Customized Product Superiority (CPS)

Abstract

The Customized Product Superiority (CPS) scale is a psychometric measurement instrument developed by Nikolaus Franke, Peter Keinz, and Christoph J. Steger in 2009 to assess consumers’ subjective evaluations of the relative performance and preference alignment of self-configured products compared to standard market alternatives. Originating within the field of marketing and consumer psychology, specifically in the context of mass customization and user co-creation, the scale captures the degree to which an individual perceives a self-designed product to be superior in terms of general quality, personal suitability, aesthetic design, and functional capabilities. The instrument consists of five unidimensional items evaluated on a 7-point Likert scale ranging from 1 (“strongly disagree”) to 7 (“strongly agree”). Across multiple empirical studies spanning diverse consumer goods categories—including technical sports equipment, mobile accessories, and media products—the CPS scale has demonstrated robust psychometric properties, consistently exhibiting high internal consistency reliability (Cronbach’s α typically exceeding .88 to .94) and strong construct, convergent, and predictive validity. Specifically, CPS has been validated as a critical psychological mediator linking preference insight and product configuration capabilities to elevated willingness to pay (WTP) and heightened purchase intention. By isolating perceived product superiority relative to the best available off-the-shelf alternative, the CPS scale serves as a foundational tool for researchers and practitioners examining customer value creation, mass customization toolkits, and consumer decision-making dynamics.

Keywords

Customized Product Superiority, Mass Customization, Consumer Preference Fit, User Co-Creation, Product Personalization, Willingness to Pay, Consumer Psychology, Psychometrics, Self-Design Effect, Value Perception

Authors

The Customized Product Superiority scale was conceptualized, operationalized, and empirically validated by a team of leading scholars in the domain of entrepreneurship, innovation management, and consumer marketing:

• Nikolaus Franke: Professor of Innovation and Entrepreneurship and Founder/Director of the Institute for Entrepreneurship and Innovation at the Vienna University of Economics and Business (WU Vienna), Austria. Dr. Franke is globally renowned for his seminal research on open innovation, lead users, and mass customization toolkits.
• Peter Keinz: Associate Professor at the Institute for Entrepreneurship and Innovation at the Vienna University of Economics and Business (WU Vienna), Austria. His research focuses on open innovation, business model development, and user-driven technology commercialization.
• Christoph J. Steger: Researcher and management consultant specializing in innovation management, customer-centric product development, and the commercial viability of mass customization architectures.

Purpose

The primary purpose of the Customized Product Superiority (CPS) scale is to measure the extent to which an individual consumer perceives a customized, self-designed product to surpass the optimal pre-manufactured, standardized alternative available in the market. In contemporary consumer research and behavioral economics, mass customization—the capability to produce individualized goods at near mass-production efficiency—is theorized to generate substantial incremental value for consumers. However, until the late 2000s, empirical research suffered from significant ambiguity regarding why, when, and to what extent customized products actually deliver superior subjective value compared to standard commercial offerings.

Franke, Keinz, and Steger (2009) designed the CPS scale to address these fundamental theoretical and practical questions. The scale establishes an explicit, comparative frame of reference: it does not merely evaluate absolute satisfaction or aesthetic appreciation of a created object, but forces a direct cognitive comparison against “the best alternative standard product.” This comparative standard is vital for both academic modeling and managerial application, as rational consumers weigh the benefits of customization (fit to personal preferences) against its associated costs (cognitive effort, waiting time, price premiums, and design risk).

In empirical research, the CPS instrument serves several core functions:

• Mediation Modeling: CPS functions as a primary psychological mediator that translates individual antecedent factors—such as consumer preference insight, preference stability, and toolkit usability—into behavioral outcomes like willingness to pay (WTP), brand loyalty, and repeat purchase intentions.
• Decomposition of Value: The scale enables researchers to disentangle the “preference fit” value of customization (i.e., objective functional and aesthetic utility) from psychological process phenomena, such as the IKEA effect or feelings of psychological ownership induced by the act of self-design.
• Boundary Condition Analysis: CPS provides an operational metric to detect when mass customization systems fail—specifically identifying scenarios where low consumer self-insight or poorly designed configurators lead to sub-optimal choices that fail to beat off-the-shelf alternatives.
• Benchmarking in New Product Development: In commercial contexts, firms utilize the scale to evaluate user satisfaction with digital configuration systems and to audit whether the custom configurations generated by consumers justify the operational overhead of customized manufacturing lines.

