Commenced in January 2007
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Edition: International
Paper Count: 32451
E-Government Continuance Intention of Media Psychology: Some Insights from Psychographic Characteristics

Authors: Azlina Binti Abu Bakar, Fahmi Zaidi Bin Abdul Razak, Wan Salihin Wong Abdullah


Psychographic is a psychological study of values, attitudes, interests and it is used mostly in prediction, opinion research and social research. This study predicts the influence of performance expectancy, effort expectancy, social influence and facilitating condition on e-government acceptance among Malaysian citizens. The survey responses of 543 e-government users have been validated and analyzed by means of covariance-based Structural Equation Modeling. The findings indicate that e-government acceptance among Malaysian citizens are mainly influenced by performance expectancy (β = 0.66, t = 11.53, p < 0.01) and social influence (β = 0.20, t = 4.23, p < 0.01). Surprisingly, there is no significant effect of facilitating condition and effort expectancy on e-government continuance intention (β = 0.01, t = 0.27, p > 0.05; β = -0.01, t = -0.40, p > 0.05). This study offers government and vendors a frame of reference to analyze citizen’s situation before initiating new innovations. In case of Malaysian e-government technology, adoption strategies should be built around fostering level of citizens’ technological expectation and social influence on e-government usage.

Keywords: Continuance intention, Malaysian citizens, media psychology, structural equation modeling.

