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The ongoing debate concerning economic losses associated with human tellers and the negative impact of queuing in the banking halls have led to new technologies and innovation diffusion. This paper applied a regression model in which end-users level data were analyzed in order to predict the adoption of automated teller machines using the theory of diffusion of innovation (e.g., relative advantage, complexity, observability, trialability, and compatibility) empirically. Applying the principal component analysis and regression as analytical techniques, the results were compatible with the adoption intention. Following from the PCA, the results show that the cumulative percentage of the predictive variables was above the 50% threshold with KMO measure and Cronbach Alphas recording scores above 70%, suggesting the appropriateness of PCA in data reduction. The predictive variables have strong predictability and were significant. Abstracting from the results there may be two reasons relating to the low adoption decisions. The first reason may be due to some inherent inefficiencies or unwarranted phenomena which may have lessened patronage and secondly, customers’ categorization on the basis of innovativeness which skewed in favour of early adopters rather than late adopters. The banks should take steps to update the existing technologies relating to automated teller machine operations in particular in order to address the challenges before enforcing any future deployment to meet end-users expectations. Because adoption can be influenced by customer categorization on the basis of innovativeness, analysis of these groupings should be conducted in order to understand the characteristics of each group.


Adoption relative advantage innovators late majority laggards

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How to Cite
Awuma, W., Danso, I., Yeboah, P., & Akentara, R. (2023). Application of the Theory of Diffusion of Innovation on the Adoption of Automated Teller Machines in the Sunyani Municipality. International Journal of Multidisciplinary Studies and Innovative Research, 11(5), 1719–1727.


