An Empirical Study of Undergraduate Satisfaction and Adoption Intentions of Artificial Intelligence in Chengdu, China

Authors

  • Jiang Aijia

Keywords:

Emotional Support, Perceived Ease of Use, Perceived Usefulness, Intention To Use, Artificial Intelligence

Abstract

Purpose: The research aimed to investigate the important factors impacting the satisfaction and intention to use Artificial Intelligence of Undergraduates in Chengdu, China. The conceptual framework presented cause-and-effect relationships between informational support, emotional support, perceived ease of use, perceived usefulness, satisfaction, attitude, and intention to use. Research design, data, and methodology: At Sichuan University of Media and Communications in Chengdu, China, undergraduate students were given a questionnaire by the researcher using a quantitative approach (n=500). Non-probability sampling included judgmental sampling to select four art majors of Sichuan University of Media and Communications, quota sampling to define the sample size, and convenience sampling to collect data and distribute the questionnaires online and offline. The researcher used structural equation modeling (SEM) and confirmatory factor analysis (CFA) to analyze the data Results: The results show that informational support, emotional support, perceived usefulness, and perceived ease of use have a significant effect on satisfaction, and satisfaction, as an intermediate variable, has a significant effect on the intention to use. Also, the attitude has a significant effect on the intention to use. Conclusions: To enhance the adoption of AI in higher education, it is essential to continuously address factors influencing student satisfaction and intention to use AI. Additionally, ongoing feedback should be provided to refine and adapt the AI implementation.

Author Biography

Jiang Aijia

Sichuan University of Media and Communications, Chengdu, China.

References

Adams, D. A., Nelson, R. R., & Todd, P. A. (1992). Perceived usefulness, ease of use, and usage of information technology: A replication. MIS Quarterly, 16(2), 227-247. https://doi.org/10.2307/249577

Aiolfi, A., Bona, D., Bonitta, G., & Bonavina, L. (2023). Effect of gastric ischemic conditioning prior to esophagectomy: Systematic review and meta-analysis. Review Updates in Surgery, 75(6), 1633-1643. https://doi.org/10.1007/s13304-023-01601-9

Aktay, S. (2022). The usability of images generated by artificial intelligence (AI) in education. International Technology and Education Journal, 6(2), 51-62.

Alam, A. (2021). Should robots replace teachers? Mobilisation of AI and learning analytics in education. Proceedings of the 2021 International Conference on Advances in Computing, Communication, and Control, 1-12.

Ashfaq, M., Yun, J., Yu, S., & Loureiro, S. M. C. (2020). I, Chatbot: Modeling the determinants of users’ satisfaction and continuance intention of AI-powered service agents. Telematics and Informatics, 54, 101473. https://doi.org/10.1016/j.tele.2020.101473

Avornyo, P., Fang, J., Odai, R. O., Vondee, J. B., & Nartey, M. N. (2019). Factors affecting continuous usage intention of mobile banking in Tema and Kumasi. International Journal of Business and Social Science, 10(3), 114-130. https://doi.org/10.30845/ijbss.v10n3p13

Benjamin, W. (2012). The autobiography of Benjamin Franklin (1st ed.). W. W. Norton.

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

Bergmann, M., Maçada, A. C. G., de Oliveira Santini, F., & Rasul, T. (2023). Continuance intention in financial technology: A framework and meta-analysis. International Journal of Bank Marketing, 41(4), 749-786. https://doi.org/10.1108/ijbm-04-2022-0168

Bugshan, H. (2015). Open innovation using Web 2.0 technologies. Journal of Enterprise Information Management, 28(4), 595-607. https://doi.org/10.1108/jeim-09-2014-099

Buis, L. R. (2008). Emotional and informational support messages in an online hospice support community. CIN: Computers, Informatics, Nursing, 26(6), 358-367. https://doi.org/10.1097/01.ncn.0000336461.94939.97

Butcher, L., Tucker, O., & Young, J. (2020). Path to discontinuance of pervasive mobile games: the case of Pokémon Go in Australia. Asia Pacific Journal of Marketing and Logistics, 33(2), 584-606. https://doi.org/10.1108/apjml-12-2019-0710

Chawla, D., & Joshi, H. (2019). Consumer attitude and intention to adopt mobile wallet in India: An empirical study. International Journal of Bank Marketing, 37(7), 1590-1618. https://doi.org/10.1108/ijbm-09-2018-0256

Churchill, G. A., & Surprenant, C. (1982). An investigation into the determinants of customer satisfaction. Journal of Marketing Research, 19(4), 491-504. https://doi.org/10.2307/3151722

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. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 25(8), 982-1003. https://doi.org/10.1287/mnsc.35.8.982

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

Erkan, I., & Evans, C. (2016). The influence of eWOM in social media on consumers’ purchase intentions: An extended approach to information adoption. Computers in Human Behavior, 61, 47-55. https://doi.org/10.1016/j.chb.2016.03.003

Ermagan, E., & Ermagan, I. (2022). Innovative technology and education: Artificial intelligence and language learning in Turkey. Shanlax International Journal of Education, 11, 201-209.

