Key Drivers of Alumni’s Satisfaction and Continuance Intention with a Private University's Service Platform in Chengdu, China

Main Article Content

Liu Kun

Abstract

Purpose: This study aims to quantitatively assess alum satisfaction and their willingness to continue using the alum information system at a university in Chengdu. Key factors examined include perceived ease of use, usefulness, perceived usefulness, information quality, system quality, service quality, satisfaction, and continuance intention. Research design, data, and methodology: A quantitative survey gathered 494 valid responses from alums using a quota sampling technique. Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) were employed to analyze the data and explore the causal relationships among the identified factors. Results: Statistical analysis supported all hypotheses. Information quality emerged as the most influential factor affecting intention to continue using the system. Additionally, perceived ease of use, usefulness, system quality, and service quality positively influenced both satisfaction levels and intention to continue. Conclusion: This study successfully achieved its objectives, suggesting that managers of alum information systems should prioritize enhancing service quality, perceived ease of use, usefulness, system quality, and information quality. By doing so, they can optimize system design, thereby boosting alum satisfaction and fostering continued usage intentions.

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How to Cite
Kun, L. (2025). Key Drivers of Alumni’s Satisfaction and Continuance Intention with a Private University’s Service Platform in Chengdu, China. AU-GSB E-JOURNAL, 18(3), 29-37. https://doi.org/10.14456/augsbejr.2025.55
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Articles
Author Biography

Liu Kun

Southwest Jiaotong University, Chengdu, China.

References

Ahn, B. H., Kroehl, H. W., Kamide, Y., & Kihn, E. A. (2000). Seasonal and solar cycle variations of the auroral electrojet indices. J. Atmos. Sol. Terr. Phys., 62(14), 1301-1310. https://doi.org/10.1016/s1364-6826(00)00073-0

Ahn, T., Ryu, S., & Han, I. (2007). The impact of Web quality and playfulness on user acceptance of online retailing. Information Management, 44(3), 263-275. https://doi.org/10.1016/j.im.2006.12.008

Allen, M. R. D. J., Frame, C., Huntingford, C. D., Jones, J. A., Lowe, M., Meinshausen, M., & Meinshausen, N. (2009). Warming caused by cumulative carbon emissions towards the trillionth tonne, Nature, 458, 1163-1166.

https://doi.org/10.1038/nature08019

Al-Mamary, Y. H., & Shamsuddin, A. (2015). Testing of The Technology Acceptance Model in Context of Yemen. Mediterranean Journal of Social Sciences, 6(4), 1-10. https://doi.org/10.5901/mjss.2015.v6n4s1p268

Awang, Z. (2012). Structural equation modeling using AMOS graphic (1st ed.). Penerbit Universiti Teknologi MARA.

Bentler, P. M. (1990). Comparative fit indexes in structural models (1st ed.). Psychological Bulletin.

Beran, T., & Violato, C. (2010). Student ratings of teaching effectiveness: Student engagement and course characteristics. Canadian Journal of Higher Education, 39(1), 1-13.

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

Brown, T. A. (2006). Confirmatory Factor Analysis for Applied Research (1st ed.). The Guilford Press.

Chang, C. C. (2012). Exploring the determinants of E-learning systems continuance intention in academic libraries. Library Management, 34(1/2), 40-55. https://doi.org/10.1108/01435121311298261

Ching, S. M., Ng, K. Y., Lee, K. W., Yee, A., Lim, P. Y., & Ranita, H. (2021). Psychological distress among healthcare providers during COVID-19 in Asia: Systematic review and meta-analysis. Plos One, 16(10), e0257983. https://doi.org/10.1371/journal.pone.0257983

Chiu, C.-M., Lin, H.-Y., Sun, S.-Y., & Hsu, M.-H. (2009). Understanding customers loyalty intentions towards online shopping: An integration of technology acceptance model and fairness theory. Behaviors & Information Technology, 28(4), 347-360. https://doi.org/10.1080/01449290801892492

Daoud, J. I. (2017). Multicollinearity and Regression Analysis. Journal of Physics: Conference Series, 949, Article ID: 012009.

https://doi.org/10.1088/1742-6596/949/1/012009

Davis, F. (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

DeLone, W. H., & McLean, E. R. (1992). Information system success: The quest for the dependent variable. Information Systems Research, 3(1), 60-95. https://doi.org/10.1287/isre.3.1.60

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/ Spring, 19(4), 9-30.

