由於 ChatGPT 的普及化和效率，越來越多大學生使用 ChatGPT 來完成學術工作。它有助於產生想法、澄清概念和語言支持，特別有利於非母語人士。然而，在沒有審慎評估的情況下可能會導致不準確或偏見的內容。因此，雖然 ChatGPT 提供了顯著的教育優勢，但使用者必須嚴格評估其可靠性。
本論文結合「技術接受模型」和「資訊系統成功模式」，了解 ChatGPT 在高等教育學生中的採用情況。「技術接受模型」中的變數是感知有用性和感知易用性，但這項研究發現，在教育的環境下，努力預期和績效預期在生成人工智慧環境中更適配。而模型中的整體品質維度，是引用資訊系統成功模式中的資訊、系統與服務品質。
本研究針對400名的高等教育學生，結果顯示努力預期在整體品質和績效預期之間發揮的中介作用，強調友好系統整體品質的重要性，可以減少努力預期並提高感知有用性，並增加對系統的信任。研究的結果目的在增強使用者體驗並確保ChatGPT在學術環境中能被持續使用，根據使用者回饋改善可以提高使用者忠誠度，並減少轉向其他替代人工智慧工具的情況。為教育工作者和ChatGPT 的軟體開發人員提供建議，強調生成式人工智慧在學術界的策略和整合的重要。

The advancement of artificial intelligence (AI) and language models has enabled generative AI tools that surpass traditional systems by fostering creativity and innovative problem-solving. These tools enhance individual capabilities but challenge conventional educational and professional frameworks. Despite their potential, research on integrating AI-generated content in higher education remains limited. University students increasingly use ChatGPT for academic tasks due to its accessibility and efficiency. It aids in idea generation, concept clarification, and language support, particularly benefiting non-native speakers. However, reliance on AI without critical evaluation may result in using content with inaccuracies or biases. Thus, while ChatGPT offers significant educational advantages, users must assess its reliability critically.
This study investigates ChatGPT adoption among university students using a modified Technology Acceptance Model (TAM). Data from 400 students with prior ChatGPT use are analyzed, incorporating TAM and the Information Systems Success Model (IS Success Model). While TAM highlights "perceived usefulness" and "ease of use," this study finds "effort expectancy" and "performance expectancy" more relevant to the generative AI when in an education context. Quality dimensions—information, system, and service quality—adapted from (DeLone & McLean, 2003) IS Success model.
The study's findings provide valuable insights for software developers aiming to enhance user experience and ensure sustained engagement with ChatGPT in academic settings. The mediating role of effort expectancy in the relationship between overall quality and performance expectancy underscores the importance of designing an intuitive, user-friendly interface that minimizes cognitive effort and maximizes perceived usefulness. Optimizing these aspects can foster greater trust and performance satisfaction, critical to students' continued use of ChatGPT. Addressing usability challenges and refining features based on feedback can improve user loyalty and reduce switching to alternative Gen. AI tools. This study extends TAM by integrating quality dimensions and offers recommendations for educators and institutions, emphasizing strategic and responsible integration of generative AI in academia.

Ajzen, I. (2020). The theory of planned behavior: Frequently asked questions. Human Behavior and Emerging Technologies, 2(4), 314-324.
Al-Azawei, A., & Alowayr, A. (2020). Predicting the intention to use and hedonic motivation for mobile learning: A comparative study in two Middle Eastern countries. Technology in Society, 62(0), 101325.
Al-Fraihat, D., Joy, M., & Sinclair, J. (2020). Evaluating E-learning systems success: An empirical study. Computers in Human Behavior, 102(0), 67-86.
Alalwan, N., Al-Rahmi, W. M., Alfarraj, O., Alzahrani, A., Yahaya, N., & Al-Rahmi, A. M. (2019). Integrated three theories to develop a model of factors affecting students’ academic performance in higher education. IEEE access, 7(0), 98725-98742.
Alasmari, T., & Zhang, K. (2019). Mobile learning technology acceptance in Saudi Arabian higher education: an extended framework and A mixed-method study. Education and Information Technologies, 24(3), 2127-2144.
