ON-DEMAND RIDE HAILING SERVICES (RHS) FOR COMMUTING PURPOSES: A CASE OF KERALA, INDIA
Keywords:
Ride hailing, Ride share, Online platform, Technology Acceptance Model (TAM), Continuous usage intentionAbstract
The evaluation of the antecedents of users’ continuous usage intention has become essential for the success of ride hailing services. This study analysed the antecedent role of customers’ perceptions and satisfaction with continuous usage intention of ride hailing services in Kerala, India. The model used in this study conceptualizes customers perceptions as a composite variable comprising four dimensions (perceived usefulness, perceived ease of use, perceived value and satisfaction) prescribed by the Technology Acceptance Model (TAM). It employed a descriptive, correlational survey approach in which the responses of 216 registered users of ride hailing were analysed using descriptive inferential statistics. Linear regression analysis indicated that the model provided a statistically significant explanation for the variation in users’ continuous usage intentions. The study also found empirical support for customers’ perceptions (perceived usefulness, perceived ease of use, perceived value and satisfaction) as antecedents of continuous usage intention with ride hailing services.
References
Agatz, N., Erera, A. L., Savelsbergh, M. W., & Wang, X. (2011). Dynamic ride-sharing: A simulation study in metro Atlanta. Procedia-Social and Behavioral Sciences, 17, 532-550.
Akbari, M., Moradi, A., Seyyed Amiri, N., Zúñiga, M. Á., Rahmani, Z., & Padash, H. (2020). Consumers’ intentions to use ridesharing services in Iran. Research in Transportation Business & Management, 100-616. 3. Doane, D. P., & Seward, L. E. (2011). Measuring skewness: a forgotten statistic? Journal of statistics education, 19(2).
Dwivedi, Y. K., Ismagilova, E., Hughes, D. L., Carlson, J., Filieri, R., Jacobson, J., ... & Wang, Y. (2021). Setting the future of digital and social media marketing research: Perspectives and research propositions. International Journal of Information Management, 59, 102-168.
Maqableh, M., & Karajeh, H. (2014). Job scheduling for cloud computing using neural networks. Communications and Network, 6(3), 191-200.
Nguyen-Phuoc, D. Q., Su, D. N., Tran, P. T. K., Le, D. T. T., & Johnson, L. W. (2020). Factors influencing customer’s loyalty towards ride-hailing taxi services–A case study of Vietnam. Transportation Research Part A: Policy and Practice, 134, 96-112.
Roberts, P., Priest, H., & Traynor, M. (2006). Reliability and validity in research. Nursing Standard, 20(44), 41-45.
RUBIN, A. & BABBIE, E. 2011. Research methods for social work. New York: Brooks/Cole Cengage Learning
Tang, B. J., Li, X. Y., Yu, B., & Wei, Y. M. (2020). How app-based ride-hailing services influence travel behaviour: an empirical study from China. International Journal of Sustainable Transportation, 14(7), 554-568.
Uber. (2018). Retrieved November 22, 2021, from https://www.uber.com/en-GB 11. Young, M., & Farber, S. (2019). The who, why, and when of Uber and other ridehailing trips: An examination of a large sample household travel survey. Transportation Research Part A: Policy and Practice, 119, 383-392
