Antecedents of Consumer Brand Engagement and Brand Equity of Apps–Measurement Model

Abstract

The Antecedents of Consumer Brand Engagement and Brand Equity of Apps–Measurement Model (Tran et al., 2024) is a psychometric framework designed to quantify how consumer motivations and technological evaluations drive relational and cognitive outcomes within mobile commerce environments. Specifically, the instrument models how two primary classes of user drivers—hedonic motivations and utilitarian motivations—interact with perceived task-service fit to stimulate consumer brand engagement (CBE) and cultivate consumer-based brand equity (CBBE) via branded smartphone applications. Spanning an operational battery of 44 survey items adapted from foundational literature in marketing, information systems, and human-computer interaction (Hollebeek et al., 2014; Yoo et al., 2000; Stocchi et al., 2018; Lin & Huang, 2008), the scale captures multiple first-order and second-order factors: Utilitarian Motivations (perceived security, perceived usefulness, and perceived ease of use), Hedonic Motivations (interpersonal utility and entertainment), Task-Service Fit, Consumer Brand Engagement (cognitive processing, affection, and activation), and Brand Equity (perceived quality, brand loyalty, and brand awareness/associations). Across validation samples of active mobile application users in the United States, the instrument demonstrates robust psychometric properties, exhibiting composite reliability (CR) and Cronbach’s alpha coefficients exceeding 0.70 across all dimensions, average variance extracted (AVE) values surpassing 0.50, and sound discriminant validity assessed via the heterotrait-monotrait (HTMT) ratio of correlations. The scale serves as an essential empirical apparatus for marketing scientists, consumer psychologists, and software experience researchers investigating relational value co-creation in digital mobile ecosystems.

Keywords

Consumer Brand Engagement, Brand Equity, Branded Mobile Apps, Utilitarian Motivation, Hedonic Motivation, Task-Service Fit, Mobile Marketing, Cognitive Processing, Consumer Psychology, Psychometrics

Authors

The measurement model was developed and validated by:

• Trang P. Tran, Ph.D. – Department of Marketing, East Carolina University, Greenville, North Carolina, USA. ORCID: 0000-0001-9000-044X.
• Ilia Gugenishvili, Ph.D. – Center for Retail and Logistics, Jyväskylä University School of Business and Economics, University of Jyväskylä, Jyväskylä, Finland. ORCID: 0000-0002-7657-9149.
• Adrienne F. Muldrow, Ph.D. – Department of Marketing, East Carolina University, Greenville, North Carolina, USA. ORCID: 0000-0003-3519-3343.

Purpose

The primary purpose of the measurement model developed by Tran, Gugenishvili, and Muldrow (2024) is to systematically conceptualize, operationalize, and evaluate the structural mechanisms through which smartphone-mediated consumer experiences translate into durable consumer-brand relationships and organizational brand equity. Branded mobile applications represent a ubiquitous touchpoint where transactional software functionality merges with relational branding. Despite this prominence, empirical research had long struggled to synthesize information technology adoption constructs with relationship marketing paradigms into a single unified psychometric battery.

From a theoretical perspective, this instrument bridges this divide by examining how dual motivational forces—functional utility versus emotional/pleasurable escapism—interact with technological alignment (task-service fit) to foster multidimensional brand engagement and subsequent downstream brand equity. The scale clarifies whether consumer interactions within proprietary software environments operate primarily through problem-solving efficiency or whether affective social connectivity and playfulness drive brand capitalization.

In applied and academic research contexts, the scale fulfills several vital objectives:

• Diagnostic Product Auditing: Product designers, software engineers, and digital marketing strategists can pinpoint whether an application’s deficits stem from transactional friction (e.g., poor usability or perceived insecurity) or experiential deficits (e.g., lack of interpersonal utility, entertainment value, or brand identification).
• Structural Equation Modeling (SEM): The scale provides verified measurement properties designed specifically for variance-based (PLS-SEM) and covariance-based (CB-SEM) structural equation modeling, permitting researchers to explore complex mediational pathways linking cognitive appraisal, affective response, and behavioral commitment.
• Cross-Platform Comparative Research: The instrument offers standardized indices allowing empirical comparison across varied mobile application categories, such as quick-service restaurant apps, retail shopping platforms, banking portals, and fitness trackers.

