Branded App Usability: User-Friendliness (BAU-UF)

1. Abstract

The Branded App Usability: User-Friendliness (BAU-UF) subscale is a psychometrically validated, three-item self-report instrument designed to evaluate consumers’ perceived ease of understanding and operating branded mobile applications. Developed by Tae Hyun Baek and Chan Yun Yoo (2018) as a core constituent of their comprehensive multidimensional branded app usability (BAU) measurement framework, the BAU-UF isolates the user-friendliness dimension from broader functional and aesthetic constructs. The instrument employs a 7-point Likert scale ranging from 1 (“Strongly Disagree”) to 7 (“Strongly Agree”). In the foundational validation study, the BAU-UF demonstrated exceptional psychometric properties, including an Average Variance Extracted (AVE) of .83, composite reliability exceeding .90, and strong factor loadings (all $lambda > .85$). The scale established robust convergent, discriminant, and nomological validity through its meaningful predictive relationships with user satisfaction, brand attitude, and multi-faceted consumer brand loyalty. By capturing the fundamental cognitive appraisal of operational simplicity and skill acquisition speed, the BAU-UF offers researchers, user experience (UX) designers, and brand managers a brief, reliable, and theoretically sound diagnostic instrument to evaluate how mobile interface ergonomics directly affect downstream relationship-marketing outcomes.

2. Keywords

Branded App Usability, User-Friendliness, BAU-UF, Mobile Marketing, Human-Computer Interaction, Technology Acceptance Model, Consumer Brand Loyalty, Perceived Ease of Use, Mobile Application UX, Psychometrics

3. Authors

The Branded App Usability (BAU) scale, including the User-Friendliness (BAU-UF) subscale, was conceived and psychometrically validated by:

• Tae Hyun Baek, Ph.D. — Professor of Advertising in the Department of Integrated Strategic Communication, College of Communication and Information, University of Kentucky, Lexington, KY, USA. Dr. Baek’s research focuses on digital advertising, mobile consumer behavior, brand-consumer relationships, and marketing communications analytics.
• Chan Yun Yoo, Ph.D. — Professor and Chair in the Department of Integrated Strategic Communication, College of Communication and Information, University of Kentucky, Lexington, KY, USA. Dr. Yoo specializes in digital media processing, computational advertising, consumer interactive psychology, and human-computer interface evaluation.

4. Purpose

Branded mobile applications have evolved into primary relational touchpoints between commercial organizations and end users. Unlike utilitarian enterprise software or standalone utility applications, branded mobile apps serve dual objectives: delivering tangible service utility while reinforcing brand equity, affective commitment, and purchase intention. However, when software interfaces impose severe cognitive friction, consumers swiftly abandon the application, generating negative spillover effects onto the parent brand. The primary purpose of the Branded App Usability: User-Friendliness (BAU-UF) subscale is to quantify the cognitive simplicity and operational fluency that users experience when navigating a brand’s mobile software.

From an applied perspective, the BAU-UF serves both academic researchers and industry practitioners. In academic advertising, marketing, and human-computer interaction (HCI) scholarship, the scale provides a standardized, parsimonious instrument to test mediation and moderation models connecting interface architecture with customer engagement and continuous usage intention. In commercial research and mobile product engineering, the tool acts as a rapid diagnostic screen during agile software development, beta testing, and competitive benchmarking. Because the scale specifically isolates “user-friendliness” from information content, aesthetic design, or transactional security, it enables product teams to pinpoint whether consumer attrition stems from operational complexity rather than aesthetic or functional deficits.

5. Psychological Construct

The psychological construct evaluated by the BAU-UF is perceived user-friendliness within the specific operational ecosystem of mobile handheld devices. Historically rooted in human factors engineering and cognitive ergonomics, user-friendliness reflects an individual’s subjective assessment that interacting with a technological artifact is effortless, intuitive, and mentally unencumbering.

In the framework established by Baek and Yoo (2018), user-friendliness comprises three complementary cognitive facets:

• Global Operability (Ease of Use): The intuitive recognition of interface mechanics, where interactive controls (e.g., buttons, navigational menus, input fields) correspond seamlessly to consumer mental models without requiring trial-and-error reasoning.
• Skill Acquisition Acceleration (Becoming Skillful): The subjective velocity with which a novice user transitions into a competent operator, characterized by automated motor interactions and the reduction of conscious cognitive supervision.
• Low Learning Curve (Ease of Learning): The minimal investment of working memory and mental effort required to master the application’s hierarchical architecture, nomenclature, and functional sequences.

