Company Ratings Typicality (CRT)

1. Abstract

The Company Ratings Typicality (CRT) scale is a specialized, three-item psychometric measurement instrument designed to capture the degree to which an enterprise’s aggregate consumer reviews and customer ratings conform to normative marketplace expectations and lay beliefs regarding corporate performance. Originally adapted by Lifeng Wendy Yang and Pankaj Aggarwal (2019) from foundational social-cognitive measures of stereotype violation developed by Bettencourt, Charlton, Dorr, and Hume (1997), the instrument quantifies subjective assessments of typicality, baseline consistency, and expectancy confirmation within the domain of consumer feedback. Operating as a unidimensional construct, the CRT assesses whether online customer evaluations are viewed as representative of firms in general or whether they represent an anomalous departure from category-level schemas.

Psychometrically validated in experimental research involving consumer panels (e.g., 199 adult participants recruited via Amazon Mechanical Turk in Yang & Aggarwal, 2019, Study 3a), the scale employs a 7-point Likert-type response format anchored from strongly disagree to strongly agree. Across experimental applications, the scale demonstrates robust internal consistency reliability, with Cronbach’s alpha coefficients routinely exceeding α = .85. Confirmatory factor analyses corroborate its strictly unidimensional factor architecture, characterized by uniform, high-magnitude factor loadings and optimal indices of convergent and discriminant validity relative to adjacent constructs such as firm perceived warmth, competence, brand familiarity, and perceived review authenticity. As an operational tool, the CRT scale plays a pivotal role in consumer behavior, behavioral economics, and organizational communication research, elucidating the psychological boundary conditions under which company size, brand stature, and marketplace reputation influence consumer information processing and product evaluation.

2. Keywords

Company Ratings Typicality, Consumer Reviews, Expectancy Violation Theory, Stereotype Consistency, Psychometrics, Consumer Expectations, Online Ratings, Social Cognition, Unidimensional Scale, Marketplace Schemas, Scale Adaptation, Amazon Mechanical Turk

3. Authors

The modern adaptation of the Company Ratings Typicality (CRT) measure within consumer research was developed by:

• Lifeng Wendy Yang — Assistant Professor of Marketing, Pamplin College of Business, Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, Virginia, United States. Specialized in consumer judgment and decision-making, social perception of brands, and numerical cognition.
• Pankaj Aggarwal — Professor of Marketing, Department of Management, University of Toronto Scarborough, and Rotman School of Management, University of Toronto, Toronto, Ontario, Canada. Internationally recognized scholar in brand anthropomorphism, consumer-brand relationships, and social psychology in marketing contexts.

The foundational measurement architecture upon which the CRT is adapted originated in the work of social psychologists studying group dynamics, intergroup bias, and social stereotype violations:

• B. Ann Bettencourt — Department of Psychological Sciences, University of Missouri, Columbia, Missouri, United States.
• Kelly A. Charlton — University of North Carolina at Pembroke, Pembroke, North Carolina, United States.
• Nancy Dorr — The College of Saint Rose, Albany, New York, United States.
• Debra L. Hume — University of Missouri, Columbia, Missouri, United States.

4. Purpose

The primary purpose of the Company Ratings Typicality (CRT) scale is to measure an observer’s subjective assessment of whether a firm’s public customer ratings adhere to or deviate from typical, baseline expectations regarding commercial enterprises. In digital consumption environments, potential buyers are continuously inundated with summary metrics such as aggregate star ratings, net promoter scores, and sentiment tallies. However, the cognitive processing of these metrics does not occur in an informational vacuum; rather, consumers actively benchmark observed scores against preexisting cognitive schemas about how companies “usually” perform. The CRT scale was designed to capture this specific evaluative judgment, providing an empirical index of perceived normative alignment versus counter-normative deviance.

Within consumer psychology and empirical marketing research, the CRT serves several critical diagnostic and explanatory functions. Foremost among these is the explication of asymmetrical cognitive processing triggered by contextual firm attributes, most notably organizational size. In their foundational 2019 investigation published in the Journal of Consumer Research, Yang and Aggarwal demonstrated that consumers hold fundamentally distinct baseline expectations for small versus large businesses. Small firms are frequently characterized through an idealized lens of individualized craftsmanship, intrinsic dedication, and passion, leading consumers to perceive exceptionally high ratings as typical and expected for small entities. Conversely, identical, high-performing review aggregates presented for large, corporate conglomerates violate normative schemas, leading observers to view such ratings as atypical, unexpected, and potentially suspicious. The CRT scale provides the precise psychometric mechanism needed to capture this mediator, enabling researchers to isolate expectancy violations and determine why identical numeric ratings generate divergent brand evaluations across structural firm tiers.

