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The rapid development of artificial intelligence (AI) technologies has transformedthe online travel industry by enabling tailored recommendations and personalized userexperiences.

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  • Subject Code :MIS703

Sample Expos

Working Title:AI-Powered Personalization in Travel Platforms and Empirical Analysis

Overall aim:The rapid development of artificial intelligence (AI) technologies has transformedthe online travel industry by enabling tailored recommendations and personalized userexperiences. As competition intensifies within the travel sector, platforms increasingly rely on AIto provide customized itineraries, accommodation suggestions, and dynamic pricing. However,despite its widespread adoption, there is a lack of comprehensive academic analysis regardingthe effectiveness, challenges, and ethical implications of AI-driven personalization in travelplatforms. This topic is highly relevant as it intersects critical areas of business, technology, andconsumer behavior, making it suitable for an academic paper. Additionally, the findings from thisresearch could provide actionable insights for travel companies seeking to balance usersatisfaction with profitability.

Objectives:The primary objective of this thesis is to analyze the role of AI-poweredpersonalization in enhancing user experience and improving operational efficiency for online

travel platforms. Specifically, the research aims to:

  • Examine how AI-driven recommendations influence customer satisfaction and loyalty.
  • Investigate the business benefits and limitations of AI-based personalization for travelcompanies. Define.
  • Explore ethical considerations, such as data privacy and algorithmic fairness, in the application of AI for personalization.

Methodology:

This research will employ a mixed-methods approach to ensure a comprehensive understandingof the topic:

  1. Literature Review: Conduct a systematic review of existing academic and industrypublications on AI personalization in travel and related fields.2. Case Studies: Analyze leading travel platforms (e.g., Expedia, Booking.com, and Airbnb) toassess their AI personalization strategies and outcomes.
  2. Questionnaire-Based Study: Distribute surveys to users of online travel platforms to gatherdata on their experiences and perceptions of AI-driven personalization. (need appointment)
  3. Expert Interviews: Conduct interviews with industry professionals to gain insights into theimplementation challenges and future trends of AI personalization.

Structure:

  1. Introduction
  • Background and relevance of AI in the travel industry
  • Research objectives and questions
  • Overview of methodology
  1. Literature Review
  • AI technologies in personalization
  • Current applications in the travel industry
  • Gaps in existing research
  1. Case Studies

Overview of selected platforms

  • Analysis of AI-powered features and their effectiveness
  1. Survey and Interview Findings

Quantitative analysis of user feedback

Qualitative insights from industry professionals

  1. Discussion
  • Implications for users and businesses

What fields can AI can be used and what are the expected outcomes and benefits.

  • Ethical considerations

Does use of AI lead to reduction or increase of labor force

  • Limitations of current AI approaches
  1. Conclusion and Recommendations
  • Summary of findings
  • Practical recommendations for travel platforms
  • Suggestions for future research

Preliminary Reading List:

  1. Buhalis, D., & Law, R. (2008). Progress in information technology and tourismmanagement: 20 years on and 10 years after the InternetThe state of eTourism research. Tourism Management, 29(4), 609-623.
  2. Tussyadiah, I., & Wang, D. (2016). Tourists’ attitudes toward proactive smartphone systems. Journal of Travel Research, 55(4), 493-507.
  3. Daz, E., & Medrano, J. (2020). Artificial intelligence in tourism: Benefits and challenges ofimplementing chatbots in the hotel industry.Journal of Tourism Futures, 6(2), 177-182.
  4. Kaplan, A., & Haenlein, M. (2019). Siri, Siri in my hand, whos the fairest in the land? Onthe interpretations, illustrations, and implications of artificial intelligence.Business Horizons, 62(1), 15-25.
  5. Zanker, M., Jannach, D., & Felfernig, A. (2019). Recommender systems: An overview.AI Magazine, 40(3), 1-7.
  6. Reis, J., Amorim, M., Melo, N., & Matos, P. (2018). Digital transformation: A literaturereview and guidelines for future research.World Conference on Information Systems and Technologies. Springer.
  7. Helbing, D. (n.d.).Title of the work (if applicable). ScienceDirect. Retrieved January 18,2025, from https://www.econstor.eu/handle/10419/216763?locale=de

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