Predicting the interrelationships among Chat GPT, tourist’s satisfaction and usage intention: Moderating role of traditional tour operator services

Tamer Hamdy Ayad, Rania M. Elsayed

Article ID: 4183
Vol 8, Issue 6, 2024

VIEWS - 326 (Abstract) 182 (PDF)

Abstract


This study aims at predicting the interrelationship between among Chat GPT with its six dimensions, tourist’s satisfaction and Chat GPT usage intention as perceived by tourist, and as well as to examine the moderating effect of traditional tour operator services on the relationships between all the variables. Data were collected from 624 tourists. The study hypotheses were tested and the direct and indirect effects between variables were examined using the PLS-SEM. The SEM results showed that Chat GPT’s six dimensions have a positive and significant direct impact on tourist’s satisfaction, and emphasis the moderating role of Traditional Tour Operator Services “TTOS” on the relationship between GPT’s six dimensions and “TS”, and on the relationship between ‘TS” and Chat GPT usage intention. These findings yield valuable insights for everyone interested in the use of IT in the tourism industry, and provide effective strategies for optimizing the use of technological applications by traditional tour operators.


Keywords


Chat GPT; traditional tour operator services; tourist’s satisfaction; AI usage intention; tourism industry

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References


Ali, B. J., Gardi, B., Jabbar Othman, B. J., et al. (2021). Hotel service quality: The impact of service quality on customer satisfaction in hospitality. International Journal of Engineering, Business and Management, 5(3), 14-28.‏ https://doi.org/10.22161/ijebm.5.3.2

Ali, F., Yasar, B., Ali, L., & Dogan, S. (2023). Antecedents and consequences of travelers' trust towards personalized travel recommendations offered by ChatGPT. International Journal of Hospitality Management, 114(1), 103588.‏ https://doi.org/10.1016/j.ijhm.2023.103588

Alotaibi, R., Ali, A., Alharthi, H., & Almehamdi, R. (2020). AI Chatbot for Tourist Recommendations: A Case Study in the City of Jeddah, Saudi Arabia. International Journal of Interactive Mobile Technologies (iJIM), 14(19), 18-30. https://doi.org/10.3991/ijim.v14i19.17201

Ara, C. (2023). The Effect of Chat GPT's e-Service Quality on Learning Performance through Perceived Value and Innovation. The Journal of the Convergence on Culture Technology, 9(5), 707-719.‏

Arman, M., & Lamiya, U. R. (2023). ChatGPT, a Product of AI, and its Influences in the Business World. Talaa: Journal of Islamic Finance, 3(1), 18-37.‏ https://doi.org/10.54045/talaa.v3i1.725

Ashfaq, M., Yun, J., Yu, S., et al. (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

Ayad, T. H. (2017). Examining the relationships between visit experience, satisfaction and behavioral intentions among tourists at the Egyptian Museum, Journal of Association of Arab Universities for Tourism and Hospitality-JAAUTH, 14 (2), 93-104. https://doi.org/10.21608/jaauth.2017.48147

Bayih, B. E., & Singh, A. (2020). Modeling domestic tourism: motivations, satisfaction and tourist behavioral intentions. Heliyon, 6(9), e04839. https://doi.org/10.1016/j.heliyon.2020.e04839

Bin-Nashwan, S. A., Sadallah, M., & Bouteraa, M. (2023). Use of ChatGPT in academia: Academic integrity hangs in the balance. Technology in Society, 75, 102370. https://doi.org/10.1016/j.techsoc.2023.102370

Braimah, S. M., Solomon, E. N. A., & Hinson, R. E. (2024). Tourists satisfaction in destination selection determinants and revisit intentions; perspectives from Ghana. Cogent Social Sciences, 10(1), 2318864.‏ https://doi.org/10.1080/23311886.2024.2318864

Bryman, A., & Cramer, D. (2011). Quantitative data analysis with IBM SPSS 17, 18 and 19: A guide for social scientists. Routledge.

Cai, Q., Lin, Y., & Yu, Z. (2023). Factors influencing learner attitudes towards ChatGPT-assisted language learning in higher education. International Journal of Human-Computer Interaction, 1-15.‏ https://doi.org/10.1080/10447318.2023.2261725

Cempena, I. B., Brahmayanti, I. A. S., Astawinetu, E. D., et al. (2021). The role of customer values in increasing tourist satisfaction in Gianyar Regency, Bali, Indonesia. The Journal of Asian Finance, Economics and Business, 8(8), 553-563.‏ https://doi.org/10.13106/JAFEB.2021.VOL8.NO8.0553

Chang, Y. (2020). Understanding the intention to use chatbots for travel: A TAM-based study. Journal of Travel & Tourism Marketing, 37(5), 546-559.

