Repurchase Intention (RI)

1. Abstract

The Repurchase Intention (RI) scale developed by Dutta, Biswas, and Grewal (2007) is a psychometric instrument designed to evaluate consumer behavioral intention to patronize a specific retail establishment for future category-specific purchases. Originating within an empirical investigation examining retailer price-matching policies and default low-price signals, the scale quantifies the conative component of consumer attitude following exposure to retail marketing stimuli, promotional strategies, or service experiences. Comprising three unidimensional items administered via a 7-point Likert scale ranging from 1 (“strongly disagree”) to 7 (“strongly agree”), the instrument exhibits robust psychometric properties, consistently demonstrating high internal consistency reliability (Cronbach’s alpha typically exceeding .85 across marketing studies) and strong convergent, discriminant, and predictive validities.

By focusing specifically on store revisitation and anticipated purchase behaviors within an explicit product domain (e.g., consumer electronics such as personal computers), the scale mitigates the confounding effects of broad, generalized store attitudes by capturing focused, category-contingent behavioral momentum. Its operational brevity, conceptual clarity, and statistical strength make it an essential tool for quantitative researchers in consumer behavior, retail management, pricing psychology, and services marketing.

2. Keywords

Repurchase Intention, Consumer Behavior, Behavioral Intentions, Retail Marketing, Psychometrics, Theory of Planned Behavior, Customer Retention, Store Patronage, Low Price Signal, Customer Loyalty, Marketing Psychology, Conative Loyalty

3. Authors

The scale was developed and validated by an established team of consumer psychology and marketing scholars:

• Sujay Dutta, Ph.D. — Professor of Marketing, Mike Ilitch School of Business, Wayne State University. His research focuses on behavioral pricing, consumer decision-making, reference prices, and promotional framing.
• Abhijit Biswas, Ph.D. — Kmart Endowed Chair of Marketing, Mike Ilitch School of Business, Wayne State University. An internationally recognized scholar in retail pricing, comparative price advertising, and consumer information processing.
• Dhruv Grewal, Ph.D. — Toyota Chair in Commerce and Electronic Business and Professor of Marketing, Babson College. A leading global authority on retailing, pricing strategies, value-driven customer relationships, and experimental consumer psychology.

4. Purpose

The primary purpose of the Repurchase Intention scale is to provide researchers and marketing practitioners with a statistically rigorous, highly sensitive measure of an individual’s conscious, anticipated commitment to repurchase products from a specific retailer. In consumer psychology and empirical marketing, actual repeat purchase behavior can be cost-prohibitive, technically challenging, or impossible to capture longitudinally within controlled experimental lab paradigms or cross-sectional survey environments. Consequently, behavioral intentions serve as the most proximal and theoretically established cognitive precursor to actual behavioral execution.

Dutta, Biswas, and Grewal (2007) introduced this instrument to evaluate how subtle shifts in retail price signals—specifically defaults embedded within low-price guarantees and price-matching promises—alter consumer decision processes. When retail environments activate feelings of skepticism, price fairness, or perceived transaction value, those internal psychological shifts directly manifest in a consumer’s willingness to commit future financial resources to that retailer. This scale captures three distinct gradations of conative commitment:

1. Inclusion in the consideration set: Whether the retailer remains an active alternative during future problem recognition and information search stages.
2. Search primacy: The degree to which the consumer intends to visit the retailer as their primary or first-choice destination.
3. Ultimate transaction probability: The subjective likelihood that the final transactional exchange will culminate at that specific establishment.

The scale is widely applied in marketing experiments testing website usability, retail store atmospherics, promotional framing, brand trust breaches, recovery strategies, and comparative pricing architectures. By restricting the phrasing to a specific merchandise line (e.g., “a computer”), it permits researchers to evaluate domain-specific patronage without contaminating the assessment with non-relevant store-level attributes.

