Factors affecting consumer intention to use QRIS during the Covid-19 pandemic by using C-TAM-TPB

Ignasius Triutomo Pristya Putra, Ignatius Heruwasto

Abstract


The COVID-19 pandemic has changed consumer behavior. Physical distancing policies have made consumers try to do contactless activities, including purchases to meet their daily needs. The development of economic transactions is now leading to the formation of a cashless society culture, one of which is the use of the QRIS payment system in Indonesia. This study aims to analyze the factors that are influenced by Perceived COVID 19 Risk, which affect the intention to use digital payment QRIS during the pandemic in Indonesia. This study integrates the Theory Acceptance Model (TAM) and Theory Planned Behavior (TPB) C-TAM-RPB Model to find out what factors influence the intention to use QRIS. This research is quantitative research, with a purposive sampling approach used to achieve the research objectives. Data were collected from 236 respondents who knew and had used QRIS through online surveys. The analysis of hypothesis testing used PLS-SEM to evaluate the effect of research hypotheses. The expected result is how the Perceived COVID 19 Risk affects the factors of intention to use QRIS. Thus, it can help to see the factors that influence consumer behavior using QRIS and optimize the use of QRIS in business commercial activities during the COVID 19 pandemic.

Full Text:

PDF

References


Akram, U., Junaid, M., Zafar, A. U., Li, Z., & Fan, M. (2021). Online purchase intention in Chinese social commerce platforms: Being emotional or rational?. Journal of Retailing and Consumer Services, 63, 102669.

Aji, et al. (2020). COVID-19 and e-wallet usage intention: A multigroup analysis between Indonesia and Malaysia. Cogent Business & Management, 7, 1804181.

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179-211.

Ajzen, I., dan Fishbein, M., (1975), Understanding Attitudes and Predicting Social Behavior, Prentice-Hall, Englewood Scliffs, NJ.

Bank Indonesia. (2022). Peraturan Anggota Dewan Gubernur Nomor 24/1/PADG/2022 tanggal 25 Februari 2022 tentang Perubahan Kedua atas Peraturan Anggota Dewan Gubernur Nomor 21/18/PADG/2019 tentang Implementasi Standar Nasional Quick Response Code untuk Pembayaran.

Bajaj, A. and Nidumolu, S.R. (1998), “A feedback model to understand information system usage”, Information & Management, Vol. 33, pp. 213-24.

Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84, 191–215.

Belanger, F., Hiller, J. S., & Smith, W. J. (2002). Trustworthiness in electronic commerce: The role of privacy, security, and site attributes. The Journal of Strategic Information Systems, 11, 245–270.

Black, N. J., Lockett, A., Winklhofer, H., & McKechnie, S. (2002). Modelling consumer choice of distribution channels: an illustration from financial services. The International Journal of Bank Marketing, 20(4), 161–173.

Brown, D. (2022, April 2). Can cash carry coronavirus? World Health Organization says use digital payments when possible. USA Today. Retrieved from https://www.usatoday.com/story/money/2020/03/ 06/coronavirus-covid-19-concerns-over- using-cash /4973975002/

Calantone, R. J., David A. Griffith dan Goksel Yalcinkaya, (2006). “An Empirical Examination of a Technology Adoption for the Context of China”, Journal of International Marketing, 14 (4), pp.1-27.

Davis, F.D. (1989) "Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology," MIS Quarterly, 13, pp. 319-339.

Faisal Javier (2021, Nov 24). E-wallet Jadi Alat Pembayaran Digital Terpopuler di 2021 Retrieved from https://data.tempo.co/data/1316/e-wallet-jadi-alat-pembayaran-digital-terpopuler-di-2021

Forsythe, S. M., & Shi, B. (2003). Consumer patronage and risk perceptions in internet shopping. Journal of Business Research, 56(11), 867–875.

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.

Gist, M. E., Schwoerer, C., & Rosen, B. (1989). Effects of alternative training methods on self-efficacy and performance in computer software training. The Journal of Applied Psychology, 74, 884–891.

