Applications of Artificial Intelligence and Machine Learning in Tourism Marketing Research
Purpose of the study: This study aims to investigate the applications of artificial intelligence and machine learning in tourism marketing research. Methodology: The importance and advantages of using artificial intelligence or machine learning in tourism marketing have been investigated. A literature review has been conducted in this field. Main Findings: As a result of this research, it has been found that using artificial intelligence or machine learning in tourism marketing will be more effective in learning customers' feelings and thoughts about businesses, more useful in making predictions about the future, and ultimately productive in customer satisfaction, which is considered the dedication point of modern marketing. Future studies can address the readiness and competencies of marketing managers employed in the tourism sector in the face of this development. In addition, researching current and potential customers in terms of user acceptance can guide businesses in tourism marketing, which can be considered as important findings of this study. Applications of the study: It is known and seen that artificial intelligence, machine learning and service robots are applied in many tourism companies today. Novelty/Originality of the study: Artificial intelligence and machine learning are making rapid progress in their use in every sector today. The innovations and benefits provided by the new and constantly developing use of artificial intelligence and machine learning for tourism marketing have been investigated.
Authors
- Yakup Durmaz (ORCID: https://orcid.org/0000-0003-0332-4185)
- Leyla Akdoğan (ORCID: https://orcid.org/0000-0002-1800-7708)
Institutions
- Mi̇lli̇ Eği̇ti̇m Bakanliği (TR)
- Hasan Kalyoncu University (TR)
Publication Details
- Journal
- International Journal of Tourism & Hospitality Reviews
- Published
- 2024-10-23
- DOI
- https://doi.org/10.18510/ijthr.2024_t24_05
- Citations
- 1
- Primary Topic
- Digital Marketing and Social Media
- Type
- article
- Field-Weighted Citation Impact
- 1.13