An Integrated Innovation DiffusionTrust-Building Framework for Understanding Mobile Payment Adoption in Indonesia’s Cross-Border Regions

Authors

  • Klaasvakumok J. Kamuri Kupang State Polytechnic Author
  • Andrias U. T. Anabuni Kupang State Polytechnic Author

DOI:

https://doi.org/10.65792/jombinov.v1i01.30

Keywords:

Mobile Payment, Cross-Border Regions, Trust, Continuance Usage Intention, Perceived Risk, Innovation Diffusion Theory, Trust-Building Theory

Abstract

Mobile payment adoption in Indonesia has expanded rapidly; however, its diffusion in cross-border regions remains limited due to infrastructural inadequacies, heightened cross-border transaction risks, and low levels of trust in digital financial platforms. These regions—marked by high population mobility, informal economic activity, and uncertain regulatory oversight—create a unique context in which conventional technology adoption models may not fully capture user behaviour. This study introduces an integrated framework that combines Innovation Diffusion Theory and Trust-Building Theory to investigate how mobility, customization, security, and reputation shape trust and influence mobile payment adoption in Indonesia’s international border areas. The framework further examines the role of trust in mitigating perceived risk and strengthening continuance usage intention, while also assessing gender as a moderating variable. Data were obtained from 225 mobile payment users residing in major border gateways between Indonesia and Malaysia, Timor-Leste, and Papua New Guinea. Using partial least squares structural equation modelling (PLS-SEM), the results indicate that security, customization, and reputation significantly enhance trust, whereas mobility does not exert a meaningful effect within the border context. Trust substantially increases continuance usage intention and reduces perceived risk; however, perceived risk does not significantly influence continuance intention. Gender is also found to have no moderating effect on any of the hypothesized relationships. This study contributes to the mobile payment literature by providing a contextualized understanding of user behaviour in high-risk, infrastructure-constrained environments. It also offers practical implications for policymakers and fintech providers aiming to expand digital financial inclusion and strengthen trust-based payment ecosystems in Indonesia’s cross-border regions.

Author Biographies

  • Klaasvakumok J. Kamuri, Kupang State Polytechnic

    Department of Businees Administration, Kupang State Polytechnic, Indonesia

  • Andrias U. T. Anabuni, Kupang State Polytechnic

    Department of Businees Administration, Kupang State Polytechnic, Indonesia

References

Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation‑confirmation model. MIS Quarterly, 25(3), 351–370. https://doi.org/10.2307/3250921

Cao, X., Yu, L., Liu, Z., Gong, M., & Adeel, L. (2018). Understanding mobile payment users’ continuance intention: A trust transfer perspective. Internet Research, 28(2), 456–476. https://doi.org/10.1108/INTR-11-2016-0359

Chawla, D., & Joshi, H. (2020). The moderating role of gender in mobile payment adoption. Journal of Financial Services Marketing, 25(1–2), 1–16. https://doi.org/10.1108/FS-11-2019-0094

Chin, W. W. (1998). The partial least squares approach to structural equation modeling. In G. A. Marcoulides (Ed.), Modern Methods for Business Research (pp. 295–336). Lawrence Erlbaum Associates. https://doi.org/10.4324/9781410604385-10

Chin, W. W., Marcolin, B., & Newsted, P. (2003). A partial least squares latent variable modeling approach for measuring interaction effects. Information Systems Research, 14(2), 189–217. https://doi.org/10.1287/isre.14.2.189.16018

Chong, A. Y. L., Chan, F. T. S., & Ooi, K. B. (2012). Predicting consumer decisions to adopt mobile commerce: Cross country empirical examination. Decision Support Systems, 53(1), 34–43.

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates. https://doi.org/10.4324/9780203771587

Cyr, D. (2008). Modeling website design across cultures: Relationships to trust, satisfaction, and e-loyalty. Journal of Management Information Systems, 24(4), 47–72.

Dahlberg, T., Guo, J., & Ondrus, J. (2015). A critical review of mobile payment research. Electronic Commerce Research and Applications, 14(5), 265–284. https://doi.org/10.1016/j.elerap.2015.07.006

Donner, J. (2008). Research approaches to mobile use in the developing world: A review of the literature. The Information Society, 24(3), 140–159.

