{n “title”: “Antecedents of Consumer Online Apparel Purchase Intention via Virtual Try On Technology–Model”,n “content”: “
1. Abstract
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The Antecedents of Consumer Online Apparel Purchase Intention via Virtual Try On Technology–Model (Chidambaram, Rana, & Parayitam, 2024) is a psychometric instrument designed to evaluate the cognitive, affective, and risk-evaluative determinants governing consumer behavioral intentions to adopt and purchase clothing through virtual try-on (VTO) technology. Integrating theoretical postulates from the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT), the instrument captures five distinct latent constructs: Perceived Usefulness, Perceived Enjoyment, Perceived Risk, Attitude Towards Virtual Try-On Technology, and Behavioral Intention (online apparel purchase intention). Comprising 21 items adapted from seminal human-computer interaction and consumer behavior frameworks (e.g., Adams et al., 1992; Kim, 2011; Zhang et al., 2019), the scale utilizes a standard five-point Likert scale ranging from 1 (Strongly Disagree) to 5 (Strongly Agree). Psychometric validation conducted among millennial consumers in India revealed robust internal consistency, with Cronbach’s alpha coefficients across all dimensions exceeding the 0.70 threshold and remaining strictly below 0.95, and composite reliability (CR) estimates ranging from 0.85 to 0.89. Convergent validity was demonstrated by Average Variance Extracted (AVE) values surpassing 0.50 across all subscales, while discriminant validity was supported by Heterotrait-Monotrait (HTMT) ratios strictly below 0.90. Confirmatory factor analysis (CFA) affirmed the empirical superiority of the hypothesized five-factor measurement model (χ² = 1991.11, df = 199, RMSEA = 0.076, RMR = 0.049, SRMR = 0.052, CFI = 0.89, TLI = 0.87) over single-factor common method bias alternatives. This scale serves as an empirical diagnostic tool for digital retailing researchers and e-commerce practitioners seeking to optimize augmented reality interfaces and mitigate transaction hesitation.
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2. Keywords
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Virtual Try-On Technology, Online Apparel Shopping, Technology Acceptance Model, Perceived Usefulness, Perceived Enjoyment, Perceived Risk, Consumer Attitude, Purchase Intention, E-Commerce Ergonomics, Millennial Consumer Behavior
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3. Authors
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The scale was developed and empirically validated by a team of researchers specializing in marketing analytics, human-computer interaction, and organizational management:
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- Vijayabanu Chidambaram — Department of Management Studies, Saranathan College of Engineering, Tiruchirappalli, Tamil Nadu, India. Specializes in consumer psychology, technology adoption, and digital marketing strategies.
- Nripendra P. Rana — College of Business and Economics, Qatar University, Doha, Qatar. Internationally recognized scholar in information systems management, e-governance, digital consumer behavior, and the application of UTAUT frameworks.
- Satyanarayana Parayitam — Department of Management and Marketing, Charlton College of Business, University of Massachusetts Dartmouth, Dartmouth, Massachusetts, USA. Expert in strategic decision-making, quantitative methodologies, psychometrics, and structural equation modeling (ORCID: 0000-0002-3580-0857).
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4. Purpose
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The primary purpose of the Antecedents of Consumer Online Apparel Purchase Intention via Virtual Try On Technology–Model is to measure the psychological mechanisms, cognitive assessments, and behavioral trade-offs that dictate whether an online consumer will engage with and consummate purchases through computer-vision and augmented reality-powered virtual fitting interfaces. Electronic apparel retailing is uniquely burdened by the \”tactile deficit\”—the inability of consumers to physically inspect fabrics, assess garment sizing, and determine bodily fit prior to payment. This physical separation historically drives high return rates, consumer frustration, and suppressed transaction completion. While Virtual Try-On (VTO) platforms simulate physical garment interaction through two-dimensional rendering, 3D avatar generation, or real-time camera-based augmented reality overlays, technology introduction alone does not guarantee consumer adoption.
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From an applied research and marketing perspective, the instrument addresses why users accept or abandon VTO systems. It accomplishes this by quantifying the interplay between utilitarian gains (functional fit assessment and shopping efficiency), hedonic rewards (entertainment, novelty, and escapism), and psychological frictions (fears of visual distortion, security vulnerabilities, or cognitive dissonance). Clinically and behaviorally, measuring these latent precursors enables researchers to determine whether technological adoption is constrained by cognitive usability deficits or by heightened perceived transactional risk.
