Download fulltext HTML
., K., Okyere-Kwakye, E., Ilona, D., & ., Z. (2020). Students’ Intention to Participate in E-learning. KnE Social Sciences, 4(14), 273–280. https://doi.org/10.18502/kss.v4i14.7885

https://doi.org/10.18502/kss.v4i14.7885

statistics

  • 319 Abstract Views
  • 238 PDF Views
  • 0 HTML Views

authors

  • Khairul .

    Khairul .

    Education Faculty, Bung Hatta University, Padang, Indonesia
  • Eugene Okyere-Kwakye

    Eugene Okyere-Kwakye

    Faculty of Business and Management Studies, Koforidua Technical University, Ghana
  • Desi Ilona

    Desi Ilona

    Economic Faculty, University of Putra Indonesia YPTK, Padang, Indonesia
  • Zaitul .

    Zaitul .

    Economic Faculty, Bung Hatta University, Padang, Indonesia

Published Date

  • Nov 11, 2020

Students’ Intention to Participate in E-learning

KnE Social Sciences / International Conference on Humanities, Education and Social Sciences (IC-HEDS) 2019 / Pages 273–280

Abstract

This study seeks to investigate the effect of controllability and responsiveness on the perceived ease of e-learning use. This study also aims to determine the relationship between the perceived ease of e-learning use and the student’s intention to participate in e- learning. This study extends the technology acceptance model by considering the external factors; controllability and responsiveness of e-learning system. Thirty-one students from Bung Hatta University were selected as research objects for this study, and three hypotheses were developed. SEM-PLS was applied to analyze the data and smart-pls 3.2.7 software was used to reject the null hypotheses. Having had a satisfied convergent and discriminant validity, this study demonstrates that the controllability and responsiveness of an e-learning system does not have a significant effect on the perceived ease of e-learning use. However, the relationship between the perceived ease of e-learning use and the students’ intention to participate in e-learning is significantly positive. The practical and theoretical implications are discussed in this paper.

References
[1] Al-gahtani, S. S. (2014). Empirical Investigation of E-Learning Acceptance and Assimilation: A Structural Equation Model. Appl. Comput. informatics, vol. 21, issue 1, pp. 27–50.

[2] Abdullah, F. and Ward, R. (2016). Developing a General Extended Technology Acceptance Model for ELearning (GETAMEL) by Analysing Commonly used External Factors. Comput. Human Behav., vol. 56, pp. 238–256.

[3] Ali, M., et al. Assessing the E-Learning System in Higher Education Institutes: Evidence from Structural Equation Modelling. Interact. Technol. Smart Educ., vol. 15, issue 1, pp. 00.

[4] Cheng, Y. (2011). Antecedents and Consequences of E-Learning Acceptance. Info Syst. J, vol. 21, pp. 269–299.

[5] Lee, Y. H., Hsieh, Y. C. and Hsu, C. N. (2011). Adding Innovation Diffusion Theory to the Technology Acceptance Model: Supporting Employees’ Intentions to use E-Learning Systems. Educ. Technol. Soc., vol. 14, issue 4, pp. 124–137.

[6] Alhabeeb, A. and Rowley, J. (2018). E-Learning Critical Success Factors: Comparing Perspectives from Academic Staff and Students. Comput. Educ., vol. 127, pp. 1–12.

[7] Bhuasiri, W., et al. Critical Success Factors for E- Learning in Developing Countries: A Comparative Analysis between ICT Experts and Faculty. Comput. Educ., vol. 58, pp. 843–855.

[8] Khairuddin, S., and Herawati, D. I. and Zaitul. (2018). Antecedents of Intention to use e-Learning. Presented at MATEC Web of Conferences, vol. 248, pp. 1–5.

[9] Fishbein, M. and Ajzen, I. (1975). Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research. Reading: Addison-Wesley.

[10] Ajzen, I. (1991). The Theory of Planned Behavior. Organ. Behav. Hum. Decis. Proccess, vol. 50, pp. 179– 211.

[11] Goodhue, D. L. and Thompson, R. L. (1995). Task-Technology Fit and Individual Performance. MIS Q., vol. 19, no. 2, pp. 213–236.

