References
- Adam, M., Wessel, M., and Benlian, A. (2021). AI-based chatbots in customer service and their effects on user compliance. Electronic Markets, 31(2), 427-445. https://doi.org/10.1007/s12525-020-00414-7
- Adil, M., Nasir, M., Sadiq, M., and Bharti, K. (2020). SSTQUAL model: Assessment of ATM service quality in an emerging economy. International Journal of Business Excellence, 22(1), 114-138. https://doi.org/10.1504/IJBEX.2020.109222
- Agarwal, R., and Prasad, J. (1998). A conceptual and operational definition of personal innovativeness in the domain of information technology. Information Systems Research, 9(2), 204-215. https://doi.org/10.1287/isre.9.2.204
- Alam, M. Z., Hoque, M. R., Hu, W., and Barua, Z. (2020). Factors influencing the adoption of mHealth services in a developing country: A patient-centric study. International Journal of Information Management, 50, 128-143. https://doi.org/10.1016/j.ijinfomgt.2019.04.016
- Anil, K., and Misra, A. (2022). Artificial intelligence in Peer-to-peer lending in India: A cross-case analysis. International Journal of Emerging Markets, 17(4), 1085-1106. https://doi.org/10.1108/IJOEM-05-2021-0822
- Ashfaq, M., Yun, J., Yu, S., and 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.
- Bandura, A., Freeman, W. H., and Lightsey, R. (1997). Self-efficacy: The exercise of control. Journal of Cognitive Psychotherapy, 13, 158-166. https://doi.org/10.1891/0889-8391.13.2.158
- Boucher, E. M., Harake, N. R., Ward, Sarah, H. E. Stoeckl, E., Vargas, J., Minkel, J., Parks, A. C. and Zilca, R. (2021). Artificially intelligent chatbots in digital mental health interventions: a review. Expert Review of Medical Devices, 18(1), 37-49 https://doi.org/10.1080/17434440.2021.2013200
- Capello, R. (1995). Network externalities: Towards a taxonomy of the concept and a theory of their effects on the performance of firms and regions. In Technological Change, Economic Development and Space (pp. 208-237).
- Chen, I. S. (2017). Computer self-efficacy, learning performance, and the mediating role of learning engagement. Computers in Human Behavior, 72, 362-370. https://doi.org/10.1016/j.chb.2017.02.059
- Chen, Q., Gong, Y., Lu, Y., and Tang, J. (2022). Classifying and measuring the service quality of AI chatbot in frontline service. Journal of Business Research, 145, 552-568. https://doi.org/10.1016/j.jbusres.2022.02.088
- Cheng, Y. M. (2021). Will robo-advisors continue? Roles of task-technology fit, network externalities, gratifications and flow experience in facilitating continuance intention. Kybernetes, 50(6), 1751-1783. https://doi.org/10.1108/K-03-2020-0185
- Choi, J. P., and Stefanadis, C. (2022). Network externalities, dominant value margins, and equilibrium uniqueness. In International Economic Review (forthcoming).
- Chun, S. Y., and Hahn, M. (2007). Network externality and future usage of Internet services. Internet Research, 17(2), 156-168. https://doi.org/10.1108/10662240710737013
- Coeurderoy, R., Guilmot, N., and Vas, A. (2014). Explaining factors affecting technological change adoption: A survival analysis of an information system implementation. Management Decision, 52(6), 1082-1100. https://doi.org/10.1108/MD-10-2013-0540
- Compeau, D. R., and Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189-211. https://doi.org/10.2307/249688
- Dehghani, M. (2018). Exploring the motivational factors on continuous usage intention of smartwatches among actual users. Behaviour & Information Technology, 37(2), 145-158. https://doi.org/10.1080/0144929X.2018.1424246
- Deva, P. (2022). India set to become the world's third-largest economy and stock market by 2030: Morgan Stanley. LiveMint. Retrieved from https://mintgenie.livemint.com/news/markets/india-set-to-become-the-world-s-third-largest-economyand-stock-market-by-2030-morgan-stanley-151667373716980
- Dijkstra, T. K., and Henseler, J. (2015). Consistent partial least squares path modeling. MIS Quarterly, 39(2), 297-316. https://doi.org/10.25300/MISQ/2015/39.2.02
- Dogra, N., and Adil, M. (2022). Should we or should we not? Examining travelers' perceived privacy, perceived security and actual behavior in online travel purchases. Journal of Vacation Marketing, 13567667221122103. https://doi.org/10.1177/13567667221122103
- Dogra, N., Adil, M., Sadiq, M., Dash, G., and Paul, J. (2023). Unraveling customer repurchase intention in OFDL context: An investigation using a hybrid technique of SEM and fsQCA. Journal of Retailing and Consumer Services, 72, 103281.