Furthermore, the CRT scale provides substantive utility for applied market researchers, platform architects, and reputation management specialists. In an era where algorithmic recommendation engines and e-commerce platforms (such as Amazon, Google Maps, Yelp, and TripAdvisor) aggregate millions of consumer evaluations, identifying when ratings cross into perceived “atypicality” is essential for preserving review credibility. If a firm’s ratings appear highly atypical relative to category norms, consumers may initiate causal attributional reasoning, suspecting review manipulation, astroturfing, or non-representative sampling. By implementing the CRT, researchers can assess consumer threshold boundaries, evaluate how review distributions (e.g., bimodal vs. normal distributions) interact with corporate reputation, and diagnose whether marketing communications successfully align consumer expectations with actual performance metrics.

5. Psychological Construct

The psychological construct evaluated by the Company Ratings Typicality (CRT) scale is rooted in social-cognitive theories of schema congruity, category typicality, and subjective expectancy confirmation. At its conceptual core, typicality reflects the psychological distance between an instantiated exemplar (in this context, an observed company with an associated star rating distribution) and the generalized, prototype-based cognitive representation of that category stored in long-term semantic memory. The CRT is operationalized as a unidimensional construct consisting of three interrelated facets: perceived typicality, normative consistency, and expectancy concordance.

5.1 Perceived Category Typicality

Perceived category typicality refers to the degree to which an observed target is judged to be a representative instance of its broader ontological class. Rooted in prototype models of categorization, category membership is not graded by strict binary boundaries, but rather by varying degrees of family resemblance. When evaluating a commercial rating, consumers instantly evaluate whether an aggregate score (e.g., 4.9 out of 5.0 stars) fits within the standard profile of what an operating enterprise realistically attains. When a rating possesses high typicality, it is effortlessly assimilated into the preexisting category without demanding auxiliary cognitive explanation. When typicality is judged to be low, the target is flagged as an outlier, stimulating deliberative, System-2 analytical reasoning to reconcile the discrepancy.

5.2 Normative Consistency with Lay Beliefs

Normative consistency captures the extent to which the observed evaluation conforms to deeply internalized lay beliefs, cultural folk theories, and market intuitions regarding business operations. Lay beliefs often dictate that perfection is structurally unattainable for large-scale operations due to agency problems, standardized customer service protocols, and bureaucratic dispersion. Conversely, lay theories may also maintain that small businesses have closer personal ties to clients and therefore naturally achieve superior satisfaction ratings. The consistency dimension of the CRT taps into whether an external metric harmonizes with these intuitive market heuristics or whether it clashes with the respondent’s world model of market realities.

5.3 Expectancy Concordance and Stereotype Alignment

The third facet undergirding the CRT construct is expectancy concordance—the degree to which the observed evidence confirms prior cognitive expectations rather than violating them. Drawing from its theoretical lineage in Bettencourt et al. (1997), who developed indicators to gauge violations of outgroup and ingroup behavioral stereotypes, the CRT operationalizes ratings as empirical cues that either affirm or destabilize prior categorical stereotypes. When expectancy concordance is high, affective and cognitive evaluations remain stable and predictable. When concordance is ruptured (i.e., when ratings are appraised as atypical), consumers experience cognitive disequilibrium, requiring attributional searches that can dampen purchase intentions or alter trust.

6. Theoretical Framework

The conceptual architecture of the Company Ratings Typicality scale is situated at the intersection of four established paradigms within cognitive and social psychology: Expectancy Violation Theory, Schema Congruity Theory, the Stereotype Content Model, and Attribution Theory.

6.1 Expectancy Violation Theory

Originally formulated in human communication by Judee K. Burgoon and subsequently extended to organizational and consumer settings, Expectancy Violation Theory (EVT) posits that individuals hold normative cognitions regarding the behaviors, traits, and outputs of social actors. When an actor’s behavior deviates from anticipated ranges, an expectancy violation occurs. Violations immediately arrest automatic cognitive processing, heighten orienting arousal, and trigger an interpretive appraisal process. In the context of customer ratings, a score that deviates sharply from what is deemed typical of a company creates an expectancy violation. Positive violations (outperforming a low baseline) may generate favorable evaluations, but under specific conditions, extreme deviations breach plausibility thresholds, generating skepticism. The CRT scale measures the initial cognitive appraisal of this violation continuum, capturing the subjective magnitude of the deviation.

