1,954
Views
5
CrossRef citations to date
0
Altmetric
Operations, Information & Technology

Fintech adoption dynamics in a pandemic: An experience from some financial institutions in Nigeria during COVID-19 using machine learning approach

ORCID Icon, , , , , & show all
Article: 2242985 | Received 17 Oct 2022, Accepted 14 Jul 2023, Published online: 10 Aug 2023

References

  • Abdus Salam, M., Saha, T., Habibur Rahman, M., & Mutsuddi, P. (2021). Challenges to mobile banking adaptation in COVID-19 pandemic. Journal of Business and Management Sciences, 9(3), 101–24. https://doi.org/10.12691/jbms-9-3-2
  • Agarwal S., Qian W., Ren Y., Tsai H., & Yeung B. (2020). The real impact of finTech: Evidence from mobile payment technology. SSRN Journal. https://doi.org/10.2139/ssrn.3556340
  • Aji, H. M., Berakon, I., Md Husin, M., & Tan, A. W. K. (2020). COVID-19 and e-wallet usage intention: A multigroup analysis between Indonesia and Malaysia. Cogent Business & Management, 7(1), 1804181. https://doi.org/10.1080/23311975.2020.1804181
  • Ajzen, I., & Fishbein, M. (1977). Attitude-behavior relations: A theoretical analysis and review of empirical research. Psychological Bulletin, 842, 888–918.
  • Akomolafe, J. A. (2022). Tax incentives and firm’s profitability: Evidence from manufacturing companies. American Journal of Research in Business and Social Sciences, 2(5), 1–10. https://doi.org/10.58314/202020
  • Akour, I., Alshurideh, M., Al Kurdi, B., Al Ali, A., & Salloum, S. (2021). Using machine learning algorithms to predict people’s intention to use mobile learning platforms during the COVID-19 pandemic: Machine learning approach. JMIR Medical Education, 7(1), e24032. https://doi.org/10.2196/24032
  • Alalwan, A. A., Dwivedi, Y. K., Rana, N. P. P., & Williams, M. D. (2016). Consumer adoption of mobile banking in Jordan: Examining the role of usefulness, ease of use, perceived risk and self-efficacy. Journal of Enterprise Information Management, 29(1), 118–139. https://doi.org/10.1108/JEIM-04-2015-0035
  • Alam, M. Z., Moudud-Ul-Huq, S., Sadekin, M. N., Hassan, M. G., & Rahman, M. M. (2021). Influence of social distancing behavior and cross-cultural motivation on consumers’ attitude to using m-payment services. Sustainability, 13(19), 1–20. https://doi.org/10.3390/su131910676
  • Allen, J., Carbo-Valverde, S., Chakravorti, S., Rodriguez-Fernandez, F., Pinar Ardic, O., & Bariviera, A. F. (2022). Assessing incentives to increase digital payment acceptance and usage: A machine learning approach. PLOS ONE, 17(11), 1–29. https://doi.org/10.1371/journal.pone.0276203
  • Alnsour, I. R. (2022). Impact of fintech over consumer experience and loyalty intentions: An empirical study on Jordanian Islamic Banks. Cogent Business & Management, 9(1). https://doi.org/10.1080/23311975.2022.2141098
  • Al-Qudah, A. A., Al-Okaily, M., Alqudah, G., & Ghazlat, A. (2022). Mobile payment adoption in the time of the COVID-19 pandemic. Electronic Commerce Research. https://doi.org/10.1007/s10660-022-09577-1
  • Al-Sabaawi, M. Y., Alshaher, A. A., & Alsalem, M. (2023). User trends of electronic payment systems adoption in developing countries: an empirical analysis. JSTPM Journal of Science and Technology Policy Management, 14(2), 246–270. https://doi.org/10.1108/JSTPM-11-2020-0162
  • Alshari, H. A., & Lokhande, M. A. (2022). The impact of demographic factors of clients’ attitudes and their intentions to use FinTech services on the banking sector in the least developed countries. Cogent Business & Management, 9(1). https://doi.org/10.1080/23311975.2022.2114305
  • Amaihian, A. B., Ogunnaike, O. O., Omankhanlen, E. A., & Peter, F. O. (2022). Optimisation of queueing efficiency and performance of Nigeria commercial banks. American Journal of Research in Business and Social Sciences, 2(4), 1–13. https://doi.org/10.58314/678900
  • Amoroso, D. L., & Lim, R. A. (2014). Why are Filipino consumers strong adopters of mobile applications? In Business technologies in contemporary organizations: Adoption, assimilation, and institutionalization (pp. 236–245 https://doi.org/10.4018/978-1-4666-6623-8.ch011).