Within the parent Branded App Usability construct, user-friendliness functions as a baseline hygiene factor. If an app lacks user-friendliness, users rarely engage deeply enough to appreciate its rich content, interactive features, or tailored personalization. High scores on the BAU-UF signify cognitive fluency, perceptual fluency, and low subjective task complexity, fostering psychological comfort and perceived operational control.

6. Theoretical Framework

The BAU-UF draws upon multiple foundational paradigms across cognitive psychology, marketing, and information systems:

Technology Acceptance Model (TAM)

Originally formulated by Fred D. Davis (1989), TAM posits that the acceptance of any technology is predominantly driven by two core beliefs: Perceived Usefulness (PU) and Perceived Ease of Use (PEOU). Davis defined PEOU as “the degree to which a person believes that using a particular system would be free of effort.” The BAU-UF adapts the core conceptualization of PEOU specifically to the constraints and paradigms of smartphone software (e.g., small screen real estate, touch-based gestural inputs, variable ambient environments).

Cognitive Load Theory

According to John Sweller’s Cognitive Load Theory, human working memory has strictly limited processing bandwidth. When interacting with an unfamiliar interface, extraneous cognitive load—caused by poor visual hierarchy, unpredictable navigation, or confusing iconography—depletes mental resources that would otherwise be allocated to germane cognitive activities, such as brand appreciation or product evaluation. The BAU-UF captures the subjective absence of extraneous cognitive load during app navigation.

Uses and Gratifications Theory (UGT) & Brand Attachment

In the context of communication studies, Uses and Gratifications Theory suggests that media consumers purposefully choose communication channels that satisfy specific utilitarian and hedonic needs. Seamless user-friendliness fulfills fundamental process gratifications. In accordance with brand equity frameworks, repeated frictionless digital interactions lower consumer frustration, which sequentially reinforces brand trust, affective attachment, and behavioral loyalty.

7. Validity

The psychometric properties of the BAU-UF subscale were rigorously verified by Baek and Yoo (2018) through a multi-stage instrument development process adhering to psychometric standards (e.g., DeVellis, Nunnally & Bernstein):

Content and Face Validity

Initial items were synthesized from literature across HCI, advertising, and marketing, followed by expert panel reviews consisting of academic advertising scholars and mobile application developers. Items exhibiting ambiguity or semantic redundancy were iteratively eliminated, preserving a focused, highly relevant three-item operationalization.

Convergent Validity

Convergent validity demonstrates that the items reliably measure the shared underlying latent construct. In confirmatory factor analysis (CFA), the BAU-UF exhibited an Average Variance Extracted (AVE) of .83, substantially outperforming the widely accepted psychometric threshold of .50 proposed by Fornell and Larcker (1981). All completely standardized factor loadings were statistically significant ($p < .001$) and exceeded .85, confirming that the three items capture common variance with high fidelity.

Discriminant Validity

Discriminant validity was established against the other four subscales within the comprehensive BAU inventory (Information, Interactivity, Visual Design, and Security/Personalization). Using the Fornell-Larcker criterion, the square root of the AVE for the User-Friendliness dimension ($\sqrt{.83} \approx .911$) was greater than any bivariate inter-factor correlation between user-friendliness and the remaining usability dimensions ($r < .75$). Furthermore, heterotrait-monotrait ratio of correlations (HTMT) criteria were systematically satisfied, verifying that user-friendliness is statistically distinguishable from aesthetic or informational constructs.

Nomological and Predictive Validity

Nomological validity was verified using structural equation modeling (SEM). As theoretically anticipated, high scores on the BAU-UF exhibited statistically significant positive paths leading to enhanced overall app satisfaction, heightened brand attitude, and increased consumer loyalty behaviors (e.g., continuous app usage, positive word-of-mouth, and repeated in-app purchases).

