A Survey to Assess Changes in Payment Transaction via Mobile Phone Behavior During the COVID-19 Pandemic

Main Article Content

Jintana Muanglen
Wannaphong Durongkaveroj

Abstract

The outbreak of the COVID-19 pandemic has affected every sector of society. Consumer payment behavior is no exception. The purpose of this study is to estimate the causal impacts of the COVID-19 pandemic on mobile payment behaviors and to examine factors underpinning such impacts. The behaviors of interest include the volume and the value of mobile payment transactions, the volume of QR code mobile payment transactions, the volume of PromptPay transactions, and the variety of mobile payment methods. The analysis is based on a survey, which is designed to recover counterfactual outcomes, of 503 people living in Bangkok between July and August 2021. The findings suggest that the COVID-19 pandemic has significantly increased all types of mobile payment transactions. The results from the logit model indicate that such changes in each mobile payment behavior are determined by different factors. The findings reflect the need to use different strategies to increase mobile payment usage to maximize the efficiency of the electronic payment system in Thailand and to help accelerate the transition to a cashless society.

Article Details

How to Cite
Muanglen, J., & Durongkaveroj, W. (2023). A Survey to Assess Changes in Payment Transaction via Mobile Phone Behavior During the COVID-19 Pandemic. Asian Journal of Applied Economics, 30(1), 97–118. Retrieved from https://so01.tci-thaijo.org/index.php/AEJ/article/view/263262
Section
Research Articles

References

Alafeef, M., Singh, D., & Ahmad, K. (2011). Influence of demographic factors on the adoption level of mobile banking applications in Jordan. Research Journal of Applied Sciences, 6(6), 373-377.

Angrist, J. D., & Pischke, J. S. (2009). Mostly harmless econometrics: An empiricist’s companion. Princeton: Princeton University Press.

Arcidiacono, P., Hotz, V. J., Maurel, A., & Romano, T. (2020). Ex ante returns and occupational choice. Journal of Political Economy, 128(12), 4387-4687.

Aucejo, E. M., French, J., Araya, M. P. U., & Zafar, B. (2020). The impact of COVID-19 on student experiences and expectations: Evidence from a survey. Journal of Public Economics, 191, 104271.

Bank of Thailand. (2021). Digital payment: A main option among Thais in the covid-19 crisis. Retrieved from https://www.bot.or.th/Thai/ResearchAndPublications/articles/Pages/Article_8Mar2021.aspx. (in Thai)

Bank of Thailand. (2022). Payment systems statistics. Retrieved from https://www.bot.or.th/Thai/Statistics/PaymentSystems/Pages/StatPaymentTransactions.aspx (in Thai)

Black, N. J., Lockett, A., Winklofer, H., & Ennew, C. (2001). The adoption of internet financial services: A qualitative study. International Journal of Retail and Distribution Management, 29(8), 390-398.

Boonsiritomachai, W., & Pitchayadejanant, K. (2019). Determinants affecting mobile banking adoption by generation Y based on the Unified Theory of Acceptance and Use of Technology Model modified by the Technology Acceptance Model concept. Kasetsart Journal of Social Sciences, 40(2), 349-358.

Bubpakham, T. (2022). Perception and attitudes affecting behavior of financial services using mobile banking application of users in Bangkok and its vicinity. Journal of Pacific Institute of Management Science (Humanities and Social Sciences), 8(2), 376-385. (in Thai)

Chang, H.-H., & Meyerhoefer, C. D. (2020). COVID-19 and the demand for online food shopping services: Empirical evidence from Taiwan. American Journal of Agricultural Economics, 103(2), 448-465.

Charness, N., & Boot, W. R. (2009). Aging and information technology use: Potential and barriers. Current Directions in Psychological Science, 18(5), 253-258.

Crowder, J., Laird, R., McLaughlin, J., Odom, L., Ragalevsky, S., ReVeal, J., Rinearson, J., & Tammero, R. (2020). COVID-19: Its impact on banking, fintech, and payments: FAQs. Retrieved from https://www.jdsupra.com/legalnews/covid-19-its-impact-on-banking-fintech-62463/

Digital Economy Promotion Agency. (n.d.). A master plan for digital economy (2018-2023). Retrieved from https://www.depa.or.th/th/master-plan-digital-economy/1st-master-plan-for-digital-economy (in Thai)

Durongkaveroj, W. (2023). Learning loss due to university closures during the COVID-19 pandemic: Evidence from Thailand’s largest public university. Thailand and the World Economy, 41(2), Advance online publication.

Gefen, D., & Straub, D. W. (1997). Gender differences in the perception and use of e-mail: An extension to the technology acceptance model. MIS Quarterly, 21(4), 389-400.

Heckman, J. J., & Vytlacil, E. (2005). Structural equations, treatment effects, and econometric policy evaluation. Econometrica, 73(3), 669-738.

