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http://dx.doi.org/10.1633/JISTaP.2022.10.3.3

Predicting Online Learning Adoption: The Role of Compatibility, Self-Efficacy, Knowledge Sharing, and Knowledge Acquisition  

Mshali, Haider (Ministry of Higher Education and Scientific Research)
Al-Azawei, Ahmed (Software Department, College of Information Technology, University of Babylon)
Publication Information
Journal of Information Science Theory and Practice / v.10, no.3, 2022 , pp. 24-39 More about this Journal
Abstract
Online learning is becoming ubiquitous worldwide because of its accessibility anytime and from anywhere. However, it cannot be successfully implemented without understanding constructs that may affect its adoption. Unlike previous literature, this research extends the Unified Theory of Acceptance and Use of Technology with three well-known theories, namely compatibility, online self-efficacy, and knowledge sharing and acquisition to examine online learning adoption. A total of 264 higher education students took part in this research. Partial Least Squares-Structural Equation Modeling was used to evaluate the proposed theoretical model. The findings suggested that performance expectancy and compatibility were significant predictors of behavioral intention, whereas behavioral intention, facilitating conditions, and compatibility had a significant and direct effect on online learning's actual use. The results also showed that knowledge acquisition, knowledge sharing, and online self-efficacy were determinates of performance expectancy. Finally, online self-efficacy was a predictor of effort expectancy. The proposed model achieved a high fit and explained 47.7%, 75.1%, 76.1%, and 71.8% of the variance of effort expectancy, performance expectancy, behavioral intention, and online learning actual use, respectively. This study has many theoretical and practical implications that have been discussed for further research.
Keywords
online learning adoption; Unified Theory of Acceptance and Use of Technology; compatibility; knowledge acquisition; knowledge sharing; self-efficacy;
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1 Bouhnik, D., & Marcus, T. (2006). Interaction in distance-learning courses. Journal of the American Society for Information Science and Technology, 57(3), 299-305. https://doi.org/10.1002/asi.20277.   DOI
2 Ashraf, S., Khan, T. A., & ur Rehman, I. (2016). E-learning for secondary and higher education sectors: A survey. International Journal of Advanced Computer Science and Applications, 7(9), 275-283. https://doi.org/10.14569/IJACSA.2016.070939.   DOI
3 Huang, C. E. (2020). Discovering the creative processes of students: Multi-way interactions among knowledge acquisition, sharing and learning environment. Journal of Hospitality, Leisure, Sport & Tourism Education, 26, 100237. https://doi.org/10.1016/j.jhlste.2019.100237.   DOI
4 Al-Emran, M., Mezhuyev, V., & Kamaludin, A. (2018). Students' perceptions towards the integration of knowledge management processes in m-learning systems: A preliminary study. International Journal of Engineering Education, 34(2A), 371-380. https://scholar.google.com/citations?view_op=view_citation&hl=da&user=J-p9z_gAAAAJ&citation_for_view=J-p9z_gAAAAJ:VaXvl8Fpj5cC.
5 Al-Emran, M., Mezhuyev, V., & Kamaludin, A. (2019). An innovative approach of applying knowledge management in m-learning application development: A pilot study. International Journal of Information and Communication Technology Education, 15(4), 94-112. https://doi.org/10.4018/IJICTE.2019100107.   DOI
6 Al-Radhi, A. A. D. J. K. (2008). Information professionals in a globalized world: Distance learning/e-learning for Iraq: Concept and road map. Bulletin of the American Society for Information Science and Technology, 34(3), 34-37. https://doi.org/10.1002/bult.2008.1720340311.   DOI
7 Al-Sabawy, A. Y. (2013). Measuring e-learning systems success. (Doctoral dissertation). https://eprints.usq.edu.au/27422/.
8 Alowayr, A., & Al-Azawei, A. (2021). Predicting mobile learning acceptance: An integrated model and empirical study based on higher education students' perceptions. Australasian Journal of Educational Technology, 37(3), 38-55. https://doi.org/10.14742/ajet.6154.   DOI
9 Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice-Hall.
