The Influence of Perceived Ease of Use and Perceived Usefulness on the Behavioral Intention of QRIS Users in Gorontalo

Fadel Muhammad; Melan Angriani Asnawi; Idris Yanto Niode

Authors

Fadel Muhammad Gorontalo State University Author
Melan Angriani Asnawi Gorontalo State University Author
Idris Yanto Niode Gorontalo State University Author

Keywords:

Perceived Ease of Use, Perceived Usefulness, Behavioral Intention, QRIS

Abstract

Interoperability is a vital strategic component in the digital payment ecosystem in Indonesia, and QRIS is a concrete manifestation of this strategic project. This study aims to analyze the influence of Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) on the Behavioral Intention of QRIS users. This study uses a quantitative approach with a survey method. The research sample consisted of 96 respondents who are QRIS users in Gorontalo City, determined through a purposive sampling technique. Data were collected using a Likert-based questionnaire, then analyzed using multiple linear regression using SPSS. The results show that PEOU and PU have a significant effect on the Behavioral Intention of QRIS users. In addition, both technology acceptance variables simultaneously drive Behavioral Intention with a significance value of 82.5%, indicating the significant influence of this technology acceptance among the Gorontalo community

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References

1. Adams, D. A., Nelson, R. R., & Todd, P. A. (1992). Perceived Usefulness, Ease of Use, and Usage of Information Technology: A Replication. MIS Quarterly, 16(2), 227. https://doi.org/10.2307/249577

2. Ahmad, S., & Abdul Latif, A. A. (2022). Controvesy Surrounding The Benefits of The E-Wallet Application Software. International Journal of Academic Research in Business and Social Sciences, 12(9), Pages 1893-1904. https://doi.org/10.6007/IJARBSS/v12-i9/14736

3. Ajzen, I. (2020). The theory of planned behavior: Frequently asked questions. Human Behavior and Emerging Technologies, 2(4), 314–324. https://doi.org/10.1002/hbe2.195

4. Alkhwaldi, A. F., & Abdulmuhsin, A. A. (2022). Understanding User Acceptance of IoT Based Healthcare in Jordan: Integration of the TTF and TAM. In S. G. Yaseen (Ed.), Digital Economy, Business Analytics, and Big Data Analytics Applications (Vol. 1010, pp. 191–213). Springer International Publishing. https://doi.org/10.1007/978-3-031-05258-3_17

5. Bhattacherjee & Premkumar. (2004). Understanding Changes in Belief and Attitude toward Information Technology Usage: A Theoretical Model and Longitudinal Test. MIS Quarterly, 28(2), 229. https://doi.org/10.2307/25148634

6. Carlos Martins Rodrigues Pinho, J., & Soares, A. M. (2011). Examining the technology acceptance model in the adoption of social networks. Journal of Research in Interactive Marketing, 5(2/3), 116–129. https://doi.org/10.1108/17505931111187767

7. Davis, F. D. (1993). User acceptance of information technology: System characteristics, user perceptions and behavioral impacts. International Journal of Man-Machine Studies, 38(3), 475–487. https://doi.org/10.1006/imms.1993.1022

8. Gangwar, H., Date, H., & Raoot, A. D. (2014). Review on IT adoption: Insights from recent technologies. Journal of Enterprise Information Management, 27(4), 488–502. https://doi.org/10.1108/JEIM-08-2012-0047

9. Gefen, D., Straub, D., & Georgia State University. (2000). The Relative Importance of Perceived Ease of Use in IS Adoption: A Study of E-Commerce Adoption. Journal of the Association for Information Systems, 1(1), 1–30. https://doi.org/10.17705/1jais.00008

10. Gupta, P., Zhang, F., Chauhan, S., Goyal, S., Bhardwaj, A. K., & Gajpal, Y. (2024). Understanding small and medium enterprises’ behavioral intention to adopt social commerce: A perceived value perspective. Journal of Enterprise Information Management, 37(3), 959–992. https://doi.org/10.1108/JEIM-09-2022-0356

11. HM, A. H., Pakaja, F., & Zainurrofiq, A. (2023). Analysis of Factors Influencing FinTech Adoption by Students of Information Systems. American Journal of Multidisciplinary Research and Development, 5(9), 06–21.