Psychological Construct

The measurement instrument models a multi-tiered network of interrelated cognitive, affective, behavioral, and evaluative constructs operating at the nexus of human-computer interaction and consumer psychology:

1. Utilitarian Motivations

Grounding user behavior in rational, functional, and goal-directed cognition, utilitarian motivation reflects the degree to which an application serves as an efficient instrument for task completion. Within this scale, utilitarian motives are captured via three distinct first-order subconstructs:

• Perceived Security: The user’s subjective confidence that the mobile application provides a dependable, shielded environment that protects sensitive personal data, transaction history, and financial credentials from breach, loss, or unauthorized interception.
• Perceived Usefulness: Defined in alignment with classical technology acceptance models, this reflects the user’s cognitive appraisal that utilizing the branded app enhances personal productivity, execution speed, and overall effectiveness in executing day-to-day shopping and management tasks.
• Perceived Ease of Use: The degree to which the consumer perceives interacting with the application’s interface, menus, navigational architectures, and operational protocols to be effortless, intuitive, and devoid of steep cognitive load.

2. Hedonic Motivations

Reflecting non-instrumental, intrinsically rewarding, and emotionally driven aspects of digital consumption, hedonic motivation captures subjective well-being, play, and peer validation derived through the software medium:

• Interpersonal Utility: The extent to which the application serves as a social conduit enabling consumers to exchange ideas, communicate within brand communities, obtain peer validation, give encouragement, and construct a digital social identity.
• Entertainment: The capacity of the mobile application to evoke sensory pleasure, escapism, enjoyment, aesthetic gratification, and emotional diversion during use.

3. Task-Service Fit

Adapted from Task-Technology Fit (TTF) theory, this construct measures the perceived correspondence between the operational characteristics and service features provided by the branded application and the specific requirements of the consumer’s shopping or informational goals. A high score signifies that the application’s capabilities directly match the user’s goal hierarchy without functional surplus or deficit.

4. Consumer Brand Engagement (CBE)

Conceptualized as an active, positively valenced psychological state reflecting a consumer’s investment in their ongoing interaction with a focal brand. CBE comprises three interrelated dimensions:

• Cognitive Processing: The level of brand-related elaboration, focused attention, and mental immersion experienced by the consumer while operating the branded application.
• Affection: The degree of positive brand-related emotion, warmth, pride, and happiness elicited during app interactions.
• Activation: The behavioral energy, time allocation, and usage frequency dedicated to the brand via the app relative to competing alternatives.

5. Brand Equity (CBBE)

Reflecting the customer-based value added to a product or service through its associated brand name, evaluated across three core operational facets:

• Perceived Quality: The consumer’s evaluative judgment regarding the brand’s overall superiority, functional dependability, and manufacturing or service standards.
• Brand Loyalty: The intentional and behavioral commitment to repurchase, consistently patronize, and maintain an ongoing affiliation with the brand over time.
• Brand Awareness and Brand Associations: The strength of the brand’s footprint in consumer memory, characterized by spontaneous logo/symbol recognition, top-of-mind recall, and positive cognitive schemas.

Theoretical Framework

The scale integrates three foundational theories across consumer behavior, social psychology, and management information systems:

Uses and Gratifications Theory (UGT)

Originally formulated within communications research (Katz, Blumler, & Gurevitch, 1973), Uses and Gratifications Theory posits that media consumers are active, goal-oriented agents who selectively seek out specific media platforms and communicative channels to fulfill distinct psychological needs. Tran et al. (2024) leverage UGT to argue that branded application users do not passively absorb brand messaging. Rather, they actively adopt branded software to fulfill dual gratification pathways: utilitarian gratifications (informational retrieval, administrative efficiency, transactional speed) and hedonic gratifications (social connection, peer recognition, play, relaxation). The measurement model captures these gratification expectations as primary drivers shaping subsequent psychological immersion.

Task-Technology Fit (TTF) Model

Formulated by Goodhue and Thompson (1995), the TTF framework asserts that information systems lead to favorable individual performance impacts and sustained usage only when the functionality of the technological artifact matches the specific activities and tasks that the individual must execute. Tran et al. translate this paradigm into mobile retail environments via the Task-Service Fit construct (Lin & Huang, 2008). In this conceptualization, motivation alone is insufficient; users must perceive that the mobile application’s service architecture congruently supports their execution of shopping tasks (e.g., product selection, coupon redemption, mobile payment, order tracking) to catalyze brand engagement.

