Building an economically sustainable online learning ecosystem with freemium model: A sequential mixed-method approach (original) (raw)

References

• Al-Emran, M., & Al-Sharafi, M. A. (2022). Revolutionizing education with industry 5.0: challenges and future research agendas. International Journal of Information Technology, 6(3), 1–5.
  Google Scholar
• Alqurashi, E. (2019). Predicting student satisfaction and perceived learning within online learning environments. Distance Education, 40(1), 133–148. https://doi.org/10.1080/01587919.2018.1553562
  Article Google Scholar
• Bai, B., Wang, J., & Chai, C. S. (2021). Understanding Hong Kong primary school English teachers’ continuance intention to teach with ICT. Computer Assisted Language Learning, 34(4), 528–551. https://doi.org/10.1080/09588221.2019.1627459
  Article Google Scholar
• Baranowski, T., Perry, C. L., & Parcel, G. S. (2002). How individuals, environments, and health behavior interact. In K. Glanz, B. K. Rimer, & F. M. Lewis (Eds.), Health behavior and health education: Theory, research, and practice (3rd ed., pp. 165–184). San Francisco: Jossey-Bass.
  Google Scholar
• Barata, M. L., & Coelho, P. S. (2021). Music streaming services: understanding the drivers of customer purchase and intention to recommend. Heliyon, 7(8), e07783. https://doi.org/10.1016/j.heliyon.2021.e07783
  Article Google Scholar
• Barclay, D. W., Higgins, C. A., & Thompson, R. (1995). The partial least squares approach to causal modeling: Personal computer adoption and use as illustration. Technology Studies, 2(2), 285–309.
  Google Scholar
• Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182. https://doi.org/10.1037/0022-3514.51.6.1173
  Article Google Scholar
• Bittner, J. V., & Shipper, J. (2014). Motivational effects and age differences of gamification in product advertising. Journal of Consumer Marketing, 31(5), 391–400. https://doi.org/10.1108/JCM-04-2014-0945
  Article Google Scholar
• Bhattacharyya, S. S., Goswami, S., Mehta, R., & Nayak, B. (2022). Examining the factors influencing adoption of over the top (OTT) services among Indian consumers. Journal of Science and Technology Policy Management, 13(3), 652–682. https://doi.org/10.1108/jstpm-09-2020-0135
  Article Google Scholar
• Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351–370. https://doi.org/10.2307/3250921
  Article Google Scholar
• Brislin, R. W. (1970). Back-translation for cross-cultural research. Journal of Cross-Cultural Psychology, 1(3), 185–216. https://doi.org/10.1177/135910457000100301
  Article Google Scholar
• Chang, C. C. (2022). Factors affecting m-learning continuance–From the perspectives of flow theory and stimulus-organism-response theory. In European Conference on Knowledge Management, 23(2), 1396–1402.
  Article Google Scholar
• Chen, L. T. (2023). The death of e-commercial & digital health & digital education in China. Internet Economy Institute. Retrieved October 20, 2023, from https://www.100ec.cn/zt/2022jkjyswbg/
• Chiang, C. F. (2006). An expectancy theory model for hotel employee motivation: The moderating role of communication satisfaction. Kansas State University.
  Google Scholar
• Fiske, D. W., & Campbell, D. T. (1959). Convergent and discriminant validation by the multitrait-mutimethod matrix. Psycho-logical Bulletin, 56, 81–105. https://doi.org/10.1037/h0046016
  Article Google Scholar
• Chen, C. F., & Wang, J. P. (2016). Customer participation, value co-creation and customer Loyalty–A case of airline online check-in system. Computers in Human Behavior, 62, 346–352. https://doi.org/10.1016/j.chb.2016.04.010
  Article Google Scholar
• Creswell, J. W., & Plano Clark, V. L. (2011). Designing and conducting mixed methods research (2nd ed.). Sage.
  Google Scholar
• Csikszentmihalyi, M., & Larson, R. (2014). Flow and the foundations of positive psychology. Springer.
