Product Price and Volume Simultaneous Changes (Typicality)

1. Abstract

The Product Price and Volume Simultaneous Changes (Typicality) Scale is a psychometric instrument designed to evaluate consumer perceptions of typicality, normality, and perceived frequency regarding retail pricing tactics that concurrently alter both product price and package volume (commonly manifested as shrinkflation or package-size adjustments). Developed and validated by Jun Yao, Harmen Oppewal, and Di Wang (2020) in the Journal of the Academy of Marketing Science, this measure quantifies how normative or anomalous consumers perceive particular dual-attribute modifications to be—specifically comparing “cheaper and smaller” (downsizing) versus “more expensive and larger” (upsizing) unit price increase strategies. The instrument consists of three semantic differential items evaluated on a 7-point continuum (anchored by Untypical/Typical, Uncommon/Common, and Infrequent/Frequent). Psychometric evaluations across multiple consumer experimental cohorts demonstrate exceptional internal consistency reliability (Cronbach’s α typically exceeding .88 to .94) and robust unidimensional construct validity verified through confirmatory factor analysis. Functioning as a key explanatory mechanism in behavioral pricing and consumer psychology, the scale illuminates how cognitive heuristics, market expectations, and category schema alignment moderate consumer tolerance, attribution of manipulative intent, and subsequent brand equity erosion when manufacturers alter unit costs.

2. Keywords

perceived typicality, shrinkflation, product downsizing, unit pricing, pricing psychology, consumer heuristics, semantic differential, package resizing, mental accounting, price perception

3. Authors

The scale was developed by a team of marketing scientists and psychometric researchers specializing in retail pricing and consumer decision-making:

• Jun Yao — Department of Marketing, Macquarie Business School, Macquarie University, Sydney, Australia; formerly affiliated with Monash University. Expertise in behavioral pricing, retail strategy, and quantitative choice modeling.
• Harmen Oppewal — Department of Marketing, Monash Business School, Monash University, Melbourne, Australia. Renowned scholar in consumer behavior, choice experiments, retail environments, and decision-making architecture.
• Di Wang — School of Advertising, Marketing and Public Relations, QUT Business School, Queensland University of Technology, Brisbane, Australia. Specialist in retail analytics, consumer heuristics, and quantitative pricing research.

4. Purpose

In modern fast-moving consumer goods (FMCG) markets, manufacturers facing inflationary pressures on raw material and operational overhead often implement unit price increases. Rather than simply raising the shelf sticker price, retailers and brand managers routinely implement dual adjustments, altering both retail price and package volume simultaneously. Two prominent executions dominate this landscape: (1) downsizing (“cheaper and smaller,” or retaining price while decreasing content, often referred to as “shrinkflation”), and (2) upsizing (“more expensive and larger,” wherein the package volume expands but the price rises disproportionately, increasing unit cost).

The primary purpose of the Product Price and Volume Simultaneous Changes (Typicality) Scale is to quantify the cognitive schema congruence and perceived market normality of these concurrent modifications. When consumers encounter a product alteration, they activate internalized cognitive benchmarks or scripts regarding how firms conventionally alter prices and packages. If an adjustment is evaluated as highly atypical or infrequent, it prompts cognitive elaboration, triggers suspicion regarding deceptive retailer intent, and amplifies perceived unfairness.

Conversely, when a pricing tactic aligns with perceived market norms—exhibiting high subjective typicality—it is processed more fluently through System 1 heuristic pathways, eliciting less consumer friction, lower reactance, and diminished punitive purchasing behaviors. The scale serves researchers investigating marketing transparency, regulatory agencies examining deceptive packaging practices, and commercial analysts seeking to forecast consumer resistance to cost-rebalancing maneuvers.

5. Psychological Construct

The core latent construct captured by this instrument is perceived typicality within the domain of transactional marketing events. Perceived typicality originates from cognitive psychology and prototype theory, reflecting the degree to which an exemplar or stimulus is judged to represent an established conceptual category or behavioral regularities within an environment.

