was imputed for
one participant using the participant’s average response on the other
scale items. In Study 2, one missing indecisiveness item was imputed for
one participant using the same method.}

\subsubsection{Measures}
Participants completed Turner et al.’s (2012) 12-item Decision Difficulty
Scale from the Maximization Inventory (MI-DD; ${\alpha}$ = .91), which was
designed to measure the decision difficulty component of maximizing, and
Germeijs and De Boeck’s (2002) 22-item Indecisiveness Scale (IS;
${\alpha}$ = .93), which was designed to measure domain-general
indecisiveness. The IS was originally developed in Dutch and then
translated into English, and we administered but did not analyze one
idiom-based item that did not quite make sense in English (“I cut the knot
myself in a decision instead of leaving the decision to others”). This
item is included in the online data so that our analyses can be
reconducted while including it. 

Participants also completed the 7-item Maximizing Tendency Scale-7 (MTS,
${\alpha}$ = .90) developed by Dalal et al.\ (2015) to measure the goal of
making the best possible choice and the 12-item Alternative Search Scale
from Turner et al.’s Maximization Inventory (MI-AS, ${\alpha}$ = .88)
developed to measure the strategy of searching extensively for
alternatives and comparing them when making a choice. These latter two
measures were recommended by Cheek and Schwartz (2016) to measure the
maximizing goal and maximizing strategy, respectively. 

%Table 3: 
\begin{table}[t]\bigskip
\caption{Correlations among variables from Study 1.}
\begin{tabular}{lC{.7in}C{.7in}C{.7in}}\toprule
 & MI-DD & IS & MTS\\\midrule
IS & .85 &  & \\
MTS & $-$.01 { ~} & $-$.15 { ~ } & \\
MI-AS & .20 & .08 & .44\\\bottomrule
\mc{4}{p{2.8in}}{\textit{Note}. \textit{p} {\textless} .01 for \textit{r} {\textgreater} .14
and \textit{p} {\textless} .001 for \textit{r} {\textgreater} .19.}
\end{tabular}
\end{table}

%Table 4: 
\begin{table*}[t!]
  \caption{Principal component analysis with only MI-DD and IS items from
    Study 1. Loadings above .35 are bolded. (R) indicates a reverse-coded
    item.}
\begin{tabular}{ld{2.2}d{2.2}}\toprule
Item \hfill Component: & \mc{1}{c}{1} & \mc{1}{c}{2}\\\midrule
\bfseries Decision Difficulty Scale (Maximization Inventory) &  & \\
I usually have a hard time making even simple decisions. & .\mathbf{73} &
.23\\
I am usually worried about making a wrong decision. & .\mathbf{60} &
.\mathbf{38}\\
I often wonder why decisions can’t be more easy. & .\mathbf{67} & .18\\
I often put off making a difficult decision until a deadline. &
.\mathbf{68} & .28\\
I often experience buyer’s remorse. & .\mathbf{67} & .17\\
I often think about changing my mind after I have already made my decision.
& .\mathbf{72} & .26\\
The hardest part of making a decision is knowing I will have to leave the
item I didn’t choose behind. & .\mathbf{69} & .16\\
I often change my mind several times before making a decision. &
.\mathbf{67} & .32\\
It’s hard for me to choose between two good alternatives. & .\mathbf{58} &
.32\\
Sometimes I procrastinate in deciding even if I have a good idea of what
decision I will make. & .\mathbf{65} & .34\\
I find myself often faced with difficult decisions. & .\mathbf{54} &
-.01\\
I do not agonize over decisions. (R) & .23 & .\mathbf{72}\\
\bfseries Indecisiveness Scale &  & \\
I find it easy to make decisions. (R) & .20 & .\mathbf{77}\\
It is hard for me to come to a decision. & .\mathbf{66} & .\mathbf{44}\\
I don’t know how to make decisions. & .\mathbf{64} & .13\\
I know which steps to take when making a decision. (R) & .24 & .\mathbf{50}\\
I would characterize myself as an indecisive person. & .\mathbf{69} &
.\mathbf{39}\\
I don’t hesitate much when I have to make a decision. (R) & .15 & .\mathbf{79}\\
While making a decision, I feel certain. (R) & .23 & .\mathbf{77}\\
While making a decision, I feel uncertain. & .\mathbf{65} & .\mathbf{40}\\
It takes a long time to weigh the pros and cons before making a decision. &
.\mathbf{57} & .24\\
I make decisions quickly. (R) & .09 & .\mathbf{72}\\
I delay deciding. & .\mathbf{64} & .33\\
I don’t postpone making decisions to a later date. (R) & .30 &
.\mathbf{57}\\
I try to avoid making a decision. & .\mathbf{70} & .28\\
I don’t avoid situations where decisions have to be made. & .17 &
.\mathbf{41}\\
I tend to leave decisions to someone else. & .\mathbf{66} & .24\\
Once I have taken a decision, I stick to that decision. (R) & .27 &
.\mathbf{54}\\
I often reconsider my decision. & .\mathbf{65} & .34\\
Once I have made a decision, I stop worrying about it. (R) & .23 &
.\mathbf{70}\\
After making a decision, I can’t get it out of my head. & .\mathbf{70} &
.20\\
After I have decided something, I believe I took the wrong decision. &
.\mathbf{76} & .15\\
After making a decision, I don’t regret the decision. (R) & .25 & .\mathbf{63}\\\bottomrule
\end{tabular}
\end{table*}

\subsection{Results}
Table 3 presents the correlations among the measures from Study 1. In
line with our proposal that decision difficulty and indecisiveness scales
are largely measuring the same construct, the MI-DD and IS were highly
correlated, \textit{r} = .85. When Spearman’s (1904) correction for
attenuation is applied, the correlation between the MI-DD and IS becomes
even higher, \textit{r} = .93. The MI-DD was positively correlated with the
MI-AS, whereas the IS was not. Neither the MI-DD nor the IS was correlated
with the MTS. The MTS and MI-AS were moderately positively correlated,
supporting the idea that people who tend to adopt the maximizing goal of
choosing the best often also adopt the maximizing strategy of alternative
search.

