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Journal article
Forecasting hospitality revenue with economic wave models: A trigonometric approach to science and non-science labor elasticity
International journal of hospitality management, Vol.134, 104580
04/2026
Web of Science ID: WOS:001678419100001
Abstract
Amid rising hotel excess expenditure driven by unconscious motives and the threat of university funding cuts, this study tackles key challenges for the U.S. hospitality sector. With science, technology, engineering, and mathematics (STEM) employment projected to grow 10.4 % by 2033, accurate forecasting of Revenue per Available Room (RevPAR) is increasingly critical. Using utility-based Generative Adversarial Networks (GANs) grounded in Economic Wave Theory, the study models how unconscious motives contribute to excess expenditure among STEM and non-STEM workforces from 2016 to 2024. By isolating these effects via Average Daily Rate (ADR) and Occupancy (OCC), the analysis identifies the STEM Non-employed group as a Nash equilibrium—an optimal target for dynamic pricing and occupancy adjustments.
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Details
- Title
- Forecasting hospitality revenue with economic wave models
- Publication Details
- International journal of hospitality management, Vol.134, 104580
- Resource Type
- Journal article
- Publisher
- Elsevier Ltd
- Number of pages
- 13
- Copyright
- © 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
- Identifiers
- WOS:001678419100001; 99381610219706600
- Academic Unit
- Commerce; Lewis Bear Jr. College of Business
- Language
- English