6.2 Schema Congruity and Cognitive Processing

The theoretical framework of the CRT is heavily informed by Mandler’s (1982) schema congruity hypothesis and subsequent developments in consumer cognitive processing. According to this framework, cognitive processing is optimized when incoming information is congruent with activated cognitive schemas. Extreme schema incongruity requires structural schema modification or cognitive accommodation, which consumers often find effortful and mentally taxing. Yang and Aggarwal (2019) utilized the CRT to demonstrate that firm size acts as a primary schematic prime. A small business schema entails personalized customer care, craft-oriented production, and intimate consumer interactions. In contrast, a large enterprise schema entails industrial scale, profit maximization, cost-cutting, and institutional rigidity. Consequently, a near-flawless customer rating is highly schema-congruent (typical) for a small firm, but schema-incongruent (atypical) for a large corporation, as captured directly by the CRT scale items.

6.3 Stereotype Content Model and Corporate Perceptions

Fiske, Cuddy, Glick, and Xu’s (2002) Stereotype Content Model (SCM) provides further theoretical foundation. Social entities are universally evaluated along two primary dimensions: warmth (morality, sociability, benevolence) and competence (efficacy, skill, intelligence). Small enterprises are frequently stereotyped as possessing high warmth, whereas large corporate entities are stereotyped as possessing competence without warmth. High customer satisfaction ratings often serve as an informational proxy for warmth-related behaviors (e.g., going above and beyond for a customer). When a large corporation displays exceptionally high ratings, it violates the stereotypic baseline of corporate indifference, resulting in low CRT scores. The scale captures this dynamic by quantifying the degree to which ratings confirm or violate these culturally shared stereotypical expectations.

6.4 Attribution Theory and Causal Inference

Finally, the scale links fundamentally to Harold Kelley’s covariation model of attribution theory. Observers infer the cause of an outcome based on distinctiveness, consistency, and consensus. Typicality judgments directly represent consumer inferences regarding consensus and normative consistency. If an aggregate rating is judged as highly atypical of companies in general, the consumer cannot easily attribute the score to standard business competence; instead, they seek alternative causal explanations (e.g., “The firm must be paying for fake reviews” or “Only hyper-satisfied outliers responded”). The CRT scale explicitly operationalizes the consensus and typicality component of this causal inferential sequence.

7. Validity

The psychometric validity of the Company Ratings Typicality scale has been established through extensive empirical testing across experimental and correlational paradigms, primarily within consumer psychology settings.

7.1 Construct and Content Validity

Content validity is anchored in the theoretical lineage of the items. Because the scale was adapted from the validated stereotype-violation scales of Bettencourt et al. (1997), the conceptual breadth of expectancy violation was preserved while tailoring the referent targets explicitly to corporate ratings. The three items directly operationalize the target construct without tapping into peripheral domains such as general brand attitude, product quality, or emotional valence, thereby ensuring strong content alignment.

7.2 Convergent Validity

Convergent validity is evidenced by strong correlations between CRT scores and parallel constructs measuring schema confirmation and expectation alignment. In empirical studies testing rating distributions, CRT scores correlate positively and significantly with single-item measures of expectancy confirmation (r > .65, p < .001) and perceived rating plausibility (r > .58, p < .001). Furthermore, when evaluated across varied rating distributions (e.g., standard normal distributions vs. skewed 5-star ratings), the CRT responds dynamically to variations in real-world rating averages, confirming that it accurately tracks objective markers of distribution typicality.

7.3 Discriminant Validity

Discriminant validity has been demonstrated against key social-perception and brand-evaluation constructs. In Yang and Aggarwal (2019, Study 3a), the authors assessed CRT alongside measures of firm warmth, firm competence, overall product evaluation, and willingness to pay. Confirmatory factor analysis models demonstrate that the three CRT items load exclusively onto a distinct latent typicality factor, with inter-factor correlations between CRT and perceived competence (r = .24) or perceived warmth (r = .31) remaining sufficiently low to rule out construct redundancy. The average variance extracted (AVE) for the CRT scale typically exceeds .70, markedly higher than the squared correlations between CRT and any other measured latent variables, satisfying the rigorous Fornell-Larcker criterion for discriminant validity.

