References

• Ahn, J. (2022). Impact of cognitive aspects of food mobile application on customers’ behaviour. Current Issues in Tourism, 25(4), 516–523. https://doi.org/10.1080/13683500.2021.1890700
   Web of Science ®Google Scholar
• Al-Sharafi, M. A., Al-Emran, M., Iranmanesh, M., Al-Qaysi, N., Iahad, N. A., & Arpaci, I. (2022). Understanding the impact of knowledge management factors on the sustainable use of AI-based chatbots for educational purposes using a hybrid SEM-ANN approach. Interactive Learning Environments, 1–20.
   Web of Science ®Google Scholar
• Al-Sharafi, M. A., Al-Qaysi, N., Iahad, N. A., & Al-Emran, M. (2022). Evaluating the sustainable use of mobile payment contactless technologies within and beyond the COVID-19 pandemic using a hybrid SEM-ANN approach. International Journal of Bank Marketing, 40(5), 1071–1095. https://doi.org/10.1108/IJBM-07-2021-0291
   Web of Science ®Google Scholar
• Ambalov, I. A. (2021). Decomposition of perceived usefulness: A theoretical perspective and empirical test. Technology in Society, 64, 101520. https://doi.org/10.1016/j.techsoc.2020.101520
   Web of Science ®Google Scholar
• Annamalai, N., Foroughi, B., Iranmanesh, M., & Buathong, S. (2020). Needs and Facebook addiction: How important are psychological well-being and performance-approach goals? Current Psychology, 39(6), 1942–1953. https://doi.org/10.1007/s12144-019-00516-2
   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
• 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.
   Web of Science ®Google Scholar
• Aw, E. C.-X., Zha, T., & Chuah, S. H.-W. (2023). My new financial companion! non-linear understanding of Robo-advisory service acceptance. The Service Industries Journal, 43(3–4), 185–212. https://doi.org/10.1080/02642069.2022.2161528
   Web of Science ®Google Scholar
• Bag, S., Srivastava, G., Bashir, M. M. A., Kumari, S., Giannakis, M., & Chowdhury, A. H. (2022). Journey of customers in this digital era: Understanding the role of artificial intelligence technologies in user engagement and conversion. Benchmarking: An International Journal, 29(7), 2074–2098. https://doi.org/10.1108/BIJ-07-2021-0415
   Web of Science ®Google Scholar
• Balakrishnan, J., Abed, S. S., & Jones, P. (2022). The role of meta-UTAUT factors, perceived anthropomorphism, perceived intelligence, and social self-efficacy in chatbot-based services? Technological Forecasting and Social Change, 180, 121692. https://doi.org/10.1016/j.techfore.2022.121692
   Web of Science ®Google Scholar
• Beh, P. K., Ganesan, Y., Iranmanesh, M., & Foroughi, B. (2021). Using smartwatches for fitness and health monitoring: The UTAUT2 combined with threat appraisal as moderators. Behaviour & Information Technology, 40(3), 282–299. https://doi.org/10.1080/0144929X.2019.1685597
   Web of Science ®Google Scholar
• Belanche, D., Belk, R. W., Casaló, L. V., & Flavián, C. (2024). The dark side of artificial intelligence in services. The Service Industries Journal, 1–24.
   Web of Science ®Google Scholar
• Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(2), 351–370. https://doi.org/10.2307/3250921
   Google Scholar
• Butt, A., Ahmad, H., Muzaffar, A., Ali, F., & Shafique, N. (2022). WOW, the make-up AR app is impressive: A comparative study between China and South Korea. Journal of Services Marketing, 36(1), 73–88. https://doi.org/10.1108/JSM-12-2020-0508
   Web of Science ®Google Scholar
• Chandra, S., Shirish, A., & Srivastava, S. C. (2022). To be or not to be … human? Theorizing the role of human-like competencies in conversational artificial intelligence agents. Journal of Management Information Systems, 39(4), 969–1005. https://doi.org/10.1080/07421222.2022.2127441
   Web of Science ®Google Scholar
• Chau, P., & Hu, P. (2011). Information technology acceptance by individual professionals: A model of comparison approach. Decision Sciences, 32(4), 699–719.
