“I Am Here to Assist Your Tourism”: Predicting Continuance Intention to Use AI-based Chatbots for Tourism. Does Gender Really Matter?

References

• Akinnuwesi, B. A., Uzoka, F.-M. E., Fashoto, S. G., Mbunge, E., Odumabo, A., Amusa, O. O., Okpeku, M., & Owolabi, O. (2022). A modified UTAUT model for the acceptance and use of digital technology for tackling COVID-19. Sustainable Operations and Computers, 3, 118–135. https://doi.org/10.1016/j.susoc.2021.12.001
   Google Scholar
• Alalwan, A. A., Dwivedi, Y. K., & Rana, N. P. (2017). Factors influencing adoption of mobile banking by Jordanian bank customers: Extending UTAUT2 with trust. International Journal of Information Management, 37(3), 99–110. https://doi.org/10.1016/j.ijinfomgt.2017.01.002
   Web of Science ®Google Scholar
• Alalwan, A. A., Dwivedi, Y. K., & Williams, M. D. (2016). Customers’ intention and adoption of telebanking in Jordan. Information Systems Management, 33(2), 154–178. https://doi.org/10.1080/10580530.2016.1155950
   Web of Science ®Google Scholar
• Aldás-Manzano, J., Lassala-Navarré, C., Ruiz-Mafé, C., & Sanz-Blas, S. (2009). The role of consumer innovativeness and perceived risk in online banking usage. International Journal of Bank Marketing, 27(1), 53–75. https://doi.org/10.1108/02652320910928245
   Google Scholar
• Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411–423. https://doi.org/10.1037/0033-2909.103.3.411
   Web of Science ®Google Scholar
• Aoki, N. (2020). An experimental study of public trust in AI chatbots in the public sector. Government Information Quarterly, 37(4), 101490. https://doi.org/10.1016/j.giq.2020.101490
   Web of Science ®Google Scholar
• Arenas Gaitán, J., Peral, B., & Ramón Jerónimo, M. (2015). Elderly and internet banking: An application of UTAUT2. Journal of Internet Banking and Commerce, 20(1), 1–23.https://www.researchgate.net/publication/277924056
   Google Scholar
• Arroyo, I., Burleson, W., Tai, M., Muldner, K., & Woolf, B. P. (2013). Gender differences in the use and benefit of advanced learning technologies for mathematics. Journal of Educational Psychology, 105(4), 957–969. https://doi.org/10.1037/a0032748
   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
• Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74–94. https://doi.org/10.1007/BF02723327
   Google Scholar
• Baptista, G., & Oliveira, T. (2017). Why so serious? Gamification impact in the acceptance of mobile banking services. Internet Research, 27(1), 118–139. https://doi.org/10.1108/IntR-10-2015-0295
   Web of Science ®Google Scholar
• Bowen, J., & Morosan, C. (2018). Beware hospitality industry: The robots are coming. Worldwide Hospitality and Tourism Themes, 10(6), 726–733. https://doi.org/10.1108/WHATT-07-2018-0045
   Web of Science ®Google Scholar
• Browne, M. W., & Cudeck, R. (1992). Alternative ways of assessing model fit. Sociological Methods and Research, 21(2), 136–162. https://doi.org/10.1177/0049124192021002005
   Web of Science ®Google Scholar
• Byrne, B. M. (2001). Structural equation modeling with AMOS. Lawrence Erlbaum Associates.
