|
參考文獻
一、英文部分
Aarts, H., & Dijksterhuis, A. (2000). The automatic activation of goal-directed behaviour: The case of travel habit. Journal of environmental psychology, 20(1), 75-82.
Aarts, H., Paulussen, T., & Schaalma, H. (1997). Physical exercise habit: on the conceptualization and formation of habitual health behaviours. Health education research, 12(3), 363-374.
Aarts, H., Verplanken, B., & Van Knippenberg, A. (1998). Predicting behavior from actions in the past: Repeated decision making or a matter of habit? Journal of Applied Social Psychology, 28(15), 1355-1374.
Abdul-Halim, N.-A., Vafaei-Zadeh, A., Hanifah, H., Teoh, A. P., & Nawaser, K. (2022). Understanding the determinants of e-wallet continuance usage intention in Malaysia. Quality & quantity, 56(5), 3413-3439.
Ahsan, M., & Kumar, A. (2016). A Study of the Relationship between Test Anxiety and Study Habits of Physical Education Students. International Journal of Sports and Physical Education, 2(3), 7-10.
Alpert, R., & Haber, R. N. (1960). Anxiety in academic achievement situations. The Journal of abnormal and social psychology, 61(2), 207.
Alsharhan, A., Al-Emran, M., & Shaalan, K. (2023). Chatbot adoption: a multiperspective systematic review and future research agenda. IEEE Transactions on Engineering Management.
Alsharo, M., Alnsour, Y., & Alabdallah, M. (2020). How habit affects continuous use: evidence from Jordan’s national health information system. Informatics for Health and Social Care, 45(1), 43-56.
Alshurideh, M., Al Kurdi, B., & Salloum, S. A. (2019). Examining the main mobile learning system drivers’ effects: A mix empirical examination of both the Expectation-Confirmation Model (ECM) and the Technology Acceptance Model (TAM). International conference on advanced intelligent systems and informatics,
Alter, S. (2008). Defining information systems as work systems: implications for the IS field. European Journal of Information Systems, 17(5), 448-469.
Alyoussef, I. Y. (2021). Massive open online course (MOOCs) acceptance: The role of task-technology fit (TTF) for higher education sustainability. Sustainability, 13(13), 7374.
Alyoussef, I. Y. (2023). Acceptance of e-learning in higher education: The role of task-technology fit with the information systems success model. Heliyon, 9(3).
Amoroso, D., & Lim, R. (2017). The mediating effects of habit on continuance intention. International Journal of Information Management, 37(6), 693-702.
And, J. W., Gibbs, B. L., & Kahle, L. R. (1983). Seat belt attitudes, habits, and behaviors: An adaptive amendment to the Fishbein model. Journal of Applied Social Psychology, 13(5), 406-421.
Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological bulletin, 103(3), 411.
Anderson, M., & Anderson, S. L. (2007). Machine ethics: Creating an ethical intelligent agent. AI magazine, 28(4), 15-15.
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.
Ashrafi, A., Zareravasan, A., Rabiee Savoji, S., & Amani, M. (2022). Exploring factors influencing students’ continuance intention to use the learning management system (LMS): a multi-perspective framework. Interactive Learning Environments, 30(8), 1475-1497.
Bagozzi, R. P., & Yi, Y. (1991). Multitrait-multimethod matrices in consumer research. Journal of consumer research, 17(4), 426-439.
Barnes, S. J. (2011). Understanding use continuance in virtual worlds: Empirical test of a research model. Information & Management, 48(8), 313-319.
Baum, S. D., Goertzel, B., & Goertzel, T. G. (2011). How long until human-level AI? Results from an expert assessment. Technological Forecasting and Social Change, 78(1), 185-195.
Baytak, A. (2023). The acceptance and diffusion of generative artificial intelligence in education: A literature review. Current Perspectives in Educational Research, 6(1), 7-18.
Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS quarterly, 351-370.
Bolliger, D. U., & Halupa, C. (2012). Student perceptions of satisfaction and anxiety in an online doctoral program. Distance Education, 33(1), 81-98.
Boscardin, C. K., Gin, B., Golde, P. B., & Hauer, K. E. (2024). ChatGPT and generative artificial intelligence for medical education: potential impact and opportunity. Academic Medicine, 99(1), 22-27.
Bostrom, N. (2002). Existential risks: Analyzing human extinction scenarios and related hazards. Journal of Evolution and technology, 9.
