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Journals / Güncel Turizm Araştırmaları Dergisi / 2026 / Cilt: 10 Sayı: 2

AI or Human? The Serial Mediating Role of Perceived Expertise and Trust in the Effect of Recommendation Source Type on Booking Intention in Travel

Yapay Zekâ mı İnsan mı? Seyahat Önerilerinde Kaynak Türünün Rezervasyon Niyetine Etkisinde Algılanan Uzmanlık ve Güvenin Sıralı Aracılık Rolü

Oguzhan Aydın(Pamukkale Üniversitesi)0000-0002-5104-2986
Pages
781–806
Publication Date
2026-09-11
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

By demonstrating the sequential roles of perceived expertise and trust in explaining how recommendation source affects consumer behaviour, this study contributes to the tourism and artificial intelligence literature.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:With the growing use of artificial intelligence technologies in the tourism sector, consumer perceptions of AI-based recommendations have become an important research topic.
📊
Key Findings & Data:For this purpose, a scenario-based experimental study was conducted with 206 participants residing in the United Kingdom.
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Conclusion & Impact:By demonstrating the sequential roles of perceived expertise and trust in explaining how recommendation source affects consumer behaviour, this study contributes to the tourism and artificial intelligence literature.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Yapay Zekâ · Turizm Pazarlaması · Kaynak Güvenilirliği

Abstract

With the growing use of artificial intelligence technologies in the tourism sector, consumer perceptions of AI-based recommendations have become an important research topic. The aim of this study is to examine the effects of recommendation source type (human or artificial intelligence) on perceived expertise, trust, and booking intention in travel recommendations. For this purpose, a scenario-based experimental study was conducted with 206 participants residing in the United Kingdom. Participants were randomly assigned to either a human-generated or an AI-generated travel recommendation scenario, and the data obtained were analysed using Hayes' PROCESS Macro Model 6 approach. The results reveal that human-generated recommendations are more effective than AI-generated recommendations in terms of perceived expertise, trust, and booking intention. This effect was found to occur not directly, but through a sequential mechanism formed by perceived expertise and trust. By demonstrating the sequential roles of perceived expertise and trust in explaining how recommendation source affects consumer behaviour, this study contributes to the tourism and artificial intelligence literature.

Özet

Yapay zekâ teknolojilerinin turizm sektöründe yaygınlaşmasıyla birlikte yapay zekâ tabanlı önerilere yönelik tüketici algıları önemli bir araştırma konusu hâline gelmiştir. Bu araştırmanın amacı, seyahat önerilerinde kaynak türünün (insan veya yapay zekâ) algılanan uzmanlık, güven ve rezervasyon niyeti üzerindeki etkilerini incelemektir. Bu kapsamda Birleşik Krallık’ta yaşayan 206 katılımcıyla senaryo temelli deneysel bir araştırma gerçekleştirilmiştir. Katılımcılar insan kaynaklı veya yapay zekâ destekli seyahat önerisi senaryolarından birine rastgele atanmış ve elde edilen veriler Hayes’in PROCESS Macro Model 6 yaklaşımı kullanılarak analiz edilmiştir. Araştırma sonuçları, insan kaynaklı önerilerin yapay zekâ destekli önerilere kıyasla algılanan uzmanlık, güven ve rezervasyon niyeti üzerinde daha etkili olduğunu ortaya koymaktadır. Bu etkinin doğrudan değil, algılanan uzmanlık ve güvenin oluşturduğu sıralı bir mekanizma aracılığıyla gerçekleştiği belirlenmiştir. Araştırma, öneri kaynağının tüketici davranışlarını nasıl etkilediğini açıklamada algılanan uzmanlık ve güven değişkenlerinin ardışık rollerini ortaya koyarak turizm ve yapay zekâ literatürüne katkı sunmaktadır.

