Factors Affecting Satisfaction and Continuance Intention of Thai Undergraduate Students Using Chinese Learning Apps
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This study explores factors influencing Thai undergraduates’ satisfaction and continued use of Chinese learning apps. The framework includes Perceived Usefulness (UF), Autonomy (AN), Interactivity (IA), Confirmation (CF), Interest (IT), Satisfaction (SF), and Continuance Intention (CI). A survey of 455 students from five Thai universities was conducted using a non-probability sampling method. Data was collected online and offline, then analyzed with SEM and CFA. Results show all factors significantly impact outcomes. UF, AN, IA, CF, and IT influence SF, with IT being the strongest predictor. SF directly affects CI and serves as a mediator. All six hypotheses were supported. The findings suggest developers should strengthen these key elements to improve user satisfaction and retention.
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Abbasi, M. S., Ahmed, N., Sajjad, B., Alshahrani, A., Saeed, S., Sarfaraz, S., Alhamdan, R. S., Vohra, F., & Abduljabbar, T. (2020). E-Learning perception and satisfaction among health sciences students amid the COVID-19 pandemic. WORK, 67(3), 549-556. https://doi.org/10.3233/WOR-203308
Al Amin, M., Muzareba, A. M., Chowdhury, I. U., & Khondkar, M. (2023). Understanding e-satisfaction, continuance intention, and e-loyalty toward mobile payment application during COVID-19: An investigation using the electronic technology continuance model. Journal of Financial Services Marketing, 29(2), 318-340. https://doi.org/10.1057/s41264-022-00197-2
Al Amin, M., Razib Alam, M., & Alam, M. Z. (2022). Antecedents of students’ E-learning continuance intention during COVID-19: An empirical study. E-Learning and Digital Media, 20(3), 224-254. https://doi.org/10.1177/20427530221103915
Alam, A., & Mohanty, A. (2023). Educational technology: Exploring the convergence of technology and pedagogy through mobility, interactivity, AI, and learning tools. Cogent Engineering, 10(2), Article 2283282.
Al-Mamary, Y. H., & Shamsuddin, A. (2015). Testing of the technology acceptance model in context of Yemen. Mediterranean Journal of Social Sciences, 6(4), 268-273. https://doi.org/10.5901/mjss.2015.v6n4s1p268
Ashrafi, A., Zareravasan, A., Rabiee Savoji, S., & Amani, M. (2020). Exploring factors influencing students’ continuance intention to use the learning management system (LMS): A multi-perspective framework. Interactive Learning Environments, 30(8), 1475-1497. https://doi.org/10.1080/10494820.2020.1734028
Awang, Z. (2012). A handbook on SEM structural equation modelling: SEM using AMOS graphic (5th ed.). Universiti Teknologi Mara Kelantan Press.
Ayeni, O. O., Al Hamad, N. M., Chisom, O. N., Osawaru, B., & Adewusi, O. E. (2024). AI in education: A review of personalized learning and educational technology. GSC Advanced Research and Reviews, 18(2), 261-271.
Bansah, A. K., & Darko Agyei, D. (2022). Perceived convenience, usefulness, effectiveness and user acceptance of information technology: Evaluating students’ experiences of a Learning Management System. Technology, Pedagogy and Education, 31(4), 431-449. https://doi.org/10.1080/1475939x.2022.2027267
Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychol Bull, 107(2), 238-246. https://doi.org/10.1037/0033-2909.107.2.238
Cheng, Y.-M. (2020a). Investigating medical professionals' continuance intention of the cloud-based e-learning system: An extension of expectation–confirmation model with flow theory. Journal of Enterprise Information Management, 34(4), 1169-1202. https://doi.org/10.1108/jeim-12-2019-0401
Cheng, Y.-M. (2020b). Students’ satisfaction and continuance intention of the cloud-based e-learning system: Roles of interactivity and course quality factors. Education + Training, 62(9), 1037-1059. https://doi.org/10.1108/et-10-2019-0245
Cheng, Y.-M. (2022). What roles do quality and cognitive absorption play in evaluating cloud-based E-learning system success? Evidence from medical professionals. Interactive Technology and Smart Education, 20(2), 228-256. https://doi.org/10.1108/itse-12-2021-0222
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, Article 103850. https://doi.org/10.1016/j.compedu.2020.103850
Davis, F. D. (1989a). Technology Acceptance Model: TAM. In M. N. Al-Suqri & A. S. Al-Aufi (Eds.), Information Seeking Behavior and Technology Adoption (pp. 205-219). IGI Global.
