The augmentation effect of artificial intelligence: can AI framing shape customer acceptance of AI-based services?

References

• Alaei, A. R., Becken, S., & Stantic, B. (2019). Sentiment analysis in tourism: Capitalizing on big data. Journal of Travel Research, 58(2), 175–191. https://doi.org/10.1177/0047287517747753
   Web of Science ®Google Scholar
• Ayeh, J. K., Au, N., & Law, R. (2013). Predicting the intention to use consumer-generated media for travel planning. Tourism Management, 35, 132–143. https://doi.org/10.1016/j.tourman.2012.06.010
   Web of Science ®Google Scholar
• Bec, A., Moyle, B., Schaffer, V., & Timms, K. (2021). Virtual reality and mixed reality for second chance tourism. Tourism Management, 83, 104256. https://doi.org/10.1016/j.tourman.2020.104256
   Web of Science ®Google Scholar
• Blöcher, K., & Alt, R. (2021). AI and robotics in the European restaurant sector: Assessing potentials for process innovation in a high-contact service industry. Electronic Markets, 31(3), 529–551. https://doi.org/10.1007/s12525-020-00443-2
   Web of Science ®Google Scholar
• Bressler, D. M., & Bodzin, A. M. (2013). A mixed methods assessment of students’ flow experiences during a mobile augmented reality science game. Journal of Computer Assisted Learning, 29(6), 505–517. https://doi.org/10.1111/jcal.12008
   Web of Science ®Google Scholar
• Brynjolfsson, E. (2022). The turing trap: The promise & peril of human-like artificial intelligence. Stanford Digital Economy Lab. http://brynjolfsson.com
   Google Scholar
• Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. Norton.
   Google Scholar
• Buhalis, D., Harwood, T., Bogicevic, V., Viglia, G., Beldona, S., & Hofacker, C. (2019). Technological disruptions in services: lessons from tourism and hospitality. Journal of Service Management, 30(4), 484–506. https://doi.org/10.1108/JOSM-12-2018-0398
   Web of Science ®Google Scholar
• Calbi, M., Langiulli, N., Ferroni, F., Montalti, M., Kolesnikov, A., Gallese, V., & Umiltà, M. A. (2021). The consequences of COVID-19 on social interactions: an online study on face covering. Scientific Reports, 11(1), 2601. https://doi.org/10.1038/s41598-021-81780-w
   PubMed Web of Science ®Google Scholar
• Chi, O. H., Denton, G., & Gursoy, D. (2020a). Artificially intelligent device use in service delivery: a systematic review, synthesis, and research agenda. Journal of Hospitality Marketing & Management, 29, 7, 757–786, https://doi.org/10.1080/19368623.2020.1721394
   Web of Science ®Google Scholar
• Chi, O. H., Gursoy, D., & Chi, C. G. (2020b). Tourists’ attitudes toward the use of artificially intelligent (AI) devices in tourism service delivery: Moderating role of service value seeking. Journal of Travel Research, 61(1), 170–185. https://doi.org/10.1177/0047287520971054
   Web of Science ®Google Scholar
• Choi, Y., Oh, M., Choi, M., & Kim, S. (2020). Exploring the influence of culture on tourist experiences with robots in service delivery environment. Current Issues in Tourism, 24(5), 717–733. https://doi.org/10.1080/13683500.2020.1735318
   Web of Science ®Google Scholar
• Daugherty, P. R., & Wilson, H. J. (2018). Human+ machine: Reimagining work in the age of AI. Harvard Business Review Press.
   Google Scholar
• Davenport, T., & Kirby, J. (2016). Only humans need apply: Winners and losers in the age of smart machines. HarperCollins.
