Benchmarking a Seasonal Resort Against Its Own Operating Months Through Dynamic Network Data Envelopment Analysis
This study introduces a Dynamic Network Data Envelopment Analysis framework that benchmarks a seasonal Mediterranean resort by evaluating its individual operating months as decision-making units, thereby isolating management performance from destination-specific factors and revealing that scale inefficiency driven by low-season operating leverage is the primary source of performance shortfalls.
Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer
To understand how a business performs, analysts often look at a group of similar companies and ask which one is doing the best job with its resources. This method, known as frontier analysis, compares a hotel to other hotels, a factory to other factories, or a school to other schools. The goal is to find the "frontier," or the most efficient way to operate, and see how far others fall short of that ideal. However, this approach has a major flaw when applied to seasonal businesses like beach resorts. A resort in July is a very different operation from the same resort in January. In summer, it is packed with guests, running at full speed; in winter, it is nearly empty, yet it must keep its core staff and facilities running. Comparing the winter month to a summer month, or to a different hotel in a different city, often measures the weather and the location rather than the skill of the manager. If a manager is judged against a different asset, the score tells you more about the destination than the management.
This study, conducted by researchers at two Turkish universities, asks a different question. Instead of comparing one hotel to another, they compared one hotel to its own past and future. They took a single, year-round resort on the Mediterranean coast of Turkey and broke its history down into sixty individual months, from January 2021 to December 2025. Each month became its own unit of measurement. By doing this, the researchers ensured that every comparison was made between the same building, the same staff, and the same capacity. The only thing that changed was the time of year. This allowed them to see how well the hotel managed its specific challenges in January versus August, without the noise of comparing it to a different property. They also looked at how the hotel's performance in one month affected the next, treating things like future booking reservations and online guest reviews as a kind of "stock" that is passed from one month to the next, much like a bank account balance that grows or shrinks based on current actions.
The researchers applied a sophisticated measurement tool to this sixty-month panel. They separated the hotel's work into two distinct stages. The first stage was the "operating" side: taking money spent on staff, energy, and supplies and turning it into occupied rooms and meals served. The second stage was the "revenue" side: taking those occupied rooms and meals and turning them into actual income. They also accounted for the fact that a month does not start from scratch; it inherits a booking position and a reputation score from the previous month, and it leaves a new position and score for the following month. To ensure that rising costs due to inflation or wage increases were not mistaken for poor management, they adjusted all money figures to reflect constant prices, stripping out the effect of general price hikes.
The results revealed a clear picture of where the hotel's efficiency problems lay. The most striking finding was that the hotel's overall shortfall in performance was almost entirely due to its size relative to its demand, rather than a lack of skill in how it operated. In technical terms, the hotel was running at the right size for its peak season but was forced to carry a heavy, fixed cost structure through the low season. When the resort was full in August, it was running at peak efficiency. When it was half-empty in January, the fixed costs of keeping the lights on and the core staff employed made it look inefficient, but this was a structural reality of the business, not a failure of management. The researchers found that the hotel was technically very good at its job, with a high level of skill in both operating the facility and converting volume into revenue. The gap between what the hotel could theoretically achieve and what it actually achieved was mostly because it had to run a large facility during months when the demand was low.
The study also showed that the specific part of the business that needed the most attention changed with the calendar. In the shoulder seasons and winter months, the bottleneck was the operating side: the challenge was managing the fixed staff and energy costs against thin volume. In the peak summer months, the bottleneck shifted to the revenue side: the challenge was setting the right prices and managing the mix of guests to maximize income. This alternation meant that a single, overall score for the year would hide these critical details. A manager looking only at an annual average would not know whether to focus on cutting costs in the winter or adjusting pricing strategies in the summer.
Furthermore, the researchers found that the hotel's productivity grew significantly over the five-year period, but this growth came almost entirely from the "frontier" moving forward—meaning the best-performing months kept getting better—rather than the other months catching up. The months that were already efficient became even more so, while the less efficient months did not close the gap. This suggests that the hotel's improvements were driven by its best performers pushing the limits of what was possible, rather than by dragging the weaker months up to a higher standard.
A crucial part of the study involved how they treated the "stock" of bookings and reputation. They found that if they ignored the fact that a month inherits a booking position from the previous month, the rankings of the months changed significantly. A month that looked efficient might have actually been efficient only because it inherited a strong booking position, or it might have been inefficient because it was forced to deal with a weak one. By accounting for these inherited stocks, the researchers could see that the hotel was often penalized for circumstances it did not create, and rewarded for circumstances it did not fully control. When they priced these inherited stocks correctly, the ranking of the months shifted, revealing that the intertemporal connections—the links between months—were more important than the specific weights given to different stages of the business.
The study concluded that for seasonal businesses, the traditional method of comparing one year to another or one hotel to another misses the point. The real challenge is managing the fixed costs of a large asset through periods of low demand. The researchers showed that by looking at each month as a distinct unit and accounting for the flow of bookings and reputation from one month to the next, managers can get a much clearer picture of their performance. They can see that the "inefficiency" of a winter month is often just the cost of staying open, and that the real opportunities for improvement lie in the specific actions taken during the peak and shoulder seasons. This approach offers a way to measure efficiency that respects the unique, cyclical nature of seasonal industries, providing a tool that could be applied to other resorts, ski lodges, or any business that faces the same ebb and flow of demand.
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