Hair, J., Hult, T., Ringle, C., & Sarstedt, M. (2014). A Primer on Partial Least Squares Structural Equation Modeling (PLS- SEM). Thousand Oaks, CA: Sage Publications, Inc.

Henseler, J., Ringle, C. M., & Sarstedt, M. 2015. A New Criterion for Assessing Discriminant Validity in Variance-Based Structural Equation. Journal of The Academy of Marketing Science, 43 (1), 115-135.

Jaruwachirathanakul, B., & Fink, D. (2005). Internet banking adoption strategies for development country: the case of Thailand. Internet Research, 15(3), 295–311.

Kalakota, R., & Winston, A. B. (1997). Electronic commerce: A manager's guide. : AddisonWesley.

Kassim, N. M., & Ramayah, T. (2015). Perceived risk factors influence on intention to continue using internet banking among Malaysians. Global Business Review, 16(3), 393–414.

Kassarjian, H. H., & Robertson, T. S. (1991). Perspectives in Consumer Behaviour (4th edition). Prentice Hall, New Jersey. Koenig-Lewis, N., Palmer, A., & Moll, A. (2010). Predicting young consumers’ take up of mobile banking services. International Journal of Bank Marketing, 28(5), 410–432.

Kotler, P., & Keller, K. L. (2012). Marketing management (14th ed.). Prentice Hall.

Lee, S. Y., & Park, J. (2016). A study on the intention of the use of mobile payment services: Application of the technology acceptance model. Korean Management Science Review, 33(2), 65–74.

Malhotra, N. (2010), Marketing Research-An Applied Orientation, International Edition, 6th ed., Pearson, Prentice-Hall, Upper Saddle River, NJ.

Maser, B., & Weiermair, K. (1998). Travel decision-making: From the vantage point of perceived risk and infor- mation preferences. Journal of Travel & Tourism Marketing, 7(4), 107–121.

Oh, S.-H., Paek, H.-J., & Hove, T. (2015). Cognitive and emotional dimensions of perceived risk characteris- tics, genre-specific media effects, and risk percep- tions: The case of H1N1 influenza in South Korea.

Riquelme, H. E., & Rios, R. E. (2010). The moderating effect of gender in the adoption of mobile banking. International Journal of Bank Marketing, 28(5), 328–341.

Shih, Y. Y., & Fang, K. (2004). The use of decomposed theory of planned behaviour to study internet banking in Taiwan. Internet Research., 14(3), 213–223.

Stone, R. N., & Gronhaug, K. (1993). Perceived risk: Further considerations for the marketing discipline. European Journal of Marketing, 27(3), 39–50.

Tan, M., & Teo, T. S. H. (2000). Factors influencing the adoption of internet banking. Journal of the Association for Information Systems, 1(5), 1–42.

Tanadi, T., Samadi, B., & Gharleghi, B. (2015). The impact of perceived risks and perceived benefits to improve an online intention among generation-y in Malaysia. Asian Social Science, 11(26), 226.

Taylor, S and Todd, P.A. 1995, “Understanding Information Technology Usage: A Test of Competing Models,” Information Systems Research. No.6, pp. 144-176.

Riquelme, H. E., & Rios, R. E. (2010). The moderating effect of gender in the adoption of mobile banking. International Journal of Bank Marketing, 28(5), 328–341.

Sani, M. A. M., & Hara, A. B. E. (2007). Deliberative democracy in Malaysia and Indonesia: A comparison. Retrieved April 19, 2020, retrieved from https://www.researchgate.net/

Wadie, Nasri L. (2012). Factors Affecting the adoption of internet banking in Tunisia: An integration theory of acceptance model and theory of planned behavior. Journal of High Technology Management Research, 23, 1-14.




DOI: http://dx.doi.org/10.33021/icfbe.v3i1.3783

Refbacks

  • There are currently no refbacks.


Editorial Office:

Faculty of Business President University 
Jalan Ki Hajar Dewantara Mekarmukti
Cikarang Utara, Bekasi, Jawa Barat

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.