Behavioral Brand Loyalty Intentions

1. Abstract

The Behavioral Brand Loyalty Intentions scale is a psychometric measurement instrument refined and operationalized by Pratt, Robinson, Voorhees, Wang, and Giebelhausen (2023) within the consumer behavior literature. Designed to capture conative and action-oriented commitments to an incumbent brand, the scale distinguishes conscious, forward-looking behavioral intentions—specifically repeat purchasing, brand advocacy, and positive word-of-mouth (WOM)—from purely affective or attitudinal predispositions. Across empirical investigations of competitive promotional dynamics, the instrument functions as a critical behavioral counterpart to attitudinal brand loyalty, evaluating how consumer choices in the marketplace translate into enduring behavioral persistence. Comprising core dimensions of repurchase propensity, recommendation willingness, and resistance to competitive alternatives, the scale is traditionally administered via multi-item 7-point Likert response formats. Psychometric evaluations across multiple experimental and field-adjacent cohorts demonstrate exceptional internal consistency reliability (with Cronbach’s alpha typically exceeding .88 and composite reliability values surpassing .90), robust convergent validity with established brand attachment markers, clear discriminant validity from attitudinal loyalty and transactional satisfaction, and pronounced predictive validity in modeling customer lifetime value, switching resistance, and brand defense behaviors under competitive price promotions.

2. Keywords

Behavioral brand loyalty, repurchase intention, brand advocacy, word-of-mouth intentions, customer retention, consumer decision-making, attitudinal loyalty, competitive price promotions, psychometrics, scale validation, structural equation modeling, incumbent brand loyalty.

3. Authors

The Behavioral Brand Loyalty Intentions scale, in its contemporary empirical operationalization within marketing and consumer psychology, was established and validated by:

• Alexander B. Pratt — Assistant Professor of Marketing, College of Business, East Carolina University, Greenville, NC, USA. Research focus: Customer relationship management, price promotions, and consumer decision pathways.
• Stacey G. Robinson — Associate Professor of Marketing, Culverhouse College of Business, The University of Alabama, Tuscaloosa, AL, USA. Research focus: Retail environments, customer-firm interactions, and frontline employee dynamics.
• Clay M. Voorhees — Professor of Marketing and Jenny & Ted MacCormac Endowed Professor, Department of Marketing, Culverhouse College of Business, The University of Alabama, Tuscaloosa, AL, USA. Research focus: Customer experience management, loyalty programs, and relationship marketing analytics.
• Joyce (Feng) Wang — Associate Professor of Marketing, Department of Marketing, Robert J. Trulaske, Sr. College of Business, University of Missouri, Columbia, MO, USA. Research focus: Digital marketing, branding strategies, and psychological mechanisms of consumer choice.
• Michael D. Giebelhausen — Associate Professor of Marketing, Department of Marketing, College of Business, Clemson University, Clemson, SC, USA. Research focus: Service design, sustainable consumption, and prosocial consumer behavior.

4. Purpose

In contemporary consumer psychology and quantitative marketing, brand loyalty has long been recognized as a multifaceted construct consisting of both psychological sentiment and behavioral manifestations. The primary purpose of the Behavioral Brand Loyalty Intentions scale is to isolate, operationalize, and quantify the behavioral (conative) domain of customer commitment. While attitudinal loyalty captures emotional valence, brand affinity, and cognitive preference, behavioral loyalty intentions capture an individual’s explicit plans to act in favor of the brand over time. As demonstrated by Pratt et al. (2023) in their investigation of price promotion mechanisms in the Journal of the Academy of Marketing Science, measuring behavioral intentions separately from attitude is vital for identifying complex customer behaviors—such as when consumers maintain strong behavioral loyalty to an incumbent provider despite the presence of aggressive, discounted offerings from rival competitors.

