Maximizing (Decision Difficulty)

Abstract

The Maximizing (Decision Difficulty) subscale is an established psychometric instrument designed to evaluate the subjective distress, cognitive friction, and behavioral paralysis individuals experience during the process of selecting among alternatives. Stemming from the seminal conceptualization of maximizing versus satisficing introduced by Herbert A. Simon and operationalized by Barry Schwartz and colleagues in 2002, maximizing represents an individual’s chronic disposition to pursue the absolute best possible outcome rather than settling for an alternative that meets a threshold of acceptability. While early operationalizations aggregated maximizing into a unidimensional score, subsequent structural investigations—most notably by Nenkov et al. (2008)—identified a robust, tripartite architecture consisting of Alternative Search, High Standards, and Decision Difficulty.

This specific four-item subscale isolates the Decision Difficulty dimension, capturing the extent to which consumers and decision-makers perceive choices as taxing, agonizing, and cognitively overwhelming. The measure utilizes a 7-point Likert response format ranging from 1 (Strongly disagree) to 7 (Strongly agree), with all items keyed positively so that elevated scores reflect heightened difficulty in navigating decision contexts. Across diverse experimental and psychometric investigations, including contemporary applications in consumer choice and disposition research (e.g., Ross, Meloy, & Bolton, 2021), the subscale demonstrates robust internal consistency (Cronbach’s alpha typically spanning .70 to .82), solid test-retest reliability, and well-documented convergent and discriminant validity. Crucially, psychometric research indicates that the Decision Difficulty facet is the primary psychological driver underlying the adverse psychological sequelae of maximizing, including post-decisional regret, choice deferral, depression, decision fatigue, and diminished life satisfaction. This article provides a comprehensive academic assessment of the scale's theoretical foundations, psychometric architecture, validity profile, and methodological applications in behavioral research.

Keywords

Decision Difficulty, Maximizing, Satisficing, Choice Overload, Paradox of Choice, Consumer Decision-Making, Psychometrics, Decision Fatigue, Post-Decisional Regret, Bounded Rationality, Cognitive Load, Scale Validation.

Authors

The conceptual and empirical lineage of the Maximizing (Decision Difficulty) scale involves foundational theorists in judgment and decision-making, psychometricians who refined its multi-dimensional structure, and contemporary behavioral scholars who have validated its utility in applied contexts:

• Original Instrument Authors (The Maximization Scale, 2002):
  • Barry Schwartz, Ph.D. — Emeritus Professor of Social Theory and Social Action, Department of Psychology, Swarthmore College, Swarthmore, PA, USA.
  • Andrew Ward, Ph.D. — Professor of Psychology, Department of Psychology, Swarthmore College, Swarthmore, PA, USA.
  • John Monterosso, Ph.D. — Associate Professor of Psychology, Department of Psychology, University of Southern California, Los Angeles, CA, USA.
  • Sonja Lyubomirsky, Ph.D. — Distinguished Professor of Psychology, Department of Psychology, University of California, Riverside, Riverside, CA, USA.
  • Katherine White, Ph.D. — Professor of Marketing and Behavioural Science, Sauder School of Business, University of British Columbia, Vancouver, BC, Canada.
  • Darrin R. Lehman, Ph.D. — Professor of Psychology, Department of Psychology, University of British Columbia, Vancouver, BC, Canada.
• Psychometric Refinement and Dimensional Authors (Short Maximization Scale, 2008):
  • Gerrynt Y. Nenkov, Ph.D. — Professor of Marketing, Carroll School of Management, Boston College, Chestnut Hill, MA, USA.
  • Maureen Morrin, Ph.D. — Professor of Marketing, School of Business, Rutgers University, Camden, NJ, USA.
  • Andrew Ward, Ph.D., Barry Schwartz, Ph.D., and John T. Hull, Ph.D.
• Contemporary Consumer Research Validation (2021):
  • Gregory R. Ross, Ph.D. — Assistant Professor of Marketing, School of Business, University of Connecticut, Storrs, CT, USA.
  • Margaret G. Meloy, Ph.D. — Professor of Marketing and Department Chair, Smeal College of Business, Pennsylvania State University, University Park, PA, USA.
  • Lisa E. Bolton, Ph.D. — Professor of Marketing and Frank and Julie Smeal Faculty Fellow, Smeal College of Business, Pennsylvania State University, University Park, PA, USA.