Almogren, A. S., Al-Rahmi, W. M., & Dahri, N. A. (2024). Exploring factors influencing the acceptance of ChatGPT in higher education: A smart education perspective. Heliyon.
Almulla, M. A. (2024). Investigating Influencing Factors of Learning Satisfaction in AI ChatGPT for Research: University Students Perspective. Heliyon.
Alzahrani, A. I., Mahmud, I., Ramayah, T., Alfarraj, O., & Alalwan, N. (2019). Modelling digital library success using the DeLone and McLean information system success model. Journal of Librarianship and Information Science, 51(2), 291-306.
Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological bulletin, 103(3), 411.
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.
Bajunaied, K., Hussin, N., & Kamarudin, S. (2023). Behavioral intention to adopt FinTech services: An extension of unified theory of acceptance and use of technology. Journal of Open Innovation: Technology, Market, and Complexity, 9(1), 100010.
Benuyenah, V. (2023). Commentary: ChatGPT use in higher education assessment: Prospects and epistemic threats. Journal of Research in Innovative Teaching & Learning, 16(1), 134-135.
Browne, M. W., MacCallum, R. C., Kim, C.-T., Andersen, B. L., & Glaser, R. (2002). When fit indices and residuals are incompatible. Psychological Methods, 7(4), 403-421.

Budhathoki, T., Zirar, A., Njoya, E. T., & Timsina, A. (2024). ChatGPT adoption and anxiety: a cross-country analysis utilising the unified theory of acceptance and use of technology (UTAUT). Studies in Higher Education, 1-16.
Cai, Q., Lin, Y., & Yu, Z. (2023). Factors influencing learner attitudes towards ChatGPT-assisted language learning in higher education. International Journal of Human–Computer Interaction, 1-15.
Camilleri, M. A. (2024). Factors affecting performance expectancy and intentions to use ChatGPT: Using SmartPLS to advance an information technology acceptance framework. Technological Forecasting and Social Change, 201, 123247.
Chan, C. K. Y., & Hu, W. (2023). Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20(1), 43.
Chao, C.-M. (2019). Factors determining the behavioral intention to use mobile learning: An application and extension of the UTAUT model. Frontiers in psychology, 10, 1652.
Chatterjee, S., Rana, N. P., & Dwivedi, Y. K. (2020). Social media as a tool of knowledge sharing in academia: an empirical study using valance, instrumentality and expectancy (VIE) approach. Journal of Knowledge Management, 24(10), 2531-2552.
Chen, Q., Gong, Y., Lu, Y., & Tang, J. (2022). Classifying and measuring the service quality of AI chatbot in frontline service. Journal of Business Research, 145, 552-568.
Chiu, C. M., Chiu, C. S., & Chang, H. C. (2007). Examining the integrated influence of fairness and quality on learners’ satisfaction and Web‐based learning continuance intention. Information systems journal, 17(3), 271-287.
Choi, E. P. H., Lee, J. J., Ho, M.-H., Kwok, J. Y. Y., & Lok, K. Y. W. (2023). Chatting or cheating? The impacts of ChatGPT and other artificial intelligence language models on nurse education. Nurse Education Today.
Chou, C.-M., Shen, T.-C., Shen, T.-C., & Shen, C.-H. (2022). Influencing factors on students’ learning effectiveness of AI-based technology application: Mediation variable of the human-computer interaction experience. Education and Information Technologies, 27(6), 8723-8750.
Choudhury, A., & Shamszare, H. (2023). Investigating the impact of user trust on the adoption and use of ChatGPT: survey analysis. Journal of Medical Internet Research, 25, e47184.
Cimperman, M., Brenčič, M. M., & Trkman, P. (2016). Analyzing older users’ home telehealth services acceptance behavior—applying an Extended UTAUT model. International journal of medical informatics, 90, 22-31.
Dağhan, G., & Akkoyunlu, B. (2016). Modeling the continuance usage intention of online learning environments. Computers in Human Behavior, 60, 198-211.