- Donaldson, L. (2001). The Contingency Theory of Organizations. London: Sage.
- Dychtwald, Z. (2021). Understanding China's Young Consumers, Harvard Business Review. Retrieved from https://hbr.org/2021/06/understanding-chinas-young-consumers
- Enholm, I. M., Papagiannidis, E., Mikalef, P., and Krogstie, J. (2022). Artificial intelligence and business value: A literature review. Information Systems Frontiers, 24(5), 1709-1734. https://doi.org/10.1007/s10796-021-10186-w
- Eren, B. A. (2021). Determinants of customer satisfaction in chatbot use: Evidence from a banking application in Turkey. International Journal of Bank Marketing, 39(2), 294-311. https://doi.org/10.1108/IJBM-02-2020-0056
- Erskine, M. A., Khojah, M., and McDaniel, A. E. (2019). Location selection using heat maps: Relative advantage, task-technology fit, and decision-making performance. Computers in Human Behavior, 101, 151-162. https://doi.org/10.1016/j.chb.2019.07.014
- Faqih, K. M. S., and Jaradat, M. I. R. M. (2021). Integrating TTF and UTAUT2 theories to investigate the adoption of augmented reality technology in education: Perspective from a developing country. Technology in Society, 67, 101787.
- Fernandes, T., and 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
- Fornell, C., and Bookstein, F. L. (1982). Two structural equation models: LISREL and PLS applied to consumer exit-voice theory. Journal of Marketing Research, 19(4), 440-452. https://doi.org/10.1177/002224378201900406
- Fornell, C., and 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/10.1177/002224378101800104
- Franque, F. B., Oliveira, T., and Tam, C. (2022). Continuance intention of mobile payment: TTF model with Trust in an African context. Information Systems Frontiers, 25(2), 1-19. https://doi.org/10.1007/s10796-022-10263-8
- Furneaux, B. (2012). Task-technology fit theory: A survey and synopsis of the literature. Information Systems Theory, 87-106.
- Goodhue, D. L., and Thompson, R. L. (1995). Task-technology fit and individual performance. MIS Quarterly, 19(2), 213-236. https://doi.org/10.2307/249689
- Gu, V. C., and Black, K. (2020). Integration of TTF and network externalities for RFID adoption in healthcare industry. International Journal of Productivity and Performance Management, 70(1), 109-129. https://doi.org/10.1108/IJPPM-11-2018-0418
- Gu, V. C., Schniederjans, M. J., and Cao, Q. (2015). Diffusion of innovation: Customer relationship management adoption in supply chain organizations. International Journal of Quality Innovation, 1(6), 1-17. https://doi.org/10.1186/s40887-015-0006-6
- Guzman, I., and Pathania, A. (2016). Chatbots in customer service. Retrieved from http://bit.ly/Accenture-Chatbots-Customer-Service.
- Hair, J. F., Ringle, C. M., and Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing theory and Practice, 19(2), 139-152. https://doi.org/10.2753/MTP1069-6679190202
- Hentzen, J. K., Hoffmann, A., Dolan, R., and Pala, E. (2021). Artificial intelligence in customer-facing financial services: A systematic literature review and agenda for future research. International Journal of Bank Marketing, 40(6), 1299-1336. https://doi.org /10.1108/IJBM-09-2021-0417
- Howard, M. C., and Rose, J. C. (2019). Refining and extending task-technology fit theory: Creation of two task-technology fit scales and empirical clarification of the construct. Information & Management, 56(6), 103134.
