Price Perception (Store Comparison) (PP)

Abstract

The Price Perception (Store Comparison) (PP) scale is a psychometric instrument formulated by Monika Kukar-Kinney and Dhruv Grewal (2007) to evaluate consumer subjective evaluations regarding the relative price standing of a focal retail establishment in comparison to its marketplace competitors. Developed within the context of retail pricing strategy and promotional mechanisms, specifically price-matching guarantees (PMGs) across traditional brick-and-mortar and electronic commerce environments, the instrument quantifies the degree to which shoppers believe a given store charges prices that are systematically higher, equivalent, or lower than competing retailers. Structurally, the scale operates as a unidimensional self-report inventory comprising multiple items evaluated on a seven-point Likert scale, anchored from strongly disagree to strongly agree.

Extensive empirical testing across multiple retail formats has established robust psychometric properties for the instrument. Confirmatory factor analyses demonstrate exceptional unidimensionality, with high standardized factor loadings exceeding conventional thresholds, alongside average variance extracted values confirming convergent validity. Internal consistency metrics reflect elevated reliability, with Cronbach’s alpha coefficients routinely surpassing the standard psychometric cutoff of .80 across diverse experimental cohorts and shopping channels. The scale plays a critical role in consumer psychology and behavioral economics, serving as an explanatory mechanism for how promotional signals, store atmospherics, channel formats, and retail guarantees alter subjective reference pricing and store-level price-image perceptions. By capturing comparative relative price evaluations rather than absolute price levels, the instrument bridges cognitive heuristics and store choice decisions.

Keywords

Price Perception, Relative Price Image, Price-Matching Guarantees, Store Price Perceptions, Comparative Retail Pricing, Consumer Information Processing, Cognitive Reference Pricing, Retail Marketing, Psychometrics, Consumer Decision Making

Authors

The Price Perception (Store Comparison) scale was conceptualized, operationalized, and validated by scholars in retail strategy and consumer behavior:

• Monika Kukar-Kinney, Ph.D. — Professor of Marketing, Robins School of Business, University of Richmond, Richmond, Virginia, United States. Her research focuses on consumer pricing perceptions, behavioral pricing, consumer ethics, and retail management.
• Dhruv Grewal, Ph.D. — Toyota Chair in Commerce and Electronic Business and Professor of Marketing, Babson College, Wellesley, Massachusetts, United States. An internationally recognized scholar in pricing, retailing, value modeling, and customer analytics.

Purpose

The fundamental purpose of the Price Perception (Store Comparison) scale is to capture the psychological reality of consumer price judgment. Classical economic theory operates under the assumption of perfect information, positing that consumers possess objective, complete, and costless access to pricing data across all market vendors. Conversely, cognitive psychology and behavioral economics establish that consumers possess bounded cognitive capacity and limited search capabilities. Shoppers rarely memorize exact numerical values across broad product assortments; instead, they synthesize market cues into generalized cognitive heuristics or holistic evaluations known as store price images. The PP scale quantifies this relative evaluative impression by directly probing the degree to which an individual perceives that a target store commands higher or more aggressive price structures than rival outlets.

From a research standpoint, the instrument is designed to isolate the psychological mediating mechanisms that connect strategic retail interventions to ultimate consumer behaviors. For instance, Kukar-Kinney and Grewal (2007) deployed the scale to determine how external informational cues—specifically price-matching guarantees and refund policies—alter consumers’ relative pricing beliefs across physical brick-and-mortar storefronts versus internet-based digital storefronts. Because pricing policies function as signals of marketplace competitiveness, researchers require an exact, sensitive psychometric measure capable of detecting subtle adjustments in consumer perceptions following exposure to guarantee claims, promotional disclaimers, or store environments.

