Determinants of intention to continue using internet banking: Indian context
-
DOIhttp://dx.doi.org/10.21511/im.17(1).2021.04
-
Article InfoVolume 17 2021, Issue #1, pp. 40-52
- Cited by
- 1743 Views
-
647 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
It is necessary to understand the customers’ perceptions of internet banking because it helps determining the direction and patterns of intention to continue using internet banking. This could also help bank policymakers to develop appropriate strategies to increase internet banking usage. The study aims to examine the determinants of user’s intention to continue using internet banking since there have been no systematic attempts to understand this aspect, especially in the Indian context. This research suggests and tests an extended model to predict the intention to continue using internet banking in India. The suggested study model was examined using survey data from 206 internet banking users. PLS-SEM was employed for data analysis. The findings imply that the most significant determinants of intention to continue using internet banking are service quality, trust, and user satisfaction. On the other hand, the study finds that intention to continue using internet banking is not impacted by system quality and information quality.
- Keywords
-
JEL Classification (Paper profile tab)M31, M15, O33, G20, L86
-
References71
-
Tables4
-
Figures2
-
- Figure 1. The proposed model
- Figure 2. PLS algorithm results
-
- Table 1. Demographic variables
- Table 2. Factor loadings and cross-loadings
- Table 3. Measurement model and multicollinearity examination
- Table 4. Structural model results
-
- Al-Fraihat, D., Joy, M., & Sinclair, J. (2020). Evaluating E-learning systems success: An empirical study. Computers in Human Behavior, 102(January), 67-86.
- Al-Hattami, H. M. (2021). Validation of the D&M IS success model in the context of accounting information system of the banking sector in the least developed countries. Journal of Management Control, 32(1), 1-27.
- Al-Hattami, H. M., Hashed, A. A., & Kabra, J. D. (2021). Effect of AIS success on performance measures of SMEs: evidence from Yemen. International Journal of Business Information Systems, 36(1), 144-164.
- Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411-423.
- Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74-94.
- Bhattacherjee, A. (2001). Understanding information systems continuance: an expectation-confirmation model. MIS Quarterly, 25(3), 351-370.
- Cheok, M. L., & Wong, S. L. (2015). Predictors of e-learning satisfaction in teaching and learning for school teachers: A literature review. International Journal of Instruction, 8(1), 75-90.
- Chin, W. W., Thatcher, J. B., & Wright, R. T. (2012). Assessing common method bias: problems with the ULMC technique. MIS Quarterly, 36(3), 1003-1019.
- Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). New York: Routledge.
- DeLone, W. H., & McLean, E. R. (1992). Information systems success: The quest for the dependent variable. Information Systems Research, 3(1), 60-95.
- DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: a ten-year update. Journal of Management Information Systems, 19(4), 9-30.
- Falk, R. F., & Miller, N. B. (1992). A primer for soft modeling. University of Akron Press.
- Fan, Y., Chen, J., Shirkey, G., John, R., Wu, S. R., Park, H., & Shao, C. (2016). Applications of structural equation modeling (SEM) in ecological studies: an updated review. Ecological Processes, 5(19), 1-12.
- 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., Straub, D., & Boudreau, M. C. (2000). Structural equation modeling and regression: Guidelines for research practice. Communications of the Association for Information Systems, 4(Article 7), 1-77.
- Gorla, N., Somers, T. M., & Wong, B. (2010). Organizational impact of system quality, information quality, and service quality. The Journal of Strategic Information Systems, 19(3), 207-228.
- Hair Jr, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM) (2nd ed.). Sage publications.
- Hair Jr., J.F., Black, W.C., Babin, B.J. and Anderson, R.E. (2014) Multivariate Data Analysis: a Global Perspective (7th ed.). Pearson Education Inc., New Jersey.
- 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.
- Hammoud, J., Bizri, R. M., & El Baba, I. (2018). The impact of e-banking service quality on customer satisfaction: Evidence from the Lebanese banking sector. SAGE Open, 8(3), 2158244018790633.
- Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS path modeling in new technology research: updated guidelines. Industrial Management & Data Systems, 116(1), 2-20.
- Hsiao, C. H., Chang, J. J., & Tang, K. Y. (2016). Exploring the influential factors in continuance usage of mobile social Apps: Satisfaction, habit, and customer value perspectives. Telematics and Informatics, 33(2), 342-355.
- Iberahim, H., Taufik, N. M., Adzmir, A. M., & Saharuddin, H. (2016). Customer satisfaction on reliability and responsiveness of self service technology for retail banking services. Procedia Economics and Finance, 37(2016), 13-20.
