Predicting M-Payment Adoption Intention in Indonesia: Integrating Technology Acceptance Model and Psychological Factors
DOI:
https://doi.org/10.23917/indigenous.v8i3.2586Keywords:
Intention, m-payment adoption, path analysis, technology acceptance model, trustAbstract
The government is currently promoting the growth of m-payment usage in Indonesia. Therefore, research is needed to identify the factors influencing the adoption intention of m-payment. One theory frequently employed to elucidate the usage intention of m-payment is the Technology Acceptance Model (TAM). According to this theory, intention arises from perceived usefulness and perceived ease of use. This study integrates TAM with psychological variables, namely, trust and subjective norms. This research aims to examine the factors influencing the adoption intention of m-payment. Before conducting the model test, this research begins with adapting and validating all measurement tools in the Indonesian language. Participants in this study are individuals aged 17 and above who own smartphones. The entire data collection is done online. The research instrument is validated with confirmatory factor analysis (CFA) on perceived usefulness, perceived ease of use, trust, subjective norm, and the adoption intention of m-payment (N=209). The model test is conducted through path analysis (N=210). The validation process confirms the theoretical model of the five instruments in the Indonesian version. The CFA results indicate that all five research instruments meet the cut-off criteria for fit indices RMSEA, CFI, TLI, and SRMR. The path analysis results reveal that perceived usefulness, perceived ease of use, and subjective norm influence the adoption intention of m-payment. In contrast, trust does not affect the adoption intention of m-payment. This research contributes both theoretically and practically, particularly regarding the factors influencing m-payment adoption.
References
AERA, APA, & NCME (2014). Standards for educational and psychological testing. American Educational Association.
Abrahão, R. de S., Moriguchi, S. N., & Andrade, D. F. (2016). Intention of adoption of mobile payment: An analysis in the light of the unified theory of acceptance and use of technology (UTAUT). RAI Revista de Administração e Inovação, 13(3), 221–230. https://doi.org/10.1016/j.rai.2016.06.003
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
Bank Indonesia. (2019). Bank Indonesia: Menavigasi sistem pembayaran nasional di era digital Bank Indonesia. In Blueprint Sistem Pembayaran Indonesia 2025. retrieved from: https://www.bi.go.id/id/publikasi/kajian/Documents/Blueprint-Sistem-Pembayaran-Indonesia-2025.pdf
Beaton, D. E., Bombardier, C., Guillemin, F., & Ferraz, M. B. (2000). Guidelines for the process of cross-cultural adaptation of self-report measures. Spine, 25(24), 3186–3191. https://doi.org/10.1097/00007632-200012150-00014
Behera, R. K., Bala, P. K., & Rana, N. P. (2022). Assessing factors influencing consumers’ non-adoption intention: exploring the dark sides of mobile payment. Information Technology and People, 36(7), 2941–2976. https://doi.org/10.1108/ITP-03-2022-0223
Busu, S., Karim, N. A., & Haron, H. (2018). Factors of adoption intention for near field communication mobile payment. Indonesian Journal of Electrical Engineering and Computer Science, 11(1), 98–104. https://doi.org/10.11591/ijeecs.v11.i1.pp98-104
Cain, M. K., Zhang, Z., & Yuan, K. H. (2017). Univariate and multivariate skewness and kurtosis for measuring nonnormality: Prevalence, influence and estimation. Behavior Research Methods, 49(5), 1716–1735. https://doi.org/10.3758/s13428-016-0814-1
Czerwiński, S. K., & Atroszko, P. A. (2023). A solution for factorial validity testing of three-item scales: An example of tau-equivalent strict measurement invariance of three-item loneliness scale. Current Psychology, 42(2), 1652–1664. https://doi.org/10.1007/s12144-021-01554-5
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