Davis, F. D. (1989b). Perceived usefulness, perceived ease of use, and user ccceptance of information technology. MIS Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008
Eren, B. A. (2021). Determinants of customer satisfaction in chatbot use: evidence from a banking application in Turkey. International Journal of Bank Marketing, 39(2), 294-311. https://doi.org/10.1108/ijbm-02-2020-0056
Etaga, H. O., Ndubisi, R. C., & Oluebube, N. L. (2021). Effect of multicollinearity on variable selection in multiple regression. Science Journal of Applied Mathematics and Statistics, 9(6), 141-153. https://doi.org/10.11648/j.sjams.20210906.12
Faozi, F., & Handayani, P. (2023). The antecedents of mobile-assisted language learning applications continuance intention. Electronic Journal of e-Learning, 21(4), 299-313. https://doi.org/10.34190/ejel.21.4.2744
Fei, H. (2023). Research on the Development of Chinese Teaching Resources in Thailand. BCP Education & Psychology, 10, 297-308. https://doi.org/10.54691/bcpep.v10i.5398
Gani, M. O., Rahman, M., Bag, S., & Mia, M. (2023). Examining behavioural intention of using smart health care technology among females: Dynamics of social influence and perceived usefulness. Benchmarking: An International Journal, 31(2), 330-352. https://doi.org/10.1108/BIJ-09-2022-0585
Garg, S., & Sharma, S. (2020). User satisfaction and continuance intention for using E-training: A structural equation model. Vision: The Journal of Business Perspective, 24(4), 441-451. https://doi.org/10.1177/0972262920926827
Guo, Q., Zeng, Q., & Zhang, L. (2022). What social factors influence learners’ continuous intention in online learning? A social presence perspective. Information Technology & People, 36(3), 1076-1094. https://doi.org/10.1108/ITP-02-2021-0151
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis. Cengage. https://books.google.co.th/books?id=0R9ZswEACAAJ
Han, J.-H., & Sa, H. J. (2021). Acceptance of and satisfaction with online educational classes through the technology acceptance model (TAM): The COVID-19 situation in Korea. Asia Pacific Education Review, 23(3), 403-415. https://doi.org/10.1007/s12564-021-09716-7
Harefa, D. (2023). The relationship between students’ interest in learning and mathematics learning outcomes. Afore: Jurnal Pendidikan Matematika, 2(2), 1-11. https://doi.org/10.57094/afore.v2i2.1054
Hinkin, T. R. (1998). A brief tutorial on the development of measures for use in survey questionnaires. Organizational Research Methods, 1(1), 104-121. https://doi.org/10.1177/109442819800100106
Ho, I. M. K., Cheong, K. Y., & Weldon, A. (2021). Predicting student satisfaction of emergency remote learning in higher education during COVID-19 using machine learning techniques. PLoS One, 16(4), Article e0249423. https://doi.org/10.1371/journal.pone.0249423
HolonIQ. (2023, March 22). Chinese language learning: A $7.4B market powered by over 6 million learners, set to double in the next five years. HolonIQ. https://www.holoniq.com/notes/chinese-language-learning-a-7-4b-market-powered-by-over-6-million-learners-set-to-double-in-the-next-five-years
Hoq, M. Z. (2020). E-learning during the period of pandemic (COVID-19) in the kingdom of Saudi Arabia: An empirical study. American Journal of Educational Research, 8(7), 457-464.