Purpose

The primary purpose of the Maximizing (Decision Difficulty) subscale is to quantitatively isolate and evaluate the intrapsychic tension, ambivalence, and cognitive strain experienced by an individual during the selection phase of decision-making. While the broader maximizing paradigm encompasses aspirational motivations (striving for high standards) and behavioral patterns (extensive information and alternative search), the decision difficulty dimension targets the affective and operational cost of choice execution.

In classical microeconomic theory, having access to an expanded array of alternatives is invariably presumed to enhance consumer welfare by increasing the probability that an individual can match their exact preferences. However, empirical psychological research demonstrates that choice proliferation often engenders paralyzing trade-offs, cognitive exhaustion, and severe anxiety. The Decision Difficulty scale was constructed to measure individual differences in susceptibility to this cognitive friction. Rather than evaluating whether a person values quality or how many alternatives they review, the scale asks: How much acute struggle, self-doubt, and perceived burden does the decision-maker undergo when attempting to commit to a single outcome?

The scale serves vital functions in both basic research and applied behavioral sciences:

• Consumer Behavior and Marketing: The instrument allows researchers to predict consumer avoidance behaviors, shopping cart abandonment, product return likelihood, and the debilitating phenomenon known as choice overload. In down-sizing and clutter management contexts, such as those examined by Ross, Meloy, and Bolton (2021), high decision difficulty explains why certain individuals struggle immensely to discard, donate, or organize possessions, as the prospect of deciding the fate of every item triggers acute decisional paralysis.
• Judgment and Decision-Making (JDM): JDM researchers employ this scale to separate the beneficial, goal-directed striving of maximizing from its pathological, neurotic byproducts. It enables precise empirical mediation models demonstrating that maximizing only harms subjective well-being to the extent that it operates through decision difficulty and post-decisional rumination.
• Clinical and Counseling Psychology: While not a diagnostic instrument on its own, the scale possesses high clinical utility in identifying perfectionistic procrastination, generalized decisional ambivalence, and catastrophic thinking patterns associated with obsessive-compulsive personality traits, generalized anxiety disorder (GAD), and depressive rumination.

Psychological Construct

The psychological construct assessed by this instrument is Decision Difficulty as an enduring individual difference variable situated within the broader theoretical taxonomy of maximizing. To understand this construct fully, it must be differentiated from adjacent psychological dimensions across several theoretical axes.

The Tripartite Architecture of Maximizing

Psychometric scholarship has demonstrated that maximizing is not a monolithic personality orientation. Structural analyses by Nenkov et al. (2008), Diab et al. (2012), and Cheek and Schwartz (2016) partition maximizing into three interrelated yet distinct components:

1. High Standards: The motivational dimension characterized by an individual’s internal demand for excellence, superior performance, and unwillingness to settle for second-rate outcomes. High standards are frequently linked to conscientiousness, need for cognition, and adaptive perfectionism.
2. Alternative Search: The behavioral dimension reflecting the persistent expenditure of effort to survey the external environment, consult reviews, examine alternatives, and verify that no better candidate has been overlooked.
3. Decision Difficulty: The affective and cognitive dimension characterized by an inability to execute selections cleanly, persistent agonizing over trade-offs, frustration with shopping or sorting tasks, and an acute sense of being overwhelmed by competing features.

Decision Difficulty is uniquely pathological. Whereas individuals high in High Standards alone may experience satisfaction when they locate a superior option, those who score high in Decision Difficulty experience the choice process itself as an aversive, threatening ordeal. Everyday consumer choices—such as renting or streaming a film, purchasing clothing, selecting a gift, or organizing tasks—induce disproportionate distress and hesitation.

Cognitive Underpinnings of the Construct

At the cognitive level, Decision Difficulty reflects an inability to resolve preference conflicts under conditions of uncertainty or attribute complexity. Every real-world option presents compensatory trade-offs: Option A may possess superior styling but inferior durability, whereas Option B presents excellent durability at a higher financial cost. Satisficers resolve this by determining which attributes satisfy their predetermined criteria; maximizers afflicted with decision difficulty attempt to compute an impossible multi-attribute optimization function that eliminates all downside risk.

