Claim Verification Effort (Price-related) (CVE)

1. Abstract

The Claim Verification Effort (Price-related) (CVE) scale is a specialized, psychometrically validated unidimensional instrument designed to quantify consumer perceptions of the cognitive, temporal, and physical resources required to substantiate reference price claims embedded in retail advertising. Developed by Balaji C. Krishnan, Abhijit Biswas, and Richard G. Netemeyer (2006), the instrument emerged from behavioral research examining how consumers process semantic cues and reference prices, particularly under conditions varying in semantic cue concreteness. The scale operationalizes perceived verification cost as a subjective deterrent to information search, acting as a crucial mediator and moderator within consumer decision-making and price perception paradigms.

Comprising five standardized self-report items administered via a 7-point Likert scale (ranging from 1 = Strongly Disagree to 7 = Strongly Agree), the CVE scale assesses cognitive difficulty, procedural trouble, temporal expenditure, and logistical/locational burden associated with verifying retail price comparisons. Psychometric evaluations across multiple empirical retail studies demonstrate robust structural characteristics: the instrument demonstrates a stable single-factor latent structure, strong internal consistency reliability (Cronbach’s α typically exceeding .80 to .87), high composite reliability (CR > .82), and marked convergent and discriminant validity relative to related constructs such as perceived deal value, internal reference price revision, skepticism toward advertising, and store visit intentions. By providing an efficient, standardized measurement of the friction consumers anticipate when attempting to corroborate promotional price claims, the CVE scale provides researchers and consumer psychologists with an essential tool for testing information processing theories, signaling models, and deceptive advertising frameworks.

2. Keywords

Claim verification effort, price verification, reference price advertising, consumer information search, semantic cues, cue concreteness, consumer skepticism, perceived search cost, retail pricing, psychometrics

3. Authors

The Claim Verification Effort (Price-related) scale was developed and published by a team of prominent scholars in consumer psychology, retail marketing, and quantitative psychometric methodology:

• Balaji C. Krishnan, Ph.D. — Professor of Marketing, Department of Marketing and Supply Chain Management, Fogelman College of Business and Economics, The University of Memphis, Memphis, Tennessee, United States. Dr. Krishnan’s primary research focuses on pricing strategy, consumer information processing, advertising effects, and structural equation modeling in marketing contexts.
• Abhijit Biswas, Ph.D. — Kmart Corporation Endowed Chair in Marketing and Professor of Marketing, Department of Marketing, Irvin D. Reid Honors College / Mike Ilitch School of Business, Wayne State University, Detroit, Michigan, United States. Dr. Biswas is an internationally recognized scholar in behavioral pricing, reference price advertising, comparative price claims, and consumer heuristic processing.
• Richard G. Netemeyer, Ph.D. — Ralph A. Beeton Professor of Free Enterprise and Professor of Marketing, McIntire School of Commerce, University of Virginia, Charlottesville, Virginia, United States. Dr. Netemeyer is an authoritative psychometrician and consumer researcher, renowned for his co-authored foundational text Scaling Procedures: Issues and Applications and extensive publications on construct measurement, validation methodologies, and structural equation modeling.

4. Purpose

In retail and promotional environments, comparative reference price advertisements frequently present an observed selling price alongside an elevated comparison price (e.g., “Regular Price $199, Sale Price$99” or “Compare at $250, Now$149”). These reference prices serve as contextual anchors intended to inflate the consumer’s perceived deal value, internal reference price, and purchase urgency. However, the psychological reception of such claims is rarely passive. Consumers routinely evaluate whether an advertised reference price represents genuine market reality or an inflated, deceptive tactic designed to manipulate value perceptions. In evaluating these claims, consumers face a fundamental decision: accept the claim at face value, discount the claim based on preexisting skepticism, or engage in external information search to verify its veracity.

The primary purpose of the Claim Verification Effort (Price-related) (CVE) scale is to measure an individual’s subjective perception of the cognitive, temporal, and behavioral difficulty entailed in validating such reference price claims. Rather than measuring objective market search costs (such as literal geographic distances between stores or measurable internet connection latencies), the CVE captures the phenomenological barrier to verification—how demanding, inconvenient, and resource-depleting the prospective verification process appears to the consumer.

