Predicting Factors Behind Students' Perceived Usefulness and Behavioral Intention to Adopt E-Learning: A Case Study of a Private University in Zhanjiang, China
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Abstract
Purpose: This study established a novel conceptual model to conduct an in-depth analysis and clarify the composition of the key factors influencing the e-learning behavioral intention of students in private undergraduate colleges in Zhanjiang, China. Concurrently, this research underscores how students' perceived ease of use influences their perceived usefulness, subsequently divulging the inherent correlation between these two factors. Research design, data, and methodology: This research endeavors to undertake quantitative data collection and analysis. In the sampling process, non-probability sampling approaches were employed, encompassing judgment sampling for selecting four representative secondary colleges, quota sampling for determining the sample size, and convenience sampling for the actual data collection. The researcher devised and executed an online survey and successfully gathered 500 valid questionnaires. In the data analysis stage, the researchers utilized two statistical methods: Structural Equation Modeling (SEM) and Confirmatory Factor Analysis (CFA). Results: Research data reveals that multiple factors, including perceived ease of use, perceived usefulness, performance expectancy, effort expectancy, social influence, and attitude, positively enhance students' behavioral intention toward e-learning. Among them, perceived ease of use and social influence have notable promoting effects on behavioral intention. It is worth noting that the perceived ease of use exerts a dual direct and indirect influence on users' behavioral intentions. Not only does it directly affect users' behavioral intentions, but it also indirectly has a profound impact on users' behavioral intentions by exerting an influence on perceived usefulness. Conclusions: This study offers a novel perspective for comprehending learners' perceived usefulness of e-learning technology and its relationship with behavioral intention and establishes a strong groundwork for subsequent related research.
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References
Abbas, M. (2016). Exploring the impact of information and communication technology on student’s learning in higher education. Journal of Educational Computing Research, 54(3), 408-426.
Abdullah, F., Ward, R., & Ahmed, E. (2016). Investigating the influence of the most used external variables of TAM on students’ Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) of e-portfolios. Computers in Human Behavior, 63, 75-90. https://doi.org/10.1016/j.chb.2016.05.014
Abu-Al-Aish, A., & Love, S. (2013). Factors influencing students’ acceptance of m-learning: An investigation in higher education. International Review of Research in Open and Distributed Learning, 14(5), 82-107. https://doi.org/10.19173/irrodl.v14i5.1631
Ain, N., Kaur, K., & Waheed, M. (2016). The influence of learning value on learning management system use: An extension of UTAUT2. Information Development, 32(5), 1306-1321. https://doi.org/10.1177/0266666915597546
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-Aulamie, A., Mansour, A., Daly, H., & Adjei, O. (2012). The effect of intrinsic motivation on learners' behavioral intention to use e-learning systems. 2012 International Conference on Information Technology Based Higher Education and Training (ITHET), 1-4.
Alharbi, S., & Drew, S. (2014). Using the technology acceptance model in understanding academics’ behavioural intention to use learning management systems. International Journal of Advanced Computer Science and Applications, 5(1), 143-155. https://doi.org/10.14569/ijacsa.2014.050120
Al-Mamary, Y. H., & Shamsuddin, A. (2015). Testing of the technology acceptance model in the context of Yemen. Mediterranean Journal of Social Sciences, 6(4), 268. https://doi.org/10.5901/mjss.2015.v6n4s1p268
Altameemi, A. F., & Al-Slehat, Z. A. F. (2021). Exploring the students’ behavior intentions to adopt e-learning technology: A survey study based on the COVID-19 crisis. International Journal of Business and Management, 16(6), 31-41.
Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411-423. https://doi.org/10.1037//0033-2909.103.3.411.
Awada, G. (2016). Effect of WhatsApp on critique writing proficiency and perceptions toward learning. Cogent Education, 3(1), 1264173. https://doi.org/10.1080/2331186x.2016.1264173
Awang, Z. (2012). Structural equation modeling using AMOS graphic (1st ed.). Penerbit Universiti Teknologi MARA.