% table 4 now A1

To further explore the similarities between decision difficulty and
indecisiveness measures, we conducted a principal component analysis with a
varimax rotation on the items from the MI-DD, IS, MTS, and MI-AS. Based on
examination of the scree plot, eigenvalues, and theoretical interpretation
components, we extracted three components.\footnote{ To transparently
  address potential disagreement about the proper number of components to
  extract, we present two- and four-component solutions in the Appendix. If
  two components are extracted, the MTS and MI-AS items load on one
  component, whereas the MI-DD and IS items load on the other. In other
  words, there is a maximizing component (comprising both the goal and
  strategy) and a decision difficulty-indecisiveness component (comprising
  decision difficulty and indecisiveness items). If four components are
  rotated, the reverse-coded items on the MI-DD and IS break off from the
  other MI-DD and IS items to load on their own component, which appears to
  be largely a methodological result, rather than theoretically-meaningful
  one. The other three components then comprise the MTS items, the MI-AS
  items, and the remaining MI-DD and IS items, respectively. In our view,
  these other potential solutions are also consistent with the
  interpretation that the MI-DD and IS are essentially tapping the same
  underlying construct of indecisiveness.} As shown in the Appendix (Table
A1), the MTS items loaded on one component, the MI-AS items loaded on a
second component, and the items from the MI-DD and the IS together loaded
on a third component. Thus, one component represents the maximizing goal,
one component represents the maximizing strategy, and one component
represents the tendency to find decisions difficult, which therefore
includes the items from both the MI-DD and the IS. This result fits with
our interpretation of decision difficulty measures as assessing the same
underlying construct as indecisiveness measures.

% table 4 was 5

Because our main goal is to explore the measurement of decision
difficulty and indecisiveness specifically, we next conducted a second
principal component analysis including only the items from those two
scales. As shown in Table 4, the two-component solution that emerges from
a varimax rotation is consistent with our proposal that the MI-DD and IS
are tapping the same underlying construct. The second component consists
entirely of reverse-coded items, suggesting that, but for a methodological
feature of some items, the two scales do not appear to measure
distinguishable constructs.

\subsection{Discussion}
The results of Study 1 provide preliminary support for our proposal
that decision difficulty and indecisiveness scales largely measure the
same underlying construct. The MI-DD and IS were highly correlated, and,
once an attenuation correction is applied, the correlation between the two
scales is essentially at ceiling. Moreover, items from both scales loaded
on the same component in multiple principle component analysis solutions;
only the methodological feature of reverse-coded items caused some items
from the two scales to load on separate factors. Consistent with previous
research (see Cheek \& Schwartz, 2016, for a review), the MTS and MI-AS
were positively correlated, and the MI-AS was related to decision
difficulty, whereas the MTS was not. The maximizing strategy of
alternative search is thus more related to decision difficulty than the
maximizing goal of choosing the best.

If decision difficulty and indecisiveness measures are essentially
measuring the same construct, then they should also show a robust pattern
of convergent correlations with related measures. We set out to test this
possibility in Study 2.

\section{Study 2}
The goal of Study 2 was to build on Study 1 by examining the correlates
of measures of decision difficulty and indecisiveness. If the MI-DD and IS
are really two measures of the same underlying construct, then they should
have similar correlations with relevant variables. Accordingly, in this
study we included several individual difference measures that we expected
would be related to decision difficulty and indecisiveness (e.g.,
intolerance of uncertainty; see below for details). In addition, we again
included the MTS and MI-AS to allow for comparisons among the two
components of maximizing and decision difficulty and indecisiveness. For
further comparison, participants additionally completed Schwartz et al.’s
(2002) original Maximization Scale (MS), out of which we scored the three
subscales — High Standards (MS-HS), Alternative Search (MS-AS), and Decision
Difficulty (MS-DD) — from Nenkov et al.\ (2008). We expected the MTS to be
most related to the MS-HS, the MI-AS to be most related to the MS-AS, and
the MI-DD and IS to be most related to the MS-DD. Importantly, however,
given our earlier suggestion that the MS-DD is less valid measure of
decision difficulty than the MI-DD, we expected that the IS would be more
strongly correlated with the MI-DD than with the MS-DD. 

To investigate how decision difficulty and indecisiveness measures related
to actual difficulty experienced during decisions, participants also made
three decisions and reported how difficult they found them.\footnote{
After completing all relevant measures for the present research,
participants in Study 2 completed a brief judgment task for an unrelated
study that is not reported here.} 

\subsection{Method}
\subsubsection{Participants}
We aimed to recruit 300 participants from MTurk using the same sample size
determination as in Study 1. In total, 303 participants completed the
study, of whom 279 were included in analyses after meeting the inclusion
criteria described in Study 1. The average age of participants was 36.63
(\textit{SD} = 11.71), and there were 142 women and 137 men. 

\subsubsection{Measures}
Participants completed the following measures in a randomly determined
order.

\paragraph{Maximizing and indecisiveness}
As in Study 1, participants completed the MTS (${\alpha}$ = .92), MI-AS
(${\alpha}$ = .92), MI-DD (${\alpha}$ = .93), and IS (${\alpha}$ = .94),
as well as the 13-item MS (${\alpha}$ = .76) developed by Schwartz et al.
(2002). We scored the two-item subscales for high standards (MS-HS;
${\alpha}$ = .75), alternative search (MS-AS; ${\alpha}$ = .34), and
decision difficulty (MS-DD; ${\alpha}$ = .51) out of the MS following
Nenkov et al.\ (2008). 

\paragraph{Regret}
Participants completed the 5-item Regret Scale (${\alpha}$ = .86; Schwartz
et al., 2002). We expected the MI-DD and IS to be positively correlated
with the tendency to experience regret. Indeed, previous research and
theory suggests that indecisive people are more likely to experience
regret and that regret may also lead to future indecisiveness (Rassin,
2007; Spunt et al., 2009).

\paragraph{Intolerance of uncertainty}
Participants completed the 12-item short form of the Intolerance of
Uncertainty Scale (${\alpha}$ = .92; Carleton, Norton \& Asmundson,
2007). Previous research has found that indecisiveness correlates with
intolerance of uncertainty (Rassin et al., 2007), and Rassin’s (2007)
theory of indecisiveness includes intolerance of uncertainty as a
potential cause of indecisiveness. Thus, we expected the MI-DD and IS to
both be positively related to intolerance of uncertainty.

\paragraph{Decisiveness}
Participants completed the revised 6-item Decisiveness Scale of the Need
for Closure Scale (${\alpha}$ = .87; Roets \& Van Hiel, 2007). The
decisiveness component of need for cognitive closure reflects the
perceived need to quickly identity a solution or make a decision (i.e.,
“seize”; Webster \& Kruglanski, 1993; Roets \& Van Hiel, 2007). We
expected that the need for decisiveness would be positively correlated
with the MI-DD and IS, because feeling pressure to quickly make a decision
should be related to a more difficult and unpleasant decision process.

