Abdullah, F., & Ward, R. (2016). Developing a General Extended Technology Acceptance Model for E-Learning (GETAMEL) by analysing commonly used external factors. Computers in Human Behavior, 56, 238–256.
Article
Google Scholar
Abyaa, A., Idrissi, M. K., & Bennani, S. (2019). Learner modelling: systematic review of the literature from the last 5 years. Educational Technology Research and Development, 1-39.
Adukaite, A., van Zyl, I., Er, Ş, & Cantoni, L. (2017). Teacher perceptions on the use of digital gamified learning in tourism education: The case of South African secondary schools. Computers & Education, 111, 172–190.
Article
Google Scholar
Agudo-Peregrina, Á. F., Hernández-García, Á., & Pascual-Miguel, F. J. (2014). Behavioral intention, use behavior and the acceptance of electronic learning systems: Differences between higher education and lifelong learning. Computers in Human Behavior, 34, 301–314.
Article
Google Scholar
Al-Azawei, A., Parslow, P., & Lundqvist, K. (2017). The effect of universal design for learning (UDL) application on e-learning acceptance: A structural equation model. The International Review of Research in Open and Distributed Learning, 18(6), 54–87.
Article
Google Scholar
Beaudry, A., & Pinsonneault, A. (2010). The other side of acceptance: studying the direct and indirect effects of emotions on information technology use. MIS Quarterly, 34(4), 689–710.
Article
Google Scholar
Bourgonjon, J., De Grove, F., De Smet, C., Van Looy, J., Soetaert, R., & Valcke, M. (2013). Acceptance of game-based learning by secondary school teachers. Computers & Education, 67, 21–35.
Article
Google Scholar
Cabada, R. Z., Estrada, M. L. B., Hernández, F. G., Bustillos, R. O., & Reyes-García, C. A. (2018). An affective and Web 3.0-based learning environment for a programming language. Telematics and Informatics, 35(3), 611–628.
Article
Google Scholar
Cheon, J., Lee, S., Crooks, S. M., & Song, J. (2012). An investigation of mobile learning readiness in higher education based on the theory of planned behavior. Computers & Education, 59(3), 1054–1064.
Article
Google Scholar
Chow, M. (2016). Determinants of presence in 3D virtual worlds: A structural equation modelling analysis. Australasian Journal of Educational Technology, 32(1).
Codish, D., & Ravid, G. (2015). Detecting playfulness in educational gamification through behavior patterns. IBM Journal of Research and Development, 59(6), 6–1.
Article
Google Scholar
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.
Article
Google Scholar
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology, 22(14), 1111–1132.
Article
Google Scholar
Dæhlen, M., & Ure, O. B. (2009). Low-skilled adults in formal continuing education: does their motivation differ from other learners? International Journal of Lifelong Education, 28(5), 661–674.
Article
Google Scholar
Deci, E., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. New York, USA: Springer Science & Business Media.
Book
Google Scholar
Delone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems, 19(4), 9–30.
Article
Google Scholar
Dicheva, D., Irwin, K., & Dichev, C. (2018). Gamifying with OneUp: For Learning, Grades or Fun?. In International Conference on Games and Learning Alliance (pp. 343-353).
Esteban-Millat, I., Martínez-López, F. J., Pujol-Jover, M., Gázquez-Abad, J. C., & Alegret, A. (2018). An extension of the technology acceptance model for online learning environments. Interactive Learning Environments, 26(7), 895–910.
Article
Google Scholar
Fiore, A. M., & Kim, J. (2007). An integrative framework capturing experiential and utilitarian shopping experience. International Journal of Retail & Distribution Management, 35(6), 421–442.
Article
Google Scholar
Gan, C. L., & Balakrishnan, V. (2017). Predicting acceptance of mobile technology for aiding student-lecturer interactions: An empirical study. Australasian Journal of Educational Technology, 33(2).