Fan, J., Zhou, W., Yang, X., Li, B., & Xiang, Y. (2019). Impact of social support and presence on swift guanxi and trust in social commerce. Industrial Management & Data Systems, 119(9), 2033-2054.

Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research (1st ed.). Addison-Wesley.

Gelbrich, K., Hagel, J., & Orsingher, C. (2021). Emotional support from a digital assistant in technology-mediated services: Effects on customer satisfaction and behavioral persistence. International Journal of Research in Marketing, 38(1), 176-193.

Glick, W. H. (1985). Conceptualizing and measuring organizational and psychological climate: Pitfalls in multilevel research. Academy of Management Review, 10(3), 601-616. https://doi.org/10.5465/amr.1985.4279045

Gonzalez Calatayud, V., Prendes, P., & Roig-Vila, R. (2021). Artificial intelligence for student assessment: A systematic review. Applied Sciences, 11(6), 5467. https://doi.org/10.3390/app11125467

Gual-Montolio, P., Jaén, I., Martínez Borba, V., & Castilla, D. (2022). Using artificial intelligence to enhance ongoing psychological interventions for emotional problems in real- or close to real-time: A systematic review. International Journal of Environmental Research and Public Health, 19(13), 7737. https://doi.org/10.3390/ijerph19137737

Guo, J., Zhang, C., Wu, Y., Li, H., & Liu, Y. (2018). Examining the determinants and outcomes of netizens’ participation behaviors on government social media profiles. Aslib Journal of Information Management, 70(4), 306-325.

https://doi.org/10.1108/ajim-07-2017-0157

Hess, T. J., McNab, A. L., & Basoglu, K. A. (2014). Reliability generalization of perceived ease of use, perceived usefulness, and behavioral intentions. MIS Quarterly, 38(1), 1-28. https://doi.org/10.25300/MISQ/2014/38.1.01

Hew, J. J., Lee, V. H., Ooi, K. B., & Wei, J. (2015). What catalyses mobile apps usage intention: An empirical analysis. Industrial Management & Data Systems, 115(7), 1269-1291. https://doi.org/10.1108/IMDS-01-2015-0028

Kashif, M., Zarkada, A., & Ramayah, T. (2018). The impact of attitude, subjective norms, and perceived behavioral control on managers’ intentions to behave ethically. Total Quality Management & Business Excellence, 29(5-6), 481-501.

https://doi.org/10.1080/14783363.2016.1209970

Kasilingam, D. L. (2020). Understanding the attitude and intention to use smartphone chatbots for shopping. Technology in Society, 62, 101280. https://doi.org/10.1016/j.techsoc.2020.101280

Kim, M., & Qu, H. (2014). Travelers' behavioral intention toward hotel self-service kiosks usage. International Journal of Contemporary Hospitality Management, 26(2), 225-245. https://doi.org/10.1108/ijchm-09-2012-0165

Krause, N. (2004). Lifetime trauma, emotional support, and life satisfaction among older adults. The Gerontologist, 44(5), 615-623. https://doi.org/10.1093/geront/44.5.615

Kuleto, V., Ilić, M., Dumangiu, M., Ranković, M., Martins, O. M., Păun, D., & Mihoreanu, L. (2021). Exploring opportunities and challenges of artificial intelligence and machine learning in higher education institutions. Sustainability, 13(18), 10424. https://doi.org/10.3390/su131810424

Kumari, N., & Biswas, A. (2023). Does M-payment service quality and perceived value co-creation participation magnify M-payment continuance usage intention? Moderation of usefulness and severity. International Journal of Bank Marketing, 3(2), 1-10. https://doi.org/10.1108/ijbm-11-2022-0500

Lai, V. S., & Li, H. (2005). Technology acceptance model for internet banking: An invariance analysis. Information & Management, 42(2), 373-386. https://doi.org/10.1016/j.im.2004.01.008

Le, X. C. (2022). Refining mobile location-based service adoption: The lens of pull effect-and push effect-related motivations. Journal of Asian Business and Economic Studies, 4(1), 30-40. https://doi.org/10.1108/jabes-09-2021-0159