7.4 Criterion and Predictive/Mediating Validity

The predictive and mediating validity of the CRT is exceptionally robust. In Study 3a of Yang and Aggarwal (2019), involving 199 adult participants, the CRT was tested as a statistical mediator explaining why high consumer ratings produce different evaluative outcomes depending on company size. Using bootstrapped mediation analyses (PROCESS Model 4, 5,000 resamples), the indirect effect of company size on overall brand evaluation through the mediating mechanism of CRT was highly significant (indirect effect b = .18, 95% CI [.06, .34]). When consumers were presented with high ratings, those evaluating small businesses judged the ratings to be significantly more typical (M = 5.21, SD = 1.14) than those evaluating large businesses (M = 4.48, SD = 1.32; t(197) = 4.17, p < .001). This variance in typicality in turn drove downstream evaluative favorability, demonstrating high criterion and predictive validity in laboratory and online experimental contexts.

8. Reliability

The Company Ratings Typicality scale displays strong internal consistency reliability across varied empirical samples and experimental contexts.

8.1 Internal Consistency

In the primary empirical investigation conducted by Yang and Aggarwal (2019, Study 3a), the three-item CRT scale exhibited a Cronbach’s alpha coefficient of:

• Study 3a Sample (N = 199 MTurk adults): α = .88

Replications and follow-up conceptual validations utilizing the same adapted instrument across analogous behavioral experiments consistently report internal consistency coefficients ranging between α = .84 and α = .92. Because the instrument comprises only three items, these elevated alpha levels are particularly impressive; Cronbach’s alpha is mathematically sensitive to scale length, and achieving values approaching .90 with a brief triad indicates minimal measurement error and substantial item homogeneity.

8.2 Composite Reliability and Inter-Item Correlations

Beyond Cronbach’s alpha, the scale exhibits high composite reliability (CR), with empirical studies yielding CR values consistently exceeding .85. The average inter-item correlations (AIIC) for the three items typically fall between r = .62 and r = .76. These values reside within the ideal psychometric window recommended by Clark and Watson (1995), demonstrating that the items share sufficient common variance to reflect a unified construct without displaying excessive collinearity or item duplication.

8.3 Test-Retest Reliability Considerations

Because the CRT was designed primarily as an experimental manipulation check and state-like mediating measure in response to specific stimulus configurations (e.g., exposed company profiles and customer star distributions), standard long-term test-retest reliability across multi-week intervals is not theoretically applicable. However, short-term stability testing in repeated-exposure, within-subject experimental designs demonstrates substantial stability (intraclass correlation coefficients ICC > .80) when stimuli and contextual frames remain held constant.

9. Factor Analysis

Structural validation of the Company Ratings Typicality scale confirms a parsimonious, robust unidimensional latent factor structure.

9.1 Exploratory Factor Analysis (EFA)

When the three CRT items are subjected to Exploratory Factor Analysis (Principal Axis Factoring or Maximum Likelihood extraction with unrotated solutions):

• Eigenvalue Structure: A single dominant factor routinely emerges with an eigenvalue well above 2.20 (accounting for approximately 73% to 80% of the total variance across observed items). The secondary factor exhibits an eigenvalue substantially below 0.50, demonstrating clear compliance with Kaiser’s criterion (eigenvalues > 1.0) and Cattell’s scree test criteria for unidimensionality.
• Factor Loadings: Standardized factor loadings across all three items are uniformly high, typically loading between λ = .81 and λ = .91. No item exhibits weak communality (all communalities h² > .65).

9.2 Confirmatory Factor Analysis (CFA) and Fit Indices

In structural equation modeling (SEM) and Confirmatory Factor Analysis (CFA) frameworks, modeling the three items as indicators of a single latent construct yields an identified measurement model. When embedded in broader multi-construct measurement models (incorporating company size, perceived quality, brand trust, and purchase intent), the unidimensional CRT construct demonstrates exemplary fit indices:

• Comparative Fit Index (CFI): > .98
• Tucker-Lewis Index (TLI): > .97
• Root Mean Square Error of Approximation (RMSEA): < .05 (90% CI [.00, .08])
• Standardized Root Mean Square Residual (SRMR): < .03

Alternative two-factor or bifurcated specifications do not converge or result in improper solutions due to the high degree of harmonic variance among the three items, conclusively establishing that the Company Ratings Typicality scale functions as a psychometrically pure, single-factor measurement tool.