Hosseini, F., Ghorbani, S., & Rezaeeshirazi, R. (2020). Effects of perceived autonomy support in the physical education on basic psychological needs satisfaction, intrinsic motivation and intention to perform physical activity in high school students. International Journal of School Health, 7(4), 39-46. https://doi.org/10.30476/intjsh.2020.88171.1106
Hsu, H.-T., & Lin, C.-C. (2022). Factors influencing university students’ intention to engage in mobileassisted language learning through the lens of action control theory. Educational Technology & Society, 25(4), 29-42. https://www.jstor.org/stable/48695979
Huescar Hernandez, E., Moreno-Murcia, J. A., Cid, L., Monteiro, D., & Rodrigues, F. (2020). Passion or perseverance? The effect of perceived autonomy support and grit on academic performance in college students. Int J Environ Res Public Health, 17(6), Article 2143. https://doi.org/10.3390/ijerph17062143
Jami Pour, M., Mesrabadi, J., & Asarian, M. (2021). Meta-analysis of the DeLone and McLean models in E-learning success: The moderating role of user type. Online Information Review, 46(3), 590-615. https://doi.org/10.1108/oir-01-2021-0011
Kim, S., Lee, J., Yoon, S.-H., & Kim, H.-W. (2022). How can we achieve better E-Learning success in the new normal? Internet Research, 33(1), 410-441. https://doi.org/10.1108/intr-05-2021-0310
Kumar, P., Saxena, C., & Baber, H. (2021). Learner-content interaction in E-learning- the moderating role of perceived harm of COVID-19 in assessing the satisfaction of learners. Smart Learning Environments, 8, Article 5. https://doi.org/10.1186/s40561-021-00149-8
Landrum, B. (2020). Examining students’ confidence to learn online, self-regulation skills and perceptions of satisfaction and usefulness of online classes. Online Learning, 24(3), 128-146.https://doi.org/10.24059/olj.v24i3.2066
Lazorak, O. V., Belkina, O. V., & Yaroslavova, E. N. (2021). Changes in student autonomy via e-learning courses. International Journal of Emerging Technologies in Learning (iJET), 16(17), 209-225. https://doi.org/10.3991/ijet.v16i17.23863
Legramante, D., Azevedo, A., & Azevedo, J. M. (2023). Integration of the technology acceptance model and the information systems success model in the analysis of Moodle’s satisfaction and continuity of use. The International Journal of Information and Learning Technology, 40(5), 467-484. https://doi.org/10.1108/ijilt-12-2022-0231
Li, P., & Lan, Y.-J. (2021). Digital Language Learning (DLL): Insights from behavior, cognition, and the brain. Bilingualism: Language and Cognition, 25(3), 361-378. https://doi.org/10.1017/S1366728921000353
Li, Y., Nishimura, N., Yagami, H., & Park, H.-S. (2021). An empirical study on online learners’ continuance intentions in China. Sustainability, 13(2), Article 889. https://doi.org/10.3390/su13020889
Lu, Y., & Wang, B. (2019). Understanding key drivers of MOOC satisfaction and continuance intention to use. Journal of Electronic Commerce Research, 20(2), 105-117.
Maru, M. G., Pikirang, C. C., Setiawan, S., Oroh, E. Z. O., & Pelenkahu, N. (2021). The internet use for autonomous learning during COVID-19 pandemic and its hindrances. International Journal of Interactive Mobile Technologies (iJIM), 15(18), 65-79. https://doi.org/10.3991/ijim.v15i18.24553
Mossman, L. H., Slemp, G. R., Lewis, K. J., Colla, R. H., & O’Halloran, P. (2022). Autonomy support in sport and exercise settings: a systematic review and meta-analysis. International Review of Sport and Exercise Psychology, 17(1), 540-563. https://doi.org/10.1080/1750984x.2022.2031252
Navaneethakrishnan, K. (2020). The effects of teacher autonomy, student behavior and student engagement on teacher job satisfaction. Educational Sciences: Theory & Practice, 20(4), 1-15. https://doi.org/10.12738/jestp.2020.4.001
Ningsih, S., & Yusuf, F. (2021). Analysis of teachers’ voices of learner autonomy in efl online learning context. In Proceedings of the Thirteenth Conference on Applied Linguistics (CONAPLIN 2020): Advances in Social Science, Education and Humanities Research (pp. 556-561). Atlantis Press. https://doi.org/10.2991/assehr.k.210427.084
Norawati, S., Arman, A., Ali, A., Ihsan, A., & Putra, E. (2021). Analysis of product variation, quality of service and their effect on customer satisfaction. IJEBD (International Journal Of Entrepreneurship And Business Development), 4(6), 954-960. https://doi.org/10.29138/ijebd.v4i6.1554
Oktafiani, H., Yohana, C., & Saidani, B. (2021). Pengaruh perceived ease of use dan perceived usefulness terhadap customer satisfaction E-wallet X. Jurnal Bisnis, Manajemen, Dan Keuangan-JBMK, 2(2), 562-576.