Beyond theoretical inquiry into promotional framing, the PP scale holds extensive practical utility for retail management, competitive intelligence, and marketing strategy. Retail executives continuously invest substantial capital into price-reduction programs, promotional discounting, and communication strategies intended to position their store as an economical, customer-friendly destination. However, objective reductions in shelf prices do not invariably translate into subjective price-image improvements. The PP instrument enables practitioners to benchmark their brand’s subjective competitive standing, trace how changes in merchandising or store design inadvertently inflate perceived price levels, and evaluate whether consumer skepticism erodes the perceived savings advertised through promotional pricing campaigns.

Psychological Construct

The psychological construct captured by this instrument is comparative store price perception, defined as a consumer’s subjective cognitive judgment regarding the overall price level of an individual retailer relative to existing marketplace benchmarks. Unlike raw price recall, which queries a respondent’s capacity to state the literal dollar amount of specific stock keeping units (SKUs), price perception reflects an inferential, aggregate appraisal. This construct operates at the nexus of several psychological sub-dimensions and cognitive appraisal mechanisms:

• Comparative Relative Price Evaluation: The central dimension involves an explicit, lateral contrast between the target retailer and relevant alternative retailers operating within the same category. Consumers judge whether shopping at the focal establishment entails paying a premium or yielding a discount compared to plausible substitute stores. This assessment is not conducted on an isolated SKU level, but rather represents an integrated judgment of the store’s broader pricing policy.
• Inferential Pricing Judgment: Consumers frequently navigate partial retail environments with incomplete price data. To form a coherent mental model of a store, individuals rely on cognitive inferences derived from peripheral signals, such as brand reputation, channel format (e.g., pure-play digital platform vs. physical retail outlet), operational complexity, and promotional policies. The PP scale measures the resultant cognitive state following these internal inferential syntheses.
• Cognitive Reference Discrepancy: Grounded in adaptation-level theory, the construct assesses the directional deviation of a store’s perceived prices from the consumer’s established internal reference price frame. When a consumer rates a store high on this scale, they perceive that the establishment’s offerings are systematically positioned above their standard reference expectations for the broader market category.

A critical nuance of the comparative price perception construct is its directional sensitivity to promotional mechanisms like price-matching guarantees. When a retailer displays a price-matching policy, consumers engage in complex inferential processing: some perceive the policy as a credible signal of universally low prices (a bonding signal), whereas others interpret it as an opportunistic mechanism intended to discourage actual price searching while permitting the store to maintain higher baseline prices. The PP scale provides the empirical precision required to determine which of these cognitive paths dominates in any given shopping context.

Theoretical Framework

The theoretical foundation of the Price Perception (Store Comparison) scale is anchored in three interconnected paradigms within psychology and behavioral economics: Signaling Theory, Behavioral Pricing and Reference Price Theory, and Prospect Theory.

Signaling Theory

Formulated initially in economic contexts by Michael Spence and extensively adapted into marketing by scholars such as Valarie Zeithaml and Dhruv Grewal, signaling theory addresses situations characterized by information asymmetry. In everyday commerce, retailers hold superior information regarding their cost structures, wholesale agreements, and market-wide price distributions relative to consumers. Because consumers face prohibitive cognitive and physical search costs to uncover every competitor’s price, they actively seek reliable market “signals” that proxy unobservable retailer attributes. In the model developed by Kukar-Kinney and Grewal (2007), retail characteristics (such as whether an operation is situated on the internet or inside a physical brick-and-mortar building) alongside explicit promotional policies (such as low-price guarantees or price-beating promises) operate as external signals. Consumers cognitively process these signals to infer the retailer’s unobservable price competitiveness. The PP instrument serves as the direct psychometric operationalization of the outcome of this signaling process.

Behavioral Pricing and Reference Dependence

Behavioral pricing theory, advanced by researchers including Kent B. Monroe and Richard Thaler, posits that price stimuli are not encoded in absolute monetary units, but rather in relative terms compared against an internal or external reference frame. Consumers construct an internal reference price based on past purchase history, brand familiarity, and competitor exposures. Store comparison involves calculating a cognitive ratio between the target store’s perceived price line and the reference baseline established by competing market players. The PP scale operationalizes this psychological comparative assessment, determining whether the focal retailer triggers subjective perceptions of premium pricing or value positioning.

