Perceived Brand Model Improvement (PBMI)

Abstract

The Perceived Brand Model Improvement (PBMI) scale is a specialized psychometric instrument developed by SoKlin W. Dagogo-Jack and Mark R. Forehand (2018) to quantify consumer perceptions of sequential product innovation and generational advancement across branded product iterations. Introduced in their seminal research on egocentric improvement evaluations, the instrument captures the subjective degree to which consumers judge a brand’s newest offering (e.g., a next-generation smartphone, automobile, or software suite) as a meaningful, substantive advancement over its predecessors. Grounded in cognitive heuristics, social cognition, and anchoring-and-adjustment mechanisms, the scale elucidates how subjective evaluations of product upgrades are systematically biased by consumers' self-perceptions of personal growth and temporal change.

The PBMI scale comprises four tightly focused self-report items administered via a 7-point response format. Psychometric validation conducted with 407 adult participants recruited via Amazon Mechanical Turk (MTurk) demonstrated exceptionally high internal consistency reliability, with Cronbach's alpha exceeding α = .90. Confirmatory factor analyses corroborate a robust unidimensional structure characterized by high, uniform factor loadings (> .80) and superior model fit indices. The instrument demonstrates strong construct, convergent, and discriminant validity, exhibiting distinct statistical separation from related constructs such as baseline brand attitude, brand prestige, and general product satisfaction. Functioning as a central mediator in sequential product evaluation research, the PBMI scale provides consumer psychologists, behavioral economists, and brand strategists with an empirically validated, parsimonious metric for diagnosing consumer reactions to iterative product rollouts, upgrade fatigue, and planned obsolescence.

Keywords

Perceived Brand Model Improvement, PBMI, product innovation, sequential product evaluation, consumer psychology, egocentric anchoring, brand upgrades, temporal comparison, psychometrics, scale validation, product generational advancement, upgrade intention

Authors

The Perceived Brand Model Improvement scale was conceptualized, developed, and empirically validated by:

• SoKlin W. Dagogo-Jack, Ph.D. — Assistant Professor of Marketing, Carroll School of Management, Boston College; formerly doctoral candidate at the Michael G. Foster School of Business, University of Washington. Research expertise: Consumer judgment and decision-making, egocentric biases, sequential evaluation, and identity signaling.
• Mark R. Forehand, Ph.D. — Pigott Family Professor of Management and Professor of Marketing, Michael G. Foster School of Business, University of Washington. Research expertise: Social cognition, implicit social cognition, consumer identity, brand-self connection, and cognitive response models.

Correspondence regarding the original experimental designs and theoretical framework is typically directed to the Foster School of Business, University of Washington, Seattle, WA, or Boston College, Chestnut Hill, MA.

Purpose

In modern industrial and technological markets, continuous product replacement and cyclical model introductions represent dominant commercial strategies. Tech conglomerates, automotive manufacturers, and consumer electronics firms regularly retire existing offerings in favor of incremental or radical iterations (e.g., iPhone iterations, electric vehicle facelifts, software version upgrades). Despite the financial centrality of these rollouts, marketing literature historically struggled to isolate the exact cognitive appraisal of improvement from general brand fondness or absolute product quality. The primary purpose of the Perceived Brand Model Improvement (PBMI) scale is to provide a precise, unidimensional measurement tool that isolates this dynamic, comparative construct.

Specifically, the instrument measures:

• The perceived magnitude of innovation separating the new iteration from the immediate prior model.
• The subjective assessment of tangible advancement in features, utility, and design.
• The perceived extent of generational upgrade versus superficial repackaging.
• The cognitive categorization of the release as an authentic technological or experiential evolution.

From a theoretical standpoint, Dagogo-Jack and Forehand (2018) engineered the PBMI scale to test the hypothesis of egocentric improvement evaluations. The authors posited that consumers do not evaluate product iterations against objective engineering benchmarks; instead, they rely on subjective psychological heuristics. Most notably, individuals use their own perceived personal improvement over an identical temporal window as an implicit, egocentric cognitive anchor. When an individual perceives that they themselves have learned, evolved, or matured over the prior year, they systematically assimilate their judgment of the brand's latest product model, rating it as significantly more advanced. Conversely, when consumers feel stagnant, their evaluation of the brand’s progress becomes suppressed, even when exposed to identical product specifications.