   Google Scholar
• Davenport, T. H., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(1), 24–42. https://doi.org/10.1007/s11747-019-00696-0
   Web of Science ®Google Scholar
• Davenport, T. H., & Miller, S. M. (2022). Working with AI: Real stories of human-machine collaboration. The MIT Press. https://books.google.pt/books?hl=en&lr=&id=f79WEAAAQBAJ&oi=fnd&pg=PR9&dq=Working+With+AI:+Real+Stories+of+Human-Machine+Collaboration&ots=WBhSY5n5sV&sig=XuThNUErxUOOxKw5tQ0uRLYoUyQ&redir_esc=y#v=onepage&q=Working%20With%20AI%3A%20Real%20Stories%20of%20Human-Machine%20Collaboration&f=false
   Google Scholar
• Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly: Management Information Systems, 13(3), 319–339. https://doi.org/10.2307/249008
   Web of Science ®Google Scholar
• de Figueiredo, C. S., Sandre, P. C., Portugal, L. C. L., Mázala-de-Oliveira, T., da Silva Chagas, L., Raony, Í., Ferreira, E. S., Giestal-de-Araujo, E., dos Santos, A. A., & Bomfim, P. O. S. (2021). COVID-19 pandemic impact on children and adolescents’ mental health: Biological, environmental, and social factors. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 106, 110171. https://doi.org/10.1016/j.pnpbp.2020.110171
   PubMed Web of Science ®Google Scholar
• de Kervenoael, R., Hasan, R., Schwob, A., & Goh, E. (2020). Leveraging human-robot interaction in hospitality services: Incorporating the role of perceived value, empathy, and information sharing into visitors’ intentions to use social robots. Tourism Management, 78, 104042. https://doi.org/10.1016/j.tourman.2019.104042
   Web of Science ®Google Scholar
• Elbagir, S., & Yang, J.. (2019, March). Twitter sentiment analysis using natural language toolkit and VADER sentiment. In Proceedings of the international multiconference of engineers and computer scientists, Chicago (Vol. 122, p. 16).
   Google Scholar
• Filieri, R., Lin, Z., Li, Y., Lu, X., & Yang, X. (2022). Customer emotions in service robot encounters: A hybrid machine-human intelligence approach. Journal of Service Research, 25(4), 614–629. https://doi.org/10.1177/10946705221103937.
   Web of Science ®Google Scholar
• Gosjen, D. I., Haryadi, K. H., Nicholas, Gunawan, A. A. S., Djuwita, C. M., & Adiati, M. P. (2023). Implementation e-concierge in hotel. Procedia Computer Science, 216, 485–498. https://doi.org/10.1016/j.procs.2022.12.161
   Google Scholar
• Granulo, A., Fuchs, C., & Puntoni, S. (2021). Preference for human (vs. robotic) labor is stronger in symbolic consumption contexts. Journal of Consumer Psychology, 31, 1, 72–80, https://doi.org/10.1002/jcpy.1181
   Web of Science ®Google Scholar
• Green, G. C., Hevner, A. R., & Collins, R. W. (2005). The impacts of quality and productivity perceptions on the use of software process improvement innovations. Information and Software Technology, 47(8), 543–553. https://doi.org/10.1016/j.infsof.2004.10.004
   Web of Science ®Google Scholar
• Gupta, S., Modgil, S., Lee, C. K., Cho, M., & Park, Y. (2022). Artificial intelligence enabled robots for stay experience in the hospitality industry in a smart city. Industrial Management & Data Systems, 122(10), 2331–2350. https://doi.org/10.1108/IMDS-10-2021-0621
   Web of Science ®Google Scholar
• Gursoy, D., Chi, O. H., Lu, L., & Nunkoo, R. (2019). Consumers acceptance of artificially intelligent (AI) device use in service delivery. International Journal of Information Management, 49, 157–169, https://doi.org/10.1016/j.ijinfomgt.2019.03.008.
