A STUDY OF FACTORS INFLUENCING UNIVERSITY STUDENTS’ SATISFACTION AND CONTINUANCE INTENTION TO USE SOCIAL MEDIA PLATFORMS IN ZHANJIANG, CHINA
คำสำคัญ:
Student Satisfaction, Continuance Intention, Higher Education, Social Media Platformบทคัดย่อ
This study examines the factors influencing university students’ satisfaction and continuance intention toward social media platforms in four representative universities in Zhanjiang City, Guangdong Province, China. Drawing on established theories of technology use and satisfaction, the proposed framework includes leisure, information sharing, entertainment, usefulness, pleasure, satisfaction, and continuance intention. Data were collected through a structured questionnaire from students aged 18 and above with at least six months of social media usage experience. We used confirmatory factor analysis and structural equation modeling to assess the measurement model and test the hypothesized relationships. The results show that leisure, information sharing, entertainment, usefulness, and pleasure significantly and positively affect user satisfaction. Satisfaction, in turn, strongly and significantly affects continuance intention. These findings extend existing research on social media use by highlighting the combined role of functional and experiential factors in shaping student satisfaction. The study provides theoretical insights and practical implications for improving social media platform design and supporting sustained student engagement in the Chinese higher education context.
เอกสารอ้างอิง
Al-Azawei, A. (2018). Predicting the adoption of social media: An integrated model and empirical study on Facebook usage. Interdisciplinary Journal of Information, Knowledge, and Management, 13, 233–258. https://doi.org/10.28945/4106
Anand, B., Chakravarty, H., Athalye, M. S. G., Varalaxmi, P., & Mishra, A. K. (2023). Understanding consumer behaviour in the digital age: A study of online shopping habits. Shodha Prabha (UGC CARE Journal), 48(3), 84–93.
Awang, Z. (2012). Structural equation modeling using AMOS graphic. Penerbit Universiti Teknologi MARA.
Baek, K., Holton, A., Harp, D., & Yaschur, C. (2011). The links that bind: Uncovering novel motivations for linking on Facebook. Computers in Human Behavior, 27(6), 2243–2248. https://doi.org/10.1016/j.chb.2011.07.003
Basak, E., & Calisir, F. (2015). An empirical study on factors affecting continuance intention of using Facebook. Computers in Human Behavior, 48, 181–189. https://doi.org/10.1016/j.chb.2015.01.045
Benlian, A., Koufaris, M., & Hess, T. (2011). Service quality in software-as-a-service: Developing the SaaS-Qual measure and examining its role in usage continuance. Journal of Management Information Systems, 28(3), 85–126. https://doi.org/10.2753/MIS0742-1222280304
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
Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351–370. https://doi.org/10.2307/3250921
Bhattacherjee, A., & Sanford, C. (2006). Influence processes for information technology acceptance: An elaboration likelihood model. MIS Quarterly, 30(4), 805–825. https://doi.org/10.2307/25148755
Casaló, L. V., Flavián, C., & Ibáñez-Sánchez, S. (2017). Antecedents of consumer intention to follow and recommend an Instagram account. Online Information Review, 41(7), 1046–1063. https://doi.org/10.1108/OIR-09-2016-0253
China Internet Network Information Center. (2022). The 49th statistical report on China’s Internet development. https://www.cnnic.com.cn/IDR/ReportDownloads/202204/P020220424336135612575.pdf
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. (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
Deng, Z., Lu, Y., Wei, K. K., & Zhang, J. (2010). Understanding customer satisfaction and loyalty: An empirical study of mobile instant messages in China. International Journal of Information Management, 30(4), 289–300. https://doi.org/10.1016/j.ijinfomgt.2009.10.001
De Vries, L., Gensler, S., & Leeflang, P. S. H. (2012). Popularity of brand posts on brand fan pages: An investigation of the effects of social media marketing. Journal of Interactive Marketing, 26(2), 83–91. https://doi.org/10.1016/j.intmar.2012.01.003
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
Foroughi, B., Iranmanesh, M., & Hyun, S. S. (2019). Understanding the determinants of mobile banking continuance usage intention. Journal of Enterprise Information Management, 32(6), 1015–1033. https://doi.org/10.1108/JEIM-10-2018-0237
Freire, T. (2017). Leisure and positive psychology: Contributions to optimal human functioning. Journal of Positive Psychology, 13(1), 1–4. https://doi.org/10.1080/17439760.2017.1374443
Gan, C., & Li, H. (2018). Understanding the effects of gratifications on the continuance intention to use WeChat in China: A uses and gratifications perspective. Computers in Human Behavior, 78, 306–315. https://doi.org/10.1016/j.chb.2017.10.003
Gupta, A., Yadav, A., & Varadarajan, R. (2020). How technology adoption affects mobile wallet usage: A meta-analysis. Journal of Retailing and Consumer Services, 52, 101903. https://doi.org/10.1016/j.jretconser.2019.101903
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis (6th ed.). Pearson Education.