Bellaaj, M., Zekri, I., & Albugami, M. (2015). The continued use of e-learning system: An empirical investigation using UTAUT model at the University of Tabuk. Journal of Theoretical and Applied Information Technology, 72(3), 464-474.
Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238-246.
https://doi.org/10.1037/0033-2909.107.2.238
Binyamin, S., Rutter, M., & Smith, S. (2019). Extending the technology acceptance model to understand students’ use of learning management systems in Saudi higher education. International Journal of Emerging Technologies in Learning, 14(3), 4-21. https://doi.org/10.3991/ijet.v14i03.9732
Boateng, R., Mbrokoh, A. S., Boateng, L., Senyo, P. K., & Ansong, E. (2016). Determinants of e-learning adoption among students of developing countries. The International Journal of Information and Learning Technology, 33(4), 248-262.
https://doi.org/10.1108/IJILT-02-2016-0008
Buabeng-Andoh, C. (2021). Exploring University students’ intention to use mobile learning: A research model approach. Education and Information Technologies, 26(1), 241-256.
Budu, K. W. A., Yinping, M., & Mireku, K. K. (2018). Investigating the effect of behavioral intention on e-learning systems usage: Empirical study on tertiary education institutions in Ghana. Mediterranean Journal of Social Sciences, 9(3), 201.
https://doi.org/10.2478/mjss-2018-0087
Byrne, B. M. (2010). Structural equation modeling with AMOS: Basic concepts, applications, and programming (2nd ed.). Routledge Taylor & Francis Group.
Chang, C.-T., Hajiyev, J., & Su, C.-R. (2017). Examining the students’ behavioral intention to use e-learning in Azerbaijan? The general extended technology acceptance model for e-learning approach. Computers & Education, 111, 128-143. https://doi.org/10.1016/j.compedu.2017.04.010
Chang, T. F., Chao, C. M., & Cheng, B. W. (2015). Framework and verification of a blended e-learning system behavioral intention model among clinical nurses. Journal of Baltic Science Education, 14(6), 733-743. https://doi.org/10.33225/jbse/15.14.733
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
Chen, Y. Y. (2017). Development and application of personalized online learning system based on learning analysis technology. Higher Education Forum, 3, 65-70.
Cheng, Y. M. (2014). Roles of interactivity and usage experience in e-learning acceptance: A longitudinal study. International Journal of Web Information Systems, 10(1), 2-23.
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
Chu, T.-H., & Chen, Y.-Y. (2016). With good we become good: Understanding e-learning adoption by theory of planned behavior and group influences. Computers & Education, 92, 37-52. https://doi.org/10.1016/j.compedu.2015.09.013
Cigdem, H., & Topcu, A. (2015). Predictors of instructors’ behavioral intention to use learning management system: A Turkish vocational college example. Computers in Human Behavior, 52, 22-28. https://doi.org/10.1016/j.chb.2015.05.049
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
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982-1003. https://doi.org/10.1287/mnsc.35.8.982
Faqih, K. M. (2020). The influence of perceived usefulness, social influence, internet self-efficacy and compatibility on users’ intentions to adopt e-learning: Investigating the moderating effects of culture. IJAEDU - International E-Journal of Advances in Education, 5(15), 300-320.
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/10.1177/002224378101800104
Gunasinghe, A., Hamid, J. A., Khatibi, A., & Azam, S. F. (2020). The adequacy of UTAUT-3 in interpreting academicians’ adoption to e-learning in higher education environments. Interactive Technology and Smart Education, 17(1), 86-106.
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis (6th ed.). Pearson Prentice Hall.
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2010). Multivariate data analysis (7th ed.). Prentice Hall.
He, X., Zhang, Y., & Wang, L. (2016). Understanding user acceptance of e-learning systems: A comprehensive study. Journal of Educational Technology, 12(3), 45-60.
Hu, X. P., & Zhang, C. M. (2024). Analysis of influencing factors of using intention of e-learning platform based on UTAUT model. Journal of Xiamen University of Technology, 32(2), 75-82.
Huang, X. M. (2021). Research on strategies to enhance learning engagement of secondary vocational students in mixed teaching [Master's thesis]. Guangzhou University.
