Branded App Usability: Personalisation (BAU-P)

Abstract

The Branded App Usability: Personalisation (BAU-P) subscale is a psychometric instrument designed to evaluate consumers’ subjective perceptions of tailored, individualized experiences within mobile branded applications. Originally developed by Tae Hyun Baek and Chan Yun Yoo (2018) as one of five foundational dimensions of the comprehensive Branded App Usability (BAU) framework, the BAU-P assesses the extent to which an application dynamically aligns content, services, and interactions with individual user preferences and behavioral profiles. Comprising three self-report items administered via a 7-point Likert response format (ranging from 1 = strongly disagree to 7 = strongly agree), the instrument captures whether users feel uniquely acknowledged and treated as distinct individuals rather than generic consumers.

During its initial validation with a heterogeneous sample of 319 adult smartphone users in the United States, the BAU-P demonstrated robust psychometric integrity. Confirmatory factor analysis (CFA) supported the unidimensionality of the subscale within the higher-order branded app usability model, reporting an average variance extracted (AVE) of .62, which exceeds the conventional threshold of .50 for convergent validity. Furthermore, the scale demonstrated high internal consistency (composite reliability and Cronbach’s alpha exceeding .80) and robust discriminant validity against related usability dimensions, including convenience, interactive messaging, ease of use, and user control. In predictive modeling, personalization emerged as a significant psychological determinant of consumer brand engagement, app stickiness, and enduring brand loyalty. The scale offers mobile marketing researchers, human-computer interaction (HCI) specialists, and digital consumer psychologists a parsimonious, psychometrically validated tool for measuring personalized mobile engagement.

Keywords

Branded App Usability, Personalization, BAU-P, Mobile Marketing, Human-Computer Interaction, Perceived Customization, Consumer Loyalty, Mobile Application Usability, User Experience Psychometrics, Digital Relationship Marketing

Authors

The Branded App Usability: Personalisation (BAU-P) subscale was conceptualized, operationalized, and psychometrically validated by:

• Tae Hyun Baek, Ph.D. — Professor of Advertising, Department of Communication, University of Kentucky (previously at Augusta University). Dr. Baek’s research focuses on digital advertising, consumer behavior, brand-consumer relationships, and mobile app interactions.
• Chan Yun Yoo, Ph.D. — Professor of Advertising, Department of Integrated Strategic Communication, University of Kentucky. Dr. Yoo specializes in interactive advertising, digital media psychology, computational advertising, and consumer decision-making processes in technology-mediated environments.

Correspondence regarding the original scale development can be directed to the lead authors through the Department of Communication, University of Kentucky, Lexington, KY 40506, USA, or via the publication channels of the Journal of Advertising.

Purpose

The emergence of smartphones as primary touchpoints for brand-consumer communication dramatically shifted the nature of customer relationship management (CRM). Branded mobile applications serve not merely as transactional portals or informational directories, but as intimate, persistent software environments operating on personal handheld devices. Despite the pervasive deployment of commercial apps, early usability measurement frameworks—such as the System Usability Scale (SUS) or general software ergonomic guidelines derived from ISO 9241—were largely utilitarian and transactional. They failed to capture the brand-building, affective, and relational nuances inherent to branded digital ecosystems.

To resolve this theoretical and empirical gap, Baek and Yoo (2018) operationalized Branded App Usability (BAU) as the extent to which a mobile app facilitates effective task completion during ongoing brand–consumer interactions. Within this overarching matrix, the Personalisation (BAU-P) subscale was developed to measure the psychological perception that a branded app adapts its offerings, data architecture, and communications to the idiosyncratic needs of the individual customer. The primary objectives and applications of the scale encompass:

• Empirical Research in Digital Consumer Psychology: Providing scholars with a reliable, standardized diagnostic tool to isolate the specific impact of tailored algorithms, recommendation systems, and individualized user interfaces on consumer cognitive processing, brand attachment, and affective commitment.
• Diagnostic Benchmarking for App Developers and User Experience (UX) Architects: Enabling practitioners to quantitatively benchmark whether algorithmically driven personalization features (e.g., dynamic content filtering, contextual push notifications, customized product feeds) are genuinely perceived as personal and valuable by end-users, rather than experienced as intrusive or superfluous.
• Predictive Analytics for Customer Lifetime Value (CLV): Assisting brand strategists in assessing how individualization within mobile applications drives downstream behavioral metrics, including app retention, repurchase frequency, in-app spending, and cross-channel brand advocacy.
• Understanding the Privacy–Personalization Paradox: Offering a precise dependent or moderating metric in empirical investigations exploring trade-offs between consumer data disclosure, perceived surveillance risks, and the psychological utility of individualized services.