   Web of Science ®Google Scholar
• Hayes, A. F. (2013). Introduction to mediation, moderation, and conditional process analysis: A … - Andrew F. Hayes - Google Books. In The Guilford Press. https://books.google.pt/books?hl=en&lr=&id=8ZM6DwAAQBAJ&oi=fnd&pg=PP1&dq=Introduction+to+mediation,+moderation,+and+conditional+process+analysis:+A+regression-based+approach+&ots=21zcpKZdXF&sig=eBfebeQOBB3JdIhkGlk6XQIf638&redir_esc=y#v=onepage&q=Introduct
   Google Scholar
• Holthöwer, J., & van Doorn, J. (2022). Robots do not judge: Service robots can alleviate embarrassment in service encounters. Journal of the Academy of Marketing Science, 1–18. https://doi.org/10.1007/s11747-022-00862-x
   Web of Science ®Google Scholar
• Huang, D., Chen, Q., Huang, J., Kong, S., & Li, Z. (2021). Customer-robot interactions: Understanding customer experience with service robots. International Journal of Hospitality Management, 99, 103078. https://doi.org/10.1016/j.ijhm.2021.103078
   Web of Science ®Google Scholar
• Huang, M.-H., Rust, R., & Maksimovic, V. (2019). The feeling economy: Managing in the next generation of artificial intelligence (AI). California Management Review, 61(4), 43–65. https://doi.org/10.1177/0008125619863436
   Web of Science ®Google Scholar
• Huang, M.-H., & Rust, R. T. (2018). Artificial intelligence in service. Journal of Service Research, 21(2), 155–172. https://doi.org/10.1177/1094670517752459
   Web of Science ®Google Scholar
• Huang, M. H., & Rust, R. T. (2021a). Engaged to a robot? The role of AI in service. Journal of Service Research, 24(1), 30–41. https://doi.org/10.1177/1094670520902266
   Web of Science ®Google Scholar
• Huang, M. H., & Rust, R. T. (2021b). A framework for collaborative artificial intelligence in marketing. Journal of Retailing, 98, 2, 209–223, https://doi.org/10.1016/J.JRETAI.2021.03.001
   Web of Science ®Google Scholar
• Hutto, C., & Gilbert, E. (2014). Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media, 8(1), 216–225. https://doi.org/10.1609/icwsm.v8i1.14550
   Google Scholar
• Ivanov, S. (2019). Ultimate transformation: How will automation technologies disrupt the travel, tourism and hospitality industries? Zeitschrift Für Tourismuswissenschaft, 11(1), 25–43. https://doi.org/10.1515/tw-2019-0003
   Web of Science ®Google Scholar
• Ivanov, S., Webster, C., & Seyitoğlu, F. (2023). Humans and/or robots? Tourists’ preferences towards the humans–robots mix in the service delivery system. Service Business, 17(1), 195–231. https://doi.org/10.1007/s11628-022-00517-5
   Web of Science ®Google Scholar
• Jabeen, F., Al Zaidi, S., & Al Dhaheri, M. H. (2021). Automation and artificial intelligence in hospitality and tourism. Tourism Review, 77(4), 1043–1061. https://doi.org/10.1108/TR-09-2019-0360
   Web of Science ®Google Scholar
• Jung, I., Quan, W., Yu, J., & Han, H. (2023). Are you ready for robot services? Exploring robot-service adoption behaviors of hotel-goers. International Journal of Hospitality Management, 109, 103404. https://doi.org/10.1016/j.ijhm.2022.103404
   Web of Science ®Google Scholar
• Kim, T., Jo, H., Yhee, Y., & Koo, C. (2022). Robots, artificial intelligence, and service automation (RAISA) in hospitality: sentiment analysis of YouTube streaming data. Electronic Markets, 32(1), 259–275. https://doi.org/10.1007/s12525-021-00514-y
   Web of Science ®Google Scholar