de Mooij, M., & Hofstede, G. (2011). Cross-cultural consumer behavior: A review of research findings. Journal of International Consumer Marketing, 23(3), 181–192. https://doi.org/10.1080/08961530.2011.578057
Field, A., Miles, J., Field, Z. (2012). Discovering statistics using R. Choice Reviews Online, 50(4). https://doi.org/10.5860/choice.50-2114
Handarkho, Y. D., Harjoseputro, Y., Samodra, J. E., & Irianto, A. B. P. (2021). Understanding proximity mobile payment continuance usage in Indonesia from a habit perspective. Journal of Asia Business Studies, 15(3), 420–440. https://doi.org/10.1108/JABS-02-2020-0046
APJII (2023). Survei APJII pengguna internet di Indonesia. Asosiasi Penyelenggara Jasa Internet Indonesia. APJII. Retrieved from: https://apjii.or.id/berita/d/survei-apjii-pengguna-internet-di-indonesia-tembus-215-juta-orang
Javier, F. (2022). E-wallet jadi alat pembayaran digital terpopuler di 2021. Tempo ID. https://data.tempo.co/data/1316/e-wallet-jadi-alat-pembayaran-digital-terpopuler-di-2021
Kamal, S. A., Shafiq, M., & Kakria, P. (2020). Investigating acceptance of telemedicine services through an extended technology acceptance model (TAM). Technology in Society, 60. https://doi.org/10.1016/j.techsoc.2019.101212
Khan, S., Khan, S. U., Khan, I. U., Khan, S. Z., & Khan, R. U. (2023). Understanding consumer adoption of mobile payment in Pakistan. Journal of Science and Technology Policy Management. https://doi.org/10.1108/JSTPM-07-2021-0110
Kim, C., Mirusmonov, M., & Lee, I. (2010). An empirical examination of factors influencing the intention to use mobile payment. Computers in Human Behavior, 26(3), 310–322. https://doi.org/10.1016/j.chb.2009.10.013
Kumar, P. (2023). A meta-analysis of trust in mobile banking: The moderating role of cultural dimensions. International Journal of Bank Marketing, 60. https://doi.org/10.1108/IJBM-02-2022-0075
Lew, S., Tan, G. W., Loh, X., Hew, J., & Ooi, K. (2020). The disruptive mobile wallet in the hospitality industry: An extended mobile technology acceptance model. Technology in Society, 63, 1–10. https://doi.org/10.1016/j.techsoc.2020.101430
Liebana-Cabanillas, F. J., Sanchez-Fernandez, J., & Munoz-Leiva, F. (2014). Role of gender on acceptance of mobile payment. Industrial Management and Data Systems, 114(2), 220–240. https://doi.org/10.1108/IMDS-03-2013-0137
Lisana, L. (2022). Understanding the key drivers in using mobile payment among generation Z. Journal of Science and Technology, 15(3), 420–440. https://doi.org/10.1108/JSTPM-08-2021-0118
Lu, J., Yao, J. E., & Yu, C. S. (2005). Personal innovativeness, social influences and adoption of wireless internet services via mobile technology. The Journal of Strategic Information Systems, 14(3), 245–268. https://doi.org/10.1016/j.jsis.2005.07.003
Marriott, H., & Williams, M. (2016). Developing a theoretical model to examine consumer acceptance behavior of mobile shopping. Proceedings of the 15th IFIP WG 6.11, Conference on e-Business, e-Services and e-Society, 261–266. https://doi.org/10.1007/978-3-319-45234-0
Matemba, E. D., & Li, G. (2018). Consumers’ willingness to adopt and use WeChat wallet: An empirical study in South Africa. Technology in Society, 53, 53–68. https://doi.org/10.1016/j.techsoc.2017.12.001
Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995). An integrative model of organizational trust. The Academy of Management Review, 20(3), 709–734. https://dMuthén, L. K., & Muthén, B. O. (2002). How to use a monte carlo study to decide on sample size and determine power. Structural Equation Modeling: A Multidisciplinary Journal, 9(4), 599–620. https://doi.org/10.1207/S15328007SEM0904_8
Mufiedah, L. A. A., Karyani, U., & Hertinjung, W. S. (2023). Determinants of health protocols compliance on office workers. Indigenous: Jurnal Ilmiah Psikologi, 8(1), 9–21. https://doi.org/10.23917/indigenous.v7i3.20346
Muthén, L. K., & Muthén, B. O. (2002). How to use a monte carlo study to decide on sample size and determine power. Structural Equation Modeling: A Multidisciplinary Journal, 9(4), 599–620. https://doi.org/10.1207/S15328007SEM0904_8
Muthén, L. K., & Muthén, B. O. (2017). Mplus user’s guide (8th ed.). Muthén & Muthén.