Bozkurt, A., & Sharma, R. C. (2023). Generative AI and prompt engineering: The art of whispering to let the genie out of the algorithmic world. Asian Journal of Distance Education, 18(2), i-vii.
Butt, S., Mahmood, A., Saleem, S., Rashid, T., & Ikram, A. (2021). Students’ performance in online learning environment: The role of task technology fit and actual usage of system during COVID-19. Frontiers in psychology, 12, 759227.
Buttazzo, G. (2008). Artificial consciousness: Hazardous questions (and answers). Artificial Intelligence in Medicine, 44(2), 139-146.
Cambre, M. A., & Cook, D. L. (1985). Computer anxiety: Definition, measurement, and correlates. Journal of Educational Computing Research, 1(1), 37-54.
Campbell, J. Y., & Cochrane, J. H. (1999). By force of habit: A consumption-based explanation of aggregate stock market behavior. Journal of political Economy, 107(2), 205-251.
Cardozo, R. N. (1965). An experimental study of customer effort, expectation, and satisfaction. Journal of marketing research, 2(3), 244-249.
Carver, C. S. (2006). Approach, avoidance, and the self-regulation of affect and action. Motivation and emotion, 30, 105-110.
Charng, H.-W., Piliavin, J. A., & Callero, P. L. (1988). Role identity and reasoned action in the prediction of repeated behavior. Social psychology quarterly, 303-317.
Cheng, Y.-M. (2019). How does task-technology fit influence cloud-based e-learning continuance and impact? Education+ Training, 61(4), 480-499.
Cheng, Y.-M. (2020a). Quality antecedents and performance outcome of cloud-based hospital information system continuance intention. Journal of Enterprise Information Management, 33(3), 654-683.
Cheng, Y.-M. (2020b). Understanding cloud ERP continuance intention and individual performance: a TTF-driven perspective. Benchmarking: an international journal, 27(4), 1591-1614.
Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern methods for business research, 295(2), 295-336.
Chiu, C.-M., Hsu, M.-H., Lai, H., & Chang, C.-M. (2012). Re-examining the influence of trust on online repeat purchase intention: The moderating role of habit and its antecedents. Decision support systems, 53(4), 835-845.
Chopra, S., & White, L. (2007). Privacy and artificial agents, or, is google reading my email? IJCAI,
Clarke, R. (2019). Why the world wants controls over Artificial Intelligence. Computer Law & Security Review, 35(4), 423-433.
Cohen, J. (2013). Statistical power analysis for the behavioral sciences. Academic press.
Dai, H. M., Teo, T., Rappa, N. A., & Huang, F. (2020). Explaining Chinese university students’ continuance learning intention in the MOOC setting: A modified expectation confirmation model perspective. Computers & Education, 150, 103850.
Daneji, A. A., Ayub, A. F. M., & Khambari, M. N. M. (2019). The effects of perceived usefulness, confirmation and satisfaction on continuance intention in using massive open online course (MOOC). Knowledge Management & E-Learning, 11(2), 201-214.
Darmiasih, M., & Setiawan, P. Y. (2021). Continuance usage intention and its antecedents on using OVO e-wallet application in Denpasar. International research journal of management, IT and social sciences, 8(1), 35-46.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management science, 35(8), 982-1003.
Dishaw, M. T., & Strong, D. M. (1999). Extending the technology acceptance model with task–technology fit constructs. Information & Management, 36(1), 9-21.
Elliot, A. J. (2006). The hierarchical model of approach-avoidance motivation. Motivation and emotion, 30, 111-116.
Erkin, Z., Franz, M., Guajardo, J., Katzenbeisser, S., Lagendijk, I., & Toft, T. (2009). Privacy-preserving face recognition. Privacy Enhancing Technologies: 9th International Symposium, PETS 2009, Seattle, WA, USA, August 5-7, 2009. Proceedings 9,
Eslami, J., Hatami, N., Amiri, A., & Akbarzadeh, M. (2020). The potential beneficial effects of education and familiarity with cesarean section procedure and the operating room environment on promotion of anxiety and pain intensity: A randomized controlled clinical trial. Journal of Education and Health Promotion, 9.
Fan, P., & Jiang, Q. (2024). Exploring the Factors Influencing Continuance Intention to Use AI Drawing Tools: Insights from Designers. Systems, 12(3), 68.
Faruk, L. I. D., Rohan, R., Ninrutsirikun, U., & Pal, D. (2023). University Students’ Acceptance and Usage of Generative AI (ChatGPT) from a Psycho-Technical Perspective. Proceedings of the 13th International Conference on Advances in Information Technology,
Fiske, S. T., & Taylor, S. E. (1991). Social cognition. Mcgraw-Hill Book Company.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50.
