Influence of region, experience, and subjective norm on the use of e-learning: Lesson from the insurance industry in Indonesia
Vol 8, Issue 11, 2024
VIEWS - 36 (Abstract) 5 (PDF)
Abstract
The effectiveness and efficiency of e-learning system in industry significantly depend on users’ acceptance and adoption. This is specifically determined by external and internal factors represented by subjective norms (SN) and experience (XP), both believed to affect users’ perceived usefulness (PU) and perceived ease of use (PEOU). Users’ acceptance of e-learning system is influenced by the immensity of region, often hampered by inadequate infrastructure support. Therefore, this study aimed to investigate behavioral intention to use e-learning in the Indonesian insurance industry by applying Technology Acceptance Model (TAM). To achieve this objective, Jabotabek and Non-Jabotabek regions were used as moderating variables in all related hypotheses. An online survey was conducted to obtain data from 800 respondents who were Indonesian insurance industry employees. Subsequently, Structural Equation Model (SEM) was used to evaluate the hypotheses, and Multi-Group Analysis (MGA) to examine the role of region. The results showed that out of the seven hypotheses tested, only one was rejected. Furthermore, XP had no significant effect on PU, and the most significant correlation was found between PEOU and PU. In each relationship path model, the role of region (Jabodetabek and Non Jabodetabek) had no significant differences. These results were expected to provide valuable insights into the components of e-learning acceptability for the development of a user-friendly system in the insurance industry.
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Abdullah, F., & Ward, R. (2016). Developing a General Extended Technology Acceptance Model for E-Learning (GETAMEL) by analysing commonly used external factors. Computers in Human Behavior, 56, 238–256. https://doi.org/10.1016/j.chb.2015.11.036
Aboderin, O. S. (2015). The Challenges and Prospects of E-learning in National Open University of Nigeria. Journal of Education and Learning (EduLearn), 9(3), 207–216. https://doi.org/10.11591/edulearn.v9i3.1728
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-T
Al-alak, B. A., Alnawas, Ibrahim, A. M. (2011). Measuring the acceptance and adoption of e-learning by academic staff. Knowledge Management & E-Learning: An International Journal, 201–221. https://doi.org/10.34105/j.kmel.2011.03.016
Alam, M. J., & Ogawa, K. (2024). Future-readiness and employable ICT skills of university graduates in Bangladesh: an analysis during the post-covid era. Cogent Social Sciences, 10(1). https://doi.org/10.1080/23311886.2024.2359014
Alam, M. J., Ogawa, K., & Islam, S. R. B. (2023). e-Learning as a Doubled-Edge Sword for Academic Achievements of University Students in Developing Countries: Insights from Bangladesh. Sustainability, 15(9), 7282. https://doi.org/10.3390/su15097282
Al-atabi, A. J., & Al-noori, B. S. M. (2020). E-Learning In Teaching (Issue May) [Master Thesis]. University of Baghdad.
Alfidella, S., Sulistyo Kusumo, D., & Dwi Jatmiko, D. S. (2015). ISO 9241-11 Based I-Caring Usability Measurement Using Partial Least Square (PLS) (Indonesian). E-Proceeding of Engineering, 1747-1754.
Aljawawdeh, H., & Nabot, A. (2021). CASL: Classical, Asynchronous, and Synchronous Learning Model. Towards a Universal Hybrid E-learning Model in Jordan Universities. In: Proceedings of the 2021 22nd International Arab Conference on Information Technology (ACIT), 19, 1–9. https://doi.org/10.1109/acit53391.2021.9677410
Amiti, F. (2020). Synchronous and asynchronous e-learning. European Journal of Open Education and E-Learning Studies, 5(2). https://doi.org/10.46827/ejoe.v5i2.3313
Asvial, M., Mayangsari, J., & Yudistriansyah, A. (2021). Behavioral Intention of e-Learning: A Case Study of Distance Learning at a Junior High School in Indonesia due to the COVID-19 Pandemic. International Journal of Technology, 12(1), 54. https://doi.org/10.14716/ijtech.v12i1.4281
Baleghi-Zadeh, S., Ayub, A. F. M., Mahmud, R., et al. (2014). The influence of subjective norm on intention to use of learning management system among Malaysian higher education students. AIP Conference Proceedings, 1635, 288–293. https://doi.org/10.1063/1.4903597
Barnes, E., & Adam, C. (2017). The Importance of Human Resources in a Globalised Economy: A Conceptual Framework. Canadian Journal of Applied Science and Technology, 5(2), 134-142.