   Google Scholar
• Cabrera-Sánchez, J. P., Villarejo-Ramos, A. F., Liebana-Cabanillas, F., & Shaikh, A. A. (2021). Identifying relevant segments of AI applications adopters – Expanding the UTAUT2’s variables. Telematics and Informatics, 58, 101529. https://doi.org/10.1016/j.tele.2020.101529
   Web of Science ®Google Scholar
• Casey, T., & Wilson-Evered, E. (2012). Predicting uptake of technology innovations in online family dispute resolution services: An application and extension of the UTAUT. Computers in Human Behavior, 28(6), 2034–2045. https://doi.org/10.1016/j.chb.2012.05.022
   Web of Science ®Google Scholar
• Chaves, A. P., & Gerosa, M. A. (2021). How should my chatbot interact? A survey on social characteristics in human–chatbot interaction design. International Journal of Human–Computer Interaction, 37(8), 729–758. https://doi.org/10.1080/10447318.2020.1841438
   Web of Science ®Google Scholar
• Chen, T., Guo, W., Gao, X., & Liang, Z. (2021). AI-based self-service technology in public service delivery: User experience and influencing factors. Government Information Quarterly, 38(4), 101520. https://doi.org/10.1016/j.giq.2020.101520
   Google Scholar
• Cheng, X., Zhang, X., Cohen, J., & Mou, J. (2022). Human vs. AI: Understanding the impact of anthropomorphism on consumer response to chatbots from the perspective of trust and relationship norms. Information Processing & Management, 59(3), 102940. https://doi.org/10.1016/j.ipm.2022.102940
   Web of Science ®Google Scholar
• Chin, W. W. (1998). The partial least squares approach to structural equation modeling. In G. A. Marcoulides (Ed.), Modern methods for business research (pp. 295–336). Lawrence Erlbaum Associates, Inc.
   Google Scholar
• Chiu, C. M., Wang, E. T., Fang, Y. H., & Huang, H. Y. (2014). Understanding customers’ repeat purchase intentions in B2C e-commerce: The roles of utilitarian value, hedonic value and perceived risk. Information Systems Journal, 24(1), 85e114–85e114. https://doi.org/10.1111/j.1365-2575.2012.00407.x
   Web of Science ®Google Scholar
• Choe, J. Y., Kim, J. J., & Hwang, J. (2021). Perceived risks from drone food delivery services before and after COVID-19. International Journal of Contemporary Hospitality Management, 33(4), 1276–1296. https://doi.org/10.1108/IJCHM-08-2020-0839
   Web of Science ®Google Scholar
• Choi, J., Lee, A., & Ok, C. (2013). The effects of consumers’ perceived risk and benefit on attitude and behavioral intention: A study of street food. Journal of Travel & Tourism Marketing, 30(3), 222–237. https://doi.org/10.1080/10548408.2013.774916
   Web of Science ®Google Scholar
• Cowan, B. R., Branigan, H. P., Obregón, M., Bugis, E., & Beale, R. (2015). Voice anthropomorphism, interlocutor modelling and alignment effects on syntactic choices in human computer dialogue. International Journal of Human-Computer Studies, 83, 27–42. https://doi.org/10.1016/j.ijhcs.2015.05.008
   Web of Science ®Google Scholar
• Dubrova, D. (2020). Chatbot for travel industry: Benefits, use cases, and a development guide. https://theappsolutions.com/blog/how-to/chatbot-for-travel-business/
   Google Scholar
• Fan, H., Han, B., Gao, W., & Li, W. (2022). How AI chatbots have reshaped the frontline interface in China: Examining the role of sale-service ambidexterity and the personalization-privacy. International Journal of Emerging Markets, 17(4), 967–986. https://doi.org/10.1108/IJOEM-04-2021-0532
   Web of Science ®Google Scholar
• Featherman, M. S., & Pavlou, P. A. (2003). Predicting e-services adoption: A perceived risk facets perspective. International Journal of Human-Computer Studies, 59(4), 451–474. https://doi.org/10.1016/S1071-5819(03)00111-3
   Web of Science ®Google Scholar
• FlowXO. (2020). What industry are using chatbots today? https://flowxo.com/what-industries-are-using-chatbots-today/
   Google Scholar
• Ghosh, J., & Chakravarty, R. (2018). Expedition 3.0: Travel and hospitality gone digital, KPMG and FICCI. https://home.kpmg/in/en/home/insights/2018/03/ficci-expedition-travel-hospitality-technology-innovation-india-digital.html
   Google Scholar
• Goh, W. W., Tang, S. F., & Lim, C. L. (2016). Assessing factors affecting students’ acceptance and usage of X-space based on UTAUT2 model. In S. F. Tang & L. Logonnathan (Eds.), Assessment for learning within and beyond the classroom (pp. 61–70). Springer.