Davis, F. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 984.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340.
Davis, F. D., & Venkatesh, V. (1996). A critical assessment of potential measurement biases in the technology acceptance model: three experiments. International Journal of human-computer studies, 45(1), 19-45.
Del Giudice, M., Scuotto, V., Orlando, B., & Mustilli, M. (2023). Toward the human–centered approach. A revised model of individual acceptance of AI. Human Resource Management Review, 33(1), 100856.
DeLone, W. H., & McLean, E. R. (1992). Information systems success: The quest for the dependent variable. Information systems research, 3(1), 60-95.
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.
Dowling, M., & Lucey, B. (2023). ChatGPT for (finance) research: The Bananarama conjecture. Finance Research Letters, 53, 103662.
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.
Duong, C. D. (2024). Modeling the determinants of HEI students’ continuance intention to use ChatGPT for learning: a stimulus–organism–response approach. Journal of Research in Innovative Teaching & Learning.
Duong, C. D., Bui, D. T., Pham, H. T., & Vu, A. T. (2023). How effort expectancy and performance expectancy interact to trigger higher education students’ uses of ChatGPT for learning. Interactive Technology and Smart Education(ahead-of-print).
Duong, C. D., Vu, T. N., & Ngo, T. V. N. (2023). Applying a modified technology acceptance model to explain higher education students’ usage of ChatGPT: A serial multiple mediation model with knowledge sharing as a moderator. The International Journal of Management Education, 21(3), 100883.
Dwivedi, Y. K., Ismagilova, E., Hughes, D. L., Carlson, J., Filieri, R., Jacobson, J., . . . Krishen, A. S. (2021). Setting the future of digital and social media marketing research: Perspectives and research propositions. International Journal of Information Management, 59, 102168.
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., . . . Ahuja, M. (2023). Opinion Paper:“So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642..
Elshan, E., Zierau, N., Engel, C., Janson, A., & Leimeister, J. M. (2022). Understanding the design elements affecting user acceptance of intelligent agents: Past, present and future. Information Systems Frontiers, 24(3), 699-730.
Fishbein, M., & Ajzen, I. (2011). Predicting and changing behavior: The reasoned action approach: Psychology press.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50.
Foroughi, B., Senali, M. G., Iranmanesh, M., Khanfar, A., Ghobakhloo, M., Annamalai, N., & Naghmeh-Abbaspour, B. (2024). Determinants of intention to use ChatGPT for educational purposes: Findings from PLS-SEM and fsQCA. International Journal of Human–Computer Interaction, 40(17), 4501-4520.
Fu, C.-J., Silalahi, A. D. K., Shih, I.-T., Phuong, D. T. T., Eunike, I. J., & Jargalsaikhan, S. (2024). Assessing ChatGPT’s information quality through the lens of user information satisfaction and information quality theory in higher education: A theoretical framework. Human behavior and emerging technologies, 2024(1), 8114315.
Gao, L., Waechter, K. A., & Bai, X. (2015). Understanding consumers’ continuance intention towards mobile purchase: A theoretical framework and empirical study–A case of China. Computers in Human Behavior, 53, 249-262.
Glikson, E., & Woolley, A. W. (2020). Human trust in artificial intelligence: Review of empirical research. Academy of Management Annals, 14(2), 627-660.
Graf, A., & Bernardi, R. E. (2023). ChatGPT in research: balancing ethics, transparency and advancement. Neuroscience, 515, 71-73.
Gu, Z., Wei, J., & Xu, F. (2016). An empirical study on factors influencing consumers' initial trust in wearable commerce. Journal of Computer Information Systems, 56(1), 79-85.
Gulati, A., Saini, H., Singh, S., & Kumar, V. (2024). Enhancing learning potiential: Investigating marketing students' behavioral intentions to sdpot ChatGPT. Marketing Education Review, 1-34.
Habibi, A., Muhaimin, M., Danibao, B. K., Wibowo, Y. G., Wahyuni, S., & Octavia, A. (2023). ChatGPT in higher education learning: Acceptance and use. Computers and Education: Artificial Intelligence, 5, 100190.