Psychological Construct

The Customized Product Superiority construct is rooted in multi-attribute utility theory and cognitive decision psychology. At its conceptual core, CPS represents a holistic, multi-faceted cognitive assessment that a customized product provides a superior vector of utility characteristics relative to the most competitive standard alternative available in the marketplace.

The construct encompasses several interrelated cognitive and evaluative facets:

1. Global Evaluative Superiority

Global superiority reflects the consumer’s overarching gestalt evaluation that the self-designed product outperforms standard market offerings. Grounded in holistic judgment models, this dimension measures the overall cognitive appraisal of value, capturing intuitive, summary impressions that emerge when integrating multiple distinct product attributes into a unified evaluation.

2. Preference-Congruence and Idiosyncratic Fit

Grounded in Lancaster’s characteristics approach to consumer demand, consumers possess idiosyncratic ideal points across multi-dimensional attribute spaces. Standard mass-produced goods, by definition, represent market compromises targeted at population averages or broad segments. Preference-congruence measures the degree to which a customized configuration matches the unique, idiosyncratic taste structure of the individual consumer, minimizing the distance between the product’s actual attributes and the user’s latent ideal preferences.

3. Personal Suitability

While preference fit emphasizes attribute alignment, personal suitability captures the perceived pragmatic compatibility between the customized good and the consumer’s lifestyle, habits, physiology, or specific usage scenarios. It reflects a contextualized judgment: the self-designed artifact is deemed exceptionally relevant and appropriate for the individual’s specific behavioral routines or self-concept.

4. Aesthetic and Design Superiority

Customization toolkits frequently afford control over visual, tactile, and expressive attributes (e.g., color schemes, surface textures, graphic motifs, geometry). The design superiority dimension captures the perceived aesthetic superiority of the customized product over standard designs. This facet reflects not only perceptual attractiveness, but also symbolic self-expression and identity signaling.

5. Functional and Performance Superiority

Beyond aesthetic stylization, many mass-customized items allow functional tailoring (e.g., ergonomic contouring, technical capacity adjustments, specialized feature modularity). Functional superiority measures the belief that the customized item provides superior tangible utility, operational effectiveness, or technical performance compared to pre-configured commercial variants.

Theoretical Framework

The Customized Product Superiority scale is conceptually underpinned by three major psychological and economic frameworks:

Lancaster’s Characteristics Approach to Consumer Demand

Traditional neoclassical economics conceptualized goods as homogeneous entities. In contrast, Kelvin Lancaster (1966) posited that goods are collections of discrete, objectively identifiable characteristics, and consumers derive utility from these underlying characteristics rather than the product per se. Because consumers possess heterogeneous utility functions, standardized market offerings inevitably generate a “compromise penalty.” When consumers configure a product using a mass customization system, they can select an optimal bundle of characteristics, thereby eliminating or substantially reducing this compromise penalty. The CPS construct operationalizes the consumer’s subjective realization that this optimal bundle has been achieved relative to the best available compromise product.

Cognitive Self-Referencing and Self-Congruence Theory

According to self-congruence theory (Sirgy, 1982) and cognitive self-referencing models, individuals actively seek objects and environments that reinforce and project their self-concept. In mass customization, the product ceases to be a purely external creation; it becomes an externalized manifestation of the self. During the co-design process, consumers encode product attributes through the prism of their own self-schemas. Consequently, the resulting customized product achieves high psychological congruence, leading the consumer to perceive it as fundamentally more suitable, appealing, and superior to any generic product conceived by a corporate designer.

Effort Justification and Psychological Ownership

Beyond rational utility maximization, the evaluation of customized products is influenced by cognitive biases and motivational mechanisms. Drawing on cognitive dissonance theory (Festinger, 1957) and effort justification (Aronson & Mills, 1959), individuals value outcomes more highly when they have invested personal cognitive effort and agency into their realization. Furthermore, the theory of psychological ownership (Pierce, Kostova, & Dirks, 2003) demonstrates that investing the self, exerting control, and intimately knowing an object fosters feelings of possession. Franke et al. (2009) established that while these psychological mechanisms contribute to the customized product’s subjective value, CPS specifically captures the cognitive output of this evaluation—the realized judgment of product excellence and fit relative to the market benchmark.

Validity

The psychometric validity of the Customized Product Superiority scale has been rigorously evaluated across laboratory experiments, field studies, and cross-sectional consumer surveys.