  1. Al-Emran, M & Granic, A (2020). Analysis of the Technology Acceptance Model from 2010 to 2020.
  2. Ahmad, S., Bhatti, S.H. & Hwang, Y., (2020). ‘E-service Quality and Actual Use of E-banking: Explanation through the Technology Acceptance Model’, Information Development 36(4),503–519.
  3. Ajzen, I (2002). Perceived Behavioral Control, Self-efficacy, Locus of Control, and the Theory of Planned Behavior, Journal of Applied Social Psychology. (32) 665-683.
  4. Ajzen, I. & Fishbein, M., (1975). A Bayesian Analysis of Attribution Processes’, Psychological Bulletin 82(2), 261–277.
  5. Ajzen, I., (1991). The Theory of Planned Behavior’, Organizational Behavior and Human Decision Processes 50(2), 179–211.
  6. Braak, V. J (2001). Individual Characteristics Influencing Teachers Class Use of Computers. Journal of Educational Computing Research 25(2): 141-157
  7. Blackwell, R. D., Engle, J. F., & Miniard, P. W. (1995). Diffusion of Innovations in Consumer Behavior. London: Dryden Press.
  8. Davis, F.D (1986). Technology Acceptance Model for Empirically Testing New End-user Information Systems: Theory and Results: Unpublished PhD thesis, Massachusetts Institute of Technology.
  9. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User Acceptance of Computer Technology: A Comparison of two Theoretical Models. Management Science, 35(8), 982-1003.
  10. Davis, F.D., (1985). A Technology Acceptance Model for Empirically Testing End-user Information Systems: Theory and Results’, Doctoral dissertation, Massachusetts Institute of Technology.
  11. Foley Curley, K. (1984). Are there any real benefits from office automation? Business Horizons, 27 (4), 37-42.
  12. Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research. Reading, MA: Addison-Wesley.
  13. Gallini, S.M., & Moely, B.E. (2003). Service-learning and Engagement, Academic Challenge and Retention. Michigan Journal of Community Service Learning, 5-14.
  14. Gefen, D., Karahanna, E and Straub, D, W. (2003). Trust and TAM in Online Shopping: An Integrated Model. MIS Quarterly, (27), 51-90
  15. Gerrard, P, & Cunningham, J. B. (2003). The Diffusion of Internet Banking among Singaporean Consumers. International Journal of Bank Marketing, 21(1), 16-28.
  16. Hair, J.F., Ortinau, D.J. & Harrison, D.E., (2021). Essentials of Marketing Research, McGraw-Hill, New York, NY
  17. Horton, R. P., Buck, T., Waterson, P. E., & Clegg, C. W. (2001). Explaining Intranet Use with the Technology Acceptance Model. Journal of Information Technology, 16(4), 237-249.
  18. Kaiser, H.F. (1960). The Application of Electronic Computers to Factor Analysis. Educational and Psychological Measurement, 20, 141–151
  19. Lee, C.K., Yiu, T.W. & Cheung, S.O., (2021). Predicting Intention to use Alternative Dispute Resolution (ADR): An Empirical Test of Theory of Planned Behaviour (TPB) model’, International Journal of Construction Management 21(1), 27–40.
  20. Leech, N.L., Barrett, K.C. and Morgan, G.A. (2005). SPSS for Intermediate Statistics, Use and Interpretation. 2nd Edition, Lawrence Erlbaum Associates Inc., Mahwah.
  21. Legris, J. Ingham, P. Collerette. (2003). Why do People use Information Technology? A Critical Review of the Technology Acceptance Model, Information and Management 40 (2003) 191-204.
  22. Menard, S. (1995). Applied Logistic Regression Analysis. Thousand Oaks, CA: Sage
  23. Mertler, C. A., & Vannatta, R. A. (2005). Advanced and Multivariate Statistical Methods: Practical Application and Interpretation. Glendale, CA: Pyrczak Publishing
  24. Menard, S. (1995). Applied Logistic Regression Analysis. Sage University Paper series on Quantitative Applications in the Social Sciences, series no. 07-106. Thousand Oaks, CA: Sage.
  25. Meimouri, N.M. Yaghoubi, M. Kazemi (2012). The Effect of Electronic Service Quality on Customers Behavioral Intentions, International Journal of Marketing Studies. 4 (2) 179-187.
  26. Nguyen, V. & Nguyen, T., (2016). Perceived Risk in the E-payment Adoption via Social Network’, Journal of Economic Development 27(12), 66–81.
  27. Ravichandran, K. Bhargavi, S. Arun-Kumar (2010). Influence of Service Quality on Banking Customers’ Behavioral Intentions, International Journal of Economics and Finance. 2 (4) 18-28.
  28. Rogers, E.M. (2003). Diffusion of Innovations (5th ed.). New York: Free Press.
  29. Sharda, R., Barr, S.H. & McDonnell, J.C. (1988). Decision Support System Effectiveness: A Review and an Empirical Test. Management Science, 34 (2), 139-159
  30. Sherry, L. (1997). The Boulder Valley Internet Project: Lessons Learned. Technological Horizons in Education. Journal, 25(2), 68-73.
  31. Singh, S. & Srivastava, R.K., (2020). Understanding the Intention to use Mobile Banking by existing Online Banking Customers: An Empirical Study’, Journal of Financial Services Marketing 25(3), 86–96.
  32. Venkatesh, V. & Davis, F.D., (1996). A Model of the Antecedents of Perceived Ease of Use: Development and Test’, Decision Sciences 27(3), 451–481.
  33. Venkatesh, V. & Davis, F.D., (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies’, Management Science 46(2), 186–204.
  34. 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.
  35. Venkatesh, V. & Bala, H., (2008). Technology Acceptance Model 3 and A Research Agenda on Interventions’, Decision Sciences 39(2), 273–315.
  36. Venkatesh, V., Thong, J.Y. & Xu, X., (2012). Consumer Acceptance and use of Information Technology: Extending the Unified Theory of Acceptance and use of Technology’, MIS Quarterly 36(1),157–178.
  37. Zhang, T., Lu, C. and Kizildag, M. (2018). “Engaging Generation Y to co-Create Through Mobile Technology”, International Journal of Electronic Commerce, Vol. 21 No. 4, pp. 489-516.
  38. Zwick, W.R., & Velicer, W.F (1986). Comparison of Five Rules of Determining the Number of Components to Retain. Psychological Bulletin 99(3): 432-442