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Cite This Article

Aydın, O. (2026). AI or Human? The Serial Mediating Role of Perceived Expertise and Trust in the Effect of Recommendation Source Type on Booking Intention in Travel. Güncel Turizm Araştırmaları Dergisi, Cilt: 10 Sayı: 2, 781–806. https://doi.org/10.32572/guntad.1958595

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References (62)

  1. Abou-Shouk, M., Abdelhakim, A. S., Elgarhy, S. D., Rabea, A., & Abdulmawla, M. (2026). ChatGPT usage intention for tourism and hospitality customers. Tourism Recreation Research, 51(2), 533–550. https://doi.org/10.1080/02508281.2025.2465962
  2. Afroogh, S., Akbari, A., Malone, E., Kargar, M., & Alambeigi, H. (2024). Trust in AI: progress, challenges, and future directions. Humanities and Social Sciences Communications, 11(1), 1568.
  3. Al-Huwail, N., Al-Hunaiyyan, A., & Alameeri, M. (2025). Consumer perceptions of AI-driven mobile marketing: trust, privacy, and behavioural intention. International Journal of Operations and Quantitative Management, 31(4), 41-56. https://doi.org/10.46970/2025.31.4.03
  4. Alomari, B. M. A., Awawdeh, A. M. H., & Alolayyan, M. N. (2017). Employee performance and quality management in the tourism sector (Case study of human resources management–employee performance). Modern Applied Science, 11(9), 1–13. https://doi.org/10.5539/mas.v11n9p1
  5. Anh, N. T. N., Minh, D. H., Cuong, T., & Chinh, T. T. Q. (2026). Can AI chatbot adoption bridge the gap between intention and behavior in tourism service e-booking: A moderated mediation model analysis. Tourism and Hospitality, 7(3), 68. https://doi.org/10.3390/tourhosp7030068
  6. Arwab, M., Adil, M., Nasir, M., & Ali, M. A. (2023). Task performance and training of employees: The mediating role of employee engagement in the tourism and hospitality industry. European Journal of Training and Development, 47(9), 900–920. https://doi.org/10.1108/EJTD-01-2022-0008
  7. Aydın, O. (2017). Utangaç tüketici ölçeğinin geliştirilmesi ve tüketici karar verme tarzlarına etkisi [Doktora Tezi, Pamukkale Üniversitesi]. https://tez.yok.gov.tr/UlusalTezMerkezi/tezSorguSonucYeni.jsp
  8. Aydın, O. (2025). The era of hyper-personalization in marketing management and the privacy paradox. In T. Yolcu (Ed.), Marketing dynamics in the digital age: Platforms, ecosystems, and innovation (pp. 1–29). Özgür Publications. https://doi.org/10.58830/ozgur.pub1133
  9. Aydın, O., & Haşıloğlu, S. B. (2021). Tüketici utangaçlık ölçeğinin geliştirilmesi. Tüketici ve Tüketim Araştırmaları Dergisi, 13(1), 1-32. https://search.trdizin.gov.tr/tr/yayin/detay/522808
  10. Baker, M. A. (2024). Sustainable tourism employment: A comprehensive overview of tourism employees’ experience from a tourist-employee interaction perspective. Journal of Hospitality and Tourism Management, 60, 228–238. https://doi.org/10.1016/j.jhtm.2024.07.009
  11. Bindawas, A. M. (2025). Promoting sustainable tourism through employee skills: Contextualizing quality education and the human resource management perspective (SDG-4). Sustainability, 17(2), 748. https://doi.org/10.3390/su17020748
  12. Castelo, N., Bos, M. W., & Lehmann, D. R. (2019). Task-dependent algorithm aversion. Journal of Marketing Research, 56(5), 809–825. https://doi.org/10.1177/0022243719851788
  13. Choung, H., David, P., & Ross, A. (2023). Trust and ethics in AI. AI & Society, 38(2), 733-745. https://doi.org/10.1007/s00146-022-01565-8
  14. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
  15. Crisci, R., & Kassinove, H. (1973). Effect of perceived expertise, strength of advice, and environmental setting on parental compliance. The Journal of Social Psychology, 89(2), 245-250. https://doi.org/10.1080/00224545.1973.9922597