Harter, S. (1981). A new self-report scale of intrinsic versus extrinsic orientation in the classroom: Motivational and informational components. Developmental Psychology, 17, 300–312.
Article
Google Scholar
Harter, S., & Jackson, B. K. (1992). Trait vs. nontrait conceptualizations of intrinsic/extrinsic motivational orientation. Motivation and Emotion, 16, 209–230.
Article
Google Scholar
Hao, S., Dennen, V. P., & Mei, L. (2017). Influential factors for mobile learning acceptance among Chinese users. Educational Technology Research and Development, 65(1), 101–123.
Article
Google Scholar
Hassenzahl, M. (2003). The thing and I: understanding the relationship between user and product. In M. Blythe, & A. Monk (Eds). Funology 2. Human–Computer Interaction Series (pp. 31-42). Cham, Switzerland: Springer.
Hassenzahl, M., Schöbel, M., & Trautmann, T. (2008). How motivational orientation influences the evaluation and choice of hedonic and pragmatic interactive products: The role of regulatory focus. Interacting with Computers, 20(4–5), 473–479.
Article
Google Scholar
Helmefalk, M., & Eklund, A. A. (2018). Fun and Function?-The impact of experiential learning styles on hedonic and utilitarian values in classrooms. Journal of Interdisciplinary Studies in Education, 7(1), 1–18.
Article
Google Scholar
Hertzum, M. (2010). Images of usability. International Journal of Human-Computer Interaction, 26(6), 567–600.
Article
Google Scholar
Hornbæk, K., & Hertzum, M. (2017). Technology acceptance and user experience: a review of the experiential component in HCI. ACM Transactions on Computer-Human Interaction (TOCHI), 24(5), 33.
Article
Google Scholar
Hong, J. C., Hwang, M. Y., Liu, M. C., Tsai, C. R., & Tai, K. H. (2020). Metacognition in covariation reasoning relevant to performance achievement mediated by experiential values in a simulation game. Educational Technology Research and Development, 68, 929–948.
Article
Google Scholar
Huang, T. K. (2018). How to lessen the effects of user resistance on the adoption of an e-learning environment: screenshot annotation on Flickr. Interactive Learning Environments, 26(4), 506–524.
Article
Google Scholar
Huang, H. M., & Liaw, S. S. (2018). An analysis of learners’ intentions toward virtual reality learning based on constructivist and technology acceptance approaches. The International Review of Research in Open and Distributed Learning, 19(1), 91–115.
Article
Google Scholar
Huang, H. M., Liaw, S. S., & Lai, C. M. (2016). Exploring learner acceptance of the use of virtual reality in medical education: A case study of desktop and projection-based display systems. Interactive Learning Environments, 24(1), 3–19.
Article
Google Scholar
Igbaria, M., Parasuraman, S., & Baroudi, J. J. (1996). A motivational model of microcomputer usage. Journal of Management Information Systems, 13(1), 127–143.
Article
Google Scholar
Ifinedo, P. (2017). Examining students’ intention to continue using blogs for learning: Perspectives from technology acceptance, motivational, and social-cognitive frameworks. Computers in Human Behavior, 72, 189–199.
Article
Google Scholar
ISO. (2010). ISO 9241: Ergonomics of Human-System Interaction - Part 210: Human-Centred Design for Interactive Systems. Geneva, CH: International Standard Organization.
Google Scholar
Islam, A. N. (2015). The moderation effect of user-type (educators vs students) in learning management system continuance. Behaviour & Information Technology, 34(12), 1160–1170.
Article
Google Scholar
Karapanos, E., Zimmerman, J., Forlizzi, J., & Martens, J. B. (2009). User experience over time: an initial framework. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 729-738), New York, US: ACM.
Kim, B. W., Lee, W. G., Lee, B. R., & Shon, J. G. (2015). Influencing factors in OER usage of adult learners in Korea. The International Review of Research in Open and Distributed Learning, 16(2).
