Consumers’ Switching from Cash to Mobile Payment under the Fear of COVID-19 in Taiwan
Abstract
:1. Introduction
2. Literature Review
2.1. Mobile Payment
2.2. Push–Pull–Mooring (PPM) and Switching Intention
2.2.1. Push (Dissatisfaction)
2.2.2. Pull (Alternative Attractiveness)
2.2.3. Mooring (Perceived Fear)
2.3. Mediating Effect
3. Research Methods
4. Results
4.1. Results of the Measurement Model
4.2. Results of the Structural Model
5. Discussions
5.1. The Findings
5.2. Theoretical Implications
5.3. Managerial Implications
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Worldpay. Global Payments Reports. 2022. Available online: https://worldpay.globalpaymentsreport.com/ (accessed on 9 April 2022).
- Zhao, Y.; Bacao, F. How does the pandemic facilitate mobile payment? An investigation on users’ perspective under the COVID-19 pandemic. Int. J. Environ. Res. Public Health 2021, 18, 1016. [Google Scholar] [CrossRef] [PubMed]
- Market Intelligence & Consulting Institute. 2021 Mobile Payment Consumer Survey. Available online: https://mic.iii.org.tw/news.aspx?id=617 (accessed on 10 April 2022).
- Dahlberg, T.; Guo, J.; Ondrus, J. A critical review of mobile payment research. Electron. Commer. Res. Appl. 2015, 14, 265–284. [Google Scholar] [CrossRef]
- Leong, L.Y.; Hew, J.J.; Wong, L.W.; Lin, B. The past and beyond of mobile payment research: A development of the mobile payment framework. Internet Res. 2022. ahead of print. [Google Scholar] [CrossRef]
- Fan, L.; Zhang, X.; Rai, L.; Du, Y. Mobile payment: The next frontier of payment systems? An empirical study based on push-pull-mooring framework. J. Theor. Appl. Electron. Commer. Res. 2021, 16, 155–169. [Google Scholar] [CrossRef]
- Talwar, S.; Dhir, A.; Khalil, A.; Mohan, G.; Islam, A.N. Point of adoption and beyond. Initial trust and mobile-payment continuation intention. J. Retail. Consum. Serv. 2020, 55, 102086. [Google Scholar] [CrossRef]
- Singh, S. An integrated model combining the ECM and the UTAUT to explain users’ post adoption behaviour to-wards mobile payment systems. Australas. J. Inf. Syst. 2020, 24, 1–27. [Google Scholar]
- Sreelakshmi, C.C.; Prathap, S.K. Continuance adoption of mobile-based payments in COVID-19 context: An integrated framework of health belief model and expectation confirmation model. Int. J. Pervasive Comput. Commun. 2020, 16, 351–369. [Google Scholar]
- Daragmeh, A.; Sagi, J.; Zeman, Z. Continuous intention to use e-wallet in the context of the COVID-19 pandemic: Integrating the health belief model (HBM) and technology continuous theory (TCT). J. Open Innov. Technol. Mark. Complex. 2021, 7, 132. [Google Scholar] [CrossRef]
- Srivastava, C.; Mahendar, G.; Vandana, V. Adoption of contactless payments during COVID-19 pandemic—An integration of protection motivation theory (PMT) and unified theory of acceptance and use of technology (UTAUT). Acad. Mark. Stud. J. 2021, 25, 2678. [Google Scholar]
- Wang, L.; Luo, X.R.; Yang, X.; Qiao, Z. Easy come or easy go? Empirical evidence on switching behaviors in mobile payment applications. Inf. Manag. 2019, 56, 103150. [Google Scholar] [CrossRef]
- Handarkho, Y.D.; Harjoseputro, Y. Intention to adopt mobile payment in physical stores: Individual switching behavior perspective based on Push–Pull–Mooring (PPM) theory. J. Enterp. Inf. Manag. 2019, 33, 285–308. [Google Scholar] [CrossRef]
- Loh, X.M.; Lee, V.H.; Tan, G.W.H.; Ooi, K.B.; Dwivedi, Y.K. Switching from cash to mobile payment: What’s the hold-up? Internet Res. 2021, 31, 376–399. [Google Scholar] [CrossRef]
- Ye, C.; Seo, D.; Desouza, K.C.; Sangareddy, S.P.; Jha, S. Influences of IT substitutes and users experience on post adoption user switching: An empirical in vestigation. J. Am. Soc. Inf. Sci. Technol. 2008, 59, 2115–2132. [Google Scholar] [CrossRef]
- Ye, C.; Potter, R. The role of habit in post-adoption switching of personal information technologies: An empirical investigation. Commun. Assoc. Inf. Syst. 2011, 28, 35. [Google Scholar] [CrossRef]
- Kim, C.; Mirusmonova, M.; Lee, I. An empirical examination of factors influencing the intention to use mobile payment. Comput. Hum. Behav. 2010, 26, 310–322. [Google Scholar] [CrossRef]
- Oliveira, T.; Thomas, M.; Baptista, G.; Campos, F. Mobile payment: Understanding the determinants of customer adoption and intention to commend the technology. Comput. Hum. Behav. 2016, 61, 404–414. [Google Scholar] [CrossRef]
- Bank for International Settlements. 2012 Innovations in Retail Payments. Available online: https://www.bis.org/cpmi/publ/d102.htm (accessed on 10 April 2022).
