Stakeholder perspectives on integrating explainable artificial intelligence into hotel revenue management system

Bibliographic Data

ID21689380
AuthorsIbrahim Mohammed (0000-0002-9583-7078, Hong Kong Polytechnic University), Basak Denizci Guillet (0000-0001-5883-1729, Griffith University, corresponding author)
Year2026
Volume32
Issue4
Pages804-824
Publication date2026-06-01
Open AccessYes
TypeARTICLE
VenueTourism Economics (JOURNAL)
Journal identifiersISSN: 1354-8166 • E-ISSN: 2044-0375
PublisherSAGE Publications (PUBLISHER • US)
DOI10.1177/13548166251382289
OpenAlexW4414484939
LanguageEN
References cited37

Revenue management systems (RMSs) are essential tools for forecasting, pricing and revenue optimisation in the hospitality and tourism industry. They are generally viewed as black boxes, whose logic is difficult to interpret, even for experts, due to information asymmetry, leading to mistrust and aversion. Despite explainable artificial intelligence (XAI)’s potential to address these problems, integrating it into RMSs is limited by a lack of understanding of stakeholder needs and integration barriers. Drawing insights from 27 interviews with hotel revenue management professionals and technology experts, this study reveals that, although XAI awareness and readiness are low, its capabilities to explain RMSs’ decision processes, such as what, how, why, why not, what-if, how-to and what-else, align with stakeholders’ informational requirements. Further, it discloses that integrating XAI can yield economic benefits, such as operational efficiency and RM effectiveness. Finally, the study identifies adoption readiness factors and suggests explanation techniques to optimise XAI-enabled RMSs

Hospitality · Hospitality industry · Hospitality management studies · Information system · Revenue · Revenue management · Stakeholder · Tourism · Yield management · Big Data and Business Intelligence · Explainable Artificial Intelligence (XAI · Forecasting Techniques and Applications

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