Nguyen, T. N., Cao, T. K., Dang, P. L., & Nguyen, H. A. (2016). Predicting consumer intention to use mobile payment services: Empirical evidence from Vietnam. International Journal of Marketing Studies, 8(1), 117–124. https://doi.org/10.5539/ijms.v8n1p117
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
Palvia, P. (2009). The role of trust in e-commerce relational exchange: A unified model. Information & Management, 46(4), 213–220. https://doi.org/https://doi.org/10.1016/j.im.2009.02.003
Phonthanukitithaworn, C., Sellitto, C., & Fong, M. W. L. (2016). A comparative study of surrent and potential users of mobile payment services. SAGE Open, 6(4), 1–14. https://doi.org/10.1177/2158244016675397
Rahardja, U., Sigalingging, C. T., Putra, P. O. H., Nizar Hidayanto, A., & Phusavat, K. (2023). The impact of mobile payment application design and performance attributes on consumer emotions and continuance intention. SAGE Open, 13(1), 1–18. https://doi.org/https://doi.org/10.1177/21582440231151919
Rahman, M., Ismail, I., & Bahri, S. (2020). Analysing consumer adoption of cashless payment in Malaysia. Digital Business, 1(1), 1–11. https://doi.org/https://doi.org/10.1016/j.digbus.2021.100004
Rai, N. G. M., Ratu, A., & Savitri, E. D. (2021). Factors mediating work-family balance to job satisfaction in higher education during pandemic. Indigenous: Jurnal Ilmiah Psikologi, 6(3), 60–72. https://doi.org/https://doi.org/10.23917/indigenous.v6i3.15505
Shamon, H., & Berning, C. (2020). Attention check items and instructions in online surveys with incentivized and nonincentivizedquality samples: Boon or bane for data quality?. Survey Research Methods, 14(1), 55–77. https://doi.org/10.18148/srm/2020.v14i1.7374
Shankar, A., & Datta, B. (2018). Factors affecting mobile payment adoption intention: An Indian perspective. Global Business Review, 19(3), S72–S89. https://doi.org/10.1177/0972150918757870
Shavitt, S., & Barnes, A. J. (2020). Culture and the consumer journey. Journal of Retailing, 96(1), 40–54. https://doi.org/10.1016/j.jretai.2019.11.009
Shaw, B., & Kesharwani, A. (2019). Moderating effect of smartphone addiction on mobile wallet payment adoption. Journal of Internet Commerce, 18(3), 291–309. https://doi.org/10.1080/15332861.2019.1620045
Skrondal, A., & Laake, P. (2001). Regression among factor scores. Psychometrika, 66(4), 563–575. https://doi.org/10.1007/BF02296196oi.org/10.2307/258792
Susiloadi, P., & Renanita, T. (2023). Understanding compulsive buying tendencies: The roles of attitude towards money. Indigenous: Jurnal Ilmiah Psikologi, 8(1), 70–81. https://doi.org/10.23917/indigenous.v8i1.20734
Tew, H. T., Tan, G. W. H., Loh, X. M., Lee, V. H., Lim, W. L., & Ooi, K. B. (2021). Tapping the next purchase: Embracing the wave of mobile payment. Journal of Computer Information Systems, 62(3), 1–9. https://doi.org/10.1080/08874417.2020.1858731
Tiwari, P., Tiwari, S. K., & Gupta, A. (2021). Examining the impact of customers’ awareness, risk and trust in m-banking adoption. FIIB Business Review, 10(4), 413–423. https://doi.org/10.1177/23197145211019924
Türker, C., Altay, B. C., & Okumuş, A. (2022). Understanding user acceptance of QR code mobile payment systems in Turkey: An extended TAM. , 184. Technological Forecasting and Social Change, 184. https://doi.org/10.1016/j.techfore.2022.121968
Waechter, L. G. & K. A. (2015). Examining the role of initial trust in user adoption of mobile payment services: an empirical investigation. Information Systems Frontiers, 19(3), 525–548. https://doi.org/10.1007/s10796-015-9611-0
Wang, J., & Wang, X. (2020). Structural equation modeling: Application using Mplus (2nd ed.). Wiley.
Wei, M., Luh, Y., Huang, Y., & Chang, Y. (2021). Young generation’s mobile payment adoption behavior: Analysis based on an extended UTAUT model. Journal Of Theoretical and Applied Electronic Commerce Research, 16(4), 618–637. https://doi.org/10.3390/ jtaer16040037
Yang, Y., Liu, Y., Li, H., & Yu, B. (2015). Understanding perceived risks in mobile payment acceptance. Industrial Management and Data Systems, 115(2), 253–269. https://doi.org/10.1108/IMDS-08-2014-0243
Yi, M. Y., Jackson, J. D., Park, J. S., & Probst, J. C. (2006). Understanding information technology acceptance by individual professionals: Toward an integrative view. Information and Management, 43(3), 350–363. https://doi.org/10.1016/j.im.2005.08.006
Zhang, Q., Khan, S., Cao, M., & Khan, S. U. (2023). Factors determining consumer acceptance of NFC mobile payment: An extended mobile technology acceptance model. Sustainability, 15(4), 1–18. https://doi.org/10.3390/su15043664
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), 1–22. https://doi.org/10.3390/ijerph18031016
Downloads
Submitted
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Priyanto Susiloadi, Theda Renanita, Julaibib
This work is licensed under a Creative Commons Attribution 4.0 International License.
