MDIM Journal of Management Review and Practice
group_logo
issue front

Muhammed Jisham1, S. Vanitha1 and Abin John1

First Published 28 May 2024. https://doi.org/10.1177/mjmrp.241253393
Article Information
Corresponding Author:

Muhammed Jisham, Department of Commerce and Financial Studies, Bharathidasan University, Tiruchirappalli, Tamil Nadu 620024, India.
Email: jisham@bdu.ac.in

Department of Commerce and Financial Studies, Bharathidasan University, Tiruchirappalli, Tamil Nadu, India

Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (http://www.creativecommons.org/licenses/by-nc/4.0/) which permits non-Commercial use, reproduction and distribution of the work without further permission provided the original work is attributed. 

Abstract

This research investigates the adoption of Wealthtech among individual investors in India using Partial Least Squares Structural Equation Modeling (PLS-SEM). A convenience sample technique was employed, gathering data from 280 participants through an online survey. The study applies the Theory of Planned Behavior and the Technology Acceptance Model as the theoretical framework to examine the factors influencing the intention to use Wealthtech. Additionally, the role of perceived ease of use and perceived usefulness in shaping attitudes toward Wealthtech adoption is explored. The results from the PLS-SEM analysis show significant positive associations between Attitude and Perceived Behavioral Control with the intention to use Wealthtech. These insights can help financial institutions tailor Wealthtech platforms to meet investor preferences, fostering increased adoption among individual investors. Regulatory authorities can use these findings to enhance accessibility and acceptance of Wealthtech solutions by fostering a conducive environment for technological innovation.

Keywords

Fintech, Wealthtech, technology adoption, technology acceptance model, theory of planned behavior

References

Abroud, A., Choong, Y. V., Muthaiyah, S., & Fie, D. Y. G. (2013). Adopting e-finance: Decomposing the technology acceptance model for investors. Service Business, 9(1), 161–182. https://doi.org/10.1007/s11628-013-0214-x

Aggarwal, M., Nayak, K. M., & Bhatt, V. (2023). Examining the factors influencing fintech adoption behaviour of gen Y in India. Cogent Economics & Finance, 11(1). https://doi.org/10.1080/23322039.2023.2197699

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

Arkorful, V. E., Zhao, S., Lugu, B. K., & Chu, J. (2022). Consumers’ mobile health adoption intention prediction utilizing an extended version of the theory of planned behavior. ACM SIGMIS Database, 53(2), 96–114. https://doi.org/10.1145/3533692.3533699

Armah, A. K., & Jin-Fa, L. (2023). Generational cohorts’ social media acceptance as a delivery tool in sub-Sahara Africa motorcycle industry: The role of cohort technical know-how in technology acceptance. Technology in Society, 75(102390). https://doi.org/10.1016/j.techsoc.2023.102390

Belanche, D., Casaló, L. V., & Flavián, C. (2019). Artificial intelligence in FinTech: Understanding robo-advisors adoption among customers. Industrial Management and Data Systems, 119(7), 1411–1430. https://doi.org/10.1108/imds-08-2018-0368

Bhatia, A., Chandani, A., & Chhateja, J. (2020). Robo advisory and its potential in addressing the behavioral biases of investors—A qualitative study in Indian context. Journal of Behavioral and Experimental Finance, 25(100281). https://doi.org/10.1016/j.jbef.2020.100281

Cao, L., Yang, Q., & Yu, P. S. (2021). Data science and AI in FinTech: An overview. International Journal of Data Science and Analytics, 12(2), 81–99. https://doi.org/10.1007/s41060-021-00278-w

Chong, L. L., Ong, H., & Tan, S. (2021). Acceptability of mobile stock trading application: A study of young investors in Malaysia. Technology in Society, 64(101497). https://doi.org/10.1016/j.techsoc.2020.101497

Cordero, D., Altamirano, K. L., Parra, J. O., & Espinoza, W. S. (2023). Intention to adopt industry 4.0 by organizations in Colombia, Ecuador, Mexico, Panama, and Peru. IEEE Access, 11, 8362–8386. https://doi.org/10.1109/access.2023.3238384

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. Management Information Systems Quarterly, 13(3), 319. https://doi.org/10.2307/249008

Diéguez, A. I. I., Velicia-Martín, F., & Aguayo-Camacho, M. (2023). Predicting Fintech innovation adoption: The mediator role of social norms and attitudes. Financial Innovation, 9(1). https://doi.org/10.1186/s40854-022-00434-6