Chen, C. (2020). The influence of smart tourism on tourist experience toward travel intention and satisfaction: Evidence from China. International Journal of Marketing Studies, 12(3), 65-70.‏ https://doi.org/10.5539/ijms.v12n3p65

Chen, C., & Chen, F. (2010). Experience quality, perceived value, satisfaction and behavioral intentions for heritage tourists. Tourism Management, 31(1), 29-35. https://doi.org/10.1016/j.tourman.2009.02.008

Chin, W.W. (1998). The partial least squares approach for structural equation modeling. In: Marcoulides G. A. (editors). Modern methods for business research. Lawrence Erlbaum Associates Publishers. pp. 295-336.

Chin, W.W. (2010). How to Write Up and Report PLS Analyses. In: Esposito Vinzi, V., Chin, W.W., Henseler, J. and Wang, H., (editors). Handbook of Partial Least Squares: Concepts, Methods and Applications. Springer, Heidelberg, Dordrecht, London, New York. pp. 655-690.

Choi, J., & Kim, K. (2020). The effect of chatbot service quality on user satisfaction and behavioral intention: The mediating role of perceived value and trust. Journal of Hospitality & Tourism Research, 44(4), 625-649. https://doi.org/10.1016/j.jhtm.2020.05.001

Choudhury, A., & Shamszare, H. (2023). Investigating the Impact of User Trust on the Adoption and Use of ChatGPT: Survey Analysis. Journal of Medical Internet Research, 25, e47184.‏ https://doi.org/10.2196/47184

Chung, M., Ko, E., Joung, H., & Kim, S. J. (2018). Chatbot e-service and customer satisfaction regarding luxury brands. Journal of Business Research, 117. https://doi.org/10.1016/j.jbusres. 2018.10.004

Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences, 2nd ed. Hillsdale, NJ: Lawrence Erlbaum Associates, Publishers, USA.

Çolak, O. (2023). The role of generative pre-trained transformers (GPT) in recreational tourism: an interview with ChatGPT. Spor Bilimleri Araştırmaları Dergisi, 8(3), 733-748.‏ https://doi.org/10.25307/jssr.1341967

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

Edwards, J. (2023). 8 Ways to Put ChatGPT to Work for Your Business. InformationWeek. Available online: https://www. informationweek.com/big-data/8-ways-to-put-chatgpt-to-work-for-your-business. (accessed on 6 September 2023).

Egypt’s Central Agency for Public Mobilization and Statistics CAPMAS. Available online: https://www.sis.gov.eg/Story/171699/CAPMAS-85.4%25-increase-in-number-of-tourists-during-1st-half-of-2022?lang=en-us (accessed on 1 January 2024).

Fareed, M. W. (2023). People-centred natural language processing for cultural tourism market: a research agenda.‏ In: Conference: The 2nd International Conference on Visual Pattern Extraction and Recognition for Cultural Heritage Understanding. 25-26 September, Zadar, Croatia.

Farhi, F., Jeljeli, R., Aburezeq, I., et al. (2023). Analyzing the students’ views, concerns, and perceived ethics about chat GPT usage. Computers and Education: Artificial Intelligence, 5, 100180. https://doi.org/10.1016/j.caeai.2023.100180

Faruk, L. I. D., Rohan, R., Ninrutsirikun, U., et al. (2023). University Students’ Acceptance and Usage of Generative AI (ChatGPT) from a Psycho-Technical Perspective. Proceedings of the 13th International Conference on Advances in Information Technology. https://doi.org/10.1145/3628454.3629552

Fatmawati, I., & Olga, F. (2023). Investigating The Determining Factors of Tourist Revisit Intention in a Natural-based Tourism Destination. In: E3S Web of Conferences 444. EDP Sciences.‏ https://doi.org/10.1051/e3sconf/202344401014

Fayed, H. A. K., Wafik, G. M., & Gerges, N. W. (2016). The impact of motivations, perceptions and satisfaction on tourists loyalty. International Journal of Hospitality and Tourism Systems, 9(2), 14.‏

Ferreira, J. M., Acuña, S. T., Dieste, O., et al. (2020). Impact of usability mechanisms: An experiment on efficiency, effectiveness and user satisfaction. Information and Software Technology Journal, 117.‏ https://doi.org/10.1016/j.infsof.2019.106195

Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104

George, A. S., & George, A. H. (2023). A review of ChatGPT AI's impact on several business sectors. Partners Universal International Innovation Journal, 1(1), 9-23.‏ https://doi.org/10.5281/zenodo.7644359

Gursoy, D., Li, Y., & Song, H. (2023). ChatGPT and the hospitality and tourism industry: an overview of current trends and future research directions. Journal of Hospitality Marketing & Management, 32(5), 579–592. https://doi.org/10.1080/19368623.2023.2211993

Hair, J.F., Hult, G.T.M., Ringle, C.M. & Sarstedt, M. (2017) A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), 2nd ed. Sage Publications Inc., Thousand Oaks, CA.