- Vizzarri, A.; Vatalaro, F. M-Payment systems: Technologies and business models. In Proceedings of the 2014 Euro Med Telco Conference (EMTC), Naples, Italy, 12–15 November 2014; pp. 1–6. [Google Scholar] [CrossRef]
- National Credit Card Center of R.O.C. Explore the Trend of NFC and QR Payment 2021. Available online: https://www.nccc.com.tw/wps/wcm/connect/zh/home/openinformation/CaseAnalysisIntroduce/CNT_05_998_20211201133516 (accessed on 10 April 2022).
- Purwandari, B.; Suriazdin, S.A.; Hidayanto, A.N.; Setiawan, S.; Phusavat, K.; Maulida, M. Factors affecting switching intention from cash on delivery to e-payment services in c2c e-commerce transactions: COVID-19, transaction, and technology perspectives. Emerg. Sci. J. 2022, 6, 136–150. [Google Scholar] [CrossRef]
- Chiu, H.C.; Hsieh, Y.C.; Roan, J.; Tseng, K.J.; Hsieh, J.K. The challenge for multichannel services: Cross-channel free-riding behavior. Electron. Commer. Res. Appl. 2011, 10, 268–277. [Google Scholar] [CrossRef]
- Mu, H.L.; Lee, Y.C. Will proximity mobile payments substitute traditional payments? Examining factors influencing customers’ switching intention during the COVID-19 pandemic. Int. J. Bank Mark. 2022, 40, 1051–1070. [Google Scholar] [CrossRef]
- Sun, Y.; Liu, D.; Chen, S.; Wu, X.; Shen, X.L.; Zhang, X. Understanding users’ switching behavior of mobile instant messaging applications: An empirical study from the perspective of push-pull-mooring framework. Comput. Hum. Behav. 2017, 75, 727–738. [Google Scholar] [CrossRef]
- Bansal, H.S.; Taylor, S.F.; James, Y.S. Migrating to new service providers: Toward a unifying framework of consumers’ switching behaviors. J. Acad. Mark. Sci. 2005, 33, 96–115. [Google Scholar] [CrossRef]
- Keaveney, S.M. Customer switching behavior in service industries: An exploratory study. J. Mark. 1995, 59, 71–82. [Google Scholar] [CrossRef]
- Hsieh, P.J. Understanding medical consumers’ intentions to switch from cash payment to medical mobile payment: A perspective of technology migration. Technol. Forecast. Soc. Chang. 2021, 173, 121074. [Google Scholar] [CrossRef]
- Peng, X.; Zhao, Y.; Zhu, Q. Investigating users switching intention for mobile instant messaging applications: Taking WeChat as an example. Comput. Hum. Behav. 2016, 64, 206–216. [Google Scholar] [CrossRef] [Green Version]
- Venkatesh, V.; Morries, M.G.; Davis, G.B. User acceptance of information technology: Toward a unified view. MIS Q. 2003, 27, 425–478. [Google Scholar] [CrossRef] [Green Version]
- Bhattacherjee, A. Understanding information systems continuance: An expectation-confirmation model. MIS Q. 2001, 25, 351–370. [Google Scholar] [CrossRef]
- Chang, I.C.; Liu, C.C.; Chen, K. The push, pull and mooring effects in virtual migration for social networking sites. Inf. Syst. J. 2014, 24, 323–346. [Google Scholar] [CrossRef]
- Fan, L.; Suh, Y.H. Why do users switch to a disruptive technology? An empirical study based on expectation-disconfirmation theory. Inf. Manag. 2014, 51, 240–248. [Google Scholar] [CrossRef]
- Bhattacherjee, A.; Park, S.C. Why end-users move to the cloud: A migration-theoretic analysis. Eur. J. Inf. Syst. 2014, 23, 357–372. [Google Scholar] [CrossRef]
- De Kerviler, G.; Demoulin, N.T.M.; Zidda, P. Adoption of in-store mobile payment are perceived risk and convenience the only drivers. J. Retail. Consum. Serv. 2016, 31, 334–344. [Google Scholar] [CrossRef]
