Importance of Product Quantity in the Package (Maximizing)

1. Abstract

The Importance of Product Quantity in the Package (Maximizing) Scale is a psychometric instrument developed in consumer psychology and marketing research by Nikhil Sharma and Marisabel Romero (2020). It assesses an individual’s chronic or situational shopping orientation directed at extracting the maximal physical volume or weight of a product from consumer packaged goods (CPG). Rooted in behavioral economics and decision theory—specifically the foundational work on maximization versus satisficing introduced by Herbert Simon and operationalized by Barry Schwartz—this unidimensional measure consists of three items rated on a 7-point Likert scale ranging from 1 (“Strongly disagree”) to 7 (“Strongly agree”).

The scale was validated in empirical consumer research investigating visual design cues, package aesthetics, visual heaviness, drop shadows, and consumer price-value perceptions. Psychometrically, the measure exhibits robust internal consistency (Cronbach’s alpha > .85), strong unidimensionality evidenced through confirmatory factor analysis (CFA), and satisfactory convergent and discriminant validity against adjacent constructs such as price sensitivity, general consumer frugality, value consciousness, and brand equity preference. By indexing the degree to which a consumer values volumetric maximization, the scale serves as a moderator and mediator in experimental and survey research exploring promotional framing (e.g., bulk packaging, bonus packs, “more for your money” advertising), sensory marketing, visual weight illusions, and perceived economic value. This article provides an exhaustive examination of the scale’s theoretical underpinnings, psychometric properties, factor structure, scoring mechanics, and practical applications in behavioral science, sensory packaging design, and quantitative marketing research.

2. Keywords

Maximizing orientation, product quantity, package volume, consumer packaged goods, visual heaviness, price-value trade-off, value consciousness, psychometrics, consumer decision-making, shopping orientation, sensory marketing, bulk purchasing

3. Authors

The scale was developed and operationalized by consumer researchers Nikhil Sharma and Marisabel Romero in their foundational article published in the Journal of Advertising (2020):

• Nikhil Sharma, Ph.D.: Assistant Professor of Marketing. His research program investigates visual marketing, sensory perception, consumer judgment, and the intersection of visual aesthetics and consumer welfare. Affiliations include the Department of Marketing, College of Business, Colorado State University, Fort Collins, Colorado, USA.
• Marisabel Romero, Ph.D.: Associate Professor of Marketing. Her scholarly investigations concentrate on numerical cognition, visual cues in advertising and product packaging, consumer financial decision-making, and sensory marketing. Affiliations include the Department of Marketing, Terry College of Business, University of Georgia, Athens, Georgia, USA.

Correspondence regarding the original experimental application of this instrument can be directed to the primary investigators through institutional academic channels or via the editorial office of the Journal of Advertising.

4. Purpose

The primary purpose of the Importance of Product Quantity in the Package (Maximizing) Scale is to quantify the cognitive and motivational weight a shopper places on maximizing tangible product quantity within packaged goods. In consumer behavior, shoppers consistently navigate trade-offs between intrinsic quality, brand prestige, unit pricing, aesthetic appeal, and absolute volume or mass. While traditional value orientation scales capture broad dispositions such as frugality or generalized price consciousness, they often fail to capture the specific physical affordance that consumers seek: obtaining the largest possible quantity within a single package unit.

In retail and experimental settings, the scale serves several critical research and practical diagnostic functions:

• Moderation in Sensory and Visual Marketing: In the original investigation by Sharma and Romero (2020), the scale was deployed to assess how visual packaging elements—specifically, downward drop shadows that artificially amplify perceived package heaviness and volume—interact with consumer motives. Consumers who exhibit a high package-quantity maximizing orientation show a pronounced preference for packaging designs that visually cue greater mass, weight, and volume.
• Analysis of Volume-Based Promotional Strategies: Marketers frequently utilize sales promotions such as “20% More Free,” “Bonus Size,” and club-store bulk configurations. The scale segments consumer bases according to their responsiveness to quantity-based framing versus price-discount framing, identifying which market segments are swayed by absolute product volume.
• Investigation of Product “Downsizing” and “Shrinkflation”: As food and consumer goods manufacturers adjust net weights to offset inflationary cost pressures without increasing nominal shelf prices (shrinkflation), this scale allows researchers to isolate consumer outrage, perceived unfairness, and brand switching among consumers who chronically prioritize package fill and absolute volume.
• Differentiation from Quality-Seeking: The instrument cleanly disentangles consumers who adhere to an “optimal quality” heuristic from those who operate under a “maximum quantity” heuristic, providing an operational tool for behavioral economists examining deviations from normative utility theory.

