Exploring the Role of Behavioral Intention and Trust in Technology Adoption: A Meta-UTAUT Model Approach
DOI:
https://doi.org/10.13052/jrss0974-8024.1826Keywords:
Behavioural intention, usage behaviour, trust, META-UTAUT, technology adoption, facilitating conditions, effort expectancy, social influenceAbstract
This paper discusses the adoption of the meta UTAUT model incorporating trust. The model consists of PE, EE, SI, FC, and trust as independent variables, attitude as a mediating variable, while BI and UB are the dependent variables. A sample size of 279 users drawn from urban and rural settings was conducted. The suggested model was experimented through structural equation modelling (SEM). The outcome of this study indicates that BI is positively affecting the UB and becoming the strongest predictor of it. Importantly, trust is also positively influencing BI and UB. FC and EE play vital roles in building the attitude of the user. EE, PE, and BI-even SI-found not so strong in predicting BI. BI as the strong predictor came in the finding so it can contribute in predicting the behaviour of the user. The study focused more on the role of trust and user experience.
Downloads
References
Al-Saedi, K., and Al-Emran, M. (2021). A Systematic Review of Mobile Payment Studies from the Lens of the UTAUT Model. Studies in Systems, Decision and Control, 79–106. https://doi.org/10.1007/978-3-030-64987-6_6.
Arfi, W. B., Nasr, I. B., Kondrateva, G., and Hikkerova, L. (2021). The role of trust in intention to use the IoT in eHealth: Application of the modified UTAUT in a consumer context. Technological Forecasting and Social Change, 167, 120688. https://doi.org/10.1016/j.techfore.2021.120688.
Alalwan, A. A., Dwivedi, Y. K., and Williams, M. D. (2016). Customers’ intention and adoption of telebanking in Jordan. Information Systems Management, 33(2), 154–178. https://doi.org/10.1080/10580530.2016.1155950.
Arfi, W. B., Nasr, I. B., Kondrateva, G., and Hikkerova, L. (2021). The role of trust in intention to use the IoT in eHealth: Application of the modified UTAUT in a consumer context. *Technological Forecasting and Social Change, 167, 120688. https://doi.org/10.1016/j.techfore.2021.120688.
Alghazi, S. S., Kamsin, A., Almaiah, M. A., Wong, S. Y., and Shuib, L. (2021b). For Sustainable Application of Mobile Learning: An Extended UTAUT model to examine the effect of technical factors on the usage of mobile devices as a learning tool. Sustainability, 13(4), 1856. https://doi.org/10.3390/su13041856.
Arfi, W. B., Nasr, I. B., Kondrateva, G., and Hikkerova, L. (2021). The role of trust in intention to use the IoT in eHealth: Application of the modified UTAUT in a consumer context. Technological Forecasting and Social Change, 167, 120688. https://doi.org/10.1016/j.techfore.2021.120688.
Aziz, S. A., and Idris, K. M. (2016). The impact of incentive alignment in behavioral acceptance. International Journal of Economics and Financial Issues, 6(4), 78–84. https://econjournals.com/index.php/ijefi/article/view/2693/pdf.
Carter, L., Christian Shaupp, L., Hobbs, J., and Campbell, R. (2011). The role of security and trust in the adoption of online tax filing. Transforming Government: People, Process and Policy, 5(4), 303–318. https://doi.org/10.1108/17506161111173568.
Chong, A. Y. L. (2013). Understanding mobile commerce continuance intentions: An empirical analysis of Chinese consumers. Journal of Computer Information Systems, 53(4), 22–30. https://doi.org/10.1080/08874417.2013.11645647.
Demombynes, G., and Thegeya, A. (2012). Kenya’s mobile revolution and the promise of mobile savings. World Bank Policy Research Working Paper No. 5988. The World Bank, Washington, DC.
Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., and Williams, M. D. (2017b). Re-examining the Unified Theory of Acceptance and Use of Technology (UTAUT): towards a revised theoretical model. Information Systems Frontiers, 21(3), 719–734. https://doi.org/10.1007/s10796-017-9774-y.
