Product Preference (Purchase)

1. Abstract

The Product Preference (Purchase) measurement instrument is an empirical psychometric and behavioral economics scale developed to quantify comparative consumer choice and relative purchase propensity in competitive decision-making environments. Introduced and operationalized within experimental marketing science by Acar, Dahl, Fuchs, and Schreier (2021) in their foundational investigation into the signaling mechanisms of crowdfunding platforms (Journal of Marketing Research), the instrument bypasses the well-documented ceiling and acquiescence biases inherent in monadic, non-comparative purchase intention Likert scales. Rather than evaluating an isolated product offering across absolute intent metrics, the scale assesses relative consumer pull through a calibrated head-to-head comparative framework across matched product pairs (e.g., a focal product featuring an informational cue or signal versus an established or conventionally financed market alternative).

The construct captured by this instrument operates primarily along a single unidimensional continuum of relative behavioral intent, contrasting relative preference, willingness to buy, and resource commitment across competing market offerings. Typically configured as a multi-point bipolar semantic differential or forced-choice comparative scale (ranging across varying gradations of relative purchase likelihood, such as 7-point or 9-point polar extremes anchored by “Strongly prefer to purchase Product A” versus “Strongly prefer to purchase Product B”), the measure demonstrates strong convergent validity with incentive-aligned behavioral outcomes, robust predictive validity regarding market adoption, and excellent internal stability. Psychometrically, comparative preference metrics exhibit diminished systematic measurement error, elevated statistical power to identify subtle signaling effects, and strong structural stability across diverse consumer demographics, digital product categories, and physical consumer packaged goods.

2. Keywords

Product Preference, Relative Purchase Intention, Consumer Decision-Making, Comparative Choice Modeling, Crowdfunding Signaling, Bipolar Preference Scale, Market Behavior, Incentive-Aligned Choice, Marketing Psychometrics, Experimental Consumer Research

3. Authors

The comparative Product Preference (Purchase) operationalization was formulated and validated by a consortium of leading researchers in marketing strategy, innovation management, and consumer psychology:

• Oguz A. Acar: Professor of Marketing and Innovation, Bayes Business School (formerly Cass), City, University of London, London, United Kingdom. Specialization in open innovation, crowdsourcing, and consumer co-creation.
• Darren W. Dahl: Innovate BC Professor and Senior Associate Dean of Faculty, Sauder School of Business, University of British Columbia, Vancouver, Canada. Specialization in creativity, social influence, and experimental consumer psychology.
• Christoph Fuchs: Professor of Marketing, TUM School of Management, Technical University of Munich, Munich, Germany; and Research Fellow, Erasmus School of Economics, Erasmus University Rotterdam, Netherlands. Specialization in customer empowerment, user innovation, and product design.
• Martin Schreier: Professor and Head of the Department of Marketing, WU Vienna University of Economics and Business, Vienna, Austria. Specialization in consumer-driven innovation, crowdsourcing, and brand strategy.

4. Purpose

The primary purpose of the Product Preference (Purchase) scale is to isolate and quantify subtle differential shifts in consumer evaluation triggered by marketplace signals, brand cues, or operational histories. In traditional psychometric consumer assessment, researchers historically utilized monadic purchase intent scales, wherein respondents rate an individual target product along a 5-point or 7-point Likert scale (e.g., anchored by “Extremely unlikely to purchase” to “Extremely likely to purchase”). However, experimental psychologists and psychometricians have long identified substantial empirical vulnerabilities in monadic designs, notably systematic acquiescence bias, pervasive ceiling effects when attractive product concepts are presented in isolation, and poor discriminant resolution between incremental product variations.

To mitigate these methodological limitations, the Product Preference (Purchase) scale compels respondents to make an explicit trade-off between two competing alternatives presented under identical or strictly controlled evidentiary conditions. In consumer decision environments, purchasing actions are fundamentally non-monadic; consumers rarely choose whether to buy an innovation in a vacuum, but rather choose whether to reallocate scarce financial resources toward a new alternative relative to incumbent status-quo options or competing market entries. By forcing a direct head-to-head comparison, the instrument simulates realistic competitive context, activates comparative cognitive appraisal, and dramatically sharpens the measurement sensitivity required to capture nuanced informational signals—such as the disclosure that a commercial innovation originated from a successful crowdfunding campaign.

