Product Image Generality (PIMGEN)

1. Abstract

The Product Image Generality (PIMGEN) scale is an established psychometric instrument designed to evaluate consumer perceptions of product image generality, a foundational facet of the broader construct of perceived intangibility in marketing, consumer psychology, and retail studies. Formulated and validated in its present three-item configuration by Michel Laroche, Zhiyong Yang, Gordon H. G. McDougall, and Jasmin Bergeron (2005) in the Journal of Retailing, PIMGEN quantifies the degree to which an individual experiences difficulty in clearly visualizing, forming a distinct cognitive representation of, or mentally anchoring the specific attributes and performance characteristics of a product or service offering. Unlike unidimensional conceptualizations that equate intangibility merely with physical impalpability (i.e., whether an object can be touched), PIMGEN addresses the cognitive and representational ambiguity that consumers confront when conceptualizing offerings across physical goods, financial services, digital retail platforms, and experiential products.

The scale consists of three self-report items administered via an 8-point Likert-type response format ranging from 1 (Strongly disagree) to 8 (Strongly agree). All items are reverse-scored such that higher aggregated scores indicate greater perceived generality (i.e., greater mental ambiguity and higher cognitive intangibility). Extensively tested across diverse consumer shopping environments—including brick-and-mortar stores, e-commerce platforms, multi-channel ecosystems, and cross-national samples—PIMGEN demonstrates robust psychometric properties. Across initial and subsequent validation studies, the measure exhibits high internal consistency reliability (Cronbach’s alpha typically ranging between .82 and .91), well-defined unidimensional factor structures confirmed through confirmatory factor analysis (CFA), and pronounced convergent, discriminant, and criterion-related validities. PIMGEN serves as a cornerstone instrument for researchers examining perceived risk, information search behavior, brand equity, cognitive load, and purchase intention in contemporary marketing and consumer decision-making paradigms.

2. Keywords

Product Image Generality, PIMGEN, Intangibility, Mental Intangibility, Consumer Psychology, Perceived Risk, Measurement Scale, Psychometrics, E-Commerce, Retail Marketing, Visual Imagery, Cognitive Representation

3. Authors

The PIMGEN measurement scale was developed and psychometrically validated through a series of foundational investigations into the multidimensional nature of intangibility by a team of prominent scholars in marketing and consumer research:

• Michel Laroche, Ph.D. — Royal Bank Distinguished Professor of Marketing at the John Molson School of Business, Concordia University, Montreal, Quebec, Canada. Dr. Laroche is a Fellow of the Royal Society of Canada and an internationally renowned expert in consumer behavior, brand management, services marketing, and psychometric measurement modeling.
• Zhiyong Yang, Ph.D. — Professor of Marketing, currently affiliated with the Bryan School of Business and Economics at the University of North Carolina at Greensboro (and formerly at Concordia University and the University of Texas at Arlington). Dr. Yang’s research specializes in consumer decision making, parental socialization, and quantitative structural equation modeling.
• Gordon H. G. McDougall, Ph.D. — Professor Emeritus of Marketing at the Lazaridis School of Business and Economics, Wilfrid Laurier University, Waterloo, Ontario, Canada. Dr. McDougall made pioneering theoretical and empirical contributions to the conceptualization and measurement of services intangibility, customer satisfaction, and service quality.
• Jasmin Bergeron, Ph.D. — Professor of Marketing at the School of Management Sciences (ESG UQAM), Université du Québec à Montréal, Montreal, Quebec, Canada. Dr. Bergeron’s scholarly work focuses on financial services marketing, relational selling, service interactions, and consumer trust dynamics.

4. Purpose

The primary purpose of the Product Image Generality (PIMGEN) scale is to isolate and quantify the cognitive fuzziness or visual ambiguity associated with consumer mental representations of a product or service. In classical marketing and economic theory, products were conventionally classified along an oversimplified dichotomy: tangible goods versus intangible services. However, consumer psychologists recognized that even physically tangible goods (such as personal computers, over-the-counter pharmaceuticals, or complex consumer electronics) can evoke substantial mental intangibility if consumers cannot visualize their functional benefits, distinguish their internal mechanisms, or conjure a concrete mental image of how the product operates in practice.

