Behavioral Intention to Use E-learning: A Case Study of Apparel School Students at Chengdu Textile College in China
DOI:
https://doi.org/10.14456/shserj.2023.30Keywords:
E-learning, Percived Ease of Use, Perceived Usefulness, Attitude, Behavioral IntentionAbstract
Purpose: To create a student-centered art teaching model, e-learning is considered a new possibility to enhance the curriculum learning approach. Therefore, this research aimed to study significant factors of school of apparel students’ behavioral intention to utilize e-learning at Chengdu Textile College. The conceptual framework consists perceived ease of use, perceived usefulness, attitude, self-efficacy, performance expectancy, social influence and behavioral intention. Research design, data, and methodology: The researcher used a quantitative approach (n=488). Questionnaires were distributed to apparel school students in Chengdu Textile College. The research data was gathered through judgmental, quota and convenience sampling. The following statistical analysis was implemented through the Confirmatory Factor Analysis (CFA) and Structural Equation Model (SEM), including model fit, validity, and reliability of each construct. Results: Perceived ease of use has a significant effect on perceived usefulness and attitude. Perceived usefulness has a significant effect on attitude and behavioral intention. Furthermore, attitude, self-efficacy, performance expectancy and social influence significantly affect behavioral intention. Conclusion: The research makes recommendations for college education policymakers, college teaching quality supervision, and teacher to encourage the integration of e-learning into the fundamental teaching process and establish a modern digital and intelligent education environment in college education.
References
Abbasi, A., & Hossaina, L. (2011). Identifying the effects of co-authorship networks on the performance of scholars: A correlation and regression analysis of performance measures and social network analysis measures. Journal of Informetrics, 5(4), 594-607.
Abdullah, F., Ward, R., & Ahmed, E. (2016). Investigating the influence of the most commonly 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
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
Akbar, F. (2013). What affects students’ acceptance and use of technology? (1st ed.). Dietrich College Honors Theses.
Al-Gahtani, S. S. (2016). Empirical investigation of e-learning acceptance and assimilation: A structural equation model. Applied Computing and Informatics, 12(1), 27-50. https://doi.org/10.1016/j.aci. 2014.09.001
Ali, M., Raza, S. A., Qazi, W., & Puah, C.-H. (2018). Assessing e-learning system in higher education institutes: Evidence from structural equation modelling. Interactive Technology and Smart Education, 15(1), 59-78. https://doi.org/10.1108/itse-02-2017-0012
Almaiah, M. A., Al-Khasawneh, A., & Althunibat, A. (2020). Exploring the critical challenges and factors influencing the E-learning system usage during COVID-19 pandemic. Education and Information Technologies, 25(6), 5261-5280. https://doi.org/10.1007/s10639-020-10219-y
Alsaif, M. (2014). Factors affecting citizens’ adoption of e-government moderated by socio-cultural values in Saudi Arabia (1st ed.). Doctoral dissertation.
Amoako-Gyampah, K., & Salam, A. F. (2004). An extension of the technology acceptance model in an ERP implementation environment. Information & Management, 41(6), 731-745. https://doi.org/10.1016/j.im.2003.08.010
Ansbacher, T. (2013). Making sense of experience: A model for meaning-making. Exhibitionist, 32, 16-19.
Awwad, M. S., & Al-Majali, S. M. (2015). Electronic library services acceptance and use: An empirical validation of unified theory of acceptance and use of technology. The Electronic Library, 33(6), 1100-1120. https://doi.org/10.1108/el-03-2014-0057
Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74-94. https://doi.org/10.1007/bf02723327
Bailey, D. R., Almusharraf, N., & Almusharraf, A. (2022). Video conferencing in the e-learning context: explaining learning outcome with the technology acceptance model. Education and Information Technologies, 27(6), 7679-7698.
https://doi.org/10.1007/s10639-022-10949-1
Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191-215.
https://doi.org/10.1037/0033-295x.84.2.191
Bandura, A. (1986). Social Foundations of Thought and Action: A Social Cognitive Theory (1st ed.). Prentice-Hall.
