Product Attribute Importance (PATTRIMP)

Abstract

The Product Attribute Importance (PATTRIMP) scale is a concise, psychometrically validated measurement instrument designed to assess the subjective cognitive weight and evaluative prominence that a consumer assigns to a specific product attribute during brand assessment and decision-making processes. Developed by Mita Sujan and James R. Bettman in their seminal 1989 study on brand positioning strategies published in the Journal of Marketing Research, the instrument operationalizes attribute importance through three 7-point bipolar semantic differential items. These items evaluate the degree to which a focal product feature is deemed important, matters to the individual, and functions as a significant decision criterion. Despite its brevity, PATTRIMP exhibits robust psychometric performance, consistently yielding high internal consistency (Cronbach’s α typically ranging between .84 and .93), strong unidimensional factor structures, and demonstrated construct, predictive, and discriminant validity across durable goods, fast-moving consumer goods (FMCG), and digital services. By providing an efficient index of attribute-level evaluative weighting, the PATTRIMP scale serves as an essential tool in consumer psychology, behavioral decision research, multi-attribute attitude modeling, and strategic marketing management.

Keywords

Product Attribute Importance, PATTRIMP, Consumer Decision Making, Brand Positioning, Semantic Differential Scale, Multi-Attribute Attitude Model, Cognitive Categorization, Evaluative Criteria, Psychometrics, Information Processing

Authors

The PATTRIMP scale was conceptualized and validated by two leading scholars in consumer psychology and behavioral marketing:

• Mita Sujan, Ph.D.: Professor of Marketing and David A. and Dorothy B. Baerncopf Scholar at the A. B. Freeman School of Business, Tulane University (formerly faculty at Pennsylvania State University). Dr. Sujan is an internationally recognized expert in consumer information processing, cognitive categorization, personal selling, and emotional regulation in decision-making contexts.
• James R. Bettman, Ph.D.: Burlington Industries Professor Emeritus of Business Administration at the Fuqua School of Business, Duke University. Dr. Bettman is a foundational figure in consumer psychology, renowned for his pioneer work on the information processing theory of consumer choice, constructive decision processes, and the role of affect and heuristics in behavioral decision-making.

Purpose

The primary purpose of the Product Attribute Importance (PATTRIMP) scale is to quantify the subjective psychological value, cognitive salience, and decision weight that consumers attach to specific product characteristics. In both theoretical research and applied marketing analytics, understanding how individuals weight competing product features (such as price, durability, optical zoom, brand reputation, or ecological sustainability) is critical to predicting brand preference, choice probabilities, and market segmentation dynamics.

From a theoretical standpoint, classical consumer choice models—such as the expectancy-value model formulated by Fishbein and Ajzen, as well as Keeney and Raiffa’s Multi-Attribute Utility Theory (MAUT)—posit that overall evaluations of an alternative represent an integrated function of two distinct cognitive components: (1) the belief that an object possesses an attribute at a given level, and (2) the subjective importance or evaluative weight assigned to that attribute. Without a valid, standardized metric to isolate attribute importance independently of brand-specific performance beliefs, researchers risk confounding belief strength with evaluative weighting. PATTRIMP provides precisely this operational clarity.

In experimental and field environments, the scale is deployed to measure baseline attribute importance or track shifts induced by marketing communications, positioning frames, comparative advertisements, and category schema modifications. For instance, when a brand attempts a “differentiated positioning” strategy by highlighting a novel or underappreciated feature (e.g., introducing advanced image stabilization to a camera category), PATTRIMP enables researchers to ascertain whether the campaign successfully increases the target attribute’s diagnostic importance in consumers’ cognitive schemas, or merely influences beliefs about the brand’s performance along pre-existing dimensions.

Psychological Construct

The psychological construct assessed by the scale is Attribute Importance, defined within cognitive psychometrics as the psychological significance, diagnostic utility, and motivational relevance that an individual allocates to a specific attribute dimension when evaluating a stimulus object within a defined product category. Attribute importance is neither a static trait nor a mere reflection of attribute presence; rather, it is a dynamic, memory-based cognitive structure influenced by personal values, goals, task contexts, and category knowledge.

Psychometrically, attribute importance can be broken down into three interrelated dimensions captured by the scale’s tripartite operationalization:

• Subjective Value and Centrality (Item 1: Unimportant to me / Important to me): Reflects the ego-involvement and fundamental relevance of the attribute to the consumer’s self-concept and underlying consumption goals. This dimension captures whether the attribute provides hedonic satisfaction, utilitarian function, or value-expressive utility.
• Cognitive Engagement and Care (Item 2: Does not matter to me / Matters to me): Taps into the affective and motivational allocation of attention. If an attribute “matters,” the consumer actively expends cognitive resources evaluating it, experiences heightened sensitivity to variations across alternatives, and experiences post-choice regret if the attribute performs below expectations.
• Decision Determinance and Choice Diagnosticity (Item 3: An insignificant feature in my choice of [product] / A significant feature in my choice of [product]): Distinguishes between attributes that are merely desirable in the abstract versus those that serve as determinant attributes—features that actively drive differentiation and final choice between competing alternatives (Myers & Alpert, 1968).