- Teo, A.; Tan, G.W.; Ooi, K.B.; Lin, B. Why consumers adopt mobile payment? A partial least squares structural equation modelling (PLS-SEM) approach. Int. J. Mob. Commun. 2015, 13, 478–497. [Google Scholar] [CrossRef]
- Porath, M. Immediate payments: Beyond ubiquity, convenience speed and security paving the road to a cashless society. J. Digit. Bank. 2017, 1, 349–357. [Google Scholar]
- Hayashi, F. Mobile Payments: What’s in it for consumers? Federal Reserve Bank of Kansas City. Econ. Rev. 2012, 97, 35–66. [Google Scholar]
- Williams, K.C. Improving fear appeal ethics. J. Acad. Bus. Ethics 2011, 5, 1–24. [Google Scholar]
- Al-Maroof, R.S.; Salloum, S.A.; Hassanien, A.E.; Shaalan, K. Fear from COVID-19 and technology adoption: The impact of Google Meet during Coronavirus pandemic. Interact. Learn. Environ. 2020, 1–16. [Google Scholar] [CrossRef]
- Mamun, M.A.; Griffiths, M.D. First COVID-19 suicide case in Bangladesh due to fear of COVID-19 and xenophobia: Possible suicide prevention strategies. Asian J. Psychiatry 2020, 51, 102073. [Google Scholar] [CrossRef]
- Pakpour, A.H.; Griffiths, M.D.; Lin, C.Y. Assessing psychological response to the COVID-19: The fear of COVID-19 scale and the COVID stress scales. Int. J. Ment. Health Addict. 2021, 19, 2407–2410. [Google Scholar] [CrossRef]
- Addo, P.C.; Jiaming, F.; Kulbo, N.B.; Liangqiang, L. COVID-19: Fear appeal favoring purchase behavior towards personal protective equipment. Serv. Ind. J. 2020, 40, 471–490. [Google Scholar] [CrossRef] [Green Version]
- Brem, A.; Viardot, E.; Nylund, P.A. Implications of the coronavirus (COVID-19) outbreak for innovation: Which technologies will improve our lives? Technol. Forecast. Soc. Chang. 2021, 163, 120451. [Google Scholar] [CrossRef]
- Makarem, S. Emotions and cognitions in consumer health behaviors: Insights from chronically ill patients into the effects of hope and control perceptions. J. Consum. Behav. 2016, 15, 208–215. [Google Scholar] [CrossRef]
- Smith, N.; Leiserowitz, A. The role of emotion in global warming policy support and opposition. Risk Anal. 2014, 34, 937–948. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Kim, J.; Yang, K.; Min, J.; White, B. Hope, fear, and consumer behavioral change amid COVID-19: Application of protection motivation theory. Int. J. Consum. Stud. 2022, 46, 558–574. [Google Scholar] [CrossRef] [PubMed]
- Janmaimool, P. Application of protection motivation theory to investigate sustainable waste management behaviors. Sustainability 2017, 9, 1079. [Google Scholar] [CrossRef] [Green Version]
- Halevy, N. Preemptive strikes: Fear, hope, and defensive aggression. J. Personal. Soc. Psychol. 2017, 112, 224–237. [Google Scholar] [CrossRef]
- Krishen, A.S.; Bui, M. Fear advertisements: Influencing consumers to make better health decisions. Int. J. Advert. 2015, 34, 533–548. [Google Scholar] [CrossRef]
- Macinnis, D.J.; Mello, G.E. The concept of hope and its relevance to product evaluation and choice. J. Mark. 2005, 69, 1–14. [Google Scholar] [CrossRef]
- Cho, H.; Lee, J.S. The influence of self-efficacy, subjective norms, and risk perception on behavioral intentions related to the H1N1 flu pandemic: A comparison between Korea and the US. Asian J. Soc. Psychol. 2015, 18, 311–324. [Google Scholar] [CrossRef]
- Prati, G.; Pietrantoni, L.; Zani, B. Influenza vaccination: The persuasiveness of messages among people aged 65 years and older. Health Commun. 2012, 27, 413–420. [Google Scholar] [CrossRef]
