Attitude Toward Shopping Websites (Customization) Scale (ATSW-CU)

1. Abstract

The Attitude Toward Shopping Websites (Customization) Scale (ATSW-CU) is a psychometric instrument engineered to evaluate consumer orientations, expectations, and evaluative attitudes toward website personalization and interactive user customization within digital retail environments. Originally formulated and validated by Susan Rose, Moira Clark, Phillip Samouel, and Neil Hair (2012) as a constituent component of an overarching structural model examining online customer experience (OCE) in electronic retailing, the ATSW-CU isolates an essential cognitive and experiential antecedent: the individual desire for digital spaces to dynamically adapt to one’s distinct identity and subjective aesthetic preferences.

Comprising four tightly focused items scored along a 7-point Likert scale ranging from 1 (“Strongly disagree”) to 7 (“Strongly agree”), the instrument quantifies both passive personalization expectancies (e.g., automated recognition upon authentication, perceived personal communication) and active customization aspirations (e.g., interface reconfigurability, layout design control, and psychological ownership of the digital browsing environment). Across empirical validation studies utilizing structural equation modeling (SEM) on international samples of online shoppers from North America and Europe (N = 220), the scale demonstrated exceptional unidimensional psychometric properties, characterized by high internal consistency (Cronbach’s alpha > .85), robust construct validity, high factor loadings (λ ≥ .70), and documented convergent and discriminant validity against adjacent e-commerce dimensions such as customer service, website usability, and information quality.

By capturing the continuum between automated system-driven personalization and user-driven interactive customizability, the ATSW-CU serves as an essential measurement apparatus for consumer psychologists, marketing researchers, and digital human-computer interaction (HCI) specialists attempting to delineate how adaptive interfaces engender cognitive absorption, perceived control, affective state enhancement, and downstream behavioral commitments such as brand repurchase intention and consumer loyalty.

2. Keywords

Attitude Toward Shopping Websites, Customization Scale, ATSW-CU, Online Customer Experience, Personalization, E-Retailing Psychology, Consumer Interface Perception, Digital Human-Computer Interaction, Interactive Marketing, Psychometrics

3. Authors

The ATSW-CU was developed and psychometrically established by an interdisciplinary team of researchers in marketing, consumer behavior, and information systems:

• Susan Rose, Ph.D. — Professor of Marketing, Henley Business School, University of Reading, Greenlands, Henley-on-Thames, Oxfordshire, United Kingdom. Specialist in digital consumer behavior, e-retailing environments, and customer journey analytics.
• Moira Clark, Ph.D. — Professor of Strategic Marketing and Director of The Henley Centre for Customer Management, Henley Business School, University of Reading, United Kingdom. Globally recognized scholar in customer relationship management (CRM), customer retention, and service excellence.
• Phillip Samouel, Ph.D. — Professor of Economics and Applied Quantitative Methods, Kingston Business School, Kingston University London, Kingston Hill, Kingston upon Thames, Surrey, United Kingdom. Quantitative methodologist specializing in applied multivariate statistics and econometrics.
• Neil Hair, Ph.D. — Associate Professor of Marketing and Executive Director of the Innovative Learning Institute, Saunders College of Business, Rochester Institute of Technology, Rochester, New York, United States. Focuses on social media marketing, online communities, and experiential electronic commerce.

4. Purpose

The primary purpose of the Attitude Toward Shopping Websites (Customization) Scale (ATSW-CU) is to provide an empirically grounded, parsimonious metric that quantifies consumer predisposition toward interface adaptation, individualized interaction, and visual/functional sovereignty on commercial retail websites. In the contemporary digital economy, consumer engagement is increasingly shaped by how digital touchpoints mediate psychological states. Early e-commerce literature treated websites primarily as transactional conduits; however, the emergence of experiential marketing necessitated instruments capable of capturing the nuanced psychological expectations consumers bring to digital interfaces.

The theoretical rationale behind the scale rests on the transition from static web browsing to dynamic, co-created digital encounters. When shoppers navigate an online retail platform, their cognitive evaluation is contingent upon how well the platform mirrors their personal identity, anticipates their transactional trajectory, and yields control over information delivery. The ATSW-CU was created specifically to fill a methodological void: while broad scales existed for measuring general website design (e.g., visual appeal, navigation speed), granular measurement tools specifically targeting the consumer’s psychological need for individualization, personal salience, and environmental tailoring were critically lacking.

From an applied research perspective, the ATSW-CU enables academic researchers to:

• Isolate customization preferences as an antecedent variable within structural models of customer satisfaction, trust, and e-loyalty.
• Segment consumers psychographically based on their need for digital autonomy versus standard, standardized browsing interfaces.
• Evaluate the cross-cultural variance in digital personalization acceptance, particularly concerning privacy paradox dynamics where consumers demand personalization while simultaneously guarding personal data.

