Archive Details

Year 2025
Volume/Issue/Review Month Volume-XVIII | Issue-I | Jan.-Jun.
Title What Drives Continuance Intention to Use Digital Postal Banking Services? A Combined Approach Using TAM and TTF Models
Authors Melby George, Dr. Anil P. V
Broad area Marketing
Abstract Abstract: The study explores the variables determining the continuance intension to make use of digital postal banking services in India through the mixture of Technology Acceptance Model (TAM) and the Task-Technology Fit (TTF) model. Focusing on constructs such as task characteristics, technology characteristics, perceived usefulness, perceived ease of use, and continuance intention to use, the research highlights the interplay between technological attributes and user perceptions. By employing a well-structured questionnaire to collect data from 214 customers of India Post financial services, an integrated model has been built and tested. The association between continuing use of digital postal banking services and its antecedents is explained by the PLS-SEM results. The study emphasizes the significance of focusing on technological features and ensuring alignment between tasks and technology to boost user engagement and stimulate the continued use of digital postal banking services. In a fast-digitizing economy, our integrated TAM-TTF strategy provides insightful information for enhancing digital banking platforms, meeting the varied needs of users, and promoting long-term adoption. The findings have important implications for India Post in order to better comprehend how customers view digital postal banking services.
DOI https://doi.org/10.63340/samt/1005
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Referenceses

Azharuddin, M. (2024). Role of India Post Payments Bank (IPPB) to Promote Financial Inclusion in West Bengal. In Perspectives in Finance and Digital Transformations in Business (pp. 85–94). Routledge India. https://doi.org/10.4324/9781003470229-10

Baabdullah, A. M., Alalwan, A. A., Rana, N. P., Patil, P., & Dwivedi, Y. K. (2019). An integrated model for m-banking adoption in Saudi Arabia. International Journal of Bank Marketing, 37(2), 452–478. https://doi.org/10.1108/IJBM-07-2018-0183

Baptista, G., & Oliveira, T. (2015). Understanding mobile banking: The unified theory of acceptance and use of technology combined with cultural moderators. Computers in Human Behavior, 50, 418–430. https://doi.org/10.1016/j.chb.2015.04.024

Bhattacherjee, A. (2001). Understanding Information Systems Continuance: An Expectation-Confirmation Model. MIS Quarterly, 25(3), 351. https://doi.org/10.2307/3250921

Cai, J., Li, Z., Dou, Y., Li, T., & Yuan, M. (2023). Understanding adoption of high off-site construction level technologies in construction based on the TAM and TTF. Engineering, Construction and Architectural Management, 30(10), 4978–5006. https://doi.org/10.1108/ECAM-05-2022-0439

Chin, W. W. (n.d.). The Partial Least Squares Approach to Structural Equation Modeling. https://www.researchgate.net/publication/311766005

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly: Management Information Systems, 13(3), 319–339. https://doi.org/10.2307/249008

Dishaw, M. T., & Strong, D. M. (1999). Extending the technology acceptance model with task–technology fit constructs. Information & Management, 36(1), 9–21. https://doi.org/10.1016/S0378-7206(98)00101-3

Gebauer, J., Shaw, M. J., & Gribbins, M. L. (2010). Task-Technology Fit for Mobile Information Systems. Journal of Information Technology, 25(3), 259–272. https://doi.org/10.1057/jit.2010.10

Goodhue, D. L., & Thompson, R. L. (1995a). Task-Technology Fit and Individual Performance. MIS Quarterly, 19(2), 213. https://doi.org/10.2307/249689

Goodhue, D. L., & Thompson, R. L. (1995b). Task-Technology Fit and Individual Performance. In Source: MIS Quarterly (Vol. 19, Issue 2).

Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R. Springer International Publishing. https://doi.org/10.1007/978-3-030-80519-7

Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139–152. https://doi.org/10.2753/MTP1069-6679190202

Hellier, P. K., Geursen, G. M., Carr, R. A., & Rickard, J. A. (2003). Customer repurchase intention. European Journal of Marketing, 37(11/12), 1762–1800. https://doi.org/10.1108/03090560310495456

Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). 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

Klopping, I. M., & Mckinney, E. (2004a). Extending the Technology Acceptance Model Extending the Technology Acceptance Model and the Task and the Task-Technology Fit Model to Technology Fit Model to Consumer E Consumer E-Commerce Commerce. In Information Technology, Learning, and Performance Journal (Vol. 22, Issue 1).