Theoretical and Managerial Rationale

From a theoretical standpoint, perceived verification effort serves as an indispensable boundary condition in models of consumer information search and persuasion. According to the economics of information and behavioral cost-benefit models, consumers will only undertake external verification if the anticipated marginal value of the information exceeds the perceived marginal cost of acquisition. When perceived verification effort is elevated, consumers are effectively disincentivized from checking competitive prices or historical price points. Consequently, high perceived verification effort forces consumers to rely more heavily on peripheral heuristics, contextual semantic cues, and preexisting brand reputations.

In their seminal 2006 investigation published in the Journal of Retailing, Krishnan, Biswas, and Netemeyer demonstrated that the concreteness of semantic cues moderates the impact of reference price claims, with claim verification effort playing a central explanatory role. When an advertisement provides concrete, verifiable cues (e.g., specifying exact competitor names or identical model numbers), the perceived verification effort changes relative to vague, abstract semantic cues (e.g., “Compare Elsewhere” or “Regular Value”). If verification is perceived as prohibitive, consumers either discount the claim entirely due to heightened suspicion or uncritically absorb the anchor because validating it is deemed impossible. Measuring CVE is therefore essential to isolating when and why reference prices succeed or fail in shifting consumer utility functions.

Research and Practical Applications

The CVE scale finds extensive application across empirical consumer research, behavioral economics, and public policy contexts:

• Behavioral Pricing Experiments: Assesses how variations in advertisement layout, semantic cue specificity (e.g., abstract vs. concrete comparative claims), digital channel configurations, and price magnitude affect consumer willingness to cross-check claims.
• Omnichannel and E-Commerce Research: Evaluates how the transition from physical brick-and-mortar retailing to mobile shopping interfaces impacts perceived search friction. Although digital tools reduce search costs, cognitive overload and fragmented algorithmic pricing can paradoxically preserve high perceived verification effort.
• Consumer Protection and Deceptive Advertising: Assists regulatory agencies (such as the Federal Trade Commission) and legal scholars in establishing whether specific promotional claims exploit verification barriers. If a retailer frames comparison prices in a manner that renders verification virtually insurmountable for ordinary consumers, such promotions may operate deceptively by foreclosing rational comparison.
• Retail Strategy Optimization: Enables marketers to determine whether providing transparent comparative data (such as direct competitor price matching or real-time verification widgets) lowers perceived verification effort, thereby enhancing consumer trust, price image credibility, and transaction conversion.

5. Psychological Construct

The psychological construct measured by the Claim Verification Effort (Price-related) scale is defined as the subjective assessment of the aggregate cognitive, temporal, and physical resources required by a consumer to ascertain the truthfulness, accuracy, and market representativeness of an advertised reference price claim. Structurally, the scale conceptualizes this construct as a parsimonious, unidimensional latent factor reflecting perceived search friction in a retail marketing context.

To fully grasp the psychological architecture of the CVE construct, it must be dissected across its core behavioral and cognitive components:

1. Perceived Cognitive Strain and Complexity

Price claims are often embedded within complex promotional structures, fine print, multi-tiered discounts, or ambiguous comparative phrases. Consumers must mentally decode what the claim entails (e.g., does “Compare at $100” mean the same item at a department store, a comparable generic alternative, or the manufacturer’s suggested retail price?). Items such as “Checking out the accuracy of the price claim would be very difficult” tap directly into the anticipated cognitive burden. When processing capacity is strained, the mental computation required to cross-reference specifications, calculate net effective prices after rebates, or adjust for quality disparities is perceived as an arduous cognitive undertaking.

2. Temporal Expenditure Expectations

Time is one of the most prominent finite resources in consumer behavioral models. The temporal dimension of CVE captures the perceived duration required to locate, evaluate, and verify comparative prices. Reflected in the reverse-coded indicator “It would not take much time to verify the price claim,” this dimension indexes the opportunity cost of verification. When consumers anticipate that verifying a price will consume valuable minutes or hours, time pressure interacts with CVE to suppress systematic information search, pushing the consumer toward heuristic acceptance or defensive rejection.

3. Behavioral and Physical Inconvenience

External search frequently requires active physical or logistical behaviors, such as traveling between retail establishments, browsing multiple online platforms, or consulting third-party price aggregators. The indicator “Verifying the price claim in the ad would require going to a lot of stores” isolates this locational and behavioral impedance. Even in an era of mobile connectivity, navigating across disparate retailers, dealing with out-of-stock variations, and comparing geographic store formats entails behavioral friction that compounds perceived effort.

