UPSI Digital Repository (UDRep)
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UPSI Digital Repository (UDRep)
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| Abstract : Universiti Pendidikan Sultan Idris |
| The technology acceptance model (TAM) is an information systems model that models how consumers use and accept technology. Scholars have implemented TAM widely to examine the effectiveness and ease of use of online learning. Therefore, this analysis comprehensively analyses TAMs role in accepting online learning platforms by conducting bibliometric and content analysis based on the PRISMA framework. Insights into technology acceptance models were determined by bibliometrics analysis with VosViewer and content analysis. Methods: This study expanded all research from 2002 to 2020. A sum of 120 publications was analysed in January 2022 as documented in the Scopus database after applying the including and excluding criteria in addition to the manual evaluation. Results: This reviews findings identified the most compelling subjects covered by the journal. Most prolific countries, educational institutions, Journals, and authors were identified. Additionally, the results demonstrate several significant models for technology acceptance; several online learning environments were outlined (MOOC, Moodle, E-learning, flipped learning, and blended learning). Conclusion: The research presents a roadmap for potential researchers, concentrating on critical areas where success is possible. However, more research is required to utilize the TAM model and incorporate different online learning environments. 2023 by the authors. |
| References |
A. Ituma, ―An evaluation of students’ perception and engagement with e-learning components in a campus-based university,‖ Active Learning in Higher Education, vol. 12, no. 1, pp. 57-68, 2011. A. W. Bates, Technology, e-Learning and Distance Education, 2nd ed. New York: Routledge Falmer, 2005. D. Warner, G. Christie, and S. Choy. (1998). Readiness of VET clients for flexible delivery, including enilno learning. Brisbane: Australian National Training Authority. [Online]. Available: http://pandora.nla.gov.au/pan/38801/20031209/www.flexiblelearning.net.au/research/ReadinessoftheVETsectorforFDCUR.rtf P. J. Smith, ―Learning preferences and readiness for online learning,‖ Educational Psychology, vol. 25, no. 1, pp. 3–12, 2005. P. J. Smith, K. L. Murphy, and S. E. Mahoney, ―Towards identifying factors underlying readiness for online learning: An exploratory study,‖ Distance Education, vol. 24, no. 1, pp. 57–67, 2003. M. Hung, C. Chou, C. H. Chen, and Z. Y. Own, ―Learner readiness for online learning: Scale development and student perceptions,‖ Computers & Education, vol. 55, no. 3, pp. 1080–1090, 2010. A. Parasuraman, ―yco oohcneiehT readiness index (TRI) is a multiple-item scale to embrace new technologies,‖ Journal of Service Research, vol. 2, no. 4, pp. 307–320, 2000. M. Blut, C. Wang, ―Technology readiness: A meta-analysis of conceptualisations of the construct and its impact on technology usage,‖ Journal of the Academy of Marketing Science, vol. 48, pp.649–669, 2020. H. Abuhassna, A. H. Busalim, B. Mamman, N. Yahaya, M. A. Z. Megat Zakaria, Q. Al-Maatouk,and F. Awae, ―From student’s experience:Does e-learning course structure influenced by learner’s prior experience, background knowledge, autonomy, and dialogue,‖ Contemporary Educational Technology, vol. 14, no. 1, p. 338, 2022. K. Shirahada, B. Q. Ho, and A. Wilson, ―Online public services usage and the elderly: Assessing determinants of technology readiness in Japan and the UK,‖ Technology in Society, 2019. H. Abuhassna, et al., ―Examining students' satisfaction and learning autonomy through web-based courses,‖ International Journal of Advanced Trends in Computer Science and Engineering, vol. 9, no. 1, pp. 356-370. 2021. N. T. Van, A. F. Abbas, H. Abuhassna, F. Awae, and D. Dike, ―Digital readiness for social educators in health care and online learning during COVID-19 pandemic: A bibliometric analysis,‖ International Journal of Interactive Mobile Technologies (iJIM), vol. 15, no. 18, pp. 104–115, 2021. H. Abuhassna, W. M. Al-Rahmi, N. Yahya et al., ―Development of a new model on utilizing online learning platforms to improve students’ academic achievements and satisfaction,‖ Int J Educ Technol High Educ., vol. 17, no. 38, 2020. M. B. Masrom, A. H. Busalim, H. Abuhassna et al., ―Understanding students’ behavior in online social networks: A systematic literature review,‖ Int J Educ Technol High Educ., vol. 18, no. 6, 2021. M. S. Summak, M. Baglibel, and M. Samancioglu, ―Technology readiness of primary school teachers: A case study in Turkey,‖ Procedia: Social and Behavioral Sciences, vol. 2, pp. 2671-2675, 2010. F. D. Davis, Information Technology Introduction, 1989, pp. 319–340. [17] K. J. Kim, D.-H. Shin, ―An acceptance model for smart watches,‖ Internet Res., 2015. S. Nikou, H. Bouwman, ―Ubiquitous use of mobile social network services,‖ Telematics Inf., vol. 31, pp. 422–433, 2014. S. Lee, J. Yu, and D. Jeong, ―BIM acceptance model in construction organizations,‖ J. Manag. Eng., vol. 31, p. 04014048, 2015. Y. Song, X. Chen, T. Hao, Z. Liu, and Z. Lan, ―Exploring two decades of research on classroom dialogue by using bibliometric analysis,‖ Computers & Education, vol. 137, pp. 12–31, 2019. J. Shin, Y. Park, D. Lee, ―Who will be savvy home users? An analysis of adoption and diffusion of smart homes,‖ Technol. Forecast. Soc. Change, vol. 134, pp. 246–253, 2018. M. Hubert, M. Blut, C. Brock, R. W. Zhang, V. Koch, and R. Riedl, ―The influence of acceptance and adoption drivers on smart home usage,‖ Eur. J. Market., vol. 53, pp. 1073–1098, 2019. F. Carlet, ―Understanding attitudes toward adopting green infrastructure: A case study of US municipal officials,‖ Environ. Sci. Pol., vol. 51, pp. 65–76, 2015. C. K. Lee, T. W. Yiu, and S. O. Cheung, ―Application of the theory of planned behavior to alternative Dispute resolution selection and use in construction projects,‖ J. Leg. Aff. Dispute Resolution. Eng. Constr., vol. 10, p. 04518003, 2018. D. A. Steinhardt and K. Manley, ―Exploring the beliefs of Australian prefabricated house builders,‖ Constr. Econ. Build., vol. 16, pp. 27–41, 2016. F. D. Davis, A Technology Acceptance Model for Empirically Testing New End-User Information Systems: Theory and Results, 1985. M. Fishbein, A. Ajzen, Understanding Attitudes and Predicting Social Behaviour, Preventive-Hall, Inc., Englewood Cliffs, 1980. S. Nikou, ―Factors driving the adoption of smart home technology: An empirical assessment,‖ Telematics Inf., vol. 45, 2019. |
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