Klopping, I. M., & Mckinney, E. (2004b). Extending the Technology Acceptance Model Extending the Technology Acceptance Model and the Task and the Task-Technology Fit Model to Technology Fit Model to Consumer E Consumer E-Commerce Commerce. In Information Technology, Learning, and Performance Journal (Vol. 22, Issue 1).

Lee, Y., Kozar, K. A., & Larsen, K. R. T. (2003). The Technology Acceptance Model: Past, Present, and Future. Communications of the Association for Information Systems, 12. https://doi.org/10.17705/1CAIS.01250

Lin, T.-C., & Huang, C.-C. (2008). Understanding knowledge management system usage antecedents: An integration of social cognitive theory and task technology fit. Information & Management, 45(6), 410–417. https://doi.org/10.1016/j.im.2008.06.004

Mahmoud, H., Ahmed Hussein, M., Jayaraman, G., Mahalakshmi Venkatachalam, D., Mahmoud Sid Ahmed, H., & Ahmed Hussien, M. (n.d.). Adoption of Online Banking Security Measures by customers-Evaluation through Extended Technology Acceptance Model (TAM) and Structural Equation Model (SEM). www.journal-innovations.com

McFarland, D. J., & Hamilton, D. (2006). Adding contextual specificity to the technology acceptance model. Computers in Human Behavior, 22(3), 427–447. https://doi.org/10.1016/j.chb.2004.09.009

Mohammadi, H. (2015). Investigating users’ perspectives on e-learning: An integration of TAM and IS success model. Computers in Human Behavior, 45, 359–374. https://doi.org/10.1016/j.chb.2014.07.044

Oliveira, T., Thomas, M., Baptista, G., & 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

Prakash, N. (2018). India Postal Banking Services-A Study on Its Growth. In Article in Sumedha Journal of Management. https://www.researchgate.net/publication/357512416

Rigopoulos, G., Askounis, D., & Prof, A. (2007). Journal of Internet Banking and Commerce A TAM Framework to Evaluate Users’ Perception towards Online Electronic Payments. In Journal of Internet Banking and Commerce (Vol. 12, Issue 3). http://www.arraydev.com/commerce/jibc/

St Joseph, J. V., & Devagiri, C. (n.d.). From Postal Service to Banking: A Paradigm Shift in the Services of India Post. https://www.researchgate.net/publication/375084018

Tam, C., & Oliveira, T. (2016a). Performance impact of mobile banking: using the task-technology fit (TTF) approach. International Journal of Bank Marketing, 34(4), 434–457. https://doi.org/10.1108/IJBM-11-2014-0169

Tam, C., & Oliveira, T. (2016b). Understanding the impact of m-banking on individual performance: DeLone & McLean and TTF perspective. Computers in Human Behavior, 61, 233–244. https://doi.org/10.1016/j.chb.2016.03.016

Venkatesh, V., & Davis, F. D. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926

Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly: Management Information Systems, 36(1), 157–178. https://doi.org/10.2307/41410412

Wang, C., Dai, J., Zhu, K., Yu, T., & Gu, X. (2023). Understanding the Continuance Intention of College Students toward New E-Learning Spaces Based on an Integrated Model of the TAM and TTF. International Journal of Human-Computer Interaction. https://doi.org/10.1080/10447318.2023.2291609

Wang, Y. S., Wang, Y. M., Lin, H. H., & Tang, T. I. (2003). Determinants of user acceptance of Internet banking: An empirical study. International Journal of Service Industry Management, 14(5), 501–519. https://doi.org/10.1108/09564230310500192

Wang, Y., Wu, M., & Wang, H. (2009). Investigating the determinants and age and gender differences in the acceptance of mobile learning. British Journal of Educational Technology, 40(1), 92–118. https://doi.org/10.1111/j.1467-8535.2007.00809.x

Yen, D. C., Wu, C.-S., Cheng, F.-F., & Huang, Y.-W. (2010). Determinants of users’ intention to adopt wireless technology: An empirical study by integrating TTF with TAM. Computers in Human Behavior, 26(5), 906–915. https://doi.org/10.1016/j.chb.2010.02.005

Yuan, S., Liu, Y., Yao, R., & Liu, J. (2016). An investigation of users’ continuance intention towards mobile banking in China. Information Development, 32(1), 20–34. https://doi.org/10.1177/0266666914522140

Zhou, T., Lu, Y., & Wang, B. (2010). Integrating TTF and UTAUT to explain mobile banking user adoption. Computers in Human Behavior, 26(4), 760–767. https://doi.org/10.1016/j.chb.2010.01.013