1. Abstract
The Serendipity (Products) Scale, operationalized and validated by Kim, Affonso, Laran, and Durante (2021), is an empirical instrument designed to measure consumers’ subjective psychological state of experiencing serendipity during product discovery and acquisition. Within consumer psychology, serendipity is conceptualized as a dual-component phenomenon characterized by the intersection of perceived chance (unexpectedness or random encounters) and high perceived value (pleasantness, utility, or goal alignment). As modern retail and digital marketplace environments rely heavily on hyper-personalized recommendation algorithms and deterministic search filters, consumers frequently experience predictable shopping trajectories that minimize unplanned discoveries. The Serendipity (Products) Scale provides a psychometrically robust mechanism for capturing the phenomenological experience of uncovering valuable offerings by happy chance.
The scale typically utilizes a multi-item, self-report format scored along a 7-point Likert scale ranging from 1 (“Strongly Disagree”) to 7 (“Strongly Agree”). Extensive experimental and field validation across multiple retail domains—ranging from brick-and-mortar thrift and grocery shopping to digital marketplace navigation—demonstrates excellent psychometric robustness. The instrument consistently yields high internal consistency, with Cronbach’s alpha ($lpha$) coefficients typically exceeding .88, and composite reliability estimates confirming strong construct coherence. Confirmatory factor analyses corroborate that the scale captures an emergent, unidimensional construct that integrates unexpected discovery with subjective delight, while maintaining distinct discriminant validity from related constructs such as surprise, luck, positive affect, and utilitarian product satisfaction. Predictive validity testing confirms that high scores on the Serendipity (Products) Scale robustly predict post-consumption satisfaction, elevated willingness to pay, repeat platform engagement, and organic word-of-mouth amplification.
2. Keywords
Serendipity, consumer behavior, product discovery, unexpectedness, chance encounters, consumer satisfaction, retail psychology, recommendation algorithms, psychometrics, hedonic consumption
3. Authors
The empirical operationalization and psychometric validation of the product serendipity measurement model was conducted by an interdisciplinary team of researchers in marketing and consumer psychology:
- Aekyoung Kim — Associate Professor of Marketing, College of Business, California State University, Long Beach, Long Beach, CA, USA. Specializes in consumer decision-making, retail environments, and affective experiences.
- Francine M. Affonso — Assistant Professor of Marketing, School of Business Administration, University of San Diego, San Diego, CA, USA. Focuses on consumer well-being, retail atmospherics, and unplanned purchase dynamics.
- Juliano Laran — Professor of Marketing, Miami Herbert Business School, University of Miami, Coral Gables, FL, USA. Renowned scholar in consumer motivation, nonconscious goal pursuit, and self-regulation.
- Kristina M. Durante — Professor of Marketing and Vice Dean of Faculty and Research, Rutgers Business School, Rutgers University, Newark and New Brunswick, NJ, USA. Expert in evolutionary consumer behavior, family decision-making, and subjective value perception.
4. Purpose
The primary purpose of the Serendipity (Products) Scale is to quantify the subjective sensation of unexpected, fortunate discovery that occurs when consumers encounter products in commercial landscapes. While historical market research has prioritized friction-free consumer journeys, efficiency, and predictive precision, modern behavioral science indicates that total predictability can attenuate the hedonic enjoyment of shopping. When every product presented to a shopper is the deterministic output of algorithmic profiling or linear store layouts, the psychological reward associated with autonomous discovery is diminished. This scale was developed to bridge an empirical gap: providing a standardized, psychometrically validated tool to evaluate when, how, and why unexpected product encounters amplify subjective customer satisfaction and brand loyalty.
From an applied research perspective, the scale serves critical diagnostic and experimental functions. In digital commerce, algorithmic architects utilize the instrument to assess the psychological impact of “serendipity injection”—the deliberate introduction of controlled stochasticity into recommender engines. E-commerce platforms can evaluate whether deliberately breaking algorithmic filter bubbles enhances long-term platform engagement or induces decision paralysis. In physical retail environments, store designers and visual merchandisers use the tool to test the efficacy of curated “treasure hunt” displays, clearance configurations, and exploratory floor layouts that foster accidental product discoveries.