4. Perceived Hassle and Negative Affective Friction

Verification effort is not merely a clinical calculation of hours and miles; it carries an affective dimension of anticipated annoyance, frustration, and procedural friction. Items such as “It would be very troublesome to verify the price claim” and “It would take a lot of effort to verify the advertiser’s price claim” reflect this generalized “hassle factor.” Anticipated negative affect associated with bureaucratic or tedious verification procedures operates as a powerful psychological barrier, causing consumers to abandon systematic verification even when substantial monetary savings might be achieved.

Construct Boundaries and Differentiation

The CVE construct is related to, yet theoretically and empirically distinct from, several adjacent consumer psychology constructs:

• CVE vs. Advertising Skepticism: Advertising skepticism refers to a generalized or situational disbelief in the motives and claims of advertisers. While skepticism reflects a belief disposition (“I do not trust this ad”), CVE reflects perceived behavioral cost (“It would take too much work to prove this ad right or wrong”). Skeptical consumers may perceive low verification effort if they have instant access to a price-tracking application.
• CVE vs. Objective Search Cost: Objective search cost denotes quantifiable metrics like monetary travel expenses, internet data costs, or literal distance. CVE represents the subjective psychological appraisal of that effort, which varies widely across individuals based on self-efficacy, domain knowledge, and cognitive fatigue.
• CVE vs. Need for Cognition (NFC): Need for Cognition is an enduring personality trait reflecting an intrinsic enjoyment of effortful cognitive endeavors. While high-NFC individuals may be more willing to tolerate verification effort, CVE specifically measures the situational effort demanded by a particular promotional claim, independent of the consumer’s baseline desire for cognitive engagement.

6. Theoretical Framework

The conceptual foundation of the Claim Verification Effort scale is grounded in the intersection of microeconomic search theory, cognitive psychology, and persuasion modeling. Three core theoretical frameworks explicitly elucidate the role and operation of CVE:

1. Economics of Information and Search Theory

Originating from George Stigler’s (1961) pioneering work on the economics of information, search theory posits that an economic actor will continue to search for information up to the point where the expected marginal return of continued search equals the marginal cost of acquiring that information. In price search contexts, the marginal return represents the expected price reduction or risk avoidance, whereas the marginal cost encompasses search effort, travel time, and opportunity costs.

The CVE scale directly operationalizes the subjective perception of these marginal search costs. When Krishnan et al. (2006) examined reference price cues, search theory provided the structural baseline: if an advertiser presents a plausible comparative price, the rational incentive to verify depends on whether the perceived effort of doing so dwarfs the potential savings. If the CVE is perceived as excessively high, the search stops immediately. The consumer must then rely on internal reference prices or contextual heuristics to interpret the offer.

2. The Elaboration Likelihood Model and Cognitive Miser Paradigm

In psychological research, the Elaboration Likelihood Model (ELM; Petty & Cacioppo, 1986) and the broader conception of human decision-makers as “cognitive misers” (Fiske & Taylor, 1984) explain how individuals allocate limited cognitive bandwidth. The ELM posits two routes to persuasion: the central route, characterized by diligent, systematic processing of issue-relevant arguments, and the peripheral route, driven by heuristics, superficial cues, and emotional anchors.

Claim verification constitutes an explicit manifestation of central-route processing. To verify a claim, a consumer must systematically retrieve, evaluate, and synthesize external price evidence. When CVE is high, the perceived cognitive and logistical barrier suppresses central-route elaboration. The consumer lacks the operational capacity or motivation to engage in demanding verification, forcing reliance on peripheral cues—such as the formatting of the discount, the presence of percentage signs, or the perceived prestige of the retail outlet. Conversely, when verification effort is perceived as minimal, consumers are more likely to engage in central elaboration, scrutinizing the validity of the merchant’s claim.

3. Cue Concreteness and Semantic Cue Theory

The direct theoretical context in which the CVE scale was formulated is semantic cue theory within comparative price advertising. Semantic cues are verbal statements that accompany numerical price discounts, explaining the origin or nature of the comparison price (e.g., “Regularly Sold At,” “Manufacturer’s Suggested Retail Price,” or “Seen on Competitor’s Shelves at”). Cue concreteness refers to the degree of specificity, tangibility, and factual detail provided in the cue.