Bashir, I., & Madhavaiah, C. (2015). Consumer attitude and behavioural intention towards Internet banking adoption in India. Journal of Indian Business Research, 7(1), 67-102. https://doi.org/10.1108/jibr-02-2014-0013
Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88(3), 588-606. https://doi.org/10.1037/0033-2909.88.3.588
Cao, Y., & Jittawiriyanukoon, C. (2022). Factors Impacting Online Learning Usage during Covid-19 Pandemic Among Sophomores in Sichuan Private Universities. AU-GSB E-JOURNAL, 15(1), 152-163. https://doi.org/10.14456/augsbejr.2022.52
Celik, H. (2016). Customer online shopping anxiety within the Unified Theory of Acceptance and Use Technology (UTAUT) framework. Asia Pacific Journal of Marketing and Logistics, 28(2). https://doi.org/10.1108/apjml-05-2015-0077
Chan, D. K.-S., & Fishbein, M. (1993). Determinants of College Women's Intentions to Tell Their Partners to Use Condoms1. Journal of Applied Social Psychology, 23(18), 1455-1470. https://doi.org/10.1111/j.1559-1816.1993.tb01043.x
Chen, K.-Y., & Chang, M.-L. (2013). User acceptance of 'near field communication' mobile phone service: an investigation based on the 'unified theory of acceptance and use of technology' model. The Service Industries Journal, 33(6), 609-623. https://doi.org/10.1080/02642069.2011.622369
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
Chien, T.-C. (2012). Computer self-efficacy and factors influencing e-learning effectiveness. European Journal of Training and Development, 36(7), 670-686. https://doi.org/10.1108/03090591211255539
Chiu, C.-M., & Wang, E. T. G. (2008). Understanding Web-based learning continuance intention: The role of subjective task value. Information & Management, 45(3), 194-201. https://doi.org/10.1016/j.im.2008.02.003
Conner, M., & Norman, P. (2005). Predicting Health Behaviour: Research and Practice with Social Cognition Models (1st ed.). Open University Press.
Conner, M., Kirk, S. F. L., Cade, J. E., & Barrett, J. H. (2001). Why do women use dietary supplements? The use of the theory of planned behaviour to explore beliefs about their use. Social Science and Medicine, 52(4), 621-633.
Cooper, D. R., & Schindler, P. S. (2014). Business Research Methods (12th ed.). McGraw-Hill/Irwin.
Curtis, M. B., & Payne, E. A. (2008). An examination of contextual factors and individual characteristics affecting technology implementation decisions in auditing. International Journal of Accounting Information Systems, 9(2), 104-121.
https://doi.org/10.1016/j.accinf.2007.10.002
Dabholkar, P. A., & Bagozzi, R. P. (2002). An Attitudinal Model of Technology-Based Self-Service: Moderating Effects of Consumer Traits and Situational Factors. Journal of the Academy of Marketing Science, 30(3), 184-201.
https://doi.org/10.1177/0092070302303001
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
Davis, F. D. (1993). User acceptance of information technology: system characteristics, user perceptions and behavioral impacts. International Journal of Man-Machine Studies, 38(3), 475-487. https://doi.org/10.1006/imms.1993.1022
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
Deb, M., & Lomo-David, E. (2014). An empirical examination of customers' adoption of m-banking in India. Marketing Intelligence & Planning, 32(4), 475-494. https://doi.org/10.1108/mip-07-2013-0119
Dishaw, M. T., & Strong, D. M. (1999). Extending the technology acceptance model with task-technology fit constructs. Information & Management, 36(1), 9-21. https://doi.org/10.1016/s0378-7206(98)00101-3
Doll, W. J., & Torkzadeh, G. (1988). The Measurement of End-User Computing Satisfaction. MIS Quarterly, 12(2), 259. https://doi.org/10.2307/248851
Dong, X., Amornsiripanitch, N., & Wang, D. (2006). A Genomic Approach to Identify Regulatory Nodes in the Transcriptional Network of Systemic Acquired Resistance in Plants. Plos Computational Biology, 51, 1-10.
Downey, J. (2006). Measuring general computer self-efficacy: the surprising comparison of three instruments in predicting performance, attitudes, and usage (12th ed.). Annual Hawaii International.