Hansemark, O. C., & Albinsson, M. (2004). Customer satisfaction and retention: The experiences of individual employees. Managing Service Quality, 14(1), 40–57. https://doi.org/10.1108/09604520410513668
Harrington, D. M. (2018). On the usefulness of “value” in the definition of creativity: A commentary. Creativity Research Journal, 30(1), 118–121. https://doi.org/10.1080/10400419.2018.1411414
Hartog, D. N. D., & 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
Hausman, A. V., & Siekpe, J. S. (2009). The effect of web interface features on consumer online purchase intentions. Journal of Business Research, 62(1), 5–13. https://doi.org/10.1016/j.jbusres.2008.01.018
Hong, S. J., & Lee, J. (2016). The influence of perceived usefulness, perceived ease of use, and user satisfaction on continuance intention in information systems. Behaviour & Information Technology, 35(5), 408–423. https://doi.org/10.1080/0144929X.2016.1141320
Hsieh, J. J. P.-A., Rai, A., & Keil, M. (2008). Understanding digital inequality: Comparing continued use behavioral models of the socioeconomically advantaged and disadvantaged. MIS Quarterly, 32(1), 97–126. https://doi.org/10.2307/25148830
Igbaria, M. (1994). An examination of the factors contributing to microcomputer technology acceptance. Accounting, Management and Information Technologies, 4(4), 205–224. https://doi.org/10.1016/0959-8022(94)90007-8
Islam, A. K. M. N. (2014). Validation of the technology satisfaction model (TSM): An empirical study. International Journal of Technology and Human Interaction, 10(3), 44–57. https://doi.org/10.4018/ijthi.2014070104
Iso-Ahola, S. E., & Weissinger, E. (1990). Perceptions of boredom in leisure: Conceptualization, reliability and validity of the leisure boredom scale. Journal of Leisure Research, 22(1), 1–17. https://doi.org/10.1080/00222216.1990.11969810
Jarvenpaa, S. L., & Staples, D. S. (2000). The use of collaborative electronic media for information sharing: An exploratory study of determinants. Journal of Strategic Information Systems, 9(2–3), 129–154. https://doi.org/10.1016/S0963-8687(00)00042-1
Junaidi, J., Chih, W.-H., & Ortiz, J. (2020). Antecedents of information seeking and sharing on social networking sites: An empirical study of Facebook users. International Journal of Communication, 14, 5705–5728.
Kaba, B. (2021). Explaining social networking sites’ use continuance from employees’ perspectives. Journal of Systems and Information Technology, 23(2), 171–198. https://doi.org/10.1108/JSIT-07-2020-0122
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.2009.11.008
Kara, F. M., Gürbüz, B., & Öncü, E. (2015). Leisure boredom, leisure satisfaction, and psychological well-being: The role of leisure participation. Social Indicators Research, 120(3), 849–863. https://doi.org/10.1007/s11205-014-0612-3
Kelly, J. R. (1996). Leisure (3rd ed.). Allyn & Bacon.
Kim, J. W. (2014). Scan and click: The uses and gratifications of social recommendation systems. Computers in Human Behavior, 33, 184–191. https://doi.org/10.1016/j.chb.2014.01.028
Krasnova, H., Veltri, N. F., Eling, N., & Buxmann, P. (2017). Why men and women continue to use social networking sites: The role of gender differences. The Journal of Strategic Information Systems, 26(4), 261–284. https://doi.org/10.1016/j.jsis.2017.01.004
Le, X. C. (2022). Charting sustained usage toward mobile social media applications: The criticality of expected benefits and emotional motivations. Asia Pacific Journal of Marketing and Logistics, 34(3), 576–593. https://doi.org/10.1108/APJML-03-2021-0182
Lee, C. S., & Ma, L. (2012). News sharing in social media: The effect of gratifications and prior experience. Computers in Human Behavior, 28(2), 331–339. https://doi.org/10.1016/j.chb.2011.10.002
Lin, H., Fan, W., & Chau, P. Y. K. (2014). Determinants of users’ continuance of social networking sites: A self-regulation perspective. Information & Management, 51(5), 595–603. https://doi.org/10.1016/j.im.2014.03.006
Lin, K. Y., & Lu, H. P. (2011). Why people use social networking sites: An empirical study integrating network externalities and motivation theory. Computers in Human Behavior, 27(3), 1152–1161. https://doi.org/10.1016/j.chb.2010.12.009
Liu, Q., Shao, Z., Tang, J., & Fan, W. (2019). Examining the influential factors for continued social media use: A comparison of social networking and microblogging. Industrial Management & Data Systems, 119(5), 1104–1127. https://doi.org/10.1108/IMDS-11-2018-0513
Ma, K. E., & Mo, Y. (2021). Analysis and research on popular science Douyin signals: Taking 21 influential popular science Douyin accounts as examples. Popular Science Research, 16(1), 39–46, 97.