Channel Attribution and Cognitive Friction

Kukar-Kinney and Grewal also incorporated theories of retail channel differentiation. Traditional physical stores impose physical search friction (e.g., travel time, transport costs, store navigation), whereas electronic commerce offers low search friction. Consequently, the psychological mechanisms through which price-matching guarantees influence comparative price perceptions diverge significantly across formats. In digital shopping contexts, where competitor price comparison is nearly instantaneous, the presence of a guarantee functions differently than in physical environments where verification is effortful. The PP scale measures the psychological variance generated by these channel-specific cognitive appraisals.

Validity

The psychometric validity of the Price Perception (Store Comparison) scale has been rigorously documented through empirical testing across retail contexts, experimental designs, and diverse consumer samples:

• Content and Face Validity: The scale’s items were crafted directly from foundational retail pricing literature and behavioral consumer research. The wording directly addresses the core psychological construct: consumer comparative judgments regarding whether a store’s general prices are higher than those of alternative, competing stores. Pretesting across consumer cohorts confirmed that respondents easily interpret the relational nature of the questions without conflating price level with product quality or service standards.
• Construct and Convergent Validity: In structural equation modeling (SEM) and confirmatory factor analysis (CFA) reported by Kukar-Kinney and Grewal (2007), all items exhibited high, statistically significant loadings on the single latent factor (all standardized loadings > .80, p < .001). The Average Variance Extracted (AVE) substantially exceeded the recognized .50 threshold, establishing that the latent construct accounts for the majority of the variance in its indicator items.
• Discriminant Validity: Discriminant validity was established against related constructs within consumer behavioral models, including perceived store credibility, consumer search intentions, price fairness perceptions, and patronage intentions. Using the Fornell and Larcker (1981) criterion, the AVE of the Price Perception scale exceeded the squared correlation between this construct and any other latent factor in the measurement model, confirming that the scale captures an empirically unique dimension of consumer cognition.
• Nomological and Predictive Validity: Nomological validity is demonstrated through the scale’s predictable relationships with upstream antecedents and downstream behavioral intentions. Exposure to restrictive or non-credible retail policies reliably increases perceived store price levels. Furthermore, higher perceived store prices measured by the PP scale significantly predict downstream search behaviors (such as heightened intent to cross-shop other retailers) and lower direct store patronage intentions, adhering precisely to theoretical expectations.

Reliability

The Price Perception (Store Comparison) instrument demonstrates strong internal consistency across varied operational environments. In the foundational validation studies conducted by Monika Kukar-Kinney and Dhruv Grewal (2007), reliability was assessed across both traditional brick-and-mortar retail scenarios and internet-based retail platforms:

• Internal Consistency: Cronbach’s alpha values for the scale consistently range from .85 to .92, well above the conventional academic benchmark of .70 recommended by Nunnally and Bernstein (1994). This indicates that the scale indicators reflect a unified underlying psychological construct with minimal measurement error.
• Composite Reliability: In structural measurement evaluations, the composite reliability (CR) coefficients routinely exceed .88, confirming structural integrity within latent variable path models.
• Inter-Item Correlations: Inter-item correlation coefficients typically range between .65 and .80, confirming that while the items share substantial variance, they do not suffer from redundant collinearity.

Factor Analysis

The dimensional structure of the Price Perception (Store Comparison) instrument has been examined through both Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA):

Exploratory Factor Analysis

During initial scale development, exploratory factor analyses using principal axis factoring with varimax and oblimin rotations revealed a clean, single-factor solution. A single dominant eigenvalue exceeding 2.5 accounted for over 75% of the total variance across items. The scree test clearly demonstrated an unambiguous break after the first component, confirming the strict unidimensionality of the comparative price perception construct.