Dumford, A. D., & Miller, A. L. (2018). Online learning in higher education: exploring advantages and disadvantages for engagement. Journal of Computing in Higher Education, 30(3), 452-465. https://doi.org/10.1007/s12528-018-9179-z
Dwivedi, Y. K., Rana, N. P., Chen, H., & Williams, M. D. (2011). A Meta-analysis of the Unified Theory of Acceptance and Use of Technology (UTAUT). Governance and Sustainability in Information Systems. Managing the Transfer and Diffusion of IT, 155-170. https://doi.org/10.1007/978-3-642-24148-2_10
Fan, X., Duangekanong, S., & Xu, M. (2021). Factors Affecting College Students’ Intention to Use English U-learning in Sichuan, China. AU-GSB E-JOURNAL, 14(2), 118-129. https://doi.org/10.14456/augsbejr.2021.20
Farah, Z., Mohamad, F., Napitupulu, D., Nazuar, S., & Roza, L. (2021). Analyzing Indonesian Students' Google Classroom Acceptance During COVID-19 Outbreak: Applying an Extended Unified Theory of Acceptance and Use of Technology Model. European Journal of Educational Research, 10(4), 1697-1710. https://doi.org/10.12973/eu-jer.10.4.1697
Fathema, N., Shannon, D., & Ross, M. (2015). Expanding the Technology Acceptance Model (TAM) to examine faculty use of Learning Management Systems (LMSs) in higher education institutions. Journal of Online Learning & Teaching, 11(2), 1-23.
Filippini, R. (1998). Trade-off and compatibility between performance: Definitions and empirical evidence. International Journal of Production Research, 36(12), 3379-3406. https://doi.org/10.1080/002075498192111
Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research (1st ed.). Wesley.
Ghalandari, K. (2012). The effect of performance expectancy, effort expectancy, social influence and facilitating conditions on acceptance of E-banking services in Iran: the moderating role of age and gender, Middle-East Journal of Scientific Research, 12(6), 801-807.
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariant Data Analysis (1st ed.). Pearson International Edition.
Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (2010). Multivariate Data Analysis (6th ed.). Prentice Hall.
Heckler, C. E., & Hatcher, L. (1996). A Step-by-Step Approach to Using the SAS® System for Factor Analysis and Structural Equation Modeling. Technometrics, 38(3), 296. https://doi.org/10.2307/1270628
Heijden, V. D. (2000). E-TAM: a revision of the technology acceptance model to explain web site revisits, research memorandum (1st ed.). September.
Hoyle, R. H. (2000). Confirmatory Factor Analysis. Handbook of Applied Multivariate Statistics and Mathematical Modeling, 465-497. https://doi.org/10.1016/b978-012691360-6/50017-3
Hu, L.-t., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55.
https://doi.org/10.1080/10705519909540118
Ifinedo, P. (2016). Applying uses and gratifications theory and social influence processes to understand students’ pervasive adoption of social networking sites: Perspectives from the Americas. International Journal of Information Management, 36(2), 192-206.
https://doi.org/10.1016/j.ijinfomgt.2015.11.007
Jahangir, N., & Begum, N. (2008). The role of perceived usefulness, perceived ease of use, security and privacy, and customer attitude to engender customer adaptation in the context of electronic banking. African Journal of Business Management, 2(2), 32-40.
Janda, S. (2016). Segmenting students based on study abroad motivations, attitudes, and preferences. Journal of International Education in Business, 9(2), 111-122. https://doi.org/10.1108/jieb-06-2016-0013
Jonassen, D. H. (1991). Objectivism versus constructivism: Do we need a new philosophical paradigm? Educational Technology Research and Development, 39(3), 5-14. https://doi.org/10.1007/bf02296434
Kelly, T. M., & Bauer, D. K. (2003). Managing Intellectual Capital ingat Cisco. Handbook on Knowledge Management.
https://doi.org/10.1007/978-3-540-24748-7_24
Kratz, S., & Merritt, E. (2011). Museums and the future of education. On the Horizon, 19(3), 188-195.