  • Ang, D., & Edo, O. C. (2022). Healthcare information system: A public healthcare facility framework. International Journal of Health Sciences, 6(S2), 15140–15147. https://doi.org/10.53730/ijhs.v6nS2.9002
  • Anouze, A., & Alamro, A. (2020). Factors affecting intention to use e-banking in Jordan. International Journal of Bank Marketing, 38(1), 86–112. https://doi.org/10.1108/IJBM-10-2018-0271
  • Anthony, C. (2021, February 2). K-Nearest neighbor.
  • Baicu, C. G., Gârdan, I. P., Gârdan, D. A., & Epuran, G. (2020). The impact of COVID-19 on consumer behavior in retail banking. Evidence from Romania. Management & Marketing, 15(s1), 534–556. https://doi.org/10.2478/mmcks-2020-0031
  • Bandura, A. (1992). Social cognitive theory of social referencing. In Social referencing and the social construction of reality in infancy (pp. 175–208 https://doi.org/10.1007/978-1-4899-2462-9_8). Springer US.
  • Barboza, F., Kimura, H., & Altman, E. (2017). Machine learning models and bankruptcy prediction. Expert Systems with Applications, 83, 405–417. https://doi.org/10.1016/j.eswa.2017.04.006
  • Bauer, R. A. (1960). Consumer behavior as risk raking. In R. S. Hancock (Ed.), Proceedings of the 43 rd Conference of the Dynamic Marketiing for a Changing World (pp. 389–398). Chicago: American Marketing Association.
  • Bhamare, D., & Suryawanshi, P. (2018). Review on reliable pattern recognition with machine learning techniques. Fuzzy Information & Engineering, 10(3), 362–377. https://doi.org/10.1080/16168658.2019.1611030
  • Bishop, C. M. (2007). Pattern recognition and machine learning. Journal of Electronic Imaging, 16(4), 049901. https://doi.org/10.1117/1.2819119
  • Bitkina, O. V., Park, J., & Kim, H. K. (2022). Measuring user-perceived characteristics for banking services: Proposing a methodology. International Journal of Environmental Research and Public Health, 19(4), 2358. https://doi.org/10.3390/ijerph19042358
  • Bonett, D. G., & Wright, T. A. (2015). Cronbach’s alpha reliability: Interval estimation, hypothesis testing, and sample size planning. Journal of Organizational Behavior, 36(1), 3–15. https://doi.org/10.1002/job.1960
  • Brewka, G. (1996). Artificial intelligence- A modern approach by Stuart Russell and Peter Norvig, Prentice Hall. Series in Artificial Intelligence, Englewood Cliffs, NJ. The Knowledge Engineering Review, 11(1), 78–79. https://doi.org/10.1017/S0269888900007724
  • Buchanan, B., & Miller, T. (2017). Machine learning for policymakers: What it is and why it matters. The Cyber Security Project.
  • Carbo-Valverde, S., Cuadros-Solas, P., Rodríguez-Fernández, F., & Xin, B. (2020). A machine learning approach to the digitalization of bank customers: Evidence from random and causal forests. PloS One, 15(10), e0240362. https://doi.org/10.1371/journal.pone.0240362
  • CC, S., & Prathap, S. K. (2020). Continuance adoption of mobile-based payments in Covid-19 context: An integrated framework of health belief model and expectation confirmation model. International Journal of Pervasive Computing and Communications, 16(4), 351–369. https://doi.org/10.1108/IJPCC-06-2020-0069
  • Coskun, M., Saygili, E., & Karahan, M. O. (2022). Exploring online payment system adoption factors in the age of covid-19—Evidence from the Turkish banking industry. International Journal of Financial Studies, 10(2), 39. https://doi.org/10.3390/ijfs10020039
  • Daragmeh, A., Lentner, C., & Sági, J. (2021). FinTech payments in the era of COVID-19: Factors influencing behavioral intentions of “Generation X” in Hungary to use mobile payment. Journal of Behavioral and Experimental Finance, 32, 100574. https://doi.org/10.1016/j.jbef.2021.100574
  • Davis, F. (1986). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
  • Davis, F. (1989). Technology acceptance model for empirically testing information system technology.