  16. Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334. https://doi.org/10.1007/BF02310555
  17. Daryanani, S. N. D., González, C. S. G., & Rallo, A. R. (2026). A user experience-based evaluation model for AI-enabled digital booking engines in tourism. International Journal of Marketing, Communication and New Media, 18, 230–252.https://doi.org/10.54663/2182-9306
  18. De Cicco, R., Francioni, B., Dini, M., Curina, I., Filieri, R., & Cioppi, M. (2025). Generative AI and human advice: User perceptions, intentions, and behavior across pre-visit and on-site travel stages. Journal of Travel Research, 1–19. https://doi.org/10.1177/00472875251364922
  19. Deák, B., & Szabó, I. (2026, January). Smarter Marketing, Stronger Trust: AI Adoption in SMEs. In 2026 IEEE 24th World Symposium on Applied Machine Intelligence and Informatics (SAMI) (pp. 000175-000180). IEEE. https://doi.org/10.1109/SAMI68106.2026.11420364
  20. Dietvorst, B. J., Simmons, J. P., & Massey, C. (2015). Algorithm aversion: People erroneously avoid algorithms after seeing them err. Journal of Experimental Psychology: General, 144(1), 114–126. https://doi.org/10.1037/xge0000033
  21. Fatema, M., Naher, K., Rifat, M. H. I., & Babu, M. A. (2026). Customer experience as a key mechanism linking AI chatbot attributes to booking intention in tourism. Quality & Quantity, 1-29. https://doi.org/10.1007/s11135-026-02800-3
  22. 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
  23. García-Madurga, M. Á., & Grilló-Méndez, A. J. (2023). Artificial Intelligence in the tourism industry: An overview of reviews. Administrative Sciences, 13(8), 172.
  24. Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51–90. https://doi.org/10.2307/30036519
  25. Gillath, O., Ai, T., Branicky, M. S., Keshmiri, S., Davison, R. B., & Spaulding, R. (2021). Attachment and trust in artificial intelligence. Computers in Human Behavior, 115, 106607.
  26. Glikson, E., & Woolley, A. W. (2020). Human trust in artificial intelligence: Review of empirical research. Academy of Management Annals, 14(2), 627-660.
  27. Gürbüzer, Ş. G., & Haşıloğlu, S. B. (2024). Dijital pazarlama güven ölçeğinin geliştirilmesi. Journal of Internet Applications and Management, 15(1), 1-23. https://doi.org/10.34231/iuyd.1398034
  28. Hayes, A. F. (2013). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. Guilford Publications.
  29. Jin, F., & Zhang, X. (2025). Artificial intelligence or human: when and why consumers prefer AI recommendations. Information Technology & People, 38(1), 279-303. https://doi.org/10.1108/ITP-01-2023-0022
  30. Khan, A. W., & Mishra, A. (2024). AI credibility and consumer-AI experiences: a conceptual framework. Journal of Service Theory and Practice, 34(1), 66-97.
  31. Khan, S., Azhar, M., & Azeem, S. (2026). Decoding the digital influence: how Trivago’s information ecosystem drives hotel booking intentions?. Journal of Hospitality and Tourism Insights, 9(4), 1585-1604. https://doi.org/10.1108/JSTP-03-2023-0108
  32. Kim, J. H., Kim, J., Kim, C., & Kim, S. (2023). Do you trust ChatGPTs? Effects of the ethical and quality issues of generative AI on travel decisions. Journal of Travel & Tourism Marketing, 40(9), 779–801. https://doi.org/10.1080/10548408.2023.2293006
  33. Kim, J., Giroux, M., & Lee, J. C. (2021). When do you trust AI? The effect of number presentation detail on consumer trust and acceptance of AI recommendations. Psychology & Marketing, 38(7), 1140-1155. https://doi.org/10.1002/mar.21498
