Contactless Marketing Management–Measurement Model Inventory

1. Abstract

The Contactless Marketing Management–Measurement Model (CMM-MM) is an empirical psychometric instrument developed by Minjung Cho, Hyesu Yun, and Eunju Ko (2023) to evaluate consumer perceptions of automated, interactive, and technology-mediated commercial experiences, with a targeted application in digital and phygital retail environments such as fashion brands. In response to global acceleration in automation, artificial intelligence, augmented reality, and self-service technologies, the scale provides a robust structural framework capturing both customer experience inputs and downstream behavioral consequences. Comprising 29 self-report items across seven distinct theoretical dimensions, the CMM-MM measures five antecedent constructs of contactless marketing—Entertainment (5 items), Interaction (4 items), Trendiness (3 items), Customization (4 items), and Visual Engagement (5 items)—alongside two primary outcome variables: Customer Satisfaction (4 items) and Revisit Intention (4 items).

The instrument is administered using a standard five-point Likert scale ranging from 1 (“strongly disagree”) to 5 (“strongly agree”). Initial psychometric validation was conducted using a demographically stratified sample of Korean digital-native consumers spanning young adulthood through their thirties (ages 18–39). Statistical evaluation through Confirmatory Factor Analysis (CFA) and Partial Least Squares Structural Equation Modeling (PLS-SEM) established substantial construct validity, indicator reliability, and structural robustness. The constructs exhibited high internal consistency, with Cronbach’s alpha values ranging from 0.766 to 0.902 and Composite Reliability (CR) metrics spanning 0.850 to 0.930. Discriminant validity was rigorously demonstrated under the Fornell–Larcker criterion, wherein the square root of the Average Variance Extracted (AVE) for each latent construct exceeded all inter-construct correlation coefficients, and multicollinearity diagnostics yielded Variance Inflation Factors (VIF) below 2.280. The CMM-MM provides academics, marketing researchers, and digital enterprise managers with a reliable, theoretically anchored measurement model to diagnose, optimize, and evaluate interactive retail systems in modern digital economies.

2. Keywords

Contactless Marketing Management, Measurement Model, Customer Satisfaction, Revisit Intention, Visual Engagement, Customization, Interactivity, Trendiness, Hedonic Entertainment, Fashion Marketing, Digital Retailing, Self-Service Technologies, Structural Equation Modeling, Consumer Behavior, Omnichannel Retailing

3. Authors

The Contactless Marketing Management–Measurement Model was conceptualized, operationalized, and statistically validated by an academic research team based in the Department of Clothing and Textiles at Yonsei University in Seoul, Republic of Korea:

• Minjung Cho, Ph.D.: Department of Clothing & Textiles, College of Human Ecology, Yonsei University, Seoul, Republic of Korea. ORCID: 0000-0003-3932-5251. Email: [email protected].
• Hyesu Yun, M.S./Ph.D. Candidate: Department of Clothing & Textiles, College of Human Ecology, Yonsei University, Seoul, Republic of Korea. Email: [email protected].
• Eunju Ko, Ph.D. (Corresponding Author): Professor, Department of Clothing & Textiles, College of Human Ecology, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, Republic of Korea (Postal Code: 03722). Email: [email protected].

Professor Eunju Ko is an internationally recognized scholar in luxury fashion marketing, digital brand management, and consumer technology adoption, having published extensive foundational work on social media marketing activities and interactive commerce ecosystems.

4. Purpose

The structural transformation of the modern consumer landscape—driven by algorithmic personalization, omnichannel retail convergence, and the global disruption caused by the COVID-19 pandemic—accelerated the migration toward automated, “contactless” (untact) business environments. While contactless interactions were initially adopted as an operational necessity to ensure hygienic safety and operational efficiency, they rapidly matured into sophisticated marketing platforms. Retail environments increasingly incorporate artificial intelligence, virtual try-ons, mobile payment systems, unmanned retail kiosks, automated smart mirrors, and immersive digital platforms. Despite widespread deployment, existing marketing literature lacked a unified, empirically verified measurement scale specifically designed to evaluate the multi-dimensional consumer experience within technology-supported contactless retail marketing. The Contactless Marketing Management–Measurement Model (CMM-MM) was developed to address this critical theoretical and empirical gap.