• Kucukusta, D., Law, R., Besbes, A., & Legohérel, P. (2015). Re-examining perceived usefulness and ease of use in online booking the case of Hong Kong online users. International Journal of Contemporary Hospitality Management, 27(2), 185–198. https://doi.org/10.1108/IJCHM-09-2013-0413
   Web of Science ®Google Scholar
• Larivière, B., Bowen, D., Andreassen, T. W., Kunz, W., Sirianni, N. J., Voss, C., Wünderlich, N. V., & De Keyser, A. (2017). “Service Encounter 2.0”: An investigation into the roles of technology, employees and customers. Journal of Business Research, 79, 238–246. https://doi.org/10.1016/j.jbusres.2017.03.008
   Web of Science ®Google Scholar
• Leung, E., Paolacci, G., & Puntoni, S. (2019). Man versus machine: Resisting automation in identity-based consumer behavior. Journal of Marketing Research, 55(6), 818–831, https://doi.org/10.1177/0022243718818423
   Web of Science ®Google Scholar
• Leung, R. (2019). Smart hospitality: Taiwan hotel stakeholder perspectives. Tourism Review, 74(1), 50–62. https://doi.org/10.1108/TR-09-2017-0149
   Web of Science ®Google Scholar
• Leung, X. Y. (2020). Technology-enabled service evolution in tourism: a perspective article. Tourism Review, 75(1), 279–282. https://doi.org/10.1108/TR-06-2019-0229
   Web of Science ®Google Scholar
• Lin, Y. (2020). 10 artificial intelligence statistics you need to know in 2021. Oberlo. https://www.oberlo.com/blog/artificial-intelligence-statistics.
   Google Scholar
• Liu, B., & Zhang, L. (2012). A survey of opinion mining and sentiment analysis. Mining Text Data, 9781461432234, 415–463. https://doi.org/10.1007/978-1-4614-3223-4_13
   Google Scholar
• Longoni, C., Bonezzi, A., & Morewedge, C. K. (2019). Resistance to medical artificial intelligence. Journal of Consumer Research, 46(4), 629–650. https://doi.org/10.1093/jcr/ucz013
   Web of Science ®Google Scholar
• Longoni, C., & Cian, L. (2022). Artificial intelligence in Utilitarian vs. Hedonic contexts: The “Word-of-Machine”. Journal of Marketing, 86(1), 91–108. https://doi.org/10.1177/0022242920957347
   Web of Science ®Google Scholar
• Loureiro, S. M. C. (2015). The role of website quality on PAD, attitude and intentions to visit and recommend island destination. International Journal of Tourism Research, 17(6), 545–554. https://doi.org/10.1002/jtr.2022
   Web of Science ®Google Scholar
• Lv, X., Yang, Y., Qin, D., Cao, X., & Xu, H. (2022). Artificial intelligence service recovery: The role of empathic response in hospitality customers’ continuous usage intention. Computers in Human Behavior, 126, 106993. https://doi.org/10.1016/j.chb.2021.106993
   Web of Science ®Google Scholar
• Makridis, C. A., & Mishra, S. (2022). Artificial intelligence as a service, economic growth, and well-being. Journal of Service Research, 25(4), 505–520. https://doi.org/10.1177/10946705221120218
   Web of Science ®Google Scholar
• Marinova, D., de Ruyter, K., Huang, M.-H., Meuter, M. L., & Challagalla, G. (2017). Getting Smart: Learning From Technology-Empowered Frontline Interactions. Journal of Service Research, 20(1), 29–42. https://doi.org/10.1177/1094670516679273
   Web of Science ®Google Scholar
• McLeay, F., Osburg, V. S., Yoganathan, V., & Patterson, A. (2021). Replaced by a Robot: Service implications in the age of the machine. Journal of Service Research, 24(1), 104–121. https://doi.org/10.1177/1094670520933354
   Web of Science ®Google Scholar
• Mingotto, E., Montaguti, F., & Tamma, M. (2021). Challenges in re-designing operations and jobs to embody AI and robotics in services. Findings from a case in the hospitality industry. Electronic Markets, 31(3), 493–510. https://doi.org/10.1007/s12525-020-00439-y