Within this psychometric framework, the construct is conceptualized as a unidimensional cognitive appraisal comprising three interrelated evaluative facets:

• Normative Representativeness (Untypical vs. Typical): The extent to which a specific simultaneous shift in price and package volume matches the consumer’s idealized or abstracted prototype of commercial transactions. For instance, a small price decline coupled with a substantial volume reduction may violate the expected prototype of a price cut, yielding a low typicality rating.
• Subjective Prevalence (Uncommon vs. Common): The perceived prevalence or ubiquity of such tactics across the retail ecosystem. This facet assesses whether consumers view the maneuver as an isolated, idiosyncratic tactic or an established industry-wide convention.
• Experienced Frequency (Infrequent vs. Frequent): The perceived temporal recurrence and encounter rate of the pricing tactic during typical grocery shopping episodes.

High typicality indicates that the market intervention is viewed as an ordinary, expected reality of retail commerce. Low typicality signifies that the intervention is categorized as an anomaly, schema violation, or potential stealth tactic designed to mislead consumer perceptions of volume or value.

6. Theoretical Framework

The scale is anchored in three primary theoretical traditions within behavioral economics and cognitive psychology:

Prototype and Categorization Theory

Pioneered by Eleanor Rosch, prototype theory posits that individuals categorize stimuli based on similarity to an abstracted category prototype rather than rigid, binary definitions. In pricing contexts, consumers hold prototypes for what constitutes a “price hike,” a “discount,” or a “package redesign.” A tactic that simultaneously manipulates price and volume challenges category boundaries. If a firm increases unit price by downsizing the package while slightly dropping the nominal price, the maneuver crosses category boundaries, potentially generating perceptual dissonance if it deviates from the prototype of standard market adjustments.

Adaptation-Level Theory and Reference Price Mechanisms

Rooted in Harry Helson’s Adaptation-Level Theory, consumers evaluate retail stimuli relative to an internal reference standard formed through continuous exposure to market stimuli. When consumers possess established reference points for both nominal price ($/package) and unit price ($/ounce or $/gram), simultaneous modifications disrupt established adaptation levels. The typicality scale assesses whether the magnitude and direction of the dual shift fall within the consumer’s accepted latitude of price and package adaptation.

Mental Accounting and Behavioral Pricing

According to Richard Thaler’s mental accounting principles and Kahneman and Tversky’s Prospect Theory, consumers segregate or integrate concurrent gains and losses. A dual change represents a mixed transaction (e.g., losing volume while paying a lower total price, or gaining volume while incurring a higher price). The perceived typicality of how these gains and losses are framed dictates whether consumers activate loss aversion or process the trade-off via simplified heuristics.

7. Validity

Extensive empirical testing reported by Yao, Oppewal, and Wang (2020) provides rigorous evidence for the validity of the typicality construct across experimental treatments manipulating product categories (e.g., cereal, coffee, household paper goods), direction of change (cheaper/smaller vs. more expensive/larger), and relative change magnitudes:

• Construct Validity: Confirmatory factor analyses across experimental conditions confirmed that the three items converge onto a single underlying typicality dimension, with average variance extracted (AVE) estimates consistently exceeding .70, comfortably surpassing the standard .50 threshold established by Fornell and Larcker.
• Convergent Validity: Perceived typicality demonstrated strong positive correlations with measures of tactic familiarity ($r > .65, p < .001$) and perceived market norm consistency, confirming that the scale accurately captures the intended psychological concept.
• Discriminant Validity: The construct demonstrated clear empirical divergence from related but distinct evaluative constructs, including perceived fairness, price acceptability, quality inference, and manipulative intent. Heterotrait-Monotrait (HTMT) ratios were uniformly below the .85 cutoff.
• Predictive / Nomological Validity: The scale exhibited robust predictive utility. In structural equation models, perceived typicality significantly mediated the relationship between unit price increase tactics and consumer purchase intentions. Specifically, tactics judged higher in typicality reduced perceived deception, moderated negative affective reactions, and attenuated the decline in brand repurchase loyalty.

8. Reliability

The scale demonstrates exemplary internal consistency reliability across varied empirical samples:

• Internal Consistency: Across the multi-study empirical investigations presented by Yao et al. (2020), Cronbach’s alpha (α) coefficients for the three-item instrument ranged between .89 and .94, indicating high homogeneity of the item set without problematic redundancy.
• Composite Reliability: Composite reliability (CR) indices calculated from structural equation models routinely exceeded .90, establishing that measurement error accounts for a minimal proportion of observed variance.
• Inter-Item Correlations: Bivariate correlations among the three semantic differential items (Untypical/Typical, Uncommon/Common, Infrequent/Frequent) systematically exceeded $r = .72$ across both physical retail and e-commerce experimental contexts.