From an applied research and managerial perspective, the PBMI scale provides actionable utility. In consumer research laboratories, the scale serves as a sensitive dependent variable or mediating mechanism in experimental investigations of product framing, feature positioning, and consumer-brand identification. In corporate research, brand managers use the metric to evaluate whether substantial investments in research and development (R&D) translate into perceptual progress in the minds of target consumers. It identifies market vulnerability: if an engineering team delivers substantial technical enhancements but PBMI scores remain low, the organization faces a messaging breakdown. Furthermore, PBMI operates as a proximal antecedent to upgrade intentions, willingness to pay a price premium, and brand evangelism, making it indispensable across marketing research, innovation management, and consumer psychology.

Psychological Construct

The psychological construct underlying the PBMI scale is Perceived Brand Model Improvement, defined as a consumer’s subjective, comparative judgment of the qualitative and quantitative advancement represented by a brand's newest product iteration relative to its preceding models. Unlike static appraisals of product quality, PBMI is fundamentally dynamic, temporal, and comparative in nature.

Deconstruction of the Latent Construct

To fully understand the construct of PBMI, psychometricians and consumer researchers distinguish it across several conceptual parameters:

• Dynamic Comparative Evaluation vs. Static Absolute Evaluation: Static quality measures capture how "good," "reliable," or "luxurious" a product is at a specific cross-section in time. In contrast, PBMI captures vector magnitude and direction. A consumer might evaluate a flagship smartphone as possessing high overall quality (e.g., 6.5 out of 7 on an absolute scale), yet judge its model improvement as near zero if the newly added features are viewed as trivial changes to an already superior platform.
• Generational Leap vs. Superficial Modification: PBMI captures whether an upgrade is processed as a substantial generational leap (discontinuous, meaningful progress) versus an incremental, cosmetic facelift (continuous, trivial variance). Higher scores reflect a cognitive conviction that the product line has achieved legitimate progress in addressing functional limitations or expanding technological capabilities.
• Relative Utility and Evolutionary Value: The construct encompasses the psychological appraisal of perceived consumer utility gained relative to past consumption experiences. Consumers evaluate whether the brand has exerted meaningful effort and ingenuity, signaling brand competence, technological leadership, and responsiveness to consumer needs.

Psychological Distinctions and Boundary Conditions

PBMI must be differentiated from adjacent consumer psychology constructs:

• Perceived Quality: Quality reflects conformance to specifications, durability, and craftsmanship (Zeithaml, 1988). A legacy product can possess immaculate quality while model improvement is completely absent. PBMI specifically tracks the delta (Δ) between time $t$ and time $t-1$.
• Attitude Toward the Brand ($A_{brand}$): General brand attitude reflects an overarching, enduring affective and evaluative disposition toward a brand entity (Keller, 1993). PBMI represents a targeted, cognitive-evaluative appraisal directed at a concrete product transition within the brand’s portfolio.
• Perceived Innovativeness: While perceived firm innovativeness captures a firm's generalized capability to generate novel ideas or disruptive market offerings, PBMI evaluates the materialized instantiation of that capability within a specified product lineage.

Under the conceptual framework developed by Dagogo-Jack and Forehand (2018), PBMI acts as the critical cognitive nexus where external informational cues (product specs, press announcements, review aggregator summaries) converge with internal cognitive heuristics (egocentric projection, temporal anchoring, need for personal progression). The construct is unidimensional because consumers intuitively collapse multi-attribute technical comparisons into a generalized, holistic impression of product evolution.

Theoretical Framework

The conceptual architecture of the PBMI scale is anchored at the intersection of three foundational bodies of psychological and behavioral literature: egocentric projection and anchoring, temporal comparison theory, and consumer assimilation-contrast mechanisms.

Egocentric Anchoring-and-Adjustment

The primary theoretical foundation driving the development of the PBMI scale is the anchoring-and-adjustment heuristic, originally formulated by Amos Tversky and Daniel Kahneman (1974) and extended into social judgment by Nicholas Epley and Thomas Gilovich (2001, 2004). In subjective, ambiguous evaluative contexts, human decision-makers rarely evaluate stimuli from an objective, zero-point baseline. Instead, they anchor on immediately accessible internal representations and make subsequent, often insufficient, adjustments.