8. Reliability

The BAU-UF subscale exhibits robust internal consistency and stability across diverse sample populations:

• Composite Reliability (CR): The construct composite reliability for the user-friendliness dimension was established at .93, substantially surpassing the standard academic benchmark of .70 (Bagozzi & Yi, 1988).
• Internal Consistency (Cronbach’s Alpha): Cronbach’s coefficient $\alpha$ for the 3-item subscale routinely exceeds .90 across validation samples, demonstrating minimal random measurement error among the indicators.
• Item-Total Correlations: Corrected item-to-total correlations for each of the three indicators exceed .80, indicating that each item provides unique yet highly congruent measurement signal to the overall user-friendliness score.
• Cross-Sample Stability: Validation testing across diverse branded application genres (e.g., retail, food and beverage, banking, hospitality) has shown invariant measurement parameters, indicating that the subscale maintains high internal consistency regardless of the specific app category tested.

9. Factor Analysis

The dimensionality of the BAU instrument was evaluated through sequential exploratory (EFA) and confirmatory (CFA) factor analyses:

Exploratory Factor Analysis (EFA)

In the initial scale-purification phase, principal axis factoring with promax (oblique) rotation was applied to the item pool. The three user-friendliness items cleanly loaded onto a discrete factor with eigenvalues > 1.0, accounting for substantial common variance. No cross-loadings above .25 were observed on secondary factors (such as visual design or interactivity).

Confirmatory Factor Analysis (CFA)

The measurement structure was subsequently tested using CFA via maximum likelihood estimation in AMOS/LISREL. The multidimensional model containing the user-friendliness factor demonstrated exceptional model fit indices:

• Chi-Square Ratio ($\chi^2 / df$): Within the desired range of < 3.0.
• Comparative Fit Index (CFI): > .95
• Tucker-Lewis Index (TLI): > .95
• Root Mean Square Error of Approximation (RMSEA): < .06 (with 90% confidence interval confirming adequate close fit)
• Standardized Root Mean Square Residual (SRMR): < .04

All standardized factor loadings ($lambda$) for Item 1, Item 2, and Item 3 were uniform, statistically significant ($p < .001$), and ranged between .86 and .93, confirming that user-friendliness operates as a unidimensional, robust first-order construct.

10. Instrument / Measurement Tool

The operational specifications of the instrument are detailed below:

• Instrument Name: Branded App Usability: User-Friendliness (BAU-UF)
• Parent Inventory: Branded App Usability (BAU) Scale (Baek & Yoo, 2018)
• Measurement Type: Standardized self-report psychometric scale
• Number of Items: 3 items
• Administration Time: Approximately 1 to 2 minutes
• Response Format: 7-point Likert scale:
  • 1 = Strongly Disagree
  • 2 = Disagree
  • 3 = Somewhat Disagree
  • 4 = Neither Agree nor Disagree
  • 5 = Somewhat Agree
  • 6 = Agree
  • 7 = Strongly Agree
• Scoring and Aggregation:
  • All three items are positively worded; there are no reverse-coded items.
  • Individual item scores may be summed (range: 3 to 21) or averaged (range: 1.00 to 7.00) to create a composite User-Friendliness index.
  • Higher scores represent greater perceived ease of use, intuitive interaction, and operational user-friendliness.

11. Permissions & Fee and Test Year

The Branded App Usability: User-Friendliness (BAU-UF) subscale was formally published in 2018. As an academic psychometric measurement published in the Journal of Advertising, the scale is free of charge for non-commercial educational, scientific, and scholarly research purposes, provided that appropriate formal attribution is given to the original authors (Baek & Yoo, 2018). Commercial entities, market research agencies, or consulting organizations intending to integrate the BAU-UF into proprietary diagnostic software platforms or fee-for-service usability audits should consult the copyright policies of the publisher (Taylor & Francis) and contact the authors regarding enterprise usage rights.

12. References

The following academic publications provide theoretical and empirical foundations for the BAU-UF instrument:

• Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74–94. https://doi.org/10.1007/BF02723327
• Baek, T. H., & Yoo, C. Y. (2018). Branded app usability: Conceptualization, measurement, and prediction of consumer loyalty. Journal of Advertising, 47(1), 70–82. https://doi.org/10.1080/00913367.2017.1405755
• Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
• 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
• Nielsen, J. (1994). Usability engineering. Morgan Kaufmann Publishers.
• Sweller, J. (2011). Cognitive load theory. In J. P. Mestre & B. H. Ross (Eds.), The psychology of learning and motivation: Cognition in education (Vol. 55, pp. 37–76). Academic Press. https://doi.org/10.1016/B978-0-12-387691-1.00002-8
• Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540

13. Items of the Scale