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

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.