- Hsiao, K. L., and Chen, C. C. (2015). How do we inspire children to learn with e-readers?. Library Hi Tech, 33(4), 584-596. https://doi.org/10.1108/LHT-04-2015-0038
- Hsiao, K. L. (2017). What drives smartwatch adoption intention? Comparing Apple and non-Apple watches. Library Hi Tech, 35(1), 186-206. https://doi.org/10.1108/LHT-09-2016-0105
- Hsu, C. L., and Lin, J. C. C. (2016). An empirical examination of consumer adoption of Internet of Things services: Network externalities and concern for information privacy perspectives. Computers in Human Behavior, 62, 516-527. https://doi.org/10.1016/j.chb.2016.04.023
- Hu, L. T., and 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. https://doi.org/10.1080/10705519909540118
- Huang, A., Chao, Y., de la Mora Velasco, E., Bilgihan, A., and Wei, W. (2021). When artificial intelligence meets the hospitality and tourism industry: an assessment framework to inform theory and management. Journal of Hospitality and Tourism Insights, 5(5), 1080-1100. https://doi.org/10.1108/JHTI-01-2021-0021
- IMARC Group. (2023, April 27). India artificial intelligence market Expanding at a CAGR of 33.28% during 2023-2028. openPR.com. Retrieved from https://www.openpr.com/news/3032270/india-artificial-intelligence-market-expanding-at-a-cagr
- Jadhav, V. V., and Mahadeokar, R. (2019). The fourth industrial revolution (I4. 0) in India: challenges and opportunities. Management, 6, 105-109.
- Jeyaraj, A. (2022). A meta-regression of tasktechnology fit in information systems research. International Journal of Information Management, 65, 102493.
- Katz, M. L., and Shapiro, C. (1985). Network externalities, competition, and compatibility. The American economic review, 75(3), 424-440.
- Kaur, S., and Arora, S. (2020). Role of perceived risk in online banking and its impact on behavioral intention: Trust as a moderator. Journal of Asia Business Studies, 15(1), 1-30. https://doi.org/10.1108/JABS-08-2019-0252
- Kim, S., Connerton, T. P., and Park, C. (2022). Transforming the automotive retail: Drivers for customers' omnichannel BOPS (Buy Online and Pick up in Store) behavior. Journal of Business Research, 139, 411-425. https://doi.org/10.1016/j.jbusres.2021.09.070
- Klumpp, M. (2018). Automation and artificial intelligence in business logistics systems: human reactions and collaboration requirements. International Journal of Logistics Research and Applications, 21(3), 224-242. https://doi.org/10.1080/13675567. 2017.1384451
- Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration, 11(4), 1-10. https://doi.org/10.4018/ijec.2015100101
- Kok, J. N., Boers, E. J., Kosters, W. A., Van der Putten, P., and Poel, M. (2009). Artificial intelligence: definition, trends, techniques, and cases. Artificial Intelligence, 1, 270-299.
- Korreck, S. (2019). The Indian startup ecosystem: Drivers, challenges and pillars of support. ORF Occasional Paper, 210.
- Kot, M., and Leszczynski, G. (2022). AI-activated value co-creation. An exploratory study of conversational agents. Industrial Marketing Management, 107, 287-299. https://doi.org/10.1016/j.indmarman.2022.10.013
- Lee, C. C., Cheng, H. K., and Cheng, H. H. (2007). An empirical study of mobile commerce in insurance industry: Task-technology fit and individual differences. Decision Support Systems, 43(1), 95-110. https://doi.org/10.1016/j.dss.2005.05.008
- Lee, J. M., and Kim, H. J. (2020). Determinants of adoption and continuance intentions toward Internet-only banks. International Journal of Bank Marketing, 38(4), 843-865. https://doi.org/10.1108/IJBM-07-2019-0269
- Li, B., Wang, X., and Tan, S. C. (2018). What makes MOOC users persist in completing MOOCs? A perspective from network externalities and human factors. Computers in Human Behavior, 85, 385-395. https://doi.org/10.1016/j.chb.2018.04.028
- Ling, E. C., Tussyadiah, I., Tuomi, A., Stienmetz, J., and Ioannou, A. (2021). Factors influencing users' adoption and use of conversational agents: A systematic review. Psychology & Marketing, 38(7), 1031-1051. https://doi.org/10.1002/mar.21491
- Ling, E. C., Tussyadiah, I., Tuomi, A., Stienmetz, J., and Ioannou, A. (2021). Factors influencing users' adoption and use of conversational agents: A systematic review. Psychology & Marketing, 38(7), 1031-1051. https://doi.org/10.1002/mar.21491
- Liu, C., Hung, K., Wang, D., and Wang, S. (2020). Determinants of self-service technology adoption and implementation in hotels: The case of China. Journal of Hospitality Marketing & Management, 29(6), 636-661. https://doi.org/10.1080/19368623.2020.1689216
- Lu, H. P., and Yang, Y. W. (2014). Toward an understanding of the behavioral intention to use a social networking site: An extension of task-technology fit to social-technology fit. Computers in Human Behavior, 34, 323-332. https://doi.org/10.1016/j.chb.2013.10.020
- Magsamen-Conrad, K., and Dillon, J. M. (2020). Mobile technology adoption across the lifespan: A mixed methods investigation to clarify adoption stages, and the influence of diffusion attributes. Computers in Human Behavior, 112, 106456.