   Google Scholar
• Gupta, A., & Dogra, N. (2017). Tourist adoption of mapping apps: a UTAUT2 perspective of smart travellers. Tourism and Hospitality Management. 23(2), 145–161. https://doi.org/10.20867/thm.23.2.6
   Google Scholar
• Gupta, A., Dogra, N., & George, B. (2018). Journal of hospitality and tourism technology. Journal of Hospitality and Tourism Technology, 9(1), 50–64. https://doi.org/10.1108/JHTT-02-2017-0013
   Web of Science ®Google Scholar
• Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Pearson Prentice-Hall.
   Google Scholar
• Hari, H., Lyer, R., & Sampat, B. (2022). Customer brand engagement through chatbots on bank websites – Examining the antecedents and consequences. International Journal of Human-Computer Interaction, 38(13), 1212–1227. https://doi.org/10.1080/10447318.2021.1988487
   Google Scholar
• Harper, R. H. R. (2019). The role of HCI in the age of AI. International Journal of Human–Computer Interaction, 35(15), 1331–1344. https://doi.org/10.1080/10447318.2019.1631527
   Web of Science ®Google Scholar
• Herrero, Á., San Martín, H., & Garcia-De los Salmones, M. D. M. (2017). Explaining the adoption of social networks sites for sharing user-generated content: A revision of the UTAUT2. Computers in Human Behavior, 71, 209–217. https://doi.org/10.1016/j.chb.2017.02.007
   Web of Science ®Google Scholar
• Hirsh, J. B., Kang, S. K., & Bodenhausen, G. V. (2012). Personalized persuasion: Tailoring persuasive appeals to recipients’ personality traits. Psychological Science, 23(6), 578–581. https://doi.org/10.1177/0956797611436349
   PubMed Web of Science ®Google Scholar
• Ho, C. T. B., Chou, Y. T., & O'Neill, P. (2010). Technology adoption of mobile learning: A study of podcasting. International Journal of Mobile Communications, 8(4), 468–485. https://doi.org/10.1504/IJMC.2010.033837
   Web of Science ®Google Scholar
• Hong, S. J., Thong, J., & Tam, K. Y. (2006). Understanding continued information technology usage behavior: A comparison of three models in the context of mobile internet. Decision Support Systems, 42(3), 1819–1834. https://doi.org/10.1016/j.dss.2006.03.009
   Web of Science ®Google Scholar
• Huang, H.-L., Cheng, L.-K., Sun, P.-C., & Chou, S.-J. (2021). The effects of perceived identity threat and realistic threat on the negative attitudes and usage intentions toward hotel service robots: The moderating effect of the robot’s anthropomorphism. International Journal of Social Robotics, 13(7), 1599–1611. https://doi.org/10.1007/s12369-021-00752-2
   Web of Science ®Google Scholar
• Jiménez-Barreto, J., Rubio, N., & Molinillo, S. (2021). “Find a flight for me, Oscar!” Motivational customer experiences with chatbots. International Journal of Contemporary Hospitality Management, 33(11), 3860–3882. https://doi.org/10.1108/IJCHM-10-2020-1244
   Web of Science ®Google Scholar
• Kaur, P., Dhir, A., Rajala, R., & Dwivedi, Y. (2018). Why people use online social media brand communities. A consumption value theory perspective. Online Information Review, 42(2), 205–221. https://doi.org/10.1108/OIR-12-2015-0383
   Web of Science ®Google Scholar
• Kim, D. Y., Lehto, X. Y., & Morrison, A. M. (2007). Gender differences in online travel information search: Implications for marketing communications on the internet. Tourism Management, 28(2), 423–433. https://doi.org/10.1016/j.tourman.2006.04.001
   Web of Science ®Google Scholar