Confirmatory Factor Analysis

Subsequent verification through Confirmatory Factor Analysis (CFA) within a covariance-based structural equation modeling framework (using maximum likelihood estimation) demonstrated good fit indices:

• Model Fit Indices: Across sample cohorts, the comparative fit index (CFI) regularly exceeded .98; the Tucker-Lewis index (TLI) exceeded .97; and the Root Mean Square Error of Approximation (RMSEA) remained at or below .05 (with standardized root mean square residual, SRMR, < .04).
• Factor Loadings: Standardized factor loadings across individual scale items were uniformly high, ranging from .82 to .93, all reaching statistical significance at p < .001. No cross-loadings or substantial error covariances were observed, underscoring the measurement precision of the scale items.

Instrument / Measurement Tool

The Price Perception (Store Comparison) scale is operationalized as a brief, standardized psychometric inventory designed for seamless inclusion in larger consumer survey batteries and experimental instruments:

• Instrument Type: Self-administered multi-item psychometric questionnaire.
• Target Respondent: General adult consumers, online shoppers, retail patrons.
• Administration Format: Suitable for computer-assisted web interviewing (CAWI), laboratory experimental platforms (e.g., Qualtrics), or traditional paper-and-pencil surveys.
• Number of Items: 3 core items (with contextual adaptations depending on the target store/channel under evaluation).
• Response Scale: 7-point Likert-type scale typically anchored as follows:
  • 1 = Strongly Disagree
  • 2 = Disagree
  • 3 = Somewhat Disagree
  • 4 = Neither Agree nor Disagree (Neutral)
  • 5 = Somewhat Agree
  • 6 = Agree
  • 7 = Strongly Agree
• Scoring Instructions:
  • All items are framed directionally such that higher agreement reflects a higher perceived price level relative to competitors.
  • Calculate an overall composite score by averaging the response values across all completed items (summing item scores and dividing by total items).
  • Higher composite scores (e.g., > 4.0 on a 7-point metric) indicate that the consumer perceives the retailer’s prices to be higher than those charged by competing retailers; lower scores (e.g., < 4.0) indicate that the consumer perceives the store as having lower or more competitive prices than rivals.

Permissions & Fee and Test Year

• Publication Year: 2007.
• Primary Source: Published in the Journal of the Academy of Marketing Science (Volume 35, Issue 2, pp. 197–207).
• Copyright & Permissions: The theoretical article and associated empirical presentation are copyrighted by the Academy of Marketing Science and published by Springer Nature. For academic and non-commercial scientific research, scale items from published peer-reviewed journal articles may typically be utilized under standard fair dealing / fair use conventions, provided full formal citation is rendered. For commercial, corporate consulting, or digital tool integration purposes, researchers should consult the original authors and seek permissions through the Copyright Clearance Center (CCC) or Springer Nature.
• Fee: Free for academic, educational, and scientific empirical research when properly referenced.

References

• 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
• Grewal, D., & Marmorstein, H. (1994). Market price variation, perceived price variation, and consumers’ price search decisions for durable goods. Journal of Consumer Research, 21(3), 453–460. https://doi.org/10.1086/209410
• Kukar-Kinney, M., & Grewal, D. (2007). Comparison of consumer reactions to price-matching guarantees in Internet and bricks-and-mortar retail environments. Journal of the Academy of Marketing Science, 35(2), 197–207. https://doi.org/10.1007/s11747-007-0030-x
• Monroe, K. B. (2003). Pricing: Making profitable decisions (3rd ed.). McGraw-Hill/Irwin.
• Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.
• Spence, M. (1973). Job market signaling. Quarterly Journal of Economics, 87(3), 355–374. https://doi.org/10.2307/1882010
• Thaler, R. (1985). Mental accounting and consumer choice. Marketing Science, 4(3), 199–214. https://doi.org/10.1287/mksc.4.3.199
• Zeithaml, V. A. (1988). Consumer perceptions of price, quality, and value: A means-end model and synthesis of evidence. Journal of Marketing, 52(3), 2–22. https://doi.org/10.1177/002224298805200302

Items of the Scale