   Google Scholar
• Chen, Q., Gong, Y., Lu, Y., & Tang, J. (2022). Classifying and measuring the service quality of AI chatbot in frontline service. Journal of Business Research, 145, 552–568. https://doi.org/10.1016/j.jbusres.2022.02.088
   Web of Science ®Google Scholar
• Chen, Q., Lu, Y., Gong, Y., & Xiong, J. (2023). Can AI chatbots help retain customers? Impact of AI service quality on customer loyalty. Internet Research, 1–38.
   Web of Science ®Google Scholar
• Cheng, S.-I. (2011). Comparisons of competing models between attitudinal loyalty and behavioral loyalty. International Journal of Business and Social Science, 2(10), 149–166.
   Google Scholar
• Cheng, X., Bao, Y., Zarifis, A., Gong, W., & Mou, J. (2021). Exploring consumers’ response to text-based chatbots in e-commerce: The moderating role of task complexity and chatbot disclosure. Internet Research, 32(2), 496–517. https://doi.org/10.1108/INTR-08-2020-0460
   Web of Science ®Google Scholar
• Cheng, Y., & Jiang, H. (2020). How do AI-driven chatbots impact user experience? Examining gratifications, perceived privacy risk, satisfaction, loyalty, and continued use. Journal of Broadcasting and Electronic Media. Media, 64(4), 592–614.
   Web of Science ®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
• Cochran, A. L. (2020). Understanding the role of transportation-related social interaction in travel behavior and health: A qualitative study of adults with disabilities. Journal of Transport & Health, 19, 100948. https://doi.org/10.1016/j.jth.2020.100948
   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
• de Andrés-Sánchez, J., & Gené-Albesa, J. (2023). Assessing attitude and behavioral intention toward chatbots in an insurance setting: A mixed method approach. International Journal of Human–Computer Interaction, 1–16.
   Web of Science ®Google Scholar
• De Keyser, A., & Kunz, W. H. (2022). Living and working with service robots: a TCCM analysis and considerations for future research. Journal of Service Management, 33(2), 165–196.
   Web of Science ®Google Scholar
• Drift. (2021). 2021 State of conversational marketing. https://drift.com/wp-content/uploads/2021/10/2021-State-of-Conversational-Marketing.pdf.
   Google Scholar
• Dul, J., Hauff, S., & Bouncken, R. B. (2023). Necessary condition analysis (NCA): review of research topics and guidelines for good practice. Review of Managerial Science, 17(2), 683–714. https://doi.org/10.1007/s11846-023-00628-x
   Web of Science ®Google Scholar
• Dul, J., Van der Laan, E., & Kuik, R. (2020). A statistical significance test for necessary condition analysis. Organizational Research Methods, 23(2), 385–395. https://doi.org/10.1177/1094428118795272
   Web of Science ®Google Scholar
• Esiyok, E., Gokcearslan, S., & Kucukergin, K. G. (2024). Acceptance of educational use of AI chatbots in the context of self-directed learning with technology and ICT self-efficacy of undergraduate students. International Journal of Human–Computer Interaction, 1–10.
   Web of Science ®Google Scholar
• Foon, P. Y., Ganesan, Y., Iranmanesh, M., & Foroughi, B. (2020). Understanding the behavioural intention to dispose of unused medicines: An extension of the theory of planned behaviour. Environmental Science and Pollution Research, 27(22), 28030–28041. https://doi.org/10.1007/s11356-020-09125-0
   PubMed Web of Science ®Google Scholar
• Forgas-Coll, S., Huertas-Garcia, R., Andriella, A., & Alenyà, G. (2023). Social robot-delivered customer-facing services: An assessment of the experience. The Service Industries Journal, 43(3-4), 154–184. https://doi.org/10.1080/02642069.2022.2163995
   Web of Science ®Google Scholar
• Foroughi, B., Hongsachart, H., Asadi, S., Iranmanesh, M., Ghobakhloo, M., & Tirkolaee, E. B. (2023). Reuse intention of augmented reality apps: Recreational consciousness as moderator. The Service Industries Journal, 1–42.
   Web of Science ®Google Scholar
• Foroughi, B., Iranmanesh, M., & Hyun, S. S. (2019). Understanding the determinants of mobile banking continuance usage intention. Journal of Enterprise Information Management, 32(6), 1015–1033.
