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Measuring Mobile Banking Adoption in Uganda Using the Technology Acceptance Model (TAM2) and Perceived Risk

DOI: 10.4236/ojbm.2021.91021, PP. 397-418

Keywords: Mobile Banking Adoption, Technology Acceptance Model, TAM2, Uganda, Mobile Banking System, Perceived Risk, Perceived Usefulness, Perceived Ease of Use, Intention to Use, Actual Usage

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Abstract:

The purpose of this study is to measure the adoption of mobile banking systems among the citizens of Uganda by analyzing the effect of perceived usefulness, perceived ease of use, and perceived risk on actual usage of mobile banking, with intention to use as a mediating variable. A closed-ended questionnaire was employed to be filled by Uganda citizens. A total of 275 questionnaires were sent out, among which only 245 were useable. A factor analysis test was run in order to establish the construct validity of the questionnaire. After that, Cronbach’s alpha was used to measure the reliability of the study, and multiple linear regression analysis, Pearson’s correlation, ANOVA table, and process analysis was used to analyze the association between the variables. The findings of our study showed a good and acceptable model fit. Perceived usefulness and perceived ease of use were significantly correlated to actual usage, with intention to use as a positive mediator. Perceived risk had a negative relationship with actual usage of mobile banking where intention to use negatively mediates the relationship. This study can be used by the banking industry of Uganda as well as clients of banks. It poses in detail the advantages and the risks of using mobile banking system. The banking industry can use it to maximize on the advantages while minimizing the risks, and the clients can make use of this study to further gain knowledge about the benefits and risks and make an informed decision on whether to use the mobile banking system or not. The respondents’ pool of this study was constricted, employing only the citizens of Uganda. Moreover, this research was conducted on a cross-sectional basis with quantitative and closed-ended questionnaire. Future researchers can employ a diverse pool of respondents to get variable responses. Moreover, the study can be conducted on a longitudinal basis, while also employing the use of open-ended questionnaire to get a better insight regarding mobile banking systems.

