Effect of Perceived Technology Acceptance on Online Stock Trading Behavior: An Empirical Analysis

Authors

DOI:

https://doi.org/10.31181/dma31202536

Keywords:

Perceived benefit, Perceived ease of use, Perceived usefulness, Online stock trading

Abstract

This study investigates the influence of perceived technology acceptance on the online stock trading behavior of retail investors. The objective of this research is to examine the predictors of online stock trading behavior and their impact. Using a regression model, the findings reveal that perceived usefulness, perceived ease of use, and perceived benefit significantly contribute to and promote online stock trading behavior. The standardized coefficients indicate a strong positive relationship between perceived usefulness (β = 0.838), perceived ease of use (β = 0.727), and perceived benefit (β = 0.783) with online stock trading behavior. The results suggest that retail investors are more inclined to engage in online stock trading when they perceive technology as useful, easy to use, and beneficial. This study underscores the pivotal role of technological applications in shaping the behavior of retail investors in the online stock trading environment, shedding light on the factors that drive participation in this increasingly digitized investment landscape.

Downloads

Download data is not yet available.

References

Malar, D. A., Arvidsson, V., & Holmstrom, J. (2019). Digital Transformation in Banking: Exploring Value Co- Creation in Online Banking Services in India. Journal of Global Information Technology Management, 22(1), 7-24. https://doi.org/10.1080/1097198X.2019.1567216.

Tan, M., & Teo, T. S. (2000). Factors influencing the adoption of Internet banking. Journal of the Association for Information Systems, 1(1), 5. https://doi.org/10.17705/1jais.00005.

Al-Gahtani, S. S. (2011). Modeling the electronic transactions acceptance using an extended technology acceptance model. Applied Computing and Informatics, 9(1), 47-77. https://doi.org/10.1016/j.aci.2009.04.001.

Xu, A., Li, W., Chen, Z., Zeng, S., Carlos, L. A., & Zhu, Y. (2021). A study of young chinese intentions to purchase “Online Paid Knowledge”: An extended technological acceptance model. Frontiers in Psychology, 12, 695600. https://doi.org/10.3389/fpsyg.2021.695600.

Deloitte. (2022). India to have 1 billion smartphone users by 2026: Deloitte report.

TRAI. (2021). Statistical Bulletin-2021.

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

Turri, A., Maniam, B., & Earl, R. (2007). Effects of online trading on the investment community. ASBBS E-Journal, 1, 146-155.

Ahmed, A. S., Schneible, R. A., & Stevens, D. E. (2003). An Empirical Analysis of the Effects of Online Trading on Stock Price and Trading Volume Reactions to Earnings Announcements. Contemporary Accounting Research, 20(3), 413-439. https://doi.org/10.1506/N2XD-TF8Y-JT4L-L6V0.

Khare, A. (2010). Online banking in India: An approach to establish CRM. Journal of Financial Services Marketing, 15(2), 176-188. https://doi.org/10.1057/fsm.2010.13.

Lee, M. C. (2009). Predicting and explaining the adoption of online trading: An empirical study in Taiwan. Decision Support Systems, 47(2), 133-142. https://doi.org/10.1016/j.dss.2009.02.003.

Teo, T. S. H., Tan, M., & Peck, S. N. (2004). Adopters and non-adopters of internet stock trading in Singapore. Behaviour and Information Technology, 23(3), 211-223. https://doi.org/10.1080/01449290410001685402.

Koivisto, J., & Hamari, J. (2014). Demographic differences in perceived benefits from gamification. Computers in Human Behavior, 35, 179-188. https://doi.org/10.1016/j.chb.2014.03.007.

Khan, S. U., Liu, X. dong, Liu, C., Khan, I. U., & Hameed, Z. (2021). Understanding uncertainty dimensions and Internet stock trading service in China from a social cognitive perspective. Information Technology and People, 34(2), 81-834. https://doi.org/10.1108/ITP-02-2019-0062.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 319-340. https://doi.org/10.2307/249008.

Li, Y., & Li, Y. (2016). Empirical Study of Influential Factors of Online Customers’ Repurchase Intention. I Business, 8(3), 48-60. https://doi.org/10.4236/IB.2016.83006.

Damghanian, H., Zarei, A., & Siahsarani Kojuri, M. A. (2016). Impact of Perceived Security on Trust, Perceived Risk, and Acceptance of Online Banking in Iran. Journal of Internet Commerce, 15(3), 214-238. https://doi.org/10.1080/15332861.2016.1191052.

Lau, A. S., Yen, J., & Chau, P. Y. (2001). Adoption of On-line Trading in the Hong Kong Financial Market. Journal of Electronic Commerce Research, 2(2), 58-65.

Jeyaraj, A., Rottman, J. W., & Lacity, M. C. (2006). A review of the predictors, linkages, and biases in IT innovation adoption research. Journal of Information Technology, 21(1), 1-23. https://doi.org/10.1057/PALGRAVE.JIT.2000056.

Wei, T. T., Marthandan, G., Chong, A. Y. L., Ooi, K. B., & ; Arumugam, S. (2009). What drives Malaysian m-commerce adoption? An empirical analysis. Industrial Management and Data Systems, 109(3), 370-388. https://doi.org/10.1108/02635570910939399.

Nunnally, J.C. (1978). An Overview of Psychological Measurement. In: Wolman, B.B. (eds) Clinical Diagnosis of Mental Disorders. Springer, Boston, MA. https://doi.org/10.1007/978-1-4684-2490-4_4.

Published

2024-10-09

How to Cite

Jayalakshmi, K., Chidananda, H., & Harshitha, K. (2024). Effect of Perceived Technology Acceptance on Online Stock Trading Behavior: An Empirical Analysis. Decision Making Advances, 3(1), 62–69. https://doi.org/10.31181/dma31202536