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In academic investigations, the scale serves as an operationalized measurement engine for structural equation modeling (SEM) applications evaluating digital human-computer interaction. It decouples general technological readiness from contextualized shopping attitudes, identifying the specific tipping points where positive attitudes convert into verifiable behavioral purchase intentions. For e-commerce systems designers, user interface (UI) engineers, and consumer behavior analysts, the scale functions as an evaluative diagnostic. By benchmarking low scoring subscales (e.g., elevated risk or depressed usefulness), retail organizations can isolate whether interface deficiencies stem from inadequate rendering accuracy, confusing navigation, or a perceived violation of user data security.
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5. Psychological Construct
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The scale models consumer decision-making as a multi-component psychological system comprising five distinct yet interlinked latent constructs:
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Perceived Usefulness (PU)
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Rooted in cognitive utilitarian motivation, Perceived Usefulness reflects the extent to which a consumer believes that utilizing VTO technology will enhance their apparel shopping effectiveness, accuracy, and efficiency. It captures functional performance: the ability of the system to simulate realistic sizing, offer dependable visualization of garment drape, streamline decision-making speed, and elevate overall consumer confidence. When an online shopper evaluates a virtual fitting tool as highly useful, the cognitive load associated with sizing ambiguity is significantly reduced.
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Perceived Enjoyment (PE)
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Capturing intrinsic and hedonic motivation, Perceived Enjoyment represents the subjective degree of pleasure, immersion, fun, and entertainment experienced by the shopper while interacting with the VTO interface, irrespective of immediate purchasing outcomes. Drawing from theories of experiential consumption and flow, this construct measures whether the simulated apparel rendering creates an engaging, playful virtual trial environment. In modern digital retailing, hedonic interactions reduce consumer resistance, stimulate lingering on web applications, and foster positive affective bonds with the brand.
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Perceived Risk (PR)
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Conceptualized as an inhibitory construct, Perceived Risk captures the consumer’s subjective expectation of loss, uncertainty, or negative consequences resulting from using VTO tools and executing an apparel purchase. In the context of virtual dressing systems, this multidimensional anxiety encompasses performance risk (e.g., discrepancies between virtual rendering and the physical garment delivered), financial risk (loss of capital, shipping costs for returns), and privacy/personal security risk (unauthorized capture, processing, or retention of bodily dimensions and facial imagery). Elevated perceived risk acts as a direct inhibitor of favorable technology attitudes and transaction intentions.
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Attitude Towards Virtual Try-On Technology (ATT)
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Positioned as an internal psychological evaluation, this construct reflects the consumer’s cumulative favorable or unfavorable affective and cognitive mindset toward utilizing virtual fitting technologies. Reflecting the core mediating link in Fishbein and Ajzen’s behavioral frameworks, attitude synthesizes cognitive assessments (usefulness, risk) and affective experiences (enjoyment). A shopper who evaluates VTO as productive and entertaining, with minimal perceived hazard, develops an enduring, positive cognitive orientation toward using such technologies across future shopping episodes.
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Behavioral Intention (BI)
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Representing the terminal outcome variable within the measurement model, Behavioral Intention quantifies the subjective probability, willingness, and deliberate plan of the consumer to purchase apparel online through the facilitation of VTO technology. In alignment with established psychometric standards, behavioral intention represents the most proximate, reliable empirical predictor of actual shopping execution and long-term technological adoption.
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6. Theoretical Framework
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The structural foundation of this measurement model synthesizes classical paradigms from information systems adoption and consumer behavioral psychology:
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The Technology Acceptance Model (TAM) and Davis’s Paradigm
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The scale adapts the foundational architecture established by Fred Davis (1989) in the Technology Acceptance Model, which posits that behavioral intentions are mediated by user attitude, which in turn is driven by primary cognitive beliefs—specifically Perceived Usefulness and Perceived Ease of Use. Chidambaram, Rana, and Parayitam (2024) specifically contextualize these cognitive beliefs within interactive augmented reality and computer-generated visualization. Utilitarian instrumentality remains a principal driver: consumers accept digital interfaces when they demonstrate demonstrable agency in solving real-world shopping dilemmas, such as garment silhouette mismatch.