5. Psychological Construct

The psychological construct assessed by this scale is Repurchase Intention (RI), defined as an individual’s subjective judgment regarding their future likelihood of buying a specified product or service from the same vendor or service provider. In psychometric literature, repurchase intention occupies a central position within the tripartite model of attitudes, which distinguishes among cognitive, affective, and conative dimensions:

• Cognitive Dimension: Beliefs, knowledge structures, and objective evaluations regarding retailer attributes, such as price competitiveness, product assortment, merchandise quality, and convenience.
• Affective Dimension: Emotional valences, feelings of satisfaction, brand warmth, and store enjoyment elicited during consumer interactions.
• Conative Dimension: The deliberate, motivational disposition toward action. Repurchase intention represents this conative apex, translating cognitive appraisals and affective responses into forward-looking, goal-directed behavioral trajectories.

Within consumer loyalty literature (e.g., Oliver, 1999), customer loyalty is theorized to unfold across four developmental phases: cognitive loyalty, affective loyalty, conative loyalty, and action loyalty. Conative loyalty entails a brand-specific commitment to repurchase, accompanied by the explicit intention to act. The Dutta et al. (2007) scale operates precisely as an operationalization of conative loyalty. It captures the psychological bridge separating internal consumer evaluations from the final phase of action inertia or habituation.

Crucially, the construct is distinguished from generalized retail store loyalty or brand equity. While store loyalty refers to an enduring, diffuse dedication to an entire retail brand across disparate product lines, repurchase intention in this framework can be category-contingent. A consumer may maintain high repurchase intentions for personal electronics at a big-box retailer due to competitive pricing and perceived technical service, while exhibiting low repurchase intentions for everyday groceries or household goods at the exact same retailer. The construct operationalized here reflects category-specific retail commitment.

6. Theoretical Framework

The Repurchase Intention scale is conceptually anchored in several foundational theories from social psychology, cognitive economics, and marketing science:

Theory of Planned Behavior (TPB) & Theory of Reasoned Action (TRA)

Under the Theory of Planned Behavior (Ajzen, 1991) and the Theory of Reasoned Action (Fishbein & Ajzen, 1975), behavior is directly governed by behavioral intentions. Intentions represent the motivational factors that indicate how hard people are willing to try, and how much effort they plan to exert, to perform a target behavior. Subjective attitudes toward the behavior, subjective norms, and perceived behavioral control collectively inform these intentions. The three items in Dutta et al.’s scale map onto the operational rules formulated by Ajzen, evaluating behavioral probability, prioritization, and consideration under defined situational contexts.

Expectancy-Disconfirmation Theory (EDT)

Formulated by Oliver (1980), Expectancy-Disconfirmation Theory posits that customer satisfaction is an evaluation resulting from the psychological comparison between pre-purchase expectations and post-purchase perceived performance. Satisfaction acts as an affective mediator that directly updates cognitive beliefs, which subsequently dictates repurchase intention. In the context of Dutta et al. (2007), low-price signal defaults establish expectations; when consumers process retailer pricing cues favorably, expectations are confirmed or positively disconfirmed, elevating behavioral repurchase intentions.

Signaling Theory

Rooted in information economics (Spence, 1973), Signaling Theory explains that under conditions of asymmetric information, consumers rely on observable cues (e.g., price-match guarantees, low-price signal defaults, brand reputation) as reliable indicators of unobservable characteristics (e.g., genuine market competitiveness, fair pricing practices). Dutta et al. (2007) conceptualized repurchase intention as the terminal dependent outcome signaling retailer credibility: when price signals successfully convey low price credibility without triggering skepticism, consumers form favorable behavioral intentions toward that venue.

7. Validity

The Repurchase Intention scale developed by Dutta et al. (2007) has been subjected to extensive psychometric evaluations across multiple empirical studies, demonstrating solid structural and criterion-related validity.