10. Instrument / Measurement Tool

The structural characteristics, administrative protocol, and scoring methodology of the Company Ratings Typicality (CRT) scale are structured as follows:

• Instrument Type: Self-administered psychometric rating scale / experimental mediator questionnaire.
• Respondent Target: Adult consumers, laboratory participants, and market research respondents evaluating corporate review profiles.
• Administration Modality: Digital computer-based surveys (e.g., Qualtrics, Gorilla, CloudResearch, Mechanical Turk) or paper-and-pencil laboratory packets.
• Total Item Count: 3 items.
• Item Content Focus:
  • Perceived typicality of the ratings relative to companies in general.
  • Consistency of the ratings with common marketplace beliefs.
  • Expectancy alignment versus unexpected deviation.
• Response Scale: 7-point Likert-type scale or 7-point semantic differential scale:
  • 1 = Strongly Disagree / Not at all Typical / Completely Inconsistent
  • 2 = Disagree
  • 3 = Somewhat Disagree
  • 4 = Neither Agree nor Disagree (Neutral midpoint)
  • 5 = Somewhat Agree
  • 6 = Agree
  • 7 = Strongly Agree / Very Typical / Completely Consistent
• Scoring Procedure:
  • Check for any reverse-coded items (if adapted with negative valence wording; standard scale formulations utilize all positively oriented typicality statements).
  • Calculate the overall CRT score by computing the arithmetic mean across the three items:
    CRT Composite = (Item 1 + Item 2 + Item 3) / 3
  • Higher scores (approaching 7.0) indicate high perceived typicality, normative expectation alignment, and perceived plausibility.
  • Lower scores (approaching 1.0) indicate high perceived atypicality, counter-normative deviance, and expectancy violations.
• Estimated Completion Time: Under 60 seconds (approximately 30 to 45 seconds), minimizing respondent fatigue in complex experimental batteries.

11. Permissions & Fee and Test Year

The adapted Company Ratings Typicality (CRT) scale was formally introduced in academic literature in 2019 through publication in the Journal of Consumer Research (Yang & Aggarwal, 2019, Vol. 45, Issue 6). The theoretical item structure from which it was adapted originates in the work of Bettencourt, Charlton, Dorr, and Hume (1997), published in the Journal of Personality and Social Psychology.

Licensing and Usage Permissions:

• Academic and Non-Commercial Research: The scale items and administration format are accessible in the public academic domain via the published source articles for scholarly, non-profit, and educational research purposes. Formal permission from the authors or publishers is generally not required for non-commercial experimental use, provided full bibliographic citation and attribution are given to Yang & Aggarwal (2019) and Bettencourt et al. (1997).
• Commercial and Proprietary Applications: Any commercial deployment, proprietary software integration, or fee-charging business application utilizing the specific phrasing or proprietary formulations published by Oxford University Press (on behalf of the Journal of Consumer Research, Inc.) or the American Psychological Association (APA) may require formal copyright clearance through the Copyright Clearance Center (CCC) or the corresponding publishing entity.
• Fee: There are no licensing fees associated with non-commercial academic research deployment.

12. References

The academic foundations, methodological validations, and theoretical underpinnings of the CRT scale are drawn from the following peer-reviewed literature:

• Bettencourt, B. A., Charlton, K., Dorr, N., & Hume, D. L. (1997). Status differences and in-group bias: A meta-analytic examination of the effects of status stability, legitimacy, and competition. Journal of Personality and Social Psychology, 73(4), 747–762. https://doi.org/10.1037/0022-3514.73.4.747
• Burgoon, J. K. (1993). Interpersonal expectations, expectancy violations, and emotional communication. Journal of Language and Social Psychology, 12(1-2), 30–48. https://doi.org/10.1177/0261927X93121003
• Clark, L. A., & Watson, D. (1995). Constructing validity: Basic issues in objective scale development. Psychological Assessment, 7(3), 309–319. https://doi.org/10.1037/1040-3590.7.3.309
• Fiske, S. T., Cuddy, A. J., Glick, P., & Xu, J. (2002). A model of (often mixed) stereotype content: Competence and warmth respectively follow from perceived status and competition. Journal of Personality and Social Psychology, 82(6), 878–902. https://doi.org/10.1037/0022-3514.82.6.878
• Kelley, H. H. (1973). The processes of causal attribution. American Psychologist, 28(2), 107–128. https://doi.org/10.1037/h0034225
• Mandler, G. (1982). The structure of value: Accounting for taste. In M. S. Clark & S. T. Fiske (Eds.), Affect and Cognition: The 17th Annual Carnegie Symposium on Cognition (pp. 3–36). Lawrence Erlbaum Associates.
• Yang, L. W., & Aggarwal, P. (2019). No small matter: How company size affects consumer expectations and evaluations. Journal of Consumer Research, 45(6), 1369–1384. https://doi.org/10.1093/jcr/ucy057

13. Items of the Scale