   Web of Science ®Google Scholar
• Ozdemir, O., Dogru, T., Kizildag, M., & Erkmen, E. (2023). A critical reflection on digitalization for the hospitality and tourism industry: Value implications for stakeholders. International Journal of Contemporary Hospitality Management, ahead-of-print. https://doi.org/10.1108/IJCHM-04-2022-0535
   Web of Science ®Google Scholar
• Peng, C., van Doorn, J., Eggers, F., & Wieringa, J. E. (2022). The effect of required warmth on consumer acceptance of artificial intelligence in service: The moderating role of AI-human collaboration. International Journal of Information Management, 66, 102533. https://doi.org/10.1016/j.ijinfomgt.2022.102533
   Web of Science ®Google Scholar
• Phillips, P., Antonio, N., de Almeida, A., & Nunes, L. (2020). The influence of geographic and psychic distance on online hotel ratings. Journal of Travel Research, 59(4), 722–741. https://doi.org/10.1177/0047287519858400
   Web of Science ®Google Scholar
• Polemis, M. L., & Stengos, T. (2021). Threshold effects during the COVID-19 pandemic crisis: evidence from international tourist destinations. Current Issues in Tourism, 25(3), 387–393. https://doi.org/10.1080/13683500.2021.1960286
   Web of Science ®Google Scholar
• Pomfret, G. (2021). Family adventure tourism: Towards hedonic and eudaimonic wellbeing. Tourism Management Perspectives, 39, 100852–100852. https://doi.org/10.1016/j.tmp.2021.100852
   Web of Science ®Google Scholar
• Ribeiro, F. N., Araújo, M., Gonçalves, P., Gonçalves, M. A., & Benevenuto, F. (2016). SentiBench - a benchmark comparison of state-of-the-practice sentiment analysis methods. EPJ Data Science, 5(1), 1–29. https://doi.org/10.1140/epjds/s13688-016-0085-1
   Google Scholar
• Rodrigues, L. F., Oliveira, A., & Costa, C. J. (2016). Does ease-of-use contributes to the perception of enjoyment? A case of gamification in e-banking. Computers in Human Behavior, 61, 114–126. https://doi.org/10.1016/j.chb.2016.03.015
   Web of Science ®Google Scholar
• Ruan, W. Q., Yang, T. T., Zhang, S. N., Liu, C. H., & Li, Y. Q. (2021). How to create tourists’ enjoyment? critical factors and strategic configurations for cultural and creative tourists’ enjoyment. Journal of Travel & Tourism Marketing, 38(6), 554–567. https://doi.org/10.1080/10548408.2021.1969318
   Web of Science ®Google Scholar
• Rust, R. T., & Huang, M.-H. (2021). The feeling economy. How artificial intelligence Is creating the Era of empathy. In Palgrave Macmillan. Springer International Publishing. https://doi.org/10.1007/978-3-030-52977-2
   Google Scholar
• Seo, K. H., & Lee, J. H. (2021). The emergence of service robots at restaurants: Integrating trust, perceived risk, and satisfaction. Sustainability, 13(8), 4431. https://doi.org/10.3390/su13084431
   Web of Science ®Google Scholar
• Seyitoğlu, F., & Ivanov, S. (2020). Service robots as a tool for physical distancing in tourism. Current Issues in Tourism, 24(12), 1631–1634. https://doi.org/10.1080/13683500.2020.1774518
   Web of Science ®Google Scholar
• Stahl, A. (2021). How AI will impact the future of work and life. Forbes. https://www.forbes.com/sites/ashleystahl/2021/03/10/how-ai-will-impact-the-future-of-work-and-life/.
   Google Scholar
• Sun, H., & Zhang, P. (2006). Causal relationships between perceived enjoyment and perceived ease of use: An alternative approach. Journal of the Association for Information Systems, 7(24), 618–645. https://doi.org/10.17705/1jais.00100.