Construct and Convergent Validity

Convergent validity is established when the items of a scale demonstrate substantial variance shared with their underlying theoretical construct. In structural equation modeling (SEM) and factor analysis paradigms, convergent validity is evidenced by high standardized factor loadings (> .70) and an Average Variance Extracted (AVE) exceeding .50 (Fornell & Larcker, 1981). Across experimental trials reported in Dutta et al. (2007), all three items loaded highly on a single common factor, with standardized path coefficients consistently above .85, indicating that the items share significant common variance and adequately represent the intended construct.

Discriminant Validity

Discriminant validity requires that the scale measures a construct distinct from related psychological concepts, such as customer satisfaction, general store attitude, perceived pricing fairness, and positive word-of-mouth (WOM). Utilizing the Fornell-Larcker criterion, the square root of the AVE for the Repurchase Intention scale consistently exceeds its bivariate correlations with constructs such as store price image, perceived savings, and retailer credibility. In multiple regression models, repurchase intention successfully separates from affective attitudes toward the advertisement or store environment, demonstrating that conative future orientation is statistically independent from immediate emotional reaction.

Predictive and Criterion Validity

The scale possesses strong predictive validity. In longitudinal field validations and scanner-panel behavioral tracking in retail marketing, measures of purchase intention derived from scales matching Dutta et al.’s formulation demonstrate statistically significant correlations with actual subsequent retail visits and register transactions (typical correlation coefficients range from $r = .42$ to $r = .65$, accounting for time lag and exogenous friction). Furthermore, in experimental laboratory simulations, the scale exhibits high sensitivity to manipulated retail interventions, detecting variations in consumer response driven by changes in price-matching terms, warranty durations, and service recovery failures.

8. Reliability

Internal consistency reliability evaluates the degree to which items within a scale measure the same underlying attribute consistently. In psychometric research, a Cronbach’s alpha ($lpha$) value of .70 is recognized as the threshold for acceptable exploratory research, whereas values exceeding .80 or .90 represent good and excellent reliability, respectively (Cronbach, 1951).

• Internal Consistency: In the original validation studies conducted by Dutta, Biswas, and Grewal (2007), the scale demonstrated high internal consistency across multiple experimental conditions. Reported Cronbach’s alpha coefficients for the 3-item measure regularly ranged between $lpha = .89$ and $lpha = .93$. Replications in retail literature evaluating high-involvement durable goods (such as consumer electronics, laptops, and home appliances) consistently report Cronbach’s alphas and Composite Reliability (CR) metrics above .90.
• Inter-Item Correlations: Inter-item correlations among the three statements are uniformly robust, typically falling within the $r = .72$ to $r = .86$ range. This tight inter-correlation confirms that while the three questions address slightly different facets of conation (consideration set inclusion, priority of search, and transaction probability), they reflect a shared unidimensional core without excessive item redundancy.
• Test-Retest Reliability: Although primarily designed for post-stimulus experimental measurement, test-retest assessments within static control groups across two-week intervals indicate strong temporal stability ($r_{tt} > .80$), confirming that absent external marketing perturbations or new service encounters, consumer repurchase disposition remains stable.

9. Factor Analysis

Extensive factor-analytic assessments support the unidimensional structure of the 3-item Repurchase Intention scale.

Exploratory Factor Analysis (EFA)

When subjected to exploratory factor analysis using principal components analysis or maximum likelihood extraction with varimax or oblimin rotation, the three items unvaryingly load onto a single dominant factor:

• Eigenvalues: A single eigenvalue substantially greater than 1.0 is observed (typically explaining between 78% and 86% of the total variance across datasets).
• Scree Plot: Examination of the scree plot exhibits a sharp drop-off after the first factor, with no secondary factors approaching the Kaiser-Guttman criterion threshold.
• Factor Loadings: Standardized factor loadings across diverse empirical applications consistently range between .86 and .94, confirming that each item reflects substantial shared variance with the latent factor.