Relationship Marketing and Consumer Brand Engagement Paradigm

The transition from transactional technological interaction to long-term equity creation is governed by relationship marketing paradigms (Hollebeek, Glynn, & Brodie, 2014; Yoo, Donthu, & Lee, 2000). Under this paradigm, customer engagement is an essential multidimensional mediating state through which repeated positive digital experiences foster cognitive loyalty and durable equity. By embedding Hollebeek et al.’s tripartite engagement model (cognitive processing, affection, activation) alongside Yoo et al.’s customer-based brand equity dimensions, the instrument operationalizes the holistic sequence: User Motivations → Task-Service Fit → Consumer Brand Engagement → Brand Equity.

Validity

The psychometric evaluation performed by Tran et al. (2024) establishes robust evidence for construct, convergent, and discriminant validity across an empirical sample of active mobile app users in the United States:

Construct and Content Validity

Content validity was established through the systematic adaptation of thoroughly validated items from premier marketing and psychometric publications. Utilitarian and hedonic subscales were adapted from Stocchi et al. (2018); task-service fit items were drawn from Lin and Huang (2008); consumer brand engagement subscales were integrated from Hollebeek et al. (2014); and brand equity items were derived from the classic multidimensional scale by Yoo, Donthu, and Lee (2000). Pre-testing procedures ensured that item wordings reflected contemporary mobile app interfaces and consumer verbiage.

Convergent Validity

Convergent validity evaluates whether indicators of a specific construct share a high proportion of common variance. In structural equation evaluations, convergent validity was confirmed across all latent factors:

• Factor Loadings: Standardized indicator loadings consistently exceeded the standard psychometric threshold of 0.70 (with all loadings significant at p < .001).
• Average Variance Extracted (AVE): The AVE scores for all first-order and second-order constructs surpassed the recommended cut-off criterion of 0.50 (Fornell & Larcker, 1981), verifying that each latent construct accounted for more than 50% of the variance observed across its respective indicator items.

Discriminant Validity

Discriminant validity confirms that each latent construct within the model is statistically distinct from other constructs:

• Heterotrait-Monotrait Ratio of Correlations (HTMT): Discriminant validity was rigorously evaluated using the modern HTMT criterion (Henseler, Ringle, & Sarstedt, 2015). Across almost all inter-construct pairings, HTMT values remained substantially below the conservative 0.85 threshold (and well below the liberal 0.90 threshold).
• Methodological Nuance: The HTMT ratio between interpersonal utility and overall hedonic benefits was found to be slightly higher, approaching or exceeding 1.0. As documented by Tran et al. (2024) and supported by methodological guidelines (Hair et al., 2017), this finding is statistically expected and conceptually sound, as the indicators of interpersonal utility serve as repeated first-order indicators within the higher-order hierarchical specification of hedonic benefits.

Reliability

The measurement model exhibits exemplary internal consistency reliability across all latent dimensions:

• Cronbach’s Alpha (α): All subscales demonstrated Cronbach’s alpha coefficients comfortably exceeding the conventional academic benchmark of 0.70, with primary dimensions such as Task-Service Fit, Perceived Quality, and Brand Loyalty frequently recording α values ranging between 0.82 and 0.92, indicating minimal random measurement error.
• Composite Reliability (CR): Because Cronbach’s alpha assumes tau-equivalence (equal factor loadings across all items) and can underestimate reliability in multidimensional designs, composite reliability coefficients were calculated. All CR values exceeded 0.70, with the majority falling between 0.85 and 0.94, demonstrating strong internal consistency across indicators.

Factor Analysis

The structural dimensionality of the 44-item battery was confirmed using structural equation modeling (SEM) protocols:

• Measurement Model Structure: The model employs a combination of first-order reflective factors and second-order hierarchical reflective constructs. Utilitarian motivations are modeled reflectively through Security, Usefulness, and Ease of Use. Hedonic motivations are modeled through Interpersonal Utility and Entertainment. Consumer Brand Engagement is modeled as a higher-order construct encompassing Cognitive Processing, Affection, and Activation. Brand Equity is evaluated through Perceived Quality, Brand Loyalty, and Brand Awareness/Associations.
• Indicator Loadings: Confirmatory factor analysis (CFA) demonstrated that every item loaded significantly onto its targeted construct (standardized λ > .70, t-values > 1.96, p < .001). Cross-loadings on non-target constructs were minimal, ensuring clean factor separation.
• Model Fit Indices: For covariance-based assessments, goodness-of-fit metrics conformed to standard methodological recommendations (Hu & Bentler, 1999), demonstrating acceptable Standardized Root Mean Square Residual (SRMR ≤ 0.08), Comparative Fit Index (CFI ≥ 0.90), and Root Mean Square Error of Approximation (RMSEA ≤ 0.06).