[12] Venkatesh, V. and Davis, F. D. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Manage. Sci., vol. 46, issue 2, pp. 186–204.

[13] Venkatesh, V., et al. (2003). User Acceptance of Information Technology: Toward A Unified View. MIS Q., vol. 27, issue 3, pp. 425–478.

[14] Aparicio, M., Bacao, F. and Oliveira, T. (2016). Cultural Impacts on E-Learning Systems’ Success. Internet High. Educ., vol. 31, pp. 58–70.

[15] Cidral, W. A., et al. (2018). E-Learning Success Determinants: Brazilian Empirical Study. Comput. Educ., vol. 122, pp. 273–290.

[16] Hubalovsky, S., Hubalovska, M. and Musilek, M. (2018). Assessment of the Influence of Adaptive Elearning on Learning Effectiveness of Primary School Pupils. Comput. Human Behav., vol. 92, pp. 691–705.

[17] Ching-ter, C., Su, C. and Hajiyev, J. (2017). Examining the Students’ Behavioral Intention to use E-Learning in Azerbaijan? The General Extended Technology Acceptance Model for E-learning Approach. Comput. Educ., vol. 111, pp. 128–143.

[18] Alsabawy, A. Y., Cater-steel, A. and Soar, J. (2016). Determinants of Perceived Usefulness of e-Learning Systems. Comput. Human Behav., vol. 64, pp. 843–858.

[19] Rui-Hsin, K. and Lin, C.-T. (2018). The Usage Intention of E-Learning for Police Education and Training. Polic. an Int. J. police Strateg. Manag., vol. 41, issue 1, pp. 98–112.

[20] Cheng, Y. M. (2014). Roles of Interactivity and Usage Experience in E-Learning Acceptance: A Longitudinal Study. Int. J. Web Inf. Syst., vol. 10, issue 1, pp. 2–23.

[21] Pituch, K. A. and Lee, Y. (2006). The Influence of System Characteristics on E-Learning Use. Comput. Educ., vol. 47, pp. 222–244.

[22] Bhattacherjee, A. (2001). An Empirical Analysis of the Antecedents of Electronic Commerce Service Continuance. Decis. Support Syst., vol. 32, pp. 201–214.

[23] Roca, J. C., Chiu, C. and Martinez, F. J. (2006). Understanding e-Learning Continuance Intention: An Extension of the Technology Acceptance Model. Int. J. Human-Computer Stud., vol. 64, pp. 683–696.

[24] Liu, Y. (2003). Developing a Scale to Measure the Interactivity of Websites. J. Advert. Res., vol. 43, issue 2, pp. 207–216.

[25] Wu, G. and Wu, G. (2006). Conceptualizing and Measuring the Perceived Interactivity of Websites. J. Curr. Issues Res. Advert. ISSN, vol. 28, issue 1, pp. 87–104.

[26] Song, J. H. and Zinkhan, G. M. (2008). Determinants of Perceived Web Site Interactivity. J. Mark., vol. 72, pp. 99–113.

[27] Hair, J. F., et al. (2017). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Los Angeles: SAGE Publications.

[28] Hulland, J. (1999). Use of Partial Least Square (PLS) in Strategic Management Research: A Review of Four Recent Studies. Strateg. Manag. J., vol. 20, pp. 195–204.

[29] Bagozzi, R. R. and Yi, Y. (1988). On the Evaluation of Structural Equation Models. J. Acad. Mark. Sci., vol. 16, issue 1, pp. 74–94.

[30] Fornell, C. and Larcker, D. F. (1981). Structural Equation Models with Unobservable Variables and Measurement Error: Algebra and Statistics. J. Mark. Res., vol. 18, issue 3, p. 382.

[31] Henseler, J., Ringle, C. M. and Sinkovics, R. R. (2009). The use of Partial Least Squares Path Modeling in International Marketing. Adv. Int. Mark., vol. 20, pp. 277–319.

[32] Hair, J., et al. (2014). Partial Least Squares Structural Equation Modeling (PLS-SEM)-An Emerging Tool in Business Research. Eur. Bus. Rev