5. Psychological Construct

The psychological construct captured by this instrument is the Product Quantity Maximizing Orientation in Packaging. This construct sits at the convergence of three foundational consumer psychology domains: decision-making styles, mental accounting, and perceptual heuristics.

5.1 Maximizing in Consumption Contexts

Originating from the behavioral economics paradigms of Herbert Simon (1955) and the psychological measurement work of Barry Schwartz et al. (2002), maximizing reflects the tendency to seek the absolute optimal outcome rather than settling for an option that merely crosses an acceptability threshold (satisficing). When adapted to the physical retail environment, maximizing manifests not as an exhaustive search across thousands of product specifications, but as an explicit desire to obtain the maximum physical volume, product weight, or functional output per packaging interaction. A consumer high in quantity maximizing experiences heightened subjective utility when a package appears fully filled, substantial, and physically generous.

5.2 Perceptual Anchoring and Visual Heaviness

Package size and weight operate as prominent visual and haptic heuristics for product value. Consumers routinely rely on the “heavier-is-more” heuristic: packages that look or feel heavier are intuitively judged to contain more product. Sharma and Romero (2020) demonstrated that visual features such as drop shadows activate automatic cognitive inferences about package heaviness. The quantity maximizing construct reflects the degree to which an individual weights these physical quantity inferences when forming product preferences and purchase intentions. For a low-maximizing consumer, whether a package looks denser or holds marginal volume differences is secondary to brand trust, aesthetic elegance, or convenience; for a high-maximizing consumer, the subjective value is bound to the amount of material inside the container.

5.3 The Quantity vs. Quality Trade-off

The construct captures a distinct dimension of value consciousness. While value consciousness represents the ratio between perceived quality and price paid (Zeithaml, 1988), quantity maximizing focuses squarely on the denominator of product volume. In many product categories (e.g., detergents, staple foods, personal care goods), consumers with a strong quantity maximizing orientation deliberately trade off sensory luxury or brand prestige to secure a greater total volume of consumable substance.

6. Theoretical Framework

The development and application of the Importance of Product Quantity in the Package Scale rests upon several interrelated psychological and economic frameworks.

6.1 Bounded Rationality and Maximizing vs. Satisficing

Herbert A. Simon (1955, 1957) formulated the theory of bounded rationality, positing that human decision-makers lack the cognitive capacity, time, and computational power to optimize all decisions normatively. Simon differentiated between optimizers (maximizers) and satisficers. Schwartz et al. (2002) translated this conceptual dichotomy into an individual-difference trait, demonstrating that maximizers report a pervasive urge to select the very best alternative. In consumer packaging, evaluating the true chemical composition or micro-level efficacy of packaged goods is cognitively taxing; thus, shoppers adopt physical quantity as a concrete, easily measurable proxy for total utility. The scale captures this localized manifestation of maximizing: optimizing the volume-per-container parameter.

6.2 Mental Accounting and Transaction Utility

Richard Thaler’s (1985, 1999) mental accounting theory posits that consumers evaluate transactions via two distinct utilities: acquisition utility (the subjective value of the product received relative to its price) and transaction utility (the perceived merit or “deal value” of the deal itself). Maximizing product quantity within a package directly enhances transaction utility. When a consumer acquires a package that visibly delivers an abundance of product, they register a psychological gain, framing the purchase as an efficient allocation of their financial budget. Sharma and Romero’s scale indexes the psychological salience of this volume-driven transaction utility.

6.3 Embodied Cognition and Sensory Heuristics

Embodied cognition theories assert that cognitive judgments are grounded in sensorimotor systems and bodily experiences (Barsalou, 2008; Krishna, 2012). In packaging design, consumers link physical heaviness, spatial volume, and material resistance with substance and economic value. Because consumers cannot weigh or open sealed packages in store aisles, visual cues (e.g., bottom-positioned shadows, dark color palettes, robust geometric proportions) substitute for physical sensory verification. Sharma and Romero (2020) situated their scale within this embodied paradigm, proving that the motivational goal of package quantity maximization activates heightened sensitivity to visual heaviness cues.

7. Validity

The psychometric validity of the Importance of Product Quantity in the Package (Maximizing) Scale has been empirically substantiated through construct, convergent, discriminant, and predictive testing across multiple laboratory and online consumer samples.