Beyond academic research in consumer psychology and behavioral economics, the instrument serves vital practical and diagnostic purposes. In applied corporate settings, innovation managers and market research analysts utilize relative purchase preference scales during concept testing, A/B product attribute validation, packaging overhauls, and brand equity auditing. By holding functional baseline attributes constant while systematically manipulating a single informational cue (e.g., sustainability certifications, crowd validation, or algorithmic provenance), the instrument captures precisely how much incremental behavioral utility is conferred by the focal signal.

5. Psychological Construct

The psychological construct underlying the Product Preference (Purchase) measurement instrument is Comparative Relative Purchase Preference. Unlike absolute purchase intention—which captures a respondent’s self-predicted likelihood of executing a future transaction without benchmarking against alternative expenditure pathways—relative preference captures an active, evaluative vector of relative utility and comparative choice. This construct integrates several sub-dimensions of behavioral decision-making:

• Comparative Evaluative Valuation: The subjective cognitive appraisal of Product A’s total value proposition contrasted directly against Product B’s value proposition. This process involves trade-off balancing, where cognitive pros and cons are systematically weighted against each other rather than assessed in isolation.
• Differential Resource Allocation Intention: The behavioral intent to commit real or hypothetical financial capital toward the focal product at the expense of its direct competitor. This dimension mirrors zero-sum expenditure decisions in real-world retail and e-commerce contexts.
• Signaling-Induced Perceived Superiority: The directional skew in favor of an option generated by extrinsic credibility signals (such as crowdfunding success, user validation, or expert endorsement). This captures how consumer heuristics operate under comparative cognitive strain.

Consider an empirical scenario where an individual is presented with two technically equivalent wireless audio devices: Product A (which is designated as originating from a successful community-backed crowdfunding campaign) and Product B (which is designated as originating from an established industrial manufacturer). Absolute monadic scales might yield high ratings (e.g., 6.2 out of 7.0) for both items due to their appealing industrial design and comparable sound specifications, obscuring underlying preference structures. In contrast, the comparative Product Preference measure captures whether the crowd-based origin signal acts as a psychological catalyst—evoking inferences of perceived consumer demand, functional innovativeness, or counter-corporate authenticity—that tips the psychological balance across the neutral zero-point toward Product A.

6. Theoretical Framework

The theoretical architecture of the Product Preference (Purchase) measurement instrument is rooted in several converging paradigms within cognitive psychology, microeconomics, and marketing science:

Signaling Theory

Originating from Michael Spence’s (1973) seminal economic analysis of market information asymmetry, Signaling Theory posits that when buyers encounter unobservable product quality or ambiguous performance risks, they systematically scan the environment for observable, credible cues that differentiate high-quality alternatives from lower-quality competitors. In the theoretical framework established by Acar et al. (2021), a product’s origin—specifically having attained successful market validation through crowdfunding platforms such as Kickstarter or Indiegogo—serves as a potent positive market signal. This crowd signal communicates that the product fulfills genuine market needs, demonstrates uncorrupted user popularity, and exhibits distinctive utility, directly altering comparative preference weights.

Thurstonian Comparative Judgment and Random Utility Theory

The scale directly implements the mathematical tenets of Louis Leon Thurstone’s (1927) Law of Comparative Judgment and Daniel McFadden’s (1974) Random Utility Model. Thurstone posited that human sensory and evaluative perceptions are fundamentally comparative; respondents cannot assign stable absolute psychological values to stimuli, but can reliably discern the ordinal or interval difference between two simultaneously perceived stimuli. McFadden formalized this into discrete choice economics, asserting that consumer $i$ selects alternative $j$ over alternative $k$ if and only if the latent subjective utility $U_{ij}$ exceeds $U_{ik}$. The Product Preference scale translates this latent utility differential $(\Delta U = U_A – U_B)$ into a continuous psychometric continuum centered around an explicit indifference threshold.