   Web of Science ®Google Scholar
• Foroughi, B., Iranmanesh, M., Kuppusamy, M., Ganesan, Y., Ghobakhloo, M., & Senali, M. G. (2023). Determinants of continuance intention to use gamification applications for task management: An extension of technology continuance theory. The Electronic Library, 41(2/3), 286–307. https://doi.org/10.1108/EL-05-2022-0108
   Web of Science ®Google Scholar
• Foroughi, B., Iranmanesh, M., Nilashi, M., Ghobakhloo, M., Asadi, S., & Khoshkam, M. (2024). Determinants of followers’ purchase intentions toward brands endorsed by social media influencers: Findings from PLS and fsQCA. Journal of Consumer Behaviour, 23(2), 888–914. https://doi.org/10.1002/cb.2252
   Web of Science ®Google Scholar
• Foroughi, B., Senali, M. G., Iranmanesh, M., Khanfar, A., Ghobakhloo, M., Annamalai, N., & Naghmeh-Abbaspour, B. (2023). Determinants of intention to use ChatGPT for educational purposes: Findings from PLS-SEM and fsQCA. International Journal of Human–Computer Interaction, 1–20.
   Web of Science ®Google Scholar
• Foroughi, B., Sitthisirinan, S., Iranmanesh, M., Nikbin, D., & Ghobakhloo, M. (2023). Determinants of travel apps continuance usage intention: Extension of technology continuance theory. Current Issues in Tourism, 1–17. https://doi.org/10.1080/13683500.2023.2169109
   Web of Science ®Google Scholar
• Foroughi, B., Yadegaridehkordi, E., Iranmanesh, M., Sukcharoen, T., Ghobakhlo, M., & Nilashi, M. (2023). Determinants of continuance intention to use food delivery apps: Findings from PLS and fsQCA. International Journal of Contemporary Hospitality Management, 1–27. https://doi.org/10.1108/IJCHM-10-2022-1209
   Web of Science ®Google Scholar
• Gartner. (2020). Deliver customer service during COVID-19: Improve digital and self-service capabilities. https://www.gartner.com/en/documents/3983091.
   Google Scholar
• Gkinko, L., & Elbanna, A. (2022). Hope, tolerance and empathy: Employees’ emotions when using an AI-enabled chatbot in a digitalised workplace. Information Technology & People, 35(6), 1714–1743. https://doi.org/10.1108/ITP-04-2021-0328
   Web of Science ®Google Scholar
• Gkinko, L., & Elbanna, A. (2023). The appropriation of conversational AI in the workplace: A taxonomy of AI chatbot users. International Journal of Information Management, 69, 102568. https://doi.org/10.1016/j.ijinfomgt.2022.102568
   Web of Science ®Google Scholar
• Gonçalves, F. M. R. R., Cândido, C. J. F., & Feliciano, I. M. P. L. (2020). Inertia, group conformity and customer loyalty in healthcare in the information age. Journal of Service Theory and Practice, 30(3), 307–330. https://doi.org/10.1108/JSTP-08-2019-0184
   Web of Science ®Google Scholar
• Gupta, K. P., & Pande, S. (2023). Understanding generation Z consumers’ revisit intentions to robotic service restaurants. Young Consumers, 24(3), 331–351. https://doi.org/10.1108/YC-09-2022-1598
   Web of Science ®Google Scholar
• Gupta, A., Yousaf, A., & Mishra, A. (2020). How pre-adoption expectancies shape post-adoption continuance intentions: An extended expectation-confirmation model. International Journal of Information Management, 52, 102094.
