2025: Crisis Management Days Book of Abstracts
Sustainability (tourism, transportation, culture, corporations)

Personalizing the travel experience through AI: Exploring the impact of AI-driven technologies on travellers satisfaction in emerging tourism markets

  • Artan Veseli,
  • Dren Bajraktari,
  • Agron Bajraktari
Artan Veseli
University of Applied Sciences in Ferizaj
Dren Bajraktari
University of Applied Sciences in Ferizaj
Agron Bajraktari
University of Applied Sciences in Ferizaj

Published 2025-05-19

Keywords

  • Artificial intelligence,
  • Personalization,
  • Traveler satisfaction,
  • Technology readiness,
  • Perceived value,
  • Tourism,
  • Kosovo
    • ...More
    Less

How to Cite

Veseli, A., Bajraktari, D., & Bajraktari, A. (2025). Personalizing the travel experience through AI: Exploring the impact of AI-driven technologies on travellers satisfaction in emerging tourism markets . Crisis Management Days. Retrieved from https://ojs.vvg.hr/index.php/DKU/article/view/711

Copyright (c) 2025 Artan Veseli, Dren Bajraktari, Agron Bajraktari

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

  1. Introduction

Artificial Intelligence (AI) is revolutionizing the tourism industry by transforming how travelers interact with services, receive information, and personalize their experiences. AI-powered technologies such as recommender systems, chatbots, upselling tools, and context-aware digital assistants are becoming integral to enhancing customer experience in tourism (Tussyadiah, 2020). In emerging tourism markets like Kosovo, such developments are still nascent but rapidly gaining momentum. Online travel agencies in Kosovo have recently adopted AI-powered upselling email systems that tailor travel suggestions to clients’ preferences, budget, language, and local weather conditions. These systems go beyond promotional messaging, offering curated experiences and practical guidance, thus shaping a more engaging and trust-enhancing digital customer journey (Lopes et al., 2025; Sağbaş & Aydogan, 2025). Despite these advancements, there is limited empirical evidence exploring how these AI-driven technologies impact traveler satisfaction and the psychological processes that mediate or moderate this relationship. This study aims to fill this gap by developing and testing a conceptual model based on Technology Acceptance Model (TAM) (Davis, 1989) and Experience Economy Theory (Pine & Gilmore, 1999).

 

  1. Methodology

A structured survey was conducted among 347 domestic and international travelers in Kosovo who interacted with AI-powered tourism services. The conceptual framework includes AI-driven Personalization (AIP) as the primary independent variable influencing perceptions of Trust in AI (TAI), Perceived Usefulness (PU), Perceived Ease of Use (PEU), and Perceived Value (PV). PU and PEU serve as antecedents to Technology Readiness (TR), while PV is the mediating pathway leading to Traveler Satisfaction (TS). TR is positioned as a moderating variable, strengthening the PV → TS relationship. The constructs were measured using multi-item Likert scales adapted from validated sources. Structural Equation Modeling (SEM) was used to test direct paths, mediation via bootstrapping, and moderation via interaction terms and multi-group analysis. Reliability and validity were assessed through Cronbach’s Alpha, Composite Reliability, AVE, and model fit indices (CFI, TLI, RMSEA, SRMR).

 

  1. Main Results and Discussion

The SEM results confirmed all hypothesized relationships. AI-driven personalization significantly influenced TAI, PU, PEU, and PV (p < 0.001), supporting the theoretical assumption that personalization enhances perceived relevance and trust (Wüst & Bremser, 2025; Sağbaş & Aydogan, 2025). PU and PEU positively impacted TR (p < 0.01), suggesting that user perceptions of ease and usefulness are critical in shaping digital readiness (Vena-Oya et al., 2024). TAI had a strong influence on PV (p < 0.001), which in turn was a significant predictor of TS (p < 0.001). The mediation analysis indicated that the path from AIP to TS is significantly mediated through TAI and PV. Furthermore, moderation analysis showed that TR significantly moderates the PV–TS relationship, with stronger effects observed among high-TR individuals. Model fit indices were satisfactory (CFI=0.945, TLI=0.932, RMSEA=0.061, SRMR=0.048), confirming the robustness of the model. These findings align with earlier work highlighting the importance of personalization, value co-creation, and digital readiness in enhancing traveler experience (Fusté-Forné & Jamal, 2021; Lopes et al., 2025).

 

  1. Conclusion

This study provides empirical evidence on the role of AI-driven personalization in shaping traveler satisfaction, mediated by trust and perceived value, and moderated by technology readiness. It offers both theoretical contributions and practical implications for tourism stakeholders in emerging markets. Policymakers and businesses should prioritize AI investments that enhance personalization and digital engagement while supporting digital literacy among travelers.

 

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