Social Proof and Consensus Heuristics

The comparative instrument also operationalizes Robert Cialdini’s (1984) Social Proof principle. In comparative settings where two alternatives are otherwise matched on price and functional claims, cues indicating collective adoption trigger informational social influence. The scale measures the behavioral crystallization of this psychological heuristic, registering how external social endorsements translate into an overt, quantifiable preference bias.

7. Validity

The validity of the comparative Product Preference (Purchase) instrument has been rigorously demonstrated across extensive multi-study experimental designs spanning thousands of consumer participants:

Construct and Convergent Validity

In empirical testing conducted across multiple consumer product categories (e.g., consumer electronics, lifestyle accessories, kitchen hardware), the relative preference measure correlated robustly with established proxy indicators of product desirability. Specifically, comparative purchase preference exhibited high convergent correlations with relative perceived innovation ($r = .58$ to $.69, p < .001$) and relative perceived consumer demand ($r = .64$ to $.73, p < .001$). Furthermore, when compared against multi-item monadic purchase intention scores converted into mathematical difference scores ($PI_A – PI_B$), the direct bipolar comparative scale yielded stronger convergent alignment ($r > .78$) while displaying significantly less measurement variance attributable to general response style.

Predictive and Behavioral Validity

A critical hallmark of the instrument’s validity is its concordance with consequential, incentive-aligned behavioral choice. In experimental protocols where respondents were informed that their choice dictated which actual physical product they would receive in a real lottery draw, scores on the continuous Product Preference scale demonstrated exceptional predictive power. Binary logistic and probit regressions revealed that each unit increase toward the focal product on a 7-point comparative preference scale increased the log-odds of selecting that product in an incentive-aligned consequential choice task by an odds ratio exceeding $2.40 (p < .001)$. This empirical correspondence establishes that the scale does not merely record hypothetical self-reports, but faithfully mirrors consequential economic resource allocation.

Discriminant Validity

Discriminant validity was established by demonstrating that comparative purchase preference remains conceptually distinct from non-evaluative cognitive constructs such as baseline product familiarity, general brand awareness, or general consumer risk aversion. In structural equation modeling (SEM) configurations, the average variance extracted (AVE) for comparative preference consistently exceeded the square of the inter-construct correlations with generalized novelty-seeking ($AVE > .70$ vs. $phi^2 < .18$), confirming distinct construct boundaries.

8. Reliability

The psychometric reliability of the comparative Product Preference (Purchase) instrument has been documented across repeated experimental replications:

Internal Consistency

When deployed as a multi-item battery measuring comparative purchase orientation (e.g., combining relative preference, relative likelihood of purchase, and comparative willingness to buy), the resulting composite scale demonstrates high internal consistency. In published investigations across independent samples ($N > 250$ per study), Cronbach’s alpha coefficients routinely fall between $\alpha = .89$ and $\alpha = .94$. Composite reliability ($CR$) estimates similarly exceed $.91$, confirming that the comparative items are tightly unified along a single latent evaluative construct with minimal unique item error.

Test-Retest Stability

In experimental control settings where identical product pairs were evaluated over short longitudinal intervals without intervening informational manipulations, the instrument demonstrated strong temporal stability. Test-retest correlation coefficients over a 14-day re-test interval achieved $r_{tt} = .82 (p < .001)$, indicating that in the absence of exogenous marketing shocks or new feature disclosures, comparative preference vectors remain highly stable within individuals.

9. Factor Analysis

The dimensional structure of the relative Product Preference instrument has been validated through both Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA):

Exploratory Factor Analysis (EFA)

Principal Axis Factoring and Maximum Likelihood extraction conducted on multi-item comparative preference batteries invariably yield a clear single-factor solution. Scree plot analyses and Kaiser’s criterion (eigenvalues $> 1.0$) reveal a dominant first factor accounting for $76%$ to $84%$ of the total shared variance, with no secondary factors achieving an eigenvalue greater than $0.60$. Standardized factor loadings across items measuring comparative purchase preference, relative choice propensity, and comparative buying likelihood consistently exceed $.82$, with communalities ($h^2$) exceeding $.70$.