Hui, T. K., Wan, D., & Ho, A. (2007). Tourists' satisfaction, recommendation and revisiting Singapore. Tourism management, 28(4), 965-975.‏ https://doi.org/10.1016/j.tourman.2006.08.008

Jacobs, O., Pazhoohi, F., & Kingstone, A. (2023). Brief exposure increases mind perception to ChatGPT and is moderated by the individual propensity to anthropomorphize. https://doi.org/10.31234/osf.io/pn29d

Jin, S. V.& Youn, S. (2023). Social Presence and Imagery Processing as Predictors of Chatbot Continuance Intention in Human-AI-Interaction. International Journal of Human-Computer Interaction, 39(9), PP.1874-1886. https://doi.org/10.1080/10447318.2022.2129277

Jo, H. (2023). Understanding AI tool engagement: A study of ChatGPT usage and word-of-mouth among university students and office workers. Telematics and Informatics, 85, 102067. https://doi.org/10.1016/j.tele.2023.102067

Kalla, D., & Smith, N. (2023). Study and Analysis of Chat GPT and its Impact on Different Fields of Study. International Journal of Innovative Science and Research Technology, 8(3).‏

Kanwal, A., Hassan, S. K., & Iqbal, I. (2023). An investigation into how university-level teachers perceive chat-gpt impact upon student learning. Gomal University Journal of Research, 39(03), 250–265. Internet Archive. https://doi.org/10.51380/gujr-39-03-001

Keni, K. (2020). How perceived usefulness and perceived ease of use affecting intent to repurchase? Jurnal Manajemen, 24(3), 481-496.‏ https://doi.org/10.24912/jm.v24i3.680

Kim, J. H., Kim, J., Park, J., et al. (2023). When ChatGPT Gives Incorrect Answers: The Impact of Inaccurate Information by Generative AI on Tourism Decision-Making. Journal of Travel Research, 0(0).‏ https://doi.org/10.1177/00472875231212996

Kim, J., Kim, J. H., Kim, C., et al. (2023). Decisions with ChatGPT: Reexamining choice overload in ChatGPT recommendations. Journal of Retailing and Consumer Services, 75, 103494. https://doi.org/10.1016/j.jretconser.2023.103494

Koc, E., Hatipoglu, S., Kivrak, O., et al. (2023). Houston, we have a problem!: The use of ChatGPT in responding to customer complaints. Technology in Society, 74, 102333. https://doi.org/10.1016/j.techsoc.2023.102333

Kock, N. (2020). Multilevel analyses in PLS-SEM: An anchor-factorial with variation diffusion approach. Data Analysis Perspectives Journal, 1(2), 1-6.

Lee, S., & Lee, H. (2021). Understanding the impact of AI chatbots on customer satisfaction: The mediating roles of perceived value, trust, and personalization. Journal of Hospitality Marketing & Management, 30(1), 104-123.

Liu, C. H., La, Q. P., Ng, Y. L., & Mamengko, R. P. (2023). Discovering the Sustainable Innovation Service Process of Organizational Environment, Information Sharing and Satisfaction: The Moderating Roles of Pressure. Sustainability, 15(14), 11445.‏ https://doi.org/10.3390/su151411445

López, P. E., Bulchand-Gidumal, J., Gutiérrez-Taño, D., & Díaz-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

Manan, A. The New Technology and Travel Revolution. Available online: https://www.wearemarketing.com/blog/tourism-and-technology-how-tech-is-revolutionizing-travel.html (accessed on 14 September 2023).

Mearian, L (2023) How enterprises can use ChatGPT and GPT-3. Available online: https://www.computerworld.com/article/3687614/how-enterprises-can-use-chatgpt-and-gpt-3.html (accessed on 6 September 2023).

