Exploring User Adoption of ChatGPT: A Technology Acceptance Model Perspective

References

• Abdelkader, O. A. (2023). ChatGPT’s influence on customer experience in digital marketing: Investigating the moderating roles. Heliyon, 9(8), e18770–e18770. https://doi.org/10.1016/j.heliyon.2023.e18770
   Web of Science ®Google Scholar
• Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
   Web of Science ®Google Scholar
• Al-Adwan, A. S., Li, N., Al-Adwan, A., Abbasi, G. A., Albelbisi, N. A., & Habibi, A. (2023). Extending the technology acceptance model (TAM) to predict university students’ intentions to use metaverse-based learning platforms. Education and Information Technologies, 28(11), 1–33. https://doi.org/10.1007/s10639-023-11816-3
   Web of Science ®Google Scholar
• 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. https://doi.org/10.1177/0961000617726123
   Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• Behrend, T. S., Wiebe, E. N., London, J. E., & Johnson, E. C. (2011). Cloud computing adoption and usage in community colleges. Behaviour & Information Technology, 30(2), 231–240. https://doi.org/10.1080/0144929X.2010.489118
   Web of Science ®Google Scholar
• Belanche, D., Casaló, L. V., & Flavián, C. (2019). Artificial intelligence in FinTech: Understanding robo-advisors adoption among customers. Industrial Management & Data Systems, 119(7), 1411–1430. https://doi.org/10.1108/IMDS-08-2018-0368
   Web of Science ®Google Scholar
• Bodani, N., Lal, A., Maqsood, A., Altamash, S., Ahmed, N., & Heboyan, A. (2023). Knowledge, attitude, and practices of general population toward utilizing ChatGPT: A cross-sectional study. SAGE Open, 13(4), 21582440231211079. https://doi.org/10.1177/21582440231211079
   Web of Science ®Google Scholar
• Bonfadelli, H. (2002). The internet and knowledge gaps: A theoretical and empirical investigation. European Journal of Communication, 17(1), 65–84. https://doi.org/10.1177/0267323102017001607
   Web of Science ®Google Scholar
• Cai, R., Cain, L. N., & Jeon, H. (2022). Customers’ perceptions of hotel AI-enabled voice assistants: Does brand matter? International Journal of Contemporary Hospitality Management, 34(8), 2807–2831. https://doi.org/10.1108/IJCHM-10-2021-1313
   Web of Science ®Google Scholar
• Cascella, M., Montomoli, J., Bellini, V., & Bignami, E. (2023). Evaluating the feasibility of ChatGPT in healthcare: An analysis of multiple clinical and research scenarios. Journal of Medical Systems, 47(1), 33–33. https://doi.org/10.1007/s10916-023-01925-4
   PubMed Web of Science ®Google Scholar
• Chatterjee, S., Chaudhuri, R., Vrontis, D., Thrassou, A., & Ghosh, S. K. (2021). Adoption of artificial intelligence-integrated CRM systems in agile organizations in India. Technological Forecasting and Social Change, 168, 120783. https://doi.org/10.1016/j.techfore.2021.120783
   Web of Science ®Google Scholar
• Choe, J. Y., Kim, J. J., & Hwang, J. (2022). Innovative robotic restUSErants in Korea: Merging a technology acceptance model and theory of planned behaviour. Asian Journal of Technology Innovation, 30(2), 466–489. https://doi.org/10.1080/19761597.2021.2005466
   Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• 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. https://doi.org/10.1287/mnsc.35.8.982
   Web of Science ®Google Scholar
• De Graaf, M. M., & Allouch, S. B. (2013). Exploring influencing variables for the acceptance of social robots. Robotics and Autonomous Systems, 61(12), 1476–1486. https://doi.org/10.1016/j.robot.2013.07.007
   Web of Science ®Google Scholar
• Degen, H., & Ntoa, S. (2022). Exploring the design context of AI-powered services: A qualitative investigation of designers’ experiences with machine learning. In Artificial Intelligence in HCI (Vol. 13336, pp.3–21). Springer International Publishing AG. https://doi.org/10.1007/978-3-031-05643-7_1
   Google Scholar