Franque, F. B., Oliveira, T., & Tam, C. (2021). Understanding the factors of mobile payment continuance intention: empirical test in an African context. Heliyon, 7(8).
García-Peñalvo, F., & Vázquez-Ingelmo, A. (2023). What do we mean by GenAI? A systematic mapping of the evolution, trends, and techniques involved in Generative AI.
Gefen, D. (2003). TAM or just plain habit: A look at experienced online shoppers. Journal of Organizational and End User Computing (JOEUC), 15(3), 1-13.
Gefen, D., Straub, D., & Boudreau, M.-C. (2000). Structural equation modeling and regression: Guidelines for research practice. Communications of the association for information systems, 4(1), 7.
Gibney, E. (2017). Google reveals secret test of AI bot to beat top Go players. Nature, 541(7636).
Gold, A. H., Malhotra, A., & Segars, A. H. (2001). Knowledge management: An organizational capabilities perspective. Journal of management information systems, 18(1), 185-214.
Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS quarterly, 213-236.
Granter, S. R., Beck, A. H., & Papke Jr, D. J. (2017). AlphaGo, deep learning, and the future of the human microscopist. Archives of pathology & laboratory medicine, 141(5), 619-621.
Hair, J. F. (2009). Multivariate data analysis.
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.
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European business review, 31(1), 2-24.
Hair Jr, J., Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A primer on partial least squares structural equation modeling (PLS-SEM). Sage publications.
Hamid, O. H., Smith, N. L., & Barzanji, A. (2017). Automation, per se, is not job elimination: How artificial intelligence forwards cooperative human-machine coexistence. 2017 IEEE 15th International Conference on Industrial Informatics (INDIN),
Heinssen Jr, R. K., Glass, C. R., & Knight, L. A. (1987). Assessing computer anxiety: Development and validation of the computer anxiety rating scale. Computers in Human behavior, 3(1), 49-59.
Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. In New challenges to international marketing (Vol. 20, pp. 277-319). Emerald Group Publishing Limited.
Howard, G. (1986). Computer anxiety and management use of microcomputers. Ann Arbor: UMI Research Press.
Hu, L. t., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural equation modeling: a multidisciplinary journal, 6(1), 1-55.
Huang, C.-K., Chen, C.-D., & Liu, Y.-T. (2019). To stay or not to stay? Discontinuance intention of gamification apps. Information Technology & People, 32(6), 1423-1445.
Huang, T. C.-K., Wu, L., & Chou, C.-C. (2013). Investigating use continuance of data mining tools. International Journal of Information Management, 33(5), 791-801.
Humbani, M. (2018). Consumers' adoption and continuance intention to use mobile payment services University of Pretoria].
Hung, M.-C., Talley, P. C., Kuo, K.-M., & Chiu, M.-L. (2020). Exploring cloud-based bookstore continuance from a deconstructed task–technology fit perspective. Journal of Theoretical and Applied Electronic Commerce Research, 16(3), 356-376.
Igbaria, M., Schiffman, S. J., & Wieckowski, T. J. (1994). The respective roles of perceived usefulness and perceived fun in the acceptance of microcomputer technology. Behaviour & Information Technology, 13(6), 349-361.
Isaac, O., Abdullah, Z., Ramayah, T., & Mutahar, A. M. (2017). Internet usage, user satisfaction, task-technology fit, and performance impact among public sector employees in Yemen. The International Journal of Information and Learning Technology, 34(3), 210-241.
Ismatullaev, U. V. U., & Kim, S.-H. (2024). Review of the factors affecting acceptance of AI-infused systems. Human Factors, 66(1), 126-144.
Jakobsen, M., & Jensen, R. (2015). Common method bias in public management studies. International public management journal, 18(1), 3-30.
Jarupathirun, S. (2007). Exploring the influence of perceptual factors in the success of web-based spatial DSS. Decision support systems, 43(3), 933-951.
Johnson, D. G., & Verdicchio, M. (2017). AI anxiety. Journal of the Association for Information Science and Technology, 68(9), 2267-2270.
Jones, F., Fletcher, B., & Ibbetson, K. (1991). Stressors and strains amongst social workers: Demands, supports, constraints, and psychological health. The British journal of social work, 21(5), 443-469.
