Consumer Experience of Mobile Wallet Platforms–Model Inventory

Abstract

The Consumer Experience of Mobile Wallet Platforms—Model Inventory (CEMWP-M), formulated by Amit Shankar and Abhishek Behl (2023), represents a psychometrically validated self-report instrument engineered to operationalize, assess, and evaluate the multifaceted nature of consumer experience within mobile financial technology environments. As digital payment ecosystems accelerate globally, understanding the micro-foundations of user experience has transitioned from an operational necessity to a strategic prerequisite for continuous adoption and consumer retention. The inventory consists of 19 carefully curated items organized into six distinct dimensions: five exogenous antecedents or functional drivers (Interactivity, Convenience, Privacy and Security, Responsiveness, and Contact) and one focal endogenous outcome dimension (Consumer Experience). Administered on an authentic five-point Likert scale ranging from 1 (Strongly Disagree) to 5 (Strongly Agree), the scale synthesizes foundational constructs from human-computer interaction (HCI), perceived service quality, and digital consumer behavior paradigms.

Empirical evaluation of the instrument was conducted through a mixed-method methodological framework utilizing big data sentiment analysis from consumer textual reviews paired with rigorous structural equation modeling (SEM) on quantitative consumer cohorts. Psychometric evaluations substantiate the instrument’s robustness: composite reliability (CR) coefficients for all constructs comfortably exceed the recommended 0.70 benchmark, indicating strong internal consistency. Convergent validity is confirmed via average variance extracted (AVE) values transcending the 0.50 cutoff, while discriminant validity is demonstrated through the classic Fornell–Larcker criterion. Furthermore, diagnostic tests including Harman’s single-factor analysis verify that common method variance does not compromise the scale’s empirical integrity. By bridging theoretical consumer psychology with actionable metrics for mobile transaction systems, the CEMWP-M provides researchers, product designers, and financial technologists with an empirically rigorous diagnostic tool for evaluating digital wallet interfaces.

Keywords

Consumer Experience, Mobile Wallet Platforms, Interactivity, Convenience, Privacy and Security, Responsiveness, Customer Contact, Human-Computer Interaction, Structural Equation Modeling, Psychometrics

Authors

The scale was developed and psychometrically validated by academic scholars specializing in marketing technology, digital transformation, and consumer information systems:

• Amit Shankar, Ph.D. — Department of Marketing Management, Indian Institute of Management Visakhapatnam (IIMV), Andhra Pradesh, India. Specializes in consumer behavior, digital platforms, service marketing, and mobile commerce adoption. Corresponding author email: [email protected].
• Abhishek Behl, Ph.D. — Jindal Global Business School, O.P. Jindal Global University, Sonipat, Haryana, India. Research foci include information systems, business analytics, electronic commerce platforms, and consumer analytics. Author ORCID: 0000-0002-5157-0121.

Purpose

The fundamental objective of the Consumer Experience of Mobile Wallet Platforms—Model Inventory is to dissect, measure, and quantify the underlying drivers that govern consumer perceptions and experiential evaluations during interactions with mobile wallet ecosystems. In an era marked by the rapid decentralization of brick-and-mortar financial institutions and the ascendancy of ubiquitous mobile commerce, consumer retention hinges on friction-free, emotionally gratifying, and technically dependable mobile applications. Despite widespread penetration, financial technology platforms routinely confront substantial consumer churn, transactional anxiety, and perceived operational ambiguity. The CEMWP-M was engineered to systematically investigate which specific digital environment parameters exert the most pronounced influence on overall consumer satisfaction, perceived value, and holistic affective engagement.

From an applied research perspective, the scale fills a critical void in contemporary psychometric literature. Historical measures of digital platform adoption—such as classic instruments rooted in the Technology Acceptance Model (TAM) or the Unified Theory of Acceptance and Use of Technology (UTAUT)—predominantly focus on cognitive instrumentality, such as perceived usefulness and perceived ease of use. While theoretically foundational, these utilitarian frameworks largely neglect the dynamic, ongoing affective journey and experiential touchpoints that characterize post-adoption usage. The CEMWP-M shifts the evaluative paradigm toward continuous consumer experience (CX), capturing both task-oriented performance attributes and emotional reassurances critical to personal financial technologies.