• Etemad-Sajadi, R., & Sturman, M. C. (2022). How to increase the customer experience by the usage of remote control robot concierge solutions. International Journal of Social Robotics, 14(2), 429–440. https://doi.org/10.1007/s12369-021-00800-x
   Web of Science ®Google Scholar
• Fernandes, T., & Oliveira, E. (2021). Understanding consumers’ acceptance of automated technologies in service encounters: Drivers of digital voice assistants adoption. Journal of Business Research, 122, 180–191. https://doi.org/10.1016/j.jbusres.2020.08.058
   Web of Science ®Google Scholar
• Forgas-Coll, S., Huertas-Garcia, R., Andriella, A., & Alenyà, G. (2022). How do consumers’ gender and rational thinking affect the acceptance of entertainment social robots? International Journal of Social Robotics, 14(4), 973–994. https://doi.org/10.1007/s12369-021-00845-y
   Web of Science ®Google Scholar
• Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.2307/3151312
   Web of Science ®Google Scholar
• Freeman, J., Park, S., & Middleton, C. (2020). Technological literacy and interrupted internet access. Information, Communication & Society, 23(13), 1947–1964. https://doi.org/10.1080/1369118X.2019.1623901
   Web of Science ®Google Scholar
• Haggstrom, D. A., Lee, J. L., Dickinson, S. L., Kianersi, S., Roberts, J. L., Teal, E., Baker, L. B., & Rawl, S. M. (2019). Rural and urban differences in the adoption of new health information and medical technologies. Journal of Rural Health: Official Journal of the American Rural Health Association and the National Rural Health Care Association, 35(2), 144–154. https://doi.org/10.1111/jrh.12358
   PubMed Web of Science ®Google Scholar
• Hair, Jr., J. F., Sarstedt, M., Matthews, L. M., & Ringle, C. M. (2016). Identifying and treating unobserved heterogeneity with FIMIX-PLS: part I–method. European Business Review, 28(1), 63–76.
   Web of Science ®Google Scholar
• Haque, M. U., Dharmadasa, I., Sworna, Z. T., Rajapakse, R. N., & Ahmad, H. (2022). “I think this is the most disruptive technology”: Exploring Sentiments of ChatGPT Early Adopters using Twitter Data. arXiv preprint arXiv:2212.05856.
   Google Scholar
• Hargittai, E., & Hinnant, A. (2008). Digital inequality: Differences in young adults’ use of the internet. Communication Research, 35(5), 602–621. https://doi.org/10.1177/0093650208321782
   Web of Science ®Google Scholar
• Hu, P. J., Chau, P. Y. K., Sheng, O. R. L., & Tam, K. Y. (1999). Examining the technology acceptance model using physician acceptance of telemedicine technology. Journal of Management Information Systems, 16(2), 91–112. https://doi.org/10.1080/07421222.1999.11518247
   Web of Science ®Google Scholar
• Jamei, E., Mortimer, M., Seyedmahmoudian, M., Horan, B., & Stojcevski, A. (2017). Investigating the role of virtual reality in planning for sustainable smart cities. Sustainability, 9(11), 2006. https://doi.org/10.3390/su9112006
   Web of Science ®Google Scholar
• Jo, H. (2023a). Understanding AI tool engagement: A study of ChatGPT usage and word-of-mouth among university students and office workers. Telematics and Informatics, 85, 102067. https://doi.org/10.1016/j.tele.2023.102067
   Web of Science ®Google Scholar
• Jo, H. (2023b). Decoding the ChatGPT mystery: A comprehensive exploration of factors driving AI language model adoption. Information Development, 02666669231202764. https://doi.org/10.1177/02666669231202764
   Web of Science ®Google Scholar
• Jo, H., & Park, D.-H. (2023). AI in the workplace: Examining the effects of ChatGPT on information support and knowledge acquisition. International Journal of Human–Computer Interaction, 1–16. https://doi.org/10.1080/10447318.2023.2278283
   Web of Science ®Google Scholar
• Klaus, P., & Zaichkowsky, J. L. (2022). The convenience of shopping via voice AI: Introducing AIDM. Journal of Retailing and Consumer Services, 65, 102490. https://doi.org/10.1016/j.jretconser.2021.102490
   Web of Science ®Google Scholar
• Kline, R. B. (2011). Principles and practice of structural equation modeling. Guilford Press.
