Student Satisfaction and Continuance Intention toward Short Video Applications: An Empirical Study from Chengdu

Main Article Content

Jiahong Li

Abstract

Purpose: This study explores the factors influencing students' Satisfaction (SAT) and Continuance Intention to Use (CIU) Short video applications. These factors are Perceived Usefulness (PU), Perceived Enjoyment (PE), Perceived Ease of Use (PEU), New Product Novelty (NPN), and Platform-Based Trust (PBT), providing insights for understanding humanities students' behavior in using short video applications. Research design, data and methodology: Data were collected through a questionnaire survey administered to humanities students at a university in Chengdu, China, yielding 500 valid responses. The validity and reliability of the data was assessed through convergent validity, composite reliability, Cronbach's alpha, factor loading, mean square extraction analysis, and discriminant validity tests, and were found to be acceptable. The conceptual framework was tested using AMOS, and the confirmatory factor analysis results indicated reasonable data fit and a suitable factor structure. Results: Results reveal that PU, PE, PEU, NPN, and PBT all significantly enhance SAT. Moreover, SAT has a strong positive effect on CIU short video applications. Conclusions: Theoretically, this work breaks new ground by bridging three distinct theoretical traditions in the short video platform literature. Practically, the findings provide actionable insights for platform developers and educators to enhance humanities students' user retention through targeted feature design and trust-building strategies.

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Li, J. (2026). Student Satisfaction and Continuance Intention toward Short Video Applications: An Empirical Study from Chengdu. AU-GSB E-JOURNAL, 19(1), 231-242. Retrieved from https://assumptionjournal.au.edu/index.php/AU-GSB/article/view/9206
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Articles
Author Biography

Jiahong Li

School of Political Science, Nanchong Vocational and Technical College, China.

References

Al-Fraihat, D., Joy, M., & Sinclair, J. (2020). Evaluating e-learning systems success: An empirical study. Computers in Human Behavior, 102, 67-86. https://doi.org/10.1016/j.chb.2019.08.004

Al Natour, S., & Woo, C. (2021). The determinants of learner satisfaction with the online video presentation method. International Journal of Emerging Technologies in Learning, 16(6), 4-15. https://doi.org/10.3991/ijet.v16i06.19895

Al-Sabawy, A. Y., Cater-Steel, A., & Soar, J. (2011). Measuring e-learning system success (Research in Progress). Proceedings of the 15th Pacific Asia Conference on Information Systems (PACIS 2011), 1-15. https://aisel.aisnet.org/pacis2011/15/

Arbuckle, J. L. (1995). Amos user's guide. SmallWaters Corporation.

Barua, B., & Urme, U. N. (2023). Assessing the online teaching readiness of faculty members. Journal of Research in Innovative Teaching & Learning, 18(1), 123-144.

https://doi.org/10.1108/JRIT-10-2022-0070

Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351-370. https://doi.org/10.2307/3250921

Casaló, L. V., Flavián, C., & Ibáñez-Sánchez, S. (2018). Influencers on Instagram: Antecedents and consequences of opinion leadership. Journal of Business Research, 117, 510-519. https://doi.org/10.1016/j.jbusres.2018.07.005

Chen, Z., He, Q., Mao, Z., Chung, H. M., & Maharjan, S. (2019). A study on the characteristics of Douyin short videos and implications for edge caching. ACM. https://doi.org/10.1145/3321408.3323082

Cheng, Y., & Jiang, H. (2020). How do AI-driven chatbots impact user experience? Examining gratifications, perceived privacy risk, satisfaction, loyalty, and continued use. Journal of Broadcasting & Electronic Media, 64(4), 592-614. https://doi.org/10.1080/08838151.2020.1834296

Collins, S. A., Currie, L. M., Bakken, S., Vawdrey, D. K., & Stone, P. W. (2012). Health literacy screening instruments for eHealth applications: A systematic review. Journal of Biomedical Informatics, 45(3), 598-607. https://doi.org/10.1016/j.jbi.2012.04.001

Csikszentmihalyi, M. (1990). Flow: The psychology of optimal experience. Harper & Row.

