Main Uses of Artificial Intelligence in Digital Marketing Strategies Linked to Tourism

Francisco Javier S. Lacárcel ()
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Francisco Javier S. Lacárcel: University of Alicante, Postal: University Institute for Tourism Research, University of Alicante, Alicante, Spain, http://www.ua.es/en/

Journal of Tourism, Sustainability and Well-being, 2022, vol. 10, issue 3, 216-227

Abstract: Migratory movements and tourism in general, together with the improvement of new technologies, have meant an increase in terms of accessibility and ease of information linked to tourism. At the same time, the use of artificial intelligence has opened new horizons in which digital marketing strategies linked to tourism can improve the industry, thus offering multiple possibilities in the short term. This new business ecosystem can analyze and extract large amounts of data for use in their marketing strategies. In this study, a systematic literature review (SLR) is conducted using the Web of Science (WOS) database. The main objective of this review is to identify the main uses of artificial intelligence in digital marketing strategies to understand the decision-making processes of future tourists, destination selection, automation of decision-making processes, and actions developed by tourists in the destination itself. In this context, through the applied methodology, 24 potential results have been identified, which have been classified in: (i)data-driven learning for decision support, (ii)decision support systems, (iii)social data analysis, (iv)artificial intelligence algorithms, and (v)artificial intelligence strategies for the improvement of the user experience. Finally, theoretical and practical implications are identified to support companies that want to develop data-driven digital marketing actions and, from the applied perspective, to help future authors who want to make new academic contributions

Keywords: Digital Marketing; Artificial Intelligence; Tourism; Machine Learning; Big Data Marketing (search for similar items in EconPapers)
JEL-codes: M31 Z32 (search for similar items in EconPapers)
Date: 2022
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