Instrument / Measurement Tool

The specifications of the measurement model are summarized below:

• Tool Name: Antecedents of Consumer Brand Engagement and Brand Equity of Apps – Measurement Model
• Primary Citation: Tran, T. P., Gugenishvili, I., & Muldrow, A. F. (2024). Enhancing brand equity in branded apps: A hedonic and utilitarian motivation’s perspective. Journal of Marketing Communications, 30(3), 318–343.
• Administration Type: Self-report electronic questionnaire (optimized for online panels, web surveys, and mobile interfaces).
• Item Count: 44 items total across the complete theoretical model.
• Target Population: Consumers, mobile application users, retail shoppers, and digital service subscribers.
• Response Scale: Typically administered on a standard 7-point Likert scale (1 = Strongly Disagree to 7 = Strongly Agree), although 5-point Likert variants have been employed in specific field replications.
• Subscale Architecture:
  • Utilitarian Motivations: Security (2 items), Usefulness (3 items), Ease of Use (3 items)
  • Hedonic Motivations: Interpersonal Utility (4 items), Entertainment (indicators reflecting enjoyment and diversion)
  • Task-Service Fit: Operational fit items (6 items)
  • Consumer Brand Engagement: Cognitive Processing (3 items), Affection (4 items), Activation (3 items)
  • Brand Equity: Perceived Quality (5 items), Brand Loyalty & Awareness/Associations (5 items)
• Scoring Protocol: Dimensional subscale scores are generated by averaging the numerical scores of the respective indicator items. Composite scores can be evaluated directly, or raw indicator scores can be inputted as manifest variables into structural equation modeling software (e.g., SmartPLS, AMOS, Mplus, lavaan in R).

Permissions & Fee and Test Year

The scale was developed and published in 2024 (with early access online in 2022) by authors Trang P. Tran, Ilia Gugenishvili, and Adrienne F. Muldrow in the Journal of Marketing Communications (Taylor & Francis). The instrument is not commercially restricted and does not require fee-based licensing for academic, educational, or non-commercial scientific research. Researchers wishing to utilize, adapt, or translate the measurement scale are expected to cite the original source paper (Tran et al., 2024). Commercial organizations seeking to deploy the scale for proprietary auditing or monetization should consult the corresponding authors and the copyright holder regarding permissions.

References

• 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
• Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS Quarterly, 19(2), 213–236. https://doi.org/10.2307/249685
• Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM) (2nd ed.). Thousand Oaks: Sage Publications. https://doi.org/10.4135/9781473957695
• Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
• Hollebeek, L. D., Glynn, M. S., & Brodie, R. J. (2014). Consumer brand engagement in social media: Conceptualization, scale development and validation. Journal of Interactive Marketing, 28(2), 149–165. https://doi.org/10.1016/j.intmar.2013.12.002
• 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
• Katz, E., Blumler, J. G., & Gurevitch, M. (1973). Uses and gratifications research. The Public Opinion Quarterly, 37(4), 509–523. https://doi.org/10.1086/268109
• Lin, T. C., & Huang, C. C. (2008). Understanding knowledge management system usage antecedents: An integration of social cognitive theory and task technology fit. Information & Management, 45(6), 410–417. https://doi.org/10.1016/j.im.2008.02.004
• Stocchi, L., Michaelidou, N., & Micevski, M. (2018). Drivers and outcomes of branded mobile app usage consumers: A uses and gratifications perspective. Journal of Retailing and Consumer Services, 45, 127–138. https://doi.org/10.1016/j.jretconser.2018.07.014
• Tran, T. P., Gugenishvili, I., & Muldrow, A. F. (2024). Enhancing brand equity in branded apps: A hedonic and utilitarian motivation’s perspective. Journal of Marketing Communications, 30(3), 318–343. https://doi.org/10.1080/13527266.2022.2113909
• Yoo, B., Donthu, N., & Lee, S. (2000). An examination of selected marketing mix elements and brand equity. Journal of the Academy of Marketing Science, 28(2), 195–211. https://doi.org/10.1016/S0148-2963(99)00098-3

Items of the Scale