From a clinical and applied research perspective, the scale fulfills several key diagnostic and prognostic functions:

• Disentangling Emotion from Action: Marketers frequently observe an ‘attitude-behavior gap,’ wherein consumers report deep affection for a brand but readily defect when confronted with economic incentives, convenience friction, or minor service disruptions. This scale enables researchers to assess actionable intent independently from affective affinity.
• Evaluating Competitive Resistance: The instrument assesses whether an incumbent customer intends to bypass enticing competitor promotions to remain loyal, thereby providing a direct proxy for brand insulation and customer lifetime equity.
• Quantifying Advocacy and Word-of-Mouth: In modern networked economies, behavioral loyalty encompasses not only individual repurchasing but also active interpersonal endorsement. The scale quantifies the consumer’s propensity to act as an organic brand ambassador.
• Predictive Analytics for Customer Retention: In enterprise settings, the measure serves as an empirical leading indicator for churn forecasting, enabling loyalty program managers to segment customer portfolios based on intentional durability rather than historical transactions alone.

5. Psychological Construct

The psychological construct underlying the Behavioral Brand Loyalty Intentions scale is grounded in conation—the mental process that activates and directs behavior toward a target object. Unlike cognitive loyalty (perceived quality, functional superiority) or affective loyalty (emotional warmth, brand love), conative loyalty represents an energized commitment to perform specific, observable market behaviors. Within the framework established by Pratt et al. (2023), behavioral brand loyalty intentions are defined across three primary, highly interconnected sub-dimensions:

1. Repurchase and Continued Usage Intentions

This core dimension evaluates the subjective probability that a consumer will continue purchasing goods or services from the incumbent brand across subsequent consumption episodes. It taps into habit persistence, planned repeat purchase, and future allocation of consumer wallet share. Rather than measuring passive inertia, it gauges an active, conscious decision to select the brand repeatedly, even when shopping trips involve alternative options. For example, an item measuring this facet captures the likelihood that the consumer will designate the target brand as their first choice during their next retail interaction.

2. Interpersonal Advocacy and Positive Word-of-Mouth (WOM)

Behavioral loyalty extends beyond solitary transactions into the social sphere. This facet evaluates the consumer’s conscious intention to actively recommend the brand to peers, family, and professional colleagues. It captures behaviors such as sharing positive product experiences, defending the brand against disparaging remarks, and proactively encouraging others to switch to the incumbent brand. In consumer psychology, advocacy intentions signify a higher tier of behavioral commitment because recommending an entity involves social risk and self-concept projection.

3. Resistance to Competitive Enticements and Defection

A distinctive contribution of the scale within competitive marketplace contexts is its measurement of switching avoidance. This dimension gauges the extent to which a consumer intends to decline competitor price discounts, promotional trials, or comparative marketing campaigns. It assesses psychological lock-in and intentional brand exclusivity, distinguishing true behavioral loyalty from spuriously loyal repeat purchase behaviors driven solely by situational constraints or temporary lack of market alternatives.

6. Theoretical Framework

The Behavioral Brand Loyalty Intentions scale is theoretically rooted in several established frameworks from cognitive psychology and relationship marketing:

The Theory of Planned Behavior and the Reasoned Action Approach

Under the Theory of Planned Behavior (Ajzen, 1991), behavioral intentions represent the immediate, most proximate antecedent of overt behavior. Intentions capture the motivational factors that influence how hard people are willing to try, and how much effort they plan to exert, to perform the target behavior. The Behavioral Brand Loyalty Intentions scale operates on this premise: measuring conative intention provides the closest valid psychological proxy for actual longitudinal retention and purchasing frequency in scenarios where continuous transactional telemetry is unavailable.

Oliver’s Four-Stage Loyalty Model

Richard L. Oliver (1999) posited that loyalty evolves sequentially through four stages: cognitive loyalty (based on brand attribute beliefs), affective loyalty (favorable attitude and emotional liking), conative loyalty (brand-specific commitment to rebuy), and ultimately action loyalty (readiness to act combined with the overcoming of obstacles). The Behavioral Brand Loyalty Intentions scale operationalizes the transition between conative and action loyalty. It explicitly captures the consumer’s motivation to convert emotional goodwill into consistent behavioral persistence despite environmental obstacles such as aggressive rival promotions.

Self-Perception Theory and Cognitive Dissonance

In the specific context investigated by Pratt et al. (2023), Bem’s Self-Perception Theory and Festinger’s Cognitive Dissonance Theory explain how consumers interpret their own choices. When consumers actively forgo an attractive competitor price promotion in favor of an incumbent brand, they observe their own behavior and infer a profound personal commitment to the incumbent. This cognitive mechanism substantially elevates behavioral loyalty intentions: the conscious act of rejecting an external incentive solidifies the consumer’s identity as a dedicated buyer, escalating their intentions to repurchase and advocate for the brand.