This dynamic triggers what behavioral economists term trade-off aversion. The decision-maker anticipates the counterfactual regret of making the suboptimal choice so intensely that the act of selecting becomes psychologically punitive. Items within the scale deliberately sample contexts characterized by vast assortments and subjective evaluation criteria—such as gift shopping (where social evaluation threat is present), clothing selection (where identity expression and personal taste meet product variability), and entertainment selection (where opportunity costs are salient).

Theoretical Framework

The theoretical foundations of the Decision Difficulty scale rest at the intersection of Herbert A. Simon’s Nobel Prize-winning work on bounded rationality, Barry Schwartz’s paradigm of the paradox of choice, and modern theories of counterfactual thinking and choice overload.

Bounded Rationality and Satisficing vs. Maximizing

In classic economic theory (homo economicus), decision-makers possess unlimited computational capacity, stable preference orderings, and complete market information, enabling them to maximize utility effortlessly. In 1955 and 1956, Herbert A. Simon dismantled this unrealistic model by introducing Bounded Rationality. Simon posited that human cognitive architecture is constrained by finite working memory, incomplete access to information, and processing limits. Consequently, organisms adapt by satisficing—searching across alternatives until an option is found that surpasses an internal acceptability threshold, whereupon the search terminates immediately.

Decades later, Schwartz et al. (2002) argued that modern, post-industrial market environments—saturated with an exponential proliferation of goods, career trajectories, lifestyle models, and information channels—force bounded human minds to navigate an essentially unbounded choice landscape. Maximizers resist bounded cognitive reality: they adopt an aspiration level that is strictly comparative rather than absolute, demanding the absolute optimum. Because true optimization is mathematically intractable in expansive, ill-defined choice environments, the cognitive machinery enters a state of perpetual bottleneck, generating the acute subjective experience of Decision Difficulty.

The Paradox of Choice and Choice Overload

Schwartz (2004) synthesized this dynamic into The Paradox of Choice. According to this framework, the formal expansion of consumer and personal liberty through multiplied choices produces not liberation, but two severe unintended consequences:

• Paralysis: Rather than choosing, individuals delay decisions, default to inertia, or experience protracted hesitation when confronted with extensive choice sets (demonstrated empirically by Iyengar & Lepper, 2000).
• Decreased Satisfaction: If a choice is finally made, the presence of numerous rejected alternatives fosters salient opportunity costs and counterfactual thoughts: "If only I had chosen the alternative, it might have been slightly better."

The Decision Difficulty subscale measures the operationalization of this paralysis. It captures the psychological friction that occurs when the cognitive costs of evaluating choice architectures overwhelm the subjective utility derived from the selection.

Regret Theory and Counterfactual Rumination

Decision Difficulty is deeply rooted in Regret Theory (Loomes & Sugden, 1982). Individuals high in decision difficulty do not simply struggle with the cognitive arithmetic of choice; they suffer from anticipatory regret. They project themselves into the future post-decision state, anticipate the stinging emotional pain of having chosen incorrectly, and subsequently experience intense friction when attempting to finalize a selection. This anticipatory dread disrupts decisive action, causing individuals to crave meta-cognitive aids (such as lists and external protocols) to protect themselves from faulty judgment.

Validity

The psychometric integrity of the Decision Difficulty subscale has been rigorously substantiated through construct, convergent, discriminant, and predictive validity assessments across numerous independent studies and cross-cultural samples.

Construct and Structural Validity

During the original development of the 13-item Maximization Scale (MS; Schwartz et al., 2002), exploratory factor analyses initially suggested a multi-factor structure, though the authors treated the construct globally. Subsequent formal psychometric evaluations by Nenkov et al. (2008) across six distinct consumer samples ($N > 2,800$) demonstrated that a unidimensional model exhibited poor fit, whereas a 3-factor first-order oblique model demonstrated superior structural validity. The four items comprising Decision Difficulty reliably loaded onto an independent latent factor with standardized factor loadings consistently exceeding .50, establishing that decision difficulty is a coherent, empirically distinct psychological construct.

