... engagement through real-time simulation and data-driven insights. Design/methodology/approach Following the PRISMA protocol, peer-reviewed studies were systematically collected from three databases (Web of Science, Scopus, ScienceDirect) and analysed using BERTopic, a BERT-based machine learning...

... information) on hotel performance prediction through a comparison of multiple machine learning models. Design/methodology/approach To achieve these goals, the present study uses an ML-driven predictive modeling approach by employing multimodal data such as social media photos, captions and financial data...

...Irem Önder Purpose This study aims to investigate the effectiveness and accessibility of GPT-4-turbo, a large language model ( LLM ), compared to traditional machine learning ( ML ) methods for sentiment analysis in restaurant reviews. Beyond predictive performance, the study emphasizes practical...

...Nasim Binesh; Kushi Vardhan Reddy Pasham; Gidon Jakar; Ahmad Mujafar Syah Purpose In this study, the authors used advanced machine learning ( ML ) models combined with game theory concepts to predict the occupancy of Airbnb properties. By leveraging big data and explainable artificial intelligence...

... evidence, nearly half a year of extensive observations at a dim sum restaurant yielded 1,784 transactional data points and corresponding food leftover records. Second, multiple machine learning models were built and compared to identify the most parsimonious model that effectively predicts food leftovers...

... at Link to the terms of the CC BY 4.0 licence. Corresponding author Miralem Helmefalk miralem.helmefalk@lnu.se Artificial intelligence Congruency Hospitality Machine learning Multi-sensory cues Sensory experiences Sensory marketing RQ1. How can AI enhance SM...

... and machine learning techniques, it aims to bridge the gap between behavioral intentions and actual behavior. Design/methodology/approach This study constructs the TPB components (attitudes, social norms and perceived control) from textual data, and analyzes their impacts on hotel customers’ behavioral...

... forecasting tasks and have proven advantageous regarding prediction accuracy and robustness. However, this promising approach has rarely been adopted for wine price prediction. Only Yeo et al. (2015) used simple machine-learning techniques in their work and investigated the potential of advanced...

... Revenue management ADR Price Forecasting Artificial neural network Machine learning LSTM Game theory Risk In times of uncertainty, pricing plays a crucial role in the operations and survival of hotels (Zaki, 2022). Given the unprecedented nature of the events in the hospitality industry...

... proximity in their formation. Design/methodology/approach Data mining and machine learning techniques were applied to data collected from the websites of tourism companies located in northeastern Italy, namely, the Veneto region. Specifically, the authors used Web scraping to extract relevant...

..., to the best of the authors’ knowledge, this study is one of the first to reveal consumers’ perceptions by a machine learning approach and econometric modeling with online big data. Our work facilitates automatic computational analysis of image data – particularly by advancing UGP processing, categorization...

... et al., 1988). It was accompanied by a significant growth in statistical learning, with algorithms such as the support vector machine (Cortes and Vapnik, 1995). The development of neural networks and statistical algorithms accelerated the acceptance and application of AI and machine learning (ML...

..., hospitality and tourism literature has shown minimal efforts to process and analyze big hospitality data through AI. Thus, this study aims to develop and compare prediction models for review helpfulness using machine learning (ML) algorithms to analyze big restaurant data. Design/methodology/approach...

... and compared data for the same tourism destination from two sources: responses from a visitor intercept survey (n = 1,336) and Flickr social media photos and metadata (n = 11,775). Content analysis, machine learning and text analysis techniques were used to analyze and compare the destination...

...Apostolos Ampountolas; Mark P. Legg Purpose This study aims to predict hotel demand through text analysis by investigating keyword series to increase demand predictions’ precision. To do so, this paper presents a framework for modeling hotel demand that incorporates machine learning techniques...

... from 116,241 reviews scraped from Tripadvisor.com using a stratified sampling technique comprising hotels in major cities in the USA. A sample comprising 1,800 reviews was randomly drawn from this larger pool of reviews and manually annotated. A manual-set rule-based model, supervised machine learning...

... a machine learning algorithm, decision tree, to analyze the influential determinants on hotel booking considering the geographical distance between origin and destination. Findings The results of the findings demonstrate that the choice of package product is the prioritized determinant for longer-stay...

.... Literature review Bibliometrics Machine learning Contemporary hospitality management Structural topic modelling This study also examined changes in the research streams before and after IJCHM was indexed in SSCI. Since 2010, the IJCHM has been indexed in SSCI and, therefore...

... to detect fraudulent reviews and create proprietary rating indexes, or which can be used as a measure of selection in recommender systems. This study applies machine learning and natural language processing approaches to combine features derived from the qualitative component of a review...

...Eunhye (Olivia) Park; Bongsug Chae; Junehee Kwon Purpose This paper aims to identify the intellectual structure of four leading hospitality journals over 40 years by applying mixed-method approach, using both machine learning and traditional statistical analyses. Design/methodology/approach...