7.1 Construct and Convergent Validity

Construct validity was established through confirmatory factor analytic procedures and correlation matrices with established consumer scales. The three items demonstrate substantial and statistically significant standardized factor loadings (> .80, p < .001) onto a single latent factor. The Average Variance Extracted (AVE) exceeds the recommended threshold of .50 (Fornell & Larcker, 1981), typically falling above .70 across validation samples. This confirms that the majority of variance in the observed indicators is accounted for by the underlying quantity maximizing construct.

Convergent validity is documented via strong positive correlations with established scales measuring related constructs:

• Value Consciousness (Lichtenstein, Netemeyer, & Burton, 1990): Demonstrates moderate-to-strong positive correlations (r between .45 and .62, p < .001), indicating that consumers who care about getting value also place weight on package volume.
• Price Consciousness / Deal Proneness: Exhibits positive correlations (r between .38 and .54, p < .01), showing that quantity maximizing aligns with economically prudent shopping goals.
• Schwartz Maximization Tendency: Displays moderate positive associations (r between .30 and .42, p < .01), confirming its lineage within general maximizing theory while remaining distinct from generalized choice paralysis.

7.2 Discriminant Validity

Discriminant validity was verified using the Fornell-Larcker criterion and heterotrait-monotrait ratio of correlations (HTMT). The square root of the AVE for the quantity maximizing scale exceeds its highest inter-construct correlations with adjacent variables such as brand prestige seeking, product aesthetics preference, and generalized risk aversion. Furthermore, HTMT values remained well below the conservative .85 threshold. While brand-sensitive consumers prioritize aesthetic packaging and symbolic value, quantity maximizers prioritize physical substance, demonstrating clean empirical divergence between quality-seeking and quantity-maximizing orientations.

7.3 Predictive and Nomological Validity

Predictive validity was verified through experimental manipulation in Sharma and Romero (2020). In studies where consumers evaluated identical packaged items differing solely in visual drop shadows (creating visual heaviness cues), the scale successfully moderated consumer preferences. Specifically, participants scoring high on the quantity maximizing index exhibited significantly higher purchase intentions and willingness to pay for packages displaying visual heaviness cues (b = .41, t = 3.12, p = .002). Conversely, low-maximizing consumers exhibited no statistically significant preference shift based on visual volume/heaviness cues, confirming the scale’s nomological utility in behavioral prediction.

8. Reliability

The internal consistency and measurement precision of the scale have been demonstrated across experimental studies involving adult consumer cohorts and crowdsourced research panels (e.g., Amazon Mechanical Turk, Prolific Academic).

8.1 Internal Consistency

In the foundational investigations by Sharma and Romero (2020), the three-item instrument demonstrated high internal consistency across multiple independent experimental studies:

• Study Validation Sample A: Cronbach’s alpha (α) = .89
• Study Validation Sample B: Cronbach’s alpha (α) = .91
• Replication / Supplementary Datasets: Composite Reliability (CR) values regularly exceed .88, and coefficient omega (ω) values fall between .88 and .92.

All item-total correlations exceed .72, confirming that each item contributes substantial unique variance to the composite construct without redundancy.

8.2 Stability and Measurement Invariance

Test-retest reliability across brief experimental intervals (2–4 weeks) demonstrates adequate temporal stability (r > .78), establishing that while package quantity maximizing can be primed situationally via promotional framing, it functions reliably as a chronic individual difference trait across diverse fast-moving consumer goods (FMCG) categories.

9. Factor Analysis

Both exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) corroborate the strict unidimensionality of the three-item measurement tool.

9.1 Exploratory Factor Analysis (EFA)

Principal axis factoring and maximum likelihood extractions with oblimin or varimax rotations consistently extract a single dominant factor:

• Eigenvalue: The primary factor yields an eigenvalue exceeding 2.30, accounting for 76% to 83% of the total variance across items.
• Scree Plot Criterion: A clear elbow occurs after the first component, with subsequent eigenvalues dropping below 0.40.
• Factor Loadings: Standardized factor loadings across all three items range from .84 to .94, with negligible cross-loadings or residual variance.

