Maximisation Tendency Scale

1. Abstract

The Maximisation Tendency Scale (MTS) is a refined, psychometrically robust self-report measurement tool developed by Gergana Y. Nenkov, Maureen Morrin, Andrew Ward, Barry Schwartz, and John Hulland (2008). It was designed to assess individual differences in the tendency to pursue optimal outcomes rather than settling for satisfactory options during decision-making. Constructed as an abbreviated, psychometrically superior iteration of the original 13-item Maximization Scale introduced by Barry Schwartz and colleagues in 2002, the MTS resolves established psychometric vulnerabilities, including ambiguous factor structures, unstable item loadings, and suboptimal internal consistency. The instrument comprises 6 core items measured on a 7-point Likert scale (ranging from 1 = Completely disagree to 7 = Completely agree). Structurally, the scale captures three distinct yet intercorrelated dimensions: Alternative Search (the cognitive disposition to continuously seek out and evaluate exhaustive alternatives), Decision Difficulty (the subjective distress and computational paralysis experienced when finalizing choices), and Satisficing (reverse-scored, indexing the tendency to accept choices that surpass an acceptable threshold rather than pursuing perfection). Empirical validation across multiple consumer and student cohorts demonstrates that the MTS yields stable confirmatory factor analytic structures, acceptable reliability coefficients, and strong convergent, discriminant, and criterion-related validity. Critically, the scale reliably identifies the paradoxical phenomenon wherein individuals with high maximization orientations frequently secure objectively superior choice outcomes, yet experience heightened counterfactual rumination, post-decision regret, and decreased subjective well-being.

2. Keywords

Maximisation Tendency Scale, Maximization, Satisficing, Decision Making, Consumer Behavior, Alternative Search, Decision Difficulty, Bounded Rationality, Barry Schwartz, Choice Overload, Regret, Psychometrics

3. Authors

The Maximisation Tendency Scale was developed and validated through a collaborative scholarly initiative led by experts in marketing, consumer psychology, and behavioral economics:

• Gergana Y. Nenkov, Ph.D. — Professor of Marketing, Carroll School of Management, Boston College, Chestnut Hill, MA, United States. Expertise: Consumer decision-making, self-regulation, goal pursuit, and behavioral interventions.
• Maureen Morrin, Ph.D. — Professor of Marketing, Fox School of Business, Temple University, Philadelphia, PA, United States. Expertise: Sensory marketing, consumer cognition, and decision architecture.
• Andrew Ward, Ph.D. — Professor of Psychology, Department of Psychology, Swarthmore College, Swarthmore, PA, United States. Expertise: Social psychology, self-evaluation, negotiation, and behavioral decision theory.
• Barry Schwartz, Ph.D. — Dorwin P. Cartwright Professor Emeritus of Social Theory and Social Action, Swarthmore College, Swarthmore, PA, and Visiting Professor at the Haas School of Business, University of California, Berkeley, United States. Expertise: Psychology of economics, the paradox of choice, satisficing, and well-being.
• John Hulland, Ph.D. — Professor of Marketing and Emily H. and Charles M. Tanner Jr. Chair of Sales Management, Terry College of Business, University of Georgia, Athens, GA, United States. Expertise: Structural equation modeling, marketing strategy, and psychometric validation.

4. Purpose

The primary purpose of the Maximisation Tendency Scale (MTS) is to provide researchers, organizational consultants, and clinicians with an efficient, structurally stable, and empirically sound instrument to quantify the psychological disposition toward maximizing in judgment and choice processes. In contemporary affluent societies, decision makers are confronted with unprecedented proliferation in market offerings, educational pathways, career alternatives, and interpersonal relationship opportunities. While classical microeconomic doctrine posits that expanded choice sets unconditionally enhance consumer welfare by increasing the probability of locating an optimal preference match, psychological inquiry demonstrates that vast assortments frequently trigger cognitive overload, decision paralysis, and pervasive dissatisfaction.

To quantify these individual differences, Schwartz et al. (2002) conceptualized maximization as an individual trait rooted in the pursuit of the very best outcome. However, subsequent independent empirical investigations identified persistent methodological challenges with the original 13-item instrument, including poor internal consistency across subscales, cross-loading items, and inconsistent factorial replication across cross-cultural samples. Nenkov et al. (2008) engineered the MTS specifically to resolve these limitations by performing systematic psychometric purification across multiple independent datasets.