Etikan, L., & Bala, K. (2017). Sampling and sampling methods. Biometrics & Biostatistics International Journal, 5(6), 215-217. https://doi.org/10.15406/bbij.2017.05.00149

Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 24(4), 337-346. https://doi.org/10.2307/3151312

Gefen, D., Karahanna, E., & Straub, D. (2003). Trust and TAM in online shopping: an integrated model. MIS Quarterly, 27(1), 51. https://doi.org/10.2307/30036519

Hong, S.-J., Thong, J., & Tam, K. (2006). Understanding Continued Information Technology Usage Behavior: A Comparison of Three Models in the Context of Mobile Internet. Decision Support Systems, 42, 1819-1834.

http://dx.doi.org/10.1016/j.dss.2006.03.00951-90.

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.2003.08.014

Hulland, J. (1999). Use of partial least squares (PLS) in strategic management research: A review of four recent studies. Strategic Management Journal, 20, 195-204. https://doi.org/10.1002/(sici)1097-0266(199902)20:2<195::aid-smj13>3.0.co;2-7

John, A. C., Glendenning, A., Marchant, P., Montgomery, A., Stewart, S., Wood, K., & Lloyd, K. (2018). Self-harm, suicidal behaviors, and cyberbullying in children and young people: systematic review [meta-analysis research support, non-U.S. gov’t systematic review. J. Med. Internet Res., 20(4), e129. https://doi.org/10.2196/jmir.9044

Lee, J.-S., Joo, E.-J., & Choi, K.-S. (2013). Perceived Stress and Self-Esteem Mediate the Effects of Work-Related Stress on Depression. Stress and Health, 29, 75-81. https://doi.org/10.1002/smi.2428

Lin, B., Lee, Y., & Hung, S. (2006). R&D Intensity and Commercialization Orientation Effects on Financial Performance. Journal of Business Research, 59, 679-685. https://doi.org/10.1016/j.jbusres.2006.01.002

Lin, H. F. (2007). The role of online and offline features in sustaining virtual communities: an empirical study. Internet Research, 17(2), 119-138. https://doi.org/10.1108/10662240710736997

Lin, L. (2013). Multiple dimensions of multitasking phenomenon. International Journal of Technology and Human Interaction, 9(1), 37-49. https://doi.org/10.4018/jthi.2013010103

Malhotra, N. K., Nunan, D., & Birks, D. F. (2017). Marketing research: An applied approach (5th ed.). Pearson Education.

McKechnie, S., Winklhofer, H., & Ennew, C. (2006). Applying the technology acceptance model to the online retailing of financial services. International Journal of Retail & Distribution Management, 34(4/5),388-410.

https://doi.org/10.1108/09590550610660297

Mourougan, S., & Sethuraman, K. (2017). Hypothesis development and testing. Journal of Business and Management, 19(5), 34-40. https://doi.org/10.9790/487X-1905013440

Munadi, M., Annur, F., & Saputra, Y. (2022). Student Satisfaction in Online Learning of Islamic Higher Education in Indonesia during the Second Wave of COVID-19 Pandemic. Journal of Education and e-Learning Research, 9(2), 87-94.

https://doi.org/10.20448/jeelr.v9i2.3952

Nugroho, M., Setyorini, D., & Novitasari, B. (2019). The Role of Satisfaction on Perceived Value and E-Learning Usage Continuity Relationship. Procedia Computer Science, 161(1), 82-89. https://doi.org/10.1016/j.procs.2019.11.102

