Hotel demand forecasting models and methods using artificial intelligence: A systematic literature review
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...
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| Format: | Article |
| Language: | English |
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University of Algarve, ESGHT/CINTURS
2024-07-01
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| Series: | Tourism & Management Studies |
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| Online Access: | https://www.tmstudies.net/index.php/ectms/article/view/2183 |
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| author | Henrique Henriques Luis Nobre Pereirsa |
| author_facet | Henrique Henriques Luis Nobre Pereirsa |
| author_sort | Henrique Henriques |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-128bd1b3c689499195735c1d66a92cd0 |
| institution | OA Journals |
| issn | 2182-8466 |
| language | English |
| publishDate | 2024-07-01 |
| publisher | University of Algarve, ESGHT/CINTURS |
| record_format | Article |
| series | Tourism & Management Studies |
| spelling | doaj-art-128bd1b3c689499195735c1d66a92cd02025-08-20T02:01:09ZengUniversity of Algarve, ESGHT/CINTURSTourism & Management Studies2182-84662024-07-012033951Hotel demand forecasting models and methods using artificial intelligence: A systematic literature review Henrique Henriques 0Luis Nobre Pereirsa 1School of Management, Hospitality and Tourism of the University of Algarve Universidade do Algarve, ESGHT/CinTurs 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.https://www.tmstudies.net/index.php/ectms/article/view/2183artificial intelligencehotel demand forecastrevenue managementmachine learningartificial neural networksdigital transformation |
| spellingShingle | Henrique Henriques Luis Nobre Pereirsa Hotel demand forecasting models and methods using artificial intelligence: A systematic literature review Tourism & Management Studies artificial intelligence hotel demand forecast revenue management machine learning artificial neural networks digital transformation |
| title | Hotel demand forecasting models and methods using artificial intelligence: A systematic literature review |
| title_full | Hotel demand forecasting models and methods using artificial intelligence: A systematic literature review |
| title_fullStr | Hotel demand forecasting models and methods using artificial intelligence: A systematic literature review |
| title_full_unstemmed | Hotel demand forecasting models and methods using artificial intelligence: A systematic literature review |
| title_short | Hotel demand forecasting models and methods using artificial intelligence: A systematic literature review |
| title_sort | hotel demand forecasting models and methods using artificial intelligence a systematic literature review |
| topic | artificial intelligence hotel demand forecast revenue management machine learning artificial neural networks digital transformation |
| url | https://www.tmstudies.net/index.php/ectms/article/view/2183 |
| work_keys_str_mv | AT henriquehenriques hoteldemandforecastingmodelsandmethodsusingartificialintelligenceasystematicliteraturereview AT luisnobrepereirsa hoteldemandforecastingmodelsandmethodsusingartificialintelligenceasystematicliteraturereview |