Hotel demand forecasting models and methods using artificial intelligence: A systematic literature review

Authors

DOI:

https://doi.org/10.18089/tms.20240304

Keywords:

Artificial Intelligence, Hotel demand Forecast, Revenue Management, Machine Learning, Artificial Neural Networks, Digital Transformation

Abstract

This systematic literature review (SLR) explores current state-of-the-art artificial intelligence (AI) methods for forecasting hotel demand. Since revenue management (RM) is crucial for business success in the hotel industry, this study aims to identify state-of-the-art effective AI-based solutions for hotel demand forecasting, including machine learning (ML), deep learning (DP), and artificial neural networks (ANNs). The study conducted an SLR using the PRISMA model and identified 20 papers indexed in Scopus and the Web of Science. It addresses the gaps in the literature on AI-based demand forecasting, highlighting the need for clarity in model specification, understanding the impact of AI on pricing accuracy and financial performance, and the challenges of available data quality and computational expertise. The review concludes that AI technology can significantly improve forecasting accuracy and empower data-driven decisions in hotel management. Additionally, this study discusses the limitations of AI-based demand forecasting, such as the need for high-quality data. It also suggests future research directions for further enhancing AI forecasting techniques in the hospitality industry.

Author Biographies

  • Henrique Henriques, School of Management, Hospitality and Tourism of the University of Algarve

    Henrique Henriques specialises in Revenue Management and Hotel Management. Over the last decade he has worked as a training coordinator, hotel director, commercial director, trainer and university lecturer. He has extensive experience in consulting and managing hotel units, having worked with dozens of units nationwide. He enjoys constant learning and is focussed on results. He enjoys new challenges that allow him to develop both personally and professionally. He has worked in several hotel chains, organised events and seminars related to tourism and hospitality. He is entrepreneurial, likes innovation and sharing knowledge. He has taught more than 5,000 hours in the areas of Revenue Management and Digital Marketing for tourism and hospitality, collaborating with various training organisations. He is a guest assistant professor at the University of the Algarve on the Master's Degree in Hotel Management and Direction, teaching Revenue Management. He works in the area(s) of Social Sciences with an emphasis on Economics and Management, predominantly in the Organisation and Management of Tourism and Hotel Companies. His research areas: Hotel Management; Revenue Management; Tourism Business Management.

  • Luis Nobre Pereirsa, Universidade do Algarve, ESGHT/CinTurs

    Luis Nobre Pereira is a passionate researcher in the field of Tourism & Hospitality Management through the application of advanced data science techniques. He is an integrated member of the Research Centre for Tourism, Sustainability and Well-being (CinTurs) since 2010 and Professor of Market Research and Data Analysis at the School of Management, Hospitality and Tourism of the University of Algarve. Luis Nobre Pereira was President of the Technical-Scientific Council of the School of Management, Hospitality and Tourism between 2019 and 2023 and Deputy Director of the School between 2016 and 2019. He holds an undergraduate degree in Mathematics Applied to Economics and Management from the ISEG - University of Lisbon (2000), and a Masters degree in Statistics and Information Management from the NOVA IMS - New University of Lisbon (2005). He completed his PhD in Quantitative Methods Applied to Economics and Management at the University of Algarve in 2009. Finally, he got his "Agregação" in Information Management - Survey Sampling and Marketing Research - from New University of Lisbon in 2017. Luis Nobre Pereira's current research interests include application of Data Science Techniques in Social Sciences (in the scientific area of Economics and Management). His research focuses on Tourism Management (sustainable tourism development, tourism demand modelling and forecasting, segmentation, measuring consumer behaviour, decision support system) and Hospitality Management (hotel demand forecasting, revenue management, segmentation, pricing, Data Science for revenue management). In addition, he also conducts research in Marketing Research (measuring consumer behavior, estimation of willingness to pay, market segmentation, quantitative models to solve marketing problems), and Health Economics (measurement of health related quality of life, modelling stated preference data). He is author or co-author of about 55 scientific papers published in academic journals with scientific refereeing. He has acted as Principal Investigator (PI) and as a team member in a number of international (EU funded) as well as national projects in the fields of Tourism & Hospitality Management and Marketing Research. Professor Luis is reviewer of many international journals, including the prestigious Tourism Management, International Journal of Hospitality Management, Current Issues in Tourism and Journal of Hospitality & Tourism Research. He has also taught short courses in the field of Survey Sampling for the Portuguese Statistical Office, Bank of Portugal, EUROSTAT and other producers of Official Statistics.

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Published

28.05.2024

Issue

Vol. 20 No. 3 (2024)

Section

Tourism/Hospitality: Research Papers

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How to Cite

Henriques, H., & Nobre Pereirsa, L. (2024). Hotel demand forecasting models and methods using artificial intelligence: A systematic literature review. Tourism & Management Studies, 20(3), 39-51. https://doi.org/10.18089/tms.20240304