  • Deloitte. (2020). Impact of COVID-19 on the banking sector. Retrieved December 20, 2020, from https://www2.deloitte.com/cn/en/pages/risk/articles/covid-19-impact-on-banks.html
  • Edo, O. C., Ang, D., Etu, E.-E., Tenebe, I., Edo, S., & Diekola, O. A. (2023). Why do healthcare workers adopt digital health technologies - A cross-sectional study integrating the TAM and UTAUT model in a developing economy. International Journal of Information Management Data Insights, 3(2), 100186. https://doi.org/10.1016/j.jjimei.2023.100186
  • Edo, O. C., Okafor, A., & Justice, A. E. (2020). Tax policy and foreign direct investment: A regime change analysis. GATR Journal of Finance and Banking Review, 5(3), 84–98. https://doi.org/10.35609/jfbr.2020.5.3(3)
  • Fawzy, S. F., & Esawai, N. (2017). Internet banking adoption in Egypt: Extending technology acceptance model. Journal of Business & Retail Management Research, 12(1). https://doi.org/10.24052/jbrmr/v12is01/ibaieetam
  • Featherman, M., & Pavlou, P. (2003). Predicting e-services adoption: A perceived risk facets perspective. International Journal of Human-Computer Studies, 59(4), 451–474. https://doi.org/10.1016/S1071-5819(03)00111-3
  • Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research. Addison-Wesley.
  • Flavián, C., Guinaliu, M., & Lu, Y. (2020). Mobile payments adoption – introducing mindfulness to better understand consumer behavior. International Journal of Bank Marketing, 38(7), 1575–1599. https://doi.org/10.1108/IJBM-01-2020-0039
  • Hasan, R., Ashfaq, M., & Shao, L. (2021). Evaluating drivers of fintech adoption in the Netherlands. Global Business Review, 1–14. https://doi.org/10.1177/09721509211027402
  • Hastie, T., Tibshirani, R., & Friedman, J. (2016). The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). Springer.
  • Hastie, T., Tibshirani, R., & Friedman, J. (2017). The elements of statistical learning data mining, inference, and prediction (12th printing). Proceedings of the 2013 International Conference on Signal-Image Technology and Internet-Based Systems, SITIS 2013, Kyoto, Japan.
  • Hayes, K., Rajabathar, R., & Balasubramaniam, V. (2019). Uncovering the machine learning “Black Box”: Discovering latent patient insights using text mining & machine learning. Innovation in Analytics via Machine Learning & AI.
  • He, Y., Chen, Q., & Kitkuakul, S. (2018). Regulatory focus and technology acceptance: Perceived ease of use and usefulness as efficacy. Cogent Business & Management, 5(1), 1–22. https://doi.org/10.1080/23311975.2018.1459006
  • Hidayat-Ur-Rehman, I., Alzahrani, S., Rehman, M. Z., Akhter, F., & Guidi, B. (2022). Determining the factors of m-wallets adoption. A twofold SEM-ANN approach. PLOS ONE, 17(1), 1–24. https://doi.org/10.1371/journal.pone.0262954
  • Hossain, M. A. (2019). Security perception in the adoption of mobile payment and the moderating effect of gender. PSU Research Review, 3(3), 179–190. https://doi.org/10.1108/prr-03-2019-0006
  • Ikechukwu, O. F., Christopher, E. O., Justice, A. E., & Osaremen, E. I. (2021). Direct taxes and income redistribution in Nigeria. GATR Global Journal of Business Social Sciences Review, 9(2), 182–196. https://doi.org/10.35609/gjbssr.2021.9.2(8)
  • Istijanto.& Handoko, I. (2022). Customers’ continuance usage of mobile payment during the COVID-19 pandemic. Spanish Journal of Marketing, 26(3), 345–362. https://doi.org/10.1108/SJME-02-2022-0016
  • Jahan, N., & Shahria, G. (2022). Factors effecting customer satisfaction of mobile banking in Bangladesh: A study on young users' perspective. SAJM, 3(1), 60–76. https://doi.org/10.1108/SAJM-02-2021-0018
  • Jayarathne, P. G. S. A., Chathuranga, B. T. K., Dewasiri, N. J., & Rana, S. (2022). Motives of mobile payment adoption during COVID-19 pandemic in Sri Lanka: A holistic approach of both customers’ and retailers’ perspectives. South Asian Journal of Marketing. https://doi.org/10.1108/sajm-03-2022-0013
  • Jehan, S. N., & Ansari, Z. A. (2018). Internet banking adoption in Saudi Arabia: An empirical study. International Journal of Marketing Studies, 10(3), 57. https://doi.org/10.5539/ijms.v10n3p57
  • Jibril, A. B., Kwarteng, M. A., Botchway, R. K., Bode, J., Chovancova, M., & Wright, L. T. (2020). The impact of online identity theft on customers’ willingness to engage in e-banking transaction in Ghana: A technology threat avoidance theory. Cogent Business & Management, 7(1), 1832825. https://doi.org/10.1080/23311975.2020.1832825
  • Kar, A. K. (2021). What affects usage satisfaction in mobile payments? Modeling user generated content to develop the “digital service usage satisfaction model. Information Systems Frontiers, 23(5), 1341–1361. https://doi.org/10.1007/s10796-020-10045-0
  • Kim, J., & Kim, M. (2022). Intention to use mobile easy payment services: Focusing on the risk perception of COVID-19. Frontiers in Psychology, 13, 1–12. https://doi.org/10.3389/fpsyg.2022.878514
  • Kumar, S., Leonie, A., & Yukita, K. (2021). Millennials behavioral intention in using mobile banking: Integrating perceived risk and trust into TAM (A Survey in Jawa Barat).
