Examination of The Factors Effective in The use of the e-government system with the technology acceptance model
Abstract. In this study, it was aimed to determine the factors affecting user behaviors in adopting the e-government system with the theory of reasoned action and technology acceptance model used in the literature. In this regard, 5500 academic and administrative staff working at Atatürk and Gümüşhane universities were included in the study using the questionnaire. In consequence of the survey application, 463 questionnaires were analyzed. The Cronbach Alpha Coefficient method was used for the reliability, and the Confirmatory Factor Analysis was used for the validity of the research scales. After determining the reliability and validity of the scales, research hypotheses were tested by the Structural Equation Model. According to the analysis results, in the first model of the study, anxiety has a negative impact on perceived usefulness and perceived ease of use. On the other hand, results showed that reliance has no significant effect on perceived usefulness and perceived ease of use. Furthermore, it has been obtained that perceived usefulness is the most important factor for the attitude with a rate of 69.2%. In the second model of the study, it has been obtained that self-efficacy is the most important factor for the perceived behavior control with a rate of 82.3% and perceived behavior control is the most important factor for the perceived behavior control with a rate of 75.6%. Moreover, the actual behavior factor for adopting the e-government system in the first model was explained with more percentage than the second model
Keywords. E-Government, Electronic government, Technology acceptance model, Theory of reasoned action, Structural equation model.JEL. C38, H11, H19.
Agag, G., & El-Masry, A.A. (2016). Understanding the determinants of hotel booking intentions and moderating role of habit. International Journal of Hospitality Management, 54, 52-67. doi. 10.1016/j.ijhm.2016.01.007
Ajzen, I. (2002). Residual effects of past on later behavior: Habituation and reasoned action perspectives. Personality and Social Psychology Review, 6(2), 107-122. doi. 10.1207/S15327957PSPR0602_02
Alrowili, T.F., Alotaibi, M.B., & Alharbi, M.S. (2015). Predicting citizens' acceptance of M-government services in Saudi Arabia an empirical investigation. In Systems Conference (SysCon), 2015 9th Annual IEEE International, (pp. 627-633). IEEE.
Altunişik, R., Coşkun, R., Bayraktaroğlu, S., & Yildirim, E. (2007). Sosyal Bilimlerde Araştırma Yöntemleri. Sakarya: Sakarya Yayıncılık.
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. doi. 10.1037/0033-2909.103.3.411
Anderson, J.E., & Schwager, P.H. (2004). SME adoption of wireless LAN technology: applying the UTAUT model. In Proceedings of the 7th annual conference of the southern association for information systems, Vol. 7, pp. 39-43.
Arık, A. (1992). Psikolojide Bilimsel Yöntem. İstanbul: İstanbul Üniversitesi Basımevi.
Bayram, N. (2010). Yapısal Eşitlik Modellemesine Giriş Amos Uygulamaları. Ezgi Kitabevi.
Bentler, P.M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238-246. doi. 10.1037/0033-2909.107.2.238
Bollen, K.A., & Long, J.S. (1992). Tests for structural equation models: introduction. Sociological Methods & Research, 21(2), 123-131. doi. 10.1177/0049124192021002001
Barbara, M.B. (2001). Structural Equation Modeling with AMOS: Basic Concepts, Applications, and Programming. (2nd edition).New York – London: Taylor & Francis Group.
Byrne, B.M. (2010). Structural Equation Modeling with AMOS: Basic Concepts, Applications, and Programming. New York – London: Taylor & Francis Group.
Carey, L. (1988). Measuring and Eevaluating School Learning. London: Allyn & Bacon.
Carmines, E.G., & Zeller, R.A. (1982). Reliability and validity assessment (Vol. 17). Beverly Hills: Sage publications.
Chou, C.P., & Bentler, P.M. (1990). Model modification in covariance structure modeling: A comparison among likelihood ratio, Lagrange multiplier, and Wald tests. Multivariate Behavioral Research, 25(1), 115-136. doi. 10.1207/s15327906mbr2501_13
Çam, H. (2012). Türkiye’deki üniversitelerde bulut bilişim teknolojisinin uygulanabilirliğinin teknoloji kabul modeli yaklaşımıyla belirlenmesi. Erzurum: Atatürk Üniversitesi Sosyal Bilimleri Enstitüsü.
Çerezci, T.E. (2010). Yapısal Eşitlik Modelleri ve Kullanılan Uyum Iyiliği Indekslerinin Karşılaştırılması. Ankara: Gazi Üniversitesi Fen Bilimleri Enstitüsü.