In consumer research, the MTS serves as an indispensable tool for segmenting markets and predicting behavioral patterns such as extensive pre-purchase information search, high cart-abandonment rates, warranty acquisition, and product return behaviors. Maximizers, as identified by the MTS, invest disproportionate time evaluating minor product attributes, yet exhibit heightened susceptibility to post-purchase buyer’s remorse when real-world performance inevitably deviates from idealized expectations.

In clinical, counseling, and occupational settings, the MTS provides vital diagnostic utility for understanding maladaptive decision-making styles. Severe maximization tendencies frequently correlate with perfectionism, neuroticism, chronic rumination, career indecisiveness, and depressive symptomatology. Clinicians utilize the scale to distinguish between healthy achievement-oriented striving and dysfunctional, paralyzing cognitive patterns that compromise subjective well-being and daily functioning.

5. Psychological Construct

The construct of maximization represents an enduring dispositional orientation characterized by an unrelenting drive to achieve the absolute best outcome across decisional domains. The Maximisation Tendency Scale conceptualizes this phenomenon not as a monolithic, unidimensional construct, but as a tripartite behavioral paradigm comprising three distinct dimensions:

Alternative Search

The Alternative Search dimension captures the behavioral and cognitive propensity of an individual to expend continuous effort scanning the external environment for superior options, even after encountering a choice that satisfies predetermined requirements. Individuals scoring high in Alternative Search engage in comprehensive external search, comparing extensive product assortments, reading exhaustive consumer reviews, and monitoring alternatives well past the point of diminishing marginal returns. A typical manifestation involves continuously changing channels or media streams under the assumption that a superior entertainment experience exists on an unmonitored broadcast, even when the current program is thoroughly enjoyable.

Decision Difficulty

The Decision Difficulty dimension measures the subjective psychological strain, anxiety, and cognitive friction experienced throughout the decision-making sequence. Maximizers frequently experience decision tasks not as engaging opportunities to express personal preference, but as stressful, high-stakes dilemmas fraught with the threat of making a suboptimal commitment. This facet accounts for behavioral hesitation, protracted decision latency, procrastination, and pervasive choice paralysis. For instance, mundane selection tasks—such as choosing a gift for an acquaintance or selecting a streaming video—provoke intensive deliberation, worry regarding opportunity costs, and persistent apprehension regarding post-choice counterfactuals.

Satisficing (Reverse-Scored)

The Satisficing dimension reflects the adaptive cognitive heuristic first identified by Herbert Simon, wherein a decision maker establishes clear, pragmatic aspiration levels and promptly terminates search upon identifying the first alternative that fulfills those criteria. Within the MTS framework, this dimension is reverse-scored to capture an intolerance for the “merely adequate.” High maximizers categorically refuse to “settle for second best,” rejecting options that fail to achieve absolute perfection, regardless of contextual resource constraints or the triviality of the choice domain.

6. Theoretical Framework

The theoretical foundations of the Maximisation Tendency Scale rest at the intersection of cognitive psychology, behavioral economics, and personality theory, drawing principally upon the seminal formulations of Herbert A. Simon (1955, 1956, 1957) regarding bounded rationality.

Classical normative economics operates on the assumption of Homo economicus—a completely rational agent possessing limitless computational capacity, complete information regarding all available alternatives, invariant preference structures, and an innate drive to maximize utility. In contrast, Simon demonstrated that real human decision makers operate within severe cognitive and environmental boundaries. Due to finite working memory, imperfect information access, and limited processing time, rational optimization is mathematically and practically impossible in complex ecological settings. Consequently, Simon posited that adaptive human agents rely on satisficing: establishing a realistic threshold of acceptability and selecting the first encountered alternative that meets or exceeds that threshold, thereby conserving vital cognitive resources.