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

Den Hartog, D. N., & Verburg, R. M. (2004). High performance work systems, organisational culture and firm effectiveness. Human Resource Management Journal, 14(1), 55-78.

https://doi.org/10.1111/j.1748-8583.2004.tb00112.x

Doll, W. J., Xia, W., & Torkzadeh, G. (1994). A confirmatory factor analysis of the end-user computing satisfaction instrument. MIS Quarterly, 18(4), 453-461. https://doi.org/10.2307/249524

González-Cutre, D., & Sicilia, Á. (2019). The importance of novelty satisfaction for multiple positive outcomes in physical education. European Physical Education Review, 25(3), 859-875. https://doi.org/10.1177/1356336X18783980

Gu, X., Wu, B., & Xu, X. (2020). Design, development and evaluation of a mobile learning application for Chinese language learning: A case study. Educational Technology Research and Development, 68(1), 1-23. https://doi.org/10.1007/s11423-020-09757-3

Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis (6th ed.). Pearson Prentice Hall.

Heller Baird, C., & Parasnis, G. (2011). From social media to social customer relationship management. Strategy & Leadership, 39(5), 30-37. https://doi.org/10.1108/10878571111161507

Heung, V. C. S., & Qu, H. (2000). Hong Kong as a travel destination: An analysis of Japanese tourists’ satisfaction levels, and the likelihood of them recommending Hong Kong to others. Journal of Travel & Tourism Marketing, 9(1-2), 57-80. https://doi.org/10.1300/J073v09n01_04

Hsu, C.-L., & Lu, H.-P. (2004). Why do people play on-line games? An extended TAM with social influences and flow experience. Information & Management, 41(7), 853-868. https://doi.org/10.1016/j.im.2003.08.014SCIRP

Jia, X., Wang, Y., & Wang, Y. (2023). Understanding college students' continuous usage intention of short-form video applications: An extended technology acceptance model perspective. Education and Information Technologies, 28(1), 123-145. https://doi.org/10.1007/s10639-023-11591-1

Jin, X., & Xu, F. (2021). Examining the factors influencing user satisfaction and loyalty on paid knowledge platforms. Aslib Journal of Information Management, 73(2), 254-270.

https://doi.org/10.1108/AJIM-07-2020-0228

Johnson, L., Adams, S., & Cummins, M. (2012). The NMC Horizon Report: 2012 Higher Education Edition. The New Media Consortium.

Johnson, L., Adams Becker, S., Cummins, M., Estrada, V., Freeman, A., & Ludgate, H. (2013). NMC Horizon Report: 2013 Higher Education Edition. The New Media Consortium.

Kalinic, Z., Marinkovic, V., Djordjevic, A., & Liebana-Cabanillas, F. (2020). What drives customer satisfaction and word of mouth in mobile commerce services? A UTAUT2 based analytical approach. Journal of Enterprise Information Management, 33(1), 71-94.

https://doi.org/10.1108/JEIM-05-2019-0136

Kang, J.-Y. M., Kim, J.-E., Lee, J. Y., & Lin, S. H. (2023). How mobile augmented reality digitally transforms the retail sector: Examining trust in augmented reality apps and online/offline store patronage intention. Journal of Fashion Marketing and Management, 27(1), 161-181.

https://doi.org/10.1108/JFMM-12-2020-0273

Kang, Y. S., & Lee, H. (2010). Understanding the role of an IT artifact in online service continuance: An extended perspective of user satisfaction. Computers in Human Behavior, 26(3), 353-364. https://doi.org/10.1016/j.chb.2009.11.006