Le, X. C. (2023). Inducing AI-powered chatbot use for customer purchase: The role of information value and innovative technology. Journal of Systems and Information Technology, 25(2), 219-241. https://doi.org/10.1108/jsit-09-2021-0206

Lee, C. T., Pan, L.-Y., & Hsieh, S. H. (2022). Artificial intelligent chatbots as brand promoters: a two-stage structural equation modeling-artificial neural network approach. Internet Research, 32(4), 1329-1356. https://doi.org/10.1108/intr-01-2021-0030

Lee, C. Y., Tsao, C. H., & Chang, W. C. (2015). The relationship between attitude toward using and customer satisfaction with mobile application services: An empirical study from the life insurance industry. Journal of Enterprise Information Management, 28(5), 680-697. https://doi.org/10.1108/JEIM-10-2014-0096

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, 1(2), 20-30. https://doi.org/10.1108/ijilt-12-2022-0231

Leong, L. Y., Hew, T. S., Ooi, K. B., & Chong, A. Y. L. (2020). Predicting the antecedents of trust in social commerce: A hybrid structural equation modeling with neural network approach. Journal of Business Research, 110, 24-40. https://doi.org/10.1016/j.jbusres.2019.12.007

Li, L., Mahowald, N. M., Miller, R. L., Pérez García-Pando, C., Klose, M., Hamilton, D. S., Gonçalves Ageitos, M., Ginoux, P., Balkanski, Y., Green, R. O., Kalashnikova, O., Kok, J. F., Obiso, V., Paynter, D., & Thompson, D. R. (2021). Quantifying the range of the dust direct radiative effect due to source mineralogy uncertainty. Atmospheric Chemistry and Physics, 21(5), 3973-4005. https://doi.org/10.5194/acp-21-3973-2021

Lin, H. F. (2011). An empirical investigation of mobile banking adoption: The effect of innovation attributes and knowledge-based trust. International Journal of Information Management, 31(3), 252-260. https://doi.org/10.1016/j.ijinfomgt.2010.07.006

Lin, R. R., & Lee, J. C. (2022). The supports provided by artificial intelligence to continuous usage intention of mobile banking: Evidence from China. Aslib Journal of Information Management, 3(4), 40-50. https://doi.org/10.1108/ajim-07-2022-0337

Loh, X. M., Lee, V. H., & Leong, L. Y. (2022). Mobile-lizing continuance intention with the mobile expectation-confirmation model: An SEM-ANN-NCA approach. Expert Systems with Applications, 205, 117659. https://doi.org/10.1016/j.eswa.2022.117659

Mazzone, M., & Elgammal, A. (2019). Art, creativity, and the potential of artificial intelligence. Arts, 8(1), 26. https://doi.org/10.3390/arts8010026

McCarthy, J. (2007). From here to human-level AI. Artificial Intelligence, 171(18), 1174-1182. https://doi.org/10.1016/j.artint.2007.10.016

Padmavathy, C., Balaji, M. S., & Sivakumar, V. J. (2012). Measuring effectiveness of customer relationship management in Indian retail banks. International Journal of Bank Marketing, 30(4), 246-266. https://doi.org/10.1108/02652321211238527

Pedroso, V. Y., Valdivié Navarro, M., Berrios Fleites, I., & Sosa Solis, E. C. (2016). Feeding systems with foliage of Morus alba and sugar cane stalks for fattening rabbits: Technical note. Revista Electrónica de Veterinaria, 17(12), 1-7.

Ram, S., & Sheth, J. N. (1989). Consumer resistance to innovations: The marketing problem and its solutions. Journal of Consumer Marketing, 6(2), 5-14. https://doi.org/10.1108/EUM0000000002565

Sarajärvi, A., Haapamäki, M. L., & Paavilainen, E. (2006). Emotional and informational support for families during their child’s illness. International Nursing Review, 53(3), 205-210. https://doi.org/10.1111/j.1466-7657.2006.00447.x

Scheider, M., Merz, S., & Stricker, J. (2018). Associations of Number Line Estimation with Mathematical Competence: A Meta-analysis. Child Development, 89(5), 1467-1484.

Shang, L., Zhou, J., & Zuo, M. (2021). Understanding older adults' intention to share health information on social media: The role of health belief and information processing. Internet Research, 31(1), 100-122. https://doi.org/10.1108/INTR-08-2019-0342

Sharma, S., Pradhan, K., Satya, S., & Vasudevan, P. (2005). Potentiality of earthworms for waste management and in other uses: A review. The Journal of American Science, 1(1), 4-16.

