العوامل المؤثرة في تبني واستخدام التعلم الإلكتروني: دراسة مقارنة بين الجامعات السعودية والأمريكية

Uthman  Alturki

doi:10.35516/edu.v49i2.1040

Authors

Uthman Alturki Educational Technology Department-King Saud University P. O. Box: 87740 - Riyadh: 11652 - Saudi Arabia

DOI:

https://doi.org/10.35516/edu.v49i2.1040

Keywords:

e-learning, acceptance of e-learning, Unified Theory of Acceptance and Use of Technology (UTAUT)

Abstract

Objectives: The research aims to identify the factors that affect students’ adoption and use of e-learning in the United States of America and Saudi Arabia by applying the research to a sample of students from the University of Phoenix in America and the Saudi Electronic University in the Kingdom of Saudi Arabia.

Methods: This research is based on The Unified Theory of Acceptance and Use of Technology (UTAUT) as a general framework with some customization to suit the research variables. The research followed descriptive and analytical approaches.

Results: The results indicated that the performance expectancy, effort expectancy, and facilitating conditions are among the factors that affect the way students adopt e-learning. The impact of performance expectancy and facilitating conditions on students in USA remains greater than in Saudi, while the effect of the effort expectancy on students in Saudi Arabia is greater than students in the USA. The results also indicated that the social influence factor has a positive effect on students in Saudi Arabia, while there is no effect or relationship between the social influence and students’ adaptation or use of e-learning in the USA. The results also indicated that students’ gender, age, and experience with technology also have an impact on the model variables. In the USA, there is no substantial effect of demographic variables on model variables and thus on the behavior of the student towards adopting e-learning.

Conclusions: The study recommends expanding the use of e-learning in other Saudi universities and studying the factors affecting the adoption of e-learning in other universities.

Downloads

Download data is not yet available.

References

Alasmari, T. (2017). Mobile Learning Technology Acceptance Among Saudi Higher Education Students. Unpublished Ph.D. dissertation, Wayne State University. Retrieved from: http://digitalcommons.wayne.edu/oa_dissertations/1676

Al-Arfaj, A. (2001). The perception of college students in Saudi Arabia towards distance web-based instruction. Unpublished Ph.D. dissertation, Ohio University).

Al-Gahtani, S.S., Hubona, G.S., & Wang, J. (2007). Information technology (IT) in Saudi Arabia: Culture and the acceptance and use of IT. Inf. Manag., 44, 681-691.

Al-Hunaiyyan, A., Alhajri, R., & Al-Sharhan, S. (2017). Instructors Age and Gender Differences in the Acceptance of Mobile Learning. Int. J. Interact. Mob. Technol., 11, 4-16.

Al-Jarf, R. (2005). Connecting students across universities in Saudi Arabia. In The annual meeting of the Asia Association of Computer Assisted Language Learning, Geongju, South Korea. Retrieved from: http://www.eric.ed.gov/ERICWebPortal/search/detailmini.jsp?_nfpb=true&_&ER ICExtSearch_SearchValue_0=ED497940&ERICExtSearch_SearchType_0=no&a ccno=ED497940

Alkharang, M. M., & Ghinea, G. (2013). E-learning in Higher Educational Institutions in Kuwait: Experiences and Challenges. International Journal of Advanced Computer Science and Applications, 4.

Alkhuwaylidee, A. R. (2019). Extended Unified Theory Acceptance and Use Technology (UTAUT) for E-Learning. Journal of Computational and Theoretical Nanoscience, 16(3), 845-852. ‏

Allen, E., & Seaman, J. (2013). The 10th Annual Survey of Online Learning: Changing Course: Ten Years of Tracking Online Education in the United States. New York, NY: The Sloan Consortium. Retrieved from: http://www.onlinelearningsurvey.com/reports/changingcourse.pdf

Al-Qeisi, K., Dennis, C., Hegazy, A., & Abbad, M. (2015). How Viable is the UTAUT Model in a Non-Western Context? International Business Research, 8 (2). 204-219. ISSN 1913-9004 (doi:10.5539/ibr.v8n2p204)

Agarwal, R., & Prasad, J. (1997). The role of innovation characteristics and perceived voluntariness in the acceptance of information technologies. Decision Sciences, 28(3), 557-582.

Ajzen, I.B., & Fishbein, M. (1980). Understanding Attitudes and Predicting Social Behavior. Englewood Cliffs: Prentice-Hall.

Jiang, B., & North-Samardzic, A. (2015). Acceptance and Use of Moodle by Students and Academics. AMCIS.

Bem, D.J., & Allen, A. (1974). On predicting some of the people some of the time: The search for cross-situational consistencies in behavior. Psychological Review, 81(6), 506-520.