References

[1]  Adamson, K. A., & Prion, S. (2013). Reliability: Measuring Internal Consistency Using Cronbach’s α. Clinical Simulation in Nursing, 9, e179-e180.
https://doi.org/10.1016/j.ecns.2012.12.001
[2]  Akturan, U., & Tezcan, N. (2012). Mobile Banking Adoption of the Youth Market: Perceptions and Intentions. Marketing Intelligence & Planning, 30, 444-459.
https://doi.org/10.1108/02634501211231928
[3]  Amin, H., Baba, R., & Muhammad, M. Z. (2007). An Analysis of Mobile Banking Acceptance by Malaysian Customers. Sunway University College Academic Journal, 4, 1-12.
[4]  Ba, S., & Pavlou, P. (2002). Evidence of the Effect of Trust Building Technology in Electronic Markets: Price Premiums and Buyer Behavior. MIS Quarterly, 26, 243-268.
https://doi.org/10.2307/4132332
[5]  Barnes, S. J., & Corbitt, B. (2003). Mobile Banking: Concept and Potential. International Journal of Mobile Communications, 1, 273-288.
https://doi.org/10.1504/IJMC.2003.003494
[6]  Bland, J. M., & Altman, D. G. (1997). Statistics Notes: Cronbach’s Alpha. BMJ, 314, 572.
https://doi.org/10.1136/bmj.314.7080.572
[7]  Cheah, C. M., Teo, A. C., Sim, J. J., Oon, K. H., & Tan, B. I. (2011). Factors Affecting Malaysian Mobile Banking Adoption: An Empirical Analysis. International Journal of Network and Mobile Technologies, 2, 149-160.
[8]  Chen, L.-D., Gillenson, M. L., & Sherrell, D. L. (2002). Enticing Online Consumers: An Extended Technology Acceptance Perspective. Information & Management, 39, 705-719.
https://doi.org/10.1016/S0378-7206(01)00127-6
[9]  Chitungo, S. K., & Munongo, S. (2013). Extending the Technology Acceptance Model to Mobile Banking Adoption in Rural Zimbabwe. Journal of Business Administration and Education, 3, 51-79.
[10]  Cho, J. (2004). Likelihood to Abort an Online Transaction: Influences from Cognitive Evaluations, Attitudes, and Behavioral Variables. Information & Management, 41, 827-838.
https://doi.org/10.1016/j.im.2003.08.013
[11]  Coursaris, C., Hassanein, K., & Head, M. (2003). M-Commerce in Canada: An Interaction Framework for Wireless Privacy. Canadian Journal of Administrative Sciences/Revue Canadienne des Sciences de l’Administration, 20, 54-73.
https://doi.org/10.1111/j.1936-4490.2003.tb00305.x
[12]  Curran, J. M., & Meuter, M. L. (2005). Self-Service Technology Adoption: Comparing Three Technologies. Journal of Services Marketing, 19, 103-113.
https://doi.org/10.1108/08876040510591411
[13]  Daoud, J. I. (2017). Multicollinearity and Regression Analysis. Journal of Physics: Conference Series, 949, Article ID: 012009.
https://doi.org/10.1088/1742-6596/949/1/012009
[14]  Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and End User Acceptance of Information Technology. MIS Quarterly, 13, 319-340.
https://doi.org/10.2307/249008
[15]  Davis, F. D. (1993). User Acceptance of Information Technology: System Characteristics, User Perceptions and Behavioral Impacts. International Journal of Man-Machine Studies, 38, 475-487.
https://doi.org/10.1006/imms.1993.1022
[16]  DeSanctis, G. (1983). Expectancy Theory as an Explanation of Voluntary Use of a Decision Support System. Psychological Reports, 52, 247-261.
https://doi.org/10.2466/pr0.1983.52.1.247
[17]  Devon, H. A., Block, M. E., Moyle-Wright, P., Ernst, D. M., Hayden, S. J., Lazzara, D. J., Savoy, S. M., & Kostas-Polston, E. (2007). A Psychometric Toolbox for Testing Validity and Reliability. Journal of Nursing Scholarship, 39, 155-164.
https://doi.org/10.1111/j.1547-5069.2007.00161.x
[18]  Field, A. P. (2009). Discovering Statistics Using SPSS: And Sex and Drugs and Rock ‘n’ Roll (3rd ed.). London: Sage.
[19]  Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research. Reading, MA: Addison-Wesley.
[20]  Forsythe, S. M., & Shi, B. (2003). Consumer Patronage and Risk Perceptions in Internet Shopping. Journal of Business Research, 56, 867-875.
https://doi.org/10.1016/S0148-2963(01)00273-9
[21]  Gerrard, P., & Barton Cunningham, J. B. (2003). The Diffusion of Internet Banking among Singapore Consumers. International Journal of Bank Marketing, 21, 16-28.
https://doi.org/10.1108/02652320310457776
[22]  Ha, S., & Stoel, L. (2009). Consumer E-Shopping Acceptance: Antecedents in a Technology Acceptance Model. Journal of Business Research, 62, 565-571.
https://doi.org/10.1016/j.jbusres.2008.06.016
[23]  Hanafizadeh, P., Behboudi, M., Koshksaray, A. A., & Tabar, M. J. S. (2014). Mobile-Banking Adoption by Iranian Bank Clients. Telematics and Informatics, 31, 62-78.
https://doi.org/10.1016/j.tele.2012.11.001
[24]  Hill, T., Smith, N. D., & Mann, M. F. (1987). Role of Efficacy Expectations in Predicting the Decision to Use Advanced Technologies: A Case of Computers. Journal of Applied Psychology, 72, 307-318.
