Leveraging Chatbots for Personalized Experience in Digital Marketing for Tourism Businesses

Alexandra Theodoropoulou, Ioanna Kalliampakou and Aris Bakalis ()
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Alexandra Theodoropoulou: University of Patras, Department of Management Science and Technology
Ioanna Kalliampakou: University of Patras, Department of Management Science and Technology
Aris Bakalis: University of Patras, Department of Management Science and Technology

A chapter in Synergizing Management, Culture, and Arts for Tourism Development - Vol. 2, 2026, pp 53-91 from Springer

Abstract: Abstract Chatbots have disrupted digital tourism marketing, yet their potential for truly personalized and pleasurable customer experiences stubbornly eludes consensus. This research penetrates beneath the veneer of technical euphoria and questions the actual tourism industry value that chatbots deliver, reaching beyond automation to probe the delicate interplay between user expectation, demographic imperatives, and constantly shifting limits of personalization. Using a structured questionnaire of travelers from six nations in Europe, the research identifies a contradictory world: though chatbots have achieved almost unanimous acceptance for rote tasks, satisfaction reports are overwhelmingly ambivalent, indifferent to factors such as gender, age, or nationality. Statistical analyses of data demonstrate that fundamental chatbot attributes: personalization, relevance, and time-gain barely propel satisfaction levels into the mediocre zone, while emotional connectivity and trust languish at the bottom. These results raise industry alarm bells: mere superficiality of efficiency no longer cuts it. Sustainable influence requires a move from automation-based transactions to emotionally intelligent, contextual interaction. The paper concludes by calling out tourism marketers and developers to escape the incremental trap and design the next generation of digital experiences in which chatbots do not merely provide answers, but build authentic connection, loyalty, and advocacy.

Keywords: User satisfaction; Artificial intelligence; Customer experience; Consumer behavior; Data-driven analysis (search for similar items in EconPapers)
JEL-codes: L86 M31 O33 Z32 Z33 (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1007/978-3-032-17534-2_2

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