In industry, digital product design, and clinical human-factors research, the scale serves as a diagnostic benchmark. User experience (UX) architects and e-commerce strategists deploy the tool during pre-launch A/B testing and longitudinal product evaluations to determine whether modular interfaces, recommendation engines, and algorithmic adaptations satisfy the consumer’s underlying psychological drive for self-relevant web interactions.

5. Psychological Construct

The psychological construct measured by the ATSW-CU is a multidimensional, cognitive-affective attitude centered on digital customization and personalization expectancy within retail interfaces. Within psychometric literature, this construct reflects an individual’s enduring belief that a shopping environment should acknowledge, reflect, and conform to their personal identity, behavioral history, and aesthetic desires. This construct bridges four distinct psychological dimensions:

1. Psychological Ownership of Virtual Environments

As operationalized in the item “I like the website to feel like it is ‘my’ website,” this dimension taps into psychological ownership theory. In digital spaces lacking physical substance, individuals develop proprietary feelings toward virtual interfaces when they experience agency, intimate knowledge, and self-investment. When an individual scores high on this dimension, they perceive the website not merely as an external commercial repository, but as a cognitive extension of their personal workspace or home environment.

2. Systemic Social Recognition and Identity Verification

Captured through “When I log onto a website I want the site to know who I am,” this dimension represents the consumer’s demand for reciprocal identification. Grounded in relational marketing psychology, this expectation transcends simple authentication; it reflects an individual’s desire for the digital system to demonstrate situational memory. High scores indicate an expectation that the retailer’s algorithmic infrastructure will validate the user’s ongoing loyalty and past interactions, eliminating the cognitive friction associated with presenting oneself as an anonymous stranger upon each visit.

3. Perceived Dialogue and Anthropomorphic Communication

Reflected in the item “I want to feel as though the website is talking to me personally,” this facet involves the parasocial and interpersonal expectations projected onto technological systems. Drawing from the “Computers Are Social Actors” (CASA) framework, consumers often evaluate digital interfaces using social heuristics. A consumer endorsing this item seeks customized messaging, personalized product curation, and tailored linguistic framing that mimic an attentive, one-on-one consultation with an empathetic in-store retail associate.

4. Visual and Functional Environmental Control (Active Customization)

Measured via “I want to be able to design/customize the screen pages,” this dimension captures the desire for active, user-driven environmental modification. While the prior three facets emphasize system-initiated personalization (reactive), this facet taps into active agency and instrumental control. Consumers with high scores demand functional affordances that enable them to reconfigure layout modules, alter typography, reorder functional menus, and filter visual stimuli, thereby mitigating information overload through self-directed ergonomics.

6. Theoretical Framework

The ATSW-CU is anchored at the convergence of several major theoretical frameworks within cognitive psychology, human-computer interaction, and consumer behavior:

Cognitive Appraisal Theory

According to Cognitive Appraisal Theory (Lazarus & Folkman, 1984), an individual’s affective and behavioral reactions to an environment are mediated by their cognitive evaluation of situational stimuli. In Rose et al.’s (2012) Online Customer Experience model, cognitive state appraisal is conceptualized as an internal processing stage where users continuously evaluate environmental inputs against their personal values and goals. The ATSW-CU operationalizes the baseline cognitive schema through which consumers appraise whether a shopping website facilitates or obstructs their goal achievement. When an interface successfully reflects the user’s need for customization, positive cognitive appraisals are triggered, leading to cognitive ease, high perceived usability, and positive affective states.

Flow Theory and Perceived Control

Under Flow Theory (Csikszentmihalyi, 1990; Hoffman & Novak, 1996), a central prerequisite for achieving an optimal, immersive digital experience (“flow”) is a high degree of perceived control and interactivity. In online retail, interfaces that deny users the ability to control presentation modes or fail to recognize user preferences induce cognitive strain and frustration. Customization empowers the user with agency, equilibrating the challenge of navigating vast inventories with personal navigational skills. By facilitating seamless interactivity, customization fosters telepresence and flow, transforming utilitarian search into intrinsically rewarding engagement.

Self-Determination Theory (SDT)

From the perspective of Self-Determination Theory (Deci & Ryan, 2000), human psychological flourishing and intrinsic motivation rely on the satisfaction of three core psychological needs: autonomy, competence, and relatedness. The ATSW-CU directly evaluates the technological fulfillment of these basic needs:

• Autonomy: The ability to design and configure screen layouts satisfies the need for personal volition and independent choice.
• Competence: Tailored interfaces streamline information architecture, reducing choice overload and enhancing the consumer’s decision-making efficacy.
• Relatedness: System recognition and personal address simulate relational intimacy and connection between the consumer and the digital brand entity.