https://doi.org/10.1037/0021-9010.72.2.307
[25]  Hinton, P. R. (2014). Statistics Explained, Statistics Explained. London: Routledge.
https://doi.org/10.4324/9781315797564
[26]  Hogarth, R. M. (1991). A Perspective on Cognitive Research in Accounting. The Accounting Review, 66, 277-290.
[27]  Huang, E. (2008). Use and Gratification in E-Consumers. Internet Research, 18, 405-426.
https://doi.org/10.1108/10662240810897817
[28]  Igbaria, M., Zinatelli, N., Cragg, P., & Cavaye, A. L. (1997). Personal Computing Acceptance Factors in Small Firms: A Structural Equation Model. MIS Quarterly, 21, 279-305.
https://doi.org/10.2307/249498
[29]  Jackson, C. M., Chow, S., & Leitch, R. A. (1997). Toward an Understanding of the Behavioral Intention to Use an Information System. Decision Sciences, 28, 357-389.
https://doi.org/10.1111/j.1540-5915.1997.tb01315.x
[30]  Kaiser, H. F. (1974). An Index of Factorial Simplicity Psychometrics. Psychometrika, 39, 31-36.
https://doi.org/10.1007/BF02291575
[31]  Koenig-Lewis, N., Palmer, A., & Moll, A. (2010). Predicting Young Consumers’ Take Up of Mobile Banking Services. International Journal of Bank Marketing, 28, 410-432.
https://doi.org/10.1108/02652321011064917
[32]  Laforet, S., & Li, X. (2005). Consumers’ Attitudes towards Online and Mobile Banking in China. International Journal of Bank Marketing, 23, 362-380.
https://doi.org/10.1108/02652320510629250
[33]  Lee, Y., Kozar, K. A., & Larsen, K. R. (2003). The Technology Acceptance Model: Past, Present, and Future. Communications of the Association for Information Systems, 12, 50.
https://doi.org/10.17705/1CAIS.01250
[34]  Legris, P., Ingham, J., & Collerette, P. (2003). Why Do People Use Information Technology? A Critical Review of the Technology Acceptance Model. Information & Management, 40, 191-204.
https://doi.org/10.1016/S0378-7206(01)00143-4
[35]  Lin, H. H., & Wang, Y. S. (2005). Predicting Consumer Intention to Use Mobile Commerce in Taiwan. Proceeding of the International Conference on Mobile Business (ICMB’05), Sydney, 11-13 July 2005, 406-412.
[36]  Lopez-Nicolas, A., Molina-Castillo, F. J., & Bouwman, H. (2008). An Assessment of Advanced Mobile Services Acceptance: Contributions from TAM and Diffusion Theory Models. Information & Management, 45, 359-364.
https://doi.org/10.1016/j.im.2008.05.001
[37]  Lu, J., Yu, C. S., Liu, C., & Yao, J. E. (2003). Technology acceptance model for wireless internet. Internet Research, 13, 206-222.
https://doi.org/10.1108/10662240310478222
[38]  Luarn, P., & Lin, H. H. (2005). Toward an Understanding of the Behavioral Intention to Use Mobile Banking. Computer in Human Behaviour, 21, 873-891.
https://doi.org/10.1016/j.chb.2004.03.003
[39]  Lule, I., Omwansa, T. K., & Waema, T. M. (2012). Application of Technology Acceptance Model (TAM) in Mobile Banking Adoption in Kenya. International Journal of Computing and ICT Research, 6, 31-43.
[40]  Luo, X., Li, H., Zhang, J., & Shim, J. P. (2010). Examining Multi-Dimensional Trust and Multi-Faceted Risk in Initial Acceptance of Emerging Technologies: An Empirical Study of Mobile Banking Services. Decision Support Systems, 49, 222-234.
https://doi.org/10.1016/j.dss.2010.02.008
[41]  Mathwick, A., Malhotra, N. K., & Rigdon, E. (2001). The Effect of Dynamic Retail Experiences of Value: An Internet and Catalog Comparison. Journal of Retailing, 78, 51-60.
https://doi.org/10.1016/S0022-4359(01)00066-5
[42]  Meuter, M. L., Bitner, M. J., Ostrom, A. L., & Brown, S. W. (2005). Choosing among Alternative Service Delivery Modes: An Investigation of Customer Trial of Self-Service Technologies. Journal of Marketing, 69, 61-83.
https://doi.org/10.1509/jmkg.69.2.61.60759
[43]  Mukaka, M. M. (2012). Statistics Corner: A Guide to Appropriate Use of Correlation Coefficient in Medical Research. Malawi Medical Journal, 24, 69-71.
[44]  Nassiwa, J. (2019). Factors Affecting Adoption of Mobile Banking in Uganda: A Case Study of Stanbic Bank, Makerere University Branch. Doctoral Dissertation, Kampala: Makerere University.
[45]  Overholser, B. R., & Sowinski, K. M. (2008). Biostatistics Preview: Part 2. Nutrition in Clinical Practice, 23, 76-84.
https://doi.org/10.1177/011542650802300176
[46]  Pavlou, P. A. (2001). Integrating Trust in Electronic Commerce with the Technology Acceptance Model: Model Development and Validation. Proceedings of Seventh Americas Conference on Information Systems (AMCIS), December 2001, 816-822.
[47]  Peevers, G., Douglas, G., & Jack, M. A. (2008). A Usability Comparison of Three Alternative Message Formats for an SMS Banking Service. International Journal of Human-Computer Studies, 66, 113-123.
https://doi.org/10.1016/j.ijhcs.2007.09.005