Hakimi, T. I., Jaafar, J. A., & Aziz, N. A. A. (2023). What factors influence the usage of mobile banking among digital natives? Journal of Financial Services Marketing, 28(4), 763–778. https://doi.org/10.1057/s41264-023-00212-0

Himel, M. T. A., Ashraf, S., Bappy, T. A., Abir, T., Morshed, K., & Hossain, M. N. (2021). Users’ attitude and intention to use mobile financial services in Bangladesh: An empirical study. South Asian Journal of Marketing, 2(1), 72–96. https://doi.org/10.1108/sajm-02-2021-0015

Henseler, J., Hubona, G. S., & Ray, P. A. (2016). Using PLS path modeling in new technology research: updated guidelines. Industrial Management & Data Systems, 116(1), 2–20. https://doi.org/10.1108/imds-09-2015-0382

Khayer, A., & Bao, Y. (2019). The continuance usage intention of Alipay. The Bottom Line: Managing Library Finances, 32(3), 211–229. https://doi.org/10.1108/bl-07-2019-0097

Kumari, A., & Devi, N. C. (2022). Blockchain technology acceptance by investment professionals: A decomposed TPB model. Journal of Financial Reporting and Accounting, 21(1), 45–59. https://doi.org/10.1108/jfra-12-2021-0466

Laksamana, P., Suharyanto, S., & Cahaya, Y. F. (2022). Determining factors of continuance intention in mobile payment: Fintech industry perspective. Asia Pacific Journal of Marketing and Logistics, 35(7), 1699–1718. https://doi.org/10.1108/apjml-11-2021-0851

Lee, J. C., & Wang, J. (2022). From offline to online: understanding users’ switching intentions from traditional wealth management services to mobile wealth management applications. International Journal of Bank Marketing, 41(2), 369–394. https://doi.org/10.1108/ijbm-08-2022-0345

Leong, M. K., & Koay, K. Y. (2023). Towards a unified model of consumers’ intentions to use drone food delivery services. International Journal of Hospitality Management, 113(103539). https://doi.org/10.1016/j.ijhm.2023.103539

Manrai, R., & Gupta, K. P. (2022). Investor’s perceptions on artificial intelligence (AI) technology adoption in investment services in India. Journal of Financial Services Marketing, 28(1), 1–14. https://doi.org/10.1057/s41264-021-00134-9

Mao, C., Bayer, J. B., Ross, M. Q., Rhee, L., Le, H., Mount, J., Chang, H. -C., Chang, Y., Hedstrom, A., & Hovick, S. R. (2023). Perceived vs. observed mHealth behavior: A naturalistic investigation of tracking apps and daily movement. Mobile Media & Communication, 11(3), 526–548. https://doi.org/10.1177/20501579221149823

Mazambani, L., & Mutambara, E. (2019). Predicting FinTech innovation adoption in South Africa: The case of cryptocurrency. African Journal of Economic and Management Studies, 11(1), 30–50. https://doi.org/10.1108/ajems-04-2019-0152

Nair, P. S., Shiva, A., Yadav, N. K., & Tandon, P. (2022). Determinants of mobile apps adoption by retail investors for online trading in emerging financial markets. Benchmarking: An International Journal, 30(5), 1623–1648. https://doi.org/10.1108/bij-01-2022-0019

Nguyen-Phuoc, D. Q., Truong, T. M., Nguyen, M. H., Pham, H. G., Li, Z., & Oviedo-Trespalacios, Ó. (2024). What factors influence the intention to use electric motorcycles in motorcycle-dominated countries? An empirical study in Vietnam. Transport Policy, 146, 193–204. https://doi.org/10.1016/j.tranpol.2023.11.013

Parthasarathy, B. (2021, September 23). 5 Ways Fintech is transforming banking in India. Forbes Advisor India. https://www.forbes.com/advisor/in/banking/5-ways-fintech-is-transforming-banking-in-india/

Ringle, C. M., Wende, S., & Becker, J. -M. (2022). SmartPLS 4. Oststeinbek: SmartPLS. https://www.smartpls.com

Sood, K., & Singh, S. (2022). Marin Laboure and Nicolas Deffrennes (2022): Democratizing finance – the radical promises of Fintech. Journal of Evolutionary Economics, 32(5), 1581–1586. https://doi.org/10.1007/s00191-022-00789-0

Wu, I., & Chen, J. (2005). An extension of trust and TAM model with TPB in the initial adoption of on-line tax: An empirical study. International Journal of Human-computer Studies, 62(6), 784–808. https://doi.org/10.1016/j.ijhcs.2005.03.003


Make a Submission Order a Print Copy