  • Liébana-Cabanillas, F., Sánchez-Fernández, J., & Muñoz-Leiva, F. (2014). The moderating effect of experience in the adoption of mobile payment tools in virtual social networks: The m-Payment Acceptance Model in Virtual Social Networks (MPAM-VSN). International Journal of Information Management, 34(2), 151–166. https://doi.org/10.1016/j.ijinfomgt.2013.12.006
  • Li, Y., & Li, Y. (2020). Study of merchant adoption in mobile payment system based on ensemble learning. American Journal of Industrial and Business Management, 10(5), 861–875. https://doi.org/10.4236/ajibm.2020.105058
  • Li, X., & Zheng, L. (2020). Consumers adoption behavior prediction through technology acceptance model and machine learning models. Statistics for Data Science and Policy Analysis. https://doi.org/10.1007/978-981-15-1735-8_24
  • Lu, H. P., & Wung, Y. S. (2021). Applying transaction cost theory and push-pull-mooring model to investigate mobile payment switching behaviors with well-established traditional financial infrastructure. Journal of Theoretical & Applied Electronic Commerce Research, 16(2), 1–21. https://doi.org/10.4067/S0718-18762021000200102
  • Lu, X., Xie, X., & Xiong, J. (2015). Social trust and risk perception of genetically modified food in urban areas of China: The role of salient value similarity. Journal of Risk Research, 18(2), 199–214. https://doi.org/10.1080/13669877.2014.889195
  • Ly, H. T. N., Khuong, N. V., & Son, T. H. (2022). Determinants affect mobile wallet continuous usage in COVID-19 pandemic: Evidence from Vietnam. Cogent Business & Management, 9(1), 12–31. https://doi.org/10.1080/23311975.2022.2041792
  • Marikyan, D., & Papagiannidis, S. (2021). Unified theory of acceptance and use of.
  • Mensah, K., Zeng, G., Luo, C., Zhi-Wu, X., & Lu, M. (2021). Factors predicting the behavioral adoption of electronic payment system (EPS). International Journal of Information Systems in the Service Sector, 13(1), 88–104. https://doi.org/10.4018/IJISSS.2021010105
  • Meyta Dewi, G. M., Joshua, L., Ikhsan, R. B., Yuniarty, Y., Sari, R. K., & Susilo, A. (2021). Perceived risk and trust in adoption e-wallet: The role of perceived usefulness and ease of use. 2021 International Conference on Information Management and Technology (ICIMTech) (pp. 120–124). IEEE. https://doi.org/10.1109/ICIMTech53080.2021.9535033
  • Mustafa, S., Zhang, W., Shehzad, M. U., Anwar, A., & Rubakula, G. (2022). Does health consciousness matter to adopt new technology? an integrated model of UTAUT2 with SEM-fsQCA approach. Frontiers in Psychology, 13, 836194. https://doi.org/10.3389/fpsyg.2022.836194
  • Mwiya, B., Katai, M., Bwalya, J., Kayekesi, M., Kaonga, S., Kasanda, E., & Mwenya, D. (2022). Examining the effects of electronic service quality on online banking customer satisfaction: Evidence from Zambia. Cogent Business & Management, 9(1). https://doi.org/10.1080/23311975.2022.2143017
  • Neal, R. M. (2007). Pattern recognition and machine learning. Technometrics, 49(3), 366–366. https://doi.org/10.1198/tech.2007.s518
  • Nguyen, T. M. A., Nguyen, T. H., & Le, H. H. (2022). Online shopping in relationship with perception, attitude, and subjective norm during COVID-19 outbreak: The case of Vietnam. Sustainability, 14(22), 15009. https://doi.org/10.3390/su142215009
  • Nguyen Thi, B., Tran, T. L. A., Tran, T. T. H., Le, T. T., Tran, P. N. H., & Nguyen, M. H. (2022). Factors influencing continuance intention of online shopping of generation Y and Z during the new normal in Vietnam. Cogent Business & Management, 9(1). https://doi.org/10.1080/23311975.2022.2143016
  • Ojo, A. O., Fawehinmi, O., Ojo, O. T., Arasanmi, C., & Tan, C. N. L. (2022). Consumer usage intention of electronic wallets during the COVID-19 pandemic in Malaysia. Cogent Business & Management, 9(1). https://doi.org/10.1080/23311975.2022.2056964