In industry settings, fintech developers, human factors engineers, and product strategists can leverage this inventory as an actionable diagnostic instrument. By systematically measuring the sub-dimensions—such as platform interactivity, transactional convenience, security infrastructure, system responsiveness, and human-mediated contact options—organizations can isolate exact friction points along the digital customer journey. For instance, low scores on the Contact dimension paired with elevated scores on Convenience indicate that while the self-service transaction pipeline is intuitive, consumer anxiety spikes whenever anomalies arise, necessitating enhanced customer resolution architecture. Thus, the inventory functions simultaneously as a theoretical framework for behavioral researchers and an applied managerial roadmap for enhancing fintech customer lifetime value.

Psychological Construct

The operational construct under examination is Consumer Experience within digital financial software, conceptualized as a multi-component psychological response comprising cognitive, affective, behavioral, and sensory evaluations formed across direct and indirect platform interactions. The scale models this construct through five exogenous cognitive-functional drivers and one endogenous overarching experiential outcome:

1. Interactivity

Interactivity within the CEMWP-M captures the degree to which users perceive that the mobile wallet platform enables dynamic, bi-directional exchange of information, facilitates feedback loops, and supports collaborative network utility. Grounded in digital communication theory, interactivity reflects user agency and active engagement rather than passive consumption. Scale items assess reciprocal communication, the ability to transmit user feedback directly through the interface, and consumer-to-consumer (C2C) functionalities such as peer-to-peer payment splitting or shared social billing. High perceived interactivity fosters a psychological sense of mastery and community belonging.

2. Convenience

Convenience represents the perceived conservation of cognitive, physical, and temporal resources during transaction execution. Based on service convenience theory, this construct evaluates time-saving benefits, temporal flexibility (anytime access), and spatial independence (anywhere access). When mobile payment platforms minimize transactional friction, reduce navigation steps, and eliminate physical distance barriers, consumers experience reduced cognitive load, which translates directly into perceived behavioral efficiency.

3. Privacy and Security

In personal finance contexts, perceived risk operates as a primary psychological inhibitor. The Privacy and Security dimension operationalizes user beliefs regarding platform safeguards, encryption integrity, data governance protocols, and fraud mitigation mechanisms. It measures both objective architectural reassurances (e.g., transaction alerts, multi-factor authentication, remote device isolation capabilities) and subjective psychological security (e.g., confidence in submitting sensitive credentials). High levels of perceived security reduce vulnerability and cultivate institutional trust.

4. Responsiveness

Responsiveness measures the platform’s operational agility, speed, and real-time feedback capacity. In psychological terms, responsiveness directly impacts perceived system latency and behavioral confirmation. Items within this construct assess how rapidly the application confirms transactions, processes complex requests, loads dynamic interfaces, and prevents system latency or freezing. Instantaneous digital feedback closes behavioral loops, reassuring users that transactional instructions have been executed without error.

5. Contact

Despite the automated, self-service architecture of mobile wallets, human safety nets remain essential for mitigating consumer risk. The Contact dimension evaluates the accessibility, efficiency, and multiplicity of support channels available when unexpected transactional failures occur. It captures the psychological availability of problem-resolution mechanisms, including conversational chatbots, direct telephone lines, omnichannel ticketing, and human representative empathy. A robust contact infrastructure diminishes consumer feelings of abandonment during anomalous payment states.

6. Consumer Experience (Overall Outcome)

The endogenous dimension of Consumer Experience represents the cumulative, holistic psychological evaluation of the mobile wallet platform. Moving beyond isolated functional metrics, this construct assesses overall emotional valence, cognitive expectation fulfillment, holistic satisfaction, and personal delight. A consumer exhibiting a high score on this outcome dimension views the m-wallet not merely as an electronic utility, but as an indispensable, gratifying, and emotionally reassuring artifact within their daily lifestyle.

Theoretical Framework

The conceptual architecture of the CEMWP-M integrates several complementary paradigms from consumer psychology, digital marketing, and Human-Computer Interaction (HCI). Foremost among these is the Stimulus-Organism-Response (S-O-R) Framework, originally formulated by environmental psychologists Mehrabian and Russell (1974) and extensively adapted for virtual retail environments by e-commerce theorists:

• Environmental Stimuli (S): The external technological attributes and functional affordances of the mobile wallet platform, operationalized through the five drivers: Interactivity, Convenience, Privacy and Security, Responsiveness, and Contact.
• Organismic Processing (O): The internal affective, perceptual, and cognitive evaluations formed by the consumer during platform navigation, reflected in reduced perceived vulnerability, heightened feelings of control, and perceived efficiency.
• Behavioral Response (R): The holistic Consumer Experience evaluation, which subsequently manifests in behavioral retention, positive word-of-mouth advocacy, sustained platform loyalty, and transaction volume escalation.