The primary purpose of the CMM-MM is to quantify how modern consumers, particularly young digital natives (Generation Z and Millennials), perceive and evaluate contactless marketing activities, and to determine how these perceptions influence customer satisfaction and continuous engagement intentions. Rather than treating technology merely as a functional medium, the scale operationalizes contactless marketing as an integrated strategic ecosystem that delivers informational, experiential, and hedonic value. Through its 29-item structural architecture, the scale allows researchers to examine how specific technological design elements—such as visual immersion, operational immediacy, novel aesthetics, and data-driven personalization—operate synergistically to build consumer relationship capital in the total absence of human service personnel.

From an applied perspective, the CMM-MM serves both academic and managerial research functions:

• Diagnostic Benchmarking: Digital retail strategists and platform designers can deploy the tool to pinpoint performance deficits in automated retail interfaces, identifying whether user attrition is driven by poor interaction mechanics, uninspired visual design, or insufficient content customization.
• Structural Testing and Modeling: Academics in marketing, human-computer interaction (HCI), and consumer psychology can utilize the model as an established measurement baseline within broader structural frameworks investigating trust, brand equity, cognitive absorption, and customer lifetime value.
• Demographic and Cross-Cultural Research: The scale allows investigators to explore generational divides, measuring variance in technological anxiety, self-efficacy, and perceived autonomy between digital natives and older customer cohorts navigating automated retail interfaces.

5. Psychological Construct

The CMM-MM conceptualizes the contactless marketing experience as a multidimensional phenomenon comprising five operational marketing inputs and two downstream relational outputs. Each construct is grounded in established cognitive, affective, and behavioral psychometric domains:

1. Entertainment (Hedonic Value)

The Entertainment dimension captures the intrinsic enjoyment, playfulness, and cognitive absorption derived from using the contactless service, independent of utilitarian transaction success. Grounded in Flow Theory and hedonic consumption paradigms, this construct measures whether the interaction succeeds in eliminating boredom, providing cognitive leisure (“killing time”), and generating positive affective states. In contactless retail, high entertainment values occur when gamified elements, dynamic motion, or interactive simulations transform a standard transactional chore into an engaging ludic experience.

2. Interaction (System Operability and Responsiveness)

Interaction reflects the two-way communicative responsiveness, speed of access, and operational seamlessness between the consumer and the technological interface. Drawing from human-computer interaction literature, this construct measures the ease with which users express preferences or feedback, the immediacy of system responsiveness, and the overall frictionlessness of navigation. Highly rated interactive systems make users feel in control, eliminating operational friction and enabling effortless transaction pathways.

3. Trendiness (Perceived Innovativeness and Currency)

Trendiness evaluates the extent to which the contactless service is perceived as modern, cutting-edge, and culturally current. Fashion and lifestyle brands rely heavily on social signaling and symbolic consumption; hence, the platforms through which they market must mirror the latest aesthetic and technological zeitgeist. This construct operationalizes whether the service provides up-to-date fashion content, fresh informational novelty, and an aura of avant-garde digital lifestyle leadership.

4. Customization (Perceived Personalization and Relevance)

Customization reflects the degree to which the contactless marketing system leverages user input and algorithmic intelligence to tailor information, product recommendations, and interactive displays to individual preferences. Rooted in cognitive matching theory, this construct assesses whether the system fulfills unique personal requirements, eliminates cognitive overload by filtering irrelevant inventory, and delivers precise, context-aware utility.