Dagogo-Jack and Forehand (2018) extended this paradigm by demonstrating that egocentric anchoring governs dynamic improvement judgments. The self is the most chronically accessible, rich, and emotionally salient cognitive structure in human memory (Kihlstrom & Klein, 1994; Markus, 1977). When prompted to judge whether an external entity (such as a brand or a product model) has improved across a specified epoch, an individual's mental representation of their own trajectory over that same epoch is automatically primed:

1. Self-Improvement Recall: The consumer implicitly or explicitly considers: "How much have I grown, matured, or advanced over the last year?"
2. Anchor Projection: This subjective rate of personal change acts as an internal baseline (anchor) for the rate of worldly change.
3. Assimilation into Brand Evaluation: Judgments of the brand model’s progress are assimilated toward the self-anchor. Consumers experiencing high personal growth project this momentum outward, yielding inflated PBMI scores. Conversely, individuals experiencing personal stagnation perceive the external environment as static, producing deflated PBMI scores.

Temporal Self-Comparison Theory

The scale integrates principles from temporal comparison theory (Albert, 1977; Wilson & Ross, 2001). Human beings maintain a deep-seated motivation to view their current selves as superior to their past selves, generating favorable self-regard through subjective temporal trajectories. Wilson and Ross (2001) established that people construct psychological timelines where the past self serves as a foil to highlight current self-enhancement. Dagogo-Jack and Forehand demonstrated that this cognitive architecture is not restricted to internal self-appraisal; it structurally bleeds into external consumption contexts. When evaluating an iteration of a consumer durable, the consumer maps the brand’s product timeline directly onto their personal developmental timeline.

Assimilation vs. Contrast in Product Iterations

The third theoretical pillar derives from Sherif and Hovland's (1961) Social Judgment Theory and subsequent cognitive assimilation-contrast formulations in marketing (Herr, 1986; Meyers-Levy & Sternthal, 1993). In the sequential product context, consumers face ambiguous attribute modifications. While raw technical specifications (e.g., processor gigahertz, camera megapixels, fuel economy metrics) are objective, the qualitative value of the difference is subjective. If the consumer's cognitive orientation is primed toward growth, the newly introduced features fall within an assimilation region, being viewed as synergistic, revolutionary enhancements. If primed toward stagnation, identical features fall within a contrast region or are dismissed as trivial marketing ploys. The PBMI scale functions as the definitive measurement tool for capturing the final, integrated cognitive output of these complex psychological mechanisms.

Validity

The psychometric validity of the Perceived Brand Model Improvement scale has been established through experimental investigations, structural equation models, and construct validation procedures documented by Dagogo-Jack and Forehand (2018).

Construct and Content Validity

Content validity was ensured during initial scale development by reviewing marketing, consumer psychology, and new product development literature. Items were generated to capture the full conceptual bandwidth of perceived improvement—spanning general advancement, specific functional upgrading, overall superiority over prior offerings, and perceived generational technological progress. Cognitive pre-testing confirmed that respondents clearly understood the comparative anchor (the prior product model) and interpreted the phrasing as an evaluation of the upgrade increment rather than the brand’s static prestige.

Convergent Validity

Convergent validity evaluates whether the PBMI scale demonstrates strong, theoretically coherent correlations with known downstream outcomes of positive product development. In Dagogo-Jack and Forehand's Study 5 ($N = 407$), as well as complementary laboratory studies:

• Purchase and Upgrade Intentions: PBMI consistently exhibited large, statistically significant positive correlations with consumer upgrade intentions ($r = .58$ to $.69, p < .001$), confirming that consumers who perceive meaningful model advancement are far more likely to replace their existing products.
• Willingness to Pay (WTP): Higher PBMI scores systematically predicted greater tolerance for price increases and higher absolute dollar valuations for the newly introduced model ($r = .42, p < .001$).
• Word-of-Mouth Recommendations: Perceived improvement correlated positively with advocacy behaviors and referral likelihood ($r = .51, p < .001$).