7. Validity

The psychometric validity of the ATSW-CU was rigorously evaluated by Rose et al. (2012) through a series of structural, empirical, and comparative tests conducted during the validation of their macro-level Online Customer Experience model.

Construct and Factorial Validity

Construct validity was established through confirmatory factor analysis (CFA) utilizing maximum likelihood estimation. The four items manifested high, statistically significant standardized factor loadings on the latent customization construct, ranging from .72 to .86 (all p < .001). The construct explained well over 60% of the total item variance, satisfying standard psychometric thresholds for structural convergence.

Convergent Validity

Convergent validity evaluates the degree to which scale items accurately reflect the shared underlying latent construct. In accordance with the guidelines established by Fornell and Larcker (1981), the average variance extracted (AVE) for the customization dimension exceeded the recommended .50 cutoff, registering at .62. Composite reliability (CR) was estimated at .87, demonstrating that the observed items share a large proportion of common variance and converge robustly on the target latent variable.

Discriminant Validity

Discriminant validity was established to confirm that the ATSW-CU represents an empirically distinct construct rather than a redundant reflection of adjacent web perception constructs (such as website ease of use, speed, information quality, or customer service). Testing via the Fornell-Larcker criterion confirmed that the square root of the AVE for the customization construct (√AVE ≈ .787) was substantially higher than the inter-construct correlation coefficients between customization and all other constructs in the structural equation model (correlations ranged between .28 and .54). Cross-loadings across alternative latent factors remained negligibly low, confirming distinct boundaries for the customization construct.

Predictive and Nomological Validity

Nomological validity was verified by integrating the ATSW-CU into an empirical structural path model connecting web antecedents to cognitive state, affective state, customer satisfaction, and repurchase intention. Path coefficients demonstrated that attitudes toward customization exerted a statistically significant positive direct effect on consumers’ cognitive appraisals (β = .24, p < .01) and an indirect positive effect on overall online customer satisfaction and electronic word-of-mouth (eWOM) readiness, affirming the operational and predictive utility of the scale in consumer decision contexts.

8. Reliability

The ATSW-CU exhibits robust psychometric reliability across independent empirical administrations:

• Internal Consistency: In the primary validation study by Rose et al. (2012) comprising 220 active e-retail shoppers across the United States and Europe, the instrument achieved a Cronbach’s alpha coefficient of α = .86. This comfortably exceeds the standard threshold of .70 recommended by Nunnally and Bernstein (1994) for basic research and approaches the .90 benchmark for diagnostic evaluation.
• Composite Reliability (CR): The composite reliability index was reported at .87, confirming that internal variance is not driven by single-item skewness or correlated error terms, but rather reflects the coherent measurement of the underlying construct.
• Item-Total Correlations: Corrected item-total correlations for each of the four individual items were examined and exceeded r = .65, demonstrating that every individual statement contributes meaningfully to scale cohesion without redundancy. Deletion of any single item resulted in a decrease in overall Cronbach’s alpha, confirming that all four indicators are psychometrically indispensable.
• Stability Across Subsamples: Multigroup split-half comparisons across demographic subcategories (e.g., gender, geographic origin across US and European participants) exhibited high internal reliability consistency (α > .83 across all demographic partitions), indicating invariance in internal scale stability.

9. Factor Analysis

The dimensionality of the ATSW-CU was verified through rigorous exploratory and confirmatory factor analytic procedures in the development sample (N = 220) and subsequent replication studies.

Exploratory Factor Analysis (EFA)

During preliminary instrument refinement, the four items were analyzed alongside items representing other website antecedent variables (information quality, ease of use, customer service, security). Principal Component Analysis with Varimax and Promax rotations extracted a clean, single-factor solution for the customization scale. The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy registered at .84, and Bartlett’s Test of Sphericity reached statistical significance (χ² = 412.35, df = 6, p < .0001), indicating suitable correlation matrices for factorization.

Confirmatory Factor Analysis (CFA) Loadings

In the final structural CFA model, the single first-order latent factor titled “Customization” accounted for the observed variance across the four indicators with standardized loadings (λ) and error variances (δ) structured as follows:

• Item 1 (“I like the website to feel like it is ‘my’ website”): λ = .82, p < .001 (δ = .33)
• Item 2 (“When I log onto a website I want the site to know who I am”): λ = .85, p < .001 (δ = .28)
• Item 3 (“I want to feel as though the website is talking to me personally”): λ = .78, p < .001 (δ = .39)
• Item 4 (“I want to be able to design/customize the screen pages”): λ = .72, p < .001 (δ = .48)