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
Delone, W.H., & McLean, E.R. (2003). The DeLone and McLean model of information systems success: a ten-year update. Journal of Management Information Systems, 19(4), 9-30. doi. 10.1080/07421222.2003.11045748
Demerouti, E. (2004). Structural equation modeling.[Retrieved from].
Duyck, P., Pynoo, B., Devolder, P., Voet, T., Adang, L., & Vercruysse, J. (2008). User acceptance of a picture archiving and communication system-applying the unified theory of acceptance and use of technology in a radiological setting. Methods of Information in Medicine, 47(2), 149-156. doi. 10.3414/ME0477
Esen, M., & Erdogmus, N. (2014). Effects of technology readiness on technology acceptance in e-HRM: Mediating role of perceived usefulness. The Journal of Knowledge Economy & Knowledge Management, 9, 7-21.
Garson, G.D. (2009). Structural Equation Modeling. Asheboro, NC: Statistical Associates Publishers.
Gay, L.R. (1985). Educational Evaluation & Measurement. CE Merrill Publishing Company.
Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E., & Tatham, R.L. (1998). Multivariate Data Analysis, (Vol. 5, No. 3, pp. 207-219). Upper Saddle River, NJ: Prentice Hall.
Hayduk, L.A. (1987). Structural Equation Modeling with LISREL: Essentials and Advances. Jhu Press.
Hooper, D., Coughlan, J., & Mullen, M. (2008). Structural equation modelling: Guidelines for determining model fit. The Electronic Journal of Business Research Methods, 6(1), 53-60.
Hu, P.J.H., Clark, T.H., & Ma, W.W. (2003). Examining technology acceptance by school teachers: a longitudinal study. Information & Management, 41(2), 227-241. doi. 10.1016/S0378-7206(03)00050-8
Hung, S.Y., Chang, C.M., & Kuo, S.R. (2013). User acceptance of mobile e-government services: An empirical study. Government Information Quarterly, 30(1), 33-44. doi. 10.1016/j.giq.2012.07.008
Hung, S.Y., Chang, C.M., & Yu, T.J. (2006). Determinants of user acceptance of the e-Government services: The case of online tax filing and payment system. Government Information Quarterly, 23(1), 97-122. doi. 10.1016/j.giq.2005.11.005
Iacobucci, D. (2010). Structural equations modeling: Fit indices, sample size, and advanced topics. Sample Size, and Advanced Topics. 20(1), 90-98. doi. 10.1016/j.jcps.2009.09.003
Jackson, D.L., Gillaspy Jr, J.A., & Purc-Stephenson, R. (2009). Reporting practices in confirmatory factor analysis: an overview and some recommendations. Psychological Methods, 14(1), 6-23. doi. 10.1037/a0014694
Kalaycı, Ş. (2010). SPSS uygulamalı çok değişkenli istatistik teknikleri (Vol. 5). Ankara, Turkey: Asil Yayın Dağıtım. Karasar, N. (1998). Bilimsel Araştırma Yöntemi, 8. Basım. Ankara: Nobel Yayım.
Kaplan, D. (2000). Structural Equation Modeling: Foundations and Extensions, Vol.3. Sage Publications.
Karasar, N. (2009). Bilimsel Araştırma Yöntemi: Kavramlar, Ilkeler, Teknikler. Nobel Yayın Dağıtım.
Kelloway, E.K. (1998). Using LISREL for Structural Equation Modeling: A Researcher's Guide. Sage.
Kim, K.H. (2005). The relation among fit indexes, power, and sample size in structural equation modeling. Structural Equation Modeling, 12(3), 368-390. doi. 10.1207/s15328007sem1203_2
Kline, R.B. (2005). Methodology in the Social Sciences.Guilford Publications.
Kline, R.B. (2011). Principles and Practice of Structural Equation Modeling: Guilford Press.
Kurtulus, K. (1998). Pazarlama Arastirmalari. Istanbul: Istanbul Üniversitesi Isletme Fakültesi Yayınları.