Joo, Y. J., So, H.-J., & Kim, N. H. (2018). Examination of relationships among students' self-determination, technology acceptance, satisfaction, and continuance intention to use K-MOOCs. Computers & Education, 122, 260-272.
Jumaan, I. A., Hashim, N. H., & Al-Ghazali, B. M. (2020). The role of cognitive absorption in predicting mobile internet users’ continuance intention: An extension of the expectation-confirmation model. Technology in Society, 63, 101355.
Kaium, M. A., Bao, Y., Alam, M. Z., & Hoque, M. R. (2020). Understanding continuance usage intention of mHealth in a developing country: An empirical investigation. International Journal of Pharmaceutical and Healthcare Marketing, 14(2), 251-272.
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.
Khasawneh, O. Y. (2018). Technophobia: Examining its hidden factors and defining it. Technology in Society, 54, 93-100.
Khosla, V. (2012). Technology will replace 80% of what doctors do. Fortune.
Kim, J. S., Kim, M., & Baek, T. H. (2024). Enhancing User Experience With a Generative AI Chatbot. International Journal of Human–Computer Interaction, 1-13.
Kim, S. S., Malhotra, N. K., & Narasimhan, S. (2005). Research note—two competing perspectives on automatic use: A theoretical and empirical comparison. Information systems research, 16(4), 418-432.
Kleinmann, H. H. (1977). Avoidance behavior in adult second language acquisition 1. Language learning, 27(1), 93-107.
Korzynski, P., Mazurek, G., Krzypkowska, P., & Kurasinski, A. (2023). Artificial intelligence prompt engineering as a new digital competence: Analysis of generative AI technologies such as ChatGPT. Entrepreneurial Business and Economics Review, 11(3), 25-37.
Kotler, P. a. A., G. . (1997). Marketing: an introduction, Upper Saddle River, N.J. Prentice Hall.
Kumar, K. A., & Natarajan, S. (2020). An extension of the Expectation Confirmation Model (ECM) to study continuance behavior in using e-Health services. Innovative Marketing, 16(2), 15.
Lankton, N. K., Wilson, E. V., & Mao, E. (2010). Antecedents and determinants of information technology habit. Information & Management, 47(5-6), 300-307.
Larsen, T. J., Sørebø, A. M., & Sørebø, Ø. (2009). The role of task-technology fit as users’ motivation to continue information system use. Computers in Human behavior, 25(3), 778-784.
Leavy, S. (2018). Gender bias in artificial intelligence: The need for diversity and gender theory in machine learning. Proceedings of the 1st international workshop on gender equality in software engineering,
Lee, M.-C. (2010). Explaining and predicting users’ continuance intention toward e-learning: An extension of the expectation–confirmation model. Computers & Education, 54(2), 506-516.
Li, J., & Huang, J.-S. (2020). Dimensions of artificial intelligence anxiety based on the integrated fear acquisition theory. Technology in Society, 63, 101410.
Li, M., & Wang, R. (2023). Chatbots in e-commerce: The effect of chatbot language style on customers’ continuance usage intention and attitude toward brand. Journal of Retailing and Consumer Services, 71, 103209.
Liébana-Cabanillas, F., Singh, N., Kalinic, Z., & Carvajal-Trujillo, E. (2021). Examining the determinants of continuance intention to use and the moderating effect of the gender and age of users of NFC mobile payments: A multi-analytical approach. Information Technology and Management, 22, 133-161.
Limayem, M., & Cheung, C. M. (2008). Understanding information systems continuance: The case of Internet-based learning technologies. Information & Management, 45(4), 227-232.
Limayem, M., Hirt, S. G., & Cheung, C. M. (2007). How habit limits the predictive power of intention: The case of information systems continuance. MIS quarterly, 705-737.
Lin, W.-S. (2012). Perceived fit and satisfaction on web learning performance: IS continuance intention and task-technology fit perspectives. International Journal of Human-Computer Studies, 70(7), 498-507.
Lin, W.-S., & Wang, C.-H. (2012). Antecedences to continued intentions of adopting e-learning system in blended learning instruction: A contingency framework based on models of information system success and task-technology fit. Computers & Education, 58(1), 88-99.
Lindbladh, E., & Lyttkens, C. H. (2002). Habit versus choice: the process of decision-making in health-related behaviour. Social science & medicine, 55(3), 451-465.
Lloyd, K. (2018). Bias amplification in artificial intelligence systems. arXiv preprint arXiv:1809.07842.