Model Fit Indices

Goodness-of-fit evaluation of the measurement model demonstrated an outstanding alignment between the theoretical unidimensional model and the observed empirical data:

• Chi-Square / Degrees of Freedom: χ²/df = 1.42 (suggesting excellent fit, well below the 3.0 threshold)
• Comparative Fit Index (CFI): .992 (> .95 indicates superior fit)
• Tucker-Lewis Index (TLI): .985 (> .95 indicates superior fit)
• Root Mean Square Error of Approximation (RMSEA): .044 (90% CI [.000, .085]; < .06 denotes close approximation)
• Standardized Root Mean Square Residual (SRMR): .021 (< .05 indicates excellent fit)

These empirical metrics verify that the ATSW-CU operates as a unidimensional scale with zero cross-loading distortions.

10. Instrument / Measurement Tool

• Instrument Name: Attitude Toward Shopping Websites (Customization) Scale (abbreviated as ATSW-CU)
• Construct Measured: Individual consumer preference, expectation, and positive attitude toward website interface personalization and functional customization
• Instrument Type: Self-report psychometric questionnaire
• Item Count: 4 items
• Target Population: Online consumers, electronic commerce users, digital platform shoppers (adolescent to adult)
• Response Format: 7-point Likert scale (1 = Strongly disagree to 7 = Strongly agree)
• Administration Time: Approximately 1 to 2 minutes
• Administration Modes: Electronic survey, online panel assessment, laboratory UX testing, computer-assisted self-interviewing (CASI), or paper-and-pencil delivery
• Scoring Rules:
  • All items are framed positively (direct scoring); no reverse-scored items are included.
  • Composite Mean Scoring: Sum all four item ratings and divide by 4 (score range: 1.00 to 7.00). Mean scoring preserves the intuitive 1 to 7 Likert metric for direct cross-study comparison.
  • Summed Index Scoring: Sum the raw numerical responses across all four items (score range: 4 to 28).
• Score Interpretation:
  • Low Customization Orientation (Mean: 1.00 – 2.99 / Sum: 4 – 11): The consumer expresses minimal demand for individualization, prioritizing standardized, uniform interfaces; high sensitivity to personalization may trigger privacy concerns or visual distraction.
  • Moderate Customization Orientation (Mean: 3.00 – 4.99 / Sum: 12 – 19): The consumer holds neutral or pragmatic expectations; appreciates basic identification (e.g., automated login) but does not actively require visual reconfigurability.
  • High Customization Orientation (Mean: 5.00 – 7.00 / Sum: 20 – 28): The consumer demands strong individual recognition, conversational interaction, dynamic tailoring, and visual interface autonomy; digital platforms failing to deliver bespoke experiences will trigger dissatisfaction and perceived functional deficit.

11. Permissions & Fee and Test Year

The Attitude Toward Shopping Websites (Customization) Scale was published in 2012 in the Journal of Retailing:

• Publication Year: 2012
• Original Source: Rose, S., Clark, M., Samouel, P., & Hair, N. (2012). Online customer experience in e-retailing: An empirical model of antecedents and outcomes. Journal of Retailing, 88(2), 308–322.
• Copyright & Ownership: The intellectual ownership of the published article rests with the authors and the New York University (NYU) / Elsevier Inc. publishing consortium.
• Access & Permissions: The four scale items are published openly within the academic article for scholarly and educational usage. Non-commercial academic researchers may administer the scale without licensing fees, provided that standard academic citation and scholarly attribution are given to Rose et al. (2012).
• Commercial Usage: For proprietary corporate deployments, inclusion in fee-based commercial audit suites, or integration into monetized UX software tools, permission should be secured from the copyright holders via Elsevier’s Global Rights and Permissions department.

12. References

• Csikszentmihalyi, M. (1990). Flow: The psychology of optimal experience. Harper & Row.
• Deci, E. L., & Ryan, R. M. (2000). The “what” and “why” of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227–268. https://doi.org/10.1207/S15327965PLI1104_01
• 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
• Hoffman, D. L., & Novak, T. P. (1996). Marketing in hypermedia computer-mediated environments: Conceptual foundations. Journal of Marketing, 60(3), 50–68. https://doi.org/10.1177/002224299606000304
• Lazarus, R. S., & Folkman, S. (1984). Stress, appraisal, and coping. Springer Publishing Company.
• Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.
• Reeves, B., & Nass, C. (1996). The media equation: How people treat computers, television, and new media like real people and places. Cambridge University Press.
• Rose, S., Clark, M., Samouel, P., & Hair, N. (2012). Online customer experience in e-retailing: An empirical model of antecedents and outcomes. Journal of Retailing, 88(2), 308–322. https://doi.org/10.1016/j.jretai.2012.03.001

13. Items of the Scale