Fang, I. C., and Fang, S. C. (2016). Factors affecting consumer stickiness to continue using mobile applications. International Journal of Mobile Communications, 14(5), 431. https://doi.org/10.1504/ijmc.2016.078720.
Hair, J. F., Hult, G. T. M., Ringle, C. M., and Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM) (2nd ed.). Sage Publications.
Hair, J. F., Risher, J. J., Sarstedt, M., and Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/ebr-11-2018-0203.
Handayani, W. P. P. (2023b). The UTAUT implementation model in defining the behavioral intention of mobile banking users. Jurnal Manajemen Bisnis, 14(2), 361–377. https://doi.org/10.18196/mb.v14i2.18649.
Henseler, J., Ringle, C. M., and Sarstedt, M. (2014). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8.
He, Q., Ma, W., Vatsa, P., and Zheng, H. (2024). Impact of mobile payment adoption on household expenditures and subjective well-being. Review of Development Economics, 28(2), 932–950. https://doi.org/10.1111/rode.13054.
Hermanto, A. H., Windasari, N. A., and Purwanegara, M. S. (2022). Taxpayers’ adoption of online tax return reporting: Extended meta-UTAUT model perspective. Cogent Business & Management, 9(1), 2110724. https://doi.org/10.1080/23311975.2022.2110724.
Hassaan, M., and Yaseen, A. (2024). Factors influencing customers’ adoption of mobile payment in Pakistan: application of the extended meta-UTAUT model. Journal of Science & Technology Policy Management. https://doi.org/10.1108/jstpm-01-2024-0029.
Jadil, Y., Rana, N. P., and Dwivedi, Y. K. (2021). A meta-analysis of the UTAUT model in the mobile banking literature: The moderating role of sample size and culture. Journal of Business Research, 132, 354–372. https://doi.org/10.1016/j.jbusres.2021.04.052.
Kang, S. (2014). Factors influencing intention of mobile application use. International Journal of Mobile Communications, 12(4), 360. https://doi.org/10.1504/ijmc.2014.063653.
Lai, I. K. W., and Shi, G. (2015). The impact of privacy concerns on the intention for continued use of an integrated mobile instant messaging and social network platform. International Journal of Mobile Communications, 13(6), 641. https://doi.org/10.1504/ijmc.2015.072086.
Loh, X. K., Lee, V. H., Loh, X. M., Tan, G. W. H., Ooi, K. B., and Dwivedi, Y. K. (2021). The dark side of mobile learning via social media: How bad can it get? Information Systems Frontiers, 1–18. https://doi.org/10.1007/s10796-021-10202-z.
Lady, Lady, Lie, K., Hesniati, H., and Candy, C. (2024). From Innovation and Compatibility to The Intention to Adopt Mobile Payment with User Expectations as The Mediating Factor. Almana, 8(3), 470–484. https://doi.org/10.36555/almana.v8i3.2667.
Marinković, V., Đorđević, A., and Kalinić, Z. (2020). The moderating effects of gender on customer satisfaction and continuance intention in mobile commerce: A UTAUT-based perspective. Technology Analysis & Strategic Management, 32(3), 306–318. https://doi.org/10.1080/09537325.2019.1655537.
Mas’ud, A., and Umar, M. A. (2019). Structural effects of trust in e-filing software on e-filing acceptance in services sector. International Journal of Enterprise Information Systems, 15(2), 76–94. https://doi.org/10.4018/IJEIS.2019040105.
Medeiros, M., Ozturk, A., Hancer, M., Weinland, J., and Okumus, B. (2022). Understanding travel tracking mobile application usage: An integration of self determination theory and UTAUT2. Tourism Management Perspectives, 42, 100949. https://doi.org/10.1016/j.tmp.2022.100949.
Mukherjee, M., and Roy, S. (2017). E-Commerce and online payment in the modern era. International Journal of Advanced Research in Computer Science and Software Engineering, 7(5), 1–5. https://doi.org/10.23956/ijarcsse/sv7i5/0250.
Nandru, R., Kumar, B., and Chendragiri, R. (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, ahead-of-print. https://doi.org/10.1108/jstpm-11-2024-246.
Oliveira, T., Thomas, M., Baptista, G., and 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.