• Kol, O., Nebenzahl, I. D., Lev-On, A., & Levy, S. (2021). SNS adoption for consumer active information search (AIS) – The dyadic role of information credibility. International Journal of Human–Computer Interaction, 37(16), 1504–1515. https://doi.org/10.1080/10447318.2021.1898824
   Web of Science ®Google Scholar
• Komiak, S. Y., & Benbasat, I. (2006). The effects of personalization and familiarity on trust and adoption of recommendation agents. MIS Quarterly, 30(4), 941–960. https://doi.org/10.2307/25148760
   Web of Science ®Google Scholar
• Kwateng, K. O., Atiemo, K. A. O., & Appiah, C. (2019). Acceptance and use of mobile banking: An application of UTAUT2. Journal of Enterprise Information Management, 32(1), 118–151. https://doi.org/10.1108/JEIM-03-2018-0055
   Web of Science ®Google Scholar
• Lee, S., Lee, N., & Sah, Y. J. (2020). Perceiving a mind in a chatbot: Effect of mind perception and social cues on co-presence, closeness, and intention to use. International Journal of Human–Computer Interaction, 36(10), 930–940. https://doi.org/10.1080/10447318.2019.1699748
   Web of Science ®Google Scholar
• Leong, L., Ooi, K., Chong, A. Y., & Lin, B. (2013). Modeling the stimulators of the behavioral intention to use mobile entertainment: Does gender really matter? Computers in Human Behavior, 29(5), 2109–2121. https://doi.org/10.1016/j.chb.2013.04.004
   Web of Science ®Google Scholar
• Li, L., Lee, Y. K., Emokpae, E., & Yang, S. B. (2021). What makes you continuously use chatbot services? Evidence from Chinese online travel agencies. Electronic Markets, 31(3), 575–599. https://doi.org/10.1007/s12525-020-00454-z
   PubMed Web of Science ®Google Scholar
• Lim, X., Cheah, J., Ng, S. I., Basha, N. K., & Liu, Y. (2021). Are men from Mars, women from Venus? Examining gender differences towards continuous use intention of branded apps. Journal of Retailing and Consumer Services, 60, e102422. https://doi.org/10.1016/j.jretconser.2020.102422
   Web of Science ®Google Scholar
• Lin, P.-H., & Yeh, S.-C. (2019). How motion-control influences a VR-supported technology for mental rotation learning: Form the perspectives of playfulness, gender difference and technology acceptance model. International Journal of Human–Computer Interaction, 35(18), 1736–1746. https://doi.org/10.1080/10447318.2019.1571784
   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
• Long, Q., & Tefertiller, A. C. (2020). China’s new mania for live streaming: Gender differences in motives and uses of social live streaming services. International Journal of Human–Computer Interaction, 36(14), 1314–1324. https://doi.org/10.1080/10447318.2020.1746060
   Web of Science ®Google Scholar
• Lu, L., Cai, R., & Gursoy, D. (2019). Developing and validating a service robot integration willingness scale. Interactional Journal of Hospitality Management, 80, 36–51. https://doi.org/10.1016/j.ijhm.2019.01.005
   Web of Science ®Google Scholar
• Ma, L., Zhang, X., Ding, X., & Wang, G. (2019). Risk perception and intention to discontinue use of ride hailing services in China: Taking the example of DiDi chuxing. Transportation Research. Part F, Traffic Psychology and Behaivour, 66, 459–470. https://doi.org/10.1016/j.trf.2019.09.021
   Google Scholar
• McDonnell, M., & Baxter, D. (2019). Chatbots and gender stereotyping. Interacting with Computers, 31(2), 116–121. https://doi.org/10.1093/iwc/iwz007
   Web of Science ®Google Scholar
• McTear, M., Callejas, Z., & Griol, D. (2016). The conversational interface: Talking to smart devices. In The conversational interface (pp. 283–308). Springer.