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[1] Floropoulos, J., Spathis, C., Halvatzis, D., & Tsipouridou, M. (2010). Measuring the success of the Greek Taxation Information System. International Journal of Information Management, 30(1), 47-56.
[2] Bigdeli, A. Z., Kamal, M. M., & de Cesare, S. (2013). Electronic information sharing in local government authorities: Factors influencing the decision-making process. International Journal of Information Management, 33(5), 816-830
[3] Cegarra-Navarro, J.-G., Pachón, J. R. C., & Cegarra, J. L. M. (2012). E-government and citizen's engagement with local affairs through e-websites: The case of Spanish municipalities. International Journal of Information Management, 32(5), 469-478.
[4] Kanat, I. E., & Özkan, S. (2009). Exploring citizens' perception of government to citizen services: A model based on theory of planned behaviour (TBP). Transforming Government: People, Process and Policy, 3(4), 406-419.
[5] Sahari, N., Zainal Abidin, N., Kasimin, H., & Mohd Idris, H. (2012). Malaysian e-Government application: Factors of actual use. Australian Journal of Basic and Applied Sciences, 6(12), 325-334.
[6] Lean, O. K., Zailani, S., Ramayah, T., & Fernando, Y. (2009). Factors influencing intention to use e-government services among citizens in Malaysia. International Journal of Information Management, 29(6), 458-475.
[7] Venkatesh, V., Thong, J. Y., Chan, F. K., Hu, P. J. H., & Brown, S. A. (2011). Extending the two‐stage information systems continuance model: incorporating UTAUT predictors and the role of context. Information Systems Journal, 21(6), 527-555.
[8] Islam, A. K. M. N. (2011). Understanding Continued Usage Intention in Intention in e-Learning Context. Paper presented at the 24th Bled eConference eFuture: Creating Solutions for the Individual, Organisations and Society, Bled, Slovenia.
[9] Loo, W. H., Yeow, P. H. P., & Chong, S. C. (2009). User acceptance of Malaysian government multipurpose smartcard applications. Government Information Quarterly, 26(2), 358-367.
[10] Suki, N. M., & Ramayah, T. (2010). User Acceptance of the E-Government Services in Malaysia: Structural Equation Modelling Approach. Interdisciplinary Journal of Information, Knowledge & Management, 5.
[11] Siddiquee, N. A. (2008). E-Government and Innovations in Service Delivery: The Malaysian Experience. International Journal of Public Administration, 31(7), 797-815.
[12] Moorthy, M. K., Samsuri, A. S. B., Hussin, S. B. M., Othman, M. S. B., & Chelliah, M. K. (2014). E-Filing Behaviour among Academics in Perak State in Malaysia. Technology and Investment, 2014.
[13] Kassim, N. M., Ramayah, T., & Kurnia, S. (2012). Antecedents and outcomes of human resource information system (HRIS) use. International Journal of Productivity and Performance Management, 61(6), 603-623.
[14] Yahya, M., Nadzar, F., & Rahman, B. A. (2012). Examining user Acceptance of E-Syariah Portal Among Syariah users in Malaysia. Procedia - Social and Behavioral Sciences, 67(0), 349-359.
[15] Bhatt, J. K. (2005). Role of information technology in the Malaysian judicial system: Issues and current trends. International Review of Law, Computers & Technology, 19(2), 199-208.
[16] Taylor, S., & Todd, P. (1995). Assessing IT usage: The role of prior experience. MIS Quarterly, 561-570.
[17] Venkatesh, V., Morris, M. G., & Ackerman, P. L. (2000). A longitudinal field investigation of gender differences in individual technology adoption decision-making processes. Organizational Behavior and Human Decision Processes, 83(1), 33-60.
[18] Venkatesh, V., Morris, M. G., Gordon, B. D., & Davis, F. D. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425-478.
[19] Hennington, A., & Janz, B. D. (2007). Information systems and healthcare XVI: physician adoption of electronic medical records: applying the UTAUT model in a healthcare context. Communications of the Association for Information Systems, 19(1), 5.
[20] Lin, P.C., Lu, H., & Liu, C. (2013). Towards an education behavioral intention model for e-learning systems: An extension of UTAUT. Journal of Theoretical and Applied Information Technology, 47(3), 1120-1127.
[21] Martins, C., Oliveira, T., & Popovič, A. (2014). Understanding the Internet banking adoption: A unified theory of acceptance and use of technology and perceived risk application. International Journal of Information Management, 34(1), 1-13.
[22] Boakye, K. G., Prybutok, V. R., & Ryan, S. D. (2012). The intention of continued web-enabled phone service usage: A quality perspective. Operations Management Research, 5(1-2), 14-24.
[23] Lian, J.-W. (2015). Critical factors for cloud based e-invoice service adoption in Taiwan: An empirical study. International Journal of Information Management, 35(1), 98-109
[24] Huang, T. C.K., Liu, C.C., & Chang, D.C. (2012). An empirical investigation of factors influencing the adoption of data mining tools. International Journal of Information Management, 32(3), 257-270.
[25] Ajzen, I. (1991). The Theory of Panned Behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211.
[26] Shin, D.-H., Shin, Y.-J., Choo, H., & Beom, K. (2011). Smartphones as smart pedagogical tools: Implications for smartphones as u-learning devices. Computers in Human Behavior, 27(6), 2207-2214.
[27] He, J. W., & Wei, K. K. (2007). Understanding Knowledge Management Systems Continuance: A Decomposed Model.
[28] Escobar-Rodríguez, T., & Carvajal-Trujillo, E. (2014). Online purchasing tickets for low cost carriers: An application of the unified theory of acceptance and use of technology (UTAUT) model. Tourism Management, 43(0), 70-88.
[29] Hair, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2013). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM): SAGE Publications.
[30] Weerakkody, V., El-Haddadeh, R., Al-Sobhi, F., Shareef, M. A., & Dwivedi, Y. K. (2013). Examining the influence of intermediaries in facilitating e-government adoption: An empirical investigation. International Journal of Information Management, 33(5), 716-725.
[31] Bhattacherjee, A. (2001). Understanding Information Systems Continuance: An Expectation-Confirmation Model. MIS Quarterly, 25(3), 351-370.
[32] McKnight, P. E., McKnight, K. M., Sidani, S., & Figueredo, A. J. (2007). Missing Data: A Gentle Introduction: Guilford Publications.
[33] Prati, G., Pietrantoni, L., & Zani, B. (2012). The prediction of intention to consume genetically modified food: Test of an integrated psychosocial model. Food Quality and Preference, 25(2), 163-170.
[34] Pai, F.-Y., & Huang, K.-I. (2011). Applying the Technology Acceptance Model to the introduction of healthcare information systems. Technological Forecasting and Social Change, 78(4), 650-660
[35] Browne, M. W., & Cudeck, R. (1992). Alternative ways of assessing model fit. Sociological Methods & Research, 21(2), 230-258.
[36] Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate Data Analysis: Pearson Prentice Hall.
[37] Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238.
[38] Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88(3), 588.
[39] Tan, G. W.-H., Ooi, K.-B., Chong, S.-C., & Hew, T.-S. (2014). NFC mobile credit card: The next frontier of mobile payment? Telematics and Informatics, 31(2), 292-307.
[40] Kline, R. B. (2011). Principles and Practice of Structural Equation Modelling. New York: The Guilford Press.
[41] Nunally, J. C., & Bernstein, I. H. (1994). Psychometric Theory. New York: McGraww-Hill.
[42] Sharma, S. (1995). Applied multivariate techniques: John Wiley & Sons, Inc.
[43] Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 39-50.
[44] Chow, M., Chan, L., Lo, B., Chu, W.-P., Chan, T., & Lai, Y.-M. (2013). Exploring the intention to use a clinical imaging portal for enhancing healthcare education. Nurse Education Today, 33(6), 655-662.
[45] Kim, B. (2010). An empirical investigation of mobile data service continuance: Incorporating the theory of planned behavior into the expectation–confirmation model. Expert Systems with Applications, 37(10), 7033-7039.
[46] Huang, T. C.K., Wu, I.L., & Chou, C.-C. (2013). Investigating use continuance of data mining tools. International Journal of Information Management, 33(5), 791-801.
[47] Youngberg, E., Olsen, D., & Hauser, K. (2009). Determinants of professionally autonomous end user acceptance in an enterprise resource planning system environment. International Journal of Information Management, 29(2), 138-144.
[48] Al-maghrabi, T., Dennis, C., & Halliday, S. V. (2011). Antecedents of continuance intentions towards e-shopping: the case of Saudi Arabia. Journal of Enterprise Information Management, 24(1), 85-111.
[49] Moreno Cegarra, J. L., Cegarra Navarro, J. G., & Córdoba Pachón, J. R. (2014). Applying the technology acceptance model to a Spanish City Hall. International Journal of Information Management, 34(4), 437-445.
[50] Riffai, M., Grant, K., & Edgar, D. (2012). Big TAM in Oman: Exploring the promise of on-line banking, its adoption by customers and the challenges of banking in Oman. International Journal of Information Management, 32(3), 239-250.
[51] Zhou, T., Lu, Y., & Wang, B. (2010). Integrating TTF and UTAUT to explain mobile banking user adoption. Computers in Human Behavior, 26(4), 760-767.
[52] Oliveira, T., Faria, M., Thomas, M. A., & Popovič, A. (2014). Extending the understanding of mobile banking adoption: When UTAUT meets TTF and ITM. International Journal of Information Management, 34(5), 689-703.
[53] 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.