Sharma, S. K., & Sharma, M. (2019). Examining the role of trust and quality dimensions in the actual usage of mobile banking services: An empirical investigation. International Journal of Information Management, 44, 65-75. https://doi.org/10.1016/j.ijinfomgt.2018.10.003

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.

Song, X., Salcianu, A., Song, Y., Dopson, D., & Zhou, D. (2021). Fast WordPiece Tokenization. In M.-F. Moens, X. Huang, L. Specia, & S. Wen-tau Yih (Eds.), Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (pp. 2089-2103). Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.emnlp-main.160

Steffens, N. K., Haslam, S. A., Reicher, S. D., Platow, M. J., Fransen, K., Yang, J., & Boen, F. (2014). Leadership as social identity management: Introducing the Identity Leadership Inventory (ILI) to assess and validate a four-dimensional model. The Leadership Quarterly, 25(5), 1001-1024. https://doi.org/10.1016/j.leaqua.2014.06.007

Steigenberger, N. (2015). Emotions in sense making: A change management perspective. Journal of Organizational Change Management, 28(3), 432-451. https://doi.org/10.1108/jocm-05-2014-0095

Tajvidi, M., Richard, M. O., Wang, Y., & Hajli, N. (2020). Brand co-creation through social commerce information sharing: The role of social media. Journal of Business Research, 121, 476-486. https://doi.org/10.1016/j.jbusres.2019.10.025

Tapalova, O., & Zhiyenbayeva, N. (2022). Artificial intelligence in education: AIEd for personalised learning pathways. Electronic Journal of e-Learning, 20(5), 639-653. https://doi.org/10.34190/ejel.20.5.2597

Tsai, H. T., Chien, J. L., & Tsai, M. T. (2014). The influences of system usability and user satisfaction on continued Internet banking services usage intention: Empirical evidence from Taiwan. Electronic Commerce Research, 14(2), 137-169.

https://doi.org/10.1007/s10660-014-9136-5

Venkatesh, V., & Davis, F. D. (1996). A model of the antecedents of perceived ease of use: Development and test. Decision Sciences, 27(3), 451-481. https://doi.org/10.1111/j.1540-5915.1996.tb01822.x

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

Wang, Y. H., Gu, T. J., & Wang, S. Y. (2019). Causes and characteristics of short video platform internet community taking the TikTok short video application as an example. 2019 IEEE International Conference on Consumer Electronics-Taiwan, 1-2.

Wheaton, B., Muthén, B., Alwin, D. F., & Summers, G. (1977). Assessing reliability and stability in panel models. Sociological Methodology, 8, 84-136. https://doi.org/10.2307/270754

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

Xing, W., Goggins, S., & Introne, J. (2018). Quantifying the effect of informational support on membership retention in online communities through large-scale data analytics. Computers in Human Behavior, 86, 227-234.

https://doi.org/10.1016/j.chb.2018.04.037

Yang, S. J., Ogata, H., Matsui, T., & Chen, N. S. (2021). Human-centered artificial intelligence in education: Seeing the invisible through the visible. Computers and Education: Artificial Intelligence, 2, 100008. https://doi.org/10.1016/j.caeai.2021.100008

Yang, X. (2019). Accelerated move for AI education in China. ECNU Review of Education, 2(3), 347-352.

https://doi.org/10.1177/2096531119878590

Yüce, M., & Dost, Ş. (2019). Examining the example generation abilities of high school students within the context of mathematics course. International Online Journal of Education and Teaching (IOJET), 6(2), 260-279.

Zaharia, S., & Wurfel, M. (2020). Voice commerce-studying the acceptance of smart speakers. International Conference on Human Interaction and Emerging Technologie, 449-454. https://doi.org/10.1007/978-3-030-55307-4_68

Zhang, K., & Aslan, A. B. (2021). AI technologies for education: Recent research & future directions. Computers and Education: Artificial Intelligence, 2, 100025. https://doi.org/10.1016/j.caeai.2021.100025

Zhu, D. H., Sun, H., & Chang, Y. P. (2016). Effect of social support on customer satisfaction and citizenship behavior in online brand communities: The moderating role of support source. Journal of Retailing and Consumer Services, 31, 287-293. https://doi.org/10.1016/j.jretconser.2016.04.003

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Published

2025-12-24

How to Cite

Aijia, J. (2025). An Empirical Study of Undergraduate Satisfaction and Adoption Intentions of Artificial Intelligence in Chengdu, China. Scholar: Human Sciences, 17(4), 291-301. Retrieved from https://assumptionjournal.au.edu/index.php/Scholar/article/view/8520