Key Drivers of Satisfaction, Perceived Usefulness, and Adoption of Flipped Classrooms at a Private University in China
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Abstract
Purpose: The study explores the key factors influencing undergraduates' intention to use flipped classrooms at a private university in Zhanjiang, China. Variables in the proposed conceptual framework include learning outcomes, cognitive engagement, social influence, perceived enjoyment, satisfaction, perceived usefulness, and intention to use. Research design, data, and methodology: The researcher adopted a quantitative research approach (n=450), administering surveys to Zhanjiang University of Science and Technology students. Nonprobability sampling involved purposive sampling to select five secondary colleges. Stratification random sampling will be used to obtain a proportional sample size, and convenience sampling will be used to collect questionnaires via Questionnaire Star online. Following this, confirmatory factor analysis (CFA) examines the relationship between potential and observed variables and measures the validity and reliability of the scale. Afterward, the structural equation model (SEM) checks the relationship between potential variables and whether the hypotheses are valid. Results: The results revealed that learning outcomes and cognitive engagement significantly affect satisfaction, while social influence and enjoyment significantly influence perceived usefulness. Both satisfaction and perceived usefulness significantly impact the intention to use. Among these factors, perceived usefulness had the strongest effect on intention to use, followed by social influence, enjoyment, and cognitive engagement. Conclusions: Policymakers, universities, colleges, and educators should work to establish the flipped classroom as an effective, engaging, and enjoyable learning method.
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References
Abdekhoda, M., Maserat, E., & Ranjbaran, F. (2020). A conceptual model of flipped classroom adoption in medical higher education. Interactive Technology and Smart Education, 17(4), 393-401. https://doi.org/10.1108/itse-09-2019-0058
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. https://doi.org/10.1016/j.chb.2015.11.036
Ackoff, R. L. (1955). The design of social research. Philosophy of Science, 22(1), 299. https://doi.org/10.2307/586972
Ajayi, I. H., Iahad, N. A., Ahmad, N., & Yusof, A. F. (2017). A conceptual model for flipped classroom: influence on continuance use intention. 2017 International Conference on Research and Innovation in Information Systems (ICRIIS), 1-6. IEEE.
Al-Mamary, Y. H., & Shamsuddin, A. (2015). Testing of The Technology Acceptance Model in Context of Yemen, Mediterranean Journal of Social Sciences, 6(4).
https://doi.org/10.5901/mjss.2015.v6n4s1p268
Avci, U., & Askar, P. (2012). The comparison of the opinions of the university students on the usage of blog and wiki for their courses. Journal of Educational Technology & Society, 15(2), 194-205.
Awang, Z. (2012). Structural equation modeling using AMOS graphic (1st ed.). Penerbit Universiti Teknologi MARA.
Balwant, M. K. (2019, July). Bidirectional LSTM based on POS tags and CNN architecture for fake news detection. 2019 10th International conference on computing, communication, and networking technologies (ICCCNT), 1-6.
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
Bergmann, J., & Sams, A. (2012). Flip your classroom: Reach every student in every class every day (1st ed.). International society for technology in education.
Bitner, M. J. (1990). Evaluating service encounters: The effects of physical surroundings and employee responses. Journal of Marketing, 54(2), 69-82. https://doi.org/10.1177/002224299005400206
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, S., Chen, T., & Bonanno, G. A. (2018). Expressive flexibility: Enhancement and suppression abilities differentially predict life satisfaction and psychopathology symptoms. Personality and Individual Differences, 126, 78-84. https://doi.org/10.1016/j.paid.2018.01.010
Chen, S. C., Yang, S. J., & Hsiao, C. C. (2016). Exploring student perceptions, learning outcome and gender differences in a flipped mathematics course. British Journal of Educational Technology, 47(6), 1096-1112. https://doi.org/10.1111/bjet.12278
Chen, S. M., & Chiou, C. H. (2014). Mult attribute decision making based on interval-valued intuitionistic fuzzy sets, PSO techniques, and evidential reasoning methodology. IEEE Transactions on Fuzzy Systems, 23(6), 1905-1916.
https://doi.org/10.1109/tfuzz.2014.2370675
Cheung, C. M., & Lee, M. K. (2009). Understanding the sustainability of a virtual community: model development and empirical test. Journal of Information Science, 35(3), 279-298. https://doi.org/10.1177/0165551508099088
Cheung, C. M., & Lee, M. K. (2012). What drives consumers to spread electronic word of mouth in online consumer-opinion platforms. Decision support systems, 53(1), 218-225.
https://doi.org/10.1016/j.dss.2012.01.015
Chow, M., Herold, D. K., Choo, T. M., & Chan, K. (2012). Extending the technology acceptance model to explore the intention to use Second Life for enhancing healthcare education. Computers & education, 59(4), 1136-1144. https://doi.org/10.1016/j.compedu.2012.05.011
Das, A., Lam, T. K., Thomas, S., Richardson, J., Kam, B. H., Lau, K. H., & Nkhoma, M. Z. (2019). Flipped classroom pedagogy: Using pre-class videos in an undergraduate business information systems management course. Education+ Training, 61(6), 756-774. https://doi.org/10.1108/et-06-2018-0133
Davis, F. D. (1985). A technology acceptance model for empirically testing new end-user (1st ed.). Information systems: Theory and results.