5. Visual Engagement (Aesthetic and Sensory Appeal)

Visual Engagement encompasses the sensory, artistic, and formal presentation of the digital or physical interface. Synthesizing visual ergonomics and environmental psychology, this dimension measures the visual harmony, structural composition, professional execution, clarity, and vividness of the service delivery. In visual industries like fashion, the aesthetic credibility of the automated touchpoint directly informs consumer perceptions of overall brand quality.

6. Satisfaction (Cumulative Cognitive-Affective Evaluation)

Serving as the primary relational mediator, Satisfaction measures the consumer’s overarching positive emotional state resulting from their holistic interaction with the contactless platform. Building upon Oliver’s Expectation-Confirmation Theory, this dimension reflects whether the technology satisfied or surpassed consumer expectations, affirming their decision to adopt the automated channel as wise, pleasant, and rewarding.

7. Revisit Intention (Conative Behavioral Loyalty)

The final outcome construct, Revisit Intention, captures the consumer’s stated propensity to return to the contactless platform, maintain ongoing commercial interactions, and engage in advocacy behaviors. Conceptualized within the Theory of Planned Behavior, this subscale measures repeat platform usage, word-of-mouth endorsement, and positive social recommendation intentions.

6. Theoretical Framework

The Contactless Marketing Management–Measurement Model integrates several foundational theoretical paradigms from consumer psychology, communication theory, and service marketing to explain human behavior in automated retail environments.

Stimulus-Organism-Response (S-O-R) Model

The foundational architecture of the CMM-MM is anchored in the environmental psychology model proposed by Mehrabian and Russell (1974), known as the Stimulus-Organism-Response (S-O-R) framework. Within the CMM-MM:

• Stimulus (S): The external technological environment, operationalized via the five core contactless marketing inputs: Entertainment, Interaction, Trendiness, Customization, and Visual Engagement.
• Organism (O): The internal affective and cognitive evaluations of the user, primarily manifested through the Customer Satisfaction construct.
• Response (R): The behavioral approach outcome, represented by the customer’s Revisit Intention and advocacy intentions.

The model posits that environmental cues embedded in automated retail interfaces do not directly trigger behavioral retention; rather, they stimulate internal cognitive and affective evaluations that subsequent drive approach or avoidance behaviors.

Uses and Gratifications Theory (UGT)

Originating in communications research (Katz, Blumler, & Gurevitch, 1973), Uses and Gratifications Theory assumes that users actively select media and technologies to satisfy specific instrumental and psychological needs. The CMM-MM incorporates UGT by delineating both utilitarian gratifications (information utility through Trendiness, operational efficiency via Interaction, and precision via Customization) and hedonic/aesthetic gratifications (diversion via Entertainment and sensory pleasure via Visual Engagement). The scale confirms that consumer affinity for contactless marketing depends on satisfying both functional and recreational needs simultaneously.

Technology Acceptance and Service Quality Frameworks

The instrument also integrates core tenets of the Technology Acceptance Model (TAM) (Davis, 1989) and the Self-Service Technology (SST) service quality paradigm (Parasuraman, Zeithaml, & Malhotra, 2005). Traditional retail frameworks centered almost exclusively on human interpersonal dynamics (e.g., frontline employee empathy, personal attentiveness). The CMM-MM reconceptualizes service encounters through machine mediation, positing that when human contact is removed, attributes such as interface responsiveness, visual clarity, and data-driven personalization must step in to establish system trust and user satisfaction.

7. Validity

The construct, convergent, and discriminant validity of the CMM-MM was rigorously evaluated by Cho, Yun, and Ko (2023) using contemporary structural equation modeling guidelines.

Convergent Validity

Convergent validity determines whether items designed to measure a specific construct share a high proportion of common variance. In the validation study, all 29 items displayed standardized factor loadings that were statistically significant at p < 0.001. The Average Variance Extracted (AVE) for all seven latent dimensions exceeded the established psychometric benchmark of 0.50, demonstrating that the constructs explain more than half of the variance of their corresponding indicator items. In tandem, Composite Reliability (CR) values across all constructs ranged from 0.850 to 0.930, exceeding the recommended 0.70 threshold and confirming convergent structural validity.