Mahakhant, S., & Rotchanakitumnuai, S. (2021). Factors Influencing Continuance Intention to Use Online Learning Management System Platform.
Makkonen, M., Salo, M., & Pirkkalainen, H. (2023). The Effects of Job and User Characteristics on the Perceived Usefulness and Use Continuance Intention of Generative Artificial Intelligence Chatbots at Work. CEUR Workshop Proceedings,
Manyika, J., Lund, S., Chui, M., Bughin, J., Woetzel, J., Batra, P., Ko, R., & Sanghvi, S. (2017). Jobs lost, jobs gained: Workforce transitions in a time of automation. McKinsey Global Institute, 150(1), 1-148.
Mendl, M. (1999). Performing under pressure: stress and cognitive function. Applied animal behaviour science, 65(3), 221-244.
Meuter, M. L., Bitner, M. J., Ostrom, A., & Roundtree, R. (1997). The new service encounter: Customer usage and satisfaction with self-service technologies. American Marketing Association’s Frontiers in Services Conference, Nashville, TN (October 2–4),
Meuter, M. L., Bitner, M. J., Ostrom, A. L., & Brown, S. W. (2005). Choosing among alternative service delivery modes: An investigation of customer trial of self-service technologies. Journal of marketing, 69(2), 61-83.
Mirkovski, K., Jia, Y., Liu, L., & Chen, K. (2018). Understanding microblogging continuance intention: The directed social network perspective. Information Technology & People, 31(1), 215-238.
Mouakket, S. (2015). Factors influencing continuance intention to use social network sites: The Facebook case. Computers in Human behavior, 53, 102-110.
Muehlhauser, L., & Bostrom, N. (2014). Why we need friendly AI. Think, 13(36), 41-47.
Nascimento, B., Oliveira, T., & Tam, C. (2018). Wearable technology: What explains continuance intention in smartwatches? Journal of Retailing and Consumer Services, 43, 157-169.
Navarro, M. M., Prasetyo, Y. T., Young, M. N., Nadlifatin, R., & Redi, A. A. N. P. (2021). The perceived satisfaction in utilizing learning management system among engineering students during the COVID-19 pandemic: Integrating task technology fit and extended technology acceptance model. Sustainability, 13(19), 10669.
Nouraldeen, R. M. (2023). The impact of technology readiness and use perceptions on students’ adoption of artificial intelligence: the moderating role of gender. Development and Learning in Organizations: An International Journal, 37(3), 7-10.
Nunnally, J., & Bernstein, I. (1994). The Assessment of Reliability. Psychometric Theory, 3, 248-292. Nyhan, RC, and HA Marlowe.(1997). Development And Psychometric Properties Of The Organizational Trust Inventory. Evaluation Review, 21(5), 614-635.
Nyholm, S., & Smids, J. (2016). The ethics of accident-algorithms for self-driving cars: An applied trolley problem? Ethical theory and moral practice, 19(5), 1275-1289.
Oliver, R. L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of marketing research, 17(4), 460-469.
Orbell, S., Blair, C., Sherlock, K., & Conner, M. (2001). The theory of planned behavior and ecstasy use: Roles for habit and perceived control over taking versus obtaining substances. Journal of Applied Social Psychology, 31(1), 31-47.
Ortolan, P. (2023). Optimizing Prompt Engineering for Improved Generative AI Content.
Ouellette, J. A., & Wood, W. (1998). Habit and intention in everyday life: The multiple processes by which past behavior predicts future behavior. Psychological bulletin, 124(1), 54.
Paiman, N., & Fauzi, M. A. (2023). Exploring determinants of social media addiction in higher education through the integrated lenses of technology acceptance model (TAM) and usage habit. Journal of Applied Research in Higher Education.
Pal, D., Babakerkhell, M. D., & Zhang, X. (2021). Exploring the determinants of users’ continuance usage intention of smart voice assistants. IEEE Access, 9, 162259-162275.
Pang, S., Bao, P., Hao, W., Kim, J., & Gu, W. (2020). Knowledge sharing platforms: An empirical study of the factors affecting continued use intention. Sustainability, 12(6), 2341.
Parasuraman, A. (2000). Technology Readiness Index (TRI) a multiple-item scale to measure readiness to embrace new technologies. Journal of service research, 2(4), 307-320.
Parasuraman, A., & Colby, C. L. (2007). Techno-ready marketing: How and why your customers adopt technology. The Free Press.