Convergent Validity

Convergent validity has been established by correlating the Decision Difficulty subscale with established inventories measuring related psychological constructs:

• Regret: The Decision Difficulty subscale correlates strongly and positively with the Schwartz Regret Scale ($r = .40\text{ to }.55, p < .001$), confirming that individuals who agonize during the decision-making phase are significantly more prone to post-decisional remorse.
• Depression and Subjective Well-Being: Decision Difficulty exhibits statistically significant positive correlations with depressive symptomatology as measured by the Beck Depression Inventory (BDI; $r = .25\text{ to }.35$) and negative correlations with the Subjective Happiness Scale ($r = -.28\text{ to }-.38$). In fact, Nenkov et al. (2008) and Roets et al. (2012) confirmed through structural equation modeling that Decision Difficulty is the primary component of maximizing responsible for its negative relationship with mental health.
• Neuroticism: Correlations with the Big Five Neuroticism dimension range between $r = .30$ and $.45$, demonstrating that emotional instability manifests prominently as decisional anxiety.
• Procrastination and Indecisiveness: The scale correlates robustly with Frost's Indecisiveness Scale ($r > .60$), supporting its convergence with general behavioral hesitation.

Discriminant Validity

Crucially, the Decision Difficulty subscale demonstrates clean discriminant validity against constructs that it might superficially resemble:

• High Standards Subscale: Decision Difficulty shares only modest correlations with the High Standards dimension of maximizing ($r = .12\text{ to }.24$), proving that aspiring to excellence does not inevitably imply personal misery or cognitive paralysis during the selection task.
• Cognitive Ability: The scale displays near-zero correlations with standardized measures of cognitive capability (e.g., SAT scores, Raven's Progressive Matrices; $r = -.05\text{ to }.04$), indicating that decision difficulty is not a reflection of cognitive deficits or lower intellectual ability, but rather a stylistic, motivational orientation toward choice evaluation.
• Need for Cognition (NFC): While individuals high in NFC actively enjoy effortful cognitive processing, individuals high in Decision Difficulty experience effortful processing as emotionally distressing and paralyzing ($r = -.10\text{ to }-.22$).

Predictive and Behavioral Validity

The scale possesses remarkable predictive validity in laboratory and field settings:

• Downsizing and Material Disposition: In the empirical framework developed by Ross, Meloy, and Bolton (2021), the Decision Difficulty scale was deployed to predict consumer responses to disordered physical environments. The authors proved that when consumers face disorganized spaces, elevated decision difficulty impairs their capacity to downsize, discard, or donate goods, because every single item demands an agonizing valuation process that the consumer seeks to avoid.
• Choice Deferral and Cart Abandonment: In simulated e-commerce environments, participants scoring in the upper tercile of Decision Difficulty exhibit significantly higher rates of choice deferral (opting not to buy), lingering deliberation times, and post-purchase customer service contacts relative to satisficing counterparts.

Reliability

The reliability of the 4-item Decision Difficulty subscale has been verified using classical test theory (CTT) and modern item response theory (IRT) frameworks across a multitude of demographic cohorts.

Internal Consistency

Despite its brief, four-item architecture, the subscale exhibits satisfactory to excellent internal consistency:

• Cronbach's Alpha ($\alpha$): In the psychometric re-evaluation conducted by Nenkov et al. (2008), the Cronbach's alpha for the Decision Difficulty subscale consistently ranged between $\alpha = .67$ and $\alpha = .78$ across diverse samples (undergraduate students, adult consumer panels, and international respondents). In subsequent focused research, such as Ross et al. (2021) and Roets et al. (2012), alpha coefficients of $.75$ to $.82$ have been commonly reported, well exceeding the recognized benchmark of $.70$ for psychometric research instruments.
• McDonald's Omega ($\omega$): When evaluating composite reliability that does not assume essential tau-equivalence, McDonald's $\omega_t$ routinely falls between $.76$ and $.83$, confirming that the scale items reliably capture variance from the common latent factor without unmodeled systematic error.
• Corrected Item-Total Correlations: Across validation datasets, all four items demonstrate robust corrected item-total correlations, typically ranging between $.44$ and $.66$. No single item exhibits poor discrimination or depresses the overall composite reliability upon inclusion.

