Consumer Experience of Mobile Wallet Platforms – Model Inventory

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Abstract

The Consumer Experience of Mobile Wallet Platforms–Model Inventory (Shankar & Behl, 2023) is a specialized 19-item psychological instrument developed to investigate the key factors influencing the consumer experience of mobile wallet (m-wallet) platforms. The instrument utilizes a five-point Likert scale for response capture.

The measure was constructed based on items adapted from previous research in technology adoption and consumer behavior. Validation was conducted using robust statistical methods, primarily structural equation modeling (SEM), applied to both a qualitative dataset of consumer reviews and a quantitative sample of mobile users in India. The study reported strong evidence supporting the instrument’s reliability and validity for assessing platform quality drivers.

Keywords

Consumer Experience, Mobile Wallet Platforms, Interactivity, Responsiveness, Privacy and Security, Convenience, Contact, Consumer Behavior, Consumer Satisfaction, Human Computer Interaction, Personal Finance, Information Security.

Authors

Shankar, Amit, Behl, Abhishek.

Purpose

The primary purpose of the Consumer Experience of Mobile Wallet Platforms–Model Inventory is to systematically assess the core drivers that shape the overall experience consumers have when interacting with mobile wallet (m-wallet) platforms.

This measurement model aims to provide researchers and marketing professionals with a validated framework for understanding which specific platform characteristics—such as responsiveness, security, and convenience—are most crucial for fostering a positive consumer perception and encouraging the continued use of digital payment technologies.

Construct

The scale measures the multi-dimensional construct of Consumer Experience within the context of digital financial services and mobile commerce. This experience is conceptualized as a latent variable influenced by several distinct platform quality factors, which collectively comprise the measurement model.

The primary factors assessed by the 19 items are: Responsiveness (system speed and stability), Interactivity (user control and interface design quality), Privacy and Security (protection of personal data and security features), Convenience (ease and effortlessness of transactions), and Contact (availability and efficiency of customer support). These dimensions provide a holistic view of the consumer’s interaction and satisfaction with the m-wallet platform.

Validity

The validity of the measurement model was established through rigorous testing of both Convergent validity and Discriminant validity, adhering to established psychometric standards.

Convergent validity was supported as the Average Variance Extracted (AVE) values for all latent constructs exceeded the recommended threshold of 0.50 (Hair et al., 2010). Furthermore, Discriminant validity was confirmed by applying the Fornell and Larcker (1981) criterion, which demonstrated that the square root of the AVE for each construct was greater than its correlation coefficients with all other latent constructs in the model, confirming the independence of the measured dimensions.

Reliability

The internal consistency and stability of the scale items were assessed using Composite Reliability. The statistical analysis indicated strong reliability across all factors, with composite reliability values consistently exceeding the generally accepted threshold of 0.70 (Hair et al., 2010).

These high reliability scores confirm that the items measuring each specific dimension (e.g., Responsiveness or Convenience) consistently and accurately capture the underlying construct, ensuring the measurement is stable and trustworthy for assessing variations in consumer experience.

Factor Analysis

Factor analysis techniques were employed primarily to test for potential methodological issues, specifically Common Method Bias (CMB), which can inflate correlations between variables when data is collected from a single source.

The researchers utilized Harman’s one-factor analysis test to evaluate whether the variance in the data could be substantially attributed to a single, underlying factor. The results showed that a single unrotated factor derived from all 19 items did not account for 50% or more of the total variance. This finding strongly suggests the absence of significant Common Method Bias (CMB), reinforcing confidence in the distinction between the measured constructs and the robustness of the structural equation modeling results.

Instrument

Test Type: Original instrument developed specifically for assessing m-wallet consumer experience.

Format: The instrument consists of 19 items rated on a five-point Likert scale (i.e., 1 = strongly disagree, 5 = strongly agree).

Language Available: English.

Population Group: Human respondents, including Male and Female consumers.

Age Group: Adulthood (18 years and older), specifically validated on samples within Young Adulthood (18-29 years) and Thirties (30-39 years).

Population Details: The initial validation sample consisted of consumers located in India.

Test Methodology: The scale was validated using Structural Equation Modeling (SEM), assessing Test Validity, Convergent Validity, Discriminant Validity, Test Reliability, Internal Consistency, and Measurement Model fit.

Keywords

Consumer Attitudes, Consumer Behavior, Consumer Satisfaction, Human Factors Engineering, Privacy, Information Security, Mobile Applications, Human Computer Interaction Measures, Personal Finance, Consumer Measures.

Authors

Shankar, Amit

Author ORCID Identifier: Not specified in source.

Affiliation Email addresses: [email protected] (Indian Institute of Management Visakhapatnam Marketing Management)

Correspondence Address: [email protected]

Behl, Abhishek

Author ORCID Identifier: http://orcid.org/0000-0002-5157-0121

Affiliation Email addresses: Not specified in source (O P Jindal Global University)

Correspondence Address: Not specified in source.

Permissions & Fee and Test Year

Permissions: Users must contact the Publisher (Journal of Strategic Marketing) for permissions regarding use or adaptation.

Fee: No fee reported for the use of the measure itself.

Test Year: 2023.

Reference’s

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

Items of the Consumer Experience of Mobile Wallet Platforms–Model Inventory

IMPORTANT: The following scale items must be preserved in their original language and must not be changed in any way.

Responsiveness

  • The m-wallet does not freeze.

  • The m-wallet enables me to complete a transaction quickly.

  • The m-wallet loads its pages quickly.

Interactivity

  • I have better control over m-wallet interface.

  • The m-wallet interface is user-friendly.

  • Interface design of the m-wallet is suitable for all types of devices.

  • The menu of the m-wallet is well organized.

Privacy and security

  • The m-wallet provides alerts about suspicious logins.

  • The m-wallet protects my personal information.

  • Users can block the m-wallet when they lose their mobile phone.

Convenience

  • I find it easy to complete the transaction over m-wallet.

  • I am able to perform transaction quickly over m-wallet.

  • It takes little effort to complete the transactions over m-wallet.

Contact

  • The m-wallet tells me what to do if a transaction fails.

  • There are several options for contacting a customer support team.

  • The customer support team responds to complaints quickly.

Consumer experience

  • I feel comfortable using the m-wallet.

  • I believe m-wallet is very useful.

  • I find that using m-wallet is enjoyable.

Note. Items are rated using a five-point Likert scale (i.e., 1 = strongly disagree, 5 = strongly agree).

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