  Book Google Scholar
• Dai, H. M., Teo, T., Rappa, N. A., & Huang, F. (2020). Explaining Chinese university students’ continuance learning intention in the MOOC setting: A modified expectation confirmation model perspective. Computers & Education, 150, 103850. https://doi.org/10.1016/j.compedu.2020.103850
  Article Google Scholar
• Dash, G., Kiefer, K., & Paul, J. (2021). Marketing-to-Millennials: Marketing 4.0, customer satisfaction and purchase intention. Journal of Business Research, 122, 608–620. https://doi.org/10.1016/j.jbusres.2020.10.016
  Article Google Scholar
• El-Tannir, A. A. (2002). The corporate university model for continuous learning, training and development. Education + Training, 44(2), 76–81. https://doi.org/10.1108/00400910210419973
  Article Google Scholar
• Fared, M. A., Darmawan, D., & Khairi, M. (2021). Contribution of E-Service Quality to Repurchase Intention with Mediation of Customer Satisfaction: Study of Online Shopping Through Marketplace. Journal of Marketing and Business Research (MARK), 1(2), 93–106. https://doi.org/10.56348/mark.v1i2.37
  Article Google Scholar
• 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/10.2307/3151312
  Article Google Scholar
• Friedman, B., Freier, N. G., Kahn, P. H., Jr., Lin, P., & Sodeman, R. (2008). Office window of the future? Field-based analyses of a new use of a large display. International Journal of Human-Computer Studies, 66(6), 452–465. https://doi.org/10.1016/j.ijhcs.2007.12.005
  Article Google Scholar
• Gefen, D., Straub, D., & Boudreau, M. C. (2000). Structural equation modeling and regression: Guidelines for research practice. Communications of the Association for Information Systems, 4(1), 7. https://doi.org/10.17705/1CAIS.00407
  Article Google Scholar
• Gold, A. H., Malhotra, A., & Segars, A. H. (2001). Knowledge management: An organizational capabilities perspective. Journal of Management Information Systems, 18(1), 185–214. https://doi.org/10.1080/07421222.2001.11045669
  Article Google Scholar
• Guo, Z., Xiao, L., Van Toorn, C., Lai, Y., & Seo, C. (2016). Promoting online learners’ continuance intention: An integrated flow framework. Information & Management, 53(2), 279–295. https://doi.org/10.1016/j.im.2015.10.010
  Article Google Scholar
• Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203
  Article Google Scholar
• Hair, J. F., Jr., Sarstedt, M., Matthews, L. M., & Ringle, C. M. (2016). Identifying and treating unobserved heterogeneity with FIMIX-PLS: Part I–method. European Business Review, 28(1), 63–76. https://doi.org/10.1108/EBR-09-2015-0094
  Article Google Scholar
• Hamari, J., Hanner, N., & Koivisto, J. (2017). Service quality explains why people use freemium services but not if they go premium: An empirical study in free-to-play games. International Journal of Information Management, 37(1), 1449–1459. https://doi.org/10.1016/j.ijinfomgt.2016.09.004
  Article Google Scholar
• Hamari, J., Hanner, N., & Koivisto, J. (2020). “Why pay premium in freemium services?” A study on perceived value, continued use and purchase intentions in free-to-play games. International Journal of Information Management, 51, 102040. https://doi.org/10.1016/j.ijinfomgt.2019.102040
  Article Google Scholar
• Hsu, C. H., Hsu, P. C., & Chen, R. S. (2023). Early childhood preservice teachers’ attitudes towards online engagement, civic responsibility, and service learning. Education and Information Technologies, 28, 3173–3190. https://doi.org/10.1007/s10639-022-11333-9
  Article Google Scholar
• Huang, Y. C., Li, L. N., Lee, H. Y., Browning, M. H., & Yu, C. P. (2023). Surfing in virtual reality: An application of extended technology acceptance model with flow theory. Computers in Human Behavior Reports, 9, 100252. https://doi.org/10.1016/j.chbr.2022.100252
  Article Google Scholar
• Hooshyar, D., Malva, L., Yang, Y., Pedaste, M., Wang, M., & Lim, H. (2021). An adaptive educational computer game: Effects on students’ knowledge and learning attitude in computational thinking. Computers in Human Behavior, 114, 106575. https://doi.org/10.1016/j.chb.2020.106575
  Article Google Scholar
• Jang, Y. T., & Chiang, I. (2023). Incorporating desire and persistence into understanding Gen Z learners’ continuance intention toward using Youtube for learning in digital learning context. Education and Information Technologies, 1–26. https://doi.org/10.1007/s10639-023-12202-9.
• Kamdjoug, J. R. K., Wamba-Taguimdje, S. L., Wamba, S. F., & Kake, I. B. E. (2021). Determining factors and impacts of the intention to adopt mobile banking app in Cameroon: Case of SARA by afriland First Bank. Journal of Retailing and Consumer Services, 61, 102509. https://doi.org/10.1016/j.jretconser.2021.102509
  Article Google Scholar
• Kline, R. B. (2015). Principles and practice of structural equation modeling. The Guilford Press.