Psychological Construct

The construct of Perceived Personalization in digital environments reflects a cognitive appraisal made by an individual regarding the degree to which an interactive medium adapts its service, communication, and functional environment to their idiosyncratic identity, preferences, and situational needs. In human-computer interaction and marketing literature, personalization is distinguished from related concepts such as user control and customization. While customization typically denotes user-driven, active modification of an interface (e.g., explicitly selecting preferences or modifying dashboard themes), personalization represents system-driven or mutually co-created adaptations wherein the application intelligently infers, curates, and delivers uniquely relevant experiences based on historical interactions, contextual parameters, and user data.

Psychologically, the BAU-P captures three interrelated facets of perceived individualization:

1. Informational Relevance and Need Congruence

The first dimension of the construct assesses the functional fit between the information furnished by the app and the consumer’s current psychological and utilitarian goals. As measured by Item 1 (“This app provides information that is personalized to my needs”), the construct evaluates the reduction of cognitive search costs. When an application filters irrelevant noise and surfaces contextually aligned recommendations, users perceive that the cognitive effort required to extract utility is minimized, fostering cognitive efficiency and positive task valence.

2. Unique Recognition and Affective Differentiation

Moving beyond purely informational mechanics, the second facet involves the user’s psychological appraisal of standing out as a distinct entity. Measured by Item 2 (“This app makes me feel like a unique customer”), this dimension addresses the consumer’s need for uniqueness and status within a commercial exchange. It taps into affective satisfaction: the application does not broadcast monolithic, mass-market communications, but rather acknowledges the consumer’s distinctive history and tastes, thereby elevating the user’s felt self-worth within the brand ecosystem.

3. Relational Individualization

The third dimension, operationalized via Item 3 (“This app treats me as an individual customer”), reflects relational agency and perceived relational reciprocity. In social exchange theory, an organization that treats a client as a standardized account triggers transactional thinking; conversely, treating a client as an individual fosters relational capital. In a branded app, this perception signifies that the software architecture mimics interpersonal responsiveness, demonstrating that the brand “knows” and respects the customer’s identity over extended longitudinal interactions.

Theoretical Framework

The theoretical architecture underpinning the Branded App Usability: Personalisation subscale synthesizes tenets from Information Systems (IS) acceptance models, Cognitive Psychology, and Relationship Marketing Theory.

1. The Technology Acceptance Model and Perceived Usefulness

Under the Technology Acceptance Model (TAM; Davis, 1989), two primary cognitive beliefs dictate the adoption and sustained usage of software systems: Perceived Usefulness (PU) and Perceived Ease of Use (PEOU). In the context of mobile devices characterized by restricted screen real estate and fragmented attention spans, traditional usability heuristics alone cannot account for sustained engagement. The BAU framework expands TAM by positing that usability in consumer-facing branded apps is inherently multidimensional. Personalization directly amplifies perceived usefulness by elevating the diagnostic value of presented information, allowing users to achieve goals with optimal precision and minimal navigational friction.

2. Cognitive Load Theory and Information Overload Mitigation

Rooted in Sweller’s (1988) Cognitive Load Theory, human working memory possesses finite processing bandwidth. In ubiquitous mobile computing environments, users are continuously subjected to sensory and informational overload. System-driven personalization operates as an external cognitive scaffolding mechanism. By automatically curating choices, prioritizing relevant items, and suppressing non-salient catalog options, the personalized app mitigates extraneous cognitive load. Users evaluate apps that streamline mental effort as fundamentally superior in usability, directly enhancing their affective evaluation of the brand.