- Thakur, R. Understanding customer engagement and loyalty: A case of mobile devices for shopping. J. Retail. Consum. Serv. 2016, 32, 151–163. [Google Scholar] [CrossRef]
- Chuah, S.H.W.; Rauschnabel, P.A.; Tseng, M.L.; Ramayah, T. Reducing temptation to switch mobile data service providers over time: The role of dedication vs constraint. Ind. Manag. Data Syst. 2018, 118, 1597–1628. [Google Scholar] [CrossRef]
- Gerhold, L. COVID-19: Risk perception and coping strategies. PsyArXiv 2020, 25. [Google Scholar] [CrossRef] [Green Version]
- Huynh, T.L.D. Data for Understanding the Risk Perception of COVID-19 from Vietnamese Sample. Data Brief 2020, 30, 105530. [Google Scholar] [CrossRef] [PubMed]
- Cheng, S.; Lee, S.J.; Choi, B. An empirical investigation of users’ voluntary switching intention for mobile personal cloud storage services based on the push-pull-mooring framework. Comput. Hum. Behav. 2019, 92, 198–215. [Google Scholar] [CrossRef]
- Zhou, T. Understanding users’ switching from online stores to mobile stores. Inf. Dev. 2016, 32, 60–69. [Google Scholar] [CrossRef] [Green Version]
- De Luna, I.R.; Liébana-Cabanillas, F.; Sánchez-Fernández, J.; Muñoz-Leiva, F. Mobile payment is not all the same: The adoption of mobile payment systems depending on the technology applied. Technol. Forecast. Soc. Chang. 2019, 146, 931–944. [Google Scholar] [CrossRef]
- Hair, J.F.; Anderson, R.E.; Tatham, R.L.; Black, W.C. Multivariate Data Analysis, 8th ed.; Prentice-Hall: Upper Saddle River, NJ, USA, 1998. [Google Scholar]
- Comrey, A.L. Factor-analytic methods of scale development in personality and clinical psychology. J. Consult. Clin. Psychol. 1988, 56, 754. [Google Scholar] [CrossRef]
- Cuieford, J.P. Fundamental Statistics in Psychology and Education, 4th ed.; MacGraw-Hill: New York, NY, USA, 1965. [Google Scholar]
- Bagozzi, R.P.; Yi, Y. On the evaluation of structural equation models. J. Acad. Mark. Sci. 1988, 16, 74–94. [Google Scholar] [CrossRef]
- Fornell, C.; Larcker, D.F. Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 1981, 18, 39–50. [Google Scholar] [CrossRef]
- Schierz, P.G.; Schilke, O.; Wirtz, B.W. Understanding consumer acceptance of mobile payment services: An empirical analysis. Electron. Commer. Res. Appl. 2010, 9, 209–216. [Google Scholar] [CrossRef]
- Trütsch, T. The impact of mobile payment on payment choice. Financ. Mark. Portf. Manag. 2016, 30, 299–336. [Google Scholar] [CrossRef] [Green Version]
- Zhou, T. An empirical examination of continuance intention of mobile payment services. Decis. Support Syst. 2013, 54, 1085–1091. [Google Scholar] [CrossRef]
- Peráček, T. E-commerce and its limits in the context of consumer protection: The case of the Slovak Republic. Jurid. Trib. Trib. Jurid. 2022, 12, 35–50. [Google Scholar] [CrossRef]
- Žofčinová, V.; Čajková, A.; Král, R. Local leader and the labour law position in the context of the smart city concept through the optics of the EU. TalTech J. Eur. Stud. 2022, 12, 3–26. [Google Scholar] [CrossRef]
Constructs | Measurement | Sources |
---|---|---|
Dissatisfaction | DIS1: I feel dissatisfied paying with cash because the change I receive often does not equal the amount I should have received. | [31,54] |
DIS2: Compared to mobile payment, I feel dissatisfied with cash payment because it would not give me more exclusive time-bound offers | ||
DIS3: I think the COVID-19 virus can be transmitted to humans from cash and coin. | ||