- Majumder, S., and Mondal, A. (2021). Are chatbots really useful for human resource management?. International Journal of Speech Technology, 24(4), 969-977. https://doi.org/10.1007/s10772-021-09834-y
- Makki, A. M., Ozturk, A. B., and Singh, D. (2016). Role of risk, self-efficacy, and innovativeness on behavioral intentions for mobile payment systems in the restaurant industry. Journal of Foodservice Business Research, 19(5), 454-473. https://doi.org/10.1080/15378020.2016.1188646
- Meryl, M. (2021). One of The Youngest Populations in the World: India's Most Valuable Asset, Retrieved from https://indbiz.gov.in/one-of-the-youngestpopulations-in-the-world-indias-most-valuable-asset/
- Mitter, S. (2022, August 24). Rise of deep-tech: India home to 3,000+ AI, Big Data and blockchain start-ups, says NASSCOM. Business Today. Retrieved from https://www.businesstoday.in/latest/corporate/story/rise-of-deep-tech-india-home-to-3000-ai-big-dataand-blockchain-start-ups-says-nasscom-345440-2022-08-24
- Mostafa, R. B., and Kasamani, T. (2021). Antecedents and consequences of chatbot initial trust. European Journal of Marketing (forthcoming).
- Mukhopadhyay, S. (2023, January 18). India has surpassed China to become the most populous country in the world, as per estimates I Mint. LIVEMINT. Retrieved from https://www.live mint.com/news/india/india-has-surpassed-china-to -become-the-most-populous-country-in-the-worldas-per-estimates-11674022881859.html
- Murtarelli, G., Gregory, A., and Romenti, S. (2021). A conversation-based perspective for shaping ethical human-machine interactions: The particular challenge of chatbots. Journal of Business Research, 129, 927-935. https://doi.org/10.1016/j.jbusres.2020.09.018
- Nasir, M., Adil, M., and Kumar, M. (2022). Phobic COVID-19 Disorder Scale: Development, Dimensionality, and Item-Structure Test. International Journal Mental and Health Addiction, 20, 2718-2730. https://doi.org/10.1007/s11469-021-00544-9
- Ogonowski, A., Montandon, A., Botha, E., and Reyneke, M. (2014). Should new online stores invest in social presence elements? The effect of social presence on initial trust formation. Journal of Retailing and Consumer Services, 21(4), 482-491. https://doi.org/10.1016/j.jretconser.2014.03.004
- Ojha, N. P., and Ingilizian, Z. (2020, February 8). How India will consume in 2030: 10 mega trends. World Economic Forum. Retrieved from https://www.weforum.org/agenda/2019/01/10-mega -trends-for-india-in-2030-the-future-of-consumptio n-in-one-of-the-fastest-growing-consumer-markets
- Oliveira, T., Faria, M., Thomas, M. A., and Popovic, A. (2014). Extending the understanding of mobile banking adoption: When UTAUT meets TTF and ITM. International Journal of Information Management, 34(5), 689-703. https://doi.org/10.1016/j.ijinfomgt.2014.06.004
- Owusu, G. M. Y., Bekoe, R. A., Addo-Yobo, A. A., and Otieku, J. (2021). Mobile banking adoption among the Ghanaian youth. Journal of African Business, 22(3), 339-360. https://doi.org/10.1080/15228916.2020.1753003
- Pal, D., and Patra, S. (2021). University students' perception of video-based learning in times of COVID-19: A TAM/TTF perspective. International Journal of Human-Computer Interaction, 37(10), 903-921. https://doi.org/10.1080/10447318.2020.1848164
- Park, C. W., Kim, D., gook, Cho, S., and Han, H. J. (2018). Adoption of multimedia technology for learning and gender difference. Computers in Human Behavior, 92, 288-296. https://doi.org/10.1016/j.chb. 2018.11.029
- Park, J., Gunn, F., Lee, Y., and Shim, S. (2015). Consumer acceptance of a revolutionary technology-driven product: The role of adoption in the industrial design development. Journal of Retailing and Consumer Services, 26, 115-124. https://doi.org/10.1016/j.jretconser.2015.05.003
- Parra-Lopez, E., Bulchand-Gidumal, J., GutierrezTano, D., and Diaz-Armas, R. (2011). Intentions to use social media in organizing and taking vacation trips. Computers in Human Behavior, 27(2), 640-654. https://doi.org/10.1016/j.chb.2010.05.022
- Patil, P., Tamilmani, K., Rana, N. P., and Raghavan, V. (2020). Understanding consumer adoption of mobile payment in India: Extending Meta-UTAUT model with personal innovativeness, anxiety, trust, and grievance redressal. International Journal of Information Management, 54, 102144.