   Google Scholar
• Koc, E., Hatipoglu, S., Kivrak, O., Celik, C., & Koc, K. (2023). Houston, we have a problem!: The use of ChatGPT in responding to customer complaints. Technology in Society, 74, 102333. https://doi.org/10.1016/j.techsoc.2023.102333
   Web of Science ®Google Scholar
• Kuang, Y.-R., Zou, M.-X., Niu, H.-Q., Zheng, B.-Y., Zhang, T.-L., & Zheng, B.-W. (2023). ChatGPT encounters multiple opportunities and challenges in neurosurgery. International Journal of Surgery (London, England), 109(10), 2886–2891. https://doi.org/10.1097/JS9.0000000000000571
   PubMed Web of Science ®Google Scholar
• Lam, L. W. (2012). Impact of competitiveness on salespeople’s commitment and performance. Journal of Business Research, 65(9), 1328–1334. https://doi.org/10.1016/j.jbusres.2011.10.026
   Web of Science ®Google Scholar
• Legris, P., Ingham, J., & Collerette, P. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information & Management, 40(3), 191–204. https://doi.org/10.1016/S0378-7206(01)00143-4
   Web of Science ®Google Scholar
• Litt, E. (2013). Measuring users’ internet skills: A review of past assessments and a look toward the future. New Media & Society, 15(4), 612–630. https://doi.org/10.1177/1461444813475424
   Web of Science ®Google Scholar
• Liu, X., He, X., Wang, M., & Shen, H. (2022). What influences patients’ continuance intention to use AI-powered service robots at hospitals? The role of individual characteristics. Technology in Society, 70, 101996. https://doi.org/10.1016/j.techsoc.2022.101996
   Web of Science ®Google Scholar
• Liu, G., & Ma, C. (2023). Measuring EFL learners’ use of ChatGPT in informal digital learning of English based on the technology acceptance model. Innovation in Language Learning and Teaching, 1–14. https://doi.org/10.1080/17501229.2023.2240316
   Web of Science ®Google Scholar
• Liu, K., & Tao, D. (2022). The roles of trust, personalization, loss of privacy, and anthropomorphism in public acceptance of smart healthcare services. Computers in Human Behavior, 127, 107026. https://doi.org/10.1016/j.chb.2021.107026
   Web of Science ®Google Scholar
• Liu, H., Wang, L., & Koehler, M. J. (2019). Exploring the intention‐behavior gap in the technology acceptance model: A mixed‐methods study in the context of foreign‐language teaching in China. British Journal of Educational Technology, 50(5), 2536–2556. https://doi.org/10.1111/bjet.12824
   Web of Science ®Google Scholar
• Lu, L., Cai, R., & Gursoy, D. (2019). Developing and validating a service robot integration willingness scale. International Journal of Hospitality Management, 80, 36–51. https://doi.org/10.1016/j.ijhm.2019.01.005
   Web of Science ®Google Scholar
• Lythreatis, S., Singh, S. K., & El-Kassar, A.-N. (2022). The digital divide: A review and future research agenda. Technological Forecasting and Social Change, 175, 121359. https://doi.org/10.1016/j.techfore.2021.121359
   Web of Science ®Google Scholar
• M. Mijwil, M., Hiran, K. K., Doshi, R., Dadhich, M., Al-Mistarehi, A.-H., & Bala, I. (2023). ChatGPT and the future of academic integrity in the artificial intelligence era: A new frontier. Al-Salam Journal for Engineering and Technology, 2(2), 116–127. https://doi.org/10.55145/ajest.2023.02.02.015
   Google Scholar
• Maad, M., & Aljanabi, M, ChatGPT. (2023). Towards artificial intelligence-based cybersecurity: The practices and ChatGPT generated ways to combat cybercrime. Iraqi Journal for Computer Science and Mathematics, 4(1), 65–70. https://doi.org/10.52866/ijcsm.2023.01.01.0019
   Google Scholar
• Mailizar, M., Burg, D., & Maulina, S. (2021). Examining university students’ behavioural intention to use e-learning during the COVID-19 pandemic: An extended TAM model. Education and Information Technologies, 26(6), 7057–7077. https://doi.org/10.1007/s10639-021-10557-5
