The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
The bright side of online consumer behavior: Continuance intention for mobile payments
Abstract
Researchers on online consumer behavior have rarely discussed the subjective well‐being aspect or improving the life quality of the next billion as the bright side. Emerging technologies such as mobile payments have added to the life quality of people by enabling fast and convenient transactions online that can be performed anywhere and anytime. The studies on mobile payments adoption or continued use are majorly focused on the utilitarian aspects. While the firms face stiff competition and struggle to retain customers it is imperative to discuss both the psychological and behavioral aspects of technology use. The proposed theoretical framework integrates the expectation confirmation model, unified theory of acceptance and use of technology (UTAUT) model, and the subjective well‐being aspect to explain the factors that affect the mobile payments continuance intention. This multidisciplinary study makes a novel contribution to the extant literature on online consumer behavior. Data were obtained by surveying 351 users of mobile payments. The structural equation modeling (SEM) revealed that continuance intention is influenced by subjective well‐being, facilitating conditions, effort expectancy, and satisfaction. Performance expectancy and effort expectancy influence satisfaction that further influences continuance intention. The study deliberates several inferences for service providers for effective strategic advancement. Findings can be used for developing innovative solutions for improved online customer experience and retention. The positioning strategy must convey the life quality improvement as a bright side of technology for better chances of success.
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Cites
Perceived usefulness, perceived ease of use, and user acceptance of information technology
Davis, F. D. (1989) · MIS Quarterly · doi:10.2307/249008 · Cited by 68,483
A new criterion for assessing discriminant validity in variance-based structural equation modeling
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-014-0403-8 · Cited by 38,459
When to Use and How to Report the Results of PLS-SEM
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019) · European Business Review · doi:10.1108/ebr-11-2018-0203 · Cited by 27,541
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012) · MIS Quarterly · doi:10.2307/41410412 · Cited by 15,756
Structural equation models with unobservable variables and measurement error: Algebra and statistics
Fornell, C., & Larcker, D. F. (1981) · Journal of Marketing Research · doi:10.1177/002224378101800313 · Cited by 13,287
Diener, E. (1984) · Psychological Bulletin · doi:10.1037/0033-2909.95.3.542 · Cited by 11,023
The Use of Cronbach’s Alpha When Developing and Reporting Research Instruments in Science Education
Taber, K. S. (2018) · Research in Science Education · doi:10.1007/s11165-016-9602-2 · Cited by 10,679
Understanding Information Systems Continuance: An Expectation-Confirmation Model1
Bhattacherjee, A. (2001) · MIS Quarterly · doi:10.2307/3250921 · Cited by 8,767
An assessment of the use of partial least squares structural equation modeling in marketing research
Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-011-0261-6 · Cited by 8,088
Venkatesh, V. (2000) · Information Systems Research · doi:10.1287/isre.11.4.342.11872 · Cited by 6,664
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Self-citationWhy Users Continue E-Commerce Chatbots? Insights from PLS-FsQCA-NCA Approach
Foroughi, B., Huy, T. Q., Iranmanesh, M., Ghobakhloo, M., Rejeb, A., & Nikbin, D. (2025) · The Service Industries Journal · doi:10.1080/02642069.2024.2371910 · Cited by 38
Metadata
- Authors
- Purohit, S., Arora, R., & Paul, J.
- Venue
- Journal of Consumer Behaviour
- Publisher
- Wiley
- Published
- 2022-01-23
- Volume
- 21
- Issue
- 3
- Pages
- 523-542
- ISSN
- 1472-0817 (ISSN-L)
- 1479-1838
- OpenAlex
- W4207084159
- Cited by
- 177
- References
- 23
- FWCI
- 33.57
- Type
- journal article
- Language
- en
- Open access
- closed
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