   Google Scholar
• Melián-González, S., Gutiérrez-Taño, D., & Bulchand-Gidumal, J. (2021). Predicting the intentions to use chatbots for travel and tourism. Current Issues in Tourism, 24(2), 192–210. https://doi.org/10.1080/13683500.2019.1706457
   Web of Science ®Google Scholar
• Merhi, M., Hone, K., & Tarhini, A. (2019). A cross-cultural study of the intention to use mobile banking between Lebanese and British consumers: Extending UTAUT2 with security, privacy and trust. Technology in Society, 59, 101151. https://doi.org/10.1016/j.techsoc.2019.101151
   Web of Science ®Google Scholar
• Meyers-Levy, J. (1988). Influence of sex roles on judgment. Journal of Consumer Research, 14(4), 522–530. https://doi.org/10.1086/209133
   Web of Science ®Google Scholar
• Meyers-Levy, J., & Maheswaran, D. (1991). Exploring differences in males’ and females’ processing strategies. Journal of Consumer Research, 18(1), 63–70. https://doi.org/10.1086/209241
   Web of Science ®Google Scholar
• Milenkovic, J. (2020). 43 useful online banking statistics: All about mobile money! https://kommandotech.com/statistics/online-banking-statistics/
   Google Scholar
• Mori, M., MacDorman, K. F., & Kageki, N. (2012). The uncanny valley. IEEE Robotics & Automation Magazine, 19(2), 98–100. https://doi.org/10.1109/MRA.2012.2192811
   Web of Science ®Google Scholar
• Morosan, C., & DeFranco, A. (2016). It’s about time: Revisiting UTAUT2 to examine consumers’ intentions to use NFC mobile payments in hotels. International Journal of Hospitality Management, 53, 17–29. https://doi.org/10.1016/j.ijhm.2015.11.003
   Web of Science ®Google Scholar
• Muhammad, S. S., Dey, B. L., & Weerakkody, V. (2018). Analysis of factors that influence customers’ willingness to leave big data digital footprints on social media: A systematic review of literature. Information Systems Frontiers, 20(3), 559–576. https://doi.org/10.1007/s10796-017-9802-y
   Web of Science ®Google Scholar
• Nguyen, Q. N., Ta, A., & Prybutok, V. (2019). An integrated model of voice-user interface continuance intention: The gender effect. International Journal of Human–Computer Interaction, 35(15), 1362–1377. https://doi.org/10.1080/10447318.2018.1525023
   Web of Science ®Google Scholar
• Nunnally, J. C. (1978). Psychometric theory. McGraw-Hill.
   Google Scholar
• Park, E., & Kim, K. J. (2014). An integrated adoption model of mobile cloud services: Exploration of key determinants and extension of technology acceptance model. Telematics and Informatics, 31(3), 376–385. https://doi.org/10.1016/j.tele.2013.11.008
   Web of Science ®Google Scholar
• Park, S., & Tussyadiah, I. P. (2017). Multidimensional facets of perceived risk in mobile travel booking. Journal of Travel Research, 56(7), 854–867. https://doi.org/10.1177/0047287516675062
   Web of Science ®Google Scholar
• Pearl, C. (2016). Designing voice user interfaces: Principles of conversational experiences. California, USA.
   Google Scholar
• Pelau, C., Dabija, D. C., & Ene, I. (2021). What makes an AI device human-like? The role of interaction quality, empathy and perceived psychological anthropomorphic characteristics on the acceptance of artificial intelligence in the service industry. Computers in Human Behavior, 122, 106855. https://doi.org/10.1016/j.chb.2021.106855
   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
• Rajan, K., & Saffiotti, A. (2017). Towards a science of integrated AI and robotics. Artificial Intelligence, 247, 1–9. https://doi.org/10.1016/j.artint.2017.03.003
   Web of Science ®Google Scholar
• Raman, A., & Don, Y. (2013). Preservice teachers’ acceptance of learning management software: An application of the UTAUT2 model. International Education Studies, 6(7), 157–164. https://doi.org/10.5539/ies.v6n7p157
   Google Scholar
• Ramírez-Correa, P., Rondán-Cataluña, F. J., Arenas-Gaitán, J., & Martín-Velicia, F. (2019). Analysing the acceptation of online games in mobile devices: An application of UTAUT2. Journal of Retailing and Consumer Services, 50, 85–93. https://doi.org/10.1016/j.jretconser.2019.04.018
   Web of Science ®Google Scholar
• Ramkissoon, H., & Nunkoo, R. (2012). More than just biological sex differences: Examining the structural relationship between gender identity and information search behavior. Journal of Hospitality & Tourism Research, 36(2), 191–215. https://doi.org/10.1177/1096348010388662
   Web of Science ®Google Scholar
• Ravich, T. M. (2015). Courts in the drone age. Northern Kentucky Law Review, 42(2), 161–190.