Etikan, I., Musa, S. A., & Alkassim, R. S. (2016). Comparison of convenience sampling and purposive sampling. American Journal of Theoretical and Applied Statistics, 5(1), 1–4.

Featherman, M. S., & Pavlou, P. A. (2003). Predicting e-services adoption: A perceived risk facets perspective. International Journal of Human-Computer Studies, 59(4), 451–474.

Fombrun, C. J. (1996). Reputation: Realizing value from the corporate image. Harvard Business School Press.

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

Gefen, D., Rigdon, E. E., & Straub, D. (2011). Editor’s comments: An update and extension to SEM guidelines for administrative and social science research. MIS Quarterly, 35(2), iii–xiv.

Geisser, S. (1974). A predictive approach to the random effect model. Biometrika, 61(1), 101–107.

Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM). Sage Publications.

Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., & Danks, N. (2021). PLS-SEM: An emerging tool in business research. Springer.

Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8

Huang, E. Y., Lin, S. W., & Fan, Y. C. (2014). MS-QUAL: Mobile service quality measurement. International Journal of Electronic Business Management, 12(4), 254–262.

Hulland, J. (1999). Use of partial least squares in strategic management research: A review. Strategic Management Journal, 20(2), 195–204.

Joshi, A., Kale, S., Chandel, S., & Pal, D. K. (2015). Likert scale: Explored and explained. British Journal of Applied Science & Technology, 7(4), 396–403.

Kalinić, Z., Marinković, V., Djordjevic, A., & Kalinić, L. (2020). Factors influencing mobile banking adoption. Technological Forecasting and Social Change, 142, 531–543.

Kim, C., Mirusmonov, M., & Lee, I. (2010). An empirical examination of intention to use mobile banking. International Journal of Information Management, 30(3), 252–259.

Köster, A., Matt, C., & Hess, T. (2016). Carefully choose your (payment) partner. Electronic Markets, 26(1), 25–42.

Liébana-Cabanillas, F., Singh, N., Kalinic, Z., & Carvajal-Trujillo, E. (2021). Determinants of mobile payment adoption. Technological Forecasting and Social Change, 165, 120567.

Lin, H. F., Wang, Y. S., & Wang, M. (2017). Understanding mobile banking continuance. Information Systems and e-Business Management, 15(1), 1–26.

Lu, Y., Yang, S., Chau, P. Y., & Cao, Y. (2011). Dynamics between trust transfer and intention to use mobile payment. Information & Management, 48(8), 393–403.

McKnight, D. H., Choudhury, V., & Kacmar, C. (2002). Developing and validating trust measures. Information Systems Research, 13(3), 334–359.

Nidumolu, S. R., & Knotts, G. W. (1998). The effects of customization on satisfaction. Information Systems Research, 9(2), 150–174.

Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.

Pavlou, P. A., & Gefen, D. (2004). Building online marketplaces with trust. Information Systems Research, 15(1), 37–59.

Ringle, C. M., Wende, S., & Becker, J. M. (2015). SmartPLS 3. SmartPLS GmbH.

Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.

Shao, Z., Zhang, L., Li, X., & Guo, Y. (2019). Antecedents of trust and continuance intention. Information Technology & People, 32(3), 603–632.

Stone, M. (1974). Cross-validatory choice and assessment. Journal of the Royal Statistical Society, 36(2), 111–147.

Wirtz, B. W., & Göttel, V. (2016). Technology acceptance in services. International Journal of Innovation Management, 20(8), 1–19.

Zhou, T. (2013). Continuance intention of mobile payment services. Decision Support Systems, 54(2), 1085–1091.

Published

2025-12-07

How to Cite

An Integrated Innovation DiffusionTrust-Building Framework for Understanding Mobile Payment Adoption in Indonesia’s Cross-Border Regions. (2025). Journal of Management and Business Innovation, 1(01), 64-77. https://doi.org/10.65792/jombinov.v1i01.30