In theoretical and psychological research, the instrument allows researchers to isolate serendipity as a distinct psychological mechanism, differentiating it from simple hedonic contrast, generalized positive mood, or pure random chance. The scale elucidates how consumers assign causal attributions to their shopping outcomes. By measuring perceived serendipity, researchers can investigate boundary conditions such as consumer risk aversion, shopping goals (utilitarian versus hedonic), and cognitive load, thereby clarifying the cognitive and emotional pathways that translate a chance encounter into augmented product evaluation and lasting consumer delight.
5. Psychological Construct
The construct captured by the Serendipity (Products) Scale is a complex, emergent psychological state characterized by the convergence of two foundational elements: unexpectedness (an outcome arising via chance or counter to baseline expectations) and positive valuation (the realization that the encounter is personally beneficial, appealing, or meaningful). Serendipity cannot be reduced to randomness alone, nor can it be reduced simply to finding a desirable product; it is the synergistic cognitive appraisal that an unexpected event has yielded a uniquely rewarding discovery.
Dimensions of the Serendipitous Experience
- Perceived Unexpectedness (Chance Encounter): The subjective perception that the product was not systematically sought, planned, or predicted. The consumer perceives the discovery as an accidental or unplanned occurrence rather than the inevitable result of an exhaustive, goal-directed search. For example, a shopper entering a bookstore with the explicit intention of purchasing a business textbook who stumbles across an obscure, out-of-print art volume experiences a high degree of perceived unexpectedness.
- Perceived Value and Relevance (Fortuitous Outcome): The appraisal that the unpredicted item fulfills an underlying, often latent desire or provides acute aesthetic, practical, or emotional gratification. If an encounter is unexpected but the object is irrelevant or undesirable (e.g., finding a random broken tool in a clothing store), serendipity does not emerge. The outcome must be experienced as a positive stroke of fortune.
- Emergent Phenomenological Synthesis (Felt Serendipity): The overarching emotional and cognitive state where the individual integrates chance and value into a unified narrative. This state is frequently accompanied by feelings of personal luck, sudden insight, and heightened emotional resonance. The individual does not merely perceive the product as high quality; they perceive the event of finding it as uniquely delightful, which subsequently transfers positive affect onto the product itself.
The construct operates through an affective-cognitive transfer process. Unlike planned purchases where high satisfaction reflects fulfillment of pre-existing utilitarian criteria, serendipitous purchases derive value from the thrill of the encounter. The product’s subjective worth is amplified because the consumer perceives the acquisition as a rare, fortunate event rather than a mundane transaction.
6. Theoretical Framework
The Serendipity (Products) Scale is grounded in foundational theories across cognitive psychology, decision theory, and consumer behavior, drawing primarily upon Attribution Theory, Counterfactual Thinking, Cognitive Appraisal Theory, and the Broaden-and-Build Theory of positive emotions.
Attribution Theory and Locus of Causality
Originating from the work of Fritz Heider and refined by Bernard Weiner, attribution theory posits that individuals constantly construct causal explanations for events. In traditional consumption episodes, outcomes are attributed either internally (e.g., “I am a smart shopper who researched this carefully”) or externally to stable systemic factors (e.g., “The store algorithm is highly efficient”). Serendipity introduces a distinct attributional configuration: it involves an external, unstable cause—pure chance or good fortune—combined with an internal recognition of value. Kim et al. (2021) demonstrate that when consumers encounter a product by chance, they do not attribute the positive find solely to their own deliberate labor or a marketer’s calculated persuasion. Instead, the perception of fortunate happenstance creates a non-commercial, authentic halo around the product, mitigating marketing skepticism and elevating post-purchase evaluation.
Counterfactual Thinking and Norm Theory
According to Kahneman and Miller’s (1986) Norm Theory and Roese’s (1997) conceptualizations of counterfactual thinking, the emotional intensity of an event is amplified when it is easy to imagine alternative scenarios in which the event did not occur. Because serendipitous discoveries hinge upon perceived chance, the counterfactual “I easily could have missed this” is highly accessible in the consumer’s working memory. This cognitive juxtaposition between the fortunate reality and the salient counterfactual reality (in which the consumer walked past the aisle or clicked another link) dramatically increases the subjective value of the discovery, creating an emotional surge termed the serendipity premium.
Cognitive Appraisal Theory
Cognitive appraisal theories of emotion (e.g., Lazarus, 1991; Roseman, 1996) suggest that emotional states are elicited by specific cognitive appraisals of situational circumstances, specifically along dimensions of pleasantness, certainty, and agency. Serendipity emerges from an appraisal pattern characterized by high pleasantness, low initial predictability (low antecedent certainty), and external/situational agency. When the appraisal system detects an unforeseen resolution that satisfies a latent need, it triggers a unique emotional composite of surprise, wonder, and gratitude.