   Google Scholar
• Tamborini, R., Bowman, N. D., Eden, A., Grizzard, M., & Organ, A. (2010). Defining media enjoyment as the satisfaction of intrinsic needs. Journal of Communication, 60(4), 758–777. https://doi.org/10.1111/j.1460-2466.2010.01513.x
   Web of Science ®Google Scholar
• Thompson, D. V., & Malaviya, P. (2013). Consumer-generated ads: Does awareness of advertising co-creation help or hurt persuasion? Journal of Marketing, 77(3), 33–47. https://doi.org/10.1509/jm.11.0403
   Web of Science ®Google Scholar
• Tong, L., Yan, W., & Manta, O. (2022). Artificial intelligence influences intelligent automation in tourism: A mediating role of internet of things and environmental, social, and governance investment. Frontiers in Environmental Science, 10, https://doi.org/10.3389/fenvs.2022.853302
   Web of Science ®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 Der Heijden, H. (2004). User acceptance of hedonic information systems. MIS Quarterly: Management Information Systems, 28(4), 695–704. https://doi.org/10.2307/25148660
   Web of Science ®Google Scholar
• Venkatesh, V. (2021). Adoption and use of AI tools: a research agenda grounded in UTAUT. Annals of Operations Research, 308(1), 641–652. https://doi.org/10.1007/s10479-020-03918-9
   Web of Science ®Google Scholar
• Vorobeva, D., Fassi, Y. El, Pinto, D. C., Hildebrand, D., Herter, M. M., & Mattila, A. S. (2022). Thinking skills don’t protect service workers from replacement by artificial intelligence. Journal of Service Research, 25(4), 601–613. https://doi.org/10.1177/10946705221104312
   Web of Science ®Google Scholar
• Webster, C., & Ivanov, S. (2020). Future tourism in a robot-based economy: a perspective article. Tourism Review, 75(1), 329–332. https://doi.org/10.1108/TR-05-2019-0172.
   Web of Science ®Google Scholar
• Whitmore, G. (2023, February 7). Will ChatGPT replace travel agents? Forbes. https://www.forbes.com/sites/geoffwhitmore/2023/02/07/will-chatgpt-replace-travel-agents/
   Google Scholar
• Wöber, K., & Gretzel, U. (2000). Tourism managers’ adoption of marketing decision support systems. Journal of Travel Research, 39(2), 172–181. https://doi.org/10.1177/004728750003900207
   Google Scholar
• WTO | Services: Tourism and travel-related services. (n.d.). Retrieved February 3, 2022, from https://www.wto.org/english/tratop_e/serv_e/tourism_e/tourism_e.htm.
   Google Scholar
• Xiao, L., & Kumar, V. (2021). Robotics for customer service: A useful complement or an ultimate substitute? Journal of Service Research, 24(1), 9–29. https://doi.org/10.1177/1094670519878881
   Web of Science ®Google Scholar
• Yang, S. (2020, October 20). Text analysis basics in Python. Bigram/Trigram, Sentiment Analysis, and Topic Modeling. Towards Data Science. https://towardsdatascience.com/text-analysis-basics-in-python-443282942ec5
   Google Scholar
• Yoganathan, V., Osburg, V. S., H. Kunz, & Toporowski, W. (2021). Check-in at the Robo-desk: Effects of automated social presence on social cognition and service implications. Tourism Management, 85, 104309. https://doi.org/10.1016/j.tourman.2021.104309
   Web of Science ®Google Scholar
• Yoo, B., & Donthu, N. (2001). Developing and validating a multidimensional consumer-based brand equity scale. Journal of Business Research, 52(1), 1–14 https://doi.org/10.1016/S0148-2963(99)00098-3
   Web of Science ®Google Scholar
• Zhang, X., Tavitiyaman, P., & Tsang, W. Y. (2022). Preferences of technology amenities, satisfaction and behavioral intention: The perspective of hotel guests in Hong Kong. Journal of Quality Assurance in Hospitality & Tourism, ahead-of-print, 1–31. https://doi.org/10.1080/1528008X.2022.2070817
   Web of Science ®Google Scholar