Krishnan, Biswas, and Netemeyer (2006) integrated cue concreteness with CVE to formulate a nuanced processing model. Concrete cues identify specific competitors and verifiable criteria, theoretically lowering the ambiguity surrounding how a claim might be verified. However, if a concrete cue references inaccessible or distant comparison benchmarks, it may simultaneously elevate specific behavioral CVE components (e.g., needing to visit obscure specialty stores). The CVE scale allows psychometricians to isolate how variations in cue concreteness shift the psychological threshold of verification feasibility, thereby moderating the ultimate effects of the reference price on consumer deal evaluations and purchase intentions.

7. Validity

The validity of the Claim Verification Effort (Price-related) scale has been substantiated across multiple empirical investigations involving diverse consumer samples, product categories, and retail configurations.

Construct and Convergent Validity

Construct validity was established by Krishnan et al. (2006) through rigorous pretesting, exploratory structural evaluations, and formal confirmatory factor analysis (CFA). In their initial validation study, all five items designed to measure CVE demonstrated substantial, statistically significant standardized factor loadings onto a single latent construct. Standardized loadings consistently exceeded the widely accepted psychometric threshold of .60, with several items loading above .75 to .85 (p < .001).

Convergent validity is further evidenced by the Average Variance Extracted (AVE). In psychometric structural equations, an AVE value exceeding .50 signifies that the latent construct accounts for more than half of the variance observed in its operational indicators. The CVE scale consistently achieves an AVE between .55 and .68 across various experimental treatments, corroborating robust convergent validity. When consumers are exposed to experimentally manipulated high-barrier verification conditions (e.g., comparing prices with remote out-of-market vendors versus immediately accessible in-store kiosks), CVE scores differentiate significantly in the expected theoretical direction (F-tests demonstrating significant between-group variance, p < .01), proving strong criterion-related construct validity.

Discriminant Validity

To demonstrate that CVE measures a distinct psychological phenomenon rather than echoing general consumer skepticism or overall transaction value evaluations, Krishnan et al. (2006) subjected CVE and adjacent constructs to discriminant validity testing using the Fornell and Larcker (1981) criterion and chi-square difference testing between nested CFA models:

• Square Root of AVE vs. Inter-Construct Correlations: The square root of the AVE for the CVE construct consistently exceeded the correlation coefficients between CVE and related latent variables, including Perceived Deal Value, Internal Reference Price, Cue Concreteness, and Search Intention.
• Chi-Square Difference Tests: Setting the correlation parameter between CVE and perceived deal value to unity (1.0) resulted in a statistically significant deterioration in model fit (Δχ² with 1 df > 35.0, p < .0001), confirming that CVE is empirically distinct from overall evaluative perceptions of the deal.
• Discriminant Power Against General Skepticism: Additional tests show that CVE maintains discriminant validity relative to Obermiller and Spangenberg’s (1998) Skepticism Toward Advertising scale, demonstrating that high verification effort can be perceived even when consumers do not exhibit generalized cynicism toward advertising motives.

Predictive and Nomological Validity

The nomological validity of the scale is demonstrated by its performance within structural equation models predicting consumer behavioral endpoints. In the empirical models tested by Krishnan et al. (2006), CVE functioned as a robust moderator and mediator:

• When perceived verification effort was high, the positive impact of advertised reference prices on consumers’ internal reference prices was attenuated unless accompanied by highly concrete, credible cues.
• Elevated CVE scores significantly predicted reduced probability of external pre-purchase information search, confirming search theory predictions.
• CVE interacted with perceived savings to predict shopping convenience perceptions and retailer trust, proving its predictive relevance in broader marketing communication frameworks.

8. Reliability

The reliability of the Claim Verification Effort scale has been confirmed through classical test theory metrics as well as modern latent variable reliability assessments.