Bergeron, F., Rivard, S., & De Serre, L. (1990). Investigating the support role of the information center. MIS Quarterly, 14(3), 247-259.

Bixter, M.T., Blocker, K., Mitzner, T., Prakash, A., & Rogers, W. (2019). Understanding the use and non-use of social communication technologies by older adults: A qualitative test and extension of the UTAUT model. Gerontechnology, 18(2), 70-88.

Bozionelos, N. (1996). Psychology of computer use: Prevalence of computer anxiety in British managers and professionals. Psychological Report, 78(3), 995-1002.

Blunch, N. (2012). Introduction to structural equation modeling using IBM SPSS statistics and AMOS. London: Sage.

Brinkerhoff, J. (2006). Effects of a long duration, professional development academy on technology skills, computer self-efficacy, and technology integration and beliefs. Journal of Research on Technology in Education, 39 (1), 22 - 43.

Bryman, A., & Cramer, D. (2001). Quantitative data analysis with SPSS Release 10 for Windows: a guide for social scientists. London: Routledge.

Brown, S.A., & Venkatesh, V. (2005). A model of adoption of technology in the household: A baseline model test and extension incorporating household life cycle. Management Information Systems Quarterly, 29(3), 4, 399-426.

Chanchary, F., & Islam, S. (2011). Is Saudi Arabia ready for e-learning? A case study. Retrieved from:

https://www.researchgate.net/publication/266892350

Chun-Hua, H., & Kai-Yu, T. (2014). Explaining undergraduates’ behavior intention of e-text book adoption: Empirical assessment of five theoretical models. Library Hi Tech, 32(1), 139-163. doi:10.1108/LHT-09-2013-0126.

Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika,16, 3, 297–334.

Davis, F.D., Bagozzi, R.P., & Warshaw, P.R. (1992). Extrinsic and Intrinsic Motivation to Use Computers in the Workplace1. Journal of Applied Social Psychology, 22(14), 1111-1132.

Decman, M. (2015). Modeling the acceptance of e-learning in mandatory environments of higher education: The influence of previous education and gender. Comput. Hum. Behav., 49, 272-281.

Dulle, F., & Minishi-Majanja, M.K. (2011). The suitability of the Unified Theory of Acceptance and Use of Technology (UTAUT) model in open access adoption studies. Information Development, 27, 32-45.

ElKheshin, S., & Saleeb, N. (2020). Unified Theory of Acceptance and Use of Technology. International Journal of Managing Information Technology (IJMIT), 12(1).

Essam, S., & Al-Ammary, J. (2013). The impact of motivation and social interaction on the e-learning at Arab Open University, kingdom of Bahrain. Creative Education, 4(10A), 21-28. doi: 10.4236/ce.2013.410A004.

Fianu, E., Blewett, C., Ampong, G.O., & Ofori, K. (2018). Factors Affecting MOOC Usage by Students in Selected Ghanaian Universities. Education Sciences, 8, 70. Retrieved from: www.mdpi.com/journal/education

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 48, 39-50. Retrieved from http://dx.doi.org/10.2307/3151312

Gunasinghe, A., Hamid, J.A., Khatibi, A., & Azam, S. (2020). The adequacy of UTAUT-3 in interpreting academician's adoption to e-Learning in higher education environments. Interact. Technol. Smart Educ., 17, 86-106.

Hair, Black, Babin & Anderson. (2014). Multivariate Data Analysis: A Global Perspective. (7th Ed.). England: Pearson Education Limited.

Hancock, G. R. & Mueller, R. O. (2010). The reviewer’s guide to quantitative methods in the social sciences. New York: Routledge.

Helsen, K., Jedidi, K., & DeSarbo, W.S. (1993). A New Approach to Country Segmentation Utilizing Multinational Diffusion Patterns. Journal of Marketing, 57, 60-71.

Hew. (2015). What catalyses mobile apps usage intention: an empirical analysis. Industrial Management and Data Systems, 115(7), 1269-1291.

Hofstede, G., & Bond, M.H. (1988). The Confucius connection: From cultural roots to economic growth. Organizational Dynamics, 16(4), 4-21.

Hofstede, G. (1993). Cultural constraints in management theories. Academy of Management Perspectives, 7, 81-94.

Ivankova, N.V., & Stick, S.L. (2006). Students’ Persistence in a Distributed Doctoral Program in Educational Leadership in Higher Education: A Mixed Methods Study. Research in Higher Education, 48, 93-135.

Gatignon, H., Eliashberg, J., & Robertson, T.S. (1989). Modeling Multinational Diffusion Patterns: An Efficient Methodology. Marketing Science, 8(3), 231-247.