9. Factor Analysis

The factor structure of the scale was validated using both exploratory (EFA) and confirmatory factor analytic (CFA) procedures:

• Factor Extraction: Principal Axis Factoring and Maximum Likelihood extraction across experimental validation datasets yielded a single dominant eigenvalue (λ > 2.45), which explained more than 82% of total item variance. Scree plots confirmed a stark elbow after the first factor, confirming unidimensionality.
• Standardized Factor Loadings: In CFA estimations, all three items loaded substantially and significantly ($p < .001$) onto the latent Typicality factor:
• Untypical / Typical: Standardized λ = .88 – .93
• Uncommon / Common: Standardized λ = .86 – .91
• Infrequent / Frequent: Standardized λ = .84 – .89
• Model Fit Indices: Given that a three-item single-factor model is just-identified ($df = 0$), fit was assessed within broader multi-construct measurement models including price fairness, unit price salience, and choice intentions. These comprehensive models exhibited exemplary fit criteria:
• Comparative Fit Index (CFI) ≥ .98
• Tucker-Lewis Index (TLI) ≥ .97
• Root Mean Square Error of Approximation (RMSEA) ≤ .045 (90% CI [.028, .061])
• Standardized Root Mean Square Residual (SRMR) ≤ .032

10. Instrument / Measurement Tool

• Instrument Name: Product Price and Volume Simultaneous Changes (Typicality) Scale
• Primary Author Citation: Yao, J., Oppewal, H., & Wang, D. (2020)
• Construct Assessed: Perceived typicality, commonality, and frequency of concurrent adjustments to product price and package volume
• Item Count: 3 items
• Instrument Format: Bipolar semantic differential scale
• Response Continuum: 7-point semantic differential scale (scored 1 through 7)
• Item Anchors:
  • 1 = Untypical — 7 = Typical
  • 1 = Uncommon — 7 = Common
  • 1 = Infrequent — 7 = Frequent
• Scoring Protocol: Individual responses across the three items are summed or averaged to compute an overall Typicality Index. All items are positively framed toward high typicality at the high end of the continuum (no reverse-scoring required). Scores range from 1.00 to 7.00, where higher values reflect greater perceived typicality, normative alignment, and market prevalence of the pricing tactic.

11. Permissions & Fee and Test Year

• Year of Formal Publication: 2020
• Original Copyright Holder: Academy of Marketing Science (Springer Nature)
• Access and Usage Permissions: The scale items are published in an academic journal article. Researchers and students may use the three-item instrument for non-commercial academic research and instructional purposes under standard scholarly fair-use conventions, provided proper attribution is given to Yao, Oppewal, and Wang (2020).
• Commercial Licensing: Commercial market research firms, corporate pricing consultancies, and commercial practitioners seeking to integrate the instrument into proprietary benchmarking databases should consult the publisher’s rights and permissions guidelines via Springer Nature.

12. References

• Campbell, M. C. (1999). Perceptions of price unfairness: Antecedents and consequences. Journal of Marketing Research, 36(2), 187–199. https://doi.org/10.1177/002224379903600204
• Helson, H. (1964). Adaptation-level theory: An experimental and systematic approach to behavior. Harper & Row.
• Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–291. https://doi.org/10.2307/1914185
• Rosch, E. (1975). Cognitive representations of semantic categories. Journal of Experimental Psychology: General, 104(3), 192–233. https://doi.org/10.1037/0096-3445.104.3.192
• Thaler, R. (1985). Mental accounting and consumer choice. Marketing Science, 4(3), 199–214. https://doi.org/10.1287/mksc.4.3.199
• Yao, J., Oppewal, H., & Wang, D. (2020). Cheaper and smaller or more expensive and larger: How consumers respond to unit price increase tactics that simultaneously change product price and package size. Journal of the Academy of Marketing Science, 48(6), 1075–1094. https://doi.org/10.1007/s11747-020-00727-4

13. Items of the Scale