Karunarathne, E. A. C. P., Wijesundara, W. M. T. H., Athukorala, A. S. T., & Chithrananda, K. P. S. P. (2025). Role of social media dependency and online reputation in instituting stress among social media users. Asian Education and Development Studies, 14(2), 283-298.

https://doi.org/10.1108/AEDS-09-2024-0200

Kaye, D. B. V., Chen, X., & Zeng, J. (2020). The co-evolution of two Chinese mobile short video apps: Parallel platformization of Douyin and TikTok. Mobile Media & Communication, 9(2), 229-253. https://doi.org/10.1177/2050157920952127

Ketema, E., & Selassie, Y. W. (2020). The impact of M-banking quality service on customer’s satisfaction during COVID-19 lockdown: The case of Bank of Abyssinia. African Journal of Marketing Management, 12, 21-37. https://doi.org/10.5897/AJMM2020.0651

Kim, J., Jin, B., & Swinney, J. L. (2009). The role of retail quality, e-satisfaction and e-trust in online loyalty development process. Journal of Retailing and Consumer Services, 16(4), 239-247. https://doi.org/10.1016/j.jretconser.2008.11.019

Kim, J. H., Kim, M., Park, M., & Yoo, J. (2021). How interactivity and vividness influence consumer virtual reality shopping experience: The mediating role of telepresence. Journal of Research in Interactive Marketing, 15(3), 502-525. https://doi.org/10.1108/JRIM-07-2020-0148

Konale, M., Panakaje, N., Parvin, S. M. R., Kulal, A., & Kambali, U. (2025). Exploring the fusion of virtual fitting rooms and social media: A study on consumer behaviour and purchase intentions. Journal of Fashion Marketing and Management: An International Journal, 29(4), 728-757. https://doi.org/10.1108/JFMM-05-2024-0195

Kumar, B. A., & Chand, S. S. (2019). Mobile learning adoption: A systematic review. Education and Information Technologies, 24(1), 471-487. https://doi.org/10.1007/s10639-018-9783-6

Lee, M. K. O., Cheung, C. M. K., & Chen, Z. (2005). Acceptance of internet-based learning medium: The role of extrinsic and intrinsic motivation. Information & Management, 42(8), 1095-1104. https://doi.org/10.1016/j.im.2003.10.007

Li, J., Zhang, Y., & Wang, X. (2024). Short video-based personalized experiences and integration with e-commerce and AR functions: Emerging trends in digital environments. Journal of Interactive Marketing, 58(3), 45-59.

Liang, M., Yang, X., & Ou, H. (2014). The measurement of the consumer trust to O2O e-commerce based on fuzzy evaluation. 2014 Seventh International Joint Conference on Computational Sciences and Optimization, 113-116. https://doi.org/10.1109/CSO.2014.30

Limayem, M., Hirt, S. G., & Cheung, C. M. (2007). How habit limits the predictive power of intention: The case of information systems continuance. MIS Quarterly, 31(4), 705-737. https://doi.org/10.2307/25148817

Lu, X. (2019). Cultural communication analysis of short video from the perspective of new media: A case study of Douyin short video. Chinese Character Culture, 18, 28-29.

McLean, G., & Wilson, A. (2019). Shopping in the digital world: Examining customer engagement through augmented reality mobile applications. Computers in Human Behavior, 101, 210-224. https://doi.org/10.1016/j.chb.2019.07.002

Moreira, B. L., Gonçalves, M. A., Laender, A. H. F., & Fox, E. A. (2009). Automatic evaluation of digital libraries with 5SQual. Journal of Informetrics, 3(2), 102-123. https://doi.org/10.1016/j.joi.2008.12.003

Mou, X., Xu, F., & Du, J. T. (2021). Examining the factors influencing college students' continuance intention to use short-form video APP. Aslib Journal of Information Management, 73(6), 992-1013. https://doi.org/10.1108/AJIM-03-2021-0080

Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.