Gefen, D., Straub, D.W., & Boudreau, M. (2000). Structural Equation Modeling and Regression: Guidelines for Research Practice. CAIS.

Jackman, G. (2016). Investigating the Factors Influencing Students’ Acceptance of Mobile Learning: The Cave Hill Campus Experience. Caribbean Educational Research Journal, 2(2), 14-32

Krejcie, R.V., & Morgan, D.W. (1970). Determining Sample Size for Research Activities. Retrieved from: https://home.kku.ac.th/sompong/guest_speaker/KrejcieandMorgan_article.pdf

Letchumanan, M. & Tarmizi, R. A. (2011). Assessing the intention to use e-book among engineering undergraduates in Universiti Putra Malaysia, Malaysia Library Hi Tech, 29(3),512 -528.

Lee, I., Choi, B., Kim, J., & Hong, S. (2007). Culture-Technology Fit: Effects of Cultural Characteristics on the Post-Adoption Beliefs of Mobile Internet Users. Int. J. Electron. Commer., 11, 11-51.

Liebenberg, J., Benadé, T., & Ellis, S. (2018). Acceptance of ICT: applicability of the Unified Theory of Acceptance and Use of Technology (UTAUT) to South African students. The African Journal of Information Systems, 10(3). Retrieved from: https://digitalcommons.kennesaw.edu/ajis/vol10/iss3/1.

Liu, J. (2013). E-learning in English classroom: Investigating factors impacting on ESL (English as Second Language) college students' acceptance and use of the Modular Object-Oriented Dynamic Learning Environment (Moodle). Retrieved from: https://lib.dr.iastate.edu/etd/13256.

Lindberg, B.C. (1982). International Comparison of Growth in Demand for a New Durable Consumer Product. Journal of Marketing Research, 19 (April), 364-371.

Leidner, D.E., & Kayworth, T.R. (2006). Review: A Review of Culture in Information Systems Research: Toward a Theory of Information Technology Culture Conflict. MIS Q., 30, 357-399.

Lonner, W., Berry, J., & Hofstede, G. (1980). Culture's Consequences: International Differences in Work-Related Values. Berverly Hills, CA: Sage Publications,.

Lwoga, E. T., & Komba, M. (2015). Antecedents of continued usage intentions of web-based learning management system in Tanzania. Education+ Training, 57(7), 738-756.

Liaw, S., & Huang, H. (2002). How Web Technology Can Facilitate Learning. Information Systems Management, 19, 56 - 61.

Lin, J., & Lin, H. (2019). User acceptance in a computer-supported collaborative learning (CSCL) environment with social network awareness (SNA) support. Australasian Journal of Educational Technology, 35, 100-115.

Maldonado, U. P., Khan, G.F., Moon, J., & Rho, J.J. (2011). E-learning motivation and educational portal acceptance in developing countries. Online Information Review, 35(1),66-85.

Marchewka, L. & Kostiwa, K. (2007). An application of the UTAUT model for understanding student perceptions using course management software. Communications of the IIMA, 7(2), 93-104.

Minton, H.L., & Schneider, F.W. (1980). Differential Psychology. Boston: Waveland Press.

Morris, M.G., & Venkatesh, V. (2000). Age difference in technology adoption decisions: Implications for a changing workforce. Personnel Psychology, 53(2), 375-403.

Mueller, R. O. (1999). Basic principles of structural equation modeling: An introduction to LISREL and EQS. New York: Springer Science & Business Media.

Nanchang, P. C. (2009). Student Acceptance of Web-based Learning System. In Proceedings of the 2009 International Symposium on Web Information Systems and application, 533 – 536.

Nguyen, T.D., Nguyen, D.T., & Cao, T.H. (2014). Acceptance and Use of Information System: E-Learning Based on Cloud Computing in Vietnam. ICT-EurAsia. Retrieved from: https://hal.inria.fr/hal-01397165

Oechslein, O., Fleischmann, M., & Hess, T. (2014). An Application of UTAUT2 on Social Recommender Systems: Incorporating Social Information for Performance Expectancy. In the 47th Hawaii International Conference on System Sciences, 3297-3306.

Oye, N.D., Iahad, A., & Rahim A. (2014). The history of UTAUT model and its impact on ICT acceptance and usage by academicians. Educ Inf Technol, 19, 251–270. Retrieved from: https://doi.org/10.1007/s10639-012-9189-9.

Park, J., & Choi, H. (2009). Factors Influencing adult learners' decision to drop out or persist in online learning. Educational Technology & Society, 12(4), 207–217. Retrieved from: http://ifets.info/journals/12_4/18.pdf

Pynoo, B., Devolder, P., Tondeur, J., Braak, J.V., Duyck, W., & Duyck, P. (2011). Predicting secondary school teachers' acceptance and use of a digital learning environment: A cross-sectional study. Comput. Hum. Behav., 27, 568-575.

