Revenue Management Analytics and Managerial Oversight Capability in U.S. Full-Service Hotels: A TOE Framework Analysis

Revenue management analytics platforms are now widely deployed in U.S. full-service hotels, automating dynamic pricing, demand forecasting, channel optimization, and total revenue management. This paper examines the relationship between revenue management analytics adoption and managerial oversight capability in U.S. full-service hotel operations, arguing that the economic value of these systems depends critically on the analytical capability of revenue managers to interpret, govern, and override automated outputs, and that current organizational and environmental conditions are systematically failing to develop this capability at the required scale. The paper conducts a literature review of peer-reviewed research on hotel revenue management, human capital theory, and technology adoption, drawing on Human Capital Theory and the Technology-Organization-Environment framework to map five revenue management analytics domains and their oversight requirements, identify barriers to effective oversight, and propose a three-level reskilling framework with explicit implementation parameters covering cost, scalability, and operational feasibility. The principal findings are that barriers to effective oversight are organizational and environmental rather than technological. Revenue management platforms are deployed; the managerial capability to govern them is not. High staff turnover, data fragmentation, algorithm interpretability limitations, absent industry competency mandates, and margin-driven underinvestment are the specific constraints identified. The study situates its findings in the context of an industry generating a nominal record RevPAR of $101.82 [2], where the governance gap is attributable to organizational and environmental constraints rather than to any limitation of the deployed technology. The three-level reskilling framework is benchmarked against existing AACSB and HSMAI competency models and calibrated to operator scale. A proposed empirical validation design using cross-sectional survey methodology and hierarchical regression analysis provides a pathway to primary data confirmation of the framework's hypotheses.