  • Oliveira, T., Thomas, M., Baptista, G., & Campos, F. (2016). Mobile payment: Understanding the determinants of customer adoption and intention to recommend the technology. Computers in Human Behavior, 61, 404–414. https://doi.org/10.1016/j.chb.2016.03.030
  • Olumide, O. (2016). Technology acceptance model as a predictor of using information system’ to acquire information literacy skills. Retrieved from http://digitalcommons.unl.edu/libphilprac/1450
  • Onakpa, S. M., & Alfred, O. G. (2022). Influence of social media COVID-19 messages on the lifestyle of students in two Nigerian universities. American Journal of Research in Business and Social Sciences, 2(2), 1–9. https://doi.org/10.58314/787890
  • Oyelami, L. O., Adebiyi, S. O., & Adekunle, B. S. (2020). Electronic payment adoption and consumers’ spending growth: Empirical evidence from Nigeria. Future Business Journal, 6(1). https://doi.org/10.1186/s43093-020-00022-z
  • Piryonesi, S. M., & El-Diraby, T. E. (2020). Role of data analytics in infrastructure asset management: Overcoming data size and quality problems. Journal of Transportation Engineering, Part B: Pavements, 146(2), 04020022. https://doi.org/10.1061/jpeodx.0000175
  • Prastiawan, D. I., Aisjah, S., & Rofiaty, R. (2021). The effect of perceived usefulness, perceived ease of use, and social influence on the use of mobile banking through the mediation of attitude toward use. Asia Pacific Management and Business Application, 009(3), 243–260. https://doi.org/10.21776/ub.apmba.2021.009.03.4
  • Rajula, H. S. R., Verlato, G., Manchia, M., Antonucci, N., & Fanos, V. (2020). Comparison of conventional statistical methods with machine learning in medicine: Diagnosis, drug development, and treatment. Medicina, 56(9), 1–10. https://doi.org/10.3390/medicina56090455
  • Ramtiyal, B., Verma, D., & Rathore, A. S. (2022). Role of risk perception and situational factors in mobile payment adoption among small vendors in unorganised retail. Electronic Commerce Research, 28, 1–39. https://doi.org/10.1007/s10660-022-09657-2
  • Raza, S. A., Amna, U., & Nida, S. (2017). New determinants of ease of use and perceived usefulness for mobile banking adoption. International Journal of Electronic Customer Relationship Management, 11(1), 44–65. https://doi.org/10.1504/IJECRM.2017.086751
  • Rodriguez-Galiano, V., Sanchez-Castillo, M., Chica-Olmo, M., & Chica-Rivas, M. (2015). Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines. Ore Geology Reviews, 71, 804–818. https://doi.org/10.1016/j.oregeorev.2015.01.001
  • Rogers, E. M., Singhal, A., & Quinlan, M. M. (2019). Diffusion of innovations. In An integrated approach to communication theory and research (3rd ed.) (pp. 415–434). https://doi.org/10.4324/9780203710753-35
  • Sharma, A., Sharma, M. K., & Dwivedi, R. K. (2021). Improved decision tree classification (idt) algorithm for social media data. Proceedings of the 2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART) (pp. 155–157). IEEE. https://doi.org/10.1109/SMART52563.2021.9676265
  • Shu’ara, J., & Amin, J. H. (2022). Implications of online payment modes on purchase behaviour of students at Baze University, Abuja-Nigeria. American Journal of Research in Business and Social Sciences, 2(4), 1–11. https://doi.org/10.58314/234500
  • Singh, N., & Sinha, N. (2020). How perceived trust mediates merchant’s intention to use a mobile wallet technology. Journal of Retailing and Consumer Services, 52, 101894. https://doi.org/10.1016/j.jretconser.2019.101894
  • Siyal, A. W., Donghong, D., Umrani, W. A., Siyal, S., & Bhand, S. (2019). Predicting mobile banking acceptance and loyalty in Chinese Bank Customers. SAGE Open, 9(2), 215824401984408. https://doi.org/10.1177/2158244019844084
  • Sona, V. K., & Swain, K. R. (2018). Awareness of mobile payment system among consumers: A comparative study in Ranchi and Kolkata.