\paragraph{Fear of negative evaluation}
Participants completed the 12-item brief Fear of Negative Evaluation Scale
(${\alpha}$ = .96; Leary, 1983). We thought that fear of negative
evaluation might be positively correlated with the MI-DD and IS. For
people who fear negative judgments from others, decisions have higher
stakes — they can influence whether the fear of negative evaluation is
realized. Thus, the decision process should be more stressful and
perceived as more related to impression management goals for those higher
in fear of negative evaluation, leading to greater decision difficulty and
rumination about choices (i.e., indecisiveness; Schlenker, 1980; Watson \&
Friend, 1969).

\paragraph{Perfectionism and excellentism}
Participants completed the 10-item Scale of Perfectionism and Excellentism
(Gaudreau \& Schellenberg, 2018), 5 items of which measure perfectionism
(${\alpha}$ = .94) and 5 of which measure excellentism (${\alpha}$ = .85).
Whereas perfectionism is the tendency to strive for perfect performance
and outcomes, excellentism is the tendency to strive for high-quality, but
not necessarily perfect, performance and outcomes (Gaudreau, 2019; Wade,
2017). We anticipated that the MTS, which measures the goal of making the
best choice, would be positively correlated with both of these traits,
whereas the MI-DD and IS would be less strongly related to both.
Furthermore, we expected that the MTS would be more related to
perfectionism than to excellentism. Because the maximization goal is
specifically striving for the \textit{best}, rather than merely a
high-quality but potentially suboptimal choice (as a satisficer with high
standards would seek; Cheek \& Schwartz, 2016), the MTS should, in theory,
be more related to perfectionism and its shared focus on the optimal
outcome than to excellentism.

\paragraph{Rational decision style}
Participants completed the 5-item Rational Decision Style Scale (${\alpha}$
= .93; Hamilton, Shih \& Mohammed, 2016). In this context, “rational
decision style” describes the tendency to carefully deliberate about
decisions after gathering all necessary information about the available
alternatives. We expected that the MI-AS would be most related to a
rational decision style, because the maximization strategy of alternative
search largely involves this style of careful deliberation after seeking
out alternatives (Hamilton et al., 2016; Cheek \& Schwartz, 2016).

\subsubsection{Experienced decision difficulty}
Participants made three decisions about what to order from three fictional
menus (one for an Italian restaurant, one for a salad restaurant, and one
for a general American restaurant). After each decision, they answered two
questions about how difficult they found the decision (“How difficult was
your decision?” and “How hard was it to choose which option you wanted?”).
Both questions were answered on scales ranging from 1 to 9, with higher
numbers indicating greater difficulty. Participants’ answers to each
question after each decision were averaged to create an index of how much
actual difficulty they experienced when making each decision. 

%Table 5: was 6
\begin{table*}[t]
\caption{Correlations among variables from Study 2.}
\begin{tabular}{@{}l*{14}{d{2.2}}@{}}\toprule
 & \mc{1}{c}{MI-DD} & \mc{1}{c}{IS} & \mc{1}{c}{MTS} & \mc{1}{c}{MI-AS} & \mc{1}{c}{MS} & \mc{1}{c}{MS-HS} & \mc{1}{c}{MS-AS} & \mc{1}{c}{MS-DD} & \mc{1}{c}{Regret} & \mc{1}{c}{IOU} &
\mc{1}{c}{Dec} & \mc{1}{c}{FNE} & \mc{1}{c}{Perf.} & \mc{1}{c}{Ex.}\\\midrule
IS & .89 &  &  &  &  &  &  &  &  &  &  &  &  & \\
MTS & -.07 & -.19 &  &  &  &  &  &  &  &  &  &  &  & \\
MI-AS & .20 & .08 & .45 &  &  &  &  &  &  &  &  &  &  & \\
MS & .58 & .45 & .37 & .44 &  &  &  &  &  &  &  &  &  & \\
MS-HS & -.05 & -.14 & .79 & .39 & .43 &  &  &  &  &  &  &  &  & \\
MS-AS & .28 & .19 & .23 & .22 & .68 & .22 &  &  &  &  &  &  &  & \\
MS-DD & .63 & .57 & -.03 & .18 & .69 & .02 & .29 &  &  &  &  &  &  & \\
Regret & .69 & .65 & -.04 & .23 & .51 & .00 & .31 & .41 &  &  &  &  &  &
\\
IOU & .63 & .54 & .03 & .33 & .50 & .10 & .19 & .47 & .49 &  &  &  &  & \\
Dec & .32 & .25 & -.06 & -.12 & .19 & -.07 & .06 & .27 & .14 & .41 & 
&  &  & \\
FNE & .56 & .51 & -.21 & -.01 & .32 & -.16 & .18 & .41 & .46 & .51 &
.41 &  &  & \\
Perf. & .01 & -.06 & .50 & .30 & .30 & .53 & .17 & .03 & .10 & .21 & .08
& .00 &  & \\
Ex. & -.09 & -.20 & .59 & .31 & .28 & .59 & .19 & -.05 & .03 & .03 &
-.03 & -.11 & .61 & \\
RDS & .02 & -.01 & .30 & .60 & .20 & .26 & .12 & .01 & .10 & .13 & -.24
& -.12 & .13 & .38\\\bottomrule
\mc{15}{@{}p{\textwidth}@{}}{\textit{Note}. IS = Indecisiveness Scale. MI-DD = Decision Difficulty Scale
of Maximization Inventory. MTS = Maximizing Tendency Scale-7. MI-AS =
Alternative Search Scale of Maximization Inventory. MS = Maximization
Scale. MS-HS = High Standards subscale of Short Form Maximization Scale.
MS-AS = Alternative Search subscale of Short Form Maximization Scale.
MS-DD = Decision Difficulty subscale of Short Form Maximization Scale. IOU
= Intolerance of Uncertainty Scale. Dec = Decisiveness Scale (need for
cognitive closure). FNE = Fear of Negative Evaluation Scale. Perf. =
Perfectionism Scale. Ex. = Excellentism Scale. RDS = Rational Decision
Style Scale. \textit{p} {\textless} .05 for {\textbar}\textit{r}{\textbar}
${\geq}$ .12. \textit{p} {\textless} .01 for {\textbar}r{\textbar}
${\geq}$ .16. \textit{p} {\textless} .001 for
{\textbar}\textit{r}{\textbar} ${\geq}$ .21.}
\end{tabular}
\end{table*}

\subsection{Results}
\subsubsection{Correlations}
Table 5 presents the correlations among the measures from Study 2. % was 6

As in Study 1, the MI-DD and IS were strongly correlated, \textit{r} = .89,
and this correlation increases when corrected for attenuation, \textit{r}
= .95. Both the MI-DD and IS were positively correlated with the MS-DD as
well, though not as strongly as they were with each other. The MI-DD and
IS were also both related to the MS-AS, regret, intolerance of
uncertainty, the decisiveness facet of need for closure, and fear of
negative evaluation, whereas they were both unrelated to the MTS,
perfectionism, excellentism, and a rational decision style. The MI-DD was
positively correlated with the MS-AS, whereas the correlation between the
MS-AS and IS was smaller. On the other hand, the IS was negatively
correlated with the MS-HS, whereas the correlation between the MS-HS and
the MI-DD was smaller. In both of these cases, the correlations were in
the same direction, and the MI-DD and IS were never meaningfully
correlated in opposite directions with any variables. 