   PubMed Web of Science ®Google Scholar
• Malik, T., Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., Albanna, H., Albashrawi, M. A., Al-Busaidi, A. S., Balakrishnan, J., Barlette, Y., Basu, S., Bose, I., Brooks, L., Buhalis, D., … Wright, R. (2023). “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. https://doi.org/10.1016/j.ijinfomgt.2023.102642
   Web of Science ®Google Scholar
• Marangunic, N., & Granic, A. (2015). Technology acceptance model: A literature review from 1986 to 2013. Universal Access in the Information Society, 14(1), 81–95. https://doi.org/10.1007/s10209-014-0348-1
   Web of Science ®Google Scholar
• Marshall, A., Turner, K., Richards, C., Foth, M., & Dezuanni, M. (2022). Critical factors of digital AgTech adoption on Australian farms: From digital to data divide. Information, Communication & Society, 25(6), 868–886. https://doi.org/10.1080/1369118X.2022.2056712
   Web of Science ®Google Scholar
• Menon, D., & Shilpa, K. (2023). “Chatting with ChatGPT”: Analyzing the factors influencing users’ intention to use the Open AI’s ChatGPT using the UTAUT model. Heliyon, 9(11), e20962–e20962. https://doi.org/10.1016/j.heliyon.2023.e20962
   Web of Science ®Google Scholar
• Mohr, N., & Kühl, R. S. (2021). Acceptance of artificial intelligence in German agriculture: An application of the technology acceptance model and the theory of planned behavior. Precision Agriculture, 22(6), 1816–1844. https://doi.org/10.1007/s11119-021-09814-x
   Web of Science ®Google Scholar
• Montes, G. A., & Goertzel, B. (2019). Distributed, decentralized, and democratized artificial intelligence. Technological Forecasting and Social Change, 141, 354–358. https://doi.org/10.1016/j.techfore.2018.11.010
   Web of Science ®Google Scholar
• Moon, J.-W., & Kim, Y.-G. (2001). Extending the TAM for a World-Wide-Web context. Information & Management, 38(4), 217–230. https://doi.org/10.1016/S0378-7206(00)00061-6
   Web of Science ®Google Scholar
• Nguyen, T. D., Nguyen, D. T., & Cao, T. H. (2014). Acceptance and use of information system: E-learning based on cloud computing in Vietnam. In Information and Communication Technology: Second IFIP TC5/8 International Conference, ICT-EurAsia 2014, Bali, Indonesia, April 14–17, 2014; Proceedings 2 (pp. 139–149). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-55032-4_14
   Google Scholar
• Ong, C.-S., & Lai, J.-Y. (2006). Gender differences in perceptions and relationships among dominants of e-learning acceptance. Computers in Human Behavior, 22(5), 816–829. https://doi.org/10.1016/j.chb.2004.03.006
   Web of Science ®Google Scholar
• Oviedo-Trespalacios, O., Peden, A. E., Cole-Hunter, T., Costantini, A., Haghani, M., Rod, J. E., Kelly, S., Torkamaan, H., Tariq, A., David Albert Newton, J., Gallagher, T., Steinert, S., Filtness, A. J., & Reniers, G. (2023). The risks of using ChatGPT to obtain common safety-related information and advice. Safety Science, 167, 106244. https://doi.org/10.1016/j.ssci.2023.106244
   Web of Science ®Google Scholar
• Pillai, R., & Sivathanu, B. (2020). Adoption of AI-based chatbots for hospitality and tourism. International Journal of Contemporary Hospitality Management, 32(10), 3199–3226. https://doi.org/10.1108/IJCHM-04-2020-0259
   Web of Science ®Google Scholar
• Pillai, R., Sivathanu, B., Metri, B., & Kaushik, N. (2023). Students’ adoption of AI-based teacher-bots (T-bots) for learning in higher education. Information Technology & People, 37(1), 328–355. https://doi.org/10.1108/ITP-02-2021-0152
   Web of Science ®Google Scholar
