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Education and Information Technologies

Erschienen in:

17.01.2023

Understanding college students’ continuous usage intention of asynchronous online courses through extended technology acceptance model

verfasst von: Kaili Lu, Feng Pang, Rustam Shadiev

Erschienen in: Education and Information Technologies | Ausgabe 8/2023

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Abstract

Asynchronous online learning has gained great popularity in higher education, especially due to the recent COVID-19 pandemic. However, few studies have investigated how to maintain students’ continuous usage intention of asynchronous online courses in the context of higher education. This study incorporated four key factors (intrinsic motivation, extrinsic motivation, perception of multiple sources, and cognitive engagement) associated with students’ continuous usage intention of asynchronous online courses into technology acceptance model (TAM) to identify the influencing factors on students’ continuous usage intention. A survey with 325 college students was conducted to explore their continuous usage intention of asynchronous online courses and structural equation modeling analysis was carried out to analyze the relationships between the key influencing factors and students’ continuous usage intention. The results showed that cognitive engagement was the only factor that directly related to continuous usage intention. Intrinsic motivation, extrinsic motivation, and perception of multiple sources indirectly correlated with students’ continuous usage intention through different pathways. The results of the study have several theoretical and practical implications. Theoretically, the study verified what key learning factors incorporated into TAM and in what way they relate to the continuous usage intention of asynchronous online courses. Practically, the present study indicated that it is required to take intrinsic motivation, extrinsic motivation, perception of multiple sources, cognitive engagement and TAM into consideration when designing and conducting asynchronous online learning courses to ensure college students’ continuous usage intention of asynchronous online courses.

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Agarwal, R., & Karahanna, E. (2000). Time flies when you're having fun: Cognitive absorption and beliefs about information technology usage. MIS Quarterly, 24(4), 665–694. https://doi.org/10.2307/3250951

Anyim, W. O. (2020). Students’ perception of electronic resources, usefulness and enhancement strategies for effective distance learning programme. Electronic Research Journal of Engineering Computer and Applied Sciences, 2, 102–116.

Bai, X., & Gu, X. (2022). Effect of teacher autonomy support on the online self-regulated learning of students during COVID‐19 in China: The chain mediating effect of parental autonomy support and students’ self‐efficacy. Journal of Computer Assisted Learning, 38(4), 1173–1184.CrossRef

Breslow, L., Pritchard, D. E., DeBoer, J., Stump, G. S., Ho, A. D., & Seaton, D. T. (2013). Studying learning in the worldwide classroom: Research into edX's first MOOC. Research & Practice in Assessment, 8, 13–25.

Broadbent, J., & Poon, W. L. (2015). Self-regulated learning strategies & academic achievement in online higher education learning environments: A systematic review. The Internet and Higher Education, 27, 1–13.CrossRef

Caffaro, F., Cremasco, M. M., Roccato, M., & Cavallo, E. (2020). Drivers of farmers’ intention to adopt technological innovations in Italy: The role of information sources, perceived usefulness, and perceived ease of use. Journal of Rural Studies, 76, 264–271.CrossRef

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.CrossRef

Eraslan Yalcin, M., & Kutlu, B. (2019). Examination of students' acceptance of and intention to use learning management systems using extended TAM. British Journal of Educational Technology, 50(5), 2414–2432.CrossRef

Fagan, M. H., Neill, S., & Wooldridge, B. R. (2008). Exploring the intention to use computers: An empirical investigation of the role of intrinsic motivation, extrinsic motivation, and perceived ease of use. Journal of Computer Information System, 48(3), 31–37.

Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. Review of educational research, 74(1), 59–109.CrossRef

Fredricks, J. A., Blumenfeld, P., Friedel, J., & Paris, A. (2005). School engagement. In K. A. Moore & L. Lippman (Eds.), What do children need to flourish? Conceptualizing and measuring indicators of positive development (pp. 305–321). Springer.CrossRef

Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.CrossRef

Fussell, S. G., & Truong, D. (2022). Using virtual reality for dynamic learning: an extended technology acceptance model. Virtual Reality, 26(1), 249–267.CrossRef

Gagné, M., & Deci, E. L. (2005). Self-determination theory and work motivation. Journal of Organizational behavior, 26(4), 331–362.CrossRef

Hair, J. F. J., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis: A global perspective (7th ed.). Pearson.