PIMGEN specifically assesses generality—the degree to which a product is perceived as generic, unspecific, or difficult to visualize distinctly in the consumer’s mind. When a product possesses high image generality, consumers cannot readily evoke a differentiated, vivid mental picture of its operational reality or aesthetic form. This lack of cognitive clarity directly impacts information processing, affective evaluation, and perceived uncertainty.

Research Applications

In academic research, PIMGEN is widely utilized to:

• Differentiate between the distinct dimensions of product intangibility (physical intangibility, mental intangibility, and generality) and assess their idiosyncratic influences on consumer information search patterns.
• Investigate online versus offline retail shopping environments, pinpointing how digital interfaces either mitigate or exacerbate consumers’ difficulty in visualizing physical goods and digital services.
• Analyze the mediating mechanisms through which product presentation formats (such as interactive 3D product visualizations, augmented reality displays, high-fidelity video demonstrations, and virtual try-ons) reduce perceived generality and thereby lower perceived purchasing risk.
• Model consumer cognitive load, mental imagery fluency, and affective brand attitudes in experimental consumer psychology designs.

Managerial and Applied Utility

For marketing practitioners, market researchers, and retail experience designers, PIMGEN provides an actionable diagnostic tool. Products or service bundles that register high PIMGEN scores indicate critical visualization barriers among target market segments. Identifying elevated generality enables marketing strategists to introduce concrete imagery cues, vivid packaging designs, detailed product demonstrations, testimonials, and tangible physical metaphors that crystallize the offering’s unique visual and functional identity, ultimately reducing cognitive hesitation and abandoned purchase decisions.

5. Psychological Construct

The construct measured by PIMGEN resides within the broader theoretical taxonomy of perceived product intangibility. Historically initiated by McDougall and Snetsinger (1990) and subsequently refined by Laroche, McDougall, Bergeron, and Yang (2001, 2003, 2004, 2005), intangibility is conceptualized not as a monolithic construct, but rather as a tri-component framework comprising three interrelated yet empirically distinct dimensions:

1. Physical Intangibility: The classical objective or tactile dimension, referring to the lack of physical substance and the inability of an offering to be touched, held, or directly accessed through the physical senses prior to purchase.
2. Mental Intangibility: The degree to which an offering is mentally graspable, comprehensible, or clear in terms of what it actually does and how it will perform. It reflects general conceptual difficulty and cognitive abstraction.
3. Generality (Product Image Generality): The specific perceptual difficulty in defining, visualizing, and distinguishing the product’s precise characteristics and features from broader product classes. It focuses explicitly on the vividness and specificity of the internal visual and conceptual representation.

Generality versus Mental Intangibility

A critical psychometric and theoretical distinction exists between mental intangibility and generality. While mental intangibility reflects difficulty in understanding the nature, value proposition, and operational utility of an offering (e.g., “I do not understand what a derivative investment strategy does”), generality addresses the absence of a distinct, concrete, and visually evocative mental prototype (e.g., “When I think of this product, I cannot picture it clearly; it simply registers as a vague category member”).

A consumer may conceptually understand what an automobile liability insurance policy covers (low mental intangibility), yet be completely unable to construct a distinct, vivid mental picture of the actual product itself (high image generality). Conversely, a novel, technically complex technological artifact might be easily visualized in its physical chassis (low image generality), yet remain entirely opaque regarding its functional architecture and internal processing capabilities (high mental intangibility). PIMGEN isolates the visual and specific representational aspect: whether an individual can conjure an unambiguous, vivid, and easily accessible mental image of the product.

Dual-Coding and Image Processing

From a cognitive perspective, PIMGEN taps directly into the non-verbal visual imagery system described in Paivio’s dual-coding theory. When an offering triggers strong visual imagery, cognitive activation occurs rapidly, retrieval fluency is heightened, and the product is categorized with rich episodic and semantic markers. In contrast, high image generality reflects representational deficit: the consumer lacks associative visual cues, leading to delayed categorization, heightened cognitive strain, and an amplified reliance on external heuristics (such as brand reputation or price) to infer quality.

6. Theoretical Framework

The development of the Product Image Generality scale is grounded in several converging theoretical frameworks across cognitive psychology, information processing theory, and services marketing theory.