- Pereira, T., Limberger, P. F., and Ardigo, C. M. (2021). The moderating effect of the need for interaction with a service employee on purchase intention in chatbots. Telematics and Informatics Reports, 1, 100003.
- Pillai, R., and Sivathanu, B. (2020). Adoption of artificial intelligence (AI) for talent acquisition in IT/ITeS organizations. Benchmarking: An International Journal, 27(9), 2599-2629 https://doi.org/10.1108/BIJ-04-2020-0186
- Pontiggia, A., and Virili, F. (2010). Network effects in technology acceptance: Laboratory experimental evidence. International Journal of Information Management, 30(1), 68-77. https://doi.org/10.1016/j.ijinfomgt.2009.07.001
- Prentice, C., Dominique Lopes, S., and Wang, X. (2020). The impact of artificial intelligence and employee service quality on customer satisfaction and loyalty. Journal of Hospitality Marketing & Management, 29(7), 739-756. https://doi.org/10.1080/19368623.2020.1722304
- Qasim, H., and Abu-Shanab, E. (2016). Drivers of mobile payment acceptance: The impact of network externalities. Information Systems Frontiers, 18(5), 1021-1034. https://doi.org/10.1007/s10796-015-9598-6
- Rafiq, F., Dogra, N., Adil, M., and Wu, J. Z. (2022). Examining consumer's intention to adopt AI-chatbots in tourism using partial least squares structural equation modeling method. Mathematics, 10(13), 2190. https://doi.org/10.3390/math10132190
- Rzepka, C., Berger, B., and Hess, T. (2022). Voice assistant vs. Chatbot-examining the fit between conversational agents' interaction modalities and information search tasks. Information Systems Frontiers, 24(3), 839-856.
- Sadiq, M., Adil, M., and Khan, M. N. (2019). Automated banks' service quality in developing economy: empirical evidences from India. International Journal of Services and Operations Management, 33(3), 331-350. https://doi.org/10.1504/ IJSOM.2019.10022582
- Sadiq, M., and Adil, M. (2021). The mediating role of customer satisfaction and its effect on service quality-customer loyalty link. International Journal of Productivity and Quality Management, 32(4), 520-535. https://doi.org/10.1504/IJPQM.2021.114256
- Sands, S., Ferraro, C., Campbell, C., and Tsao, H. Y. (2020). Managing the human-chatbot divide: how service scripts influence service experience. Journal of Service Management, 32(2), 246-264.
- Sheehan, B. T. (2018). Customer Service Chatbots: Anthropomorphism, Adoption and Word of Mouth (Doctoral dissertation). Queensland University of Technology, Australia.
- Shin, H. H., and Jeong, M. (2022). Redefining luxury service with technology implementation: The impact of technology on guest satisfaction and loyalty in a luxury hotel. International Journal of Contemporary Hospitality Management, 34(4), 1491-1514. https://doi.org/10.1108/IJCHM-06-2021-0798
- Shumanov, M., and Johnson, L. (2021). Making conversations with chatbots more personalized. Computers in Human Behavior, 117, 106627.
- Singh, N., Sinha, N., and Liebana-Cabanillas, F. J. (2020). Determining factors in the adoption and recommendation of mobile wallet services in India: Analysis of the effect of innovativeness, stress to use and social influence. International Journal of Information Management, 50, 191-205 https://doi.org/10.1016/j.ijinfomgt.2019.05.022
- Suhaili, S. M., Salim, N., and Jambli, M. N. (2021). Service chatbots: A systematic review. Expert Systems with Applications, 184, 115461.
- Syvanen, S., and Valentini, C. (2020). Conversational agents in online organization-stakeholder interactions: A state-of-the-art analysis and implications for further research. Journal of Communication Management, 24(4), 339-362. https://doi.org/10.1108/JCOM-11-2019-0145
- Szymkowiak, A., Melovic, B., Dabic, M., Jeganathan, K., and Kundi, G. S. (2021). Information technology and Gen Z: The role of teachers, the internet, and technology in the education of young people. Technology in Society, 65, 101565.