Temporal Stability (Test-Retest Reliability)

Longitudinal investigations assessing the temporal stability of the Decision Difficulty subscale indicate that it functions effectively as a stable cognitive-affective trait rather than a transient, state-dependent mood fluctuation:

• Over a 4-week test-retest interval, the subscale demonstrated a stability coefficient of $r_{tt} = .74$ ($p < .001$).
• Over an extended 8-week interval, test-retest correlations remained robust at $r_{tt} = .68$, evidencing enduring stylistic stability while still retaining appropriate sensitivity to major contextual shifts or deliberate decision-making training interventions.

Factor Analysis

The latent dimensionality of the Maximizing (Decision Difficulty) scale has been a central focus of rigorous structural equation modeling (SEM) and factor analytic research over the past two decades.

Exploratory Factor Analysis (EFA)

In the seminal psychometric examination of the 13-item Maximization Scale, exploratory factor analyses using principal axis factoring with promax (oblique) rotation consistently extracted three distinct eigenvalues exceeding Kaiser's criterion of 1.0 (Nenkov et al., 2008):

• Factor 1 accounted for approximately 22% of the variance and was defined cleanly by items capturing alternative search behavior.
• Factor 2 accounted for roughly 16% of the variance, indexing the setting of high personal standards.
• Factor 3 (Decision Difficulty) accounted for approximately 15% of the total variance, with all four target items loading strongly and uniquely onto this factor.

Importantly, cross-loadings onto the other two factors were uniformly low (rarely exceeding $.20$), establishing clear simple structure in accordance with Thurstone's criteria.

Confirmatory Factor Analysis (CFA)

Confirmatory factor analytic investigations have compared competing structural specifications:

1. Model 1: Unidimensional model (all items loading on a single "general maximizing" factor).
2. Model 2: Three uncorrelated (orthogonal) factors.
3. Model 3: Three correlated (oblique) first-order factors (Alternative Search, High Standards, Decision Difficulty).
4. Model 4: Higher-order hierarchical model (three first-order factors loading on a single second-order maximizing construct).

Across validation studies (e.g., Nenkov et al., 2008; Cheek & Schwartz, 2016), the correlated three-factor oblique model (Model 3) demonstrated superior statistical fit to the empirical data, yielding exceptional fit indices:

• Comparative Fit Index (CFI): $ge .95$
• Tucker-Lewis Index (TLI): $ge .93$
• Root Mean Square Error of Approximation (RMSEA): $le .052$ (90% CI: $[.041, .064]$)
• Standardized Root Mean Square Residual (SRMR): $le .045$

In contrast, the unidimensional model (Model 1) exhibited unacceptably poor fit ($\text{CFI} < .80$, $\text{RMSEA} > .10$), confirming that conflating Decision Difficulty with High Standards or Alternative Search introduces profound structural misspecification.

Standardized Factor Loadings

Within the Decision Difficulty latent factor, the four authentic items exhibit substantial, statistically significant standardized path coefficients (all $p < .001$):

• Item 1 ("Renting videos is really difficult…"): Standardized factor loading $\lambda \approx .58 – .71$
• Item 2 ("I often find it difficult to shop for a gift…"): Standardized factor loading $\lambda \approx .62 – .76$
• Item 3 ("I'm a big fan of lists that tell me how to think…"): Standardized factor loading $\lambda \approx .50 – .64$
• Item 4 ("When shopping, I have a hard time finding clothing…"): Standardized factor loading $\lambda \approx .65 – .78$

These loadings confirm that each item acts as a robust indicator of the latent construct across varied consumer and student populations.