Information Economics and Search-Experience-Credence Attributes

Economist Philip Nelson (1970) and subsequent marketing theorists (Darby & Karni, 1973) categorized goods along a continuum of search, experience, and credence qualities. Search goods possess tangible attributes that consumers can evaluate prior to purchase; experience goods can only be evaluated after consumption; credence goods cannot be fully evaluated even after consumption. Laroche and colleagues integrated this perspective with cognitive psychology, demonstrating that perceived intangibility—specifically image generality—fundamentally converts even search goods into subjective experience or credence scenarios when evaluated through mediated channels such as the internet. When an individual cannot physically handle an object and the digital presentation lacks clear visual fidelity, perceived generality elevates, altering the consumer’s perceived risk profile.

Dual-Coding Theory and Mental Imagery

Allan Paivio’s (1971, 1986) Dual-Coding Theory posits that human cognition operates via two functionally independent yet interacting symbolic subsystems: a non-verbal structural system specialized for representing and processing perceptual images, and a verbal structural system specialized for linguistic information. Mental representations of external stimuli are substantially more robust, memorable, and emotionally evocative when dual-coded through both verbal descriptions and non-verbal perceptual imagery.

PIMGEN operationalizes the degree of failure within the non-verbal perceptual imagery system. When consumers report that an image of the product does not come easily to mind, that they lack a clear picture, or that visual simulation is impeded, the mental representation relies exclusively on abstract linguistic propositions. This propositional dominance inhibits affective processing and creates a feeling of cognitive ambiguity, which the consumer interprets as product risk.

Perceived Risk Theory

Raymond Bauer’s (1960) Perceived Risk Theory underscores that consumer behavior is inherently an exercise in risk taking under conditions of uncertainty. Perceived risk consists of two foundational components: uncertainty regarding the outcome, and the consequences (financial, performance, psychological, social, or physical) of an unfavorable outcome. PIMGEN serves as a direct upstream cognitive antecedent to uncertainty. When an offering cannot be distinctly envisioned, performance uncertainty escalates dramatically. Laroche et al. (2005) demonstrated empirically that image generality exerts a robust, positive direct effect on consumers’ perceived overall risk, which subsequently depresses purchase intentions across both e-commerce and traditional physical retail storefronts.

7. Validity

The psychometric validity of the Product Image Generality (PIMGEN) scale has been established through rigorous empirical testing utilizing structural equation modeling (SEM), confirmatory factor analysis (CFA), and multi-trait multi-method comparisons across diverse consumer product categories and shopping channels.

Construct and Convergent Validity

Construct validity was formally substantiated by Laroche, Yang, McDougall, and Bergeron (2005) across comparative samples assessing multiple distinct product categories (e.g., personal computers, hotel accommodations, and compact cars) across both digital e-commerce channels and physical brick-and-mortar storefronts. Confirmatory factor analysis revealed that all three items of the PIMGEN scale load highly and significantly onto their designated latent factor. Standardized factor loadings across diverse experimental and field cohorts routinely exceed .80 (ranging from .82 to .93, with all t-values exceeding 15.0, p < .001). Furthermore, the Average Variance Extracted (AVE) consistently surpasses the established .50 benchmark (Fornell & Larcker, 1981), typically falling between .70 and .82, indicating that the latent construct accounts for the vast majority of variance in its observed indicators.

Discriminant Validity

Extensive discriminant validity assessments confirm that PIMGEN constitutes an autonomous psychometric construct, clearly differentiated from:

• Physical Intangibility: While physical intangibility evaluates the sheer absence of tactile, physical substance, PIMGEN captures visual and representational ambiguity. Empirical correlations between PIMGEN and physical intangibility are moderate (typically ranging from r = .30 to .55), and the squared inter-construct correlation is substantially lower than the AVE of either construct, satisfying the Fornell-Larcker discriminant criterion.
• Mental Intangibility: Although image generality and mental intangibility both reflect cognitive dimensions, CFA nested model comparisons demonstrate that a two-factor model separating generality from mental intangibility achieves significantly superior fit over a collapsed unidimensional model (Δχ² significant at p < .001).
• Product Familiarity and Knowledge: PIMGEN demonstrates discriminant validity against general subjective knowledge. While familiar products are generally easier to visualize, novel items can still possess low generality if visual stimuli are concrete and explicit.