Hair, J. F. Jr., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A primer on partial least squares structural equation modeling (PLS-SEM). Sage Publications.

Hart, C. (2012). Factors associated with student persistence in an online program of study: A review of the literature. Journal of Interactive Online Learning, 11(1), 19–42.MathSciNet

Hepola, J., Karjaluoto, H., & Shaikh, A. A. (2016). Consumer engagement and behavioral intention toward continuous use of innovative mobile banking applications: A case study of Finland. In International Conference on Information Systems. Association for Information Systems (AIS).

Holbrook, M. B. (2006). Consumption experience, customer value, and subjective personal introspection: An illustrative photographic essay. Journal of Business Research, 59(6), 714–725.CrossRef

Hollebeek, L. D., Glynn, M. S., & Brodie, R. J. (2014). Consumer brand engagement in social media: Conceptualization, scale development and validation. Journal of Interactive Marketing, 28(2), 149–165.CrossRef

Hsu, H. T., & Lin, C. C. (2022). Extending the technology acceptance model of college learners' mobile-assisted language learning by incorporating psychological constructs. British Journal of Educational Technology, 53(2), 286–306.CrossRef

Jo, I. H., Kim, D., & Yoon, M. (2015). Constructing proxy variables to measure adult learners’ time management strategies in LMS. Journal of Educational Technology & Society, 18(3), 214–225.

John, O. P., & Benet-Martínez, V. (2000). Measurement: Reliability, construct validation, and scale construction. In H. T. Reis & C. M. Judd (Eds.), Handbook of research methods in social and personality psychology (pp. 473–503). Cambridge University Press.

Joo, Y. J., So, H. J., & Kim, N. H. (2018). Examination of relationships among students' self-determination, technology acceptance, satisfaction, and continuance intention to use K-MOOCs. Computers & Education, 122, 260–272.CrossRef

Jung, Y., & Lee, J. (2018). Learning engagement and persistence in massive open online courses (MOOCS). Computers & Education, 122, 9–22.CrossRef

Kim, D., Jo, I. H., Song, D., Zheng, H., Li, J., Zhu, J., & Xu, Z. (2021). Self-regulated learning strategies and student video engagement trajectory in a video-based asynchronous online course: A Bayesian latent growth modeling approach. Asia Pacific Education Review, 22(2), 305–317.CrossRef

Kim, D., Yoon, M., Jo, I. H., & Branch, R. M. (2018). Learning analytics to support self-regulated learning in asynchronous online courses: A case study at a women's university in South Korea. Computers & Education, 127, 233–251.CrossRef

Kim, S., & Kim, D. J. (2021). Structural relationship of key factors for student satisfaction and achievement in asynchronous online learning. Sustainability, 13(12), 6734.CrossRef

Liang, D., Jia, J., Wu, X., Miao, J., & Wang, A. (2014). Analysis of learners' behaviors and learning outcomes in a massive open online course. Knowledge Management & E-Learning: An International Journal, 6(3), 281–298.

Liu, H. J. (2012). Understanding EFL undergraduate anxiety in relation to motivation, autonomy, and language proficiency. Electronic Journal of Foreign Language Teaching, 9(1), 123–139.MathSciNet

Lu, K., Pang, F., & Shadiev, R. (2022). How to deepen college students' approach to using technologies in T-O‐IBL? Examining the mediating influence of deep approaches to using technologies between learning factors and higher order thinking skills. Journal of Computer Assisted Learning. https://doi.org/10.1111/jcal.12738CrossRef

Lu, K., Pang, F., & Shadiev, R. (2021a). Understanding the mediating effect of learning approach between learning factors and higher order thinking skills in collaborative inquiry-based learning. Educational Technology Research and Development, 69(5), 2475–2492.CrossRef

Lu, K., Yang, H. H., Shi, Y., & Wang, X. (2021b). Examining the key influencing factors on college students’ higher-order thinking skills in the smart classroom environment. International Journal of Educational Technology in Higher Education, 18(1), 1–13.CrossRef

Lv, X., Yang, Y., Qin, D., Cao, X., & Xu, H. (2022). Artificial intelligence service recovery: The role of empathic response in hospitality customers’ continuous usage intention. Computers in Human Behavior, 126, 106993.CrossRef

MacLeod, J., Yang, H. H., Zhu, S., & Li, Y. (2018). Understanding students’ preferences toward the smart classroom learning environment: Development and validation of an instrument. Computers & Education, 122, 80–91.CrossRef

Meyer, K. A. (2003). Face-to-face versus threaded discussions: The role of time and higher-order thinking. Journal of Asynchronous Learning Networks, 7(3), 55–65.