3. Social Exchange Theory and Consumer-Brand Relationship Norms

From the perspective of Social Exchange Theory (Blau, 1964) and Fournier’s (1998) framework of brand relationships, interactions between a consumer and a brand can evolve from purely transactional exchanges into enduring relational partnerships. Branded apps serve as continuous behavioral touchpoints. When a branded application utilizes consumer data transparently and effectively to treat the user as a unique individual, it signals commitment, empathy, and bilateral investment. This perception of individualized treatment triggers the psychological norm of reciprocity: consumers reciprocate the brand’s perceived investment of technological attentiveness through increased brand attachment, attitudinal loyalty, and behavioral commitment.

Validity

The psychometric evaluation of the BAU-P was conducted by Baek and Yoo (2018) across rigorous scale-development stages adhering to Churchill’s (1979) paradigm and contemporary structural equation modeling (SEM) standards.

Construct and Content Validity

Content validity was established through extensive literature syntheses across marketing, HCI, and telecommunications disciplines, followed by qualitative focus groups with active smartphone users. An initial pool of prospective items underwent formal expert review by scholars and marketing industry professionals to ensure domain clarity, representativeness, and semantic distinctiveness. Ambiguous or redundant items were removed prior to quantitative testing.

Convergent Validity

Convergent validity evaluates whether the observed indicators of a construct correlate strongly with one another, sharing substantial variance. In the confirmatory factor analysis (CFA) executed on the validation sample (N = 319 U.S. adult smartphone users across diverse branded app categories such as retail, dining, travel, and entertainment), the BAU-P subscale yielded:

• Standardized Factor Loadings ($lambda$): All three items loaded significantly on the personalization latent factor (loadings well exceeding the recommended .70 threshold, $p < .001$).
• Average Variance Extracted (AVE): The AVE for the personalization construct was .62. Because this figure exceeds the established benchmark of .50 (Fornell & Larcker, 1981), it demonstrates that the latent factor explains more than 62% of the variance among its constituent indicators, confirming strong convergent validity.

Discriminant Validity

Discriminant validity confirms that the personalization subscale measures a phenomenon empirically distinct from other usability dimensions. Baek and Yoo (2018) validated this using the Fornell-Larcker criterion: the square root of the AVE for personalization ($\sqrt{.62} pprox .787$) was notably greater than its bivariate correlation coefficients with any of the other four branded app usability dimensions (i.e., user control, interactive messaging, ease of use, and convenience). This confirmed that while personalization correlates moderately with overall usability, it represents an empirically non-redundant psychometric dimension.

Nomological and Predictive Validity

The predictive utility of the BAU-P was substantiated through structural path modeling. Controlling for general mobile usage habits, perceived personalization demonstrated significant positive paths toward key downstream relational outcomes:

• Brand Engagement: Elevated personalization scores directly predicted enhanced consumer cognitive, affective, and behavioral brand engagement.
• Brand Loyalty: Personalization served as a pivotal indirect and direct antecedent to long-term consumer brand loyalty and ongoing intention to retain the branded app.

Reliability

The reliability of a psychometric instrument reflects its precision, stability, and internal consistency across items. The BAU-P exhibits robust statistical reliability across empirical evaluations:

• Internal Consistency (Cronbach’s $lpha$): In the initial validation study of 319 smartphone users, the Cronbach’s alpha coefficient for the three-item personalization subscale comfortably exceeded the conventional .70 heuristic and the stringent .80 benchmark for research instruments, reflecting high homogeneity among items.
• Composite Reliability (CR): Structural equation modeling yielded a Composite Reliability metric greater than .80, indicating that the latent construct is reliably indicated by its observed measures without excessive measurement error.
• Item-Total Correlations: Corrected item-to-total correlations for all three indicators exceeded .65, ensuring that each statement contributes significantly to the measurement of the underlying latent construct.
• Test-Retest Stability Considerations: While mobile app software undergoes frequent updates that may alter actual personalization mechanics over time, empirical field tests examining static app versions have demonstrated consistent test-retest reliability across short administrative intervals (e.g., two to four weeks).