Hair, E., Halle, T., Terry-Humen, E., Lavelle, B., & Calkins, J. (2006). Children's school readiness in the ECLS-K: Predictions to academic, health, and social outcomes in first grade. Early Childhood Research Quarterly, 21(4), 431-454.
Hair, J. F., Gabriel, M., & Patel, V. (2014). AMOS covariance-based structural equation modeling (CB-SEM): Guidelines on its application as a marketing research tool. Brazilian Journal of Marketing, 13(2).
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139-152.
Halloran, D., Manchester, S., Moriarty, J., Riley, R., Rohrman, J., & Skramstad, T. (1978). Systems development quality control. MIS quarterly, 1-13.
Hayes, A. F. (2017). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. Guilford publications.
Hengstler, M., Enkel, E., & Duelli, S. (2016). Applied artificial intelligence and trust—The case of autonomous vehicles and medical assistance devices. Technological Forecasting and Social Change, 105, 105-120.
Hii, P. K., Goh, C. F., Tan, O. K., Amran, R., & Ong, C. H. (2023). An information system success model for e-learning postadoption using the fuzzy analytic network process. Education and Information Technologies, 28(8), 10731-10752.
Hoi, V. N. (2020). Understanding higher education learners' acceptance and use of mobile devices for language learning: A Rasch-based path modeling approach. Computers & Education, 146, 103761.
Hooda, A., Gupta, P., Jeyaraj, A., Giannakis, M., & Dwivedi, Y. K. (2022). The effects of trust on behavioral intention and use behavior within e-government contexts. International Journal of Information Management, 67, 102553.
Hoque, R., & Sorwar, G. (2017). Understanding factors influencing the adoption of mHealth by the elderly: An extension of the UTAUT model. International journal of medical informatics, 101, 75-84.
Hsia, J.-W., & Tseng, A.-H. (2008). An enhanced technology acceptance model for e-learning systems in high-tech companies in Taiwan: analyzed by structural equation modeling. Paper presented at the 2008 International Conference on Cyberworlds.
Hsu, M.-H., & Chiu, C.-M. (2004). Predicting electronic service continuance with a decomposed theory of planned behaviour. Behaviour & Information Technology, 23(5), 359-373.
Huang, M.-H., & Rust, R. T. (2018). Artificial intelligence in service. Journal of service research, 21(2), 155-172.
Hulin, C. (2001). Can a reliability coefficient be too high? J Consum Psychol, 10(1–2), 55.
Issa, T., & Isaias, P. (2022). Usability and human–computer interaction (hci). In Sustainable design: HCI, usability and environmental concerns (pp. 23-40): Springer.
Jeon, J., Lee, S., & Choe, H. (2023). Beyond ChatGPT: A conceptual framework and systematic review of speech-recognition chatbots for language learning. Computers & Education, 104898.
Jin, J. H., & Lee, J. H. (2024). Impact of ChatGPT Service Quality on the Intention of Generation Z to Continue Using it for Tourism Information Search. Asia-pacific Journal of Convergent Research Interchange (APJCRI), 223-233.
Kao, W.-K., & Huang, Y.-S. S. (2023). Service robots in full-and limited-service restaurants: Extending technology acceptance model. Journal of Hospitality and Tourism Management, 54, 10-21.
Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., . . . Hüllermeier, E. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and individual differences, 103, 102274.
Kim, Y., Blazquez, V., & Oh, T. (2024). Determinants of Generative AI System Adoption and Usage Behavior in Korean Companies: Applying the UTAUT Model. Behavioral Sciences, 14(11), 1035.
Kohnke, L., Moorhouse, B. L., & Zou, D. (2023). Exploring generative artificial intelligence preparedness among university language instructors: A case study. Computers and Education: Artificial Intelligence, 5, 100156.
Lai, I. K., Hitchcock, M., Yang, T., & Lu, T.-W. (2018). Literature review on service quality in hospitality and tourism (1984-2014) Future directions and trends. International Journal of Contemporary Hospitality Management, 30(1), 114-159.