Kitchenham B. (2007). Guidelines for Performing Systematic Literature Reviews in Software Engineering. Version 2.3, EBSE Technical Report, EBSE-2007-01. Keele, UK: Keele University. Retrieved from https://www.elsevier.com/__data/promis_misc/525444systematicreviewsguide.pdf.
Lepper, M. R., Corpus, J. H., & Iyengar, S. S. (2005). Intrinsic and extrinsic motivational orientations in the classroom: Age differences and academic correlates. Journal of educational psychology, 97(2), 184.
Article
Google Scholar
Mac Callum, K., & Jeffrey, L. (2014). Comparing the role of ICT literacy and anxiety in the adoption of mobile learning. Computers in Human Behavior, 39, 8–19.
Article
Google Scholar
Malaquias, R. F., Malaquias, F. F., & Hwang, Y. (2018). Understanding technology acceptance features in learning through a serious game. Computers in Human Behavior, 87, 395–402.
Article
Google Scholar
Mei, B., Brown, G. T., & Teo, T. (2018). Toward an understanding of preservice English as a Foreign Language teachers’ acceptance of computer-assisted language learning 20 in the People’s Republic of China. Journal of Educational Computing Research, 56(1), 74–104.
Article
Google Scholar
Merhi, M. I. (2015). Factors influencing higher education students to adopt podcast: An empirical study. Computers & Education, 83, 32–43.
Article
Google Scholar
Moghavvemi, S., Paramanathan, T., Rahin, N. M., & Sharabati, M. (2017). Student’s perceptions towards using e-learning via Facebook. Behaviour & Information Technology, 36(10), 1081–1100.
Article
Google Scholar
Mohammadi, H. (2015). Investigating users’ perspectives on e-learning: An integration of TAM and IS success model. Computers in Human Behavior, 45, 359–374.
Article
Google Scholar
Moreno, V., Cavazotte, F., & Alves, I. (2017). Explaining university students’ effective use of e-learning platforms. British Journal of Educational Technology, 48(4), 995–1009.
Article
Google Scholar
O’Brien, H. L. (2010). The influence of hedonic and utilitarian motivations on user engagement: The case of online shopping experiences. Interacting with Computers, 22(5), 344–352.
Article
Google Scholar
Oluwajana, D., Idowu, A., Nat, M., Vanduhe, V., & Fadiya, S. (2019). The adoption of students’ Hedonic motivation system model to gamified learning environment. Journal of theoretical and applied electronic commerce research, 14(3), 156–167.
Article
Google Scholar
Online supplementary document. (2019). Data Extraction. https://docs.google.com/spreadsheets/d/1-XBdHWmkIxNa7QAXcj4Cm_7vTdAEEdNINoNsaacDn-M/edit?usp=sharing
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.
Google Scholar
Philipsen, B., Tondeur, J., Roblin, N. P., Vanslambrouck, S., & Zhu, C. (2019). Improving teacher professional development for online and blended learning: A systematic meta-aggregative review. Educational Technology Research and Development, 1-30.
Pribeanu, C., Balog, A., & Iordache, D. D. (2017). Measuring the perceived quality of an AR-based learning application: a multidimensional model. Interactive Learning Environments, 25(4), 482–495.
Article
Google Scholar
Roca, J. C., & Gagné, M. (2008). Understanding e-learning continuance intention in the workplace: A self-determination theory perspective. Computers in Human Behavior, 24(4), 1585–1604.
Article
Google Scholar
Rodríguez-Ardura, I., & Meseguer-Artola, A. (2016). What leads people to keep on e-learning? An empirical analysis of users’ experiences and their effects on continuance intention. Interactive Learning Environments, 24(6), 1030–1053.
Article
Google Scholar
Rothes, A., Lemos, M. S., & Gonçalves, T. (2017). Motivational profiles of adult learners. Adult Education Quarterly, 67(1), 3–29.