- India Brand Equity Foundation (IBEF) (2020). Indian Telecommunications Industry Report.
- Karat, C. M., Blom, J. O., & Karat, J. (Eds.) (2004). Designing personalized user experiences in eCommerce (Vol. 5). Springer Science & Business Media.
- Kassim, E. S., Jailani, S. F. A. K., Hairuddin, H., & Zamzuri, N. H. (2012). Information system acceptance and user satisfaction: The mediating role of trust. Procedia-Social and Behavioral Sciences, 57(October), 412-418.
- Kaur, A., & Malik, G. (2019). Examining factors influencing Indian customers’ intentions and adoption of internet banking: Extending TAM with electronic service quality. Innovative Marketing, 15(2), 42-57.
- Kelloway, E. K. (1998). Using LISREL for structural equation modeling: A researcher’s guide. Sage.
- Kesharwani, A., & Singh Bisht, S. (2012). The impact of trust and perceived risk on internet banking adoption in India: An extension of technology acceptance model. International Journal of Bank Marketing, 30(4), 303-322.
- Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration (IJeC), 11(4), 1-10.
- Kumar, A., Dhingra, S., Batra, V., & Purohit, H. (2020). A Framework of Mobile Banking Adoption in India. Journal of Open Innovation: Technology, Market, and Complexity, 6(2), 40.
- Lee, J. M., & Kim, H. J. (2020). Determinants of adoption and continuance intentions toward Internet-only banks. International Journal of Bank Marketing, 38(4), 843-865.
- Lin, F. T., Wu, H. Y., & Tran, T. N. N. (2015). Internet banking adoption in a developing country: an empirical study in Vietnam. Information Systems and e-Business Management, 13(2), 267-287.
- Lin, H. F. (2007). The impact of website quality dimensions on customer satisfaction in the B2C e-commerce context. Total Quality Management and Business Excellence, 18(4), 363-378.
- Lin, W. R., Wang, Y. H., & Hung, Y. M. (2020). Analyzing the factors influencing adoption intention of internet banking: Applying DEMATEL-ANP-SEM approach. Plos one, 15(2), 1-25.
- Malhotra, P., & Singh, B. (2009). The impact of internet banking on bank performance and risk: The Indian experience. Eurasian Journal of Business and Economics, 2(4), 43-62.
- Molla, A., & Licker, P. S. (2001). E-commerce systems success: An attempt to extend and respecify the Delone and MacLean model of IS success. J. Electron. Commerce Res., 2(4), 131-141.
- Nabavi, A., Taghavi-Fard, M. T., Hanafizadeh, P., & Taghva, M. R. (2016). Information technology continuance intention: A systematic literature review. International Journal of E-Business Research (IJEBR), 12(1), 58-95.
- O’brien, R. M. (2007). A caution regarding rules of thumb for variance inflation factors. Quality & quantity, 41(5), 673-690.
- Petter, S., DeLone, W., & McLean, E. (2008). Measuring information systems success: models, dimensions, measures, and interrelationships. European Journal of Information Systems, 17(3), 236-263.
- Rahi, S., & Ghani, M. A. (2019). Integration of DeLone and McLean and self-determination theory in internet banking continuance intention context. International Journal of Accounting & Information Management, 27(3), 512-528.
- Rahman, M. N. A., Zamri, S. N. A. S., & Eu, L. K. (2017). A meta-analysis study of satisfaction and continuance intention to use educational technology. International Journal of Academic Research in Business and Social Sciences, 7(4), 1059-1072.
- Ramayah, T., Ahmad, N. H., & Lo, M. C. (2010). The role of quality factors in intention to continue using an e-learning system in Malaysia. Procedia-Social and Behavioral Sciences, 2(2), 5422-5426.
- Rönkkö, M., McIntosh, C. N., Antonakis, J., & Edwards, J. R. (2016). Partial least squares path modeling: Time for some serious second thoughts. Journal of Operations Management, 47-48(November), 9-27.
- Safeena, R., Date, H., & Kammani, A. (2011). Internet Banking Adoption in an Emerging Economy: Indian Consumer’s Perspective. Int. Arab. J. e Technol., 2(1), 56-64.
- Saunders, M., Lewis, P., & Thornhill, A. (2009). Research methods for business students. Pearson education.
- Schamberger, T., Schuberth, F., Henseler, J., & Dijkstra, T. K. (2020). Robust partial least squares path modeling. Behaviormetrika, 47(1), 307-334.