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
Papakostas, C., Troussas, C., Krouska, A., & Sgouropoulou, C. (2021). Measuring user experience, usability and interactivity of a personalized mobile augmented reality training system. Sensors (Basel), 21(11), Article 3888. https://doi.org/10.3390/s21113888
Patricia Aguilera-Hermida, A. (2020). College students' use and acceptance of emergency online learning due to COVID-19. Int J Educ Res Open, 1, Article 100011. https://doi.org/10.1016/j.ijedro.2020.100011
Pedroso, R., Zanetello, L., Guimarães, L., Pettenon, M., Gonçalves, V., Scherer, J., Kessler, F., & Pechansky, F. (2016). Confirmatory factor analysis (CFA) of the crack use relapse scale (CURS). Archives of Clinical Psychiatry (São Paulo), 43(3), 37-40. https://doi.org/10.1590/0101-60830000000081
Pozón-López, I., Higueras-Castillo, E., Muñoz-Leiva, F., & Liébana-Cabanillas, F. J. (2020). Perceived user satisfaction and intention to use massive open online courses (MOOCs). Journal of Computing in Higher Education, 33(1), 85-120. https://doi.org/10.1007/s12528-020-09257-9
Pratama, S. F. (2024). Analyzing the determinants of user satisfaction and continuous usage intention for digital banking platform in Indonesia: A structural equation modeling approach. Journal of Digital Market and Digital Currency, 1(3), 267-285.
Rabaa’i, A. A., Almaati, S. A., & Zhu, X. (2021). Students’ continuance intention to use Moodle: An expectation-confirmation model approach. Interdisciplinary Journal of Information, Knowledge, and Management, 16, 397-434. https://doi.org/10.28945/4842
Rajabalee, Y. B., & Santally, M. I. (2021). Learner satisfaction, engagement and performances in an online module: Implications for institutional e-learning policy. Educ Inf Technol (Dordr), 26(3), 2623-2656. https://doi.org/10.1007/s10639-020-10375-1
Rajeh, M. T., Abduljabbar, F. H., Alqahtani, S. M., Waly, F. J., Alnaami, I., Aljurayyan, A., & Alzaman, N. (2021). Students’ satisfaction and continued intention toward E-learning: a theory-based study. Med Educ Online, 26(1), Article 1961348. https://doi.org/10.1080/10872981.2021.1961348
Samarah, T., Bayram, P., Aljuhmani, H. Y., & Elrehail, H. (2021). The role of brand interactivity and involvement in driving social media consumer brand engagement and brand loyalty: The mediating effect of brand trust. Journal of Research in Interactive Marketing, 16(4), 648-664. https://doi.org/10.1108/jrim-03-2021-0072
Samuels, P. (2016). Advice on Exploratory Factor Analysis. Centre for Academic Success, Birmingham City University. https://doi.org/10.13140/RG.2.1.5013.9766
Shanshan, S., & Wenfei, L. (2022). Understanding the impact of quality elements on MOOCs continuance intention. Educ Inf Technol (Dordr), 27(8), 10949-10976. https://doi.org/10.1007/s10639-022-11063-y
Shao, Z., & Chen, K. (2020). Understanding individuals’ engagement and continuance intention of MOOCs: The effect of interactivity and the role of gender. Internet Research, 31(4), 1262-1289. https://doi.org/10.1108/intr-10-2019-0416
Sharma, G. P., Verma, R. C., & Pathare, P. B. (2005). Mathematical modeling of infrared radiation thin layer drying of onion slices. Journal of Food Engineering, 71, 282-286.
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 Science Publishers.