Jameel, A. S., Abdalla, S. N., & Karem, M. A. (2020). Behavioral intention to use e-learning from students' perspective during COVID-19 pandemic. 2020 2nd Annual International Conference on Information and Sciences (AiCIS), 165-171.
Jere, J. N. (2020). Investigating university academics' behavioural intention in the adoption of e-learning in a time of COVID-19. South African Journal of Information Management, 22(1), 1-9. https://doi.org/10.4102/sajim.v22i1.1208
Joo, Y. J., Lee, H. W., & Ham, Y. (2014). Integrating user interface and personal innovativeness into the TAM for mobile learning in Cyber University. Journal of Computing in Higher Education, 26, 143-158. https://doi.org/10.1007/s12528-014-9081-2
Kongchan, C. (2012). How a non-digital-native teacher makes use of Edmodo. In ICT for Language Learning Conference.
Lee, J. (2006). The effects of e-learning on organizational performance: A case study in a Korean company. Journal of Educational Technology & Society, 9(3), 82-91.
Lee, M. C. (2010). Explaining and predicting users’ continuance intention toward e-learning: An extension of the expectation–confirmation model. Computers & Education, 54(2), 506-516. https://doi.org/10.1016/j.compedu.2009.09.002
Liao, P. W., Yu, C., & Yi, C. C. (2011). Exploring effect factors of e-learning behavioral intention on cross-level analysis. Advanced Materials Research, 204, 174-177. https://doi.org/10.4028/www.scientific.net/AMR.204-210.174
Lin, X. (2011). The impact of e-learning on student performance: A case study. Journal of Educational Technology, 12(3), 45-56.
Liu, C. H. (2010). The comparison of learning effectiveness between traditional face-to-face learning and e-learning among goal-oriented users. 6th International Conference on Digital Content, Multimedia Technology, and its applications, 180-185.
Liu, S.-H., Liao, H.-L., & Pratt, J. A. (2009). Impact of media richness and flow on e-learning technology acceptance. Computers & Education, 52(3), 599-607. https://doi.org/10.1016/j.compedu.2008.11.002
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), 2. https://doi.org/10.9743/jeo.2019.16.2.6
Mailizar, M., Burg, D., & Maulina, S. (2021). Examining university students’ behavioural intention to use e-learning during the COVID-19 pandemic: An extended TAM model. Education and Information Technologies, 26(6), 7057-7077.
Masadeh, R., Tarhini, A., Mohammed, A. B., & Maqableh, M. (2016). Modeling factors affecting student’s usage behaviour of e-learning systems in Lebanon. International Journal of Business and Management, 11(2), 299. https://doi.org/10.5539/ijbm.v11n2p299
Mohammadi, H. (2015). Investigating users’ perspectives on e-learning: An integration of TAM and IS success model. Computers in Human Behavior, 45, 359-374. https://doi.org/10.1016/j.chb.2014.07.044
Muangmee, C., Kot, S., Meekaewkunchorn, N., Kassakorn, N., Tiranawatananun, S., & Khalid, B. (2021). Students' use behavior towards e-learning tools during COVID-19 pandemics: Case study of higher educational institutions of Thailand. International Journal of Evaluation and Research in Education, 10(4), 1166-1175.