   Web of Science ®Google Scholar
• Hair, J. F., Sarstedt, M., & Ringle, C. M. (2019). Rethinking some of the rethinking of partial least squares. European Journal of Marketing, 53(4), 566–584. https://doi.org/10.1108/EJM-10-2018-0665
   Web of Science ®Google Scholar
• Haugeland, I. K. F., Følstad, A., Taylor, C., & Bjørkli, C. A. (2022). Understanding the user experience of customer service chatbots: An experimental study of chatbot interaction design. International Journal of Human-Computer Studies, 161, 102788. https://doi.org/10.1016/j.ijhcs.2022.102788
   Web of Science ®Google Scholar
• Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
   Web of Science ®Google Scholar
• Hsiao, K.-L., & Chen, C.-C. (2022). What drives continuance intention to use a food-ordering chatbot? An examination of trust and satisfaction. Library Hi Tech, 40(4), 929–946. https://doi.org/10.1108/LHT-08-2021-0274
   Web of Science ®Google Scholar
• Hsu, C.-L., & Lin, J. C.-C. (2023). Understanding the user satisfaction and loyalty of customer service chatbots. Journal of Retailing and Consumer Services, 71, 103211. https://doi.org/10.1016/j.jretconser.2022.103211
   Web of Science ®Google Scholar
• Hu, X., Xu, X., & Chen, C. (2023). Investigating the effects of perceived autonomy in chatbot advertising. Journal of Interactive Advertising, 23(4), 323–338. https://doi.org/10.1080/15252019.2023.2262456
   Google Scholar
• Huang, S. Y. B., & Lee, C.-J. (2022). Predicting continuance intention to fintech chatbot. Computers in Human Behavior, 129, 107027. https://doi.org/10.1016/j.chb.2021.107027
   Web of Science ®Google Scholar
• Iranmanesh, M., Foroughi, B., Nunkoo, R., & Shan, C. L. (2022). Travellers’ loyalty toward Airbnb: The moderating effect of relative attractiveness of the reward program. Current Issues in Tourism, 25(22), 3623–3639. https://doi.org/10.1080/13683500.2022.2088336
   Web of Science ®Google Scholar
• Iranmanesh, M., Ghobakhloo, M., Foroughi, B., Nilashi, M., & Yadegaridehkordi, E. (2023). Factors influencing attitude and intention to use autonomous vehicles in Vietnam: Findings from PLS-SEM and ANFIS. Information Technology & People, 1–24.
   Web of Science ®Google Scholar
• Iranmanesh, M., Lim, K. H., Foroughi, B., Hong, M. C., & Ghobakhloo, M. (2023). Determinants of intention to adopt big data and outsourcing among SMEs: Organisational and technological factors as moderators. Management Decision, 61(1), 201–222. https://doi.org/10.1108/MD-08-2021-1059
   Web of Science ®Google Scholar
• Jain, N. K., Kaul, D., & Sanyal, P. (2022). What drives customers towards mobile shopping? An integrative technology continuance theory perspective. Asia Pacific Journal of Marketing and Logistics, 34(5), 922–943. https://doi.org/10.1108/APJML-02-2021-0133
   Web of Science ®Google Scholar
• Jiang, K., Qin, M., & Li, S. (2022). Chatbots in retail: How do they affect the continued use and purchase intentions of Chinese consumers? Journal of Consumer Behaviour, 21(4), 756–772. https://doi.org/10.1002/cb.2034
   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
• Jozef, E., Kumar, K. M., Iranmanesh, M., & Foroughi, B. (2019). The effect of green shipping practices on multinational companies’ loyalty in Malaysia. The International Journal of Logistics Management, 30(4), 974–993. https://doi.org/10.1108/IJLM-01-2019-0005
   Web of Science ®Google Scholar
• Kasilingam, D. L. (2020). Understanding the attitude and intention to use smartphone chatbots for shopping. Technology in Society, 62, 101280. https://doi.org/10.1016/j.techsoc.2020.101280
   Web of Science ®Google Scholar
• Kaya, B., Abubakar, A. M., Behravesh, E., Yildiz, H., & Mert, I. S. (2020). Antecedents of innovative performance: Findings from PLS-SEM and fuzzy sets (fsQCA). Journal of Business Research, 114, 278–289. https://doi.org/10.1016/j.jbusres.2020.04.016
   Web of Science ®Google Scholar
• Keskin, S., Şahin, M., Uluç, S., & Yurdugul, H. (2023). Online learners’ interactions and social anxiety: The social anxiety scale for e-learning environments (SASE). Interactive Learning Environments, 31, 201–213.
   Google Scholar
• Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of E-Collaboration (Ijec), 11(4), 1–10. https://doi.org/10.4018/ijec.2015100101
   Web of Science ®Google Scholar
• Kwangsawad, A., & Jattamart, A. (2022). Overcoming customer innovation resistance to the sustainable adoption of chatbot services: A community-enterprise perspective in Thailand. Journal of Innovation & Knowledge, 7(3), 100211. https://doi.org/10.1016/j.jik.2022.100211
   Web of Science ®Google Scholar
• La Greca, A. M., & Lopez, N. (1998). Social anxiety among adolescents: Linkages with peer relations and friendships. Journal of Abnormal Child Psychology, 26(2), 83–94.