- Shaikh, A. A., & Karjaluoto, H. (2015). Mobile banking adoption: A literature review. Telematics and Informatics, 32(1), 129-142.
- Shankar, A., & Jebarajakirthy, C. (2019). The influence of e-banking service quality on customer loyalty. International Journal of Bank Marketing, 37(5), 1119-1142.
- Sharma, M., & Sharma, S. K. (2019a, June). Theoretical Framework for Digital Payments in Rural India: Integrating UTAUT and Empowerment Theory. In International Working Conference on Transfer and Diffusion of IT (pp. 212-223). Springer, Cham.
- Sharma, S. K., & Sharma, M. (2019b). Examining the role of trust and quality dimensions in the actual usage of mobile banking services: An empirical investigation. International Journal of Information Management, 44(February), 65-75.
- Sharma, S. K., Govindaluri, S. M., & Al Balushi, S. M. (2015). Predicting determinants of Internet banking adoption. Management Research Review, 38(7), 750-766.
- Sujeet, K. S., & Srikrishna, G. (2014). Internet banking adoption in India: structural equation modelling approach. Journal of Indian Business Research, 6(2), 155-169.
- Susanto, A., Chang, Y., & Ha, Y. (2016). Determinants of continuance intention to use the smartphone banking services. Industrial Management & Data Systems, 116(3), 508-525.
- Tam, C., & Oliveira, T. (2016). Understanding the impact of m-banking on individual performance: DeLone & McLean and TTF perspective. Computers in Human Behavior, 61(August), 233-244.
- Tam, C., & Oliveira, T. (2017). Literature review of mobile banking and individual performance. International Journal of Bank Marketing, 35(7), 1044-1067.
- Tella, A. (2012). Determinants of E-Payment Systems Success: A User’s Satisfaction Perspective. International Journal of E-Adoption, 4(3), 15-38.
- Tenenhaus, M., Vinzi, V. E., Chatelin, Y. M., & Lauro, C. (2005). PLS path modeling. Computational Statistics & Data Analysis, 48(1), 159-205.
- Teo, T. S., Srivastava, S. C., & Jiang, L. (2008). Trust and electronic government success: An empirical study. Journal of Management Information Systems, 25(3), 99-132.
- Urbach, N., & Ahlemann, F. (2010). Structural equation modeling in information systems research using partial least squares. Journal of Information Technology Theory and Application, 11(2), 5-40.
- Urbach, N., Smolnik, S., & Riempp, G. (2010). An empirical investigation of employee portal success. The Journal of Strategic Information Systems, 19(3), 184-206.
- Valaei, N., & Baroto, M. B. (2017). Modelling continuance intention of citizens in government Facebook page: A complementary PLS approach. Computers in Human Behavior, 73(August), 224-237.
- Vatanasombut, B., Igbaria, M., Stylianou, A. C., & Rodgers, W. (2008). Information systems continuance intention of web-based applications customers: The case of online banking. Information & Management, 45(7), 419-428.
- Veeramootoo, N., Nunkoo, R., & Dwivedi, Y. K. (2018). What determines success of an e-government service? Validation of an integrative model of e-filing continuance usage. Government Information Quarterly, 35(2), 161-174.
- Vinzi, V. E., Trinchera, L., & Amato, S. (2010). PLS path modeling: from foundations to recent developments and open issues for model assessment and improvement. In Handbook of partial least squares (pp. 47-82). Springer, Berlin, Heidelberg.
- Wang, Y. S. (2008). Assessing e-commerce systems success: a respecification and validation of the DeLone and McLean model of IS success. Information Systems Journal, 18(5), 529-557.
- Wetzels, M., Odekerken-Schröder, G., & Van Oppen, C. (2009). Using PLS path modeling for assessing hierarchical construct models: Guidelines and empirical illustration. MIS Quarterly, 33(1), 177-195.
- Xu, J., Benbasat, I., & Cenfetelli, R. T. (2013). Integrating service quality with system and information quality: an empirical test in the e-service context. MIS Quarterly, 37(3), 777-794.
- Yoon, S. J. (2002). The antecedents and consequences of trust in online-purchase decisions. Journal of Interactive Marketing, 16(2), 47-63.
- Yuan, Y., Lai, F., & Chu, Z. (2019). Continuous usage intention of Internet banking: a commitment-trust model. Information Systems and e-Business Management, 17(1), 1-25.
- Zheng, Y., Zhao, K., & Stylianou, A. (2013). The impacts of information quality and system quality on users’ continuance intention in information-exchange virtual communities: An empirical investigation. Decision Support Systems, 56(December), 513-524.