Uncovering Optimal Gains in Hotel Mergers in the Presence of Correlated Inputs: An Integrated OWA-Inverse DEA Framework

Though Mergers and Acquisitions (M&A) are strategic transactions that have long been intertwined with the hospitality sector, the application of data envelopment analysis (DEA) to hotel M&A has been largely overlooked. In this paper, we introduce a new methodological framework that integrates inverse DEA (IDEA) and the ordered weighted averaging (OWA) operator to evaluate pairwise consolidations among hotels in the event of strong input correlation. While IDEA enables effective identification of all productive post-merger hotels, i.e., hotel mergers with potential input gains, the OWA aggregation is essentially prompted by the possible occurrence of inputs’ correlation. Rather than discarding inputs arbitrarily, which may lead to information loss and, hence, biased results, the proposed methodology resorts to OWA aggregation to (1) ensure information integrity through a regulated fusion of correlated inputs, (2) explore a broader spectrum of decision making scenarios by mimicking pessimistic, neutral and optimistic stances, (3) assess the impact of different decision making scenarios on the merging process, (4) show that high levels of post-merger input savings can be reached, regardless of the aggregation stance, and (5) demonstrate empirically that there always exist productive post-mergers implicating pairs of strongly efficient hotels. Individually, these hotels do not require savings, yet when merged, they exhibit significant savings potential. These studies’ objectives are examined by using a case study of 58 hotels in the Sultanate of Oman.