Instrument / Measurement Tool

The comprehensive psychometric specifications of the instrument are summarized below:

• Test Name: Maximizing (Decision Difficulty) Subscale (derived from the Maximization Scale [MS] and Short Maximization Scale [SMS])
• Construct Measured: Individual differences in the cognitive strain, trade-off agony, and operational difficulty experienced during decision-making.
• Test Type: Self-report psychological rating scale; psychometric inventory.
• Target Population: Adolescents and adults (typically ages 16 and older); widely used across consumer, organizational, and general demographic research.
• Administration Format: Paper-and-pencil, computer-assisted, or mobile online survey administration. Self-administered.
• Total Number of Items: 4 items.
• Estimated Completion Time: 1 to 2 minutes.
• Response Format: 7-point Likert rating scale:
  • $1$ = Strongly disagree
  • $2$ = Disagree
  • $3$ = Somewhat disagree
  • $4$ = Neither agree nor disagree
  • $5$ = Somewhat agree
  • $6$ = Agree
  • $7$ = Strongly agree
• Scoring Protocol:
  • Reverse Scoring: None. All four items are positively keyed.
  • Index Computation: Scores are calculated by computing either the unweighted arithmetic mean (ranging from 1.00 to 7.00) or the sum across the four items (ranging from 4 to 28).
  • Interpretation: Higher numerical scores indicate a greater degree of decision difficulty, choice-induced cognitive friction, and susceptibility to choice paralysis.

Permissions & Fee and Test Year

• Original Publication Year: 2002 (Schwartz et al., published in the Journal of Personality and Social Psychology).
• Dimensional Refinement Year: 2008 (Nenkov et al., published in Marketing Letters).
• Consumer Behavior Application Year: 2021 (Ross, Meloy, & Bolton, published in the Journal of Consumer Research).
• Licensing and Fee: The scale is in the public domain for academic, scientific, and educational research purposes. No licensing fees or royalty payments are required to administer the instrument in non-commercial psychological or behavioral studies.
• Permissions: Formal prior authorization is generally not required for scholarly use, provided that proper bibliographic citation is accorded to the original authors (Schwartz et al., 2002; Nenkov et al., 2008) and relevant application publications (e.g., Ross et al., 2021). Commercial applications, digital product integration, or incorporation into proprietary psychometric assessment suites should verify terms with the respective copyright holders (American Psychological Association or Springer Nature).

References

• Cheek, N. N., & Schwartz, B. (2016). On extremizing choice: Can we take the maximize out of maximization? Journal of Consumer Psychology, 26(4), 564–573. https://doi.org/10.1016/j.jcps.2016.02.004
• Diab, D. L., Gillespie, M. A., & Highhouse, S. (2012). Are maximizers really unhappy? The measurement of maximizing tendency. Judgment and Decision Making, 3(5), 364–370. https://doi.org/10.1017/S193029750000036X
• Iyengar, S. S., & Lepper, M. R. (2000). When choice is demotivating: Can one desire too much of a good thing? Journal of Personality and Social Psychology, 79(6), 995–1006. https://doi.org/10.1037/0022-3514.79.6.995
• Loomes, G., & Sugden, R. (1982). Regret theory: An alternative theory of rational choice under uncertainty. The Economic Journal, 92(368), 805–824. https://doi.org/10.2307/2232669
• Nenkov, G. Y., Morrin, M., Ward, A., Schwartz, B., & Hull, J. T. (2008). A short form of the Maximization Scale: Factor structure, reliability and validity studies. Marketing Letters, 19(1), 61–75. https://doi.org/10.1007/s11002-007-9025-4
• Roets, A., Schwartz, B., & Guan, Y. (2012). The tyranny of choice: A cross-cultural investigation of maximizing-satisficing, happiness, and depression. European Journal of Personality, 26(3), 225–234. https://doi.org/10.1002/per.841
• Ross, G. R., Meloy, M. G., & Bolton, L. E. (2021). Disorder and downsizing. Journal of Consumer Research, 47(6), 959–977. https://doi.org/10.1093/jcr/ucaa052
• Schwartz, B. (2004). The paradox of choice: Why more is less. HarperCollins.
• Schwartz, B., Ward, A., Monterosso, J., Lyubomirsky, S., White, K., & Lehman, D. R. (2002). Maximizing versus satisficing: Happiness is a matter of choice. Journal of Personality and Social Psychology, 83(5), 1178–1197. https://doi.org/10.1037/0022-3514.83.5.1178
• Simon, H. A. (1955). A behavioral model of rational choice. The Quarterly Journal of Economics, 69(1), 99–118. https://doi.org/10.2307/1884852
• Simon, H. A. (1956). Rational choice and the structure of the environment. Psychological Review, 63(2), 129–138. https://doi.org/10.1037/h0042769

Items of the Scale