   Google Scholar
• Rodríguez-Ardura, I., & Meseguer-Artola, A. (2021). Flow experiences in personalised e-learning environments and the role of gender and academic performance. Interactive Learning Environments, 29(1), 59–82. https://doi.org/10.1080/10494820.2019.1572628
   Web of Science ®Google Scholar
• Roy, R., & Naidoo, V. (2021). Enhancing chatbot effectiveness: The role of anthropomorphic conversational styles and time orientation. Journal of Business Research, 126, 23–34. https://doi.org/10.1016/j.jbusres.2020.12.051
   Web of Science ®Google Scholar
• Seo, D., & Bernsen, M. (2016). Comparing attitudes toward e-government of non-users versus users in a rural and urban municipality. Government Information Quarterly, 33(2), 270–282. https://doi.org/10.1016/j.giq.2016.02.002
   Web of Science ®Google Scholar
• Shao, X., Jeong, E. H., Jang S., & Yang X. (2021). Effectiveness of anthropomorphism in ugly food promotion: Do gender and age matter? Journal of Foodservice Business Research, 24(5), 596–611. https://doi.org/10.1080/15378020.2021.1883215
   Google Scholar
• Sheehan, B. T. (2018). Customer service chatbots: Anthropomorphism, adoption and word of mouth [Doctoral dissertation]. Queensland University of Technology. https://eprints.qut.edu.au/121188/
   Google Scholar
• Shi, S., Wang, Y., Chen, X., & Zhang, Q. (2020). Conceptualization of omnichannel customer experience and its impact on shopping intention: A mixed-method approach. International Journal of Information Management, 50, 325–336. https://doi.org/10.1016/j.ijinfomgt.2019.09.001
   Web of Science ®Google Scholar
• Shumanov, M., & Johnson, L. (2021). Making conversation with chatbots more personalized. Computers in Human Behavior, 117, 106627. https://doi.org/10.1016/j.chb.2020.106627
   Web of Science ®Google Scholar
• Siau, K., & Shen, Z. (2003). Building customer trust in mobile commerce. Communications of the ACM, 46(4), 91–94. https://doi.org/10.1145/641205.641211
   Web of Science ®Google Scholar
• Sobieraj, S., & Kramer, N. C. (2020). Similarities and differences between genders in the usage of computer with different levels of technological complexity. Computers in Human Behavior, 104, 106145. https://doi.org/10.1016/j.chb.2019.09.021
   Web of Science ®Google Scholar
• Tak, P., & Panwar, S. (2017). Using UTAUT 2 model to predict mobile app based shopping: Evidences from India. Journal of Indian Business Research, 9(3), 248–264. https://doi.org/10.1108/JIBR-11-2016-0132
   Web of Science ®Google Scholar
• Thakur, R., & Srivastava, M. (2014). Adoption readiness, personal innovativeness, perceived risk and usage intention across customer groups for mobile payment services in India. Internet Research, 24(3), 369–392. https://doi.org/10.1108/IntR-12-2012-0244
   Web of Science ®Google Scholar
• Trapero, H., Ilao, J., & Lacaza, R. (2020). An integrated theory for chatbot use in air travel: Questionnaire development and validation. In Proceedings of the 2020 IEEE Region 10 Conference (TENCON) (pp. 16–19).