  Google Scholar
• 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.
  Article Google Scholar
• Lee, C. H., Chiang, H. S., & Hsiao, K. L. (2018). What drives stickiness in location-based AR games? An examination of flow and satisfaction. Telematics and Informatics, 35(7), 1958–1970. https://doi.org/10.1016/j.tele.2018.06.008
  Article Google Scholar
• Lee, Y., & Choi, J. (2013). A structural equation model of predictors of online learning retention. The Internet and Higher Education, 16, 36–42. https://doi.org/10.1016/j.iheduc.2012.01.005
  Article Google Scholar
• Leo, S., Alsharari, N. M., Abbas, J., & Alshurideh, M. T. (2021). From offline to online learning: A qualitative study of challenges and opportunities as a response to the COVID-19 pandemic in the UAE higher education context. The effect of coronavirus disease (COVID-19) on business intelligence, 203–217. https://doi.org/10.1007/978-3-030-67151-8_12
• Li, S., Zheng, J., & Zheng, Y. (2021). Towards a new approach to managing teacher online learning: Learning communities as activity systems. The Social Science Journal, 58(3), 383–395. https://doi.org/10.1016/j.soscij.2019.04.008
  Article Google Scholar
• Li, X., Zhang, J., & Zhang, J. (2023). From free to paid: Effect of knowledge differentiation on market performance of paid knowledge products. Information Processing & Management, 60(2), 103239. https://doi.org/10.1016/j.ipm.2022.103239
  Article Google Scholar
• Lin, S., Cheng, K., & Chuang, S. H. (2021). Three needs and information anxiety on knowledge purchase intentions across online knowledge platforms. Behavioral Sciences, 11(10), 127. https://doi.org/10.3390/bs11100127
  Article Google Scholar
• Lowry, P. B., Gaskin, J., & Moody, G. D. (2015). Proposing the multi-motive information systems continuance model (MISC) to better explain end-user system evaluations and continuance intentions. Journal of the Association for Information Systems, 16(7), 515–579. https://doi.org/10.17705/1jais.00403
  Article Google Scholar
• Lu, Y., Wang, B., & Lu, Y. (2019). Understanding key drivers of MOOC satisfaction and continuance intention to use. Journal of Electronic Commerce Research, 20(2), 105–117.
  MathSciNet Google Scholar
• Moore, S. L., & Piety, P. J. (2022). Online learning ecosystems: Comprehensive planning and support for distance learners. Distance Education, 43(2), 179–203. https://doi.org/10.1080/01587919.2022.2064820
  Article Google Scholar
• Mäntymäki, M., Islam, A. N., & Benbasat, I. (2020). What drives subscribing to premium in freemium services? A consumer value-based view of differences between upgrading to and staying with premium. Information Systems Journal, 30(2), 295–333. https://doi.org/10.1111/isj.12262
  Article Google Scholar
• Ngo, H. T., & Eichelberger, A. (2020). Learning ecologies in online language learning social networks: A netnographic study of EFL learners using italki. International Journal of Social Media and Interactive Learning Environments, 6(3), 181–199.
  Article Google Scholar
• Oh, S., Ji, H., Kim, J., Park, E., & del Pobil, A. P. (2022). Deep learning model based on expectation-confirmation theory to predict customer satisfaction in hospitality service. Information Technology & Tourism, 24(1), 109–126. https://doi.org/10.1007/s40558-022-00222-z
  Article Google Scholar
• Osterwalder, A., & Pigneur, Y. (2010). Business model generation: a handbook for visionaries, game changers, and challengers (Vol. 1). John Wiley & Sons.
  Google Scholar
• Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879. https://doi.org/10.1037/0021-9010.88.5.879
  Article Google Scholar
• Qu, Y., Lin, Z., & Zhang, X. (2022). The optimal pricing model of online knowledge payment goods in C2C sharing economy. Kybernetes, 51(1), 31–51. https://doi.org/10.1108/K-11-2020-0756
  Article Google Scholar
• Rahimi, A. R., & Cheraghi, Z. (2022). Unifying EFL learners’ online self-regulation and online motivational self-system in MOOCs: A structural equation modeling approach. Journal of Computers in Education, 1–27. https://doi.org/10.1007/s40692-022-00245-9.