  34. Kim, T., Kim, M. J., & Promsivapallop, P. (2025). Investigating the influence of generative AI’s credibility and utility on travel consumer behaviour and recommendations through the lens of personal innovativeness. Current Issues in Tourism, 28(14), 2220–2224. https://doi.org/10.1080/13683500.2024.2364764
  35. Kumar, A., & Dayal, M. (2025). AI-powered marketing: A content analysis of bias, transparency, and consumer trust. International Journal of Contemporary Research in Multidisciplinary, 4(3), 146–151. https://doi.org/10.5281/zenodo.15491261
  36. Lam, T., & Hsu, C. H. C. (2006). Predicting behavioral intention of choosing a travel destination. Tourism Management, 27(4), 589–599. 10.1016/j.tourman.2005.02.003
  37. Lee, J. D., & See, K. A. (2004). Trust in automation: Designing for appropriate reliance. Human Factors, 46(1), 50–80. https://doi.org/10.1518/hfes.46.1.50_30392
  38. Lee, W. C., Lee, J. S., & Suh, J. (2026). Semantic divergence in AI-generated and human influencer product recommendations: A computational analysis of dual-agent communication in social commerce. Applied Sciences, 16(6), 2816. https://doi.org/10.3390/app16062816
  39. Liu, W., & Wang, Y. (2025). Evaluating trust in recommender systems: A user study on the impacts of explanations, agency attribution, and product types. International Journal of Human–Computer Interaction, 41(2), 1280-1292. https://doi.org/10.1080/10447318.2024.2313921
  40. Logg, J. M., Minson, J. A., & Moore, D. A. (2019). Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes, 151, 90–103. https://doi.org/10.1016/j.obhdp.2018.12.005
  41. Mazzù, M. F., Andria, A., Baccelloni, A., & De Angelis, M. (2025). The impact of humans vs. AI recommendation on consumer reactions to products exposure. Electronic Commerce Research, 1-41. https://doi.org/10.1007/s10660-025-10048-6
  42. Mesquita, E., Herrero, E., Lopes, E. L., Ribeiro, T. D. L. S. A., & Scrivano, P. (2026). Artificial influence: how AI and influencers shape choices in tourism and hospitality. International Journal of Contemporary Hospitality Management, 38(1), 86-103. https://doi.org/10.1108/IJCHM-12-2024-1898
  43. Mladenović, D., Ismagilova, E., Mogaji, E., & Dwivedi, Y. K. (2026). Travel recommendations of tomorrow: Generative artificial intelligence and travel planning. Journal of Consumer Behaviour, 25(3), 1365-1384. https://doi.org/10.1002/cb.70126
  44. Mollay, M. H., Sharma, D., Anawade, P., Rafique, A. A. A., & Akpabio, E. (2024). Trust in artificial intelligence marketing: How AI-powered chatbots and algorithms influence consumer trust in automated systems. In International Conference on Security, Privacy and Data Analytics (pp. 365–382). Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-96-5330-8_19
  45. Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill. Ohanian, R. (1990). Construction and validation of a scale to measure celebrity endorsers’ perceived expertise, trustworthiness, and attractiveness. Journal of Advertising, 19(3), 39–52. https://doi.org/10.1080/00913367.1990.10673191
  46. Oluwafemi, I. O., Clement, T., Adanigbo, O. S., Gbenle, T. P., & Adekunle, B. I. (2021). A review of ethical considerations in AI-driven marketing analytics: Privacy, transparency, and consumer trust. International Journal of Multidisciplinary Research and Growth Evaluation, 2(2), 428-435.
  47. Omrani, N., Rivieccio, G., Fiore, U., Schiavone, F., & Agreda, S. G. (2022). To trust or not to trust? An assessment of trust in AI-based systems: Concerns, ethics and contexts. Technological Forecasting and Social Change, 181, 121763. https://doi.org/10.1016/j.techfore.2022.121763