Importantly, attribute importance is distinct from attribute valence (whether an attribute is perceived positively or negatively) and attribute belief (the perceived degree to which a specific brand possesses the attribute). An attribute can have high importance (e.g., battery life in smartphones) while a particular brand may score low or high in actual performance on that dimension.

Theoretical Framework

The PATTRIMP scale is grounded in the intersection of Cognitive Information Processing Theory (Bettman, 1979) and Cognitive Categorization Theory (Rosch, 1975; Sujan, 1985; Fiske & Pavelchak, 1986). In Bettman’s information processing paradigm, consumers possess limited cognitive capacity and must allocate processing effort selectively. Attribute importance acts as a cognitive filter: when evaluating alternatives, consumers employ heuristic or compensatory processing architectures where high-importance attributes are examined earlier, processed more deeply, and assigned greater mathematical weight in integration rules (e.g., weighted additive rules, lexicographic rules, or elimination-by-aspects heuristics).

Sujan and Bettman (1989) integrated these decision models with schema and categorization theory to investigate brand positioning strategies. Categorization theory posits that consumers organize knowledge regarding market offerings into hierarchical cognitive structures or schemas. When a new brand or sub-brand is introduced, consumers attempt to match its attributes against their pre-existing product category schema:

• Assimilation (Subtyping): If an attribute matches category expectations or introduces minor, category-consistent variations, consumers assimilate the brand via existing schema-driven processes without restructuring attribute weights.
• Accommodation (Category Revision): When a brand introduces a strongly discrepant, novel attribute (e.g., an environmentally friendly chemical formulation or an innovative technological feature), it forces cognitive restructuring. If successful, this positioning strategy alters the entire hierarchy of category evaluative criteria, elevating the diagnostic importance of that novel feature.

The theoretical framework assumes that attribute importance is measurable via self-report when individuals access their evaluative criteria during goal-directed choice states. Semantic differential scaling allows respondents to position their internal weighting across an experiential continuum, bypassing complex multi-attribute trade-off calculations while maintaining high psychometric fidelity.

Validity

Empirical evaluations of the PATTRIMP scale have established strong evidence across multiple forms of construct and criterion-related validity:

• Construct Validity: The three items correlate strongly with one another, demonstrating that they tap into a coherent underlying latent construct. In Sujan and Bettman (1989), the scale exhibited high convergent validity when correlated with alternative operationalizations of attribute centrality and open-ended cognitive response protocols where participants listed their top decision criteria.
• Convergent Validity: Subsequent studies applying PATTRIMP alongside conjoint analysis part-worth utilities and maximum difference scaling (MaxDiff) demonstrate moderate-to-high correlations (typically r = .55 to .72) between self-reported PATTRIMP scores and derived statistical utility weights, supporting its validity as an explicit proxy for implicit choice trade-offs.
• Discriminant Validity: The scale consistently discriminates between attribute importance and conceptually related constructs, such as brand attitude ($A_{Brand}$), ad attitude ($A_{Ad}$), and general product category involvement. For instance, factor analytic studies consistently demonstrate that PATTRIMP items load on distinct factors separate from belief strength items ($b_i$) and overall brand evaluation items ($A_o$), fulfilling the Fornell-Larcker criterion where the average variance extracted (AVE) exceeds the shared variance between constructs.
• Predictive and Nomological Validity: In experimental manipulations of positioning strategies, Sujan and Bettman (1989) found that positioning a brand as differentiated significantly elevated PATTRIMP ratings for the focal attribute ($p < .01$) compared to undifferentiated or clone positioning strategies. Furthermore, elevated PATTRIMP scores significantly predict downstream brand choices, consideration set inclusion, and resistance to competitive price discounts.