As expected, the MI-AS was positively correlated with a rational decision
style, whereas the MTS was positively correlated with both perfectionism
and excellentism. However, contrary to expectations, the MTS was not more
strongly related to perfectionism; rather, the MTS and excellentism were
more strongly correlated than the MTS and perfectionism. Consistent with
the suggestion that the maximization strategy of alternative search is
potentially more maladaptive than the maximization goal of wanting the
best, the MI-AS was correlated with regret and intolerance of uncertainty,
whereas the MTS was unrelated to regret and intolerance of uncertainty and
negatively correlated with fear of negative evaluation.

%Table 6: was 7 
\begin{table}[t]
\caption{Correlations between decision difficulty, indecisiveness, and
experienced difficulty}
\begin{tabular}{@{}lC{.9in}@{}C{.9in}@{}}\toprule
 & Decision difficulty & Indecisiveness\\\midrule
Menu 1 decision difficulty & \textit{r}=.34, \textit{p}{\textless}.001 
& \textit{r}=.27, \textit{p}{\textless}.001\\
Menu 2 decision difficulty & \textit{r}=.34, \textit{p}{\textless}.001
& \textit{r}=.27, \textit{p}{\textless}.001\\
Menu 3 decision difficulty & \textit{r}=.20, \textit{p}=.001 &
\textit{r}=.14, \textit{p}=.017\\\bottomrule
\end{tabular}
\end{table}

\subsubsection{Experienced decision difficulty}
Table 6 shows the correlation between the actual difficulty reported after
making decisions from three menus and the MI-DD and IS. Both scales were
related to greater experienced difficulty for all three decisions, though
correlations were higher for the first two decisions than for the third.

\subsection{Discussion }
The results of Study 2 provide further evidence for our proposal that
decision difficulty and indecisiveness scales measure the same underlying
construct. The MI-DD and IS were again highly correlated, \textit{r} =
.89, and, as in Study 1, their correlation further increases when
corrected for attenuation, \textit{r} = .95. For reference, the MS-HS has
been argued to measure the same construct as the MTS and is often used as
evidence of construct validity (e.g., Diab et al., 2008; Dalal et al.,
2015), and these two scales were correlated at \textit{r} = .79. Thus, if
researchers were developing the MI-DD as an indecisiveness scale, this
high correlation between the MI-DD and IS would be compelling evidence of
construct validity.

It is worth noting that while the MI-DD and IS were both correlated with
the MS-DD, these correlations were somewhat smaller, \textit{r}’s = .63
and .57. This finding fits with our suggestion that previous research on
maximizing and indecisiveness has obscured the extent to which decision
difficulty and indecisiveness conceptually overlap because the MS-DD scale
has limited validity and weaker psychometric properties — it does not
capture the construct as well as the MI-DD. Many researchers have
criticized the Schwartz et al.’s (2002) MS, while still using it in scale
validation or scale comparison work, but this approach has important
limitations because of the very weaknesses of the original scale
highlighted by critics. Thus, the MS and its subscales should not be used
as the only means of validating or comparing maximizing and related
constructs. 

The MI-DD and IS also showed a convergent pattern of correlations: they
were correlated in the same direction and at similar magnitudes with the
other variables. Moreover, both the MI-DD and IS were positively
correlated with actual difficulty experienced during decision making,
providing evidence for the criterion validity of both measures.
Interestingly, the correlation between the IS and other variables was
always lower — not in magnitude, but numerically — than the correlation
between the MI-DD and other variables. One possibility is that this
difference arose because only 1 of the 12 items of the MI-DD is
reverse-coded, whereas 10 of the 21 IS items we scored (leaving out one
item that did not translate well) are reverse-coded. This possibility is
supported by the results of the principal component analysis in Study 1,
in which the reverse-coded items loaded on a separate, though
theoretically uninteresting, component. Reverse-coded items can
potentially weaken the psychometric properties of scales, even when
respondents pay close attention, and can also cause confusion or mistakes
(e.g., Netemeyer, Bearden \& Sharma, 2003; Swain, Weather \& Niedrich,
2008). Thus, the fact that half of the items in the IS are reverse-coded
may account for the differences in correlations between the MI-DD and IS.
Of course, the MI-DD and IS were also developed independently and it is
therefore not surprising that they do not show \textit{identical} patterns
of correlations with other variables. Nonetheless, it may be worthwhile
for future research to further examine the similarities and differences
between the two scales with a particular focus on reverse-coded items.
Indeed, the present research may suggest that, although the two scales are
very similar, the MI-DD may be a slightly more useful measure of
indecisiveness than the IS if the many reverse-coded items of the IS
result in greater participant error or noise. 

Although the maximizing goal and strategy are not the primary focus of the
present research, one additional finding merits discussion. It was
unexpected that the MTS was more correlated with excellentism than with
perfectionism (though the latter two also correlated relatively strongly
with each other). This finding fits with other findings on the relative
adaptiveness of higher scores on the MTS (e.g., Diab et al., 2008; Dalal
et al., 2015), but also suggests that the MTS may not be completely, or
only, measuring the pursuit of the \textit{very best possible} choice.
Indeed, if the MTS is at least partly capturing the pursuit of a very good
choice — that is, if it is capturing having “high standards” instead of
wanting the best (Dalal et al., 2015) — then it may not fully capture what
is specific to the maximizing goal. Future psychometric research may be
needed into how best to distinguish having high standards, which may
characterize satisficers and be adaptive like excellentism, from being
happy with only the best choice, which characterizes only maximizers and
may be less adaptative. For now, the MTS and MI-AS appear to be the
highest-quality measures of, respectively, the maximization goal and
strategy, but future research, such as work on developing a more specific
measure of the goal of wanting the \textit{very best}, should perhaps
continue to rework and refine maximizing measures. 