Complementing the S-O-R paradigm, the instrument draws substantially upon the Expectation-Confirmation Theory (ECT) formulated by Richard L. Oliver (1980). ECT posits that consumers establish initial baseline expectations prior to interaction; post-adoption cognitive appraisal compares experienced performance against these reference points. When system attributes such as prompt responsiveness and seamless convenience exceed baseline thresholds, positive confirmation emerges, generating elevated satisfaction and subjective delight. Conversely, latency or security ambiguity leads to negative disconfirmation, precipitating immediate cognitive dissonance and platform abandonment.

Finally, the scale incorporates core tenets from Perceived Risk Theory (Bauer, 1960) and E-Service Quality (E-S-QUAL) models (Parasuraman et al., 2005). Because mobile wallet transactions involve real-time capital transfer and sensitive personal identity metadata, consumers experience heightened psychological vulnerability compared to standard informational browsing. Thus, structural assurances (Privacy and Security) and recovery mechanisms (Contact) serve as primary cognitive boundary conditions, neutralizing perceived financial and privacy risks so that utilitarian advantages (Convenience and Responsiveness) can effectively cultivate positive consumer sentiment.

Validity

The structural, convergent, and discriminant validity of the CEMWP-M was established through rigorous psychometric testing following protocols delineated by Hair et al. (2010) and Fornell and Larcker (1981):

Convergent Validity

Convergent validity demonstrates that items hypothesized to reflect a specific latent dimension correlate strongly and share substantial common variance. In the validation dataset, Confirmatory Factor Analysis (CFA) demonstrated that standardized factor loadings across all 19 manifest items were statistically significant (p < .001) and substantially exceeded the standard 0.70 threshold. Furthermore, the Average Variance Extracted (AVE) for each latent construct exceeded the established benchmark of 0.50, demonstrating that the constructs explain more than 50% of the variance observed among their respective indicator items rather than residual measurement error.

Discriminant Validity

Discriminant validity confirms that each latent construct within the model is statistically distinct from every other dimension. This was confirmed using the established Fornell–Larcker criterion: the square root of the AVE calculated for each individual construct was systematically greater than the highest bivariate inter-construct correlation observed across all off-diagonal pairings. Additionally, the Heterotrait-Monotrait (HTMT) ratio of correlations remained well beneath the conservative threshold of 0.85, confirming robust conceptual independence among closely related technological dimensions (such as Responsiveness and Convenience).

Nomological and Predictive Validity

Nomological validity was verified through structural equation modeling, where path coefficients between the exogenous drivers (Interactivity, Convenience, Privacy/Security, Responsiveness, Contact) and the endogenous Consumer Experience dimension exhibited statistically significant, positive trajectories consistent with underlying theoretical postulates. Predictive validity was underscored by the substantial coefficient of determination (R²) observed for the overall Consumer Experience construct, confirming that the identified functional antecedents account for a dominant proportion of the variance in digital consumer evaluations.

Reliability

The internal consistency and precision of the CEMWP-M were verified through multiple psychometric reliability metrics:

• Composite Reliability (CR): To circumvent the underestimation biases frequently associated with traditional Cronbach’s alpha under conditions of unequal factor loadings (tau-equivalence violation), Composite Reliability coefficients were computed. All six subscale dimensions achieved CR values comfortably surpassing the 0.70 threshold proposed by Hair et al. (2010), with values consistently ranging between 0.82 and 0.91 across dimensions.
• Cronbach’s Alpha (α): Standard unweighted internal consistency coefficients corroborated the CR findings, with subscale alpha estimates exceeding the conventional 0.75 minimum cutoff, demonstrating high internal homogeneity within each item cluster.
• Common Method Bias (CMB) Diagnostics: Because self-report survey instruments deployed in cross-sectional designs carry potential exposure to common method variance, the authors conducted Harman’s Single-Factor Test. An exploratory unrotated factor extraction encompassing all 19 manifest indicators revealed that the primary emerging factor accounted for substantially less than the 50% variance ceiling, establishing that common method bias does not undermine the empirical relationships.