Moody, J. (2004). Distance education: Why are the attrition rates so high? Quarterly Review of Distance Education, 5(3), 205–210.MathSciNet

Moon, J. W., & Kim, Y. G. (2001). Extending the TAM for a World-Wide-Web context. Information & Management, 38(4), 217–230.CrossRef

Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory. McGraw-Hill.

Oyman, M., Bal, D., & Ozer, S. (2022). Extending the technology acceptance model to explain how perceived augmented reality affects consumers' perceptions. Computers in Human Behavior, 128, 107127.CrossRef

Pintrich, P. R., Smith, D. A. F., Garcia, T., & Mckeachie, W. J. A. (1991). A Manual for the Use of the Motivated Strategies for Learning Questionnaire (MSLQ). University of Michigan.

Pursel, B. K., Zhang, L., Jablokow, K. W., Choi, G. W., & Velegol, D. (2016). Understanding MOOC students: Motivations and behaviours indicative of MOOC completion. Journal of Computer Assisted Learning, 32(3), 202–217.CrossRef

Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25(1), 54–67.CrossRef

Saadé, R., & Bahli, B. (2005). The impact of cognitive absorption on perceived usefulness and perceived ease of use in on-line learning: an extension of the technology acceptance model. Information & Management, 42(2), 317–327.CrossRef

Segars, A. H. (1997). Assessing the unidimensionality of measurement: A paradigm and illustration within the context of information systems research. Omega International Journal of Management Science, 25(1), 107–121.CrossRef

Sun, J. C. Y., & Rueda, R. (2012). Situational interest, computer self-efficacy and self-regulation: Their impact on student engagement in distance education. British Journal of Educational Technology, 43(2), 191–204.CrossRef

Sun, Y., & Gao, F. (2020). An investigation of the influence of intrinsic motivation on students’ intention to use mobile devices in language learning. Educational Technology Research and Development, 68(3), 1181–1198.CrossRef

Tsai, P. S., Tsai, C. C., & Hwang, G. J. (2012). Developing a survey for assessing preferences in constructivist context-aware ubiquitous learning environments. Journal of Computer Assisted Learning, 28(3), 250–264.CrossRef

Ullman, J. B., & Bentler, P. M. (2012). Structural equation modeling. Journal of Handbook of Psychology, 2, 607–634.

Ushioda, E. (1996). Learner autonomy 5: The role of motivation (p. 40). Authentik.

Wang, L. C., & Chen, M. P. (2010). The effects of game strategy and preference-matching on flow experience and programming performance in game‐based learning. Innovations in Education and Teaching International, 47(1), 39–52.CrossRef

Yeo, S., Rutherford, T., & Campbell, T. (2022). Understanding elementary mathematics teachers’ intention to use a digital game through the technology acceptance model. Education and Information Technologies, 27, 11515–11536. https://doi.org/10.1007/s10639-022-11073-w.

Zhang, L., Carter Jr, R. A., Qian, X., Yang, S., Rujimora, J., & Wen, S. (2022). Academia's responses to crisis: A bibliometric analysis of literature on online learning in higher education during COVID-19. British Journal of Educational Technology, 53(3), 620–646.CrossRef

Titel: Understanding college students’ continuous usage intention of asynchronous online courses through extended technology acceptance model
verfasst von: Kaili Lu
Feng Pang
Rustam Shadiev
Publikationsdatum: 17.01.2023
Verlag: Springer US
Erschienen in: Education and Information Technologies / Ausgabe 8/2023
Print ISSN: 1360-2357
Elektronische ISSN: 1573-7608
DOI: https://doi.org/10.1007/s10639-023-11591-1

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