Behavioural Intent of Indian Consumers to Accept Mobile Banking  Services

Bushra  Khalid; Aalia  Sheerin

doi:10.48165/sajssh.2020.1305

Authors

Bushra Khalid Senior Research Fellow, Department of Economics, Aligarh Muslim University, Aligarh, India
Aalia Sheerin Senior Research Fellow, Department of Economics, Aligarh Muslim University, Aligarh, India

DOI:

https://doi.org/10.48165/sajssh.2020.1305%20

Keywords:

Mobile banking, Technology Acceptance Model, Payment and Settlement System, Theory of Reasoned Action, Behavioral Intention.

Abstract

Banking system around the globe is becoming digitally advanced and India, too, has adopted the digital mode to transform the payment and settlement system. The number of mobile internet users is continuously increasing owing to the easy accessibility of mobile phones and cost efficiency of their usage. Mobile banking allows the consumers to conduct the financial transactions and other banking activities using mobile phones. Mobile banking has ensured easy and rapid accessibility of banking facility 24*7 and served as a medium to reach the unbanked, thereby becoming a gateway to financial inclusion. Digital innovations are critical for achieving and sustaining an inclusive economic growth, hence, eliminating poverty. In this paper, we have applied the extended version of the Technology Acceptance Model (TAM), originally given by Davis in 1989, to study the key determinants of behavioral intent of consumers to accept mobile banking services. TAM in itself is an adaptation of the Theory of Reasoned Action (TRA) (Fishbien and Ajzen, 1975). According to TRA, the actual action of an individual is determined by his/her behavioral intention, which, in turn, is influenced by his/her attitude and subject norm. The key constructs that have been studied in this paper are perceived ease-of-use, perceived usefulness, perceived credibility, normative pressures, self-efficacy and attitude to use. In order to achieve the objective, we have adapted a five-point Likert scale questionnaire containing 20items.The behavioral intent was then regressed against these key constructs. The study attempts to trace the causality between the behavioral intent and the main key constructs of TAM.

References

Agarwal, R., & Karahanna, E. (2000). Time flies when you’re having fun: Cognitive absorption and beliefs about information technology usage. MIS Quarterly, 24(4), 665–694

Agarwal, R., Sambamurthy, V., & Stair, R.M. (2000). Research report: The evolving relationship between general and specific computer self-efficacy-An empirical assessment. Information Systems Research, 11(4), 418-430.

Ajzen, I., &Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs, NJ: Prentice-Hall

Amin, H., Rizal, A. H. M., Suddin, L. & Zuraidah, A. (2008). The adoption of mobile banking in Malaysia: The case of bank Islam Malaysia berhad (Bimb). International Journal of Business and Society, 9(2), 43-53.

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, http://dx.doi.org/10.1037/0033-2909.103.3.411

Bagozzi, R. P. (1994). Structural Equation Models in Marketing Research: Basic Principles. In Bagozzi (Ed.), Principles of Marketing Research, Blackwell Business.

Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173-1182.

Brown, I., Cajee, Z., Davies, D. & Stroebel, S. (2003). Cell phone banking: Predictors of adoption in South Africa – An exploratory study. International Journal of Information Management, 23(5), 381-394. doi:10.1016/S0268-4012(03)00065-3

Chauhan, S. (2015). Acceptance of Mobile Money by Poor Citizens of India: Integrating Trust into the Technology Acceptance Model. Info, 17 (3), pp. 58-68. https://doi.org/10.1108/info 02-2015-0018

Compeau, D. R., and Higgins, C. A. (1995). Computer Self-Efficacy: Development of a Measure and Initial Test. MIS Quarterly (19:2), 1995b, pp. 189-211.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. doi:10.2307/249008

Dabholkar, P. A., & Bagozzi, R. P. (2002). An attitudinal model of technology-based self-service: Moderating effects of consumer traits and situational factors. Journal of the Academy of Marketing Science, 30(3), 184-201. doi:10.1177/0092070302303001

Dasgupta, S., Paul, R., & Fuloria, S. (2011). Factors affecting behavioral intentions towards mobile banking usage: Empirical evidence from India. Romanian Journal of Marketing, 6(1), 6- 28.

Deng, Z., Lu, Y., Deng, S., and Zhang, J. (2010). Exploring user adoption of mobile banking: An empirical study in China. International Journal of Information Technology and Management, Vol. 9, No. 3, 289-301.

Fishbein, M., and Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Addison-Wesley, Reading, MA

Fornell, C., and Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, Vol. 18, No. 1, 39-50.

Gefen, D., & Straub, D. W. (1997). Gender differences in the perception and use of email: An extension to the technology acceptance model. MIS Quarterly, 21(4),389–400.

Gu, J.-C., Lee, S.-C., and Suh, Y.-H. (2014). Determinants of behavioural intention to mobile banking. Expert Systems with Applications, 36 (2009), 11605-11616.

Howcroft, B., Hamilton, R., and Hewer, P. (2002). Consumer attitude and the usage and adoption of home-based banking in the United Kingdom. International Journal of Bank Marketing, 20(3), 111- 121.Islamic Banking Act (IBA) 1983.

Hair, J.F. Jr, Anderson, R.E., Tatham, R.L., and Black, W.C. (1998). Multivariate data Analysis. 5th ed. New Jersey: Prentice Hall Inc.

Hsu, C. L., & Lu, H. P. (2004). Why do people play on-line games? An extended TAM with social influences and flow experience. Information and Management, 41(7), 853–868.