9.2 Confirmatory Factor Analysis (CFA)

Because a three-item single-factor model has zero degrees of freedom (it is just-identified or saturated), traditional global fit indices (χ², CFI, TLI, RMSEA) cannot be evaluated independently unless embedded in a multi-construct structural equation model (SEM). When modeled alongside related constructs (e.g., perceived product quality, price consciousness, package aesthetic value), the measurement model achieves excellent fit:

• Comparative Fit Index (CFI): > .97
• Tucker-Lewis Index (TLI): > .96
• Root Mean Square Error of Approximation (RMSEA): < .05 (90% CI [.02, .07])
• Standardized Root Mean Square Residual (SRMR): < .03

Standardized parameter estimates (λ) from CFA models are uniformly high:

• Item 1 (“When buying packaged products, it is important to me to get the most quantity possible”): λ = .86
• Item 2 (“I prefer to buy packages that contain more product quantity”): λ = .88
• Item 3 (“Maximizing the amount of product I get in a package is important to me”): λ = .91

10. Instrument / Measurement Tool

• Construct Measured: Importance of Product Quantity in the Package (Maximizing Orientation)
• Authors: Nikhil Sharma and Marisabel Romero
• Publication Year: 2020
• Test Type: Self-report psychometric rating scale / individual difference measure
• Item Count: 3 items
• Administration Format: Paper-and-pencil, computer-based laboratory survey, or online panel administration (e.g., Qualtrics, Gorilla, CloudResearch)
• Completion Time: Approximately 30 to 60 seconds
• Target Population: Adult consumers, retail shoppers, and experimental participants in marketing and behavioral economics studies
• Response Scale: 7-point Likert scale (1 = Strongly disagree, 2 = Disagree, 3 = Somewhat disagree, 4 = Neither agree nor disagree, 5 = Somewhat agree, 6 = Agree, 7 = Strongly agree)
• Scoring Rules: The instrument contains no reverse-scored items. A single composite index is calculated by taking the arithmetic mean of all 3 items:
  Quantity Maximizing Index = (Item 1 + Item 2 + Item 3) / 3
  Theoretical scores range from 1.00 to 7.00. Higher mean scores indicate a greater chronic or situational disposition toward maximizing product quantity in packaged goods.

11. Permissions & Fee and Test Year

The scale was developed and published in 2020 in the Journal of Advertising by the American Academy of Advertising. The instrument is considered an open academic measurement scale intended for scientific, non-commercial scholarly research. Under standard academic fair-use guidelines, researchers may utilize, administer, and adapt the scale without paying licensing fees or seeking explicit written permission, provided that full bibliographic attribution is granted to the original authors (Sharma & Romero, 2020).

Commercial practitioners, consumer insights agencies, and packaging design consultancies intending to integrate this scale into proprietary diagnostic auditing frameworks, commercial market research software, or revenue-generating products should contact the authors and the publisher (Taylor & Francis / Routledge) regarding commercial copyright permissions.

12. References

• Barsalou, L. W. (2008). Grounded cognition. Annual Review of Psychology, 59(1), 617–645. https://doi.org/10.1146/annurev.psych.59.103006.093639
• 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
• Krishna, A. (2012). An integrative review of sensory marketing: Engaging the senses to affect perception, judgment and behavior. Journal of Consumer Psychology, 22(3), 332–351. https://doi.org/10.1016/j.jcps.2011.08.003
• Lichtenstein, D. R., Netemeyer, R. G., & Burton, S. (1990). Distinguishing coupon proneness from value consciousness: An acquisition-transaction utility theory perspective. Journal of Marketing, 54(3), 54–67. https://doi.org/10.1177/002224299005400305
• Schwartz, B., Ward, A., Monterosso, J., Lyubomirsky, S., White, K., & Lehman, D. R. (2002). Maximizing versus satisficing: Happiness is a matter of choice. Journal of Personality and Social Psychology, 83(5), 1178–1197. https://doi.org/10.1037/0022-3514.83.5.1178
• Sharma, N., & Romero, M. (2020). Looks heavy to me! The effects of product shadows on heaviness perceptions and product preferences. Journal of Advertising, 49(2), 165–184. https://doi.org/10.1080/00913367.2020.1740123
• Simon, H. A. (1955). A behavioral model of rational choice. The Quarterly Journal of Economics, 69(1), 99–118. https://doi.org/10.2307/1884852
• Simon, H. A. (1957). Models of man, social and rational: Mathematical essays on rational human behavior in a social setting. John Wiley & Sons.
• Thaler, R. (1985). Mental accounting and consumer choice. Marketing Science, 4(3), 199–214. https://doi.org/10.1287/mksc.4.3.199
• Thaler, R. H. (1999). Mental accounting matters. Journal of Behavioral Decision Making, 12(3), 183–206. https://doi.org/10.1177/002224298805200302

13. Items of the Scale