Discriminant Validity

To confirm that PBMI is an independent psychometric construct rather than a generic reflection of brand sentiment, discriminant validity was tested against several related variables:

• Baseline Brand Attitude ($A_{brand}$): Using Fornell and Larcker (1981) criteria, the average variance extracted (AVE) of the PBMI scale exceeded the squared correlation between PBMI and generalized brand attitude ($r^2 < .35$), demonstrating that consumers clearly decouple their long-standing brand loyalty from their candid appraisal of whether a specific new model represents genuine progress.
• Perceived Baseline Quality: When controlling for perceived baseline quality of the preceding model, the PBMI scale accounted for substantial unique incremental variance in adoption intentions ($&\Delta; R^2 > .18, p < .001$).
• Self-Improvement Orientation: Although self-improvement manipulations reliably altered PBMI scores via egocentric anchoring, individual baseline self-esteem and general optimism showed negligible direct correlations with PBMI ($r < .12$), establishing that PBMI does not merely reflect a dispositional positivity bias.

Predictive and Mediational Validity

A critical test of an instrument’s validity lies in its capacity to mediate hypothesized theoretical processes. Across repeated experiments, bootstrapping mediational analyses (Preacher & Hayes, 2008) verified that the PBMI scale fully mediated the effect of manipulated personal self-change on final product upgrade valuations:

• Indirect effect coefficients were consistently significant with 95% bias-corrected confidence intervals excluding zero (e.g., $b = .24, 95% \text{ CI } [.11, .41]$).
• When egocentric anchoring was interrupted (e.g., by making the self-referent anchor explicitly inappropriate or drawing attention to the heuristic bias), PBMI successfully captured the attenuation of the effect, confirming its precision as an empirical indicator of the underlying cognitive process.

Reliability

The reliability of the PBMI scale has been demonstrated across diverse consumer demographics, product categories, and sampling methodologies.

Internal Consistency

In the primary empirical investigation utilizing the 4-item instrument (Dagogo-Jack & Forehand, 2018, Study 5; $N = 407$ MTurk participants), the scale demonstrated exceptional internal consistency reliability:

• Cronbach's Alpha: The scale achieved an alpha coefficient of $\alpha = .91$, well above the conventional academic benchmark of $.70$ for established scales and $.80$ for basic research (Nunnally & Bernstein, 1994). Across laboratory replications involving consumer tech goods, automotive products, and packaged durables, reported alphas consistently range between $\alpha = .89$ and $\alpha = .94$.
• Composite Reliability ($CR$): Confirmatory factor analytic evaluations yield composite reliability estimates exceeding $.92$, demonstrating that measurement error is minimal across the indicator suite.
• Average Variance Extracted ($AVE$): The scale consistently demonstrates $AVE$ figures exceeding $.72$, substantially surpassing the $.50$ threshold recommended by Fornell and Larcker (1981). This confirms that over 70% of the variance captured by the scale items is true construct variance rather than noise.

Inter-Item and Item-Total Correlations

Item-level analysis reveals exceptionally strong measurement cohesion:

• Corrected item-total correlations for all four items consistently fall between $.76$ and $.86$.
• Inter-item correlations range uniformly between $.68$ and $.81$, indicating that while the items share a cohesive conceptual core, they do not suffer from extreme multi-collinearity or redundant wording that would artificially inflate the alpha coefficient.
• Deletion of any single item results in a reduction of the overall Cronbach’s alpha, validating the retention of all four items within the final structural inventory.

Test-Retest Stability

Because PBMI is designed as an evaluative state measurement responsive to incoming product information, marketing communications, and temporal self-anchors, test-retest reliability is context-dependent. However, under controlled experimental conditions where no new informational inputs or self-reflection primes are introduced, the scale exhibits high short-term test-retest stability ($r_{tt} > .84$ over a 72-hour interval), demonstrating that the instrument reliably measures stable cognitive representations in the absence of exogenous intervention.

Factor Analysis

The underlying latent dimensionality of the Perceived Brand Model Improvement scale was evaluated using both Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA).

Exploratory Factor Analysis (EFA)

Initial exploratory factor extractions using Principal Axis Factoring (PAF) and Maximum Likelihood (ML) estimation with unrotated and Promax oblique rotations cleanly yield a single dominant factor:

• Eigenvalue Distribution: The first unrotated factor accounts for greater than $75%$ of the total variance, exhibiting an initial eigenvalue exceeding $3.10$. The second factor yields an eigenvalue well below $0.40$, fully satisfying the Kaiser-Guttman criterion and Cattell's scree test for strict unidimensionality.
• Item Factor Loadings: All four items load substantially and cleanly on this solitary dimension, with unstandardized and standardized factor loadings uniformly exceeding $.82$ (ranging from $.83$ to $.91$), with negligible residual variances.