https://doi.org/10.1108/10748121111163896
Kurfalı, M., Arifoğlu, A., Tokdemir, G., & Paçin, Y. (2017). Adoption of e-government services in Turkey. Computers in Human Behavior, 66, 168-178. https://doi.org/10.1016/j.chb.2016.09.041
Lee, B.-C., Yoon, J.-O., & Lee, I. (2009). Learners' acceptance of e-learning in South Korea: Theories and results. Computers & Education, 53(4), 1320-1329. https://doi.org/10.1016/j.compedu.2009.06.014
Liaw, S.-S. (2008). Investigating students' perceived satisfaction, behavioral intention, and effectiveness of e-learning: A case study of the Blackboard system. Computers & Education, 51(2), 864-873. https://doi.org/10.1016/j.compedu.2007.09.005
Lin, K.-M., Chen, N.-S., & Fang, K. (2011). Understanding e-learning continuance intention: a negative critical incidents perspective. Behaviour & Information Technology, 30(1), 77-89. https://doi.org/10.1080/01449291003752948
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
Ma, Q., & Liu, L. (2004). The Technology Acceptance Model: A Meta-Analysis of Empirical Findings. Journal of Organizational and End User Computing, 16(1), 59-72. https://doi.org/10.4018/joeuc.2004010104
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. https://doi.org/10.1007/s10639-021-10557-5
Marsh, H. W., Hau, K.-T., & Wen, Z. (2004). In Search of Golden Rules: Comment on Hypothesis-Testing Approaches to Setting Cutoff Values for Fit Indexes and Dangers in Overgeneralizing Hu and Bentler's (1999) Findings. Structural Equation Modeling: A Multidisciplinary Journal, 11(3), 320-341. https://doi.org/10.1207/s15328007sem1103_2
Martinho, D. S., Santos, E. M., Miguel, M. I., & Cordeiro, D. S. (2018). Factors that Influence the Adoption of Postgraduate Online Courses. International Journal of Emerging Technologies in Learning (iJET), 13(12), 123. https://doi.org/10.3991/ijet.v13i12.8864
Mathieson, K. (1991). Predicting User Intentions: Comparing the Technology Acceptance Model with the Theory of Planned Behavior. Information Systems Research, 2(3), 173-191. https://doi.org/10.1287/isre.2.3.173
Mathwick, C., Malhotra, N. K., & Rigdon, E. (2002). The effect of dynamic retail experiences on experiential perceptions of value: An Internet and catalog comparison. Journal of Retailing, 78(1), 51-60.
Mohd Nizar, N. N., Rahmat, M. K., Maaruf, S. Z., & Damio, S. M. (2019). Examining the use behaviour of augmented reality technology through marlcario : adapting the utaut model. Asian Journal of University Education, 15(3), 198.
https://doi.org/10.24191/ajue.v15i3.7799
Morosan, C. (2014). Toward an integrated model of adoption of mobile phones for purchasing ancillary services in air travel. International Journal of Contemporary Hospitality Management, 26(2), 246-271. https://doi.org/10.1108/ijchm-11-2012-0221
Nassuora, A. (2012). Students Acceptance of Mobile Learning for Higher Education in Saudi Arabia. International Journal of Learning Management Systems, 1(1), 1-9. https://doi.org/10.12785/ijlms/010101
Ngai, E. W. T., Poon, J. K. L., & Chan, Y. H. C. (2007). Empirical examination of the adoption of WebCT using TAM. Computers & Education, 48(2), 250-267. https://doi.org/10.1016/j.compedu.2004.11.007
Ozkan, S., & Koseler, R. (2009). Multi-dimensional students' evaluation of e-learning systems in the higher education context: An empirical investigation. Computers & Education, 53(4), 1285-1296. https://doi.org/10.1016/j.compedu.2009.06.011
Padumadasa, E. (2012). E-learning-fication of Sri Lanka higher education sector: adoption perspective. AIS eLibrary, 27(3), 1-32.
Park, S. Y., Nam, M.-W., & Cha, S.-B. (2012). University students' behavioral intention to use mobile learning: Evaluating the technology acceptance model: Factors related to use mobile learning. British Journal of Educational Technology, 43(4), 592-605. https://doi.org/10.1111/j.1467-8535.2011.01229.x
Pino, G., Peluso, A. M., & Guido, G. (2012). Determinants of Regular and Occasional Consumers' Intentions to Buy Organic Food. Journal of Consumer Affairs, 46(1), 157-169. https://doi.org/10.1111/j.1745-6606.2012.01223.x
Polonsky, M., & Waller, D. (2019). Designing and Managing a Research Project: A Business Student's Guide (4th Ed.). SAGE. https://doi.org/10.4135/9781544316499
Riquelme, H. E., & Rios, R. E. (2010). The moderating effect of gender in the adoption of mobile banking. International Journal of bank marketing, 28(5), 328-341. https://doi.org/10.1108/02652321011064872
Sahin, F., Dogan, E., Okur, M. R., & Sahin, Y. L. (2022). Emotional outcomes of e-learning adoption during compulsory online education. Education and Information Technologies, 27(6), 7827-7849. https://doi.org/10.1007/s10639-022-10930-y
Salloum, S. A., Al-Emran, M., Shaalan, K., & Tarhini, A. (2019). Factors affecting the E-learning acceptance: A case study from UAE. Education and Information Technologies, 24(1), 509-530. https://doi.org/10.1007/s10639-018-9786-3
San Martín, H., & Herrero, Á. (2012). Influence of the user's psychological factors on the online purchase intention in rural tourism: Integrating innovativeness to the UTAUT framework. Tourism Management, 33(2), 341-350.
https://doi.org/10.1016/j.tourman.2011.04.003
Sarmento, R., & Costa, V. (2019). Confirmatory Factor Analysis - A Case study. Researchgate, 10(1), 11-23.