• Rahmansyah, M. R., & Hati, S. R. H. (2020). The influence of customer engagement experiences between satisfaction and loyalty relationships on freemium business model. In the International Conference on Business and Management Research (pp. 79–84). Atlantis Press.
• Ranellucci, J., Rosenberg, J. M., & Poitras, E. G. (2020). Exploring pre-service teachers’ use of technology: The technology acceptance model and expectancy–value theory. Journal of Computer Assisted Learning, 36(6), 810–824. https://doi.org/10.1111/jcal.12459
  Article Google Scholar
• Razmerita, L., Kirchner, K., Hockerts, K., & Tan, C. W. (2020). Modeling collaborative intentions and behavior in Digital Environments: The case of a Massive Open Online Course (MOOC). Academy of Management Learning & Education, 19(4), 469–502. https://doi.org/10.5465/amle.2018.0056
  Article Google Scholar
• Ringle, C. M., Wende, S., & Becker, J. M. (2015). SmartPLS 3. SmartPLS GmbH, Boenningstedt. Journal of Service Science and Management, 10(3), 32–49.
• Rossin, D., Ro, Y. K., Klein, B. D., & Guo, Y. M. (2009). The effects of flow on learning outcomes in an online information management cou. Journal of Information Systems Education, 20(1), 87–98.
  Google Scholar
• Sarosa, S. (2019). The role of brand reputation and perceived enjoyment in accepting compulsory device’s usage: Extending UTAUT. Procedia Computer Science, 161, 115–122. https://doi.org/10.1016/j.procs.2019.11.106
  Article Google Scholar
• Sarnok, K., Wannapiroon, P., & Nilsook, P. (2019). Digital learning ecosystem by using digital storytelling for teacher profession students. International Journal of Information and Education Technology, 9(1), 21–26. https://doi.org/10.18178/ijiet.2019.9.1.1167
  Article Google Scholar
• Shang, S. S., Wu, Y. L., & Sie, Y. J. (2017). Generating consumer resonance for purchase intention on social network sites. Computers in Human Behavior, 69, 18–28. https://doi.org/10.1016/j.chb.2016.12.014
  Article Google Scholar
• Shanshan, S., & Wenfei, L. (2022). Understanding the impact of quality elements on MOOCs continuance intention. Education and Information Technologies, 27(8), 10949–10976. https://doi.org/10.1007/s10639-022-11063-y
  Article Google Scholar
• Shanyun, K., Qin, S., & Guolin, Z. (2015). Research on the construction of MOOC learning community ecosystem circle. In 2015 International Conference of Educational Innovation through Technology (EITT) (pp. 199–203). IEEE.
• Shin, N. (2006). Online learner’s “flow” experience: An empirical study. British Journal of Educational Technology, 37(5), 705–720. https://doi.org/10.1111/j.1467-8535.2006.00641.x
  Article Google Scholar
• Syahrivar, J., Chairy, C., Juwono, I. D., & Gyulavári, T. (2022). Pay to play in freemium mobile games: A compensatory mechanism. International Journal of Retail & Distribution Management, 50(1), 117–134. https://doi.org/10.1108/IJRDM-09-202-0358
  Article Google Scholar
• Tai, K. H., Hong, J. C., Tsai, C. R., Lin, C. Z., & Hung, Y. H. (2022). Virtual reality for car-detailing skill development: Learning outcomes of procedural accuracy and performance quality predicted by VR self-efficacy, VR using anxiety, VR learning interest and flow experience. Computers & Education, 182, 104458. https://doi.org/10.1016/j.compedu.2022.104458
  Article Google Scholar
• Tang, Q., Zhang, T., & Jiang, L. (2023). Influence of blended instruction on students’ learning effectiveness: The role of Flow. Education and Information Technologies, 28(2), 1891–1909. https://doi.org/10.1007/s10639-022-11224-z
  Article Google Scholar
• Thompson, J., Taheri, B., & Scheuring, F. (2022). Developing esport tourism through fandom experience at in-person events. Tourism Management, 91, 104531. https://doi.org/10.1016/j.tourman.2022.104531
  Article Google Scholar
• Voigt, S., & Hinz, O. (2016). Making digital freemium business models a success: Predicting customers’ lifetime value via initial purchase information. Business & Information Systems Engineering, 58, 107–118. https://doi.org/10.1007/s12599-015-0395-z
  Article Google Scholar
• Wagner, T. M., Benlian, A., & Hess, T. (2014). Converting freemium customers from free to premium—the role of the perceived premium fit in the case of music as a service. Electronic Markets, 24, 259–268. https://doi.org/10.1007/s12525-014-0168-4