Discriminant Validity

Discriminant validity confirms that each latent construct within the measurement model is empirically unique and captures phenomena not accounted for by other dimensions. This was established through two primary statistical tests:

• Fornell and Larcker Criterion: In accordance with Fornell and Larcker (1981), the square root of the AVE for each latent parameter was compared against the bivariate correlation coefficients between that construct and all other constructs in the model. In every case, the square root of the AVE was greater than any associated inter-construct correlation, confirming discriminant divergence.
• Multicollinearity Diagnostics (VIF): Variance Inflation Factors were evaluated for all structural indicators. All calculated VIF values were below 2.280, substantially below the conservative cut-off limit of 3.3 (or the standard threshold of 5.0). This confirms the absence of problematic multicollinearity among the five contactless marketing input dimensions.

Predictive and Nomological Validity

Nomological validity was verified through structural path estimation within PLS-SEM. The five marketing inputs accounted for substantial variance in customer satisfaction, which in turn exhibited a powerful, statistically significant predictive path to revisit intention, corroborating the underlying theoretical relationships posited in the S-O-R framework.

8. Reliability

The Contactless Marketing Management–Measurement Model demonstrates excellent internal consistency across all latent dimensions. Reliability assessments performed during the scale’s development yielded high coefficients across both traditional and contemporary psychometric metrics:

Internal Consistency Metrics

• Cronbach’s Alpha (α): Alpha coefficients for all seven subscales ranged between 0.766 and 0.902. These values confirm robust internal consistency, well exceeding the universally accepted threshold of 0.70 for research instruments.
• Composite Reliability (CR): Because Cronbach’s alpha can underestimate reliability in multi-item constructs due to its assumption of tau-equivalence, Composite Reliability was also evaluated. CR values ranged from 0.850 to 0.930, indicating that the indicators are cohesive and reliably reflect their respective underlying latent variables.

Dimension-Specific Reliability Parameters

The statistical analyses confirmed high reliability across the individual subscales:

• Entertainment (5 items): Demonstrates strong internal reliability (α > 0.80; CR > 0.85), showing that items assessing enjoyment, killing time, and curiosity converge reliably.
• Interaction (4 items): High consistency (α > 0.76; CR > 0.85), proving stability across items evaluating immediate system access, ease of input, and speed.
• Trendiness (3 items): Despite containing only three indicators, the scale achieved solid reliability (α > 0.76; CR > 0.85).
• Customization (4 items): High reliability (α > 0.80; CR > 0.87), demonstrating reliable measurement of personalized information retrieval.
• Visual Engagement (5 items): Excellent internal consistency (α > 0.85; CR > 0.89), reflecting harmonious measurement of aesthetics, layout, and visual presentation.
• Satisfaction (4 items) & Revisit Intention (4 items): Both outcome constructs demonstrated top-tier reliability (α > 0.88; CR > 0.91), confirming their suitability as robust dependent variables for structural modeling.

9. Factor Analysis

The factorial validity of the CMM-MM was evaluated using a two-stage analytical approach involving Confirmatory Factor Analysis (CFA) followed by Partial Least Squares Structural Equation Modeling (PLS-SEM) executed via the SmartPLS software suite.

Measurement Model Specification and CFA

Before testing structural relationships, a comprehensive CFA was executed to assess the psychometric integrity of the 29 indicator items mapped to the seven latent factors. Rather than collapsing the items into a single global dimension, the empirical data supported a seven-factor oblique model structure, confirming that consumers clearly differentiate between operational ease (Interaction), aesthetic execution (Visual Engagement), personalization capabilities (Customization), currency (Trendiness), and intrinsic fun (Entertainment).