Factor Analysis

The structural dimensionality of the CEMWP-M was validated using a multi-step analytical sequence involving qualitative data synthesis followed by quantitative Confirmatory Factor Analysis (CFA) executed via covariance-based structural equation modeling (CB-SEM):

Exploratory Phase & Measurement Model Specification

Initial item pools adapted from foundational human-computer interaction and mobile adoption literature (e.g., Cyr et al., 2009; Huang et al., 2015; Shankar et al., 2019) were cross-validated against sentiment extraction performed on large-scale consumer reviews. The resulting 19 items were specified into a 6-factor oblique measurement model representing the six theoretical dimensions.

Goodness-of-Fit Parameters

Maximum likelihood estimation yielded excellent global goodness-of-fit indices, satisfying standard psychometric cutoff criteria (Hu & Bentler, 1999):

• Chi-Square to Degrees of Freedom Ratio (χ²/df): Exhibited values < 3.0, indicating satisfactory model parsimony.
• Comparative Fit Index (CFI): ≥ 0.95, indicating that the hypothesized factor model provides a superior fit relative to the null baseline.
• Tucker–Lewis Index (TLI): ≥ 0.94, confirming strong structural fit after penalizing model complexity.
• Root Mean Square Error of Approximation (RMSEA): ≤ 0.05 (with 90% confidence intervals bounded below 0.08), reflecting minimal residual discrepancy per degree of freedom.
• Standardized Root Mean Square Residual (SRMR): ≤ 0.05, substantiating the accuracy of the model-implied correlation matrix.

All indicator variables exhibited standardized factor loadings well above the 0.60 minimum threshold, with the majority exceeding 0.75, confirming that the 19 items load cleanly onto their intended theoretical factors without substantial cross-loadings.

Instrument / Measurement Tool

• Test Type: Standardized multi-dimensional self-report survey inventory / psychometric rating scale.
• Target Population: Adult consumers (ages 18 and older, spanning young adulthood and mature cohorts) who actively utilize mobile wallet platforms for digital transactions and personal finance management.
• Item Count: 19 manifest items.
• Construct Dimensions: 6 distinct dimensions comprising:
  • Interactivity: Items 1, 2, 3
  • Convenience: Items 4, 5, 6
  • Privacy and Security: Items 7, 8, 9
  • Responsiveness: Items 10, 11, 12
  • Contact: Items 13, 14, 15
  • Consumer Experience (Outcome): Items 16, 17, 18, 19
• Response Scale: Five-point Likert scale (1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree).
• Scoring Protocol: No items require reverse scoring. Subscale scores are derived by calculating the unweighted arithmetic mean of the items within each designated dimension. An overall Consumer Experience score can be analyzed independently as the focal outcome variable or operationalized within structural equation models to examine direct and indirect path effects. Higher mean scores reflect more favorable evaluations of platform affordances and superior customer experience.
• Administration Time: Approximately 5 to 8 minutes.

Permissions & Fee and Test Year

• Test Year: Published in 2023 (online first 2021).
• Original Authors: Dr. Amit Shankar and Dr. Abhishek Behl.
• Original Publishing Outlet: Journal of Strategic Marketing, Taylor & Francis.
• Access Fee: The psychometric scale items are published for academic and educational evaluation without charge. Commercial deployments, large-scale software integrations, or derivative commercial benchmarking should request formal permission through the corresponding author or the journal publisher (Taylor & Francis Group).

References

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• 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
• Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate Data Analysis (7th ed.). Prentice Hall.
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• Huang, E. Y., Lin, S. W., & Fan, Y. C. (2015). M-S-QUAL: Mobile service quality measurement. Electronic Commerce Research and Applications, 14(2), 126–142. https://doi.org/10.1016/j.elerap.2015.01.003
• 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
• Shankar, A., & Behl, A. (2023). How to enhance consumer experience over mobile wallet: A data-driven approach. Journal of Strategic Marketing, 31(4), 838–855. https://doi.org/10.1080/0965254X.2021.1999306
• Shankar, A., Datta, B., & Jebarajakirthy, C. (2019). Are games effective in mobile marketing? An empirical study on the role of flow experience and gamification features. Electronic Commerce Research and Applications, 38, Article 100896. https://doi.org/10.1016/j.elerap.2019.100896

Items of the Scale