• Porto, N., Huang, Y., & Xiao, J. J. (2019). Credit card adoption and usage in china: Urban–rural comparisons. Singapore Economic Review, 64(01), 41–56. https://doi.org/10.1142/S021759081743010X
   Google Scholar
• Quan, S. J. (2022). Urban-GAN: An artificial intelligence-aided computation system for plural urban design. Environment and Planning B: Urban Analytics and City Science, 49(9), 2500–2515. https://doi.org/10.1177/23998083221100550
   Google Scholar
• Quan, W., Moon, H., Kim, S. S., & Han, H. (2023). Mobile, traditional, and cryptocurrency payments influence consumer trust, attitude, and destination choice: Chinese versus Koreans. International Journal of Hospitality Management, 108, 103363. https://doi.org/10.1016/j.ijhm.2022.103363
   Web of Science ®Google Scholar
• Rahimzadeh, V., Kostick-Quenet, K., Blumenthal Barby, J., & McGuire, A. L. (2023). Ethics education for healthcare professionals in the era of ChatGPT and other large language models: Do we still need it? American Journal of Bioethics: AJOB, 23(10), 17–27. https://doi.org/10.1080/15265161.2023.2233358
   PubMed Web of Science ®Google Scholar
• Riddell, W. C., & Song, X. (2017). The role of education in technology use and adoption: Evidence from the Canadian workplace and employee survey. ILR Review, 70(5), 1219–1253. https://doi.org/10.1177/0019793916687719
   Web of Science ®Google Scholar
• Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. Computers & Education, 128, 13–35. https://doi.org/10.1016/j.compedu.2018.09.009
   Web of Science ®Google Scholar
• Serenko, A. (2008). A model of user adoption of interface agents for email notification. Interacting with Computers, 20(4–5), 461–472. https://doi.org/10.1016/j.intcom.2008.04.004
   Web of Science ®Google Scholar
• Shah, V., & Kwak, N. (2001). “Connecting” and “disconnecting” with civic life: Patterns of internet use and the production of social capital. Political Communication, 18(2), 141–162. https://doi.org/10.1080/105846001750322952
   Web of Science ®Google Scholar
• Shaikh, A. A., & Karjaluoto, H. (2015). Mobile banking adoption: A literature review. Telematics and Informatics, 32(1), 129–142. https://doi.org/10.1016/j.tele.2014.05.003
   Web of Science ®Google Scholar
• Sharma, S., Singh, G., Gaur, L., & Afaq, A. (2022). Exploring customer adoption of autonomous shopping systems. Telematics and Informatics, 73, 101861. https://doi.org/10.1016/j.tele.2022.101861
   Web of Science ®Google Scholar
• Shoufan, A. (2023). Exploring students’ perceptions of ChatGPT: Thematic analysis and follow-up survey. IEEE Access, 11, 38805–38818. https://doi.org/10.1109/ACCESS
   Web of Science ®Google Scholar
• Stahl, B. C., & Eke, D. (2024). The ethics of ChatGPT – Exploring the ethical issues of an emerging technology. International Journal of Information Management, 74, 102700. https://doi.org/10.1016/j.ijinfomgt.2023.102700
   Web of Science ®Google Scholar
• Tandon, A., Sharma, H., & Aggarwal, A. G. (2019). Studying the moderating effect of a respondent’s locality in M-commerce adoption intention. Ingeniería Solidaria, 15(29), 1–23. https://doi.org/10.16925/2357-6014.2019.03.07
   Web of Science ®Google Scholar
• Taylor, A., & Dalal, H. A. (2017). Gender and information literacy: Evaluation of gender differences in a student survey of information sources. College & Research Libraries, 78(1), 90–113. https://doi.org/10.5860/crl.78.1.90
   Web of Science ®Google Scholar
• Terzis, V., & Economides, A. A. (2011). Computer based assessment: Gender differences in perceptions and acceptance. Computers in Human Behavior, 27(6), 2108–2122. https://doi.org/10.1016/j.chb.2011.06.005
   Web of Science ®Google Scholar
• Uludag, K. (2023). The use of AI-supported Chatbot in Psychology. Available at SSRN 4331367.