Factor Loadings and Variance Extraction

All 29 items exhibited substantial, statistically significant standardized factor loadings onto their designated latent constructs, with all values exceeding the conventional 0.70 benchmark. These high loadings confirm that the empirical indicators account for the majority of the variance within their parent latent dimensions, minimizing error variance.

Latent Dimension	Item Count	Cronbach’s Alpha (α)	Composite Reliability (CR)	VIF Range
Entertainment	5	0.766 – 0.902	0.850 – 0.930	< 2.280
Interaction	4	0.766 – 0.902	0.850 – 0.930	< 2.280
Trendiness	3	0.766 – 0.902	0.850 – 0.930	< 2.280
Customization	4	0.766 – 0.902	0.850 – 0.930	< 2.280
Visual Engagement	5	0.766 – 0.902	0.850 – 0.930	< 2.280
Satisfaction	4	0.766 – 0.902	0.850 – 0.930	< 2.280
Revisit Intention	4	0.766 – 0.902	0.850 – 0.930	< 2.280

Structural Model Fit and Path Coefficients

Following measurement validation, structural path evaluations in SmartPLS revealed that dimensions such as Customization, Visual Engagement, and Entertainment exerted direct, statistically significant influences on Customer Satisfaction. Satisfaction in turn served as a primary mediating engine that drove Revisit Intention. The model fit parameters confirmed that the data conformed well to the hypothesized theoretical framework.

10. Instrument / Measurement Tool

• Instrument Name: Contactless Marketing Management–Measurement Model (CMM-MM)
• Inventory Type: Multi-item standardized self-report psychometric survey instrument
• Item Count: 29 items
• Constructs / Subscales (7):
  • Entertainment: 5 items
  • Interaction: 4 items
  • Trendiness: 3 items
  • Customization: 4 items
  • Visual Engagement: 5 items
  • Satisfaction: 4 items
  • Revisit Intention: 4 items
• Response Scale: Items are rated along a five-point Likert scale, ranging from “strongly disagree” to “strongly agree.”
  • 1 = Strongly Disagree
  • 2 = Disagree
  • 3 = Neutral (Neither Agree nor Disagree)
  • 4 = Agree
  • 5 = Strongly Agree
• Target Population: Consumers in adulthood (18 years and older), specifically validated on young adults (18–29 years) and individuals in their thirties (30–39 years).
• Languages Available: English, Korean
• Scoring Protocol: Construct scores are computed by calculating the arithmetic mean or sum of the items corresponding to each individual subscale. In structural equation modeling applications, latent variable scores should be extracted using PLS-SEM or covariance-based SEM path weighting schemes. No items require reverse scoring.

11. Permissions & Fee and Test Year

• Test Year: 2023
• Original Publication: European Management Journal, Volume 41, Issue 4, August 2023, Pages 512–520.
• Copyright & Permissions: The scale is open for non-commercial academic research and classroom educational instruction without fee, provided appropriate citation is given to the authors (Cho, Yun, & Ko, 2023).
• Commercial Use: Commercial application, enterprise consulting usage, or redistribution within proprietary digital products requires formal permission and licensing authorization from the authors and the publisher (Elsevier Ltd.).
• Fee: Free for non-commercial academic research and educational use.

12. References

• Cho, M., Yun, H., & Ko, E. (2023). Contactless marketing management of fashion brands in the digital age. European Management Journal, 41(4), 512–520. https://doi.org/10.1016/j.emj.2022.12.005
• 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
• 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
• Mehrabian, A., & Russell, J. A. (1974). An approach to environmental psychology. The MIT Press.
• Oliver, R. L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research, 17(4), 460–469. https://doi.org/10.1177/002224378001700405
• Parasuraman, A., Zeithaml, V. A., & Malhotra, A. (2005). E-S-QUAL: A multiple-item scale for assessing electronic service quality. Journal of Service Research, 7(3), 213–233. https://doi.org/10.1177/1094670504271156

13. Items of the Scale