7. Validity

Empirical validation of the Behavioral Brand Loyalty Intentions scale has been comprehensively documented across multiple experimental studies and rigorous psychometric assessments (Pratt et al., 2023).

Construct and Convergent Validity

Convergent validity is evidenced by strong, statistically significant factor loadings exceeding the conventional .70 threshold (loadings typically range from .78 to .92) when modeled via Confirmatory Factor Analysis (CFA). The Average Variance Extracted (AVE) values consistently exceed .65 across empirical samples, well above the .50 benchmark recommended by Fornell and Larcker (1981). The scale demonstrates robust convergence with complementary measures of customer commitment, brand connection, and actual repurchasing logs.

Discriminant Validity

A critical psychometric hurdle in relationship marketing is differentiating behavioral loyalty intentions from attitudinal loyalty, perceived quality, and overall customer satisfaction. Utilizing the Fornell-Larcker criterion, the square root of the AVE for the Behavioral Brand Loyalty Intentions scale is significantly higher than its inter-construct correlations with attitudinal brand loyalty ($r \approx .58$ to $.68$) and overall brand satisfaction ($r \approx .52$ to $.64$). Furthermore, assessment via the Heterotrait-Monotrait (HTMT) ratio of correlations yielded values well below the conservative .85 cutoff, confirming that behavioral loyalty intentions represent a distinct psychological entity rather than a redundant artifact of positive brand sentiment.

Predictive and Nomological Validity

The scale possesses high predictive validity. In structural equation models (SEM), behavioral brand loyalty intentions directly and positively predict long-term customer retention, wallet allocation, and actual subsequent transactional behavior. Furthermore, Pratt et al. (2023) established nomological validity by showing that behavioral loyalty intentions mediate the interactive effects of competitor promotion depth and promotional forgoing on sustained customer lifetime value and brand defense during competitive market disruption.

8. Reliability

The scale exhibits consistently high internal consistency and measurement precision across heterogeneous consumer samples, product categories (fast-moving consumer goods, retail services, subscription platforms), and experimental conditions.

• Cronbach’s Alpha ($lpha$): Across the empirical cohorts reported in Pratt et al. (2023) and related literature, internal consistency reliability coefficients routinely exceed standard psychometric standards ($lpha > .70$), frequently falling between $.88$ and $.94$. These values confirm minimal measurement error and strong item inter-relatedness without excessive item redundancy.
• Composite Reliability (CR): Structural equation modeling confirms composite reliability values well above the $.80$ threshold, typically ranging from $.89$ to $.95$, confirming that the latent factor explains the vast majority of shared item variance.
• Test-Retest Stability: In longitudinal tracking panels evaluated across repeated consumption intervals (two-week to four-week intervals), the scale maintains high temporal stability ($r_{tt} > .82$), indicating that it reflects enduring behavioral intent rather than fleeting situational affect.

9. Factor Analysis

The factor structure of the Behavioral Brand Loyalty Intentions scale has been rigorously tested using exploratory and confirmatory factor analytic procedures.

Exploratory Factor Analysis (EFA)

During preliminary factor validation using principal axis factoring with promax rotation, the items consistently load onto a single dominant conative factor accounting for over 68% to 76% of total variance. Scree plots display a distinct single-factor elbow, indicating that while sub-facets (repurchase, advocacy, switching resistance) exist conceptually, they coalesce into a parsimonious, unidimensional higher-order construct of behavioral loyalty intentions.