Lankton, N. K., & McKnight, D. H. (2011). What does it mean to trust facebook? Examining technology and interpersonal trust beliefs. ACM SIGMIS Database: The DATABASE for Advances in Information Systems, 42(2), 32-54.
Lee, O.-K. D., Ayyagari, R., Nasirian, F., & Ahmadian, M. (2021). Role of interaction quality and trust in use of AI-based voice-assistant systems. Journal of Systems and Information Technology, 23(2), 154-170.
Lewis, M., Sycara, K., & Walker, P. (2018). The role of trust in human-robot interaction. Foundations of trusted autonomy, 135-159.
Li, X., & Zhu, W. (2022). System quality, information quality, satisfaction and acceptance of online learning platform among college students in the context of online learning and blended learning. Frontiers in psychology, 13, 1054691.
Li, Y., & Wang, J. (2021). Evaluating the impact of information system quality on continuance intention toward cloud financial information system. Frontiers in psychology, 12(0), 713353.
Lim, W. M., Gunasekara, A., Pallant, J. L., Pallant, J. I., & Pechenkina, E. (2023). Generative AI and the future of education: Ragnarök or reformation? A paradoxical perspective from management educators. The International Journal of Management Education, 21(2), 100790.
Limayem, M., & Cheung, C. M. (2011). Predicting the continued use of Internet-based learning technologies: the role of habit. Behaviour & Information Technology, 30(1), 91-99.
Lin, H., & Chen, Q. (2024). Artificial intelligence (AI) -integrated educational applications and college students’ creativity and academic emotions: students and teachers’ perceptions and attitudes. BMC Psychology, 12(1), 487. doi:10.1186/s40359-024-01979-0
Lu, Y., Papagiannidis, S., & Alamanos, E. (2019). Exploring the emotional antecedents and outcomes of technology acceptance. Computers in Human Behavior, 90, 153-169.
MacCallum, R. C., & Hong, S. (1997). Power analysis in covariance structure modeling using GFI and AGFI. Multivariate behavioral research, 32(2), 193-210.
Mcknight, D. H., Carter, M., Thatcher, J. B., & Clay, P. F. (2011). Trust in a specific technology: An investigation of its components and measures. ACM Transactions on management information systems (TMIS), 2(2), 1-25.
Mayer, R. (1995). An Integrative Model of Organizational Trust. Academy of Management Review.
Mogavi, R. H., Deng, C., Kim, J. J., Zhou, P., Kwon, Y. D., Metwally, A. H. S., . . . Gujar, S. (2024). ChatGPT in education: A blessing or a curse? A qualitative study exploring early adopters’ utilization and perceptions. Computers in Human Behavior: Artificial Humans, 2(1), 100027.
Mohammadi, H. (2015). Investigating users’ perspectives on e-learning: An integration of TAM and IS success model. Computers in Human Behavior, 45, 359-374.
Moriuchi, E., & Murdy, S. (2024). The role of robots in the service industry: Factors affecting human-robot interactions. International Journal of Hospitality Management, 118, 103682.
Nandankar, S., Sachan, A., Mukherjee, A., & Adhikari, A. (2023). Electronic service quality (e-SQ) measurement: a cross-functional review. International Journal of Quality & Reliability Management, 40(1), 148-168.
Nelson, R. R., Todd, P. A., & Wixom, B. H. (2005). Antecedents of information and system quality: an empirical examination within the context of data warehousing. Journal of management information systems, 21(4), 199-235.
Nikolopoulou, K., Gialamas, V., & Lavidas, K. (2021). Habit, hedonic motivation, performance expectancy and technological pedagogical knowledge affect teachers’ intention to use mobile internet. Computers and Education Open, 2(0), 100041.
Nunkoo, R., Teeroovengadum, V., Ringle, C. M., & Sunnassee, V. (2020). Service quality and customer satisfaction: The moderating effects of hotel star rating. International Journal of Hospitality Management, 91, 102414.
