Influencers of University Students' Satisfaction and Continued Use of Online Review Websites in Chengdu, China
Keywords:
Satisfaction, Continuance Intention, Online Review Website, Undergraduate StudentsAbstract
Purpose: This quantitative study examines undergraduate students' satisfaction (SAT) with online review platforms, their continuance intention (CI) to use these platforms, and the key factors influencing both SAT and CI. The researchers assessed several critical factors affecting the continuance intention of Xi Hua University undergraduate students, including quality (QUL), value (VAL), confirmation (CNFM), service quality (SEQ), information quality (INQ), and user satisfaction (SAT). Research design, data and methodology: A quantitative survey targeting undergraduate students at selected universities was conducted, yielding 482 valid responses. These responses were analyzed using statistical techniques. Quota sampling was employed in this study. To assess the causal relationships among the investigated factors, Structural Equation Modeling (SEM) and Confirmatory Factor Analysis (CFA) were utilized. Results: Statistical analysis confirms that all hypotheses are supported, with customer satisfaction having the strongest direct impact on continuance intention. Conclusions: The findings of this study have significant implications for operators and managers of online review platforms. To enhance student satisfaction and encourage continued use, it is essential to optimize information quality, refine interface design, improve social interaction features, and establish effective incentive mechanisms. Additionally, by enhancing the overall user experience and promoting social engagement, students' sense of involvement and loyalty can be significantly strengthened.
References
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211.
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
Alshurideh, M., Al Dmour, H., & Shishan, F. (2014). The influence of mobile application quality and attributes on the continuance intention of mobile shopping. Life Science Journal, 11(10), 172-181.
Awang. (2012). Research methodology and data analysis (2nd ed.). UiTM Press.
Baka, V. (2016). The becoming of user-generated reviews: Looking at the past to understand the future of managing reputation in the travel sector. Tourism Management, 53, 148-162.
Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238-246.
Beran, T. N., & Violato, C. (2010). Structural equation modeling in medical research: A primer. BMC Research Notes, 3, 267.
Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351-370. https://doi.org/10.2307/3250921
Chang, C. C. (2012). Exploring the determinants of e-learning systems continuance intention in academic libraries. Library Management, 34(1/2), 40-55.
Chen, S. C., & Lin, C. P. (2019). Understanding the effect of social media marketing activities: The mediation of social identification, perceived value, and satisfaction. Technological Forecasting and Social Change, 140, 22-32. https://doi.org/10.1016/j.techfore.2018.11.025
Chen, Y., & Xie, J. (2008). Online consumer review: Word-of-mouth as a new element of the marketing communication mix. Management Science, 54(3), 477-491.
Chiu, W., & Cho, H. (2019). E-commerce brand: The effect of perceived brand leadership on consumers' satisfaction and repurchase intention on e-commerce websites. Asia Pacific Journal of Marketing and Logistics, 33(6), 1339-1362.
DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean Model of Information Systems Success: A ten-year update. Journal of Management Information Systems, 19(4), 9-30.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 24(4), 337-346.
Frontiers in Psychology. (2024). Research on the influence of perceived quality on users' continuance intention in live streaming class platforms. Frontiers in Psychology. https://www.frontiersin.org/articles/10.3389/fpsyg.2024.1455597/full
Haslina, H., Hashim, I. C., Abdullah, S., Isa, F. M., & Talib, N. (2014). Geographical information systems (GIS) approach for mapping the aboriginal children malnutrition growth: A case in Kemar, Perak. Proceedings of the Postgraduate Conference on Global Green Issues (Go Green), 1-7.
Hulland, J. (1999). Use of partial least squares (PLS) in strategic management research: A review of four recent studies. Strategic Management Journal, 20(2), 195-204.
Hussein, E., Daoud, S., Alrabaiah, H., & Badawi, R. (2020). Exploring undergraduate students’ attitudes towards emergency online learning during COVID-19: A case from the UAE. Children and Youth Services Review, 119, 1-7.