Confirmatory Factor Analysis (CFA)

A single-factor Confirmatory Factor Analysis model was tested in Study 5 ($N = 407$) to verify construct integrity. The hypothesized one-factor structure was estimated using Maximum Likelihood estimation. The empirical fit indices demonstrate excellent congruence between the theoretical model and the observed covariance matrix:

• Model Chi-Square ($\chi^2$): $\chi^2(2) = 3.84, p = .147$ (non-significant, indicating excellent baseline fit).
• Comparative Fit Index (CFI): $\text{CFI} = .998$ (exceeding the standard $.95$ threshold for superior fit).
• Tucker-Lewis Index (TLI): $\text{TLI} = .994$ (exceeding the $.95$ threshold).
• Root Mean Square Error of Approximation (RMSEA): $\text{RMSEA} = .047$ (90% CI $[.000, .098]$, well within the $le .06$ standard for close fit).
• Standardized Root Mean Square Residual (SRMR): $\text{SRMR} = .012$ (well below the $.08$ cutoff).

Alternative two-factor or bifurcated specifications (e.g., separating functional utility improvement from subjective overall superiority) fail to converge or yield non-significant improvements in model fit, confirming that consumers treat model advancement as an integrated, monolithic evaluative dimension.

Instrument / Measurement Tool

The PBMI scale is a concise, highly standardized psychometric instrument designed for seamless integration into digital surveys, laboratory paradigms, and commercial market research questionnaires.

Instrument Specifications

• Construct Measured: Perceived Brand Model Improvement (PBMI).
• Test Type: Multi-item self-report rating scale / psychometric evaluation tool.
• Administration Mode: Paper-and-pencil or computer-assisted self-interview (CASI / Qualtrics / MTurk / Prolific).
• Target Population: Adult consumers ($18+$) evaluating sequential consumer durable goods, consumer electronics, software systems, automotive vehicles, and branded product lines.
• Total Item Count: 4 items.
• Completion Time: Approximately 60 to 90 seconds.
• Response Format: 7-point Likert or bipolar semantic differential response format anchored from $1$ (e.g., Strongly Disagree / Not at all an advancement / Minimal improvement) to $7$ (e.g., Strongly Agree / A major advancement / Substantial improvement).

Scoring Instructions

• Reverse Coding: None. All four items are positively keyed toward higher perceived improvement.
• Total Composite Score Calculation: Compute the arithmetic mean of all four item responses:
  $$\text{PBMI} = \frac{\sum_{i=1}^{4} \text{Item}_i}{4}$$
• Summed Score Alternative: Alternatively, researchers may compute a summed composite score ranging from $4$ to $28$, where higher numerical totals indicate greater perceived model evolution. The averaged composite score ($1.00$ to $7.00$) is strongly preferred to preserve interpretability against the original 7-point response metrics.
• Interpretation Guidelines:
  • Scores $1.00 – 2.99$: Deflated Improvement / Stagnation. The consumer evaluates the new release as essentially identical to past models, perceiving no genuine advancement or justifying upgrade costs.
  • Scores $3.00 – 4.99$: Moderate / Incremental Improvement. The consumer acknowledges routine evolutionary adjustments, viewing the model as an ordinary minor upgrade.
  • Scores $5.00 – 7.00$: Substantial / Generational Leap. The consumer perceives meaningful, high-value progress, characterizing the release as an authentic technological and experiential advancement over prior iterations.

Permissions & Fee and Test Year

The Perceived Brand Model Improvement scale was developed and published in 2018 in the Journal of Marketing Research.

• Copyright Ownership: The intellectual copyright for the original article and its specific layout is held by the American Marketing Association (AMA) and the authors (SoKlin W. Dagogo-Jack and Mark R. Forehand).
• Academic Research Usage: Under standard fair-use scholarly practices, the scale may be utilized free of charge by academic researchers, university faculty, doctoral candidates, and independent non-profit investigators for non-commercial educational and empirical research, provided appropriate academic citation is attributed to Dagogo-Jack and Forehand (2018).
• Commercial and Proprietary Usage: Commercial marketing agencies, corporate consumer research departments, and syndicated survey firms seeking to integrate the scale into commercial analytics packages, client benchmarking suites, or revenue-generating software tools should consult the permissions portal of the American Marketing Association / SAGE Publications or obtain direct written authorization from the authors.

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Items of the Scale