10 Ameen, N., & Willis, R. (2018). Towards closing the gender gap in Iraq: Understanding gender differences in smartphone adoption and use. Information Technology for Development, 25(4), 660-685. https://doi.org/10.1080/02681102.2018.1454877.   DOI
11 Chang, M. K., Cheung, W., & Lai, V. S. (2005). Literature derived reference models for the adoption of online shopping. Information & Management, 42(4), 543-559. https://doi.org/10.1016/j.im.2004.02.006.   DOI
12 Cheng, Y. M. (2015). Towards an understanding of the factors affecting m-learning acceptance: Roles of technological characteristics and compatibility. Asia Pacific Management Review, 20(3), 109-119. https://doi.org/10.1016/j.apmrv.2014.12.011.   DOI
13 Cimperman, M., Makovec Brencic, M., & Trkman, P. (2016). Analyzing older users' home telehealth services acceptance behavior-applying an Extended UTAUT model. International Journal of Medical Informatics, 90, 22-31. https://doi.org/10.1016/j.ijmedinf.2016.03.002.   DOI
14 Cheung, R., & Vogel, D. (2013). Predicting user acceptance of collaborative technologies: An extension of the technology acceptance model for e-learning. Computers & Education, 63, 160-175. https://doi.org/10.1016/j.compedu.2012.12.003.   DOI
15 Chin, K. L. (1999, July 12-15). A study into students' perceptions of web-based learning environment. Proceedings of the 1999 HERDSA Annual International Conference. Cornerstones: What do we value in higher education? (pp. 12-15). Higher Education Research and Development Society of Australasia.
16 Chin, W. W. (1998). Commentary: Issues and opinion on structural equation modeling. MIS Quarterly, 22(1), vii-xvi. https://www.jstor.org/stable/249674.
17 Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems: Theory and results. (Doctoral dissertation). http://hdl.handle.net/1721.1/15192.
18 Kim, B., & Park, M. J. (2018). Effect of personal factors to use ICTs on e-learning adoption: Comparison between learner and instructor in developing countries. Information Technology for Development, 24(4), 706-732.   DOI
19 Wixom, B. H., & Todd, P. A. (2005). A theoretical integration of user satisfaction and technology acceptance. Information Systems Research, 16(1), 85-102. https://doi.org/10.1287/isre.1050.0042.   DOI
20 Tsai, Y. Y., Chao, C. M., Lin, H. M., & Cheng, B. W. (2018). Nursing staff intentions to continuously use a blended e-learning system from an integrative perspective. Quality & Quantity, 52(6), 2495-2513. https://doi.org/10.1007/s11135-017-0540-5.   DOI
21 Kim, J., & Lee, K. S. S. (2020). Conceptual model to predict Filipino teachers' adoption of ICT-based instruction in class: Using the UTAUT model. Asia Pacific Journal of Education. https://doi.org/10.1080/02188791.2020.1776213.   DOI
22 Chao, C. M. (2019). Factors determining the behavioral intention to use mobile learning: An application and extension of the UTAUT model. Frontiers in Psychology, 10, 1652. https://doi.org/10.3389/fpsyg.2019.01652.   DOI
23 Farid, S., Ahmad, R., Niaz, I. A., Arif, M., Shamshirband, S., & Khattak, M. D. (2015). Identification and prioritization of critical issues for the promotion of e-learning in Pakistan. Computers in Human Behavior, 51(Pt A), 161-171. https://doi.org/10.1016/j.chb.2015.04.037.   DOI
24 Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008.   DOI
25 Dutton, J., Dutton, M., & Perry, J. (2001). Do online students perform as well as lecture students? Journal of Engineering Education, 90(1), 131-136. https://doi.org/10.1002/j.2168-9830.2001.tb00580.x.   DOI
26 Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50. https://doi.org/10.2307/3151312.   DOI
27 Shin, W. S., & Kang, M. (2015). The use of a mobile learning management system at an online university and its effect on learning satisfaction and achievement. International Review of Research in Open and Distributed Learning, 16(3), 110-130. https://doi.org/10.19173/irrodl.v16i3.1984.   DOI
28 Khalilzadeh, J., Ozturk, A. B., & Bilgihan, A. (2017). Security-related factors in extended UTAUT model for NFC based mobile payment in the restaurant industry. Computers in Human Behavior, 70, 460-474. https://doi.org/10.1016/j.chb.2017.01.001.   DOI
29 Lau, A., & Tsui, E. (2009). Knowledge management perspective on e-learning effectiveness. Knowledge-Based Systems, 22(4), 324-325. https://doi.org/10.1016/j.knosys.2009.02.014.   DOI
30 Mtebe, J. S., Mbwilo, B., & Kissaka, M. M. (2016). Factors influencing teachers' use of multimedia enhanced content in secondary schools in Tanzania. International Review of Research in Open and Distributed Learning, 17(2), 65-84.
