The Determinants of User Behavior of Computer Based Transaction Processing Systems: The Case of Minimarket Employees in Padang, Indonesia

  • Authors

    • Yuhelmi .
    • Surya Dharma
    • Mery Trianita
    • Listiana Sri Mulatsih
  • Technology Acceptance Model (TAM), User Behavior of Computer Based Transaction Processing System.
  • This study was aimed to investigate the determinants of actual use of computer based transaction processing system among employees in minimarkets in Padang, Indonesia. In addition to Perceived ease of use and perceived usefulness which are the basic models of Technology Acceptance Model (TAM), Subjective norm was conceptualized as an external variable that affecting Technology Acceptance among users of transaction processing system. In total, 246 employees participated in this study. The results show that the perceived ease of use positively affects Perceived Usefulness and Attitude. Furthermore, perceived usefulness and subjective norm have positively affected on Attitude. Likewise Attitude has positively affected on Actual Use. This study reveals that employees tend to comply the peers’ opinion on using transaction processing system. For future research is expected to expand the TAM model by adding external variables and individual characteristics as a moderator variable



  • References

    1. [1] Mahar, E. F. (2003). Role of Information Technology in Transaction Processing System. Pakistan Journal of Information and Technology, 2(2), 128–134.

      [2] Vlahos, G. E., Ferratt, T. W., & Knoepfle, G. (2004). The use of computer-based information systems by German managers to support decision making. Information and Management, 41(6), 763–779.

      [3] Davis, F. (1989). Perceived Usefulness, Perceived Ease Of Use, And User Accep. MIS Quarterly, 13(3), 319.

      [4] 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.

      [5] Schierz, P. G., Schilke, O., & Wirtz, B. W. (2010). Understanding consumer acceptance of mobile payment services: An empirical analysis. Electronic Commerce Research and Applications, 9(3), 209–216.

      [6] Taylor, S., & Todd, P. A. (1995b). Understanding information technology usage: A test of competing models. Information Systems Research.

      [7] Fishbein, M. & Ajzen, I. (1975). Belief, attitude, attitude, intention and behavior: An introduction to theory of research. Reading, MA : Addison-Wesley Addison-Wesley, 578.

      [8] Venkatesh, Viswanath., Davis, F. D. (2000). Theoretical Acceptance Extension Model : Field Four Studies of the Technology Longitudinal. Management Science, 46(2), 186–204.

      [9] Wei, W. C. (2009). A technology acceptance model: Mediate and moderate effect. Asia Pacific Management Review, 14(4), 461–476.

      [10] Wallace, L. G., & Sheetz, S. D. (2014). Information & Management The adoption of software measures : A technology acceptance model ( TAM ) perspective. Information & Management, 51(2), 249–259.

      [11] Fathema, N., Shannon, D., & Ross, M. (2015). Expanding The Technology Acceptance Model ( TAM ) to Examine Faculty Use of Learning Management Systems ( LMSs ) In Higher Education Institutions. MERLOT Journal of Online Learning and Teaching, 11(2), 210–232.

      [12] Venkatesh Viswanath ; Bala, H. (2008). Technology Acceptance Model 3 and a Research Agenda on Interventions. Decision Sciences, 39(2), 273–315.

      [13] Natarajan, T., Balasubramanian, S. A., & Kasilingam, D. L. (2017). Understanding the intention to use mobile shopping applications and its influence on price sensitivity. Journal of Retailing and Consumer Services, 37, 8–22.

      [14] Cheng, Y. M. (2015). Towards an understanding of the factors affecting m-learning acceptance: Roles of technological characteristics and compatibility. Asia Pacific Management Review, 1–11.

      [15] Darsono, L. I. (2005). Examining Information Technology Acceptance by Individual Professionals. Gadjah Mada International Journal of Business, 7(2), 155—178.

      [16] Premkumar, G., & Bhattacherjee, A. (2008). Explaining information technology usage: A test of competing models. Omega, 36, 64–75.

      [17] Hsu, M. H., & Chiu, C. M. (2004). Predicting electronic service continuance with a decomposed theory of planned behaviour. Behaviour and Information Technology, 23(5), 359–373.

      [18] Macedo, I. M. (2017). Predicting the acceptance and use of information and communication technology by older adults: An empirical examination of the revised UTAUT2. Computers in Human Behavior, 75.

      [19] Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2017). Re-examining the Unified Theory of Acceptance and Use of Technology (UTAUT): Towards a Revised Theoretical Model. Information Systems Frontiers, 1–16.

      [20] 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. Forthcoming in MIS Quarterly, Vol. 36, No. 1 (2012), Pp. 157-178, 36(1), 157–178.

      [21] Wu, B., & Chen, X. (2016). Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model. Computers in Human Behavior, 67, 221–232.

      [22] Hair, J. F., Black. W. C., Babin. B. J.; and Anderson. R. E. (2010), Multivariate Data Analysis, 7th ed. Pearson Prentice Hall, New Jersey.

      [23] Erasmus, E., Rothmann, S., & Van Eeden, C. (2015). A structural model of technology acceptance. SA Journal of Industrial Psychology, 41(1), 1–12.

      [24] Rauniar, R., Rawski, G., Yang, J., & Johnson, B. (2013). Technology acceptance model ( TAM ) and social media usage : an empirical study on Facebook.

      [25] Giovanis, A. N., Binioris, S., & Polychronopoulos, G. (2012). An extension of TAM model with IDT and security/privacy risk in the adoption of internet banking services in Greece. EuroMed Journal of Business, 7(1).

      [26] Ayeh, J. K. (2015). Travelers acceptance of consumer-generated media: An integrated model of technology acceptance and source credibility theories. Computers in Human Behavior, 48, 173–180.

      [27] Taylor, S., & Todd, P. (1995a). Assessing IT Usage: The Role of Prior Experience. MIS Quarterly, 19(4), 561.

      [28] Bashir, I., & Madhavaiah, C. (2015). Consumer attitude and behavioral intention towards Internet banking adoption in India. Journal of Indian Business Research, 7(1), 67–102.

      [29] Alsamydai, M. J. (2014). Adaptation of the Technology Acceptance Model (TAM) to the Use of Mobile Banking Services. International Review of Management and Business Research, 3(4), 2016–2028. Retrieved from

      [30] Afshan, S., & Sharif, A. (2016). Acceptance of mobile banking framework in Pakistan. Telematics and Informatics, 33(2).

      [31] Chaouali, W., Ben Yahia, I., & Souiden, N. (2016). The interplay of counter-conformity motivation, social influence, and trust in customers’ intention to adopt Internet banking services: The case of an emerging country. Journal of Retailing and Consumer Services, 28, 209–218.

      [32] Moshki, M. K., Teimouri, H., & Ansari, R. (2013). A Survey on the Level of Organization Agility and Proposition of a Comprehensive Model (the Case of Nir Pars Company). International Journal of Human Resource Studies, 3(3), 62.

      [33] Gao, Lingling and Bai, Xuesong, (2014). A Unified perspective on the factors influencing consumer acceptance of internet of things technology, Asia Pacific Journal of Marketing and Logistics, 26(2), 211 - 231.

  • Downloads

  • How to Cite

    ., Y., Dharma, S., Trianita, M., & Sri Mulatsih, L. (2018). The Determinants of User Behavior of Computer Based Transaction Processing Systems: The Case of Minimarket Employees in Padang, Indonesia. International Journal of Engineering & Technology, 7(4.9), 90-95.