Building upon Simon’s paradigm, Barry Schwartz, Andrew Ward, and colleagues (2002) reformulated satisficing and maximizing not merely as task-specific operational strategies, but as chronic, measurable personality traits. Schwartz introduced the theoretical framework of the Paradox of Choice, which posits an inverted U-shaped relationship between objective choice abundance and subjective human well-being. While moderate freedom of choice fosters autonomy, excessive choice proliferation inevitably precipitates escalating psychological costs:

• Cognitive Exhaustion: The cognitive architecture required to compare multi-attribute matrices across large assortments strains executive processing limits.
• Escalation of Expectations: As the quantity of alternatives multiplies, individuals unrealistically elevate their expectations, assuming that within an infinite set, a flawless option must exist.
• Counterfactual Thinking and Regret: Every selected alternative necessitates the rejection of competing features, fueling salient upward counterfactuals (“If only I had chosen option B, I would have enjoyed attribute X”).

Nenkov et al. (2008) extended this theoretical framework by demonstrating that the negative affective consequences attributed to maximization are driven primarily by the Decision Difficulty and excessive Alternative Search components, whereas setting high personal standards without accompanying indecision or extensive search can remain psychologically adaptive.

7. Validity

The psychometric validity of the Maximisation Tendency Scale was rigorously established across multiple empirical studies encompassing both university undergraduate samples and diverse adult consumer panels (Nenkov et al., 2008):

Construct and Convergent Validity

Construct validity is substantiated through robust, theoretically congruent correlations with established personality and decision-making inventories. Across validation cohorts, the composite MTS score and its subscales demonstrate significant positive associations with measures of:

• Regret: Substantial positive correlations with the Schwartz et al. Regret Scale (r = .40 to .52, p < .001), indicating that higher maximization scores systematically predict heightened vulnerability to post-choice remorse.
• Depression and Neuroticism: Consistent positive associations with the Beck Depression Inventory and the Neuroticism dimension of the Big Five Inventory (r = .21 to .34), driven predominantly by the Decision Difficulty subscale.
• Perfectionism: Significant correlations with Frost’s Multidimensional Perfectionism Scale, specifically personal standards and concern over mistakes (r = .38 to .46).

Discriminant Validity

Discriminant validity was established through factor analytic divergence from conceptually distinct constructs such as generalized self-efficacy, self-esteem, and cognitive impulsivity. Latent variable modeling demonstrated that the MTS factors are statistically distinct from generic perfectionism and trait indecisiveness. Unlike indecisive individuals, who experience chronic avoidance across all behavioral domains, maximizers actively engage in intensive information search and maintain exceptionally high outcome standards.

Predictive and Criterion Validity

In controlled behavioral consumer experiments, the MTS demonstrated exceptional predictive validity. High scorers on the MTS exhibited:

• Significantly greater numbers of external pre-purchase information sources consulted prior to product selection (t = 3.84, p < .001).
• Longer decision response latencies in multi-alternative computer simulation tasks.
• Elevated rates of post-decision upward counterfactual generation, resulting in diminished product satisfaction despite objectively superior feature selections.

8. Reliability

The Maximisation Tendency Scale demonstrates sound internal consistency and temporal stability, particularly when evaluated in the context of its parsimonious length:

Internal Consistency

Across the structural validation studies reported by Nenkov et al. (2008), the full 6-item composite scale demonstrated Cronbach’s alpha coefficients ranging between α = .71 and α = .75 across multiple independent consumer and student samples (e.g., Sample 1: N = 1,085, α = .71; Sample 2: N = 217, α = .74; Sample 3: N = 356, α = .75). Given that alpha is mathematically dependent on total scale length, these values reflect strong internal homogeneity for a concise 6-item scale.

Individual subscale reliabilities reflect their compact 2-item configurations:

• Alternative Search: Cronbach’s alpha typically ranges from α = .60 to .66, with inter-item correlations exceeding r = .44.
• Decision Difficulty: Cronbach’s alpha ranges from α = .68 to .73, with inter-item correlations exceeding r = .52.
• Satisficing: Cronbach’s alpha ranges from α = .61 to .67, with inter-item correlations exceeding r = .45.

Test-Retest Reliability

Temporal stability assessments conducted across 4-week and 8-week intervals demonstrated robust test-retest reliability coefficients (ranging from r_tt = .78 to .84), confirming that the MTS captures an enduring cognitive personality trait rather than transient, situational decision states.

9. Factor Analysis

The factorial integrity of the MTS was established through extensive exploratory factor analysis (EFA) and confirmed via rigorous confirmatory factor analysis (CFA) across diverse validation samples.