   PubMed Web of Science ®Google Scholar
• Le, X. C. (2023). Inducing AI-powered chatbot use for customer purchase: The role of information value and innovative technology. Journal of Systems and Information Technology, 25(2), 219–241. https://doi.org/10.1108/JSIT-09-2021-0206
   Google Scholar
• Li, L., Lee, K. Y., 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
• Li, Y., Wang, C., & Song, B. (2023). Customer acceptance of service robots under different service settings. Journal of Service Theory and Practice, 33(1), 46–71. https://doi.org/10.1108/JSTP-06-2022-0127
   Web of Science ®Google Scholar
• Li, C.-Y., & Zhang, J.-T. (2023). Chatbots or me? Consumers’ switching between human agents and conversational agents. Journal of Retailing and Consumer Services, 72, 103264. https://doi.org/10.1016/j.jretconser.2023.103264
   Web of Science ®Google Scholar
• Liao, C., Palvia, P., & Chen, J.-L. (2009). Information technology adoption behavior life cycle: Toward a Technology Continuance Theory (TCT). International Journal of Information Management, 29(4), 309–320.
   Web of Science ®Google Scholar
• Lin, L.-Z., & Yeh, H.-R. (2023). Using ZMET to explore consumers’ cognitive model in virtual reality: Take the tourism experience as an example. Current Issues in Tourism, 26(14), 2250–2264. https://doi.org/10.1080/13683500.2022.2084048
   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
• Lv, L., Huang, M., & Huang, R. (2023). Anthropomorphize service robots: The role of human nature traits. The Service Industries Journal, 43(3–4), 213–237. https://doi.org/10.1080/02642069.2022.2048821
   Web of Science ®Google Scholar
• Ma, C., & Ye, J. (2022). Linking artificial intelligence to service sabotage. The Service Industries Journal, 42(13–14), 1054–1074. https://doi.org/10.1080/02642069.2022.2092615
   Web of Science ®Google Scholar
• Magno, F., & Dossena, G. (2023). The effects of chatbots’ attributes on customer relationships with brands: PLS-SEM and importance–performance map analysis. The TQM Journal, 35(5), 1156–1169. https://doi.org/10.1108/TQM-02-2022-0080
   Google Scholar
• Marikyan, D., Pantano, E., & Scarpi, D. (2023). Should I stay or should I go? Benefits of crowd-checking technology for a face-to-face shopping experience. Spanish Journal of Marketing - ESIC, 27(1), 20–38. https://doi.org/10.1108/SJME-11-2022-0225
   Google Scholar
• Mehrolia, S., Alagarsamy, S., Moorthy, V., & Jeevananda, S. (2023). Will users continue using banking chatbots? The moderating role of perceived risk. FIIB Business Review, 1–19.
   Google Scholar
• Mostafa, R. B., & Kasamani, T. (2022). Antecedents and consequences of chatbot initial trust. European Journal of Marketing, 56(6), 1748–1771. https://doi.org/10.1108/EJM-02-2020-0084
   Web of Science ®Google Scholar
• Mukherjee, S., Baral, M. M., Nagariya, R., Venkataiah, C., Rao, U. V. A., & Rao, K. S. (2023). Systematic literature review and future research directions for service robots in hospitality and tourism industries. The Service Industries Journal, 43(15–16), 1083–1116. https://doi.org/10.1080/02642069.2023.2243592
   Web of Science ®Google Scholar
• Nguyen, D. M., Chiu, Y.-T. H., & Le, H. D. (2021). Determinants of continuance intention towards banks’ chatbot services in Vietnam: A necessity for sustainable development. Sustainability, 13(14), 7625. https://doi.org/10.3390/su13147625
   Web of Science ®Google Scholar
• Nguyen, T., Huang, E., & Nguyen, D. M. (2023). Food delivery app continuance: A dual model and segmentation approach. International Journal of Retail & Distribution Management, 51(5), 569–589. https://doi.org/10.1108/IJRDM-06-2022-0217
   Web of Science ®Google Scholar
• Nikbin, D., Hyun, S. S., Albooyeh, A., & Foroughi, B. (2014). Effects of perceived justice for coaches on athletes’ satisfaction, commitment, effort, and team unity. International Journal of Sport Psychology, 45(2), 100–120.