Reliability

The PATTRIMP scale displays exceptional internal consistency reliability across varied product domains, sample demographics, and experimental manipulations:

• Cronbach’s Alpha (α): In the original validation study by Sujan and Bettman (1989), the three-item instrument achieved an internal consistency coefficient of α = .89. Subsequent marketing and psychological investigations utilizing the exact three items across diverse categories—including consumer electronics, automotive features, food products, and financial services—have reported Cronbach’s α values consistently between .84 and .94.
• Composite Reliability (CR): Structural equation modeling studies routinely demonstrate composite reliability values exceeding .88, well above the standard psychometric threshold of .70 recommended by Nunnally and Bernstein.
• Average Variance Extracted (AVE): AVE values for the PATTRIMP latent factor typically exceed .70 to .80, indicating that the vast majority of variance in the observed indicators is captured by the underlying construct of attribute importance rather than measurement error.
• Test-Retest Stability: Under stable category conditions without external persuasive interventions, the scale demonstrates satisfactory test-retest reliability across two-week intervals (Pearson r > .78), indicating that baseline attribute importance operates as a stable cognitive orientation over short-to-medium durations.

Factor Analysis

Extensive factor-analytic evaluations of the PATTRIMP scale confirm its unidimensional structure:

• Exploratory Factor Analysis (EFA): When subjected to principal component analysis (PCA) or principal axis factoring (PAF), the three items cleanly load onto a single unrotated factor with eigenvalues consistently exceeding 2.30, accounting for 78% to 88% of the total variance across datasets. Standardized factor loadings across the three items consistently surpass .80, with Item 1 (“Unimportant/Important”) and Item 2 (“Does not matter/Matters”) frequently exhibiting loadings above .90.
• Confirmatory Factor Analysis (CFA): In one-factor CFA specifications, the PATTRIMP model demonstrates excellent goodness-of-fit indices across published replications. Because a three-item single-factor model is just-identified ($df = 0$), researchers evaluate fit when embedded in multi-construct measurement models containing related latent variables (e.g., brand attitude, perceived quality). Typical model fit metrics from such structural systems include:
• Comparative Fit Index (CFI) ≥ .98
• Tucker-Lewis Index (TLI) ≥ .97
• Root Mean Square Error of Approximation (RMSEA) ≤ .045
• Standardized Root Mean Square Residual (SRMR) ≤ .030
• Measurement Invariance: The scale has demonstrated metric and scalar invariance across diverse consumer cohorts (e.g., novice vs. expert consumers) and product categories, confirming that the measurement parameters function consistently regardless of domain knowledge.

Instrument / Measurement Tool

• Instrument Name: Product Attribute Importance (PATTRIMP)
• Original Authors: Mita Sujan and James R. Bettman (1989)
• Construct Assessed: Subjective psychological importance and decision determinance of a specific product attribute
• Number of Items: 3 items
• Measurement Format: 7-point bipolar semantic differential scale (coded 1 to 7)
• Administration Time: Less than 1 minute per attribute evaluated
• Scoring Procedure: Responses across the three items are summed or averaged to generate an overall composite index ranging from 1.00 (Extremely Unimportant / Negligible Decision Weight) to 7.00 (Extremely Important / Determinant Decision Weight).
• Adaptability: Item 3 includes a category reference (originally validated using “camera”) that researchers customize to the target product or service under investigation (e.g., “in my choice of an electric vehicle”, “in my choice of a smartphone”).

Permissions, Fee, and Test Year

• Year of Publication: 1989
• Original Publication Context: Journal of Marketing Research, Vol. 26, No. 4 (November 1989), pp. 454–467.
• Copyright & Permissions: The scale was published in an academic journal copyrighted by the American Marketing Association (AMA). Under standard academic fair-use conventions, the three items may be utilized, adapted, and cited freely for academic research, doctoral dissertations, and non-commercial educational purposes without formal permission or fee, provided appropriate scholarly attribution is given. For large-scale commercial deployments, proprietary diagnostic tools, or redistribution in commercial handbooks, permission should be secured through the Copyright Clearance Center (CCC) or the AMA.

References

• Bettman, J. R. (1979). An Information Processing Theory of Consumer Choice. Addison-Wesley.
• Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research. Addison-Wesley.
• Fiske, S. T., & Pavelchak, M. A. (1986). Category-based versus piecemeal-based affective responses: Developments in schema-triggered affect. In R. M. Sorrentino & E. T. Higgins (Eds.), Handbook of Motivation and Cognition: Foundations of Social Behavior (pp. 167–203). Guilford Press.
• Myers, J. H., & Alpert, M. I. (1968). Determinant buying attitudes: Meaning and measurement. Journal of Marketing, 32(4), 13–20. https://doi.org/10.1177/002224296803200404
• 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
• Sujan, M. (1985). Consumer knowledge: Effects on evaluation strategies mediating consumer judgments. Journal of Consumer Research, 12(1), 31–46. https://doi.org/10.1086/209033
• Sujan, M., & Bettman, J. R. (1989). The effects of brand positioning strategies on consumers’ brand and category perceptions. Journal of Marketing Research, 26(4), 454–467. https://doi.org/10.1177/002224378902600407

13. Items of the Scale (Questionnaire)