   Google Scholar
• van Dis, E. A. M., Bollen, J., Zuidema, W., van Rooij, R., & Bockting, C. L. (2023). ChatGPT: Five priorities for research. Nature (London), 614(7947), 224–226. https://doi.org/10.1038/d41586-023-00288-7
   PubMed Web of Science ®Google Scholar
• Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315. https://doi.org/10.1111/j.1540-5915.2008.00192.x
   Web of Science ®Google Scholar
• Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926
   Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• Vimalkumar, M., Sharma, S. K., Singh, J. B., & Dwivedi, Y. K. (2021). “Okay Google, what about my privacy?”: User’s privacy perceptions and acceptance of voice based digital assistants. Computers in Human Behavior, 120, 106763. https://doi.org/10.1016/j.chb.2021.106763
   Web of Science ®Google Scholar
• Wang, C., Ahmad, S. F., Bani Ahmad Ayassrah, A. Y. A., Awwad, E. M., Irshad, M., Ali, Y. A., Al-Razgan, M., Khan, Y., & Han, H. (2023). An empirical evaluation of technology acceptance model for artificial intelligence in E-commerce. Heliyon, 9(8), e18349–e18349. https://doi.org/10.1016/j.heliyon.2023.e18349
   PubMed Web of Science ®Google Scholar
• Wu, M., Wang, N., & Yuen, K. F. (2023). Can autonomy level and anthropomorphic characteristics affect public acceptance and trust towards shared autonomous vehicles? Technological Forecasting and Social Change, 189, 122384. https://doi.org/10.1016/j.techfore.2023.122384
   Web of Science ®Google Scholar
• Yang, D. (2023). Income growth, income uncertainty, and urban–rural household savings rate in China. Regional Science and Urban Economics, 99, 103855. https://doi.org/10.1016/j.regsciurbeco.2022.103855
   Web of Science ®Google Scholar
• Yang, F., & Zhang, X. (2020). Focal fields in literature on the information divide: The USA, China, UK and India. Journal of Documentation, 76(2), 373–388. https://doi.org/10.1108/JD-02-2019-0032
   Web of Science ®Google Scholar
• Yao Tian, T., Lik-Hang, C., Reza Hadi Mogavi, L., Liao, Y., & Zhou, P. (2023). Last week with ChatGPT: A Weibo study on social perspective regarding ChatGPT for education and beyond. ArXiv.Org. https://doi.org/10.48550/arxiv.2306.04325
   Google Scholar
• Yu, X., & Liu, S. (2022). Disparities in online use behaviours and Chinese digital inclusion: A 10-year comparison. International Journal of Environmental Research and Public Health, 19(19), 11937. https://doi.org/10.3390/ijerph191911937
   PubMed Web of Science ®Google Scholar
• Zhang, B., Zhu, Y., Deng, J., Zheng, W., Liu, Y., Wang, C., & Zeng, R. (2023). “I am here to assist your tourism”: Predicting continuance intention to use AI-based chatbots for tourism. Does gender really matter? International Journal of Human–Computer Interaction, 39(9), 1887–1903. https://doi.org/10.1080/10447318.2022.2124345
   Web of Science ®Google Scholar