  • Sudarsono, H., Kholid, M. N., Trisanty, A., & Maisaroh, M. (2022). The intention of Muslim customers to adopt mobile banking: The case of Islamic banks in Indonesia. Cogent Business & Management, 9(1). https://doi.org/10.1080/23311975.2022.2154102
  • Suttharattanagul, S. L., Cai, Y., & Moschis, G. P. (2022). Life course explanations of consumer responses to threats: The case of COVID-19. Cogent Business & Management, 9(1), 1–25. https://doi.org/10.1080/23311975.2022.2151193
  • Taber, K. S. (2018). The use of cronbach’s alpha when developing and reporting research instruments in science education. Research in Science Education, 48(6), 1273–1296. https://doi.org/10.1007/s11165-016-9602-2
  • Tavakol, M., & Dennick, R. (2011, June 27). Making sense of Cronbach’s alpha. International Journal of Medical Education, 2, 53–55. https://doi.org/10.5116/ijme.4dfb.8dfd
  • Thompson, R. L., Higgins, C. A., & Howell, J. M. (1991). Personal computing: Toward a conceptual model of utilization. MIS Quarterly, 15(1), 124–143. https://doi.org/10.2307/249443
  • Tolles, J., & Meurer, W. J. (2016). Logistic regression. JAMA, 316(5), 533–534. https://doi.org/10.1001/jama.2016.7653
  • To, A. T., & Trinh, T. H. M. (2021). Understanding behavioral intention to use mobile wallets in Vietnam: Extending the tam model with trust and enjoyment. Cogent Business & Management, 8(1). https://doi.org/10.1080/23311975.2021.1891661
  • Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
  • Venkatesh, V., Thong, J.& Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178. https://doi.org/10.2307/41410412
  • von Wachter, T. (2020). Lost generations: Long-term effects of the COVID-19 crisis on job losers and labour market entrants, and options for policy*. Fiscal Studies, 41(3), 549–590. https://doi.org/10.1111/1475-5890.12247
  • Weinstein, N. D. (1982). Unrealistic optimism about susceptibility to health problems. Journal of Behavioral Medicine, 5(4), 441–460. https://doi.org/10.1007/BF00845372
  • WHO. (2020). Corona virus disease (Covid-19) situation report 198. Retrieved from https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200805-covid-19-sitrep-198.pdf?sfvrsn=f99d1754_2
  • Yan, C., Siddik, A. B., Akter, N., & Dong, Q. (2021). Factors influencing the adoption intention of using mobile financial service during the COVID-19 pandemic: The role of FinTech. Environmental Science and Pollution Research, 30(22), 61271–61289. https://doi.org/10.1007/s11356-021-17437-y
  • Yin, L. X., & Lin, H. C. (2022). Predictors of customers’ continuance intention of mobile banking from the perspective of the interactivity theory. Economic Research-Ekonomska Istrazivanja. https://doi.org/10.1080/1331677X.2022.2053782
  • Yoon, C., Lim, D., & Tan, A. W. K. (2020). An empirical study on factors affecting customers’ acceptance of internet-only banks in Korea. Cogent Business & Management, 7(1), 1792259. https://doi.org/10.1080/23311975.2020.1792259
  • Yu, S. Y., & Chen, D. C. (2022). Consumers’ switching from cash to mobile payment under the fear of COVID-19 in Taiwan. Sustainability, 14(14), 8489. https://doi.org/10.3390/su14148489
  • Zhang, J., & Mao, E. (2020). Cash, credit, or phone? An empirical study on the adoption of mobile payments in the United States. Psychology & Marketing, 37(1), 87–98. https://doi.org/10.1002/mar.21282
  • Zhao, Y., & Bacao, F. (2021). How does the pandemic facilitate mobile payment? An investigation on users’ perspective under the COVID-19 pandemic. International Journal of Environmental Research and Public Health, 18(3), 1016. https://doi.org/10.3390/ijerph18031016