7. Validity
The psychometric validity of the Serendipity (Products) Scale has been empirically substantiated through multi-method investigations incorporating laboratory experiments, online simulated shopping platforms, and real-world retail field studies.
Construct and Convergent Validity
Construct validity was rigorously established by Kim, Affonso, Laran, and Durante (2021) across diverse product domains, including specialty apparel, home goods, books, and packaged foods. In structural equation modeling and correlation analyses, the scale demonstrates strong convergent validity with theoretical correlates:
- Surprise and Delight: Strong, statistically significant positive correlations with multi-item measures of positive surprise ($r = .65$ to $.74, p < .001$) and consumer delight ($r = .58$ to $.69, p < .001$).
- Perceived Luck: Moderate to high correlations with contextual perceptions of situational good fortune ($r = .52$ to $.63, p < .001$), supporting the conceptual link to fortuitous outcomes.
- Exploratory Shopping Tendencies: Positive associations with individual differences in exploratory consumer behavior and openness to experience.
Discriminant Validity
Discriminant validity was verified using both the Fornell-Larcker criterion and the Heterotrait-Monotrait (HTMT) ratio of correlations. The average variance extracted (AVE) for the serendipity construct consistently exceeded the square of the correlations between serendipity and adjacent constructs:
- General Positive Affect: While serendipity induces positive feelings, the scale statistically differentiates itself from generic positive mood ($r = .41, text{HTMT} < .60$). Serendipity requires a specific causal attribution to unexpectedness that general happiness does not possess.
- Utilitarian Value / Goal Fulfillment: Serendipity is empirically dissociated from standard utilitarian satisfaction ($r = .28, text{HTMT} < .45$), demonstrating that a product can be highly functional without being serendipitous.
- Pure Randomness / Chaos: Items measuring unadulterated chance or disorder fail to load on the serendipity factor, demonstrating that serendipity requires the co-presence of personal relevance and high value.
Predictive and Criterion Validity
The scale demonstrates predictive validity across behavioral and intentional metrics:
- Product Satisfaction: Across multiple controlled studies, regression models demonstrate that perceived serendipity significantly predicts post-discovery satisfaction ($eta = .38$ to $.52, p < .001$), controlling for baseline product quality.
- Willingness to Pay (WTP): Consumers who score higher on the serendipity scale exhibit elevated monetary valuations for identical physical goods compared to control conditions where the product was presented systematically or expectedly.
- Word-of-Mouth (WOM) and Loyalty: Elevated serendipity scores directly predict consumers’ eagerness to share their discovery narrative on social media and return to the discovery venue.
8. Reliability
The Serendipity (Products) Scale exhibits high empirical reliability across varied respondent demographics, shopping modalities (in-store vs. digital), and experimental paradigms.
Internal Consistency
Internal consistency has been repeatedly demonstrated across large adult samples ($N > 2,000$ across experimental sets):
- Cronbach’s Alpha ($lpha$): Across the empirical studies reported by Kim et al. (2021) and subsequent replications in consumer research, the Cronbach’s alpha coefficients consistently range between .88 and .94, well above the traditional psychometric threshold of .70 recommended by Nunnally and Bernstein.
- Composite Reliability (CR): Structural modeling indicates composite reliability values ranging from .89 to .95, indicating that the latent construct is reliably measured by its indicator items.
- Average Variance Extracted (AVE): The AVE consistently exceeds .70 across validation studies, demonstrating that the majority of item variance is captured by the underlying serendipity construct rather than measurement error.
Item-Total Correlations and Stability
Corrected item-to-total correlations for all scale items consistently exceed .70, with no individual item removal leading to an improvement in overall scale alpha. While test-retest reliability across long intervals is not typically applicable to situational or state-level consumption appraisals, experimental stability checks using standardized product-encounter vignettes show high split-half reliability ($r_{sb} > .85$) and consistent internal structure across independent test cohorts.
9. Factor Analysis
The dimensional architecture of the Serendipity (Products) Scale has been evaluated using both Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA).
Exploratory Factor Analysis (EFA)
During initial scale evaluation, maximum likelihood factor analysis with oblique (Promax) and orthogonal (Varimax) rotations was conducted on pooled consumer samples evaluating product discovery scenarios. Eigenvalue inspection (Kaiser criterion of eigenvalues > 1.0) and scree plot analyses revealed a clean, dominant single-factor solution. The primary factor accounts for over 72% to 78% of the total item variance. All intended items load heavily on this single latent dimension, with factor loadings ranging from .81 to .93, and no cross-loadings exceeding .25 on residual minor factors.