Seif, M. H., Sarmadi, M. R., Ebrahimzadeh, I., & Zare, H. (2012). A model for predicting intention to use e-learning based on epistemological beliefs. Life Science Journal, 9, 926-929.
Sukendro, S., Habibi, A., Khaeruddin, K., Indrayana, B., Syahruddin, S., Makadada, F. A., & Hakim, H. (2020). Using an extended Technology Acceptance Model to understand students' use of e-learning during Covid-19: Indonesian sport science education context. Heliyon, 6(11), e05410. https://doi.org/10.1016/j.heliyon.2020.e05410
Sumak, B., Polancic, G., & Hericˇko, M. (2010). An empirical study of virtual learning environment adoption using UTAUT. Second International Conference on Mobile, Hybrid, and On-Line Learning. St Maarten, 5(1), 17-22.
Talboys, G. K. (2018). Museum Educator’s Handbook (3rd ed). Routledge.
Tan, M., & Teo, T. (2000). Factors Influencing the Adoption of Internet Banking. Journal of the Association for Information Systems, 1(1), 1-44. https://doi.org/10.17705/1jais.00005
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
Teo, T. (2011). Factors influencing teachers' intention to use technology: Model development and test. Computers & Education, 57(4), 2432-2440. https://doi.org/10.1016/j.compedu.2011.06.008
Thomas, T. D., Singh, L., & Gaffar, K. (2013). The utility of the UTAUT model in explaining mobile learning adoption in higher education in Guyana. International Journal of Education and Development Using Information and Communication Technology, 9(3), 71-85.
Toh, C. H. (2013). Assessing adoption of wikis in a Singapore secondary school: using the UTAUT model. International Council for Educational Media, 2, 1-9.
Tsu wei, T., Marthandan, G., Yee‐Loong Chong, A., Ooi, K.-B., & Arumugam, S. (2009). What drives Malaysian m-commerce adoption? An empirical analysis. Industrial Management & Data Systems, 109(3), 370-388. https://doi.org/10.1108/02635570910939399
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.11.4.342.1187
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. https://doi.org/10.2307/30036540
Venkatesh, V., Thong, J., & 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. https://doi.org/10.2307/41410412
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
Wang, Y.-S., Wang, H.-Y., & Shee, D. Y. (2007). Measuring e-learning systems success in an organizational context: Scale development and validation. Computers in Human Behavior, 23(4), 1792-1808. https://doi.org/10.1016/j.chb.2005.10.006
Wiafe, I., Nakata, K., Moran, S., & Gulliver, S. R. (2011). Considering user attitude and behaviour in persuasive systems design: the 3D-Rabmodel, ECIS, 2(1),186.
Williams, B., Onsman, A., & Brown, T. (2010). Exploratory factor analysis: A five-step guide for novices. Australasian Journal of Paramedicine, 8(3). https://doi.org/10.33151/ajp.8.3.93
Yakubu, M. N., & Dasuki, S. I. (2019). Factors affecting the adoption of e-learning technologies among higher education students in Nigeria: A structural equation modelling approach. Information Development, 35(3), 492-502. https://doi.org/10.1177/0266666918765907
Yeo, G. B., & Neal, A. (2013). Revisiting the Functional Properties of Self-Efficacy: A Dynamic Perspective. Journal of Management, 39(6), 1385-1396. https://doi.org/10.1177/0149206313490027
Yu, C. S. (2012). Factors affecting individuals to adopt mobile banking: empirical evidence from the UTAUT model, Journal of Electronic Commerce Research, 13(2), 104-121.
Yu, T.-K., & Yu, T.-Y. (2010). Modelling the factors that affect individuals' utilisation of online learning systems: An empirical study combining the task technology fit model with the theory of planned behaviour: Modelling factors affecting e-learning systems. British Journal of Educational Technology, 41(6), 1003-1017. https://doi.org/10.1111/j.1467-8535.2010.01054.x
Zhang, S., Zhao, J., & Tan, W. (2008). Extending TAM for online learning systems: An intrinsic motivation perspective. Tsinghua Science and Technology, 13(3), 312-317. https://doi.org/10.1016/s1007-0214(08)70050-6
Zhou, T. (2011). Understanding mobile internet continuance usage from the perspectives of UTAUT and flow. Information Development, 27(3), 207-218. https://doi.org/10.1177/0266666911414596