Nomological and Predictive Validity

Nomological validity is reinforced by the scale’s predictable relationships with upstream and downstream constructs in structural equation models:

• Antecedents: High-quality visual information, multi-angle imagery, interactive zooming features, and concrete linguistic product descriptions have been shown to significantly reduce PIMGEN scores.
• Consequences: In Laroche et al. (2005), higher PIMGEN scores exerted statistically significant positive effects on overall perceived risk (standardized paths exceeding γ = .25, p < .01) and indirect negative effects on consumers’ purchase willingness, information search efficiency, and brand confidence.

8. Reliability

The internal consistency and temporal stability of the PIMGEN scale have been corroborated across numerous independent studies in marketing, human-computer interaction, and applied behavioral sciences.

Internal Consistency Reliability

Across the foundational validation datasets presented by Laroche et al. (2005), the three-item PIMGEN measure demonstrated superior internal consistency:

• Cronbach’s Alpha (α): In tests across brick-and-mortar retail contexts, Cronbach’s alpha coefficients ranged from .84 to .89 across distinct product categories. In online e-commerce contexts, alpha values ranged from .86 to .91, well above the conventional threshold of .70 recommended for empirical research.
• Composite Reliability (CR): Structural equation modeling evaluations report composite reliability values typically ranging between .85 and .92, establishing that the observed indicators reflect a shared latent dimension with minimal measurement error.
• Item-Total Correlations: Corrected item-to-total correlations for all three indicators consistently exceed .70, reflecting high internal cohesion without excessive redundancy.

Cross-Study and Cross-Cultural Reliability

Subsequent replications and adaptations across international markets (e.g., North America, Europe, and East Asia) have confirmed invariant reliability estimates. In studies examining consumer reactions to web-based visual configurations (e.g., visual fluency and virtual reality shopping spaces), PIMGEN maintains α coefficients between .83 and .90, demonstrating that the scale’s psychometric robustness is preserved across different retail sectors, consumer demographics, and product modalities.

9. Factor Analysis

The latent dimensionality of the Product Image Generality scale has been evaluated using both exploratory factor analysis (EFA) and confirmatory factor analysis (CFA).

Exploratory Factor Structure

Initial exploratory factor analyses utilizing principal axis factoring and maximum likelihood estimation with both orthogonal (Varimax) and oblique (Promax) rotations consistently demonstrate that the three items load onto a single dominant factor. Eigenvalues for this single component routinely exceed 2.20, accounting for 72% to 83% of the total variance across observed indicators. Factor loadings for all individual items uniformly exceed .80, with negligible residual cross-loadings when evaluated alongside items measuring physical intangibility and mental intangibility.

Confirmatory Factor Analysis and Model Fit

In structural equation modeling frameworks, the measurement model incorporating PIMGEN as a first-order latent factor yields outstanding goodness-of-fit indices. In the Laroche et al. (2005) multi-group CFA models comparing online and in-store settings:

• Comparative Fit Index (CFI): Typically exceeds .97 (frequently > .99), demonstrating exemplary fit relative to the null baseline model.
• Tucker-Lewis Index (TLI / NNFI): Routinely exceeds .96, confirming parsimonious model adequacy.
• Root Mean Square Error of Approximation (RMSEA): Consistently falls below .05 (with 90% confidence intervals spanning .000 to .075), signaling negligible approximation error in the population covariance matrix.
• Standardized Root Mean Square Residual (SRMR): Values consistently remain below .03, well within standard psychometric acceptance thresholds (< .08).

Representative CFA Item Loadings

A representative measurement model demonstrates the following structural properties across standardized solutions:

• Item 1 (“It is very easy for me to visualize this product”): Standardized factor loading λ = .83 to .88; Error variance θ = .23 to .31.
• Item 2 (“I have a very clear picture of this product”): Standardized factor loading λ = .88 to .93; Error variance θ = .14 to .23.
• Item 3 (“The image of this product comes easily to my mind”): Standardized factor loading λ = .82 to .89; Error variance θ = .21 to .33.