Lederer, A.L., Maupin, D.J., Sena, M.P., & Zhuang, Y. (2000). The technology acceptance model and the World Wide Web. Decision Support Systems, 29(3), 269-282. doi. 10.1016/S0167-9236(00)00076-2
Lin, H.F. (2008). Predicting consumer intentions to shop online: An empirical test of competing theories. Electronic Commerce Research and Applications, 6(4), 433-442. doi. 10.1016/j.elerap.2007.02.002
Lin, H. F. (2011). An empirical investigation of mobile banking adoption: The effect of innovation attributes and knowledge-based trust. International Journal of Information Management, 31(3), 252-260. doi. 10.1016/j.ijinfomgt.2010.07.006
Kyriakidou, M., Banks, F., & Chrisostomou, C. (2000). Primary teachers’ attitude to the use of ICT: a comparative study between Cyprus and the UK. European Conference on Educational Research, Lahti, Finland. [Retrieved from]
Mahadeo, J.D. (2009). Towards an understanding of the factors ınfluencing the acceptance and diffusion of e-government services. Electronic Journal of E-government, 7(4). 381-392. doi. 10.1155/2012/490647
Mohd, H., & Syed Mohamad, S.M. (2005). Acceptance model of electronic medical record. Journal of Advancing Information and Management Studies, 2(1), 75-92. [Retrieved from].
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
Mulaik, S.A., James, L.R., Van Alstine, J., Bennett, N., Lind, S., & Stilwell, C.D. (1989). Evaluation of goodness-of-fit indices for structural equation models. Psychological Bulletin, 105(3), 430-445. doi. 10.1037/0033-2909.105.3.430
Munro, B.H. (2005). Statistical methods for health care research, (Vol. 1). Lippincott Williams & Wilkins.
Oostrom, J.K., Van Der Linden, D., Born, M.P., & Van Der Molen, H.T. (2013). New technology in personnel selection: How recruiter characteristics affect the adoption of new selection technology. Computers in Human Behavior, 29(6), 2404-2415. doi. 10.1016/j.chb.2013.05.025
Raykov, T., & Marcoulides, G.A. (2006). A First Course in Structural Equation Modeling (2nd ed.). Mahlah, New Jersey, London: Lawrence Erlbaum Associates.
Saadé, R.G., & Kira, D. (2007). Mediating the impact of technology usage on perceived ease of use by anxiety. Computers & Education, 49(4), 1189-1204. doi. 10.1016/j.compedu.2006.01.009
Sahni, A. (1994). Incorporating perceptions of financial control in purchase prediction: an empirical examination of the theory of planned behavior. Advances in Consumer Research, 21(1). [Retrieved from]
Schermelleh-Engel, K., Moosbrugger, H., & Müller, H. (2003). Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures. Methods of Psychological Research Online, 8(2), 23-74.
Schierz, P.G., Schilke, O., & Wirtz, B.W. (2010). Understanding consumer acceptance of mobile payment services: An empirical analysis. Electronic Eommerce Research and Applications, 9(3), 209-216. doi. 10.1016/j.elerap.2009.07.005
Sharma, S.K., Al-Badi, A.H., Govindaluri, S.M., & Al-Kharusi, M.H. (2016). Predicting motivators of cloud computing adoption: A developing country perspective. Computers in Human Behavior, 62, 61-69. doi. 10.1016/j.chb.2016.03.073
Schumacker, R.E., & Lomax, R.G. (2004). A Beginner's Guide to Structural Equation Modeling. New Jersey: Psychology Press.
Şimşek, Ö.F. (2007). Yapısal Eşitlik Modellemesine Giriş: Temel İlkeler ve LISREL Uygulamaları. Ankara: Ekinoks Yayıncılık.
Suki, N.M., & Ramayah, T. (2010). User acceptance of the e-government services in Malaysia: structural equation modelling approach. Interdisciplinary Journal of Information, Knowledge, and Management, 5(1), 395-413.
Tabachnick, B.G. & Fidell, L.S. (2007). Using Multivariate Statistics. Boston: Allyn and Bacon.
Taylor, A.B. (2008). Two New Methods of Studying the Performance of SEM Fit Indexes. Arizona State University.
Tung, F.C., Chang, S.C., & Chou, C.M. (2008). An extension of trust and TAM model with IDT in the adoption of the electronic logistics information system in HIS in the medical industry. International Journal of Medical Informatics, 77(5), 324-335. doi. 10.1016/j.ijmedinf.2007.06.006
Venkatesh, V., & Davis, F.D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204. doi. 10.1287/mnsc.188.8.131.5226
Walczuch, R., Lemmink, J., & Streukens, S. (2007). The effect of service employees’ technology readiness on technology acceptance. Information & Management, 44(2), 206-215. doi. 10.1016/j.im.2006.12.005
Wu, C.S., Cheng, F.F., Yen, D.C., & Huang, Y.W. (2011). User acceptance of wireless technology in organizations: A comparison of alternative models. Computer Standards & Interfaces, 33(1), 50-58. doi. 10.1016/j.csi.2010.03.002
Snedecor, G.W., & Cochran, W.G. (1989). Analysis of variance: the random effects model. Statistical Methods. Iowa State University Press, Ames, IA, 237-252.
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