Construct and Structural Validity

Franke, Keinz, and Steger (2009) tested the CPS scale across multiple distinct product domains featuring varying levels of functional and hedonic complexity (specifically: high-performance technical skis, aesthetic mobile phone covers, and personalized online news portals). Confirmatory factor analysis demonstrated that the five items loaded strongly onto a single underlying construct, confirming unidimensionality. Standardized factor loadings across studies were high, consistently exceeding .80, with robust average variance extracted (AVE) values well above the recommended .50 threshold (typically ranging from .68 to .81).

Convergent Validity

Convergent validity has been confirmed through strong and statistically significant correlations with closely related psychological and behavioral constructs. Across experimental conditions, CPS correlated strongly with:

• Preference Fit: High positive correlations (typically r = .65 to .78, p < .001) with independent measures of how closely the product matched the user’s pre-existing preferences.
• Purchase Intention: Robust positive associations with the intention to buy the self-designed product over competitive items (r > .60).
• Product Satisfaction: Strong alignment with post-design evaluative satisfaction metrics (r > .70).

Discriminant Validity

To establish discriminant validity, Franke et al. applied the Fornell–Larcker criterion, demonstrating that the square root of the AVE for CPS exceeded its inter-construct correlations with other key dimensions in the customization ecosystem, including:

• Design Process Enjoyment (Flow): Demonstrating that evaluating the physical product as superior is distinct from simply having fun during the configuration process.
• Product Category Involvement: Confirming that CPS does not merely capture general enthusiasm or domain expertise for the product category.
• Psychological Ownership: Confirming that cognitive judgments of product superiority are structurally distinct from feelings of psychological possession.

Predictive and Criterion Validity

A hallmark of the Franke et al. (2009) investigation was establishing the predictive validity of the CPS scale using incentive-aligned, behavioral outcome measures. The authors utilized real-money Becker–DeGroot–Marschak (BDM) experimental auctions to measure consumers’ actual willingness to pay (WTP). CPS significantly predicted WTP premiums; consumers who rated their customized product higher on the CPS scale exhibited substantially higher real-dollar bid prices for their self-designed items relative to standard, pre-manufactured alternatives (often exceeding a 100% price premium in high-involvement conditions).

Reliability

The Customized Product Superiority scale consistently exhibits exemplary internal consistency reliability across varied empirical investigations, cultural contexts, and product categories.

Internal Consistency Reliability

In the original validation studies conducted by Franke, Keinz, and Steger (2009), the internal consistency of the 5-item instrument was systematically assessed across independent study samples:

• Study 1 (Customized Skis – High Functional Complexity): Cronbach’s α = .93.
• Study 2 (Customized Cell Phone Covers – High Aesthetic/Visual Salience): Cronbach’s α = .91.
• Study 3 (Customized Daily Newspapers – High Informational Complexity): Cronbach’s α = .89.

Subsequent studies by independent researchers in human-computer interaction, digital marketing, and retail management have corroborated these findings, reporting Cronbach’s alpha coefficients routinely ranging between .88 and .95. Furthermore, composite reliability (CR) metrics calculated in structural equation modeling contexts regularly surpass .90, significantly higher than the standard academic benchmark of .70.

Item-Total Correlations and Stability

Corrected item-total correlations across all five items consistently exceed .70, indicating that each individual item contributes meaningfully and homogeneously to the aggregate score. Deletion of any single item does not result in an increase in Cronbach’s alpha, supporting the retention of all five indicators. While test-retest reliability is context-dependent—given that the scale is administered following a specific product configuration task—longitudinal studies tracking consumers over multi-week delivery intervals confirm that superiority evaluations remain stable post-delivery, provided the physical product matches the digital configuration.

Factor Analysis

The psychometric structure of the Customized Product Superiority scale was developed and corroborated using rigorous exploratory and confirmatory factor analytic procedures.

Exploratory Factor Analysis (EFA)

During initial instrument development, exploratory factor analysis utilizing principal axis factoring and maximum likelihood estimation with varimax and oblimin rotations revealed a clean, single-factor solution. The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy consistently exceeded .85, verifying sampling suitability, while Bartlett’s test of sphericity yielded highly significant results (p < .001). A single dominant eigenvalue well above 3.5 was extracted, explaining between 68% and 78% of the total variance across product samples. All five items loaded heavily onto this single factor (loadings > .75), with no problematic secondary cross-loadings.

