The Extension of TAM Model in the Use of Point of Sale (Pos) in Minimarkets in Padang, Indonesia

Abstract

The use of information technology system in the form of Point of Sale (POS) becomes essential in assisting operational minimarket. The purpose of this study is to extend the original TAM model to investigate factors affecting the behavior of Point of Sales (POS) users. Information quality of POS and subjective norm is conceptualized as an external variable that affects POS acceptance through perceived usefulness and perceived ease of use. The number of respondents who participated in this study was 270 employees of minimarket in Padang. The results show that the quality of information positively affects subjective norms, perceived usefulness and attitude, as well as subjective norms positively affect perceived ease of use, perceived usefulness and attitude. Perceived usefulness mediates the full perceived ease of use effect on attitude. Lastly, attitude has a positive effect on actual use. The results of this study show that the quality of relevant and accurate information and opinions of colleagues are taken into consideration for employees in using POS.


 


 


Keywords: Information Quality, Point of Sale, Subjective Norm, TAM

References
[1] Zadeh, M. H. K., Karkon, A., & Golnari, H. (2013). The Effect of Information Technology on the Quality of Accounting Information. Shiraz Journal of System Management, 3(3), 61–76.


[2] Utami, C. W. (2010). Manajemen ritel (2nd ed.). Jakarta: Salemba Empat.


[3] Yuhelmi., Dharma, S., Trianita, M., & Mulatsih, L. S. (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.


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


[5] Jogiyanto, 2007. Sistem Informasi Keperilakuan,. Edisi Revisi. Yogyakarta: Andi Offset


[6] 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.


[7] Fan, C.-W. (2014). Applied the Technology Acceptance Model to Survey the mobilelearning adoption behavior in Science Museum. International Journal of Innovation and Scientific Research, 12(1), 22–29.


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


[9] Lin, F., Fofanah, S. S., & Liang, D. (2011). Assessing citizen adoption of e-Government initiatives in Gambia: A validation of the technology acceptance model in information systems success. Government Information Quarterly, 28(2), 271–279.


[10] Gao, L., & Bai, X. (2014). A unified perspective on the factors influencing consumer acceptance of internet of things technology, 26(2), 211–231.


[11] Alambaigi, A., & Ahangari, I. (2015). Technology Acceptance Model (TAM) As a Predictor Model for Explaining Agricultural Experts Behavior in Acceptance of ICT. International Journal of Agricultural Management and Development (IJAMAD), 6(2), 235–247.


[12] Venkatesh, V., & Davis, F. D. (1996). A Model of the Antecedents of Perceived Ease of Use: Development and Test. Decision Science, 27(3), 451–481.


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


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


[15] Venkatesh, V. (2000). Determinants of Perceived Ease of Use: Integrating Control, Intrinsic Motivation, and Emotion into the Technology Acceptance Model. Information Systems, 11(4), 342–365.


[16] Davis, G. B. (2000). Information systems conceptual foundations: looking backward and forward. Organizational and Social Perspectives on Information Technology IFIP - The International Federation for Information Processing, 41, 61–82.


[17] Delone, W. H., & Mclean, E. R. (2004). Measuring e-Commerce Success: Applying the DeLone & McLean Information Systems Success Model Value of Information Technology in e-Business Environments (Fall, 2004), International Journal of Electronic Commerce, 9(1) pp. 31-47., 31–47.


[18] Shih, H. P. (2004). An empirical study on predicting user acceptance of e-shopping on the Web. Information and Management, 41(3), 351–368.


[19] Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decession Processes, 50, 179–211.


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


[22] 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.


[23] Yi, M. Y., Jackson, J. D., Park, J. S., & Probst, J. C. (2006). Understanding information technology acceptance by individual professionals: Toward an integrative view. Information and Management, 43(3), 350–363.


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


[25] 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.


[26] . Agarwal, R., & Prasad, J. (1999). Are individual differences germane to the acceptance of new information technologies? Decision Sciences, 30(2), 361–391


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


[28] Nagy, J. T. (2018). Evaluation of Online Video Usage and Learning Satisfaction: An Extension of the Technology Acceptance Model. The International Review of Research in Open and Distributed Learning, 19(1), 160–184.


[29] Shipps, B., & Phillips, B. (2013). Social Networks, Interactivity and Satisfaction: Assessing Socio-Technical Behavioral Factors as an Extension to Technology Acceptance. Journal of Theoretical and Applied Electronic Commrce Research, 8(1), 35–52


[30] Bhattacherjee, A., & Sanford, C. (2006). Influence Processes For Information Technology Acceptance: An Elaboration Likelihood Model 1. MIS Quarterly, 30(4), 805–825.


[31] 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


[32] Chen, H., Rong, W., Ma, X., Qu, Y., & Xiong, Z. (2017). An Extended Technology Acceptance Model for Mobile Social Gaming Service Popularity Analysis. Hindawi Mobile Information Systems, 2017, 1–13.


[33] Chau, P. Y. K., & Hu, P. J.-H. (2001). Information Technology Acceptance by Individual Professionals: A Model Comparison Approach. Decision Sciences, 32(4), 699–719.


[34] 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.


[35] 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.


[36] Al-Gahtani, S. S., & King, M. (1999). Attitudes, satisfaction and usage: Factors contributing to each in the acceptance of information technology. Behaviour and Information Technology, 18(4), 277–297.


[37] Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). In Multivariate Data Analysis (Seventh Ed, p. 761). Harlow, United Kingdom: Pearson Education Limited.


[38] DeLone, W. H., & Mclean, E. R. (2003). The DeLone and McLean Model of Information Systems Success. Journal of Management Information Systems, 19(4), 9–30.


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


[40] Yakasai, A. B. M., & Jusoh, W. J. W. (2015). Testing the Theory of Planned Behavior in Determining Intention to Use Digital Coupon among University Students. Procedia Economics and Finance, 31(15), 186–193.


[41] Taylor, S., & Todd, P. (1995a). Assessing IT Usage: The Role of Prior Experience. MIS Quarterly, 19(4), 561-570


[42] Taylor, S., & Todd, P. A. (1995b). Understanding information technology usage: A test of competing models. Information Systems Research. 6 (2), 144- 176