O'Connor, C., Joffe, H., & Brown, G. (2022). How trauma is represented on social media: Analysis of Trauma content on TikTok. Psychology of Popular Media, 11(3), 345-357.

Omar, B., & Dequan, W. (2020). Watch, share or create: The influence of personality traits and user motivation on TikTok mobile video usage. International Journal of Interactive Mobile Technologies, 14(4), 121-137. https://doi.org/10.3991/ijim.v14i04.12429

Pedroso, B., Silva, C., & Tavares, D. (2016). Model fit measures for structural equation modeling: Complete guidelines. Revista Brasileira de Biometria, 34(2), 135-147.

Reichheld, F. F., & Schefter, P. (2000). E-loyalty: Your secret weapon on the web. Harvard Business Review, 78(4), 105-113.

Ribbink, D., Van Riel, A. C. R., Liljander, V., & Streukens, S. (2004). Comfort your online customer: Quality, trust and loyalty on the internet. Managing Service Quality: An International Journal, 14(6), 446-456. https://doi.org/10.1108/09604520410569784

Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68-78.

https://doi.org/10.1037/0003-066X.55.1.68

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.

Thong, J. Y. L., Hong, S. J., & Tam, K. Y. (2006). The effects of post-adoption beliefs on the expectation-confirmation model for information technology continuance. International Journal of Human-Computer Studies, 64(9), 799-810. https://doi.org/10.1016/j.ijhcs.2006.05.001

Tian, Y., Zhang, L., & Chen, H. (2025). Global media and digital trends through 2024: Annual report. Financial Corporation.

Tran, V. D. (2020). The relationship among product risk, perceived satisfaction and purchase intentions for online shopping. Journal of Asian Finance, Economics and Business, 7(6), 221-231. https://doi.org/10.13106/jafeb.2020.vol7.no6.221

Tsai, Y. H., Lin, C. H., Hong, J. C., & Tai, K. H. (2018). The effects of metacognition on online learning interest and continuance to learn with MOOCs. Computers & Education, 121, 18-29. https://doi.org/10.1016/j.compedu.2018.02.011

Turner, R. C., & Carlson, L. (2003). Indexes of item-objective congruence for multidimensional items. International Journal of Testing, 3(2), 163-171. https://doi.org/10.1207/S15327574IJT0302_5

Van der Heijden, H. (2004). User acceptance of hedonic information systems. MIS Quarterly, 28(4), 695-704. https://doi.org/10.2307/25148660

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., 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, 36(1), 157-178. https://doi.org/10.2307/41410412

Wang, Y. (2025). The essence of social media: Information communication technology and its role in information dissemination and exchange. Journal of Communication Technology, 12(1), 15-27.

Watson, R. (2015). Quantitative research. Nursing Standard, 29(31), 44-48. https://doi.org/10.7748/ns.29.31.44.e8681

Wei, W., Ozturk, A. B., Fairley, J., & Hua, N. (2023). What drives event attendees’ intention to continue using mobile event apps? The role of app attributes, social exchange and social-image. Journal of Hospitality and Tourism Technology, 14(3), 476-489. https://doi.org/10.1108/JHTT-04-2022-0097

Whiting, A., & Williams, D. (2013). Why people use social media: A uses and gratifications approach. Qualitative Market Research: An International Journal, 16(4), 362-369.

Zacharis, N. Z. (2012). Predicting college students' acceptance of online learning courses using the Technology Acceptance Model. International Journal of Technology and Human Interaction, 8(4), 37-57. https://doi.org/10.4018/jthi.2012100103

Zhao, X., & Wagner, C. (2023). Personalization algorithms in short video platforms: Enhancing user engagement through intelligent content delivery. Journal of Digital Media Research, 18(3), 150-165.

Zhou, T. (2013). An empirical examination of continuance intention of mobile payment services. Decision Support Systems, 54(2), 1085-1091. https://doi.org/10.1016/j.dss.2012.10.034