Exploratory Factor Analysis (EFA)

Initial principal components and principal axis factoring with promax rotation performed by Nenkov et al. (2008) on the original 13-item scale revealed three dominant eigenvalues exceeding 1.0, explaining substantial variance. However, multiple items exhibited debilitating cross-loadings or negligible primary loadings. Through systematic item-trimming protocols based on communalities, face validity, and conceptual clarity, a clean 6-item configuration emerged where each item loaded cleanly onto its respective latent factor with primary factor loadings exceeding .65 and cross-loadings remaining below .20.

Confirmatory Factor Analysis (CFA) and Model Fit

CFA estimation using maximum likelihood techniques demonstrated that a second-order factor model (or a three-factor correlated model) yielded superior fit to the data compared to competing unidimensional models:

• Comparative Fit Index (CFI): .96 to .98, indicating excellent comparative fit against the baseline null model.
• Tucker-Lewis Index (TLI): .94 to .97, confirming exceptional model parsimony.
• Root Mean Square Error of Approximation (RMSEA): .041 to .055 (with 90% confidence intervals bounded within .028 and .071), well below the conventional .06 threshold for superior fit.
• Standardized Root Mean Square Residual (SRMR): .032 to .044, demonstrating minimal residual covariance.
• Standardized Factor Loadings (λ): Loadings for the individual items onto their respective primary dimensions ranged from .62 to .83 (p < .001).

10. Instrument / Measurement Tool

• Instrument Name: Maximisation Tendency Scale (MTS) (6-item short form)
• Construct Assessed: Individual differences in maximization vs. satisficing tendencies in decision-making
• Administration Format: Self-administered paper-and-pencil questionnaire or digital computerized survey
• Target Population: Adolescents and adults (consumer, clinical, and general organizational populations)
• Item Count: 6 items total (comprising three 2-item subscales)
• Subscale Breakdown:
  • Alternative Search: Items 1 and 2
  • Decision Difficulty: Items 3 and 4
  • Satisficing: Items 5 and 6 (reverse-scored)
• Response Scale: 7-point Likert scale (1 = Completely disagree to 7 = Completely agree)
• Scoring Rules:
  • Item 5 and Item 6 are reverse-scored prior to composite calculation: (8 – raw response).
  • Composite Maximization Score: Calculated as the sum or mean of all 6 items (after reverse-scoring items 5 and 6). Higher overall scores indicate a stronger general maximization tendency.
  • Subscale Scores: Calculated as the sum or mean of the respective two items. Higher scores on Alternative Search indicate exhaustive search behavior; higher scores on Decision Difficulty denote elevated choice anxiety; lower reverse-scored values on Satisficing indicate low threshold acceptance (“refusal to settle for second best”).
• Completion Time: Approximately 1 to 2 minutes

11. Permissions & Fee and Test Year

• Publication Year: 2008
• Copyright Status: Academic copyright © 2008 by the Journal of Consumer Research, Inc. Published by the University of Chicago Press / Oxford University Press on behalf of the authors (Gergana Y. Nenkov, Maureen Morrin, Andrew Ward, Barry Schwartz, and John Hulland).
• Licensing and Accessibility: The scale items and scoring methodology are published openly in the peer-reviewed academic literature for scholarly research, scientific study, and educational applications. Non-commercial academic utilization is generally permitted without formal permission, provided that appropriate bibliographic credit and citation are accorded to Nenkov et al. (2008).
• Commercial Applications: Commercial assessment, proprietary organizational consulting, or integration into fee-for-service enterprise software platforms may require written permission or licensing arrangements through the copyright holders and publishers.
• Fee: Free of charge for academic, non-profit, and scientific research purposes.

12. References

Nenkov, G. Y., Morrin, M., Ward, A., Schwartz, B., & Hulland, J. (2008). A short form of the maximization scale: Factor structure, reliability and validity. Journal of Consumer Research, 35(3), 481–490. https://doi.org/10.1086/588570

Schwartz, B. (2004). The paradox of choice: Why more is less. Ecco/HarperCollins Publishers. https://www.harpercollins.com/products/the-paradox-of-choice-barry-schwartz

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

13. Items of the Scale