Berawi, M. A. (2020). Empowering Healthcare, Economic, and Social Resilience during Global Pandemic Covid-19. International Journal of Technology, 11(3), 436. https://doi.org/10.14716/ijtech.v11i3.4200
Blaga, P. (2020). The Importance of Human Resources in the Continuous Improvement of the Production Quality. Procedia Manufacturing, 46, 287–293. https://doi.org/10.1016/j.promfg.2020.03.042
Buabeng-Andoh, C. (2018). Predicting students’ intention to adopt mobile learning. Journal of Research in Innovative Teaching & Learning, 11(2), 178–191. https://doi.org/10.1108/jrit-03-2017-0004
Cascio, W. F., & Montealegre, R. (2016). How Technology Is Changing Work and Organizations. Annual Review of Organizational Psychology and Organizational Behavior, 3(1), 349–375. https://doi.org/10.1146/annurev-orgpsych-041015-062352
Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319. https://doi.org/10.2307/249008
Dhawan, S. (2020). Online Learning: A Panacea in the Time of COVID-19 Crisis. Journal of Educational Technology Systems, 49(1), 5–22. https://doi.org/10.1177/0047239520934018
Drobne, S., Garre, A., Hontoria, E., et al. (2020). Comparison of Two Network-Theory-Based Methods for detecting Functional Regions. Business Systems Research Journal, 11(2), 21–35. https://doi.org/10.2478/bsrj-2020-0013
Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39. https://doi.org/10.2307/3151312
Ghozali, I., & Latan, H. (2015). Concepts, Techniques, Applications Using Smart PLS 3.0 for Empirical Research (Indonesian). Badan Penerbit Universitas Diponegoro.
Hair, J. F., Hult, G. T. M., Ringle, C. M., et al. (2021). Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R. In Classroom Companion: Business. Springer International Publishing. https://doi.org/10.1007/978-3-030-80519-7
Heathfield, S. M. (2021). What is Human Resource? Definition and Examples of a Human Resource. Available online: https://www.thebalancecareers.com/what-is-a-human-resource-1918144 (accessed on 2 June 2023).
Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS path modeling in new technology research: updated guidelines. Industrial Management & Data Systems, 116(1), 2–20. https://doi.org/10.1108/imds-09-2015-0382
Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. Advances in International Marketing, 20, 277-319. https://doi.org/10.1108/S1474-7979(2009)0000020014
Holmes-Smith, P. (2001). Introduction to Structural Equation Modeling Using LISREL. ACSPRI 2001 Winter Program, School Research, Evaluation and Measurement Services.
Kalayou, M. H., Endehabtu, B. F., & Tilahun, B. (2020). The Applicability of the Modified Technology Acceptance Model (TAM) on the Sustainable Adoption of eHealth Systems in Resource-Limited Settings. Journal of Multidisciplinary Healthcare, Volume 13, 1827–1837. https://doi.org/10.2147/jmdh.s284973
Kanwal, F., & Rehman, M. (2017). Factors Affecting E-Learning Adoption in Developing Countries–Empirical Evidence From Pakistan’s Higher Education Sector. IEEE Access, 5, 10968–10978. https://doi.org/10.1109/access.2017.2714379
Khine, M. S. (2013). Application of Structural Equation Modeling in Educational Research and Practice. SensePublishers. https://doi.org/10.1007/978-94-6209-332-4
Koran, J. K. C. (2001). In teaching and learning in Malaysian schools (Indonesian). In Elearning.
Kustono, A. S. (2021). Improving actual e-learning usage: Evidence from Indonesia. Journal of Hunan University Natural, 48(1), 1-11.