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.
https://doi.org/10.1111/j.1559-1816.1992.tb00945.x
Dele-Ajayi, O., Strachan, R., Sanderson, J., & Pickard, A. (2017, April). A modified TAM for predicting acceptance of digital educational games by teachers. 2017 IEEE Global Engineering Education Conference (EDUCON), 961-968.
Doo, M. Y. (2021). Understanding flipped learners’ perceptions, perceived usefulness, registration intention, and learning engagement. Contemporary Educational Technology, 14(1), ep331.
Doolin, B., Dillon, S., Thompson, F., & Corner, J. L. (2005). Perceived risk, the Internet shopping experience and online purchasing behavior: A New Zealand perspective. Journal of Global Information Management (JGIM), 13(2), 66-88. https://doi.org/10.4018/jgim.2005040104
Evans, H. K., & Cordova, V. (2015). Lecture videos in online courses: A follow-up. Journal of Political Science Education, 11(4), 472-482. https://doi.org/10.30935/cedtech/11368
Fan, X., Duangekanong, S., & Xu, M. (2022). Factors Affecting College Students’ Intention to Use U-Learning For English Studies In Sichuan, China. Journal of Global Business Review., 24(1), 51-70.
Fishbein, M., & Ajzen, I. (1975). Attitude-behaviour relations: A theoretical analysis and review of empirical research. Psychological Bulletin, 84(5), 888-918.
https://doi.org/10.1037/0033-2909.84.5.888
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
Francescucci, A., & Rohani, L. (2019). Exclusively synchronous online (VIRI) learning: The impact on student performance and engagement outcomes. Journal of marketing Education, 41(1), 60-69. https://doi.org/10.1177/0273475318818864
Ha, S., & Stoel, L. (2009). Consumer e-shopping acceptance: Antecedents in a technology acceptance model. Journal of Business Research, 62(5), 565-571. https://doi.org/10.1016/j.jbusres.2008.06.016
Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate data analysis (1st ed.). Prentice-Hall.
Hsieh, P. A. J., & Cho, V. (2011). Comparing e-Learning tools’ success: The case of instructor-student interactive vs. self-paced tools. Computers & Education, 57(3), 2025-2038.
https://doi.org/10.1016/j.compedu.2011.05.002
Huang, G. (2006). The determinants of capital structure: Evidence from China. China economic review, 17(1), 14-36. https://doi.org/10.1016/j.chieco.2005.02.007
Hunde, M. K., Demsash, A. W., & Walle, A. D. (2023). Behavioral intention to use e-learning and its associated factors among health science students in Mettu university, southwest Ethiopia: Using modified UTAUT model. Informatics in Medicine Unlocked, 36, 101154.
https://doi.org/10.1016/j.imu.2022.101154
Hung, H.-T. (2015). Flipping the classroom for English language learners to foster active learning. Computer Assisted Language Learning, 28(1), 81-96. https://doi.org/10.1080/09588221.2014.967701
Jin, L. (2012). What does "flipped classroom" flip?. Journal of Chinese Information Technology Education, 2012(9), 18.
Kelman, H. C. (1958). Compliance, identification, and internalization three processes of attitude change. Journal of Conflict Resolution, 2(1), 51-60.
https://doi.org/10.1177/002200275800200106
Kerkhoff, S. N. (2017). Designing global futures: A mixed methods study to develop and validate the teaching for global readiness scale. Teaching and Teacher Education, 65, 91-106.