  Article Google Scholar
• Walsh, G., Shiu, E., & Hassan, L. M. (2014). Replicating, validating, and reducing the length of the consumer perceived value scale. Journal of Business Research, 67(3), 260–267. https://doi.org/10.1016/j.jbusres.2013.05.012
  Article Google Scholar
• Wang, L., Gao, Y., Yan, J., & Qin, J. (2021). From freemium to premium: The roles of consumption values and game affordance. Information Technology & People, 34(1), 297–317. https://doi.org/10.1108/ITP-10-2019-0527
  Article Google Scholar
• Wang, W. T., Lin, Y. L., & Lu, H. E. (2023). Exploring the effect of improved learning performance: A mobile augmented reality learning system. Education and Information Technologies, 28, 7509–7541. https://doi.org/10.1007/s10639-022-11487-6
  Article Google Scholar
• Wang, Y., Liu, X., & Zhang, Z. (2018a). An overview of e-learning in China: History, challenges and opportunities. Research in Comparative and International Education, 13(1), 195–210. https://doi.org/10.1177/1745499918763421
  Article Google Scholar
• Wang, S., Zhang, K., Du, M., & Wang, Z. (2018b). Development and measurement validity of an instrument for the impact of technology-mediated learning on learning processes. Computers & Education, 121, 131–142. https://doi.org/10.1016/j.compedu.2018.03.006
  Article Google Scholar
• West, S. G., & Aiken, L. S. (1997). Toward understanding individual effects in multicomponent prevention programs: Design and analysis strategies. In K. J. Bryant, M. Windle, & S. G. West (Eds.), The science of prevention: Methodological advances from alcohol and substance abuse research (pp. 167–209). American Psychological Association.
  Chapter Google Scholar
• Wigfield, A. (1994). Expectancy-value theory of achievement motivation: A developmental perspective. Educational Psychology Review, 6, 49–78. https://doi.org/10.1007/BF02209024
  Article Google Scholar
• Wu, L., Chiu, M. L., & Chen, K. W. (2020a). Defining the determinants of online impulse buying through a shopping process of integrating perceived risk, expectation-confirmation model, and flow theory issues. International Journal of Information Management, 52, 102099. https://doi.org/10.1016/j.ijinfomgt.2020.102099
  Article Google Scholar
• Wu, F., Fan, W., Arbona, C., & de la Rosa-Pohl, D. (2020b). Self-efficacy and subjective task values in relation to choice, effort, persistence, and continuation in engineering: An Expectancy-value theory perspective. European Journal of Engineering Education, 45(1), 151–163. https://doi.org/10.1080/03043797.2019.1659231
  Article Google Scholar
• Wu, Y., Xu, X., Xue, J., & Hu, P. (2023). A cross-group comparison study of the effect of interaction on satisfaction in online learning: The parallel mediating role of academic emotions and self-regulated learning. Computers & Education, 104776. https://doi.org/10.1016/j.compedu.2023.104776.
• Xu, Y., Jeong, E., Baiomy, A. E., & Shao, X. (2020). Investigating onsite restaurant interactive self-service technology (ORISST) use: Customer expectations and intentions. International Journal of Contemporary Hospitality Management, 32(10), 3335–3360. https://doi.org/10.1108/IJCHM-02-2020-0157
  Article Google Scholar
• Yan, J., & Wakefield, R. O. B. I. N. (2018). The freemium (two-tiered) model for individual cloud services: Factors bridging the free tier and the paying tier. Journal of Information Technology Management, 29(1), 47–61.
  Google Scholar
• Yang, X. (2023). Mobile learning application characteristics and learners’ continuance intentions: The role of flow experience. Education and Information Technologies, 1–17. https://doi.org/10.1007/s10639-023-11910-6.
• Zhao, H., McLoughlin, L., Adzhiev, V., & Pasko, A. (2019). “ Why do we not buy mass customised products?”-An investigation of consumer purchase intention of mass customised products. International Journal of Industrial Engineering and Management, 10(2), 181–190. https://doi.org/10.24867/IJIEM-2019-2-238
  Article Google Scholar
• Zheng, X., Wang, C., Shen, Z., & Fang, X. (2020). Associations of private tutoring with Chinese students’ academic achievement, emotional well-being, and parent-child relationship. Children and Youth Services Review, 112, 104934. https://doi.org/10.1016/j.childyouth.2020.104934
  Article Google Scholar

Download references