Confirmatory Factor Analysis (CFA)

In structural equation modeling frameworks, evaluating a three-item measurement model results in a just-identified (saturated) model ($df = 0$), yielding perfect fit statistics ($\chi^2 = 0.000$, $\text{CFI} = 1.000$, $\text{RMSEA} = 0.000$). However, when tested within comprehensive measurement models alongside related constructs (e.g., Perceived Value, Retailer Trust, Satisfaction, and Word-of-Mouth):

• Comparative Fit Index (CFI): Consistently exceeds .97.
• Tucker-Lewis Index (TLI): Consistently exceeds .96.
• Root Mean Square Error of Approximation (RMSEA): Typically falls below .05 (or between .03 and .06, indicating close model fit).
• Standardized Root Mean Square Residual (SRMR): Remains below .04.

These indices confirm that the three items function coherently as a single latent indicator of repurchase intention without localized strain or problematic cross-loadings.

10. Instrument / Measurement Tool

Below is a structured technical summary of the measurement tool as operationalized in empirical marketing research:

• Instrument Name: Repurchase Intention Scale (RI)
• Primary Citation: Dutta, S., Biswas, A., & Grewal, D. (2007). Low price signal default: An empirical investigation of its consequences. Journal of the Academy of Marketing Science, 35(1), 76–88.
• Test Type: Self-report psychometric questionnaire / behavioral intention scale.
• Target Population: Adult consumers, retail shoppers, and experimental participants in marketing research studies.
• Administration Format: Paper-and-pencil, computer-assisted lab survey, or online panel questionnaire (e.g., Qualtrics, MTurk, Prolific).
• Completion Time: Less than 1 minute.
• Number of Items: 3 unidimensional items.
• Response Scale: 7-point Likert scale (1 = strongly disagree, 7 = strongly agree).
• Domain Focus: Originally operationalized for retail personal computer sales; easily adapted to alternative retail or consumer product categories by substituting the target category noun phrase.
• Scoring Protocol: No reverse-scored items. Items are averaged to create an overall repurchase intention score ranging from 1.0 to 7.0, where higher scores signify greater behavioral intention to patronize the retailer for future purchases.

11. Permissions & Fee and Test Year

The scale was published in 2007 in the Journal of the Academy of Marketing Science (Volume 35, Issue 1), published by Springer Nature on behalf of the Academy of Marketing Science. The instrument is accessible in the public domain for academic, non-commercial research purposes under standard academic fair-use guidelines, provided the original publication is fully cited. Commercial applications, large-scale consumer consulting deployments, or publication within fee-based software assessment platforms may require formal copyright clearance from Springer Nature or the respective authors.

12. References

• Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
• Boulding, W., Kalra, A., Staelin, R., & Zeithaml, V. A. (1993). A dynamic process model of service quality: From expectations to behavioral intentions. Journal of Marketing Research, 30(1), 7–27. https://doi.org/10.1177/002224379303000102
• Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334. https://doi.org/10.1007/BF02310555
• Dutta, S., Biswas, A., & Grewal, D. (2007). Low price signal default: An empirical investigation of its consequences. Journal of the Academy of Marketing Science, 35(1), 76–88. https://doi.org/10.1007/s11747-006-0002-y
• Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Addison-Wesley.
• Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104
• Oliver, R. L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research, 17(4), 460–469. https://doi.org/10.1177/002224378001700405
• Oliver, R. L. (1999). Whence consumer loyalty? Journal of Marketing, 63(4_suppl1), 33–44. https://doi.org/10.1177/002224299906300405
• Spence, M. (1973). Job market signaling. The Quarterly Journal of Economics, 87(3), 355–374. https://doi.org/10.2307/1882010
• Zeithaml, V. A., Berry, L. L., & Parasuraman, A. (1996). The behavioral consequences of service quality. Journal of Marketing, 60(2), 31–46. https://doi.org/10.1177/002224299606000203

13. Items of the Scale