Kim, N. (2017). Analyzing continuance intention of recommendation algorithms. Proceedings of the 27th European Conference on Information Systems (ECIS).
Koo, C., Wati, Y., & Jung, J. J. (2016). Examination of how social aspects moderate the relationship between task characteristics and usage of social communication technologies (SCTs) in organizations. International Journal of Information Management, 36(6), 1005-1017.
Lee, H.-M., & Chen, T. (2014). Perceived quality as a key antecedent in continuance intention on mobile commerce. International Journal of Electronic Commerce Studies, 5(2), 123-142. https://doi.org/10.7903/ijecs.1150
Lee, M. C., & Chung, N. (2009). Understanding factors affecting trust in and satisfaction with mobile banking: A modified DeLone and McLean model perspective. Interacting with Computers, 21(5-6), 385-392.
Lee, M. C., & Kwon, H. S. (2011). A dynamic diffusion model for the continuous use of mobile internet services. Industrial Management & Data Systems, 111(4), 531-550.
Liang, T. P., Wu, S. P. J., & Huang, C. W. (2018). Why funders invest in crowdfunding projects: Role of project description and perceived benefits. Information & Management, 55(3), 358-368. https://doi.org/10.1016/j.im.2017.09.003
Liao, C., Liu, C.-C., & Chen, K. (2019). Examining the impact of privacy, trust, and risk perceptions beyond monetary transactions: An integrated model. Information Systems Frontiers, 21(3), 517-533.
Lightner, N. J. (2004). Evaluating e-commerce functionality with a focus on customer service. Communications of the ACM, 47(10), 88-92.
Limayem, M., & Cheung, C. M. K. (2008). Understanding information systems continuance: The case of internet-based learning technologies. Information & Management, 45(4), 227-232.
Mudambi, S. M., & Schuff, D. (2010). What makes a helpful online review? A study of customer reviews on Amazon.com. MIS Quarterly, 34(1), 185-200.
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.
Oliver, R. L. (1993). Cognitive, affective, and attribute bases of the satisfaction response. Journal of Consumer Research, 20(3), 418-430.
Schmitt, N., & Stults, D. (1986). Methodology review: Analysis of multitrait-multimethod matrices. Applied Psychological Measurement, 10(1), 1-22.
Sharma, S., Mukherjee, S., Kumar, A., & Dillon, W. R. (2005). A simulation study to investigate the use of cutoff values for assessing model fit in covariance structure models. Journal of Business Research, 58(7), 935-943. https://doi.org/10.1016/j.jbusres.2003.10.007
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).
Stein, C., Morris, N., & Nock, N. (2012). Structural equation modeling. Methods in Molecular Biology, 850, 495-512.
Tenenhaus, M., Amato, S., & Esposito Vinzi, V. (2004). A global goodness-of-fit index for PLS structural equation modeling. Proceedings of the XLII SIS Scientific Meeting, 739-742.
Tsai, H. T., & Bagozzi, R. P. (2014). Contribution behavior in virtual communities: Cognitive, emotional, and social influences. MIS Quarterly, 38(1), 143-163. https://doi.org/10.25300/MISQ/2014/38.1.07
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, S. P. J., & Chen, Y. J. (2021). The impact of perceived value on continuance intention in mobile government services. Government Information Quarterly, 38(1), 101-110.
https://doi.org/10.1016/j.giq.2020.101-110
Zhang, H., Lu, Y., Gupta, S., & Gao, P. (2015). Understanding group-buying websites continuance: An extension of expectation confirmation model. Internet Research, 25(5), 767-793.
Zhou, T. (2011). Understanding continuance usage of mobile sites. Industrial Management & Data Systems, 111(4), 609-626. https://doi.org/10.1108/02635571111133545
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Pengjiang Yang
This work is licensed under a Creative Commons Attribution 4.0 International License.
A separate Copyright Form will be sent to authors whose paper is accepted for publication.
