The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
Understanding perceived risks in mobile payment acceptance
Abstract
Purpose: – The purpose of this paper is to examine and quantify how various uncertainties result in different perceived risk dimensions that hinder mobile payment (m-payment) acceptance. Design/methodology/approach: – An uncertainty-risk-value framework was proposed based on perceived risk theory, prospect theory, and perceived value theory. Structural equation modeling method was used to test the research model. Findings: – Perceived information asymmetry, perceived technology uncertainty, perceived regulatory uncertainty, and perceived service intangibility are confirmed as the main determinants of perceived risk, while perceived performance risk, perceived financial risk, and perceived privacy risk were found to have strong negative effects on perceived value and acceptance intention. Practical implications: – The findings may help businesses and policy makers better understand the sources of perceived risk and help support the development of appropriate strategies to mitigate the risk concerns consumers have regarding m-payment. Originality/value: – Although the hindering effects of perceived risk regarding m-payment acceptance have been confirmed in previous studies, the sources of perceived risk were rarely investigated. By examining the determinants of perceived risk in m-payment acceptance, this paper offers insights into how consumers perceive risks when adopting new innovations. Additionally, it bridges the gap between the antecedents and consequences of perceived risk.
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Cites
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Featherman, M. S., & Pavlou, P. A. (2003) · International Journal of Human-Computer Studies · doi:10.1016/s1071-5819(03)00111-3 · Cited by 3,351
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Cited by
Oliveira, T., Thomas, M., Baptista, G., & Campos, F. (2016) · Computers in Human Behavior · doi:10.1016/j.chb.2016.03.030 · Cited by 1,361
Use of AI-based tools for healthcare purposes: a survey study from consumers’ perspectives
Esmaeilzadeh, P. (2020) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-020-01191-1 · Cited by 507
Shao, Z., Zhang, L., Li, X., & Guo, Y. (2019) · Electronic Commerce Research and Applications · doi:10.1016/j.elerap.2018.100823 · Cited by 423
Adoption of in-store mobile payment: Are perceived risk and convenience the only drivers?
de Kerviler, G., Demoulin, N. T. M., & Zidda, P. (2016) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2016.04.011 · Cited by 374
Point of adoption and beyond. Initial trust and mobile-payment continuation intention
Talwar, S., Dhir, A., Khalil, A., Mohan, G., & Islam, A. K. M. N. (2020) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2020.102086 · Cited by 363
Metadata
- Authors
- Yang, Y., Liu, Y., Li, H., & Yu, B.
- Venue
- Industrial Management & Data Systems
- Publisher
- Emerald
- Published
- 2015-03-09
- Volume
- 115
- Issue
- 2
- Pages
- 253-269
- ISSN
- 0263-5577 (ISSN-L)
- OpenAlex
- W1999806522
- Cited by
- 390
- References
- 39
- FWCI
- 34.09
- Type
- journal article
- Language
- en
- Open access
- closed
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- Yang, Y., Liu, Y., Li, H., & Yu, B. (2015). Understanding perceived risks in mobile payment acceptance. Industrial Management & Data Systems. https://doi.org/10.1108/imds-08-2014-0243
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.