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The Unified Theory of Acceptance and Use of Technology (UTAUT)
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Expanding beyond basic TAM formulations, the model incorporates insights from the UTAUT framework (Venkatesh et al., 2003, 2012). UTAUT emphasizes performance expectancy, effort expectancy, and hedonic motivation as direct determinants of adoption intention. By recognizing that interactive technologies in consumer retail are evaluated through both cognitive (performance) and experiential (hedonic) lenses, the authors incorporated Perceived Enjoyment as an essential parallel antecedent, acknowledging that apparel shopping is intrinsically expressive and multisensory.
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Perceived Risk Theory and Cognitive Mediation
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Drawing on Bauer’s (1960) and Jacoby and Kaplan’s (1972) classical formulations of Perceived Risk Theory, the scale incorporates risk as a counterbalancing force against technological enthusiasm. Consumer behavior involves risk-taking under imperfect information; in VTO environments, cognitive evaluations balance expected benefits against perceived transactional vulnerabilities. Furthermore, in accordance with the Theory of Reasoned Action (Fishbein & Ajzen, 1975), the architecture posits that cognitive perceptions (PU, PE, PR) do not trigger behavioral outcomes in an emotional vacuum; rather, they form an integrated consumer Attitude, which acts as the direct psychological mediator transmitting cognitive inputs into Behavioral Purchase Intentions.
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7. Validity
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The psychometric validity of the instrument was examined through a series of empirical checks among a large cohort of millennial consumers in India, an emerging digital consumer powerhouse characterized by rapid smartphone proliferation and an expanding e-commerce infrastructure:
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Content and Construct Validity
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Content validity was established through systematic item adaptation from thoroughly validated measurement scales in the literature (e.g., Adams et al., 1992; Kim, 2011; Zhang et al., 2019). The adaptation process ensured that domain-specific terminology regarding virtual garment sizing, augmented visualization, and digital checkout processes aligned directly with contemporary virtual trial applications. Construct validity was corroborated via confirmatory measurement model estimation, ensuring that observed indicators loaded significantly onto their designated latent factors.
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Convergent Validity
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Convergent validity captures whether the items representing a specific construct share a high proportion of common variance. In this instrument, convergent validity was confirmed using the Average Variance Extracted (AVE) criterion. The empirical study reported that the AVE values for all five latent dimensions exceeded the established benchmark of 0.50 (Fornell & Larcker, 1981). This demonstrates that more than 50% of the variance observed in the individual measurement items was accounted for by their underlying latent constructs rather than measurement error.
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Discriminant Validity
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To confirm that each construct measures a distinct empirical reality rather than capturing overlapping psychological phenomena, discriminant validity was rigorously evaluated. The modern Heterotrait-Monotrait Ratio of Correlations (HTMT) criterion was applied. All calculated HTMT correlation values were well below the conservative methodological threshold of 0.85 to 0.90 (Henseler et al., 2015). This finding confirms that constructs such as Perceived Usefulness and Perceived Enjoyment exhibit robust empirical divergence, maintaining statistical and conceptual independence.
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8. Reliability
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The scale exhibits robust internal consistency and metric stability, as demonstrated across multiple psychometric indicators reported by Chidambaram, Rana, and Parayitam (2024):
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Internal Consistency Reliability
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The traditional index of internal consistency, Cronbach’s alpha (α), was computed for each of the five subscales. Methodologists consider an alpha between 0.70 and 0.90 to be exemplary; values exceeding 0.95 may suggest indicator redundancy. In the validation study, Cronbach’s alpha values for all constructs met the condition of being strictly greater than 0.70 while remaining below 0.95, establishing strong internal consistency without excessive item collinearity.
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Composite Reliability (CR)
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Because Cronbach’s alpha assumes tau-equivalence (equal factor loadings across all items) and can underestimate reliability in multi-item scales, Composite Reliability (CR) was calculated. All latent constructs yielded CR values substantially higher than the standard 0.70 benchmark, spanning an empirical range from 0.85 to 0.89. These high CR coefficients confirm that the standardized factor loadings of the individual manifest variables consistently converge to represent their assigned theoretical constructs.