Igbaria, M., Guimaraes, T., & Davis, G. B. (1995). Testing the determinants of microcomputer usage via a structural equation model. Journal of Management Information Systems, 11(4), 87–114.

Javiya, V. B. (2017). Analyzing the Factor Influencing Acceptance of Mobile Banking Services. International Journal of Research in Engineering, IT & Social Sciences, 7(12), 45-49.

Kleijnen, M., Wetzels, M., and de Ruyter, K. (2004). Consumer acceptance of wireless finance. Journal of Financial Services Marketing, 8(3), 206-217.

Lucas, H. C., &Spitler, V. K. (1999). Technology use and performance: A field study of broker workstations. Decision Sciences, 30(2), 291-311. doi:10.111/j.1540- 5915.1999.tb01611.x

Luarn, P. and Lin, H.H. (2005). Toward an understanding of the behavioural intention to use mobile banking. Computers in Human Behavior, Vol. 21, No. 6, pp.873–891

Moon, J.-W., & Kim, Y. G. (2001). Extending the TAM for a World-Wide-Web Context. Information& Management, 38(4), 217-230. doi:10.1016/S0378-7206(00)00061-6

Natarajan, T., Balasubramanian, S. A., & Manickavasagam, S. (2010). Customer’s choice amongst self-service technology (SST) channels in retail banking: A study using analytical hierarchy process (AHP). Journal of Internet Banking & Commerce, 15(2), 1-16.

Nui Polatoglu, V. and Ekin, S. (2001). An empirical investigation of the Turkish consumers’ acceptance of Internet banking services. International Journal of Bank Marketing, Vol. 19 No. 4, pp. 156-165. https://doi.org/10.1108/02652320110392527

Nysveen, H., Pedersen, P.E., and Thorbjornsen, H. (2005). Explaining intention to use mobile chat services: Moderating effects of gender. Journal of Consumer Marketing, 33(5), 247-256.

Norazah, M. S., & Norbayah, M. S. (2009). Exploring the relationship between perceived usefulness, perceived ease of use, perceived enjoyment, attitude and subscribers’ intention towards using 3G mobile service. Internet Journal, 3(3), 1-11.

O’ Cass, A., & Fenech, T. (2003). Web retailing adoption: Exploring the nature of internet users Web retailing behavior. Journal of Retailing and Consumer Services, 10(2), 81-94. doi:10.1016/S0969-6989(02)00004-8

Ong, C. S., Laia, J. Y., & Wang, Y. S. (2004). Factors affecting engineers’ acceptance of asynchronous e-learning systems in high-tech companies. Information and Management, 41(6), 795–804.

Purwanto, E. & A. M. Mutahar. (2020). Examine the Technology Acceptance Model among Mobile Banking Users in Indonesia. Technology Reports of Kansai University, Vol 62(7), pp. 3969-3979.

Riquelme, H. E., & Rios, R. E. (2010). The moderating effect of gender in the adoption of mobile banking. International Journal of Bank Marketing, 28(5), 328-341. doi:10.1108/02652321011064872

Shanmugan, A., Savarimurthu, M. T., Wen, T. C. (2014). Factors affecting Malaysian Behavioural Intention to use Mobile Banking with Mediating Effects of Attitude. Academic Research International, Vol. 5 (2), 236-253.

Sathye, M. (1999). Adoption of Internet banking by Australian consumers: an empirical investigation. International Journal of Bank Marketing, Vol.17 No.7, pp.324- 334. https://doi.org/10.1108/02652329910305689

Shankar, A. & B. Datta (2018). Factors Affecting Mobile Banking Payment Adoption Intention: An Indian Perspective. Global Business Review, 19(35), pp. 72S – 89S https://doi.org/10.1108/info-02-2015-0018

Sharma, D. (2020). Predicting the Antecedents of Mobile Banking Acceptance in India by Structural Equation Modelling. Pacific Business Review International, Vol. 12 (12).

Sobel, Michael E. (1982). Asymptotic Confidence Intervals for Indirect Effects in StructuralEquationModels. SociologicalMethodology. 13:290312. CiteSeerX 10.1.1.452. 5935. doi:10.2307/270723

Thakur, R. (2013). Consumer Adoption of Mobile Payment Services by Professionals across Two Cities in India: An Emerging Study using Modified Technology Acceptance Model. Business Perspective and Research, 1(2), pp. 17-30. https://doi.org/10.1177/2278533720130203

Taylor, S. and Todd, P.A. (1995). Understanding information technology usage: a test of competing models. Information Systems Research, Vol. 6, No. 2, pp.144–176

Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information System Research, 11(4), 342–365.

Venkatesh, V., and Morris, M.G. (2000). Why don’t men ever stop to ask for directions: Gender, social influence and their role in technology acceptance and usage behavior. MIS Quarterly, 24(1), 115-139

Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2),186–204.

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.

Vijayasarathy, L. R. (2004). Predicting consumer intentions to use online shopping: The case for an augmented technology acceptance model. Information and Management, 41(6), 747– 762.

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, Vol. 14, No. 5, pp.501–519.

Wu, J. H., & Wang, S. C. (2005). What drives mobile commerce? An empirical evaluation of the revised technology acceptance model. Information and Management, 42(5), 719–729.

Zainuddin, A. (2012). Structural Equation Modelling. Malaysia: UiTM Press.