Nahla Aljojo, B. A. (2020). Investigating effort expectancy and facilitating conditions affecting behavioral intention to use mobile learning. Journal of Southwest Jiaotong University, 55(5), 1-10. https://doi.org/10.35741/issn.0258-2724.55.5.8
Ndubisi, N. O. (2004). Factors influencing e-learning adoption intention: Examining the determinant structure of the decomposed theory of planned behaviour constructs. Proceedings of the 27th Annual Conference of HERDSA, 252-262. https://doi.org/10.1.1.212.5974
Odegbesan, O. A., Ayo, C., Oni, A. A., Tomilayo, F. A., Gift, O. C., & Nnaemeka, E. U. (2019). The prospects of adopting e-learning in the Nigerian education system: A case study of Covenant University. Journal of Physics: Conference Series, 1299(1), 12058. https://doi.org/10.1088/1742-6596/1299/1/012058
Olasina, G. (2019). Human and social factors affecting the decision of students to accept e-learning. Interactive Learning Environments, 27(3), 363-376. https://doi.org/10.1080/10494820.2018.1474232
Ouadoud, M., Rida, N., & Chafiq, T. (2021). Overview of e-learning platforms for teaching and learning. International Journal of Recent Contributions to Engineering, Science, and IT, 9(1), 50-70. https://doi.org/10.3991/ijes.v9i1.20159
Pedroso, R., Zanetello, L., Guimarães, L., Pettenon, M., Gonçalves, V., Scherer, J., & Pechansky, F. (2016). Confirmatory factor analysis (CFA) of the crack use relapse scale (CURS). Archives of Clinical Psychiatry (São Paulo), 43(3), 37-40. https://doi.org/10.1590/0101-60830000000081
Perera, H. N., & Abeysekera, I. (2022). The influence of e-learning on academic performance: Evidence from Sri Lankan universities. Journal of Educational Technology & Society, 25(1), 107-119.
Pituch, K. A., & Lee, Y.-K. (2006). The influence of system characteristics on e-learning use. Computers & Education, 47(2), 222-244. https://doi.org/10.1016/j.compedu.2004.10.007
Polonsky, M. J., & Waller, D. S. (2018). Designing and managing a research project: A business student's guide (3rd ed.). Sage Publications.
Purdie, D. M., Dunne, M. P., Boyle, F. M., Cook, M. D., & Najman, J. M. (2002). Health and demographic characteristics of respondents in an Australian national sexuality survey: Comparison with population norms. Journal of Epidemiology & Community Health, 56(10), 748-753. https://doi.org/10.1136/jech.56.10.748
Qin, H. X., Li, Z., & Zhou, J. H. (2020). Satisfaction and willingness to continue using online teaching in different disciplines: An empirical analysis based on the technology acceptance model (TAM). Educational Research, 41(11), 91-103.
Rabaai, A. A. (2016). Extending the technology acceptance model (TAM) to assess students' behavioral intentions to adopt an e-learning system: The case of Moodle as a learning tool. Journal of Emerging Trends in Engineering and Applied Sciences, 7(1), 13-30. https://doi.org/10.1.1.212.208
Raman, A., Don, Y., Khalid, R., Hussin, F., Omar Fauzee, M. S., & Ghani, M. (2014). Technology acceptance on smart board among teachers in Terengganu using UTAUT model. Asian Social Science, 10(11), 84-91. https://doi.org/10.5539/ass.v10n11p84
Ratheeswari, K. (2018). Information communication technology in education. Journal of Applied and Advanced Research, 3(1), 45-47. https://doi.org/10.21839/jaar.2018.v3iS1.169
Sánchez-Franco, M. J., Martínez-López, F. J., & Martín-Velicia, F. A. (2009). Exploring the impact of individualism and uncertainty avoidance in web-based electronic learning: An empirical analysis in European higher education. Computers & Education, 52(3), 588-598. https://doi.org/10.1016/j.compedu.2008.11.006
Shah, H. J., & Attiq, S. (2016). Impact of technology quality, perceived ease of use and perceived usefulness in the formation of consumer’s satisfaction in the context of e-learning. Abasyn Journal of Social Sciences, 9(1), 124-140.
Shaqrah, A. A. (2015). Explain the behavior intention to use e-learning technologies: A unified theory of acceptance and use of technology perspective. International Journal of Web-Based Learning and Teaching Technologies (IJWLTT), 10(4), 19-32. https://doi.org/10.4018/IJWLTT.2015100102
Sharma, G., Verma, R., & Pathare, P. (2005). Mathematical modeling of infrared radiation thin layer drying of onion slices. Journal of Food Engineering, 71(3), 282-286. https://doi.org/10.1016/j.jfoodeng.2004.10.041
Sica, C., & Ghisi, M. (2007). The Italian versions of the Beck Anxiety Inventory and the Beck Depression Inventory-II: Psychometric properties and discriminant power. Nova Science Publishers.