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9. Factor Analysis
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Evaluation of the underlying structural properties of the 21-item instrument involved confirmatory factor analysis (CFA) and diagnostic testing for common method variance (CMV):
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Assessment of Common Method Bias (CMB)
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Because self-report survey methodologies administered at a single cross-sectional point can induce artificial covariance, the researchers conducted Harman’s single-factor test using confirmatory structural modeling. A rival one-factor model, wherein all 21 items were forced to load onto a solitary general factor, was tested against the empirical data. The one-factor model yielded a very poor fit to the data:
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- Chi-square (χ²): 3305.55
- Degrees of Freedom (df): 209
- Root Mean Square Error of Approximation (RMSEA): 0.078
- Root Mean Square Residual (RMR): 0.094
- Standardized RMR (SRMR): 1.12 (indicating severe residual misfit)
- Comparative Fit Index (CFI): 0.71
- Tucker-Lewis Index (TLI): 0.68
- Goodness-of-Fit Index (GFI): 0.63
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Confirmatory Factor Analysis (CFA) Results
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In contrast to the single-factor model, the hypothesized five-factor measurement model demonstrated a significantly superior and acceptable empirical fit (Δχ² = 1314.44; Δdf = 10, p < .001). The global fit statistics for the five-factor model were:
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- Chi-square (χ²): 1991.11
- Degrees of Freedom (df): 199
- Root Mean Square Error of Approximation (RMSEA): 0.076 (within acceptable bounds for large structural samples)
- Root Mean Square Residual (RMR): 0.049
- Standardized RMR (SRMR): 0.052 (well below the 0.08 threshold)
- Comparative Fit Index (CFI): 0.89
- Tucker-Lewis Index (TLI): 0.87
- Goodness-of-Fit Index (GFI): 0.86
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The statistical divergence between the models verified that common method bias was not a confounding threat, confirming that the 21 items cleanly represent five distinct latent psychological dimensions.
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10. Instrument / Measurement Tool
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The operational specifications of the measurement scale are structured as follows:
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- Instrument Name: Antecedents of Consumer Online Apparel Purchase Intention via Virtual Try On Technology–Model
- Instrument Type: Self-administered quantitative questionnaire / psychological inventory
- Administration Format: Originally administered via structured paper-and-pencil surveys; equally suitable for web-based surveys (e.g., Qualtrics, Google Forms) and laboratory-based post-interaction assessments
- Target Population: Millennial consumers (young adults aged 18–29 and adults in their thirties aged 30–39) engaged in online apparel browsing or shopping
- Total Item Count: 21 items
- Dimensional Structure:\n
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- Subscale 1: Perceived Usefulness (PU) — Utilitarian efficiency and sizing evaluation
- Subscale 2: Perceived Enjoyment (PE) — Hedonic pleasure, fun, and immersion
- Subscale 3: Perceived Risk (PR) — Visual misrepresentation, return costs, and transactional security concerns
- Subscale 4: Attitude Towards Virtual Try-On Technology (ATT) — General evaluative valence toward the technology
- Subscale 5: Behavioral Intention (BI) — Forward-looking willingness and decision to purchase apparel online using VTO
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- Response Format: Symmetrical 5-point Likert scale:\n
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- 1 = Strongly Disagree
- 2 = Disagree
- 3 = Neutral (Neither Agree nor Disagree)
- 4 = Agree
- 5 = Strongly Agree
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- Scoring Procedures: Dimension scores are calculated by averaging or summing item scores within each subscale. Higher scores on PU, PE, ATT, and BI reflect elevated utilitarian value, hedonic engagement, positive attitude, and purchase readiness, respectively. Conversely, higher scores on PR indicate heightened consumer reservation and perceived vulnerability.
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11. Permissions & Fee and Test Year
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The Antecedents of Consumer Online Apparel Purchase Intention via Virtual Try On Technology–Model was published in 2024. The instrument is non-commercial and is free of licensing fees when utilized for non-profit academic research, educational instruction, and scientific study. Researchers intending to replicate, translate, or adapt the 21 items for empirical research should cite the original peer-reviewed publication by Chidambaram, Rana, and Parayitam (2024). Commercial organizations wishing to integrate this diagnostic instrument into proprietary software architectures, consumer benchmarking audits, or commercial e-commerce analytics suites are advised to consult the corresponding authors and the publishing journal regarding terms of use.