12. Isiaku, L., & Adalier, A. (2024). Determinants of business intelligence systems adoption in Nigerian banks: The role of perceived usefulness and ease of use. Information Development, 02666669241307024. https://doi.org/10.1177/02666669241307024

13. Jameaba, M. (2020). Digitization, FinTech Disruption, and Financial Stability: The Case of the Indonesian Banking Sector. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3529924

14. Karahanna, E., & Straub, D. W. (1999). The psychological origins of perceived usefulness and ease-of-use. Information & Management, 35(4), 237–250. https://doi.org/10.1016/S0378-7206(98)00096-2

15. Kredina, A. (2021). Transformation of Fintech: Impact of POS and ATM on Non-Cash Payments. Eurasian Journal of Economic and Business Studies, 2(60). https://doi.org/10.47703/ejebs.v2i60.51

16. Kumar, R. L., Smith, M. A., & Bannerjee, S. (2004). User interface features influencing overall ease of use and personalization. Information & Management, 41(3), 289–302. https://doi.org/10.1016/S0378-7206(03)00075-2

17. Lederer, A. L., Maupin, D. J., Sena, M. P., & Zhuang, Y. (2000). The technology acceptance model and the World Wide Web. Decision Support Systems, 29(3), 269–282. https://doi.org/10.1016/S0167-9236(00)00076-2

18. Moghavvemi, S., Mei, T. X., Phoong, S. W., & Phoong, S. Y. (2021). Drivers and barriers of mobile payment adoption: Malaysian merchants’ perspective. Journal of Retailing and Consumer Services, 59, 102364. https://doi.org/10.1016/j.jretconser.2020.102364

19. Mugo, D., Njagi, K., Chemwei, B., & Motanya, J. (2017). The Technology Acceptance Model (TAM) and its Application to the Utilization of Mobile Learning Technologies. British Journal of Mathematics & Computer Science, 20(4), 1–8. https://doi.org/10.9734/BJMCS/2017/29015

20. Nabila, A. P., Raharso, S., & Tiorida, E. (2025). The Influence of Trust and Transaction Security on Interest in Using The QRIS Payment System: Study: QRIS Users in Bandung City. Airlangga Journal of Innovation Management, 6(2), 248–260. https://doi.org/10.20473/ajim.v6i2.72597

21. Nada, D. Q., Suryaningsum, S., & Negara, H. K. S. (2021). Digitalization of the Quick Response Indonesian Standard (QRIS) Payment System for MSME Development. Journal of International Conference Proceedings, 4(3). https://doi.org/10.32535/jicp.v4i3.1358

22. Nandru, P., S.A., S. K., & Chendragiri, M. (2024). Adoption intention of mobile QR code payment system among marginalized street vendors: An empirical investigation from an emerging economy. Journal of Science and Technology Policy Management, 15(6), 1709–1733. https://doi.org/10.1108/JSTPM-03-2023-0035

23. Ngan, N. T., & Khoi, B. H. (2020). Behavioral Intention to Accept and Use Banking Service. The Journal of Asian Finance, Economics and Business, 7(11), 393–400. https://doi.org/10.13106/JAFEB.2020.VOL7.NO11.393

24. Nisa, S., & Adinugraha, H. H. (2024). The Effectiveness of the Implementation of the Quick Response Code Indonesia Standard (QRIS) Payment System for MSMEs. Journal of Economics, Management, Accounting and Computer Applications, 1(1), 34–39. https://doi.org/10.69693/jemaca.v1i1.5

25. Omar, N., Munir, Z. A., Kaizan, F. Q., Noranee, S., & Malik, S. A. (2019). The Impact of Employees Motivation, Perceived Usefulness and Perceived Ease of Use on Employee Performance among Selected Public Sector Employees. International Journal of Academic Research in Business and Social Sciences, 9(6), Pages 1128-1139. https://doi.org/10.6007/IJARBSS/v9-i6/6074

26. Pakaja, F. (2025). Overconfidence Bias Measures and Herd Behavior on Information System Security. Journal of Computer Information Systems, 1–17. https://doi.org/10.1080/08874417.2025.2507707

27. Pakaja, F., & Wafa, M. (2023). Social family, parental involvement and intentions: Predicting the technology acceptance and interest students learning online. Interactive Learning Environments, 31(8), 5331–5346. https://doi.org/10.1080/10494820.2021.2005105

