This study develops an intra-chain benchmarking framework using data envelopment analysis (DEA) to evaluate the economic-operational and environmental performance of 20 hotels belonging to the same international chain.
A two-stage DEA framework is applied to 2024 hotel-level data. The Slack-Based Inefficiency (SBI) model identifies efficient and inefficient hotels using accommodation capacity as a non-discretionary input, employment as a discretionary input, revenue and guest nights as desirable outputs and water consumption as an undesirable output. The Super-SBI model ranks efficient hotels and identifies internal benchmarks, while variable-specific slacks provide improvement targets for non-frontier hotels. Robustness is assessed through data perturbations, a no-revenue model and a cost-adjusted specification incorporating operating expenses (OPEX).
The results show that 9 of the 20 hotels operate on the efficient frontier, while 11 exhibit positive inefficiency scores. The Super-SBI model identifies the leading internal benchmarks, whereas the SBI results reveal heterogeneous performance gaps in revenue, guest nights, labour utilization and water consumption. Despite shared corporate standards, performance remains uneven across establishments. Robustness checks confirm substantial stability: all nine baseline efficient hotels are preserved under the perturbation scenarios, the no-AR model produces one marginal reclassification, and the OPEX-adjusted specification retains 19 of the 20 classifications.
The study contributes to hotel-chain benchmarking by integrating established SBI and Super-SBI models into a sequential internal benchmarking framework. Its value lies in combining efficiency classification, Super-SBI ranking, benchmark identification and variable-specific improvement targets within a single intra-chain setting. By doing so, the study shows how DEA results can be translated into actionable improvement priorities for multi-unit service organizations operating under shared corporate standards.