\section{General discussion}
Across two studies, we found that measures of the decision difficulty
component of maximizing and indecisiveness are highly correlated, load on
the same component in a principal component analysis, and show a
convergent pattern of correlations with other variables — essentially, the
two scales appear indistinguishable within the limits of error. Taken
together, along with the lack of a compelling theoretical reason to
distinguish between the two constructs, the present results support our
proposal that the decision difficulty component of maximizing is actually
indecisiveness by another name. In other words, considering decision
difficulty to be part of maximizing, rather than simply being
indecisiveness, is an instance of the jingle fallacy — incorrectly believing
that two constructs (maximizing and indecisiveness) are the same because
they are measured with scales of the same name. On the other hand,
considering decision difficulty and indecisiveness to be two unique
constructs is an instance of the jangle fallacy — incorrectly believing that
two scales (decision difficulty and indecisiveness) measure different
constructs simply because they have different names (Kelley, 1927;
Thorndike, 1904).

Distinguishing between maximizing and the related but distinct
construct of indecisiveness has at least three important implications for
research. First, much of the debate around maximizing has focused on
whether pursuing the goal of making the best possible choice has negative
consequences (e.g., Cheek \& Schwartz, 2016; Diab et al., 2008; Dalal et
al., 2015; Lai, 2010; Kim \& Miller, 2017; Schwartz et al., 2002; Zhu,
Dalal \& Hwang, 2017). One prominent approach to answering this question
has been to suggest that, independent of decision strategies, the
maximization goal of wanting the best is not necessarily negative, and may
even be related to positive outcomes in some cases. When this goal is
pursued through the maximization strategy of alternative search, however,
the picture may be less positive, because this strategy is associated with
more negative outcomes such as regret (e.g., Cheek \& Schwartz, 2016;
Cheek \& Ward, 2019; Hughes \& Scholer, 2017). To this first approach to
understanding whether and how maximizing may be detrimental we seek to
explicitly add another — properly measuring maximizing as a construct
separate from indecisiveness. Measuring indecisiveness (“decision
difficulty”) as part of maximizing may serve to unintentionally build in
correlations with negative outcomes and individual differences because of
the potential maladaptive correlates of indecisiveness (e.g., Bavolar,
2018; Frost \& Shows, 1993; Rassin, 2007; Rassin et al., 2007). Maximizing
may not seem as problematic when indecisiveness is not included in its
measurement.

Previous calls to distinguish the measurement of maximizing from the
measurement of more potentially negative or maladaptive constructs (e.g.,
Diab et al., 2008; Dalal et al., 2015; Lai, 2010) have not always been
heeded in the past, and this may be due in part to ambiguity around the
construct of decision difficulty. We hope that the present research
magnifies the perceived importance of properly measuring maximizing by
clearly demonstrating that “decision difficulty” is already a separate
existing construct, much like regret. If researchers want to argue in
favor of retaining decision difficulty as a component of maximizing, we
suggest that the burden of proof now belongs to those wishing to argue
that decision difficulty and indecisiveness are meaningfully
distinguishable. Until and unless a new theory is proposed that provides a
compelling case for their separation, the two should not be considered
conceptually distinct.

A second benefit of properly distinguishing between maximizing and
indecisiveness is that they can be accurately studied in relation to one
another. Indecisiveness may be both a potential cause and a potential
consequence of maximizing, and studying when and why they are related will
advance the literature on both of these individual differences. Yet, it is
impossible to study their relation if maximizing scales inappropriately
include items measuring indecisiveness, and thus our proposal to interpret
decision difficulty as indecisiveness also facilitates future research on
how potentially related constructs are connected.

A third potential benefit of properly recognizing the overlap in the
constructs of decision difficulty and indecisiveness is that the existing
literatures on these constructs can now more clearly inform future
research. For example, Kim and Miller (2017) found that the post-choice
experience of maximizers is more vulnerable than that of satisficers to
negative feedback about choices, but that this effect is mainly driven by
the decision difficulty component of maximizing. Based on the present
research, we suggest that this is really a novel and important finding
about indecisiveness — people who are more indecisive may be more upset when
they receive negative feedback about their choices. Thus, previous
research on “decision difficulty” can inform our current and future
understanding of indecisiveness. Similarly, the maximization strategy of
alternative search is frequently found to be related to “decision
difficulty” (Cheek \& Schwartz, 2016), and these previous findings can now
be interpreted as showing a relation between indecisiveness and
alternative search. The literature on indecisiveness can inform our
understanding of maximizing because of the relation between alternative
search and indecisiveness — for instance, it would be interesting to examine
how the greater distraction experienced by more indecisive people during
choice is related to alternative search (Patalano et al., 2010). Much is
known about maximizing, “decision difficulty,” and indecisiveness, and
these literatures can be better connected after properly understanding and
correcting the jingle-jangle fallacies in previous research.

\subsection{Cultural differences}
There is only limited research comparing maximizing and indecisiveness
across different cultural contexts, but previous findings may both provide
further support for our interpretation of decision difficulty as
indecisiveness and highlight important limitations to our conclusions. At
the mean level, previous research has found that Japanese individuals tend
to be higher in indecisiveness than U.S. individuals, whereas people from
the U.S. and China may not differ in average levels of indecisiveness
(Yates et al., 2010; see also Ng \& Hynie, 2014, 2016). This pattern of
variation is notably similar for the decision difficulty subscale of the
Short Form of the Maximization Scale — people from Japan tend to have higher
average levels of decision difficulty than people from the U.S. (Oishi,
Tsutsui, Eggleston \& Galinha, 2014), whereas people in the U.S. have
similar average levels of decision difficulty as people in China (Roets,
Schwartz \& Guan, 2012). On the other hand, people from the U.S. tend to
be higher than people from Japan in maximization goal and strategy (Oishi
et al., 2014). These patterns of differences provide further convergent
patterns of variation between decision difficulty scales and
indecisiveness scale, as well as divergent patterns between those two
scales and other maximizing measures. 

Beyond mean differences across cultures, there appears to be important
variation in the implications of maximizing. For instance, whereas the
maximization goal may be related to positive outcomes in the U.S., it may
be related to more negative outcomes in Western Europe and Japan (Roets,
et al., 2012; Oishi et al., 2014). Oishi et al.\ (2014) suggested that,
because, in Japan, making good personal decisions is not as important as
adjusting to others’ expectations, the maximization goal may not be
related to positive outcomes. In fact, because seeking to make the best
personal choice may run counter to cultural values, maximizing may be
especially negative in a Japanese cultural context. Relatedly, the
negative effects of indecisiveness may be particularly strong for
individuals, such as people of East Asian descent living in a Canadian
cultural context, whose cultural background may encourage indecisiveness
through naïve dialecticism, but whose broader cultural context may instead
value decisiveness (Ng \& Hynie, 2014, 2016). On the other hand,
maximizing may not be related to negative outcomes in a society lacking
abundant choice and a cultural emphasis on the pursuit of
self-determination through individual choice, as Roets et al.\ (2012)
argued when demonstrating no negative correlation between maximizing and
well-being in Chinese participants. 