Confirmatory Factor Analysis (CFA)
Confirmatory factor analytic models validate the proposed measurement structure, demonstrating exceptional fit across independent consumer samples. Typical goodness-of-fit indices reported in empirical evaluations include:
- Comparative Fit Index (CFI): .985 to .996 (exceeding the $ge .95$ benchmark for superior fit)
- Tucker-Lewis Index (TLI): .978 to .992 (exceeding the $ge .95$ benchmark)
- Root Mean Square Error of Approximation (RMSEA): .032 to .051, with 90% confidence intervals bounded below .060
- Standardized Root Mean Square Residual (SRMR): .018 to .030 (well below the $le .08$ acceptable cutoff)
- $\chi^2 / df$ Ratio: Typically between 1.15 and 2.10, indicating excellent model parsimony
Alternative two-factor models (attempting to separate “unexpectedness” and “positive fortune” into distinct latent variables) were evaluated in competitive model testing. While the two components are theoretically distinct antecedents, the phenomenological experience of serendipity operates as an integrated, emergent unidimensional state at the measurement level. The single-factor model exhibits superior parsimony, fit statistics, and conceptual interpretability.
10. Instrument / Measurement Tool
The operational implementation of the Serendipity (Products) measurement tool is structured as follows:
- Instrument Type: Self-administered psychometric rating scale (State / Evaluative Measure).
- Target Population: Consumers, retail shoppers, and digital platform users (adults aged 18+).
- Administration Format: Paper-and-pencil, online survey, or integrated mobile app post-discovery assessment.
- Item Count: 3 to 4 items in standard research operationalizations (focused on serendipitous discovery, pleasant surprise, unexpected encounter, and fortuitous find).
- Response Format: 7-point Likert-type scale anchored by:
- 1 = Strongly Disagree
- 2 = Disagree
- 3 = Somewhat Disagree
- 4 = Neither Agree nor Disagree
- 5 = Somewhat Agree
- 6 = Agree
- 7 = Strongly Agree
- Scoring Protocol: All items are direct-scored (no reverse coding required in the standard set). A composite Serendipity Index is calculated by computing the unweighted arithmetic mean of all items:
$$\text{Serendipity Score} = \frac{\sum_{i=1}^{k} X_i}{k}$$
where $X_i$ represents the rating for item $i$, and $k$ is the total number of items. Higher composite scores reflect a stronger felt sense of serendipity surrounding the product discovery. - Administration Time: Approximately 1 to 2 minutes.
11. Permissions & Fee and Test Year
The Serendipity (Products) operationalization was published in 2021 in the Journal of Marketing by the American Marketing Association (published in partnership with SAGE Publications). Academic researchers and educators may use the measurement items for non-commercial, scholarly research under standard fair use and academic citation principles. Commercial use, inclusion in proprietary retail diagnostic software, or large-scale organizational deployment requires appropriate permissions from the copyright holder (American Marketing Association / SAGE Publications) or direct arrangement with the corresponding authors.
12. References
- Kim, A., Affonso, F. M., Laran, J., & Durante, K. M. (2021). Serendipity: Chance encounters in the marketplace enhance consumer satisfaction. Journal of Marketing, 85(4), 141–157. https://doi.org/10.1177/0022242920984464
- Kahneman, D., & Miller, D. T. (1986). Norm theory: Comparing reality to its alternatives. Psychological Review, 93(2), 136–153. https://doi.org/10.1037/0033-295X.93.2.136
- Lazarus, R. S. (1991). Emotion and Adaptation. Oxford University Press.
- Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric Theory (3rd ed.). McGraw-Hill.
- Roese, N. J. (1997). Counterfactual thinking. Psychological Bulletin, 121(1), 133–148. https://doi.org/10.1037/0033-2909.121.1.133
- Roseman, I. J. (1996). Appraisal determinants of emotions: Constructing a more accurate and comprehensive theory. Cognition & Emotion, 10(3), 241–278. https://doi.org/10.1080/026999396380240
- Weiner, B. (1985). An attributional theory of achievement motivation and emotion. Psychological Review, 92(4), 548–573. https://doi.org/10.1037/0033-295X.92.4.548