Kim, D. J., Ferrin, D. L., & Rao, H. R. (2009). Trust and satisfaction, two steppingstones for successful e-commerce relationships: A longitudinal exploration. Information Systems Research, 20(2), 237-257. https://doi.org/10.1287/isre.1080.0188
Kim, D. Y., Park, J., & Morrison, A. M. (2008). A model of traveller acceptance of mobile technology. International Journal of Tourism Research, 10(5), 393-407.
https://doi.org/10.1002/jtr.669
Kulviwat, S., Bruner, G. C., Kumar, A., Nasco, S. A., & Clark, T. (2007). Toward a unified theory of consumer acceptance technology. Psychology & Marketing, 24(12), 1059-1084.
https://doi.org/10.1002/mar.20196
Lai, C.-L., & Hwang, G. J. (2016). A self-regulated flipped classroom approach to improving students’ learning performance in a mathematics course. Computers and Education, 100, 126-140. https://doi.org/10.1016/j.compedu.2016.05.006
Le, M. H., Le, D. M., Baez, T. C., Wu, Y., Ito, T., Lee, E. Y., & Nguyen, M. H. (2023). Global incidence of non-alcoholic fatty liver disease: A systematic review and meta-analysis of 63 studies and 1,201,807 persons. Journal of Hepatology, 79(2), 287-295. https://doi.org/10.1016/j.jhep.2023.03.040
Le, N. T., & Nguyen, D. T. (2023). Student satisfaction with EMI courses: the role of motivation and engagement. Journal of Applied Research in Higher Education, 15(3), 762-775.
https://doi.org/10.1108/jarhe-02-2022-0050
Lee, M.-C. (2010). Explaining and predicting users’ continuance intention toward e-learning: An extension of the expectation-confirmation model. Computers and Education, 54(2), 506-516. https://doi.org/10.1016/j.compedu.2009.09.002
Long, S. (2018). Socioanalytic methods: discovering the hidden in organisations and social systems (1st ed.). Routledge.
Long, T., Cummins, J., & Waugh, M. (2017). Use of the flipped classroom in higher education: Instructors’ perspectives. Journal of Computing in Higher Education, 29(2), 179-200. https://doi.org/10.1007/s12528-016-9119-8
Luo, N., Wang, Y., Jin, C., Ni, Y., & Zhang, M. (2019). Effects of socialization interactions on customer engagement in online travel communities. Internet Research, 29(6), 1509-1525.
https://doi.org/10.1108/intr-08-2018-0354
Manwaring, K. C., Larsen, R., Graham, C. R., Henrie, C. R., & Halverson, L. R. (2017). Investigating student engagement in blended learning settings using experience sampling and structural equation modeling. The Internet and Higher Education, 35, 21-33.
https://doi.org/10.1016/j.iheduc.2017.06.002
Mitchell, T. R., & Beach, L. R. (1976). A review of occupational preference and choice research using expectancy theory and decision theory. Journal of Occupational Psychology, 49(4), 231-248. https://doi.org/10.1111/j.2044-8325.1976.tb00348.x
Mohamed, H., & Lamia, M. (2018). Implementing flipped classroom that used an intelligent tutoring system into learning process. Computers & Education, 124, 62-76.
https://doi.org/10.1016/j.compedu.2018.05.011
Moore, M. M. (2011). Student Satisfaction and Graduate Part-Time Students. Continuing Higher Education Review, 75, 113-120.
Murillo-Zamorano, L. R., Sánchez, J. Á. L., & Godoy-Caballero, A. L. (2019). How the flipped classroom affects knowledge, skills, and engagement in higher education: Effects on students' satisfaction. Computers & Education, 141, 103608. https://doi.org/10.1016/j.compedu.2019.103608
Novak, E., Brannon, M., Librea‐Carden, M. R., & Haas, A. L. (2021). A systematic review of empirical research on learning with 3D printing technology. Journal of Computer Assisted Learning, 37(5), 1455-1478. https://doi.org/10.1111/jcal.12585
Oliver, R. L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research, 17(4), 460-469. https://doi.org/10.1177/002224378001700405
Oliver, R. L. (1981). Measurement and evaluation of satisfaction processes in retail settings. Journal of retailing, 4(2), 20-30.
Orús, C., Barlés, M. J., Belanche, D., Casaló, L., Fraj, E., & Gurrea, R. (2016). The effects of learner-generated videos for YouTube on learning outcomes and satisfaction. Computers & Education, 95, 254-269. https://doi.org/10.1016/j.compedu.2016.01.007
Pedroso, R., Zanetello, L., Guimaraes, L., Pettenon, M., Goncalves, V., Scherer, J., Kessler, F., & Pechansky, F. (2016). Confirmatory factor analysis (CFA) of the crack use relapse scale (CURS). Archives of Clinical Psychiatry, 43(3), 37-40. https://doi.org/10.1590/0101-60830000000081
Pfeffer, R. I., Kurosaki, T. T., Harrah, C. H., Chance, J. M., & Filos, S. (1982). Measurement of functional activities in older adults in the community. Journal of gerontology, 37(3), 323-329. https://doi.org/10.1093/geronj/37.3.323
Rabren, K., Eaves, R. C., Dunn, C., & Darch, C. (2013). Students with learning disabilities’ satisfaction, employment, and postsecondary education outcomes. Journal of Education and Learning, 2(2), 14-22. https://doi.org/10.5539/jel.v2n2p14
Ray, A., Bala, P. K., & Dasgupta, S. A. (2019). Role of authenticity and perceived benefits of online courses on technology-based career choice in India: A modified technology adoption model based on career theory. International Journal of Information Management, 47, 140-151.