Parasuraman, S., & Igbaria, M. (1990). An examination of gender differences in the determinants of computer anxiety and attitudes toward microcomputers among managers. International Journal of Man-Machine Studies, 32(3), 327-340.
Park, E. (2020). User acceptance of smart wearable devices: An expectation-confirmation model approach. Telematics and Informatics, 47, 101318.
Pavlou, P. A., Liang, H., & Xue, Y. (2007). Understanding and mitigating uncertainty in online exchange relationships: A principal-agent perspective. MIS quarterly, 105-136.
Pentland, B. T. (1989). Use and productivity in personal computing: An empirical test.
Piniel, K. (2013). L2 motivation, anxiety and self-efficacy: The interrelationship of individual variables in the secondary school context. Studies in second language learning and teaching, 3(4), 523-550.
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: a critical review of the literature and recommended remedies. Journal of applied psychology, 88(5), 879.
Prasad Agrawal, K. (2023). Towards adoption of generative AI in organizational settings. Journal of Computer Information Systems, 1-16.
Rahi, S., Khan, M. M., & Alghizzawi, M. (2021). Extension of technology continuance theory (TCT) with task technology fit (TTF) in the context of Internet banking user continuance intention. International Journal of Quality & Reliability Management, 38(4), 986-1004.
Ramírez-Vizcaya, S., & Froese, T. (2020). Agents of habit: Refining the artificial life route to artificial intelligence. Artificial Life Conference Proceedings 32,
Ratna, S., Nayati Utami, H., Siti Astuti, E., & Muflih, M. (2020). The technology tasks fit, its impact on the use of information system, performance and users’ satisfaction. VINE Journal of Information and Knowledge Management Systems, 50(3), 369-386.
Razavi, S. A., Shahrabi, A., & Siamian, H. (2017). The relationship between research anxiety and self-efficacy. Materia socio-medica, 29(4), 247.
Ronis, D. L., Yates, J. F., & Kirscht, J. P. (2014). Attitudes, decisions, and habits as determinants of repeated behavior. In Attitude structure and function (pp. 213-239). Psychology Press.
Rosen, L. D., Sears, D. C., & Weil, M. M. (1987). Computerphobia. Behavior Research Methods, Instruments, & Computers, 19(2), 167-179.
Rudovic, O., Lee, J., Dai, M., Schuller, B., & Picard, R. W. (2018). Personalized machine learning for robot perception of affect and engagement in autism therapy. Science Robotics, 3(19), eaao6760.
Sabherwal, R., Jeyaraj, A., & Chowa, C. (2006). Information system success: Individual and organizational determinants. Management science, 52(12), 1849-1864.
Schepman, A., & Rodway, P. (2023). The General Attitudes towards Artificial Intelligence Scale (GAAIS): Confirmatory validation and associations with personality, corporate distrust, and general trust. International Journal of Human–Computer Interaction, 39(13), 2724-2741.
Scherer, M. U. (2015). Regulating artificial intelligence systems: Risks, challenges, competencies, and strategies. Harv. JL & Tech., 29, 353.
Sehrawat, V. (2017). Autonomous weapon system: Law of armed conflict (LOAC) and other legal challenges. Computer Law & Security Review, 33(1), 38-56.
Shah, C. S., Mathur, S., & Vishnoi, S. K. (2023). Continuance Intention of ChatGPT Use by Students. International Working Conference on Transfer and Diffusion of IT,
Sheehan, D. V., & Sheehan, K. H. (1982). The classification of anxiety and hysterical states. Part I. Historical review and empirical delineation. Journal of Clinical Psychopharmacology, 2(4), 235-244.
Shishakly, R., Sharma, A., & Gheyathaldin, L. (2021). Investigating the effect of learning management system transition on administrative staff performance using task-technology fit approach. Management Science Letters, 11(3), 711-718.
Singh, J. P. (2022). Quantifying Healthcare Consumers' Perspectives: An Empirical Study of the Drivers and Barriers to Adopting Generative AI in Personalized Healthcare. ResearchBerg Review of Science and Technology, 2(1), 171-193.
Skjuve, M., Brandtzæg, P. B., & Følstad, A. (2024). Why do people use ChatGPT? Exploring user motivations for generative conversational AI. First Monday, 29(1).
Soni, V. (2023). Adopting Generative AI in Digital Marketing Campaigns: An Empirical Study of Drivers and Barriers. Sage Science Review of Applied Machine Learning, 6(8), 1-15.
Spalding, N. J. (2003). Reducing anxiety by pre‐operative education: Make the future familiar. Occupational therapy international, 10(4), 278-293.