31 Hulland, J. (1999). Use of partial least squares (PLS) in strategic management research: A review of four recent studies. Strategic Management Journal, 20(2), 195-204. https://doi.org/10.1002/(SICI)1097-0266(199902)20:2%3C195::AIDSMJ13%3E3.0.CO;2-7.   DOI
32 Isaac, O., Aldholay, A., Abdullah, Z., & Ramayah, T. (2019). Online learning usage within Yemeni higher education: The role of compatibility and task-technology fit as mediating variables in the IS success model. Computers & Education, 136, 113-129. https://doi.org/10.1016/j.compedu.2019.02.012.   DOI
33 Lin, T. J. (2021). Exploring the differences in Taiwanese university students' online learning task value, goal orientation, and self-efficacy before and after the COVID-19 outbreak. The Asia-Pacific Education Researcher, 30(3), 191-203. https://doi.org/10.1007/s40299-021-00553-1.   DOI
34 Park, S. Y. (2009). An analysis of the technology acceptance model in understanding university students' behavioral intention to use e-learning. Educational Technology & Society, 12(3), 150-162. https://www.jstor.org/stable/jeductechsoci.12.3.150.
35 Mahande, R. D., & Malago, J. D. (2019). An e-learning acceptance evaluation through UTAUT model in a postgraduate program. Journal of Educators Online, 16(2). https://eric.ed.gov/?id=EJ1223779.
36 Ozturk, A. B., Bilgihan, A., Nusair, K., & Okumus, F. (2016). What keeps the mobile hotel booking users loyal? Investigating the roles of self-efficacy, compatibility, perceived ease of use, and perceived convenience. International Journal of Information Management, 36(6 Pt B), 1350-1359. https://doi.org/10.1016/j.ijinfomgt.2016.04.005.   DOI
37 Pallant, J. (2013). SPSS survival manual: A step by step guide to data analysis using IBM SPSS. 5th ed. McGraw Hill.
38 Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478. https://doi.org/10.2307/30036540.   DOI
39 Rogers, E. M. (1995). Diffusion of innovations: Modifications of a model for telecommunications. In M. W. Stoetzer, & A. Mahler (Eds.), Die diffusion von innovationen in der telekommunikation. Schriftenreihe des wissenschaftlichen instituts fur kommunikationsdienste (pp. 25-38). Springer. German. https://doi.org/10.1007/978-3-642-79868-9_2.   DOI
40 Tarhini, A., Hone, K., & Liu, X. (2014). Measuring the moderating effect of gender and age on e-learning acceptance in England: A structural equation modeling approach for an extended technology acceptance model. Journal of Educational Computing Research, 51(2), 163-184. https://doi.org/10.2190/EC.51.2.b.   DOI
41 Wu, J. H., Tennyson, R. D., & Hsia, T. L. (2010). A study of student satisfaction in a blended e-learning system environment. Computers & Education, 55(1), 155-164. https://doi.org/10.1016/j.compedu.2009.12.012.   DOI
42 Wallden, S., Makinen, E., & Raisamo, R. (2016). A review on objective measurement of usage in technology acceptance studies. Universal Access in the Information Society, 15(4), 713-726. https://doi.org/10.1007/s10209-015-0443-y.   DOI
43 Wang, A. Y., & Newlin, M. H. (2002). Predictors of web-student performance: The role of self-efficacy and reasons for taking an on-line class. Computers in Human Behavior, 18(2), 151-163. https://doi.org/10.1016/S0747-5632(01)00042-5.   DOI
44 Wang, W. T., & Lin, Y. L. (2021). The relationships among students' personal innovativeness, compatibility, and learning performance: A social cognitive theory perspective. Educational Technology & Society, 24(2), 14-27. https://www.jstor.org/stable/27004928.