Pérez-Morote, R., Pontones-Rosa, C., and Núñez-Chicharro, M. (2020). The effects of e-government evaluation, trust and the digital divide in the levels of e-government use in European countries. Technological Forecasting and Social Change, 154, 119973. https://doi.org/10.1016/j.techfore.2020.119973.
Patil, P., Tamilmani, K., Rana, N. P., and Raghavan, V. (2020). Understanding consumer adoption of mobile payment in India: Extending meta-UTAUT model with personal innovativeness, anxiety, trust, and grievance redressal. International Journal of Information Management, 54, 102144. https://doi.org/10.1016/j.ijinfomgt.2020.102144.
R, A., Kuanr, A., and Kr, S. (2021b). Developing banking intelligence in emerging markets: Systematic review and agenda. International Journal of Information Management Data Insights, 1(2), 100026. https://doi.org/10.1016/j.jjimei.2021.100026.
Rahman, T., Noh, M., Kim, Y. S., and Lee, C. K. (2021b). Effect of word of mouth on m-payment service adoption: a developing country case study. Information Development, 38(2), 268–285. https://doi.org/10.1177/0266666921999702.
Rastogi, S., Panse, C., Sharma, A., and Bhimavarapu, V. M. (2021b). Unified Payment Interface (UPI): a digital innovation and its impact on financial inclusion and economic development. Universal Journal of Accounting and Finance, 9(3), 518–530. https://doi.org/10.13189/ujaf.2021.090326.
Sarkar, S., Chauhan, S., and Khare, A. (2020). A meta-analysis of antecedents and consequences of trust in mobile commerce. International Journal of Information Management, 50, 286–301. https://doi.org/10.1016/j.ijinfomgt.2019.08.008.
Sarstedt, M., Ringle, C. M., Cheah, J. H., Ting, H., Moisescu, O. I., and Radomir, L. (2020). Structural model robustness checks in PLS-SEM. Tourism Economics, 26(4), 531–554. https://doi.org/10.1177/1354816618823921.
Singh, S., and Srivastava, R. (2018b). Predicting the intention to use mobile banking in India. International Journal of Bank Marketing, 36(2), 357–378. https://doi.org/10.1108/ijbm-12-2016-0186.
Slade, E. L., Williams, M. D., Dwivedi, Y. K., and Piercy, N. C. (2015). Exploring consumer adoption of proximity mobile payments. Journal of Strategic Marketing, 23(3), 209–232. https://doi.org/10.1080/0965254X.2013.840347.
Song, K., Wu, P., and Zou, S. (2023). The adoption and use of mobile payment: Determinants and relationship with bank access. China Economic Review, 77, Article 101893. https://doi.org/10.1016/j.chieco.2022.101893.
Thakur, R., and Srivastava, M. (2014b). Adoption readiness, personal innovativeness, perceived risk and usage intention across customer groups for mobile payment services in India. Internet Research, 24(3), 369–392. https://doi.org/10.1108/intr-12-2012-0244.
Thusi, P., and Maduku, D. K. (2020b). South African millennials’ acceptance and use of retail mobile banking apps: An integrated perspective. Computers in Human Behavior, 111, 106405. https://doi.org/10.1016/j.chb.2020.106405.
Venkatesh, N., Morris, N., Davis, N., and Davis, N. (2003). User acceptance of information Technology: toward a unified view. MIS Quarterly, 27(3), 425. https://doi.org/10.2307/30036540.
Vatsa, P., Ma, W., and Zheng, H. (2023). Mobile payment adoption in China: Do demographic and socioeconomic factors matter? Managerial and Decision Economics, 45(2), 201–211. https://doi.org/10.1002/mde.4086.
Wang, X., Lin, X., and Spencer, M. K. (2018). Exploring the effects of extrinsic motivation on consumer behaviors in social commerce: Revealing consumers’ perceptions of social commerce benefits. International Journal of Information Management, 45, 163–175. https://doi.org/10.1016/j.ijinfomgt.2018.11.010.
Zhou, T. (2012). An empirical examination of continuance intention of mobile payment services. Decision Support Systems, 54(2), 1085–1091. https://doi.org/10.1016/j.dss.2012.10.034.