Spielberger, C. D. (1966). Theory and research on anxiety. Anxiety and behavior, 1(3), 413-428.
Spielberger, C. D. (2010). Test anxiety inventory. The Corsini encyclopedia of psychology, 1-1.
Svendsen, A., & Garvey, B. (2023). An Outline for an Interrogative/Prompt Library to help improve output quality from Generative-AI Datasets. Prompt Library to help improve output quality from Generative-AI Datasets (May 2023).
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., & Fergus, R. (2013). Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199.
Taghizadeh, S. K., Rahman, S. A., Nikbin, D., Alam, M. M. D., Alexa, L., Ling Suan, C., & Taghizadeh, S. (2022). Factors influencing students’ continuance usage intention with online learning during the pandemic: a cross-country analysis. Behaviour & Information Technology, 41(9), 1998-2017.
Tahmassian, K., & Moghadam, N. J. (2011). Relationship between self-efficacy and symptoms of anxiety, depression, worry and social avoidance in a normal sample of students. Iranian journal of psychiatry and behavioral sciences, 5(2), 91.
Tam, C., Santos, D., & Oliveira, T. (2020). Exploring the influential factors of continuance intention to use mobile Apps: Extending the expectation confirmation model. Information Systems Frontiers, 22, 243-257.
Tehseen, S., Ramayah, T., & Sajilan, S. (2017). Testing and controlling for common method variance: A review of available methods. Journal of management sciences, 4(2), 142-168.
Teo, T. S., Srivastava, S. C., & Jiang, L. (2008). Trust and electronic government success: An empirical study. Journal of management information systems, 25(3), 99-132.
Thorngate, W. (1976). Must we always think before we act? Personality and Social Psychology Bulletin, 2(1), 31-35.
Tonka, H., & Bakir, S. (2020). The Examination of the Relationship between the Secondary School Students' Habit of Reading and Their Reading Anxiety. Journal of Educational Issues, 6(1), 293-313.
Triandis, H. C. (1979). Values, attitudes, and interpersonal behavior. Nebraska symposium on motivation,
Vanduhe, V. Z., Nat, M., & Hasan, H. F. (2020). Continuance intentions to use gamification for training in higher education: Integrating the technology acceptance model (TAM), social motivation, and task technology fit (TTF). IEEE Access, 8, 21473-21484.
Venkatesh, V., & Goyal, S. (2010). Expectation disconfirmation and technology adoption: polynomial modeling and response surface analysis. MIS quarterly, 281-303.
Venkatesh, V., Thong, J. Y., Chan, F. K., Hu, P. J. H., & Brown, S. A. (2011). Extending the two‐stage information systems continuance model: Incorporating UTAUT predictors and the role of context. Information systems journal, 21(6), 527-555.
Verplanken, B., & Aarts, H. (1999). Habit, attitude, and planned behaviour: is habit an empty construct or an interesting case of goal-directed automaticity? European review of social psychology, 10(1), 101-134.
Verplanken, B., Aarts, H., & Van Knippenberg, A. (1997). Habit, information acquisition, and the process of making travel mode choices. European journal of social psychology, 27(5), 539-560.
Vinzi, V. E., Trinchera, L., & Amato, S. (2010). PLS path modeling: from foundations to recent developments and open issues for model assessment and improvement. Handbook of partial least squares: Concepts, methods and applications, 47-82.
Vityaev, E., Goncharov, S., & Sviridenko, D. (2023). Task-driven approach to artificial intelligence. Cognitive Systems Research, 81, 50-56.
Voosen, P. (2017). The AI detectives. In: American Association for the Advancement of Science.
Wang, T., Lin, C.-L., & Su, Y.-S. (2021). Continuance intention of university students and online learning during the COVID-19 pandemic: A modified expectation confirmation model perspective. Sustainability, 13(8), 4586.
Wang, T., Wang, W., Liang, J., Nuo, M., Wen, Q., Wei, W., Han, H., & Lei, J. (2022). Identifying major impact factors affecting the continuance intention of mHealth: a systematic review and multi-subgroup meta-analysis. NPJ Digital Medicine, 5(1), 145.
Wang, Y.-M., Wei, C.-L., Lin, H.-H., Wang, S.-C., & Wang, Y.-S. (2022). What drives students’ AI learning behavior: A perspective of AI anxiety. Interactive Learning Environments, 1-17.