Qiao, P., Xiaoman, Z., Yangzhi, G., Ying, S., & Chuan, Q. (2021). The Development and Adoption of Online Learning in Preand Post-COVID-19: Combination of Technological System Evolution Theory and Unified Theory of Acceptance and Use of Technology. Journal of Risk and Financial Management, 14(162). https:// doi.org/10.3390/jrfm14040162

Raman, A., Don, Y.B., Khalid, R., & Rizuan, M. (2014). Usage of Learning Management System (Moodle) among Postgraduate Students: UTAUT Model. Canadian Center of Science and Education, Asian Social Science, 10(14).

Seaman, J.E., Allen, I.E., & Seaman, J.S. (2018). Grade Increase: Tracking Distance Education in the United States. Retrieved from: http://www.onlinelearningsurvey.com/highered.html.

Straub, D.W., Keil, M., & Brenner, W. (1997). Testing the technology acceptance model across cultures: A three country study. Inf. Manag., 33, 1-11.

Sun, H., & Zhang, P. (2006). The role of moderating factors in user technology acceptance. International Journal of Human-Computer Studies, 64, 53-78.

Tamilmani, K., Rana, N.P., Wamba, S.F., & Dwivedi, R. (2021). The extended Unified Theory of Acceptance and Use of Technology (UTAUT2): A systematic literature review and theory evaluation. Int. J. Inf. Manag., 57, 102269.

Tan, P.J. (2013). Applying the UTAUT to Understand Factors Affecting the Use of English E-Learning Websites in Taiwan. Sage Open, 3, 1–12.

Takada, H., & Jain, D.C. (1991). Cross-National Analysis of Diffusion of Consumer Durable Goods in Pacific Rim Countries. Journal of Marketing, 63 (April), 55-69.

Taylor, S., & Todd, P.A. (1995). Understanding Information Technology Usage: A Test of Competing Models. Inf. Syst. Res., 6, 144-176.

Tellefsen, T.E., & Takada, H. (1999). The Relationship between Mass Media Availability and the Multicountry Diffusion of Consumer Products. Journal of International Marketing, 7(1), 77-96.

Teo, T. (2011). Technology Acceptance Research in Education. In Technology Acceptance in Education. Sense Publishers.

Teo, T., and Noyes, J. (2014). Explaining the intention to use technology among pre-service teachers: a multi-group analysis of the Unified Theory of Acceptance and Use of Technology. Interactive Learning Environments, 22(1), 51-66.

Tsourela, M., & Roumeliotis, M. (2015). The moderating role of technology readiness, gender, and sex in consumer acceptance and actual use of Technology-based services. The Journal of High Technology Management Research, 26(2), 124-136.

Venkatesh, V., Morris, M.G., Davis, G.B., & Davis, F.D. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27, 425-478.

Venkatesh, V., & Bala, H. (2008). Technology Acceptance Model 3 and a Research Agenda on Interventions. Decision Sciences, 39(2), 273-315

Venkatesh, V., Thong, J.Y., & Xu, X. (2016). Unified Theory of Acceptance and Use of Technology: A Synthesis and the Road Ahead. J. Assoc. Inf. Syst., 17, 1.

Yueh, H. P., Huang, J. Y., & Chang, C. (2015). Exploring factors affecting students' continued Wiki use for individual and collaborative learning: An extended UTAUT perspective. Australasian Journal of Educational Technology, 31(1), 16-31. Retrieved from https://doi.org/10.14742/ajet.170

Wang, M. (2016). Factors Influencing Usage of E-learning Systems in Taiwan’s Public Sector: Applying the UTAUT Model. Advances in Management and Applied Economics, 6, 1-5.

Wang, Y., Wu, M., & Wang, H. (2009). Investigating the determinants and age and gender differences in the acceptance of mobile learning. BJET, 40, 92-118.

Wong, K., Teo, T., & Russo, S. (2013). Interactive Whiteboard Acceptance: Applicability of the UTAUT Model to Student Teachers. The Asia-Pacific Education Researcher, 22, 1-10.

Xu, D., & Jaggars, S. (2013). Adaptability to online learning: differences across types of students and academic subject areas. Community College Research Center, 8, 1- 27. Retrieved from: http://ccrc.tc.columbia.edu/media/k2/attachments/adaptabilityto- online-learning.pdf.

Zhenghao, C., Alcorn, B., Christensen, G., Eriksson, N., Koller, D., & Emanuel, E. (2015). Who’s benefiting from MOOCs, and Why. Harv. Bus. Rev., September 22.