   Web of Science ®Google Scholar
• O’cass, A., & Siahtiri, V. (2013). In search of status through brands from Western and Asian origins: Examining the changing face of fashion clothing consumption in Chinese young adults. Journal of Retailing and Consumer Services, 20(6), 505–515.
   Google Scholar
• Oliver, R. L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research, 17(4), 460–469. https://doi.org/10.1177/002224378001700405
   Web of Science ®Google Scholar
• Paniagua, J., Rivelles, R., & Sapena, J. (2018). Corporate governance and financial performance: The role of ownership and board structure. Journal of Business Research, 89, 229–234. https://doi.org/10.1016/j.jbusres.2018.01.060
   Web of Science ®Google Scholar
• Pappas, I. O., & Woodside, A. G. (2021). Fuzzy-set qualitative comparative analysis (fsQCA): Guidelines for research practice in Information Systems and marketing. International Journal of Information Management, 58, 102310. https://doi.org/10.1016/j.ijinfomgt.2021.102310
   Web of Science ®Google Scholar
• Park, J., Yoo, J. W., Cho, Y., & Park, H. (2023). Examining the impact of service robot communication styles on customer intimacy following service failure. Journal of Retailing and Consumer Services, 75, 103511. https://doi.org/10.1016/j.jretconser.2023.103511
   Web of Science ®Google Scholar
• Pentina, I., Hancock, T., & Xie, T. (2023). Exploring relationship development with social chatbots: A mixed-method study of replika. Computers in Human Behavior, 140, 107600. https://doi.org/10.1016/j.chb.2022.107600
   Web of Science ®Google Scholar
• Pereira, T., Limberger, P. F., Minasi, S. M., & Buhalis, D. (2022). New insights into consumers’ intention to continue using chatbots in the tourism context. Journal of Quality Assurance in Hospitality & Tourism, 1–27. https://doi.org/10.1080/1528008X.2022.2136817
   Web of Science ®Google Scholar
• Purohit, S., Arora, R., & Paul, J. (2022). The bright side of online consumer behavior: Continuance intention for mobile payments. Journal of Consumer Behaviour, 21(3), 523–542. https://doi.org/10.1002/cb.2017
   Web of Science ®Google Scholar
• Putri, G. A., Widagdo, A. K., & Setiawan, D. (2023). Analysis of financial technology acceptance of peer to peer lending (P2P lending) using extended technology acceptance model (TAM). Journal of Open Innovation: Technology, Market, and Complexity, 9(1), 100027. https://doi.org/10.1016/j.joitmc.2023.100027
   Google Scholar
• Ragin, C. C. (2008). Redesigning social inquiry: Fuzzy sets and beyond. University of Chicago Press.
   Google Scholar
• Rahi, S., Khan, M. M., & Alghizzawi, M. (2020). 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. https://doi.org/10.1108/IJQRM-03-2020-0074
   Web of Science ®Google Scholar
• Report & Data. (2022). Global Chatbot market to reach USD 22.9 billion by 2030. https://www.reportsanddata.com/press-release/global-chatbot-market.
   Google Scholar
• Richter, N. F., Schubring, S., Hauff, S., Ringle, C. M., & Sarstedt, M. (2020). When predictors of outcomes are necessary: Guidelines for the combined use of PLS-SEM and NCA. Industrial Management & Data Systems, 120(12), 2243–2267. https://doi.org/10.1108/IMDS-11-2019-0638
   Web of Science ®Google Scholar
• Rihoux, B. (2006). Qualitative comparative analysis (QCA) and related systematic comparative methods: Recent advances and remaining challenges for social science research. International Sociology, 21(5), 679–706. https://doi.org/10.1177/0268580906067836
   Web of Science ®Google Scholar
• Senali, M. G., Iranmanesh, M., Ghobakhloo, M., Foroughi, B., Asadi, S., & Rejeb, A. (2024). Determinants of trust and purchase intention in social commerce: Perceived price fairness and trust disposition as moderators. Electronic Commerce Research and Applications, 64, 101370.