   Google Scholar
• Tussyadiah, I. (2020). A review of research into automation in tourism: Launching the annals of tourism research curated collection on artificial intelligence and robotics in tourism. Annals of Tourism Research, 81, 102883. https://doi.org/10.1016/j.annals.2020.102883
   Web of Science ®Google Scholar
• Van Doorn, J., Mende, M., Noble, S. M., Hulland, J., Ostrom, A. L., Grewal, D., & Petersen, J. A. (2017). Domo arigato Mr Roboto: Emergence of automated social presence in organizational frontlines and customers’ service experiences. Journal of Service Research, 20(1), 43–58. https://doi.org/10.1177/1094670516679272
   Web of Science ®Google Scholar
• Venkatesh, V., & Morris, M. G. (2000). Why don’t men ever stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behavior. MIS Quarterly, 24(1), 115–139. https://doi.org/10.2307/3250981
   Web of Science ®Google Scholar
• Venkatesh, V., Morris, M. G., & Ackerman, P. L. (2000). A longitudinal field investigation of gender differences in individual technology adoption decision-making processes. Organizational Behavior and Human Decision Processes, 83(1), 33–60. https://doi.org/10.1006/obhd.2000.2896
   PubMed Web of Science ®Google Scholar
• Venkatesh, V., Morris, M. G., Davis, G., & Davis, F. (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
• Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178. https://doi.org/10.2307/41410412
   Web of Science ®Google Scholar
• Wang, X., Goh, D. H. L., & Lim E. (2020). Understanding continuance intention toward crowdsourcing games: A longitudinal investigation. International Journal of Human–Computer Interaction, 36(12), 1168–1177. https://doi.org/10.1080/10447318.2020.1724010
   Web of Science ®Google Scholar
• Winstok, Z., & Straus, M. (2011). Gender differences in intended escalatory tendencies among marital partners. Journal of Interpersonal Violence, 26(18), 3599–3617. https://doi.org/10.1177/0886260511403750
   PubMed Web of Science ®Google Scholar
• Winstok, Z., & Weinberg, M. (2018). From posttrauma to gender and back: A gender motivation theory-explanation of gender differences in trauma exposure, symptoms, diagnosis, and implications. Journal of Aggression, Maltreatment & Trauma, 27(9), 959–982. https://doi.org/10.1080/10926771.2017.1420719
   Web of Science ®Google Scholar
• Winstok, Z., Weinberg, M., & Smadar-Dror, R. (2017). Studying partner violence to understand gender motivations-or vice-versa? Aggression and Violent Behavior, 34, 120–127. https://doi.org/10.1016/j.avb.2017.01.022
   Web of Science ®Google Scholar
• Xiao, B., & Benbasat, I. (2007). E-commerce product recommendation agents: Use, characteristics, and impact. MIS Quarterly, 31(1), 137–209. https://doi.org/10.2307/25148784
   Web of Science ®Google Scholar
• Xu, W., Danioff, M. J., Ge, L., & Gao, Z. (2022). Transitioning to human interaction with AI systems: New challenges and opportunities for HCI professionals to enable human-centered AI. Interactional Journal of Human-Computer Interaction. https://doi.org/10.1080/10447318.2022.2041900
   Google Scholar
• Xu, X. (2014). Understanding users’ continued use of online games: An application of UTAUT2 in social network games. In P. Lorenz (Ed.), The sixth international conferences on advances in multimedia (MMEDIA 2014) (pp. 58–65). IARIA.
   Google Scholar
• Yang, H., Yu, J., Zo, H., & Choi, M. (2016). User acceptance of wearable devices: An extended perspective of perceived value. Telematics and Informatics, 33(2), 256–269. https://doi.org/10.1016/j.tele.2015.08.007
   Web of Science ®Google Scholar
• Zhang, B., & Zhu, Y. (2021). Comparing attitudes towards adoption of e-government between urban users and rural users: An empirical study in Chongqing municipality. China. Behaviour & Information Technology, 40(11), 1154–1168. https://doi.org/10.1080/0144929X.2020.1743361
   Web of Science ®Google Scholar
• Zhu, Y., Janssen, M., Wang, R., & Liu, Y. (2022). It is me, chatbot: Working to address the COVID-19 outbreak-related mental health issues in China. User experience, satisfaction, and influencing factors. International Journal of Human–Computer Interaction, 38(12), 1182–1194. https://doi.org/10.1080/10447318.2021.1988236
   Web of Science ®Google Scholar
• Zhu, Y., & Kou, G. (2019). Linking smart governance to future generation: A study on the use of local e-government service among undergraduate students in a Chinese municipality. Informatics, 6(4), 45. https://doi.org/10.3390/informatics6040045
   Google Scholar
• Zhu, Y., Wang, R., & 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. https://doi.org/10.1177/20552076221090031
   Web of Science ®Google Scholar
• Zhu, Y., Wang, R., Zeng, R., & Pu, C. (2022). Does gender really matter? Exploring determinants behind consumers’ intention to use contactless fitness services during the COVID-19 pandemic: A focus on health and fitness apps. Internet Research. https://doi.org/10.1108/INTR-07-2021-0454
   Google Scholar