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12. References
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- Adams, D. A., Nelson, R. R., & Todd, P. A. (1992). Perceived usefulness, ease of use, and usage of information technology: A replication. MIS Quarterly, 16(2), 227–247. https://doi.org/10.2307/249577
- Bauer, R. A. (1960). Consumer behavior as risk taking. In R. S. Hancock (Ed.), Dynamic Marketing for a Changing World (pp. 389–398). American Marketing Association.
- Chidambaram, V., Rana, N. P., & Parayitam, S. (2024). Antecedents of consumer online apparel purchase intention via virtual try on technology. Journal of Retailing and Consumer Services, 78, 103732. https://doi.org/10.1016/j.jretconser.2024.103732
- 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
- Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Addison-Wesley.
- 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
- 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
- Jacoby, J., & Kaplan, L. B. (1972). The components of perceived risk. Advances in Consumer Research, 3(3), 382–383.
- Kim, J. (2011). The effect of 3D virtual try-on on consumers’ apparel shopping behaviors: The mediating role of sensory experience and presence. Family and Consumer Sciences Research Journal, 40(2), 173–187. https://doi.org/10.1111/j.1552-3934.2011.02102.x
- 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
- Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178. https://doi.org/10.2307/41410412
- Zhang, T., Wang, W. Y., Cao, L., & Wang, Y. (2019). The role of virtual try-on technology in online purchase decisions. Information Technology & People, 32(6), 1620–1643. https://doi.org/10.1108/ITP-12-2017-0440
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13. Items of the Scale
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The official, exact survey questions and item wording of this 21-item measurement model are proprietary and copyrighted by the respective authors and publisher; they are not reproduced here in full verbatim text in the open public domain.
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To employ this scale for empirical administration or scientific replication, researchers must consult the original publication or contact the authors directly. The 21 items are distributed across the following five theoretical dimensions:
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Dimension 1: Perceived Usefulness (PU)
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Contains operational indicators evaluating functional utility, fit assessment accuracy, efficiency gains, and overall shopping enhancement through VTO technology (adapted from Adams et al., 1992; Kim, 2011).
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Dimension 2: Perceived Enjoyment (PE)
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Contains operational indicators assessing the hedonic pleasure, fun, immersion, visual fascination, and entertaining experience of manipulating garments virtually (adapted from Kim, 2011; Venkatesh et al., 2012).
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Dimension 3: Perceived Risk (PR)
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Contains operational indicators quantifying consumer apprehension regarding garment depiction inaccuracies, sizing discrepancy, personal visual privacy concerns, and potential financial/shipping losses (adapted from Zhang et al., 2019).
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Dimension 4: Attitude Towards Virtual Try-On Technology (ATT)
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Contains operational indicators gauging overall positive, neutral, or negative affective evaluation, perceived favorability, and endorsement of VTO integration in e-retailing (adapted from Davis, 1989; Kim, 2011).
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Dimension 5: Behavioral Intention (BI)
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Contains operational indicators measuring personal commitment, intention, likelihood, and willingness to complete online apparel transactions utilizing VTO technology (adapted from Venkatesh et al., 2003; Zhang et al., 2019).
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Response Format
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All items in the scale are structured on a five-point Likert scale rated as follows:
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- 1: Strongly Disagree
- 2: Disagree
- 3: Neutral (Neither Agree nor Disagree)
- 4: Agree
- 5: Strongly Agree
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“,n “excerpt”: “The Antecedents of Consumer Online Apparel Purchase Intention via Virtual Try On Technology–Model is a 21-item psychometric instrument evaluating utilitarian, hedonic, and risk-oriented determinants of digital fashion consumption.”,n “slug”: “antecedents-consumer-online-apparel-purchase-intention-virtual-try-on-technology-model”,n “categories”: [n “Psychological Tests”,n “Consumer Psychology”,n “Technology Acceptance”n ],n “tags”: [n “Virtual Try-On”,n “Technology Acceptance Model”,n “Consumer Behavior”,n “Online Apparel Shopping”,n “Perceived Usefulness”,n “Perceived Enjoyment”,n “Perceived Risk”,n “UTAUT”,n “Psychometrics”n ],n “seo_title”: “Consumer Online Apparel Purchase Intention via Virtual Try-On Model”,n “seo_description”: “Explore the 21-item model measuring consumer online apparel purchase intention via virtual try-on technology, including validity, reliability, and factor structure.”,n “focus_keyword”: “Virtual Try On Technology Model”n}