Oliveira, T., Alhinho, M., Rita, P., & Dhillon, G. (2017). Modelling and testing consumer trust dimensions in e-commerce. Computers in Human Behavior, 71, 153-164.
Overono, A. L., & Ditta, A. S. (2023). The Rise of Artificial Intelligence: A Clarion Call for Higher Education to Redefine Learning and Reimagine Assessment. College Teaching, 1-4.
Park, S. (2020). Multifaceted trust in tourism service robots. Annals of Tourism Research, 81, 102888.
Patel, S. B., & Lam, K. (2023). ChatGPT: the future of discharge summaries? The Lancet Digital Health, 5(3), e107-e108.
Petter, S., DeLone, W., & McLean, E. R. (2013). Information systems success: The quest for the independent variables. Journal of management information systems, 29(4), 7-62.
Pham, Q.-V., Nguyen, D. C., Huynh-The, T., Hwang, W.-J., & Pathirana, P. N. (2020). Artificial intelligence (AI) and big data for coronavirus (COVID-19) pandemic: a survey on the state-of-the-arts. IEEE access, 8, 130820-130839.
Prentice, C., & Nguyen, M. (2021). Robotic service quality–Scale development and validation. Journal of Retailing and Consumer Services, 62, 102661.
Rahman, M. S., Sabbir, M. M., Zhang, J., Moral, I. H., & Hossain, G. M. S. (2022). Examining students’ intention to use ChatGPT: Does trust matter? Australasian Journal of Educational Technology, 51-71.
Ramayah, T., Ahmad, N. H., & Lo, M.-C. (2010). The role of quality factors in intention to continue using an e-learning system in Malaysia. Procedia-Social and Behavioral Sciences, 2(2), 5422-5426.
Rasul, T., Nair, S., Kalendra, D., Robin, M., de Oliveira Santini, F., Ladeira, W. J., . . . Heathcote, L. (2023). The role of ChatGPT in higher education: Benefits, challenges, and future research directions. Journal of Applied Learning and Teaching, 6(1), 41-56.
Ratten, V., & Jones, P. (2023). Generative artificial intelligence (ChatGPT): Implications for management educators. The International Journal of Management Education, 21(3), 100857.
Roca, J. C., Chiu, C.-M., & Martínez, F. J. (2006). Understanding e-learning continuance intention: An extension of the Technology Acceptance Model. International Journal of human-computer studies, 64(8), 683-696.
Sabah, N. M. (2016). Exploring students' awareness and perceptions: Influencing factors and individual differences driving m-learning adoption. Computers in Human Behavior, 65, 522-533.
Schumacker, R. E., & Lomax, R. G. (2004). A beginner's guide to structural equation modeling: psychology press.
Senali, M. G., Iranmanesh, M., Ghobakhloo, M., Foroughi, B., Asadi, S., & Rejeb, A. (2024). Determinants of trust and purchase intention in social commerce: Perceived price fairness and trust disposition as moderators. Electronic Commerce Research and Applications, 64, 101370.
Sepasgozar, S. M. (2022). Immersive on-the-job training module development and modeling users’ behavior using parametric multi-group analysis: A modified educational technology acceptance model. Technology in Society, 68, 101921.
Shahzad, M., Qu, Y., Rehman, S. U., & Zafar, A. U. (2022). Adoption of green innovation technology to accelerate sustainable development among manufacturing industry. Journal of Innovation & Knowledge, 7(4), 100231.
Shahzad, M. F., Xu, S., & Javed, I. (2024). ChatGPT awareness, acceptance, and adoption in higher education: the role of trust as a cornerstone. International Journal of Educational Technology in Higher Education, 21(1), 46.
Shin, D. (2021). The effects of explainability and causability on perception, trust, and acceptance: Implications for explainable AI. International Journal of human-computer studies, 146, 102551.
Shin, D., Zhong, B., & Biocca, F. A. (2020). Beyond user experience: What constitutes algorithmic experiences? International Journal of Information Management, 52, 102061.