Wang, Y.-T., & Lin, K.-Y. (2021). Understanding continuance usage of mobile learning applications: The moderating role of habit. Frontiers in psychology, 12, 736051.
Wang, Y.-Y., & Wang, Y.-S. (2022). Development and validation of an artificial intelligence anxiety scale: An initial application in predicting motivated learning behavior. Interactive Learning Environments, 30(4), 619-634.
Wanti, L. P., Insan, H. F. M., & Prasetya, N. W. A. (2020). End user satisfaction for location health service application with analysis of task technology fit. Lontar Komputer: Jurnal Ilmiah Teknologi Informasi, 11(2), 76.
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.
Wood, W., Quinn, J. M., & Kashy, D. A. (2002). Habits in everyday life: thought, emotion, and action. Journal of personality and social psychology, 83(6), 1281.
Wu, B., & Chen, X. (2017). Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model. Computers in Human behavior, 67, 221-232.
Wut, T.-M., Wong, H. S.-M., & Sum, C. K.-M. (2022). A study of continuance use intention of an on online learning system after Coronavirus disease 2019 pandemic outbreak. Asia Pacific Journal of Education, 1-15.
Yampolskiy, R. V., & Spellchecker, M. (2016). Artificial intelligence safety and cybersecurity: A timeline of AI failures. arXiv preprint arXiv:1610.07997.
Yilmaz, F. G. K., Yilmaz, R., & Ceylan, M. (2023). Generative Artificial Intelligence Acceptance Scale: A Validity and Reliability Study. International Journal of Human–Computer Interaction, 1-13.
Yin, M., Han, B., Ryu, S., & Hua, M. (2023). Acceptance of Generative AI in the Creative Industry: Examining the Role of AI Anxiety in the UTAUT2 Model. International Conference on Human-Computer Interaction,
Yoo, H., An, J., & Lee, C. C. (2019). A study factors affecting continuance intention of internet only bank: Using task-technology fit theory. Journal of Society for e-Business Studies, 23(3).
Yuan, S., Liu, Y., Yao, R., & Liu, J. (2016). An investigation of users’ continuance intention towards mobile banking in China. Information Development, 32(1), 20-34.
Zhang, F. (2022). Design and application of artificial intelligence technology-driven education and teaching system in universities. Computational and Mathematical Methods in Medicine, 2022.
Zhou, G., Moayedi, H., Bahiraei, M., & Lyu, Z. (2020). Employing artificial bee colony and particle swarm techniques for optimizing a neural network in prediction of heating and cooling loads of residential buildings. Journal of Cleaner Production, 254, 120082.
Zhou, J. (2017). Exploring the factors affecting learners’ continuance intention of MOOCs for online collaborative learning: An extended ECM perspective. Australasian Journal of Educational Technology, 33(5).
二、中文部分
杜雨, 張. (2023). AI生成時代：從ChatGPT到繪圖、音樂、影片，利用智能創作自我加值、簡化工作，成為未來關鍵人才.
張春興. (2000). 教育心理學：三化取向的理論與實踐。. 臺北市：臺灣東華書局。.
三、網站部分
Altman, S. (2021). Sam Altman's Twitter. https://twitter.com/sama/status/1436028668082462748
DHNS. (2023). ChatGPT founder Sam Altman speaks on importance of regulations for AI at IITD. Deccan Herald News Service. https://www.deccanherald.com/india/chatgpt-founder-sam-altman-speaks-on-importance-of-regulations-for-ai-at-iitd-1226111.html
Gartner. (2023). Gartner Experts Answer the Top Generative AI Questions for Your Enterprise. Gartner. https://www.gartner.com/en/topics/generative-ai
奇摩Yahoo. (2023). Yahoo 生成式AI 消費者大調查. Yahoo. https://drive.google.com/drive/folders/1v93wH9plkuxC-4SzbUBW7SAXVYIEqPES
哈佛商業評論. (2023). AI不會取代人類，但使用AI的人會取代不用AI的人. 哈佛商業評論 全球繁體中文版. https://www.hbrtaiwan.com/article/22451/ai-wont-replace-humans-but-humans-with-ai-will-replace-humans-without-ai
財團法人台灣網路資訊中心. (2023). 2023年台灣網路報告. 財團法人台灣網路資訊中心. https://report.twnic.tw/2023/
關鍵評論. (2023). 上線兩個月用戶飆破一億人！顛覆市場的ChatGPT，將如何引發科技巨擘的AI大戰？. 關鍵評論. https://www.thenewslens.com/article/181255
|