[48]  Pikkarainen, T., Pikkarainen, K., Karjaluoto, H., & Pahnila, S. (2004). Consumer Acceptance of Online Banking: An Extension of the Technology Acceptance Model. Internet Research, 14, 224-235.
https://doi.org/10.1108/10662240410542652
[49]  Poon, W. C. (2008). Users’ Adoption of E-Banking Services: The Malaysian Perspective. Journal of Business & Industrial Marketing, 23, 59-69.
[50]  Porteous, D. (2006). The Enabling Environment for Mobile Banking in Africa. Boston MA: DFID.
[51]  Püschel, J., Mazzon, J. A., & Hernandez, J. M. C. (2010). Mobile Banking: Proposition of an Integrated Adoption Intention Framework. International Journal of Bank Marketing, 28, 389-409.
https://doi.org/10.1108/02652321011064908
[52]  Ramdhony, D., & Munien, S. (2013). An Investigation on Mobile Banking Adoption and Usage: A Case Study of Mauritius. Proceedings of 3rd Asia-Pacific Business Research Conference, Kuala Lumpur, Malaysia, 25-26 February 2013.
[53]  Ringle, C. M., Wende, S., & Becker, J.-M. (2015). SmartPLS 3. Bonningstedt: SmartPLS.
http://www.smartpls.com
[54]  Riquelme, H. E., & Rios, R. E. (2010). The Moderating Effect of Gender in the Adoption of Mobile Banking. International Journal of Bank Marketing, 28, 328-341.
https://doi.org/10.1108/02652321011064872
[55]  Rodgers, J. L., & Nicewander, W. A. (1988). Thirteen Ways to Look at the Correlation Coefficient. The American Statistician, 42, 59-66.
https://doi.org/10.2307/2685263
[56]  Sheppard, B. H., Hartwick, J., & Warshaw, P. R. (1988). The Theory of Reasoned Action: A Meta-Analysis of Past Research with Recommendations for Modifications for Future Research. Journal of Consumer Research, 15, 325-343.
https://doi.org/10.1086/209170
[57]  Shih, Y. Y., & Fang, K. (2004). The Use of a Decomposed Theory of Planned Behavior to Study Internet Banking in Taiwan. Internet Research, 14, 213-223.
https://doi.org/10.1108/10662240410542643
[58]  Singh, S., Singh, D. K., Singh M. K., & Singh, S. K. (2010). The Forecasting of 3G Market in India Based on Revised Technology Acceptance Model. International Journal of Next-Generation Networks, 2, 61-68.
https://doi.org/10.5121/ijngn.2010.2206
[59]  Sulaiman, A., Jaafar, N. I., & Mohezar, S. (2007). An Overview of Mobile Banking Adoption among the Urban Community. International Journal of Mobile Communications, 5, 157-168.
https://doi.org/10.1504/IJMC.2007.011814
[60]  Tan, G. W. H., Chong, C. K., Ooi, K. B., & Chong, A. Y. L. (2010). The Adoption of Online Banking in Malaysia: An Empirical Analysis. International Journal of Business and Management Science, 3, 169-193.
[61]  Tan, G. W. H., Ooi, K. B., Leong, L. Y., & Lin, B. (2014). Predicting the Drivers of Behavioral Intention to Use Mobile Learning: A hybrid SEM-Neural Networks Approach. Computers in Human Behavior, 36, 198-213.
https://doi.org/10.1016/j.chb.2014.03.052
[62]  Taylor, J. W. (1974). The Role of Risk in Consumer Behavior: A Comprehensive and Operational Theory of Risk Taking in Consumer Behavior. Journal of Marketing, 38, 54-60.
https://doi.org/10.1177/002224297403800211
[63]  Taylor, S., & Todd, P. (1995). Assessing IT Usage: The Role of Prior Experience. MIS Quarterly, 19, 561-570.
https://doi.org/10.2307/249633
[64]  Teo, A. C., Cheah, C. M., Leong, L. Y., Hew, T. S., & Shum, Y. L. (2012). What Matters Most in Mobile Payment Acceptance? A Structural Analysis. International Journal of Network and Mobile Technologies, 3, 49-69.
[65]  Turban, E., King, D., Viehland, D., & Lee, J. (2006). Electronic Commerce 2006: A Management Perspective. Upper Saddle River, NJ: Prentice Hall.
[66]  Venkatesh, V., & Davis, F. D. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, 46, 186-204.
https://doi.org/10.1287/mnsc.46.2.186.11926
[67]  Venkatesh, V., & 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, 115-139.
https://doi.org/10.2307/3250981
[68]  Wackerly, D. D., Mendenhall III, W., & Scheaffer, R. L. (2008). Multivariate Probability Distributions. In Mathematical Statistics with Applications (7th ed., pp. 223-295). Belmont, CA: Brooks/Cole.
[69]  Wang, Y. S., Wang, Y., Lin, Y. M., & Tang, T. I. (2003). Determinants of User Acceptance of Internet Banking: An Empirical Study. International Journal of Service Industry Management, 14, 501-519.
https://doi.org/10.1108/09564230310500192
[70]  Wu, J. H., & Wang, S. C. (2005). What Drives Mobile Commerce?: An Empirical Evaluation of the Revised Technology Acceptance Model. Information & Management, 42, 719-729.
https://doi.org/10.1016/j.im.2004.07.001
[71]  Yousafzai, S. Y., Pallister, J. G., & Foxall, G. R. (2003). A Proposed Model of E-Trust for Electronic Banking. Technovation, 23, 847-860.
https://doi.org/10.1016/S0166-4972(03)00130-5

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