- Tam, C., and Oliveira, T. (2016). Performance impact of mobile banking: Using the task-technology fit (TTF) approach. International Journal of Bank Marketing, 34(4), 434-457. https://doi.org/10.1108/IJBM-11-2014-0169
- Thakur, R., Angriawan, A., and Summey, J. H. (2016). Technological opinion leadership: The role of personal innovativeness, gadget love, and technological innovativeness. Journal of Business Research, 69(8), 2764-2773 https://doi.org/10.1016/j.jbusres.2015.11.012
- Thormundsson, B. (2022). Chatbot market worldwide 2016 and 2025, Retrieved from https://www.statista.com/statistics/656596/worldw ide-chatbot-market
- Venkatesh, V., Morris, M. G., Davis, G. B., and 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, C., Teo, T. S., and Janssen, M. (2021). Public and private value creation using artificial intelligence: An empirical study of AI voice robot users in Chinese public sector. International Journal of Information Management, 61, 102401.
- Wang, S. L., and Lin, H. I. (2019). Integrating TTF and IDT to evaluate user intention of big data analytics in mobile cloud healthcare system. Behaviour & Information Technology, 38(9), 974-985. https://doi.org/10.1080/0144929X.2019.1626486
- Wang, X., Wong, Y. D., Chen, T., and Yuen, K. F. (2021). Adoption of shopper-facing technologies under social distancing: A conceptualisation and an interplay between task-technology fit and technology trust. Computers in Human Behavior, 124, 106900.
- Wu, B., and 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. https://doi.org/10.1016/j.chb.2016.10.028
- Wu, J. H., Chen, Y. C., and Lin, L. M. (2007). Empirical evaluation of the revised end user computing acceptance model. Computers in Human Behavior, 23(1), 162-174. https://doi.org/10.1016/j.chb.2004.04.003
- Wu, K., Vassileva, J., and Zhao, Y. (2017). Understanding users' intention to switch personal cloud storage services: Evidence from the Chinese market. Computers in Human Behavior, 68, 300-314. https://doi.org/10.1016/j.chb.2016.11.039
- Wu, X., and Lai, I. K. W. (2021). The acceptance of augmented reality tour app for promoting film-induced tourism: the effect of celebrity involvement and personal innovativeness. Journal of Hospitality and Tourism Technology, 12(3), 454-470. https://doi.org/10.1108/JHTT-03-2020-0054
- Yen, C., and Chiang, M. C. (2021). Trust me, if you can: A study on the factors that influence consumers' purchase intention triggered by chatbots based on brain image evidence and self-reported assessments. Behaviour & Information Technology, 40(11), 1177-1194. https://doi.org/10.1080/0144929X.2020.1743362
- Yen, D. C., Wu, C. S., Cheng, F. F., and Huang, Y. W. (2010). Determinants of users' intention to adopt wireless technology: An empirical study by integrating TTF with TAM. Computers in Human Behavior, 26(5), 906-915. https://doi.org/10.1016/j.chb.2010.02.005
- Yoo, Y., and Alavi, M. (2001). Media and group cohesion: Relative influences on social presence, task participation, and group consensus. MIS Quarterly, 25(3), 371-390. https://doi.org/10.2307/3250922
- Yoon, S. B., and Cho, E. (2016). Convergence adoption model (CAM) in the context of a smart car service. Computers in Human Behavior, 60, 500-507. https://doi.org/10.1016/j.chb.2016.02.082
- Zhang, C. B., Li, Y. N., Wu, B., and Li, D. J. (2017). How WeChat can retain users: Roles of network externalities, social interaction ties, and perceived values in building continuance intention. Computers in Human Behavior, 69, 284-293. https://doi.org/10.1016/j.chb.2016.11.069
- Zhou, L., Gao, J., Li, D., and Shum, H. Y. (2020). The design and implementation of xiaoice, an empathetic social chatbot. Computational Linguistics, 46(1), 53-93. https://doi.org/10.1162/coli_a_00368
- Zhu, Y., Wang, R., and Pu, C. (2022). "I am chatbot, your virtual mental health adviser." What drives citizens' satisfaction and continuance intention toward mental health chatbots during the COVID-19 pandemic? An empirical study in China. Digital Health, 8, 20552076221090031.
- Zigurs, I., and Buckland, B. K. (1998). A theory of task/technology fit and group support systems effectiveness. MIS Quarterly, 22(3), 313-334. https://doi.org/10.2307/249668