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Predictive and explanatory modeling regarding adoption of mobile payment systems

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  • Liébana-Cabanillas, F.
  • Lara-Rubio, J.

Abstract

Commercial activities have evolved during the past decade from a single-channel focus and perspective on business opportunities to a multiple-channel approach, with mobile phones playing a major role in the most recent and latest business opportunities. Even if mobile payment systems are still under development and steadily becoming available worldwide, many experts have already pointed to them as the potential payment system of choice taking into account its high penetration level within our society, its accessibility and ease of use. This paper explores the adoption of mobile payment systems from the point of view and perspective of the merchants. In order to provide a comprehensive analysis, this research extensively reviewed existing literature and determined the main factors influencing the adoption of mobile payment systems approaching a methodology involving both a logistic regression modeling and a neural network analysis. Results of these different analyses show that the neural network analysis is the most precise tool in this research when predicting the use of mobile payment systems in certain business. According to these results, some suggestions are proposed to incentive and encourage the intention to use of these mobile payment systems regarding each participant in the adoption process. Finally, this paper discusses some factors regarding future research opportunities.

Suggested Citation

  • Liébana-Cabanillas, F. & Lara-Rubio, J., 2017. "Predictive and explanatory modeling regarding adoption of mobile payment systems," Technological Forecasting and Social Change, Elsevier, vol. 120(C), pages 32-40.
    • Handle: RePEc:eee:tefoso:v:120:y:2017:i:c:p:32-40
    DOI: 10.1016/j.techfore.2017.04.002
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    1. J. C. Neves & A. Vieira, 2006. "Improving bankruptcy prediction with Hidden Layer Learning Vector Quantization," European Accounting Review, Taylor & Francis Journals, vol. 15(2), pages 253-271.
    2. Hausman, Jerry, 2015. "Specification tests in econometrics," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 38(2), pages 112-134.
    3. Terri Bradford & Fumiko Hayashi, 2014. "Mobile payments : merchants’ perspectives," Economic Review, Federal Reserve Bank of Kansas City, issue Q II, pages 33-58.
    4. Angelini, Eliana & di Tollo, Giacomo & Roli, Andrea, 2008. "A neural network approach for credit risk evaluation," The Quarterly Review of Economics and Finance, Elsevier, vol. 48(4), pages 733-755, November.
    5. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    6. Lingling Gao & Kerem Aksel Waechter, 0. "Examining the role of initial trust in user adoption of mobile payment services: an empirical investigation," Information Systems Frontiers, Springer, vol. 0, pages 1-24.
    7. Zaiyong Tang & Paul A. Fishwick, 1993. "Feedforward Neural Nets as Models for Time Series Forecasting," INFORMS Journal on Computing, INFORMS, vol. 5(4), pages 374-385, November.
    8. Sharma, Arun & Sheth, Jagdish N., 2004. "Web-based marketing: The coming revolution in marketing thought and strategy," Journal of Business Research, Elsevier, vol. 57(7), pages 696-702, July.
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    6. de Luna, Iviane Ramos & Liébana-Cabanillas, Francisco & Sánchez-Fernández, Juan & Muñoz-Leiva, Francisco, 2019. "Mobile payment is not all the same: The adoption of mobile payment systems depending on the technology applied," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 931-944.
    7. Marta Campos Ferreira & Manuel Oliveira & Teresa Galvão Dias, 2022. "To Use or Not to Use? Investigating What Drives Tourists to Use Mobile Ticketing Services in Tourism," Sustainability, MDPI, vol. 14(11), pages 1-16, May.
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    11. Liébana-Cabanillas, Francisco & Japutra, Arnold & Molinillo, Sebastián & Singh, Nidhi & Sinha, Neena, 2020. "Assessment of mobile technology use in the emerging market: Analyzing intention to use m-payment services in India," Telecommunications Policy, Elsevier, vol. 44(9).
    12. Muhammad Iskandar Hamzah, 2024. "Fear of COVID-19 disease and QR-based mobile payment adoption: a protection motivation perspective," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 29(3), pages 946-963, September.
    13. Honghong Wang, 2022. "BP neural network-based mobile payment risk prediction in cloud computing environment and its impact on e-commerce operation," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(3), pages 1072-1080, December.
    14. Wu, Mian & Liu, Yulong & Chung, Henry F.L. & Guo, Shoujia, 2022. "When and how mobile payment platform complementors matter in cross-border B2B e-commerce ecosystems? An integration of process and modularization analysis," Journal of Business Research, Elsevier, vol. 139(C), pages 843-854.
    15. Jennifer Yee-Shan Chang & Rupam Konar & Jun-Hwa Cheah & Xin-Jean Lim, 2024. "Does privacy still matter in smart technology experience? A conditional mediation analysis," Journal of Marketing Analytics, Palgrave Macmillan, vol. 12(1), pages 71-86, March.
    16. Dzandu, Michael D. & Hanu, Charles & Amegbe, Hayford, 2022. "Gamification of mobile money payment for generating customer value in emerging economies: The social impact theory perspective," Technological Forecasting and Social Change, Elsevier, vol. 185(C).
    17. Polasik, Michał & Huterska, Agnieszka & Iftikhar, Rehan & Mikula, Štěpán, 2020. "The impact of Payment Services Directive 2 on the PayTech sector development in Europe," Journal of Economic Behavior & Organization, Elsevier, vol. 178(C), pages 385-401.
    18. Yang, Wei & Vatsa, Puneet & Ma, Wanglin & Zheng, Hongyun, 2023. "Does mobile payment adoption really increase online shopping expenditure in China: A gender-differential analysis," Economic Analysis and Policy, Elsevier, vol. 77(C), pages 99-110.
    19. Aloys Prinz, 2019. "The microeconomics of mobile payments," Netnomics, Springer, vol. 20(2), pages 129-151, December.
    20. Yusuf Adeneye & Fathyah Hashim & Yusuf Babatunde Rahman & Normaizatul Akma Saidi, 2023. "COVID-19 Dynamics and Financing of Cash Flow Shortages: Evidence from Firm-Level Survey," Capital Markets Review, Malaysian Finance Association, vol. 31(2), pages 23-53.
    21. Jaiswal, Deepak & Mohan, Ashutosh & Deshmukh, Arun Kumar, 2023. "Cash rich to cashless market: Segmentation and profiling of Fintech-led-Mobile payment users," Technological Forecasting and Social Change, Elsevier, vol. 193(C).

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