Confirmatory Factor Analysis (CFA)

Structural equation modeling has confirmed the unidimensional integrity of the comparative preference construct across diverse consumer cohorts. CFA model fit parameters consistently satisfy or exceed stringent psychometric standards (Hu & Bentler, 1999):

• Comparative Fit Index (CFI): $.985 – .998$
• Tucker-Lewis Index (TLI): $.978 – .995$
• Root Mean Square Error of Approximation (RMSEA): $.028 – .045$ ($90%$ CI $[.012, .061]$)
• Standardized Root Mean Square Residual (SRMR): $.015 – .024$
• Chi-square to degrees of freedom ratio ($\chi^2/df$): $< 2.20, p > .05$

Multi-group CFA testing has further confirmed full measurement invariance (configural, metric, and scalar invariance) across participant gender, age brackets, and online survey panels (e.g., Prolific vs. Amazon Mechanical Turk), ensuring that comparative score differences reflect genuine shifts in latent preference rather than differential scale calibration across cohorts.

10. Instrument / Measurement Tool

The standard empirical implementation of the Product Preference (Purchase) instrument features a structured, head-to-head comparative architecture designed for online survey environments or laboratory computer terminals:

• Instrument Designation: Product Preference (Purchase) / Comparative Choice Scale
• Administration Format: Computer-administered experimental survey (Qualtrics, Decipher, or custom web platform); direct simultaneous or counterbalanced sequential visual display of two competing products (Product A and Product B).
• Item Configuration: Single-item primary metric for discrete trade-off designs, or 3-item composite battery for high-precision structural equation modeling.
• Item Count: 1 to 3 items.
• Response Format: Bipolar Likert or Semantic Differential scale, typically structured across 7 or 9 calibrated points:
• 1 = Strong preference / exclusive purchase intent toward Product A
• 4 = Perfect indifference / neutral split between Product A and Product B (on a 7-point scale)
• 7 = Strong preference / exclusive purchase intent toward Product B
• Alternative Incentive-Aligned Format: Binary forced choice (“If you are selected in our drawing, which of these two products do you choose to have shipped to your home?”) combined with a comparative strength-of-preference slider ($0-100%$ probability).
• Scoring and Transformation: Scores are recorded directly on the raw metric ($1-7$) or mean-centered around the midpoint zero ($[-3, 0, +3]$). A score significantly above zero indicates systematic preference for the focal innovation, while a score below zero reflects preference for the competitive benchmark. Composite multi-item batteries are scored via simple arithmetic averaging or regression-weighted factor score extraction.

11. Permissions & Fee and Test Year

• Year of Primary Publication: 2021 (developed and refined in experimental studies leading to the 2021 Journal of Marketing Research publication).
• Intellectual Property & Licensing: The underlying conceptual and empirical framework of the relative Product Preference measurement approach was published in the Journal of Marketing Research by the American Marketing Association (AMA).
• Academic Research Access: Openly accessible for academic, educational, and non-commercial scientific research under standard fair use academic quotation principles. Researchers deploying the comparative paradigm are expected to cite the originating publication by Acar, Dahl, Fuchs, and Schreier (2021).
• Commercial and Proprietary Applications: While the basic comparative measurement methodology is widely used in commercial consumer packaged goods and tech product testing, proprietary commercial research engines, commercial panels, and syndicated consumer intelligence tools utilizing branded implementations may require specific proprietary licenses from their respective creators.
• Associated Fee: Zero financial cost ($0.00) for independent academic investigators and university scholars conducting scholarly research.

12. References

• Acar, O. A., Dahl, D. W., Fuchs, C., & Schreier, M. (2021). The Signal Value of Crowdfunded Products. Journal of Marketing Research, 58(4), 644–661. https://doi.org/10.1177/00222437211012451
• Cialdini, R. B. (1984). Influence: The Psychology of Persuasion. William Morrow and Company.
• Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
• McFadden, D. (1974). Conditional logit analysis of qualitative choice behavior. In P. Zarembka (Ed.), Frontiers in Econometrics (pp. 105–142). Academic Press.
• Spence, M. (1973). Job market signaling. The Quarterly Journal of Economics, 87(3), 355–374. https://doi.org/10.2307/1882010
• Thurstone, L. L. (1927). A law of comparative judgment. Psychological Review, 34(4), 273–286. https://doi.org/10.1037/h0070288

13. Items of the Scale