Confirmatory Factor Analysis (CFA)

Confirmatory factor analysis conducted using covariance-based structural equation modeling (CB-SEM) confirmed that the unidimensional measurement model possesses excellent goodness-of-fit to empirical data across multiple experimental settings. Standard fit indices across validation cohorts yielded exemplary metrics:

• Comparative Fit Index (CFI): .97 to .99 (threshold > .95)
• Tucker-Lewis Index (TLI): .96 to .98 (threshold > .95)
• Root Mean Square Error of Approximation (RMSEA): .035 to .058 (threshold < .06 to .08)
• Standardized Root Mean Square Residual (SRMR): .018 to .032 (threshold < .05)
• Chi-Square / Degrees of Freedom Ratio (χ²/df): Ranged between 1.2 and 2.4 (threshold < 3.0)

Standardized factor loadings (λ) for the individual items onto the latent CPS construct in the primary CFA models were uniformly high:

• Item 1 (Overall better): λ ≈ .86 to .91
• Item 2 (Preference fit): λ ≈ .88 to .92
• Item 3 (Personal suitability): λ ≈ .87 to .90
• Item 4 (Design superiority): λ ≈ .80 to .85
• Item 5 (Functional superiority): λ ≈ .79 to .86

These empirical findings provide unambiguous evidence that the five items reflect a cohesive, highly reliable unidimensional psychometric construct.

Instrument / Measurement Tool

• Instrument Name: Customized Product Superiority (CPS) Scale
• Authors: Nikolaus Franke, Peter Keinz, and Christoph J. Steger (2009)
• Construct Assessed: Consumer perceived superiority of a customized/self-designed product relative to the best available standard alternative across overall utility, preference alignment, suitability, aesthetics, and functionality
• Test Format: Self-administered paper-and-pencil or digital/online psychometric questionnaire
• Number of Items: 5 items
• Dimensionality: Unidimensional
• Response Scale: 7-point Likert scale (1 = strongly disagree, 7 = strongly agree)
• Administration Time: Approximately 1 to 2 minutes
• Scoring Protocol: There are no reverse-coded items. An aggregate Customized Product Superiority score is computed by calculating the arithmetic mean of all five items. The resulting continuous index ranges from 1.0 to 7.0, where higher scores reflect greater perceived superiority of the customized product over standard market alternatives.
• Target Population: Consumers, study participants, or end-users who have engaged with a mass customization system, configurator, or co-design process and evaluated the resulting artifact.

Permissions & Fee and Test Year

The Customized Product Superiority scale was formally published in the peer-reviewed academic literature in 2009 by the American Marketing Association in the Journal of Marketing. The instrument is accessible for academic, scientific, and non-commercial educational research purposes without licensing fees, provided that standard scholarly attribution and formal citation are given to the original authors (Franke, Keinz, & Steger, 2009). Organizations or commercial entities seeking to incorporate the instrument into proprietary customer satisfaction software, commercial market research platforms, or commercial consulting frameworks should consult the American Marketing Association (AMA) or contact the corresponding author regarding copyright and licensing permissions.

References

• Aronson, E., & Mills, J. (1959). The effect of severity of initiation on liking for a group. The Journal of Abnormal and Social Psychology, 59(2), 177–181. https://doi.org/10.1037/h0042351
• Festinger, L. (1957). A Theory of Cognitive Dissonance. Stanford University Press.
• Franke, N., Keinz, P., & Steger, C. J. (2009). Testing the value of customization: When do customers really prefer products tailored to their preferences? Journal of Marketing, 73(5), 103–121. https://doi.org/10.1509/jmkg.73.5.103
• Franke, N., & Schreier, M. (2010). Why customers value self-designed products: The importance of process effort and enjoyment. Journal of Product Innovation Management, 27(7), 1020–1031. https://doi.org/10.1111/j.1540-5885.2010.00768.x
• Lancaster, K. J. (1966). A new approach to consumer theory. Journal of Political Economy, 74(2), 132–157. https://doi.org/10.1086/259131
• Norton, M. I., Mochon, D., & Ariely, D. (2012). The IKEA effect: When labor leads to love. Journal of Consumer Psychology, 22(3), 453–460. https://doi.org/10.1016/j.jcps.2011.08.002
• Pierce, J. L., Kostova, T., & Dirks, K. T. (2003). The state of psychological ownership: Integrating and extending a century of research. Review of General Psychology, 7(1), 84–107. https://doi.org/10.1037/1089-2680.7.1.84
• Sirgy, M. J. (1982). Self-concept in consumer behavior: A critical review. Journal of Consumer Research, 9(3), 287–300. https://doi.org/10.1086/208924

Items of the Scale