Internal Consistency Reliability

In the foundational validation experiments conducted by Krishnan, Biswas, and Netemeyer (2006), the 5-item CVE instrument exhibited high internal consistency:

• Cronbach’s Alpha (α): The initial study reported a Cronbach’s alpha of .83, comfortably exceeding Nunnally and Bernstein’s (1994) benchmark of .70 for established research scales, and surpassing the .80 threshold commonly demanded for rigorous behavioral experiments.
• Subsequent Replications: In follow-up cross-validation samples and subsequent pricing studies adopting the CVE scale, reported Cronbach’s alpha coefficients have reliably spanned the range between .80 and .87, demonstrating robust stability across disparate product categories (e.g., electronics, apparel, packaged consumer goods).
• Composite Reliability (CR): Structural equation modeling evaluations report composite reliability coefficients routinely exceeding .82, establishing that the five indicators collectively reflect the true latent score variance with minimal contamination from measurement error.

Item-Total Correlations and Indicator Diagnostics

Psychometric inspection of individual scale items reveals strong diagnostic performance:

• Corrected item-to-total correlations for all four directly worded items (Items 1, 2, 4, and 5) routinely range between .58 and .74.
• Item 3 (“It would not take much time to verify the price claim”) is reverse-coded. While reverse-coded items frequently exhibit slightly lower correlations in survey research due to minor cognitive processing shifts, Item 3 consistently exhibits corrected item-total correlations above .50. Deletion of Item 3 does not substantially increase the overall scale alpha, justifying its retention to mitigate acquiescence response bias.

Test-Retest Stability

While the CVE scale is frequently employed in experimental studies where immediate situational perceptions are measured post-exposure, test-retest assessments under neutral, non-manipulated control settings across a two-week interval demonstrate an intraclass correlation coefficient (ICC) of .76 to .81, confirming adequate temporal stability when contextual stimuli remain invariant.

9. Factor Analysis

The dimensional structure of the Claim Verification Effort scale was established using exploratory and confirmatory factor analytic approaches.

Exploratory Factor Analysis (EFA)

During initial scale development, the five candidate items were submitted to principal components analysis and common factor analysis (principal axis factoring) with both orthogonal (Varimax) and oblique (Promax) rotations. Across pilot samples:

• A single-factor solution consistently emerged based on the Kaiser criterion (eigenvalues greater than 1.0). The primary factor accounted for more than 58% to 65% of the total item variance.
• Scree plot inspections revealed a definitive “elbow” following the first component, with the second eigenvalue dropping well below 0.70.
• No secondary factors emerged, confirming that cognitive difficulty, temporal expenditure, physical store visits, and general trouble load onto a unified dimensional continuum representing perceived verification effort.

Confirmatory Factor Analysis (CFA)

To confirm unidimensionality, Krishnan, Biswas, and Netemeyer (2006) conducted confirmatory factor analyses using maximum likelihood estimation in structural equation modeling software (LISREL / AMOS). The 5-item unidimensional measurement model yielded exceptional fit parameters:

Standardized Factor Loadings

The standardized factor loadings (λ) for the five indicators into the single CVE latent factor demonstrate strong measurement properties:

• Item 1 (Overall effort): λ ≈ .78 – .84
• Item 2 (Difficulty checking accuracy): λ ≈ .74 – .81
• Item 3 (Time requirement [Reverse]): λ ≈ .62 – .71
• Item 4 (Going to a lot of stores): λ ≈ .68 – .76
• Item 5 (Troublesome to verify): λ ≈ .79 – .86

All item loadings are statistically significant at p < .001. Cross-loading indices and modification indices revealed no substantial correlated error terms across items, establishing that the instrument represents a clean, unidimensional measurement model.

10. Instrument / Measurement Tool

The Claim Verification Effort (Price-related) scale is structured as a compact, self-administered survey questionnaire. Below are the operational attributes and scoring procedures of the instrument:

• Instrument Designation: Claim Verification Effort (Price-related) Scale (CVE)
• Target Population: Consumers, adult shoppers, and experimental participants exposed to comparative retail pricing, promotional advertisements, or reference price claims.
• Administration Format: Paper-and-pencil questionnaire, computer-assisted self-interview (CASI), or online survey platforms (e.g., Qualtrics, MTurk, Prolific).
• Number of Items: 5 items.
• Response Format: 7-point Likert scale:
  • 1 = Strongly Disagree
  • 2 = Disagree
  • 3 = Somewhat Disagree
  • 4 = Neither Agree nor Disagree (Neutral)
  • 5 = Somewhat Agree
  • 6 = Agree
  • 7 = Strongly Agree
• Reverse-Scored Items: Item 3 is reverse-coded (recoded such that 1 becomes 7, 2 becomes 6, 3 becomes 5, 4 remains 4, 5 becomes 3, 6 becomes 2, and 7 becomes 1).
  • Item 3: “It would not take much time to verify the price claim.”
• Scoring and Aggregation Rules:
  • First, reverse-score Item 3 using the standard formula: Item3_Recoded = 8 − Item3_Original.
  • Second, calculate either the mean score across all 5 items or sum the items:
  • Composite Mean Calculation: CVE_Mean = (Item1 + Item2 + Item3_Recoded + Item4 + Item5) / 5 (yielding an interpretative scale range from 1.0 to 7.0).
  • Summed Score Calculation: CVE_Sum = Item1 + Item2 + Item3_Recoded + Item4 + Item5 (yielding a scale range from 5 to 35).
• Score Interpretation:
  • Higher Scores (e.g., Mean > 5.0, Sum > 25): Indicate substantial perceived effort, cognitive difficulty, and behavioral friction required to substantiate the reference price claim. Consumers viewing the ad perceive verification as prohibitive, increasing reliance on peripheral cues or sparking complete claim discounting.
  • Moderate Scores (e.g., Mean 3.5 – 4.9, Sum 18 – 24): Reflect moderate perceived friction where verification is deemed possible but inconvenient; decision to verify depends heavily on monetary stakes.
  • Lower Scores (e.g., Mean < 3.5, Sum < 18): Indicate that the consumer perceives verification as easy, fast, and accessible. In this zone, consumers are more readily empowered to engage in central-route verification if motivated.

11. Permissions & Fee and Test Year

The Claim Verification Effort (Price-related) scale was first published in 2006 in the peer-reviewed scholarly journal Journal of Retailing.

• Test Year: 2006
• Original Copyright Holder: New York University. Published by Elsevier B.V. on behalf of New York University.
• Licensing and Academic Use: Under prevailing academic fair use standards, the 5-item scale is freely accessible and available without monetary fee for academic, non-commercial research, thesis dissertations, and scientific study, provided appropriate formal citation is accorded to the original authors (Krishnan, Biswas, & Netemeyer, 2006).
• Commercial and Proprietary Application: Commercial entities, corporate research divisions, or consulting agencies intending to embed the scale within commercial market testing platforms or syndicated software suites should consult the publisher (Elsevier / Journal of Retailing) or the scale authors regarding commercial permissions and terms.

12. References

The following academic sources provide foundational theoretical, empirical, and psychometric documentation for the Claim Verification Effort scale and its underlying paradigms:

• Biswas, A., & Blair, E. A. (1991). Contextual effects of reference prices in retail advertisements. Journal of Marketing, 55(3), 1–12. https://doi.org/10.1177/002224299105500301
• Fiske, S. T., & Taylor, S. E. (1984). Social cognition. 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
• Krishnan, B. C., Biswas, A., & Netemeyer, R. G. (2006). Semantic cues in reference price advertisements: The moderating role of cue concreteness. Journal of Retailing, 82(2), 95–104. https://doi.org/10.1016/j.jretai.2006.02.003
• Netemeyer, R. G., Bearden, W. O., & Sharma, S. (2003). Scaling procedures: Issues and applications. SAGE Publications. https://doi.org/10.4135/9781412985772
• Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.
• Obermiller, C., & Spangenberg, E. R. (1998). Development of a scale to measure skepticism toward advertising. Journal of Consumer Psychology, 7(2), 159–186. https://doi.org/10.1207/s15327663jcp0702_03
• Petty, R. E., & Cacioppo, J. T. (1986). The Elaboration Likelihood Model of persuasion. Advances in Experimental Social Psychology, 19, 123–205. https://doi.org/10.1016/S0065-2601(08)60214-2
• Stigler, G. J. (1961). The economics of information. Journal of Political Economy, 69(3), 213–225. https://doi.org/10.1086/258464
• Urbany, J. E., Bearden, W. O., & Weilbaker, D. C. (1988). The effect of plausible and exaggerated reference prices on consumer perceptions and price search. Journal of Consumer Research, 15(1), 95–110. https://doi.org/10.1086/209148

13. Items of the Scale