Sousa, S., Lamas, D., & Dias, P. (2014). A model for human-computer trust: contributions towards leveraging user engagement. Paper presented at the Learning and Collaboration Technologies. Designing and Developing Novel Learning Experiences: First International Conference, LCT 2014, Held as Part of HCI International 2014, Heraklion, Crete, Greece, June 22-27, 2014, Proceedings, Part I 1.
Sweeney, S. (2023). Who wrote this? Essay mills and assessment–Considerations regarding contract cheating and AI in higher education. The International Journal of Management Education, 21(2), 100818.
Tan, C. N.-L., Tee, M., & Koay, K. Y. (2024). Discovering students' continuous intentions to use ChatGPT in higher education: a tale of two theories. Asian Education and Development Studies, 13(4), 356-372.
Thomas, R. J., O'Hare, G., & Coyle, D. (2023). Understanding technology acceptance in smart agriculture: A systematic review of empirical research in crop production. Technological Forecasting and Social Change, 189, 122374.
Tossell, C. C., Tenhundfeld, N. L., Momen, A., Cooley, K., & de Visser, E. J. (2024). Student perceptions of ChatGPT use in a college essay assignment: Implications for learning, grading, and trust in artificial intelligence. IEEE Transactions on Learning Technologies.
Ullman, J. B., & Bentler, P. M. (2012). Structural equation modeling. Handbook of Psychology, Second Edition, 2.
Vaishya, R., Misra, A., & Vaish, A. (2023). ChatGPT: Is this version good for healthcare and research? Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 17(4), 102744.
Vatolkina, N., Gorbashko, E., Kamynina, N., & Fedotkina, O. (2020). E-service quality from attributes to outcomes: The similarity and difference between digital and hybrid services. Journal of Open Innovation: Technology, Market, and Complexity, 6(4), 143.
Veeramootoo, N., Nunkoo, R., & Dwivedi, Y. K. (2018). What determines success of an e-government service? Validation of an integrative model of e-filing continuance usage. Government information quarterly, 35(2), 161-174.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 425-478.
Venkatesh, V., Thong, J. Y., & Xu, X. (2016). Unified theory of acceptance and use of technology: A synthesis and the road ahead. Journal of the association for Information Systems, 17(5), 328-376.
Wach, K., Duong, C. D., Ejdys, J., Kazlauskaitė, R., Korzynski, P., Mazurek, G., . . . Ziemba, E. (2023). The dark side of generative artificial intelligence: A critical analysis of controversies and risks of ChatGPT. Entrepreneurial Business and Economics Review, 11(2), 7-30.
Wanner, J., Herm, L.-V., Heinrich, K., & Janiesch, C. (2022). The effect of transparency and trust on intelligent system acceptance: Evidence from a user-based study. Electronic Markets, 32(4), 2079-2102.
Williams, L. J., & Hazer, J. T. (1986). Antecedents and consequences of satisfaction and commitment in turnover models: A reanalysis using latent variable structural equation methods. Journal of applied psychology, 71(2), 219.
Wollny, S., Schneider, J., Di Mitri, D., Weidlich, J., Rittberger, M., & Drachsler, H. (2021). Are we there yet?-a systematic literature review on chatbots in education. Frontiers in artificial intelligence, 4, 654924.
Wong, I. A., Lian, Q. L., & Sun, D. (2023). Autonomous travel decision-making: An early glimpse into ChatGPT and generative AI. Journal of Hospitality and Tourism Management, 56, 253-263.
Wu, K., Zhao, Y., Zhu, Q., Tan, X., & Zheng, H. (2011). A meta-analysis of the impact of trust on technology acceptance model: Investigation of moderating influence of subject and context type. International Journal of Information Management, 31(6), 572-581.
Yang, R., & Wibowo, S. (2022). User trust in artificial intelligence: A comprehensive conceptual framework. Electronic Markets, 32(4), 2053-2077.
Zeithaml, V. A., Berry, L. L., & Parasuraman, A. (1988). Communication and control processes in the delivery of service quality. Journal of marketing, 52(2), 35-48.