These findings suggest that the meaning of maximizing and
indecisiveness may be inherently shaped by cultural context. Thus,
conclusions about whether maximizing is “positive” or “negative” are
likely culturally bounded — limited by both empirical data and researchers’
value-based interpretations of what is good and bad, adaptive and
maladaptive. As one example, indecisiveness may account for some of the
negative correlations between maximizing scales and well-being in the
U.S., whereas it may not in Japan. Indeed, perhaps in Japan indecisiveness
is valuable, fitting with a cultural emphasis on thoroughness (Yates et
al., 2010), whereas individually seeking the best is frowned upon (Oishi
et al., 2014). Even in the U.S., difficulty during decision making may be
valuable at times, such as when it prompts reflections about and
development of the self-concept (Newark, 2014). The implications of the
present research for broader conclusions about maximizing and
indecisiveness therefore need to be carefully contextualized in our
particular research setting.

In addition, Patalano and Wengrovits (2006) found that the factor
structure of indecisiveness scales may differ across cultures — they factor
analyzed Frost and Shows’ (1993) scale and found evidence for different
factor structures in the U.S. and China. Hence, cultural variation may
matter for more than outcomes — even the measures, and thus
conceptualizations of constructs, may differ considerably. Within debates
about the meaning and measurement of maximizing, an explicit interrogation
of how cultural values shape understandings, interpretations, and
consequences of decision making tendencies has played only a minimal role.
In future research, it will be important to more carefully and thoroughly
integrate cultural research into theories and investigations of maximizing
and indecisiveness.

\subsection{Conclusion}
In the present research, we proposed that the “decision difficulty”
component of maximizing scales and definitions actually captures the
existing construct of indecisiveness. Thus, the tendency to experience
difficulty in decision making is not part of maximizing, but rather a
distinct construct, though it may be a cause or consequence of maximizing.
Maximizing and indecisiveness are both important individual differences,
but advancing research on either requires accurate conceptualizations of
both.

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\vfill
\break
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\onecolumn

\renewcommand\arraystretch{1.0}

%\section{Appendix}

%Table 4: -> A1
\begin{table*}[t!]
 \caption*{\textbf{Appendix Table A1:} Principal component analysis with all measures from Study
  1. Loadings above .35 are bolded. (R) indicates a reverse-coded item.}
{\small
\begin{tabular}{ld{2.2}d{2.2}d{2.2}}\toprule
Item \hfill Component: & \mc{1}{c}{1} & \mc{1}{c}{2} & \mc{1}{c}{3}\\\midrule
\textbf{Maximizing Tendency Scale-7} & & & \\
No matter what I do, I have the highest standards for myself. & -.14 &
.31 & .\mathbf{66}\\
I never settle for second best. & .04 & .31 & .\mathbf{77}\\
No matter what it takes, I always try to choose the best thing. & -.13 &
.\mathbf{48} & .\mathbf{43}\\
I don’t like having to settle for “good enough.” & -.01 & .29 &
.\mathbf{61}\\
I am a maximizer. & -.01 & .34 & .\mathbf{53}\\
I will wait for the best option, no matter how long it takes. & .14 &
.\textbf{43} & .\mathbf{61}\\
I never settle. & .05 & .25 &  .\mathbf{75}\\
\bfseries Alternative Search Scale (Maximization Inventory) & & & \\
I can’t come to a decision unless I have carefully considered all of my
options. & .12 & .\mathbf{67} & .15\\
I take time to read the whole menu when dining out. & .13 & .\mathbf{41} &
.14\\
I will continue shopping for an item until it reaches all of my criteria. &
-.02 & .\mathbf{70} & .13\\
I usually continue to search for an item until it reaches my expectations.
& -.06 & .\mathbf{75} & .13\\
When shopping, I plan on spending a lot of time looking for something. &
.19 & .\mathbf{57} & .14\\
When shopping, if I can’t find exactly what I’m looking for, I will
continue to search for it. & -.05 & .\mathbf{74} & .06\\
I find myself going to many different stores before finding the thing I
want. & .20 & .\mathbf{49} & .18\\
When shopping for something, I don’t mind spending several hours looking
for it. & .14 & .\mathbf{49} & .21\\
I take the time to consider all alternatives before making a decision. &
-.04 & .\mathbf{73} & -.01\\
When I see something that I want, I always try to find the best deal before
purchasing it. & .01 & .\mathbf{65} & -.09\\
If a store doesn’t have exactly what I’m shopping for, then I will go
somewhere else. & -.10 & .\mathbf{73} & .01\\
I just won’t make a decision until I am comfortable with the process. & .09
& .\mathbf{68} & .11\\
\bfseries Decision Difficulty Scale (Maximization Inventory) & & & \\
I usually have a hard time making even simple decisions. & .\mathbf{77} &
.01 & .11\\
I am usually worried about making a wrong decision. & .\mathbf{68} & .23 &
-.11\\
I often wonder why decisions can’t be more easy. & .\mathbf{68} & .00 &
.09\\
I often put off making a difficult decision until a deadline. &
.\mathbf{73} & .05 & -.04\\
I often experience buyer’s remorse. & .\mathbf{67} & .00 & .03\\
I often think about changing my mind after I have already made my decision.
& .\mathbf{74} & .12 & .01\\
The hardest part of making a decision is knowing I will have to leave the
item I didn’t choose behind. & .\mathbf{69} & .09 & .12\\
I often change my mind several times before making a decision. &
.\mathbf{71} & .15 & -.11\\
It’s hard for me to choose between two good alternatives. & .\mathbf{62} &
.21 & -.14\\
Sometimes I procrastinate in deciding even if I have a good idea of what
decision I will make. & .\mathbf{70} & .13 & -.16\\
I find myself often faced with difficult decisions. & .\mathbf{47} & .08 &
.19\\
I do not agonize over decisions. (R) & .\mathbf{53} & .19 & -.36\\
\bfseries Indecisiveness Scale & & & \\
I find it easy to make decisions. (R) & .\mathbf{52} & .14 & -.39\\
It is hard for me to come to a decision. & .\mathbf{78} & .13 & -.11\\
I don’t know how to make decisions. & .\mathbf{67} & -.24 & .12\\
I know which steps to take when making a decision. (R) & .\mathbf{48} &
-.24 & -.21\\
I would characterize myself as an indecisive person. & .\mathbf{79} & .04 &
-.06\\
I don’t hesitate much when I have to make a decision. (R) & .\mathbf{48} &
.25 & -.50\\
While making a decision, I feel certain. (R) & .\mathbf{55} & .01 &
-.47\\
While making a decision, I feel uncertain. & .\mathbf{74} & .05 & -.16\\
It takes a long time to weigh the pros and cons before making a decision. &
.\mathbf{59} & .32 & -.03\\
I make decisions quickly. (R) & .\mathbf{38} & .32 & -.47\\
I delay deciding. & .\mathbf{70} & .06 & -.16\\
I don’t postpone making decisions to a later date. (R) & .\mathbf{51} &
.09 & -.32\\
I try to avoid making a decision. & .\mathbf{75} & -.05 & -.02\\
I don’t avoid situations where decisions have to be made. & .\mathbf{37} &
-.12 & -.17\\
I tend to leave decisions to someone else. & .\mathbf{71} & -.12 & .00\\
Once I have taken a decision, I stick to that decision. (R) & .\mathbf{52}
& -.23 & -.27\\
I often reconsider my decision. & .\mathbf{71} & .18 & -.05\\
Once I have made a decision, I stop worrying about it. (R) & .\mathbf{53} &
.03 & -.36\\
After making a decision, I can’t get it out of my head. & .\mathbf{71} &
.02 & .07\\
After I have decided something, I believe I took the wrong decision. &
.\mathbf{75} & -.06 & .13\\
After making a decision, I don’t regret the decision. (R) & .\mathbf{52} &
-.03 & -.33\\\bottomrule
\end{tabular}}
\end{table*}