45 Abbad, M. (2021). Using the UTAUT model to understand students' usage of e-learning systems in developing countries. Education and information technologies, 26(6), 7205-7224. https://doi.org/10.1007/s10639-021-10573-5.   DOI
46 Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Prentice-Hall.
47 Al-Azawei, A., & Alowayr, A. (2020). Predicting the intention to use and hedonic motivation for mobile learning: A comparative study in two Middle Eastern countries. Technology in Society, 62, 101325. https://doi.org/10.1016/j.techsoc.2020.101325.   DOI
48 Al-Emran, M., Arpaci, I., & Salloum, S. A. (2020). An empirical examination of continuous intention to use m-learning: An integrated model. Education and Information Technologies, 25(4), 2899-2918. https://doi.org/10.1007/s10639-019-10094-2.   DOI
49 Al-Azawei, A., Parslow, P., & Lundqvist, K. (2016). Barriers and opportunities of e-learning implementation in Iraq: A case of public universities. The International Review of Research in Open and Distributed Learning, 17(5), 126-146. https://doi.org/10.19173/irrodl.v17i5.2501.   DOI
50 Al-Azawei, A. H. S. (2017). Modelling e-learning adoption: The influence of learning style and universal learning theories. (Doctoral dissertation). https://centaur.reading.ac.uk/77921/.
51 Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157-178. https://doi.org/10.2307/41410412.   DOI
52 Wu, J. Y. (2017). The indirect relationship of media multitasking self-efficacy on learning performance within the personal learning environment: Implications from the mechanism of perceived attention problems and self-regulation strategies. Computers & Education, 106, 56-72. https://doi.org/10.1016/j.compedu.2016.10.010.   DOI
53 Yilmaz, R. (2016). Knowledge sharing behaviors in e-learning community: Exploring the role of academic self-efficacy and sense of community. Computers in Human Behavior, 63, 373-382. https://doi.org/10.1016/j.chb.2016.05.055.   DOI
54 Zhang, S., & Liu, Q. (2019). Investigating the relationships among teachers' motivational beliefs, motivational regulation, and their learning engagement in online professional learning communities. Computers & Education, 134, 145-155. https://doi.org/10.1016/j.compedu.2019.02.013.   DOI
55 Al-Azawei, A. (2019). What drives successful social media in education and e-learning? A comparative study on Facebook and Moodle. Journal of Information Technology Education: Research, 18, 253-274. https://doi.org/10.28945/4360.   DOI
56 Ringle, C. M., Wende, S., & Becker, J. M. (2015). SmartPLS 3. http://www.smartpls.com.
57 Qiao, P., Zhu, X., Guo, Y., Sun, Y., & Qin, C. (2021). The development and adoption of online learning in pre- and postCOVID-19: Combination of technological system evolution theory and unified theory of acceptance and use of technology. Journal of Risk and Financial Management, 14(4), 162. https://doi.org/10.3390/jrfm14040162.   DOI
58 Al-Emran, M., & Teo, T. (2020). Do knowledge acquisition and knowledge sharing really affect e-learning adoption? An empirical study. Education and Information Technologies, 25(3), 1983-1998. https://doi.org/10.1007/s10639-019-10062-w.   DOI
59 Aliano, A. M., Hueros, A. M. D., Franco, M. D. G., & Aguaded, I. (2019). Mobile learning in university contexts based on the unified theory of acceptance and use of technology (UTAUT). Journal of New Approaches in Educational Research, 8(1), 7-17. https://doi.org/10.7821/naer.2019.1.317.   DOI
60 Ameen, N., Willis, R., Abdullah, M. N., & Shah, M. (2019). Towards the successful integration of e-learning systems in higher education in Iraq: A student perspective. British Journal of Educational Technology, 50(3), 1434-1446. https://doi.org/10.1111/bjet.12651.   DOI
61 Bernard, H. R. (2012). Social research methods: Qualitative and quantitative approaches. 2nd ed. Sage.