Lai, P. (2017). The literature review of technology adoption models and theories for the novelty technology. Journal of Information Systems and Technology Management, 14(1), 21–38. https://doi.org/10.4301/s1807-17752017000100002
Lee, Y.-H., Hsieh, Y.-C., & Chen, Y.-H. (2013). An investigation of employees’ use of e-learning systems: applying the technology acceptance model. Behaviour & Information Technology, 32(2), 173–189. https://doi.org/10.1080/0144929x.2011.577190
Lee, Y.-H., Hsieh, Y.-C., & Ma, C.-Y. (2011). A model of organizational employees’ e-learning systems acceptance. Knowledge-Based Systems, 24(3), 355–366. https://doi.org/10.1016/j.knosys.2010.09.005
Lee, Y.-H., Hsieh, Y.-C., & Ma, C.-Y. (2011). A model of organizational employees’ e-learning systems acceptance. Knowledge-Based Systems, 24(3), 355–366. https://doi.org/10.1016/j.knosys.2010.09.005
Lu, J., Yu, C., Liu, C., et al. (2003). Technology acceptance model for wireless Internet. Internet Research, 13(3), 206–222. https://doi.org/10.1108/10662240310478222
Maatuk, A. M., Elberkawi, E. K., Aljawarneh, S., et al. (2021). The COVID-19 pandemic and E-learning: challenges and opportunities from the perspective of students and instructors. Journal of Computing in Higher Education, 34(1), 21–38. https://doi.org/10.1007/s12528-021-09274-2
Mailizar, M., Almanthari, A., & Maulina, S. (2021). Examining Teachers’ Behavioral Intention to Use E-learning in Teaching of Mathematics: An Extended TAM Model. Contemporary Educational Technology, 13(2), ep298. https://doi.org/10.30935/cedtech/9709
Malik, S., & Rana, A. (2020). E-Learning: Role, Advantages, and Disadvantages of its implementation in Higher Education. JIMS8I International Journal of Information Communication and Computing Technology, 8(1), 403. https://doi.org/10.5958/2347-7202.2020.00003.1
Marikyan, D., & Papagiannidis, S. (2023). Unified Theory of Acceptance and Use of Technology: A review (S. Papagiannidis, Ed.). THEORYHUB BOOK. https://open.ncl.ac.uk/theories/2/unified-theory-of-acceptance-and-use-of-technology/
Mohajan, H. K. (2020). Quantitative Research: A Successful Investigation in Natural and Social Sciences. Journal of Economic Development, Environment and People, 9(4). https://doi.org/10.26458/jedep.v9i4.679
Mohd Basar, Z., Mansor, A. N., Jamaludin, K. A., et al. (2021). The Effectiveness and Challenges of Online Learning for Secondary School Students – A Case Study. Asian Journal of University Education, 17(3), 119. https://doi.org/10.24191/ajue.v17i3.14514
Munajatisari, R. R. (2014). Effectiveness Analysis of Classical and E-Learning Training Methods (Indonesian). Jurnal Administrasi Bisnis, 10(2), 173-185.
Pateda, S. A., Badu, R. W., Isa, Abd. H., & Rahmat, A. (2020). Evaluation of The Kirkpatrick Model Program on Towards Class at The Gorontalo District. JournalNX - A Multidisciplinary Peer Reviewed Journal, 6(7), 40-47.
Purnomo, S. H., & Lee, Y.-H. (2012). E-learning adoption in the banking workplace in Indonesia. Information Development, 29(2), 138–153. https://doi.org/10.1177/0266666912448258
Qashou, A. (2022). Obstacles to effective use of e-learning in higher education from the viewpoint of faculty members. Turkish Online Journal of Distance Education, 23(1), 144-177.
Sanderson, P., & Rosenberg, M. J. (2002). E-Learning: strategies for delivering knowledge in the digital age. Internet and Higher Education - internet high educ, 5. https://doi.org/10.1016/S1096-7516(02)00082-9
Sukma, E. L., Rachmadi, A., & Wardani, N. H. (2020). Analysis of the Influence of Perceived Usefulness, Perceived Ease Of Use, Behavioral Intention To Use, on Actual System Use in Using the Esensus System at AJB Bumiputera 1912 Wlingi Branch Office (Indonesian). Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 4(9), 2753-2761.
Sullivan, G. M., & Artino, A. R. (2013). Analyzing and Interpreting Data From Likert-Type Scales. Journal of Graduate Medical Education, 5(4), 541–542. https://doi.org/10.4300/jgme-5-4-18
Teo, T. (2009). The Impact of Subjective Norm and Facilitating Conditions on Pre-Service Teachers’ Attitude toward Computer Use: A Structural Equation Modeling of an Extended Technology Acceptance Model. Journal of Educational Computing Research, 40(1), 89–109. https://doi.org/10.2190/ec.40.1.d
Van Langenhove, L. (2013). What is a region? Towards a statehood theory of regions. Contemporary Politics, 19(4), 474–490. https://doi.org/10.1080/13569775.2013.853392
Venkatesh, Morris, Davis, & Davis. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425. https://doi.org/10.2307/30036540
Venkatesh, V., & Davis, F. D. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926
Vukovic, D., & Kochetkov, D. M. (2017). Defining region. R-Economy, 3(2), 76–81. https://doi.org/10.15826/recon.2017.3.2.009
Yousafzai, S. Y., Foxall, G. R., & Pallister, J. G. (2007). Technology acceptance: a meta‐analysis of the TAM: Part 1. Journal of Modelling in Management, 2(3), 251–280. https://doi.org/10.1108/17465660710834453
DOI: https://doi.org/10.24294/jipd.v8i11.5902
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