Homboon, N., Pongpraniti, N., & Srisatanon, P. (2020). Acceptance and attitudes towards the cashless society of consumers in Bangkok. Journal of Arts Management, 4(2), 472-488. (in Thai)

Hosmer, D. W., & Lemeshow, S. (1980). Goodness of fit tests for the multiple logistic regression model. Communications in Statistics – Theory and Methods, 9(10), 1043-1069.

Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied Logistic Regression (3rd edition). New Jersey: John Wiley & Sons.

Kim, J., & Kwan, M.-P. (2021). The impact of the COVID-19 pandemic on people’s mobility: A longitudinal study of the U.S. from March to September of 2020. Journal of Transport Geography, 93, 103039.

Khemngoen, J. (2021). A Study on the Spread of Coronavirus (Covid-19) Affecting Generation Y and Z’s Electronic Payment Behavior in Thailand and the Perception to Step into a Cashless Society [Master’s thesis, National Institute of Development Administration]. Retrieved from https://repository.nida.ac.th/handle/662723737/6059. (in Thai)

Liu, T., Pan, B., & Yin, Z. (2020). Pandemic, Mobile Payment, and Household Consumption: Micro-Evidence from China. Journal of Emerging Markets Finance and Trade, 56(10), 2378-2389.

Marangunic, N., & Granic, A. (2015). Technology acceptance model: A literature review from 1986 to 2013. Universal Access in the Information Society, 14, 81-95.

Martin, S., & Bergmann, J. (2021). (Im)mobility in the age of COVID-19. International Migration Review, 55(3), 660-687.

McKinsey. (2020). How COVID-19 has pushed companies over the technology tipping point—and transformed business forever. Retrieved from https://www.mckinsey.com/business-functions/strategy-and-corporate-finance/our-insights/how-covid-19-has-pushed-companies-over-the-technology-tipping-point-and-transformed-business-forever

Monsuwe, T. P., Dellaert, B. G. C., & Ruyter, K. D. (2004). What drives consumers to shop online? A literature review. International Journal of Service Industry Management, 15(1), 102-121.

National Statistical Office. (2020). ICT statistical report 2020. Retrieved from http://www.nso.go.th/sites/2014en/Pages/Statistical%20Themes/ICT.aspx

Nouvellet, P. et al. (2021). Reduction in mobility and COVID-19 transmission. Nature Communications, 12. Retrieved from https://doi.org/10.1038/s41467-021-21358-2

Office of the National Digital Economy and Society Commission. (2017). Digital economy and society development plan. Retrieved from https://onde.go.th/view/1/Digital_Development_for_National_Economic_and_Social_Development/EN-US (in Thai)

Rahman, M., Ismail, I., Bahri, S. (2020). Analysing consumer adoption of cashless payment in Malaysia. Digital Business, 1(1), 100004.

Shapiro, M., & Giustinelli, P. (2019). SeaTE: Subjective ex Ante treatment effect of health on retirement. National Bureau of Economic Research Working Paper Number 26087. Cambridge, MA: National Bureau of Economic Research. Retrieved from https://www.nber.org/papers/w26087

Srichan, T., & Phosing, P. (2021). Social assistance policies and government assistance measures through the application system for persons affected by Covid19. Journal of Modern Learning Development, 6(6), 324-337. (in Thai)

Subawa, N. S., Dewi, N. K. A., & Gama, A. W. O. (2021). Differences of gender perception in adopting cashless transaction using technology acceptance model. Journal of Asian Finance, Economics and Business, 8(2), 617-624.

Sunarjo, W. A., Nurhayati, S., & Muhardono, A. (2021). Consumer behavior toward adoption of mobile payment: A case study in Indonesia during the COVID-19 pandemic. Journal of Asian Finance, Economics and Business, 8(4), 581-590.

Roscoe, J. T. (1975). Fundamental research statistics for the behavioral sciences. New York: Holt Rinehart and Winston.

UNCTAD. (2020). COVID-19 and e-commerce: A global review. New York: United Nations Conference on Trade and Development.

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Towards a unified view. MIS Quarterly, 27(3), 425-478.

Wagner, N., Hassanein, K., & Head, M. (2010). Computer use by older adults: A multi-disciplinary review. Computers in Human Behavior, 26(5), 870-882.

Wannasilp, T. (2020). Factors affecting touchless payment service use in the digital cashless society after the COVID-19 pandemic outbreak in the Bangkok and metropolitan area [Independent study, Thammasat University]. Retrieved from https://digital.library.tu.ac.th/tu_dc/frontend/Info/item/dc:184271 (in Thai)

Wiswall, M., & Zafar, B. (2021). Human capital investments and expectations about career and family. Journal of Political Economy, 129(5), 1361-1424.

Yusif, S., Soar, J., & Hafeez-Baig, A. (2016). Older people, assistive technologies, and the barriers to adoption: A systematic review. International Journal of Medical Informatics, 94, 112-116.

Zhao, Y., & Bacao. (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.