  48. Ozturkcan, S., & Bozdağ, A. A. (2025). Responsible AI in marketing: AI booing and AI washing cycle of AI mistrust. International Journal of Market Research, 67(6), 696-722. https://doi.org/10.1177/14707853251379285
  49. Pai, C. K., Dai, A., Yang, C. Y., & Ge, Y. (2022). Exploring factors affecting customer trust in social distancing technology and impact on hotel booking intentions. Cogent Social Sciences, 8(1), 1-20. https://doi.org/10.1080/23311886.2022.2110200
  50. Park, J. E., Fan, A., & So, K. K. F. (2025). Enhancing the Effectiveness of Generative AI Travel Recommendations: Balancing Source Credibility and Cognitive Load. Journal of Hospitality & Tourism Research, 10963480251401822. https://doi.org/10.1177/10963480251401822
  51. Park, Y. E., & Son, H. (2025). AI recommendation vs. crowdsourced recommendation vs. travel expert recommendation: The moderating role of consumption goal on travel destination decision. PLOS ONE, 20(3), e0318719. https://doi.org/10.1371/journal.pone.0318719
  52. Qi, Y., Du, R., & Yang, R. (2022). Consumers’ trust mechanism and trust boundary on humanizing customer service chatbots in E-commerce. In F. F.-H. Nah & K. Siau (Eds.), HCI in Business, Government and Organizations (pp. 493–509). Springer. https://doi.org/10.1007/978-3-031-05544-7_36
  53. Rejón-Guardia, F., Molinillo, S., & Anaya-Sánchez, R. (2026). AI hallucinations in tourism: How errors impact consumer trust and recommendation acceptance. Journal of Consumer Behaviour, 25(2), 923–938. https://doi.org/10.1002/cb.70105
  54. Ryan, M. (2020). In AI we trust: Ethics, artificial intelligence, and reliability. Science and Engineering Ethics, 26(5), 2749–2767. https://doi.org/10.1007/s11948-019-00137-3
  55. Shi, S., Gong, Y., & Gursoy, D. (2021). Antecedents of Trust and Adoption Intention toward Artificially Intelligent Recommendation Systems in Travel Planning: A Heuristic–Systematic Model. Journal of Travel Research, 60(8), 1714-1734. https://doi.org/10.1177/0047287520966395
  56. Song, Y., & He, Y. (2023). Toward an intelligent tourism recommendation system based on artificial intelligence and IoT using Apriori algorithm. Computers, Materials & Continua, 77(2), 19159–19177. https://doi.org/10.32604/cmc.2023.044162
  57. Talebi, A., Mukherjee, S., Gera, N., Kaur, K., & Das, G. (2026). Unveiling coping mechanisms in marketplace discrimination: the allure of artificial intelligence recommendations. Journal of Product Innovation Management, 43(1), 215-247. https://doi.org/10.1111/jpim.12774
  58. Tasnim, K., Abdullah, M. S., Karim, M. Z., & Hasan, R. (2025). AI-Driven Innovation, Privacy Issues, and Gaining Consumer Trust: The Future of Digital Marketing. Business and Social Sciences, 3(1), 1-7. https://doi.org/10.25163/business.3110212
  59. Trong-Tuan, L. (2020). Can managing employee diversity be a pathway to creativity for tour companies? International Journal of Contemporary Hospitality Management, 32(1), 81–107. https://doi.org/10.1108/IJCHM-06-2018-0488
  60. Wüst, K., & Bremser, K. (2025). Artificial intelligence in tourism through chatbot support in the booking process: An experimental investigation. Tourism and Hospitality, 6(1), 36. https://doi.org/10.3390/tourhosp6010036
  61. Yang, Y., Zheng, J., Yu, Y., Qiu, Y., & Wang, L. (2024). The role of recommendation sources and attribute framing in online product recommendations. Journal of Business Research, 174, 114498. https://doi.org/10.1016/j.jbusres.2024.114498
  62. Zwane, F. N., Du Plessis, L., & Slabbert, E. (2014). Analysing employers' expectations of employee skills in the South African tourism industry. SA Journal of Human Resource Management, 12(1), 1–9. https://doi.org/10.4102/sajhrm.v12i1.550