DIS4: I feel dissatisfied with my overall experience using cash payment | ||
Alternative attractiveness (AA) | AA1: There are good mobile payment provider options if I have to switch to mobile payment. | [25,55] |
AA2: For me, the benefits of using mobile payment is higher than cash | ||
AA3: I would probably be really pleased and happy with the functions and services of mobile payment | ||
AA4: Using mobile payment may make me more satisfied and delighted than cash payment | ||
Perceived Fear (PF) | PF1: The COVID-19 pandemic worries me. | [56,57] |
PF2: I am afraid of being infected by COVID-19. | ||
PF3: How likely do you think it is to get COVID-19 in general? | ||
PF4: Overall, to what extent do you worry about COVID-19? | ||
Switching Intention (SI) | SI1: I am thinking about switching from cash to mobile payment. | [58,59] |
SI2: I would like to switch from cash to mobile payment in the future. | ||
SI3: There is a high probability that I will switch from cash to mobile payment | ||
SI4: I am ready to switch from cash to mobile payment. |
Constructs and Items | Loading | CR | AVE | Cronbach’s Alpha |
---|---|---|---|---|
Dissatisfaction | ||||
DIS1 | 0.705 | 0.793 | 0.500 | 0.791 |
DIS2 | 0.651 | |||
DIS3 | 0.666 | |||
DIS4 | 0.772 | |||
Alternative attractiveness | ||||
AA 1 | 0.621 | 0.825 | 0.544 | 0.824 |
AA 2 | 0.819 | |||
AA 3 | 0.804 | |||
AA 4 | 0.689 | |||
Perceived fear | ||||
PF 1 | 0.890 | 0.794 | 0.502 | 0.81 |
PF 2 | 0.765 | |||
PF 3 | 0.592 | |||
PF 4 | 0.528 | |||
Switching intention | ||||
SI 1 | 0.769 | 0.820 | 0.535 | 0.788 |
SI 2 | 0.842 | |||
SI 3 | 0.662 | |||
SI 4 | 0.635 |
Construct | Mean | SD | DIS | AA | PM | SI |
---|---|---|---|---|---|---|
Dissatisfaction | 3.527 | 0.702 | 1 | |||
Alternative attractiveness | 3.371 | 0.730 | 0.491 | 1 | ||
Perceived fear | 3.464 | 0.776 | 0.560 | 0.518 | 1 | |
Switching intention | 3.595 | 0.702 | 0.590 | 0.448 | 0.753 | 1 |
Paths | Estimates | t-Values | Results |
---|---|---|---|
H1: Dissatisfaction → Switching intention | 0.302 | 5.476 *** | Supported |
H2: Alternative attractiveness → Switching intention | −0.008 | −0.164 | Not supported |
H3: Perceived fear → Switching intention | 0.631 | 8.532 *** | Supported |
H4: Dissatisfaction → Perceived fear | 0.394 | 6.44 *** | Supported |
H5: Alternative attractiveness → Perceived fear | 0.320 | 5.714 *** | Supported |
Paths | DIS | AA | PM | SI |
---|---|---|---|---|
Standardized total effects | ||||
Perceived fear | 0.395 | 0.319 | - | - |
Switching intention | 0.548 | 0.200 | 0.628 | - |
Standardized direct effects | ||||
Perceived fear | 0.395 | 0.319 | - | - |
Switching intention | 0.300 | 0.628 | - | |
Standardized indirect effects | ||||
Perceived fear | - | - | - | - |
Switching intention | 0.248 | 0.200 | - | - |
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Yu, S.-Y.; Chen, D.C. Consumers’ Switching from Cash to Mobile Payment under the Fear of COVID-19 in Taiwan. Sustainability 2022, 14, 8489. https://doi.org/10.3390/su14148489
Yu S-Y, Chen DC. Consumers’ Switching from Cash to Mobile Payment under the Fear of COVID-19 in Taiwan. Sustainability. 2022; 14(14):8489. https://doi.org/10.3390/su14148489
Chicago/Turabian StyleYu, Shih-Yi, and Der Chao Chen. 2022. "Consumers’ Switching from Cash to Mobile Payment under the Fear of COVID-19 in Taiwan" Sustainability 14, no. 14: 8489. https://doi.org/10.3390/su14148489
APA StyleYu, 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