Park, D. H., Lee, J., & Han, I. (2007). The Effect of On-Line Consumer Reviews on Consumer Purchasing Intention: The Moderating Role of Involvement. International Journal of Electronic Commerce, 11, 125-148.

https://doi.org/10.2753/JEC1086-4415110405

Pedroso, R., Zanetello, L., Guimaraes, L., Pettenon, M., Goncalves, V., Scherer, J., Kessler, F., & Pechansky, F. (2016). Confirmatory factor analysis (CFA) of the crack use relapse scale (CURS). Archives of Clinical Psychiatry, 43(3), 37-40. https://doi.org/10.1590/0101-60830000000081

Petter, S., DeLone, W., & McLean, E. (2008). Measuring information systems success: Models, dimensions, measures, and interrelationships. European Journal of Information Systems, 17(3), 236-263. https://doi.org/10.1057/ejis.2008.15

Roca, J. C., Chiu, C.-M., & López, F. J. M. (2006). Understanding e-Learning continuance intention: An extension of the technology acceptance model. International Journal of Human-Computer Studies, 64(8), 683-696.

https://doi.org/10.1016/j.ijhcs.2006.01.003.

Rughoobur-Seetah, S., & Zuberia, Z. A. (2021). An evaluation of the impact of confinement on the quality of e-learning in higher education institutions. Quality Assurance in Education, 29(4), 422–444. https://doi.org/10.1108/qae-03-2021-0043

Saeed, K. A., Hwang, Y., & Yi, M. Y. (2003). Toward an integrative framework for online consumer behavior research: A meta-analysis approach. Journal of Organizational and End User Computing, 15(4), 1-26. https://doi.org/10.4018/joeuc.2003100101

Santos, J. (2003). E-service quality: a model of virtual service quality dimensions. Managing Service Quality: An International Journal, 13(3), 233-246. https://doi.org/10.1108/09604520310476490

Schmitt, N., & Stults, D. M. (1986). Methodology review: Analysis of multitrait-multimethod matrices. Applied Psychological Measurement, 10(1), 1-22. https://doi.org/10.1177/014662168601000101

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.2005.02.010

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.

Stein, C., Morris, N., & Nock, N. (2012). Structural Equation Modeling. Methods in Molecular Biology, 850, 495-512. https://doi.org/10.1007/978-1-61779-555-8_27

Teo, H. H., Chan, H.-C., Wei, K.-K., & Zhang, Z. (2003). Evaluating information accessibility and community adaptivity features for sustaining virtual learning communities. International Journal of Human-Computer Studies, 59(5), 671-697. https://doi.org/10.1016/S1071-5819(03)00087-9

Wang, F., & Zhang, H. (2016). An Empirical Study on the Influencing Factors of University Librarians’ Psychological Capital. Shandong Library Journal, 4, 10-16.

Wang, R. Y., & Strong, D. M. (1996). Beyond Accuracy: What Data Quality Means to Data Consumers. Journal of Management Information Systems, 12(4), 5-33. https://doi.org/10.1080/07421222.1996.11518099

Wu, J. H., & Wang, Y. M. (2006). Measuring KMS success: A specification of the DeLone and McLean’s model. Information and Management, 43(6), 728-739. https://doi.org/10.1016/j.im.2006.05.002

Yu, M., & Zhao, R. (2015). Sustainability and Firm Valuation: An International Investigation. International Journal of Accounting and Information Management, 23, 289-307.https://doi.org/10.1108/IJAIM-07-2014-0050

Zaied, A. N. H. (2012). An Integrated Success Model for Evaluating Information System in Public Sectors. Journal of Emerging Trends in Computing and Information Sciences, 6(3), 814-825.

Zhang, J., Wang, K., Wei, Y., & Liu, Z. (2017). Synthesis and characterization of hexagonal boron nitride nanosheets via low-temperature solid-state reaction. Journal of Chinese Society for Nonferrous Metals, 27(4), 901-908.