https://doi.org/10.1016/j.ijinfomgt.2019.01.015
Reeve, J., & Tseng, C. M. (2011). Agency as a fourth aspect of students’ engagement during learning activities. Contemporary Educational Psychology, 36(4), 257-267.
https://doi.org/10.1016/j.cedpsych.2011.05.002
Reynoso, C. (2010). The use of case studies in preparing postgraduate dissertations on small and medium sized firms. Review of Business & Finance Case Studies, 1(1), 81-94.
Sedaghat, A., Mokhtarzade, M., & Ebadi, H. (2011). Uniform robust scale-invariant feature matching for optical remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 49(11), 4516-4527. https://doi.org/10.1109/tgrs.2011.2144607
Sharma, G. P., Verma, R. C., & 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.2005.02.010
Sica, C., & Ghisi, M. (2007). The Italian versions of the Beck Anxiety Inventory and the Beck Depression Inventory-II: Psychometric properties and discriminant power. In M.A. Lange (Ed.), Leading - Edge Psychological Tests and Testing Research (pp. 27-50). Nova.
Skinner, E. A., & Belmont, M. J. (1993). Motivation in the classroom: Reciprocal effects of teacher behavior and student engagement across the school year. Journal of Educational Psychology, 85(4), 571-581. https://doi.org/10.1037/0022-0663.85.4.571
Stanislaus, I. (2022). Forming digital shepherds of the Church: evaluating participation and satisfaction of blended learning course on communication theology. Interactive Technology and Smart Education, 19(1), 58-74. https://doi.org/10.1108/itse-10-2020-0217
Straub, D. W. (1989). Validating instruments in MIS research. MIS Quarterly, 13(2), 147-169. https://doi.org/10.2307/248922
Teo, T., Sang, G., Mei, B., & Hoi, C. K. W. (2019). Investigating pre-service teachers’ acceptance of Web 2.0 technologies in their future teaching: A Chinese perspective. Interactive Learning Environments, 27(4), 530-546.
Terzis, V., & Economides, A. A. (2011). The acceptance and use of computer-based assessment. Computers & Education, 56(4), 1032-1044. https://doi.org/10.1016/j.compedu.2010.11.017
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204.
https://doi.org/10.1287/mnsc.46.2.186.11926
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), 425-478.
Venkatesh, V., Thong, J. Y. L., Chan, F. K. Y., Hu, P. J. H., & Brown, S. A. (2011). Extending the two-stage information systems continuance model: Incorporating UTAUT predictors and the role of context. Information Systems Journal, 21(6), 527-555. https://doi.org/10.2307/30036540
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 157-178. https://doi.org/10.2307/41410412
Vogelsang, K., Hagerer, I., Liere-Netheler, K., & Hoppe, U. (2017). Analysis of the use of digital media to design a blended learning environment by the example of a master course lecture. Proceedings of the 11th International Multi-Conference on Society, Cybernetics, and Informatics (IMSCI 2017), 127-131.
Wang, J. C., & Chiang, M. J. (2009). Social interaction and continuance intention in online auctions: A social capital perspective. Decision Support Systems, 47(4), 466-476. https://doi.org/10.1016/j.dss.2009.04.013
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
Wu, L., & Liu, Y. J. (2007). Development of dendritic-cell lineages. Immunity, 26(6), 741-750.
https://doi.org/10.1016/j.immuni.2007.06.006
Yi, N., Yandell, B. S., Churchill, G. A., Allison, D. B., Eisen, E. J., & Pomp, D. (2005). Bayesian model selection for genome-wide epistatic quantitative trait loci analysis. Genetics, 170(3), 1333-1344. https://doi.org/10.1534/genetics.104.040386
Yoshida, H. (2016). Perceived usefulness of "flipped learning" on instructional design for elementary and secondary education: With focus on pre-service teacher education. International Journal of Information and Education Technology, 6(6), 430- 434. https://doi.org/10.7763/ijiet.2016.v6.727
Zhai, X., Gu, J., Liu, H., Liang, J. C., & Tsai, C. C. (2017). An experiential learning perspective on students’ satisfaction model in a flipped classroom context. Educational Technology and Society, 20(1), 198-210.
Zhang, D., Zhou, L., Briggs, R. O., & Nunamaker, J. F. (2006). Instructional video in e-learning: Assessing the impact of interactive video on learning effectiveness. Information and Management, 43(1), 15-27. https://doi.org/10.1016/j.im.2005.01.004