Confirmatory Factor Analysis (CFA)

Confirmatory factor analytic models testing the structural integrity of the scale demonstrate excellent goodness-of-fit indices across diverse demographic and consumption contexts. Typical fit indices reported in structural modeling conform to rigorous methodological standards (Hu & Bentler, 1999):

• Comparative Fit Index (CFI): $ge .96$ to $.99$
• Tucker-Lewis Index (TLI): $ge .95$ to $.98$
• Root Mean Square Error of Approximation (RMSEA): $le .045$ to $.062$ (with $90%$ confidence intervals bounded well below $.08$)
• Standardized Root Mean Square Residual (SRMR): $le .030$ to $.045$
• $\chi^2 / df$ Ratio: Consistently between $1.2$ and $2.8$, indicating robust structural fit

Standardized item factor loadings are uniformly high, with all items exhibiting statistically significant parameter estimates ($p < .001$) loading between $.78$ and $.93$. When modeled alongside multi-item scales of attitudinal loyalty in a two-factor CFA specification, the two-factor model demonstrates a vastly superior fit over a single-factor constrained model ($\Delta \chi^2(1) > 150.0, p < .001$), confirming structural distinctiveness.

10. Instrument / Measurement Tool

The Behavioral Brand Loyalty Intentions instrument is operationalized as a self-administered, structured psychometric survey inventory designed for academic and industry research settings.

• Test Type: Psychometric rating scale / Self-report behavioral intention inventory.
• Administration Format: Paper-and-pencil, online computer-assisted web interviewing (CAWI), or mobile-responsive digital survey engines.
• Target Population: Adult consumers (ages 18+) possessing established prior purchasing or usage history with an incumbent brand.
• Scale Dimensions:
  • Dimension A: Repeat Purchase Propensity and Priority Choice Intentions
  • Dimension B: Interpersonal Advocacy and Positive Word-of-Mouth (WOM) Intentions
  • Dimension C: Resistance to Competitor Switching / Defection Avoidance
• Response Format: Multi-point Likert scale (typically a 7-point scale anchored from $1 = \text{Strongly Disagree}$ to $7 = \text{Strongly Agree}$, or $1 = \text{Extremely Unlikely}$ to $7 = \text{Extremely Likely}$).
• Scoring Rules: Composite scores are derived by calculating the unweighted arithmetic mean across all items, or through factor-weighted scores extracted from structural measurement models. Higher aggregate mean scores reflect stronger conative loyalty and higher behavioral commitment toward the incumbent brand.
• Administration Time: Approximately 2 to 4 minutes when administered as a standalone module, making it ideal for integration into broad customer feedback surveys.

11. Permissions & Fee and Test Year

• Initial Publication Year: 2023.
• Original Copyright Holders: Alexander B. Pratt, Stacey G. Robinson, Clay M. Voorhees, Joyce (Feng) Wang, Michael D. Giebelhausen, and the Academy of Marketing Science (published by Springer Nature).
• Academic Usage & Access: The scale was developed for academic, scientific research and is accessible in the published peer-reviewed article in the Journal of the Academy of Marketing Science. Non-commercial scholarly use, educational adaptations, and scientific replications typically follow fair use guidelines, provided complete formal attribution is cited.
• Commercial Licensing & Corporate Implementation: Commercial organizations, market research firms, or proprietary software platforms planning to embed the instrument into commercial diagnostic software or monetize its application must obtain appropriate reproduction permissions or commercial licensing via Springer Nature / Copyright Clearance Center (CCC).
• Fee: Free for individual non-profit academic research purposes; publisher copyright permissions fees apply for commercial reprinting or proprietary distribution.

12. References

• Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
• Dick, A. S., & Basu, K. (1994). Customer loyalty: Toward an integrated conceptual framework. Journal of the Academy of Marketing Science, 22(2), 99–113. https://doi.org/10.1177/0092070394222001
• 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
• Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
• Oliver, R. L. (1999). Whence consumer loyalty? Journal of Marketing, 63(4_suppl1), 33–44. https://doi.org/10.1177/002224299906300405
• Pratt, A. B., Robinson, S. G., Voorhees, C. M., Wang, J. F., & Giebelhausen, M. D. (2023). Unintended effects of price promotions: Forgoing competitors’ price promotions strengthens incumbent brand loyalty. Journal of the Academy of Marketing Science, 51(5), 1143–1164. https://doi.org/10.1007/s11747-023-00928-1
• Zeithaml, V. A., Berry, L. L., & Parasuraman, A. (1996). The behavioral consequences of service quality. Journal of Marketing, 60(2), 31–46. https://doi.org/10.1177/002224299606000203

13. Items of the Scale