28. Rafferty, N. E., & Fajar, A. N. (2022). Integrated QR Payment System (QRIS): Cashless Payment Solution in Developing Country from Merchant Perspective. Asia Pacific Journal of Information Systems, 32(3), 630–655. https://doi.org/10.14329/apjis.2022.32.3.630

29. Rahmalia, W., Majid, M. S. Abd., Halim, H., Agustina, M., Sabila, S., & Hafidzah, F. M. (2024). The Effects of Perceived Benefits and Ease of Use on the Reuse Intention of Islamic Banking QRIS through Satisfaction Among Culinary MSMEs: Does Fintech Literacy play a role? 2024 International Conference on Sustainable Islamic Business and Finance (SIBF), 268–273. https://doi.org/10.1109/SIBF63788.2024.10883866

30. Ramayanti, R. (2024). Understanding User Perceptions of QRIS in Indonesia: Exploring the Impact of Perceived Usefulness, Ease of Use, and Demographic Factors. International Journal of Finance & Banking Studies (2147-4486), 13(4), 90–99. https://doi.org/10.20525/ijfbs.v13i4.3887

31. Rani Das, K. (2016). A Brief Review of Tests for Normality. American Journal of Theoretical and Applied Statistics, 5(1), 5. https://doi.org/10.11648/j.ajtas.20160501.12

32. Setiawan, A. D., Rahman, I., Hidayatno, A., & Zelin, A. D. E. (2019). Modeling Adoption of Electronic Money in Indonesia: Conceptual Approach for Less Cash Society Development. Proceedings of the 2019 5th International Conference on Industrial and Business Engineering, 370–373. https://doi.org/10.1145/3364335.3364398

33. Shrestha, N. (2020). Detecting Multicollinearity in Regression Analysis. American Journal of Applied Mathematics and Statistics, 8(2), 39–42. https://doi.org/10.12691/ajams-8-2-1

34. Srivastava, S., & Singh, N. (2023). An integrated model predicting customers’ continuance behavioral intention and recommendations of users: A study on mobile payment in emerging markets. Journal of Financial Services Marketing, 28(2), 236–254. https://doi.org/10.1057/s41264-022-00147-y

35. Sun, J. (2012). Why different people prefer different systems for different tasks: An activity perspective on technology adoption in a dynamic user environment. Journal of the American Society for Information Science and Technology, 63(1), 48–63. https://doi.org/10.1002/asi.21670

36. Susilo, J. (2024). Cooperation in Digital Innovation Under the Master Plan on Asean Community (MPAC) in Muslim Asean Countries. Airlangga Journal of Innovation Management, 5(1), 107–125. https://doi.org/10.20473/ajim.v5i1.54332

37. Taherdoost, H. (2018). A review of technology acceptance and adoption models and theories. Procedia Manufacturing, 22, 960–967. https://doi.org/10.1016/j.promfg.2018.03.137

38. Tanha, M., Dolon, Md. M. A., Al-Amin, A.-A., Nadi, N. A., Islam, Md. M., & Ali, Md. H. (2024). Factors influencing the development of the cashless payment system: Comprehending the function of the involved participants. Annals of Management and Organization Research, 5(4), 255–270. https://doi.org/10.35912/amor.v5i4.1959

39. Thalib, R. R., Machmud, R., & Isa, R. A. (2025). The Effect of Price, Promotion and E-Service Through The McDonald’s Application On Consumer Buying Interest. International Journal of Multidisciplinary Applied and Science Research, 01(03), 115–124.

40. Uzun, E., Yıldırım, A., & Özden, M. (2013). tudents’ Perceptions About Learning Environment of a Distance Course Based on Technology Acceptance Model: A Descriptive Study. Mersin Üniversitesi Eğitim Fakültesi Dergisi, 9(1), 201–211.

41. Xia, M., Zhang, Y., & Zhang, C. (2018). A TAM-based approach to explore the effect of online experience on destination image: A smartphone user’s perspective. Journal of Destination Marketing & Management, 8, 259–270. https://doi.org/10.1016/j.jdmm.2017.05.002

42. Zulfa, D., & Syahnur, S. (2025). The dynamic effect of cash and non-cash payment instruments on money velocity in Indonesia. Economic Journal of Emerging Markets, 57–69. https://doi.org/10.20885/ejem.vol17.iss1.art5