\begin{table}[t]
\caption*{\textbf{Appendix Table A2:} Study 1 principal component analysis with 2 components rotated.}
{\small
\begin{tabular}{ld{2.2}d{2.2}}\toprule
Item \hfill Component: & \mc{1}{c}{1} & \mc{1}{c}{2}\\\midrule
\textbf{Maximizing Tendency Scale-7} & & \\
No matter what I do, I have the highest standards for myself. & -.25 & .\mathbf{59}\\
I never settle for second best. & -.10 & .\mathbf{66}\\
No matter what it takes, I always try to choose the best thing. & -.18 &
.\mathbf{62}\\
I don’t like having to settle for “good enough.” & -.11 & .\mathbf{56}\\
I am a maximizer. & -.10 & .\mathbf{56}\\
I will wait for the best option, no matter how long it takes. & .04 &
.\mathbf{69}\\
I never settle. & -.08 & .\mathbf{60}\\
\bfseries Alternative Search Scale (Maximization Inventory) & & \\
I can’t come to a decision unless I have carefully considered all of my
options. & .15 & .\mathbf{66}\\
I take time to read the whole menu when dining out. & .13 & .\mathbf{43}\\
I will continue shopping for an item until it reaches all of my criteria. &
.01 & .\mathbf{67}\\
I usually continue to search for an item until it reaches my expectations.
& -.03 & .\mathbf{71}\\
When shopping, I plan on spending a lot of time looking for something. &
.21 & .\mathbf{56}\\
When shopping, if I can’t find exactly what I’m looking for, I will
continue to search for it. & .00 & .\mathbf{66}\\
I find myself going to many different stores before finding the thing I
want. & .20 & .\mathbf{52}\\
When shopping for something, I don’t mind spending several hours looking
for it. & .13 & .\mathbf{53}\\
I take the time to consider all alternatives before making a decision. &
.03 & .\mathbf{63}\\
When I see something that I want, I always try to find the best deal before
purchasing it. & .08 & .\mathbf{51}\\
If a store doesn’t have exactly what I’m shopping for, then I will go
somewhere else. & -.04 & .\mathbf{63}\\
I just won’t make a decision until I am comfortable with the process. & .12
& .\mathbf{65}\\
\bfseries Decision Difficulty Scale (Maximization Inventory) & & \\
I usually have a hard time making even simple decisions. & .\mathbf{73} &
.09\\
I am usually worried about making a wrong decision. & .\mathbf{71} & .17\\
I often wonder why decisions can’t be more easy. & .\mathbf{65} & .07\\
I often put off making a difficult decision until a deadline. &
.\mathbf{72} & .05\\
I often experience buyer’s remorse. & .\mathbf{64} & .03\\
I often think about changing my mind after I have already made my decision.
& .\mathbf{73} & .14\\
The hardest part of making a decision is knowing I will have to leave the
item I didn’t choose behind. & .\mathbf{65} & .16\\
I often change my mind several times before making a decision. &
.\mathbf{73} & .10\\
It’s hard for me to choose between two good alternatives. & .\mathbf{65} &
.13\\
Sometimes I procrastinate in deciding even if I have a good idea of what
decision I will make. & .\mathbf{73} & .06\\
I find myself often faced with difficult decisions. & .\mathbf{43} & .18\\
I do not agonize over decisions. (R) & .\mathbf{61} & .00\\
\textbf{Indecisiveness Scale} & & \\
I find it easy to make decisions. (R) & .\mathbf{60} & -.06\\
It is hard for me to come to a decision. & .\mathbf{79} & .08\\
I don’t know how to make decisions. & .\mathbf{60} & -.13\\
I know which steps to take when making a decision. (R) & .\mathbf{49} &
-.30\\
I would characterize myself as an indecisive person. & .\mathbf{79} &
.04\\
I don’t hesitate much when I have to make a decision. (R) & .\mathbf{59} &
-.02\\
While making a decision, I feel certain. (R) & .\mathbf{64} & -.21\\
While making a decision, I feel uncertain. & .\mathbf{76} & -.01\\
It takes a long time to weigh the pros and cons before making a decision. &
.\mathbf{61} & .28\\
I make decisions quickly. (R) & .\mathbf{50} & .05\\
I delay deciding. & .\mathbf{72} & .00\\
I don’t postpone making decisions to a later date. (R) & .\mathbf{57} &
-.07\\
I try to avoid making a decision. & .\mathbf{74} & -.03\\
I don’t avoid situations where decisions have to be made. & .\mathbf{38} &
-.18\\
I tend to leave decisions to someone else. & .\mathbf{68} & -.08\\
Once I have taken a decision, I stick to that decision. (R) & .\mathbf{55}
& -.31\\
I often reconsider my decision. & .\mathbf{72} & .15\\
Once I have made a decision, I stop worrying about it. (R) & .\mathbf{60} &
-.14\\
After making a decision, I can’t get it out of my head. & .\mathbf{68} &
.08\\
After I have decided something, I believe I took the wrong decision. &
.\mathbf{70} & .04\\
After making a decision, I don’t regret the decision. (R) & .\mathbf{58} &
-.18\\\bottomrule
\end{tabular}}
\end{table}