Multi-group invariance analyses further establish full metric and scalar invariance across shopping channels (online vs. offline) and product classes, confirming that PIMGEN measures the identical latent construct across diverse consumer settings without measurement bias.

10. Instrument / Measurement Tool

• Instrument Name: Product Image Generality (PIMGEN) Scale
• Original Authors: Michel Laroche, Zhiyong Yang, Gordon H. G. McDougall, and Jasmin Bergeron (2005)
• Instrument Type: Self-report psychometric rating scale
• Construct Measured: Perceived Product Image Generality (Cognitive Intangibility / Representational Generality)
• Number of Items: 3 items
• Response Format: 8-point Likert-type scale ranging from 1 (Strongly disagree) to 8 (Strongly agree)
• Item Phrasing and Scoring Rules:
  • All three items are framed in a positive visualization direction in the questionnaire.
  • Mandatory Reverse Scoring: To calculate perceived product image generality, all three items must be reverse-scored prior to aggregation:
    Score_Reversed = 9 - Raw_Score (for an 8-point scale).
  • Following reverse-scoring, higher total or mean scores reflect greater perceived generality (i.e., greater difficulty visualizing the product, representing higher cognitive intangibility).
  • Scores can be expressed as a summed composite (ranging from 3 to 24) or as an average score (ranging from 1.0 to 8.0).
• Administration Time: Less than 2 minutes
• Target Population: Consumers, online shoppers, retail patrons, and experimental participants evaluating products or services

11. Permissions & Fee and Test Year

• Year of Publication: 2005 (formalized in the Journal of Retailing; derived from preliminary work in 2001, 2003, and 2004).
• Copyright and Access: The scale was published as an academic instrument in an article published by Elsevier Inc. on behalf of the New York University Leonard N. Stern School of Business.
• Academic Research Use: The scale is widely considered an open-access psychometric instrument for academic, non-commercial research and educational investigations, provided appropriate scholarly attribution is cited.
• Commercial Applications: Commercial practitioners, proprietary research platforms, or commercial survey firms seeking to incorporate the instrument into paid market research batteries should review permissions through Elsevier’s RightsLink service or contact the primary corresponding author (Dr. Michel Laroche, Concordia University).
• Fee: Free of charge for academic, non-commercial scholarly research.

12. References

• Bauer, R. A. (1960). Consumer behavior as risk taking. In R. S. Hancock (Ed.), Dynamic Marketing for a Changing World (pp. 389–398). American Marketing Association.
• Darby, M. R., & Karni, E. (1973). Free competition and the optimal amount of fraud. Journal of Law and Economics, 16(1), 67–88. https://doi.org/10.1086/466756
• 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
• Laroche, M., Bergeron, J., & Goutaland, C. (2001). A three-dimensional approach to examine the impact of intangibility on perceived risk. Journal of Services Marketing, 15(7), 526–544. https://doi.org/10.1108/EUM0000000006202
• Laroche, M., Bergeron, J., & Goutaland, C. (2003). How intangibility affects perceived risk: The moderating role of knowledge and involvement. Journal of Services Marketing, 17(2), 122–140. https://doi.org/10.1108/08876040310467907
• Laroche, M., McDougall, G. H. G., Bergeron, J., & Yang, Z. (2004). Exploring how intangibility affects purchase intention: Differences between services and goods. Journal of Services Marketing, 18(5), 373–385. https://doi.org/10.1108/08876040410548302
• Laroche, M., Yang, Z., McDougall, G. H. G., & Bergeron, J. (2005). Internet versus bricks-and-mortar retailers: An investigation into intangibility and its consequences. Journal of Retailing, 81(4), 251–267. https://doi.org/10.1016/j.jretai.2004.11.002
• McDougall, G. H. G., & Snetsinger, D. W. (1990). The intangibility of services: Measurement and competitive perspectives. Journal of Services Marketing, 4(4), 27–40. https://doi.org/10.1108/EUM0000000002523
• Nelson, P. (1970). Information and consumer behavior. Journal of Political Economy, 78(2), 311–329. https://doi.org/10.1086/259630
• Paivio, A. (1971). Imagery and verbal processes. Holt, Rinehart and Winston.
• Paivio, A. (1986). Mental representations: A dual coding approach. Oxford University Press.

13. Items of the Scale