Melián-González, S., Gutiérrez-Taño, D., & Bulchand-Gidumal, J. (2019). 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

Menon, D., & Shilpa, K. (2023). "Chatting with ChatGPT": Analyzing the factors influencing users' intention to Use the Open AI's ChatGPT using the UTAUT model. Heliyon, 9(11).‏ https://doi.org/10.1016/j.heliyon.2023.e20962

Paul, J., Ueno, A., & Dennis, C. (2023). ChatGPT and consumers: Benefits, Pitfalls and Future Research Agenda. International Journal of Consumer Studies, 47(4), 1213–1225. Portico. https://doi.org/10.1111/ijcs.12928

Permana, D. (2018). Tourist's re-visit intention from perspective of value perception, destination image and satisfaction.‏ European Research Studies Journal, 21(3), 254-265. https://doi.org/10.35808/ersj/1058

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

Pratminingsih, S. A., Rudatin, C. L., & Rimenta, T. (2014). Roles of motivation and destination image in predicting tourist revisit intention: A case of Bandung-Indonesia. International Journal of Innovation, Management and Technology, 5(1), 19.‏ https://doi.org/10.7763/IJIMT.2014.V5.479

Reisinger, Y., & Turner, L. W. (2003). Cross-cultural Behavior in Tourism: Concepts and Analysis. International Journal of Tourism Research, 6(1). https://doi.org/10.1002/jtr.463

Salles, A., Evers, K., & Farisco, M. (2020). Anthropomorphism in AI. AJOB neuroscience, 11(2), 88-95.‏ https://doi.org/10.1080/21507740.2020.1740350

Severt, D., Wong, Y., Chen, P., & Breiter, D. (2007). Examining the motivation, perceived performance and behavioral intentions of convention attendees: Evidence from a regional conference. Tourism Management, 28(2), 399-408. https://doi.org/10.1016/j.tourman.2006.04.003

Skjuve, M., Følstad, A., & Brandtzaeg, P. B. (2023). The user experience of ChatGPT: findings from a questionnaire study of early users. In: Proceedings of the 5th International Conference on Conversational User Interfaces. Association for Computing Machinery, pp. 1-10.‏

Sohail, S. S., Farhat, F., Himeur, Y., et al. (2023). Decoding ChatGPT: a taxonomy of existing research, current challenges, and possible future directions. Journal of King Saud University-Computer and Information Sciences, 35(8).‏ https://doi.org/10.1016/j.jksuci.2023.101675

Stocchi, L., Michaelidou, N. & Micevski, M. (2019). Drivers and outcomes of branded mobile app usage intention. Journal of Product & Brand Management, 28(1), pp. 28-49. https://doi.org/10.1108/JPBM-02-2017-1436

Subagja, A. D., Ausat, A. M. A., Sari, A. R., et al. (2023). Improving Customer Service Quality in MSMEs through the Use of ChatGPT. Jurnal Minfo Polgan, 12(2), 380-386.‏ https://doi.org/10.33395/jmp.v12i1.12407

Topsakal, Y., Icoz, O., & Icoz, O. (2022). Digital Transformation and Tourist Experiences. In L. Oliveira (Ed.), Handbook of Research on Digital Communications, Internet of Things, and the Future of Cultural Tourism (pp. 19-41). IGI Global. https://doi.org/10.4018/978-1-7998-8528-3.ch002

Venkatesh, Thong, & Xu. (2012). Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly, 36(1), 157. https://doi.org/10.2307/41410412

Wang, T., Wang, D., Li, B., et al. (2023). The Impact of Anthropomorphism on Chatgpt Actual Use: Roles of Interactivity, Perceived Enjoyment, and Extraversion. Perceived Enjoyment, and Extraversion. 1-34. https://doi.org/10.2139/ssrn.4547430

Wetzels, M., Odekerken-Schroder, G. & Van Oppen, C. (2009) Using PLS Path Modeling for Assessing Hierarchical Construct Models: Guidelines and Empirical Illustration. MIS Quarterly, 33(1), 177-195. https://doi.org/10.2307/20650284

Wong, I. A., Lian, Q. L., & Sun, D. (2023). Autonomous travel decision-making: An early glimpse into ChatGPT and generative AI. Journal of Hospitality and Tourism Management, 56, 253–263. https://doi.org/10.1016/j.jhtm.2023.06.022

Yilmaz, H., Maxutov, S., Baitekov, A., & Balta, N. (2023). Student Attitudes towards Chat GPT: A Technology Acceptance Model Survey. International Educational Review, 1(1), 57-83. https://doi.org/10.58693/ier.114

Zeng, L., & Li, R. Y. M. (2021). Tourist satisfaction, willingness to revisit and recommend, and mountain kangyang tourism spots sustainability: A structural equation modelling approach. Sustainability, 13(19), 10620.‏ https://doi.org/10.3390/su131910620

Zhai, C., & Wibowo, S. (2022). A systematic review on cross-culture, humor and empathy dimensions in conversational chatbots: The case of second language acquisition. Heliyon, 8(12). https://doi.org/10.1016/j.heliyon.2022.e12056




DOI: https://doi.org/10.24294/jipd.v8i6.4183

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