Technical Resources in E-training Acceptance

  • Zainab Bello Waziri Umaru Federal Polytechnic, Birnin Kebbi
Keywords: E- Training Acceptance, Technical Resources, TAM, Nigeria


Purpose: This paper examines the role of availability of resources in the acceptance of e-training in the Nigerian civil service. Perceived ease of use (PEOU) and Perceived usefulness (PU) of Technology Acceptance Model (TAM) was used as the base for consideration

Design/methodology: Questionnaires were used to collect data from 450 heads of departments. The framework of the paper made up of technological infrastructure, internet facility, PEOU and PU was tested with SmartPLS 2.0 M3 software.

Findings- This paper found both that PU and PEOU indicated strong predictive role in e-training acceptance. In addition, technological infrastructure was found significant. However, internet facility had in significant effect in e-training acceptance.  

Practical implications: This paper showed that availability of resources can help in the acceptance of e-training in the Nigerian civil service. This will help to improve the outlook and overall performance in the civil service. It will be beneficial to policy makers and government agencies in developing policies regarding e-training, create awareness of the benefits of accepting e-training in the public sector leading to better performance and efficiency.

Originality: Relationships of technological infrastructure and internet facility which are necessary in the acceptance of e-training in the Nigerian civil service were examined in this paper


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Amara, N. B., & Atia, L. (2016). E-training and its role in human resources development. Global Journal of Human Resource Management, 4(1), 1-12.

Bankole, O. M. (2013). The use of internet services and resources by scientists at Olabisi Onabanjo University, Ago Iwoye, Nigeria. Program, 47(1), 15-33. DOI:

Bhattacherjee, A., & Hikmet, N., (2008). Re-conceptualizing organizational support and its effect on information technology usage: evidence from the health care sector. The Journal of Computer Information Systems, 48(4), 69-75.

Chin, W. W. (1998). The partial least squares approach to structural equation modeling. In G. A. Marcoulides (Ed.), Modern Methods for Business Research (295-336). Mahwah, New Jersey: Laurence Erlbaum Associates.

Chong, A. Y. L., Ooi, K. B., Lin, B., & Tan, B. I. (2010). Online banking adoption: an empirical analysis. International Journal of Bank Marketing, 28(4), 267-287. DOI:

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 13 (3), 319-340. DOI:

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. DOI:

Duarte, P., & Raposo, M. (2010). A PLS model to study brand preference: An application to the mobile phone market. In V. Esposito Vinzi, W. W. Chin, J. Henseler & H. Wang (Eds). Handbook of Partial Least Squares (449-485), Springer Berlin Heidelberg. DOI:

Ehikhamenor, F. A., (2003). Internet facilities: use and non-use by Nigerian university scientists. Journal of Information Science, 29(1), 35-48. DOI:

El-Rufia, N. (2011). Reforming Nigeria: Reforming our dysfunctional public service. White paper. Retrieved from:

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research 18, 39-50. DOI:

Geladi, P., & Kowalski, B. (1986). Partial least-squares regression: A tutorial. Analytica Chimica Acta, 185, 1-17. DOI:

Hair Jr, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2013). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Sage Publications

Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 18, 139-152. DOI:

Harfoushi, O., & Obiedat, R. (2011). E-Training Acceptance Factors in Business Organizations. iJET, 6(2), 15-18. DOI:

Hsbollah, H. M., & Idris, K. M. (2009). E-learning adoption: the role of relative advantages, trialability and academic specialisation. Campus-Wide Information Systems, 26(1), 54-70. DOI:

JebaKumar, C., & Govindaraju, P., (2009). Virtual learning environment: A study of virtual learning environment with reference to perception of college students in Tamilnadu (South India). Communication policy research South 4th Conference, Negombo. DOI:

Lee, Y. H., Hsieh, Y. C., & Hsu, C. N. (2011). Adding Innovation Diffusion Theory to the Technology Acceptance Model: Supporting Employees' Intentions to use E-Learning Systems. Educational Technology & Society, 14(4), 124-137.

Lee, Y. H., Hsiao, C., & yah, S. H, Purnomo, S. H. (2014). An empirical examination of individual and system characteristics on enhancing e-learning acceptance. Australasian Journal of Educational Technology, 30(5). DOI:

McKay, E. and Vilela, C. (2011). Corporate sector practice informs online workforce training for Australian government agencies: towards effective educational-learning systems design. Australian Journal of Adult Learning. 51(2), 302.

Mohsin, M., & Sulaiman, R. (2013). A study on e-training adoption for higher learning institutions. International Journal of Asian Social Science, 3(9), 2006-2018.

Nchunge, D.M., Sakwa, M. & Mwangi, W. (2013). Assessment of ICT infrastructure on ICT adoption in educational institutions: A descriptive survey of secondary schools in Kiambu county Kenya. Journal of Computer Science & Information Technology, 1 (1), 32-45.

Obi-Anike, H. O., & Ekwe, M. C. (2014). Impact of training and development on organizational effectiveness: Evidence from selected public sector organizations in Nigeria. European Journal of Business and Management, 6(29), 66-75.

Ong, C. S., Lai, J. Y., & Wang, Y. S. (2004). Factors affecting engineers’ acceptance of asynchronous e-learning systems in high-tech companies. Information & management, 41(6), 795-804. DOI:

Osborne, J. W. (2010). Improving your data transformations: Applying the box-cox transformation. Practical Assessment, Research & Evaluation, 15, 12, 1-9.

Özgen, C. (2012). Toward an understanding of acceptance of electronic performance support systems: What drives users ‘perceptions regarding usefulness and ease of use? Doctoral dissertation. Middle East Technical University.

Purnomo, S. H., & Lee, Y. H. (2013). E-learning adoption in the banking workplace in Indonesia an empirical study. Information Development, 29(2), 138-153. DOI:

Ramayah, T., Ahmad, N. H., & Hong, T. S. (2012). An assessment of e-training effectiveness in multinational companies in Malaysia. Educational Technology & Society, 15(2), 125-137.

Ringle, C. M., Wende, S., & Will, S. (2005). SmartPLS 2.0 beta: University of Hamburg, Hamburg. Retrieved from

Rym, B., Olfa, B. & Mélika, B. M. B. (2013). Determinants of E-Learning Acceptance: An Empirical Study in the Tunisian Context. American Journal of Industrial and Business Management, 3(3), 307-321. DOI:

Selim, H.M. (2007). Critical success factors for e-learning acceptance: Confirmatory factor models. Computers and Education, 49(2), 396-413. DOI:

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:

Yiong, B.L.C., Sam, H.K. & Wah, T.K. (2008). Acceptance of e-learning among distance learners: a Malaysian perspective. Proceedings: Ascilite Conference. Retrieved from:

How to Cite
Bello, Z. (2019). Technical Resources in E-training Acceptance. Journal of Business and Social Review in Emerging Economies, 5(1), 201-212.