\begin{table}[t]
\caption*{\textbf{Appendix Table A3:} Study 1 principal component analysis with 4 components rotated.}
{\small
\begin{tabular}{ld{2.2}d{2.2}d{2.2}d{2.2}}\toprule
Item \hfill Component: & \mc{1}{c}{1} & \mc{1}{c}{2} & \mc{1}{c}{3} & \mc{1}{c}{4}\\\midrule
\bfseries Maximizing Tendency Scale-7 & & & & \\
No matter what I do, I have the highest standards for myself. & -.15 &
.17 & -.12 & .\mathbf{78}\\
I never settle for second best. & .07 & .19 & -.18 & .\mathbf{84}\\
No matter what it takes, I always try to choose the best thing. & -.18 &
.\mathbf{36} & .01 & .\mathbf{61}\\
I don’t like having to settle for “good enough.” & -.06 & .14 & .00 &
.\mathbf{79}\\
I am a maximizer. & -.04 & .21 & -.02 & .\mathbf{67}\\
I will wait for the best option, no matter how long it takes. & .13 & .32 &
-.05 & .\mathbf{72}\\
I never settle. & .08 & .14 & -.18 & .\mathbf{81}\\
\bfseries Alternative Search Scale (Maximization Inventory) & & & & \\
I can’t come to a decision unless I have carefully considered all of my
options. & .13 & .\mathbf{66} & .03 & .21\\
I take time to read the whole menu when dining out. & .13 & .\mathbf{39} &
.01 & .17\\
I will continue shopping for an item until it reaches all of my criteria. &
.02 & .\mathbf{72} & -.06 & .15\\
I usually continue to search for an item until it reaches my expectations.
& -.07 & .\mathbf{72} & .02 & .22\\
When shopping, I plan on spending a lot of time looking for something. &
.22 & .\mathbf{58} & .00 & .14\\
When shopping, if I can’t find exactly what I’m looking for, I will
continue to search for it. & .00 & .\mathbf{77} & -.08 & .05\\
I find myself going to many different stores before finding the thing I
want. & .20 & .\mathbf{47} & .04 & .23\\
When shopping for something, I don’t mind spending several hours looking
for it. & .20 & .\mathbf{51} & -.10 & .16\\
I take the time to consider all alternatives before making a decision. &
-.05 & .\mathbf{74} & .05 & .07\\
When I see something that I want, I always try to find the best deal before
purchasing it. & .01 & .\mathbf{68} & .05 & -.05\\
If a store doesn’t have exactly what I’m shopping for, then I will go
somewhere else. & -.11 & .\mathbf{72} & .04 & .10\\
I just won’t make a decision until I am comfortable with the process. & .10
& .\mathbf{69} & .01 & .15\\
\textbf{Decision Difficulty Scale (Maximization Inventory)} & & & & \\
I usually have a hard time making even simple decisions. & .\mathbf{75} &
.00 & .18 & .10\\
I am usually worried about making a wrong decision. & .\mathbf{59} & .20 &
.\mathbf{37} & .00\\
I often wonder why decisions can’t be more easy. & .\mathbf{67} & .00 & .16
& .06\\
I often put off making a difficult decision until a deadline. &
.\mathbf{71} & .07 & .21 & -.06\\
I often experience buyer’s remorse. & .\mathbf{69} & .04 & .09 & -.07\\
I often think about changing my mind after I have already made my decision.
& .\mathbf{72} & .13 & .21 & .01\\
The hardest part of making a decision is knowing I will have to leave the
item I didn’t choose behind. & .\mathbf{69} & .10 & .13 & .09\\
I often change my mind several times before making a decision. &
.\mathbf{68} & .17 & .27 & -.10\\
It’s hard for me to choose between two good alternatives. & .\mathbf{55} &
.20 & .32 & -.06\\
Sometimes I procrastinate in deciding even if I have a good idea of what
decision I will make. & .\mathbf{66} & .15 & .29 & -.13\\
I find myself often faced with difficult decisions. & .\mathbf{51} & .09 &
.00 & .13\\
I do not agonize over decisions. (R) & .28 & .08 & .\mathbf{71} & .00\\
\bfseries Indecisiveness Scale & & & & \\
I find it easy to make decisions. (R) & .24 & .01 & .\mathbf{77} & .01\\
It is hard for me to come to a decision. & .\mathbf{68} & .10 &
.\mathbf{40} & .00\\
I don’t know how to make decisions. & .\mathbf{70} & -.22 & .04 & .01\\
I know which steps to take when making a decision. (R) & .34 & -.31 &
.\mathbf{42} & -.04\\
I would characterize myself as an indecisive person. & .\mathbf{73} & .03
& .32 & -.01\\
I don’t hesitate much when I have to make a decision. (R) & .21 & .14 &
.\mathbf{77} & -.10\\
While making a decision, I feel certain. (R) & .30 & -.09 & .\mathbf{74}
& -.13\\
While making a decision, I feel uncertain. & .\mathbf{66} & .05 &
.\mathbf{36} & -.09\\
It takes a long time to weigh the pros and cons before making a decision. &
.\mathbf{55} & .32 & .24 & .02\\
I make decisions quickly. (R) & .12 & .22 & .\mathbf{73} & -.08\\
I delay deciding. & .\mathbf{65} & .08 & .28 & -.14\\
I don’t postpone making decisions to a later date. (R) & .34 & .02 &
.\mathbf{53} & -.09\\
I try to avoid making a decision. & .\mathbf{74} & -.03 & .19 & -.07\\
I don’t avoid situations where decisions have to be made. & .24 & -.18 &
.\mathbf{36} & -.01\\
I tend to leave decisions to someone else. & .\mathbf{71} & -.10 & .15 &
-.07\\
Once I have taken a decision, I stick to that decision. (R) & .\mathbf{37}
& -.28 & .\mathbf{46} & -.11\\
I often reconsider my decision. & .\mathbf{65} & .16 & .31 & .02\\
Once I have made a decision, I stop worrying about it. (R) & .31 & -.07 &
.\mathbf{65} & -.05\\
After making a decision, I can’t get it out of my head. & .\mathbf{71} &
.04 & .15 & .03\\
After I have decided something, I believe I took the wrong decision. &
.\mathbf{79} & -.03 & .06 & .03\\
After making a decision, I don’t regret the decision. (R) & .33 & -.11 &
.\mathbf{57} & -.09\\\bottomrule
\end{tabular}}
\end{table}

\end{document}