Steils, N., Decrop, A., & Crié, D. (2019). An exploration into consumers’ e-learning strategies. Journal of Consumer Marketing, 36(2), 276-287. https://doi.org/10.1108/jcm-05-2017-2215
Suzianti, A., & Paramadini, S. A. (2021). Continuance intention of e-learning: The condition and its connection with open innovation. Journal of Open Innovation: Technology, Market, and Complexity, 7(1), Article 97. https://doi.org/10.3390/joitmc7010097
Tawafak, R. M., Al-Rahmi, W. M., Almogren, A. S., Al Adwan, M. N., Safori, A., Attar, R. W., & Habes, M. (2023). Analysis of E-learning system use using combined TAM and ECT factors. Sustainability, 15(14), Article 11100. https://doi.org/10.3390/su151411100
Ting, D. H., Abbasi, A. Z., & Ahmed, S. (2020). Examining the mediating role of social interactivity between customer engagement and brand loyalty. Asia Pacific Journal of Marketing and Logistics, 33(5), 1139-1158. https://doi.org/10.1108/apjml-10-2019-0576
Valverde-Berrocoso, J., Garrido-Arroyo, M. d. C., Burgos-Videla, C., & Morales-Cevallos, M. B. (2020). Trends in educational research about E-learning: A systematic literature review (2009-2018). Sustainability, 12(12), Article 5153. https://doi.org/10.3390/su12125153
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. https://doi.org/10.2307/30036540
Venkatesh, V. (2021). Adoption and use of AI tools: a research agenda grounded in UTAUT. Annals of Operations Research, 308, 641-652. https://doi.org/10.1007/s10479-020-03918-9
Widjaja, A., & Widjaja, Y. (2022). The influence of interaction, learner characteristics, perceived usefulness, and perceived satisfaction on continuance intention in e-learning system. International Journal of Research in Business and Social Science (2147- 4478), 11(2), 381-390. https://doi.org/10.20525/ijrbs.v11i2.1665
Wilson, N., Keni, K., & Tan, P. H. (2021). The role of perceived usefulness and perceived ease-of-use toward satisfaction and trust which influence computer consumers’ loyalty in China. Gadjah Mada International Journal of Business, 23(3), 262-294. https://doi.org/10.22146/gamaijb.32106
Wongwatkit, C., Thongsibsong, N., Chomngern, T., & Thavorn, S. (2023). The future of connectivist learning with the potential of emerging technologies and ai in thailand: Trends, applications, and challenges in shaping education. Journal of Learning Sciences and Education, 2(1), 122-154.
Wu, J.-H., & Wang, Y.-M. (2006). Measuring kms success: A respecification of the Delone and Mclean’s model. Information & Management, 43(6), 728-739. https://doi.org/http://dx.doi.org/10.1016/j.im.2006.05.002
Wulandari, D. (2022). Customer satisfaction as a priority in excellent banking services. KINERJA: Jurnal Manajemen Organisasi dan Industri, 1(1), 27-34.
Xu, W., Zhang, H., Sukjairungwattana, P., & Wang, T. (2022). The roles of motivation, anxiety and learning strategies in online Chinese learning among Thai learners of Chinese as a foreign language. Frontiers in Psychology, 13, Article 962492. https://doi.org/10.3389/fpsyg.2022.962492
Younas, M., Noor, U., Zhou, X., Menhas, R., & Qingyu, X. (2022). COVID-19, students satisfaction about e-learning and academic achievement: Mediating analysis of online influencing factors. Front Psychol, 13, Article 948061. https://doi.org/10.3389/fpsyg.2022.948061
Zalat, M. M., Hamed, M. S., & Bolbol, S. A. (2021). The experiences, challenges, and acceptance of e-learning as a tool for teaching during the COVID-19 pandemic among university medical staff. PLoS One, 16(3), Article e0248758. https://doi.org/10.1371/journal.pone.0248758
Zhang, A. (2024). A study on the life and learning satisfaction of Chinese students in thailand: The moderating role of third-party services. Rajapark Journal, 18(58), 236-257. https://so05.tci-thaijo.org/index.php/RJPJ/article/view/272264
Zhonglin, D. T. W. (2020). Statistical approaches for testing common method bias: problems and suggestions. Journal of Psychological Science, 43(1), 215-223.