Article
Google Scholar
Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55, 68–78.
Article
Google Scholar
Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25(1), 54–67.
Article
Google Scholar
Sabah, N. M. (2016). Exploring students’ awareness and perceptions: Influencing factors and individual differences driving m-learning adoption. Computers in Human Behavior, 65, 522–533.
Article
Google Scholar
Sadaf, A., Newby, T. J., & Ertmer, P. A. (2016). An investigation of the factors that influence preservice teachers’ intentions and integration of Web 2.0 tools. Educational Technology Research and Development, 64(1), 37–64.
Article
Google Scholar
Sánchez-Franco, M. J., & Roldán, J. L. (2005). Web acceptance and usage model: A comparison between goal-directed and experiential web users. Internet Research, 15(1), 21–48.
Article
Google Scholar
Song, Y., & Kong, S. C. (2017). Investigating students’ acceptance of a statistics learning platform using technology acceptance model. Journal of Educational Computing Research, 55(6), 865–897.
Article
Google Scholar
Šumak, B., Heričko, M., & Pušnik, M. (2011). A meta-analysis of e-learning technology acceptance: The role of user types and e-learning technology types. Computers in Human Behavior, 27(6), 2067–2077.
Article
Google Scholar
Teo T. (2011) Technology Acceptance Research in Education. In Teo T. (ed.) Technology Acceptance in Education. Leiden: SensePublishers.
Teo, T., & Wong, S. L. (2013). Modeling key drivers of e-learning satisfaction among student teachers. Journal of Educational Computing Research, 48(1), 71–95.
Article
Google Scholar
Teo, T., Doleck, T., Bazelais, P., & Lemay, D. J. (2019). Exploring the drivers of technology acceptance: a study of Nepali school students. Educational Technology Research and Development, 1-23.
Teo, T., Huang, F., & Hoi, C. K. W. (2018). Explicating the influences that explain intention to use Technology among English teachers in China. Interactive Learning Environments, 26(4), 460–475.
Article
Google Scholar
Teo, T., Zhou, M., & Noyes, J. (2016). Teachers and technology: development of an extended theory of planned behavior. Educational Technology Research and Development, 64(6), 1033–1052.
Article
Google Scholar
Traxler, J. (2005). Defining mobile learning. In IADIS International Conference Mobile Learning (pp. 261-266).
Van Schaik, P., & Ling, J. (2011). An integrated model of interaction experience for information retrieval in a Web-based encyclopedia. Interacting with Computers, 23(1), 18–32.
Article
Google Scholar
Voss, K. E., Spangenberg, E. R., & Grohmann, B. (2003). Measuring the hedonic and utilitarian dimensions of consumer attitude. Journal of Marketing Research, 40(3), 310–320.
Article
Google Scholar
Wan, Z. H. (2019). Exploring the effects of intrinsic motive, utilitarian motive, and self-efficacy on students’ science learning in the classroom using the expectancy-value theory. Research in Science Education. https://doi.org/10.1007/s11165-019-09891-z.
Article
Google Scholar
Wang, W. T., & Wang, C. C. (2009). An empirical study of instructor adoption of web-based learning systems. Computers & Education, 53(3), 761–774.
Article
Google Scholar
Weng, C., Tsai, C. C., & Weng, A. (2015). Social support as a neglected e-learning motivator affecting trainee’s decisions of continuous intentions of usage. Australasian Journal of Educational Technology, 31(2).
Yang, M., Shao, Z., Liu, Q., & Liu, C. (2017). Understanding the quality factors that influence the continuance intention of students toward participation in MOOCs. Educational Technology Research and Development, 65(5), 1195–1214.
Article
Google Scholar
Yoo, S. J., & Huang, W. D. (2013). Engaging online adult learners in higher education: Motivational factors impacted by gender, age, and prior experiences. The Journal of Continuing Higher Education, 61(3), 151–164.
Article
Google Scholar