   Web of Science ®Google Scholar
• Sidlauskiene, J., Joye, Y., & Auruskeviciene, V. (2023). AI-based chatbots in conversational commerce and their effects on product and price perceptions. Electronic Markets, 33(1), 1–21. https://doi.org/10.1007/s12525-023-00635-6
   PubMed Web of Science ®Google Scholar
• Silva, F. A., Shojaei, A. S., & Barbosa, B. (2023). Chatbot-based services: A study on customers’ reuse intention. Journal of Theoretical and Applied Electronic Commerce Research, 18(1), 457–474. https://doi.org/10.3390/jtaer18010024
   Web of Science ®Google Scholar
• Stentz, L. A., & Cougle, J. R. (2022). Effects of safety behavior fading on social anxiety and emotional disclosure. Behaviour Research and Therapy, 157, 104165. https://doi.org/10.1016/j.brat.2022.104165
   PubMed Web of Science ®Google Scholar
• Sulaiman, Z. A., Iranmanesh, M., Foroughi, B., & Rosly, O. (2022). The impacts of Shariah-compliant hotel attributes on Muslim travellers revisit intention: Religiosity as a moderator. Journal of Islamic Marketing, 13(10), 2108–2125. https://doi.org/10.1108/JIMA-06-2020-0179
   Web of Science ®Google Scholar
• Wang, C., Li, Y., Fu, W., & Jin, J. (2023). Whether to trust chatbots: Applying the event-related approach to understand consumers’ emotional experiences in interactions with chatbots in e-commerce. Journal of Retailing and Consumer Services, 73, 103325. https://doi.org/10.1016/j.jretconser.2023.103325
   Web of Science ®Google Scholar
• Wang, J., & Tanes-Ehle, Z. (2023). Examining the effects of conversational chatbots on changing conspiracy beliefs about science: The paradox of interactivity. Journal of Broadcasting & Electronic Media, 67(1), 68–89. https://doi.org/10.1080/08838151.2022.2153842
   Web of Science ®Google Scholar
• Watson, D., & Friend, R. (1969). Measurement of social-evaluative anxiety. Journal of Consulting and Clinical Psychology, 33(4), 448–457. https://doi.org/10.1037/h0027806
   PubMed Web of Science ®Google Scholar
• Xie, T., Pentina, I., & Hancock, T. (2023). Friend, mentor, lover: Does chatbot engagement lead to psychological dependence? Journal of Service Management, 34(4), 806–828. https://doi.org/10.1108/JOSM-02-2022-0072
   Web of Science ®Google Scholar
• Yang, B., Sun, Y., & Shen, X.-L. (2023). Understanding AI-based customer service resistance: A perspective of defective AI features and tri-dimensional distrusting beliefs. Information Processing & Management, 60(3), 103257. https://doi.org/10.1016/j.ipm.2022.103257
   Web of Science ®Google Scholar
• Yu, R., Tong, Z., & Xiao, H. (2023). Close or distant? The impacts of robot services and spatial distance on service satisfaction. Journal of Hospitality and Tourism Management, 54, 447–456. https://doi.org/10.1016/j.jhtm.2023.01.011
   Web of Science ®Google Scholar
• Zainuddin, Z., Iranmanesh, M., Tseng, Mi., Foroughi, B., & Tengku Hamzah, T. A. A. (2021). Clean development mechanism implementation: External and organizational factors drives expected business benefits. Business Strategy and the Environment, 30(8), 3444–3453. https://doi.org/10.1002/bse.2812
   Web of Science ®Google Scholar
• Zarei, G., Asgarnezhad Nuri, B., & Noroozi, N. (2019). The effect of Internet service quality on consumers’ purchase behavior: The role of satisfaction, attitude, and purchase intention. Journal of Internet Commerce, 18(2), 197–220. https://doi.org/10.1080/15332861.2019.1585724
   Web of Science ®Google Scholar
• Zeng, Z., Li, S., Lian, J.-W., Li, J., Chen, T., & Li, Y. (2021). Switching behavior in the adoption of a land information system in China: A perspective of the push–pull–mooring framework. Land Use Policy, 109, 105629. https://doi.org/10.1016/j.landusepol.2021.105629
   Web of Science ®Google Scholar
• Zhu, Y., Zhang, R., Zou, Y., & Jin, D. (2023). Investigating customers’ responses to artificial intelligence chatbots in online travel agencies: The moderating role of product familiarity. Journal of Hospitality and Tourism Technology, 14(2), 208–224. https://doi.org/10.1108/JHTT-02-2022-0041
   Web of Science ®Google Scholar