Factor Analysis

The structural composition of the BAU instrument, including the BAU-P subscale, was determined through a rigorous two-phase analytical sequence comprising Exploratory Factor Analysis (EFA) followed by Confirmatory Factor Analysis (CFA).

Exploratory Factor Analysis (EFA)

During scale purification, an initial battery of candidate items representing various dimensions of app interaction was administered to mobile users. Principal Axis Factoring with oblique (Promax) rotation was employed to allow for theoretically expected correlations between usability dimensions. The analysis clearly extracted five distinct factors with eigenvalues greater than 1.0, accounting for substantial cumulative variance. The three personalization items loaded unambiguously onto a single latent factor with minimal cross-loadings (< .20 on non-target factors).

Confirmatory Factor Analysis (CFA)

To confirm the structural integrity and dimensionality of the instrument, CFA was executed using covariance structure analysis (maximum likelihood estimation) on an independent calibration sample (N = 319). The five-factor measurement model—specifying Personalisation, User Control, Interactive Messaging, Ease of Use, and Convenience—demonstrated excellent overall model fit indices that met or exceeded strict psychometric criteria:

• Fit Indices: The measurement model yielded an acceptable Chi-Square to degrees of freedom ratio ($\chi^2/df < 3.0$), Comparative Fit Index ($ ext{CFI} ge .95$), Tucker-Lewis Index ($ ext{TLI} ge .94$), and a Root Mean Square Error of Approximation ($ ext{RMSEA} le .06$) with a Standardized Root Mean Square Residual ($ ext{SRMR} le .05$).
• Factor Loadings: The standardized factor loadings ($lambda$) for the three BAU-P items were all highly significant ($p < .001$), ranging robustly above .75, indicating that the latent factor of Personalisation is captured with minimal error variance.

Instrument / Measurement Tool

The BAU-P subscale is an objective, brief, self-administered rating scale tailored for post-interaction evaluation of branded mobile smartphone software.

• Instrument Type: Standardized self-report psychometric subscale.
• Domain / Application: Evaluates perceived algorithmic and service personalization within branded iOS and Android applications.
• Number of Items: 3 items.
• Administration Format: Self-administered electronically (via mobile or web survey) or on paper immediately following an app interaction session.
• Completion Time: Approximately 30 to 60 seconds.
• Response Scale: 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 Instructions:
  • No items are reverse-coded. All statements are positively phrased.
  • An overall subscale score is computed by calculating the arithmetic mean of the three completed items: $ ext{BAU-P Score} = rac{ ext{Item 1} + ext{Item 2} + ext{Item 3}}{3}$.
  • Composite scores range from 1.00 to 7.00. Higher mean scores reflect greater degrees of perceived personalization, customized utility, and individualized customer recognition delivered by the branded app.

Permissions & Fee and Test Year

The Branded App Usability: Personalisation (BAU-P) subscale was formally published in 2018 in the Journal of Advertising. As an academic psychometric instrument developed for scientific progress, the scale items are accessible in the public domain for non-commercial academic research, pedagogical investigations, and thesis projects, provided that appropriate scholarly attribution is accorded to the original authors (Baek & Yoo, 2018).

Commercial enterprises, software development firms, and market research agencies intending to integrate the BAU-P into proprietary usability testing suites, software analytics platforms, or commercial audits should review the copyright policies established by the publisher (Taylor & Francis) and the American Academy of Advertising, and contact the scale authors regarding commercial licensing and consulting applications.

References

• 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
• Blau, P. M. (1964). Exchange and power in social life. John Wiley & Sons.
• Churchill, G. A., Jr. (1979). A paradigm for developing better measures of marketing constructs. Journal of Marketing Research, 16(1), 64–73. https://doi.org/10.1177/002224377901600110
• 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
• Fournier, S. (1998). Consumers and their brands: Developing relationship theory in consumer research. Journal of Consumer Research, 24(4), 343–373. https://doi.org/10.1086/209515
• Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285. https://doi.org/10.1207/s15516709cog1202_4

Items of the Scale