Researchers have developed a data-driven analytical framework that reveals how hotel mergers can generate significant resource savings, even among properties that already operate efficiently.
The study analyses potential merger scenarios among 58 hotels in Oman using an integrated framework that combines inverse data envelopment analysis (IDEA) with an ordered weighted averaging (OWA) operator. The approach evaluates how operational inputs—such as rooms, beds, staff, and salaries—can be optimised when two hotels merge.
Unlike traditional merger evaluations that often remove correlated variables and risk introducing bias, the proposed framework preserves relationships among operational inputs. By simulating all possible two-hotel combinations, the model identifies “productive post-mergers,” or pairings that could produce genuine efficiency gains.
Results suggest that even hotels classified as strongly efficient on their own may achieve substantial reductions in resource use when strategically merged. In some simulated cases, required room and bed capacity dropped by more than 90 per cent compared with the combined pre-merger requirements.
The findings highlight how predictive analytics can support more informed pre-merger planning in the hospitality sector. The framework allows hotel operators and decision-makers to identify partnerships that maximise operational efficiency, improve asset utilisation, and reduce staffing and operational costs.
Beyond full mergers, the analysis suggests that strategic alliances between hotels could also deliver comparable efficiency improvements when consolidation is not feasible.
Although the model currently examines 58 hotels in Oman, the researchers note that future studies could expand the framework to larger datasets and incorporate sustainability indicators to support long-term sector planning.
Overall, the framework offers hotel operators and policymakers a forward-looking analytical tool for identifying hidden efficiency gains and evaluating consolidation opportunities in competitive hospitality markets.
The Journal of Engineering Research [TJER]
Data/statistical analysis
Not applicable
Uncovering Optimal Gains in Hotel Mergers in the Presence of Correlated Inputs: An Integrated OWA-Inverse DEA Framework