Moon, J. W., & Kim, Y. G. (2001). Extending the TAM for a World-Wide-Web context. Information & Management, 38(4), 217–230. https://doi.org/10.1016/S0378-7206(00)00061-6
Nadkarni, A., & Hofmann, S. G. (2012). Why do people use Facebook? Personality and Individual Differences, 52(3), 243–249. https://doi.org/10.1016/j.paid.2011.11.007
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
Park, E. (2020). User acceptance of smart wearable devices: An expectation–confirmation model approach. Telematics and Informatics, 47, 101318. https://doi.org/10.1016/j.tele.2019.101318
Park, N., Kee, K. F., & Valenzuela, S. (2009). Being immersed in a social networking environment: Facebook groups, uses and gratifications, and social outcomes. CyberPsychology & Behavior, 12(6), 729–733. https://doi.org/10.1089/cpb.2009.0003
Park, S. Y. (2009). An analysis of the technology acceptance model in understanding university students’ behavioral intention to use e-learning. Educational Technology & Society, 12(3), 150–162.
Pedroso, R., Pilati, R., & Bartholomeu, D. (2016). Investigating the adequacy of RMSEA for structural equation models: A simulation study. Psicologia: Reflexão e Crítica, 29, Article 18. https://doi.org/10.1186/s41155-016-0024-2
Pontiggia, A., & Virili, F. (2010). Network effects in technology acceptance: Laboratory experimental evidence. International Journal of Information Management, 30(1), 68–77. https://doi.org/10.1016/j.ijinfomgt.2009.04.002
Ray, S., Kim, S. S., & Morris, J. G. (2012). Online users’ switching costs: Their nature and formation. Information Systems Research, 23(1), 197–213. https://doi.org/10.1287/isre.1100.0340
Sago, D. (2013). Factors influencing social media adoption and frequency of use: An examination of Facebook, Twitter, Pinterest and Google+. International Journal of Business and Commerce, 3(1), 1–14.
Sarkar, S., & Khare, A. (2019). Influence of expectation confirmation, network externalities, and flow on use of mobile shopping apps. International Journal of Human–Computer Interaction, 35(16), 1449–1460. https://doi.org/10.1080/10447318.2018.1543080
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.2005.01.015
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.
Sledgianowski, D., & Kulviwat, S. (2009). Using social network sites: The effects of playfulness, critical mass, and trust in a hedonic context. Journal of Computer Information Systems, 49(4), 74–83. https://doi.org/10.1080/08874417.2009.11645342
Venkatesh, V., & Brown, S. A. (2001). A longitudinal investigation of personal computers in homes: Adoption determinants and emerging challenges. MIS Quarterly, 25(1), 71–98. https://doi.org/10.2307/3250959
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
Wasko, M. M., & Faraj, S. (2005). Why should I share? Examining social capital and knowledge contribution in electronic networks of practice. MIS Quarterly, 29(1), 35–57. https://doi.org/10.2307/25148667
Weisberg, R. W. (2015). On the usefulness of “value” in the definition of creativity. Creativity Research Journal, 27(2), 111–124. https://doi.org/10.1080/10400419.2015.1030304
Wen, J. F. (2018). The viral spread of imitation: Taking imitation behavior in TikTok videos as an example. Research in Communication, 2(20), 120.
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/10.1016/j.im.2006.05.002
Wu, X., & Fitzgerald, R. (2021). “Hidden in plain sight”: Expressing political criticism on Chinese social media. Discourse Studies, 23(3), 365–385. https://doi.org/10.1177/1461445621994583
Yu, L., Jiang, W., Ren, Z., Xu, S., Zhang, L., & Hu, X. (2021). Detecting changes in attitudes toward depression on Chinese social media: A text analysis. Journal of Affective Disorders, 280, 354–363. https://doi.org/10.1016/j.jad.2020.11.082
Zhang, S., Pian, W., Ma, F., Ni, Z., & Liu, Y. (2021). Characterizing the COVID-19 infodemic on Chinese social media: An exploratory study. JMIR Public Health and Surveillance, 7(2), e26090. https://doi.org/10.2196/26090
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