Song, J. Y. (2023). Study on the influencing factors of college students' learning satisfaction and continuous learning willingness in online ideological and political courses: An empirical study from the perspective of students. Journal of Taiyuan City Vocational and Technical College, 2(7), 145-150.
Song, Y. L. (2022). Research and application of online learning resource recommendation system for adaptive learning (1st ed.). Yunnan Normal University.
Suhr, D. (2006, June 23). The basics of structural equation modeling. Presented at the SAS User Group of the Western Region of the United States (WUSS), Irvine, CA. https://support.sas.com/resources/papers/proceedings/proceedings/sugi31/200-31.pdf
Tarhini, A., Masa’deh, R. e., Al-Busaidi, K. A., Mohammed, A. B., & Maqableh, M. (2017). Factors influencing students’ adoption of e-learning: A structural equation modeling approach. Journal of International Education in Business, 10(2), 164-182. https://doi.org/10.1108/JIEB-09-2016-0032
Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach's alpha. International Journal of Medical Education, 2, 53-55. https://doi.org/10.5116/ijme.4dfb.8dfd
Thongsri, N., Shen, L., & Bao, Y. (2019). Investigating factors affecting learner’s perception toward online learning: Evidence from ClassStart application in Thailand. Behavior & Information Technology, 38(12), 1243-1258. https://doi.org/10.1080/0144929X.2019.1581259
Tinio, V. (2002). ICT in education (1st ed.). UN Development Programme.
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, 2495-2513.
Twum, A., Agyemang, E., & Osei, E. (2022). Analyzing the factors affecting e-learning adoption among higher education students: Evidence from Ghana. Journal of Educational Technology & Society, 25(3), 48-61.
Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342-365. https://doi.org/10.1287/isre.11.4.342.11872
Venkatesh, V., Morris, M. G., Davis, F. D., & Davis, G. B. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478. https://doi.org/10.2307/30036540
Vululleh, P. (2018). Determinants of students’ e-learning acceptance in developing countries: An approach based on Structural Equation Modeling (SEM). International Journal of Education and Development using ICT, 14(1), 141-151. Retrieved from https://www.learntechlib.org/p/184687/
Wang, Y. F. (2024). Research on the hindrance factors and coping strategies of overseas learners in online Chinese learning (1st ed.). Shandong University of Finance and Economics.
Wu, J. H., & Wang, Y. M. (2006). Measuring KMS success: A respecification of the DeLone and McLean’s model. Information and Management, 43(6), 728-739. https://doi.org/10.1016/j.im.2006.05.002
Xiong, F. (2023). Research on influencing factors of university students' willingness to use network teaching platform based on UTAUT model. Theoretical Research and Practice of Innovation and Entrepreneurship, 6(9), 166-168.
Yang, H.-D., & Yoo, Y. (2004). It is all about attitude: Revisiting the technology acceptance model. Decision Support Systems, 38(1), 19-31. https://doi.org/10.1016/S0167-9236(03)00062-9
Yang, H. Q., Shao, B. R., Niu, Q., Ding, Y. S., Hu, Y. H., Ma, R. L., Sun, J. L., Wang, H. X., Li, Y., & Xu, S. Z. (2022). Online learning was investigated in universities during the epidemic. Agricultural Reclamation Medicine, 44(2), 174-179.
Yang, L. T. (2020). Analysis of influencing factors and mechanism of BIM user satisfaction (1st ed.). Kunming University of Science and Technology.
Yang, T. (2016). Research on e-book users' persistent use behavior: An extension of the expectation confirmation model. Library Science Research, 3(22), 76-83.
Zawaideh, F. H. (2017). Acceptance model for e-learning services: A case study at Al-Madinah International University in Malaysia. International Journal of Academic Research in Accounting, Finance and Management Sciences, 7(2), 14-20. https://doi.org/10.6007/IJARAFMS/v7-i2/2820
Zhang, J. X., Zhang, H., Zhu, Y., Chen, X., & Xu, Q. (2022). Research on college students' online learning behavior and its influencing factors based on UTAUT model. Journal of Hubei Normal University (Natural Science Edition), 42(2), 50-58.