Data envelopment analysis for measuring performance in a competitive market

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In today’s increasingly competitive markets, it is essential to be able to determine the position of a company as opposed to its competitors. Today the traditional financial ratios are most widely used to measure corporate performance, but more and more authors begin to criticize their use. It is difficult to use financial ratios as a complex measurement tool. It is crucial to use an appropriate method or tool to measure corporate performance, which can measure the company’s performance in a complex way represented by one indicator. In this study, the Data Envelopment Analysis (DEA) method is used, which is one of the potential tools available. Several researchers have used the DEA method to measure corporate performance. Many authors consider DEA as a useful tool for measuring corporate performance, while others criticize it. The authors analyze the performance of retail food companies in Hungary’s Northern Great Plain region. The companies analyzed were chosen from the region investigated, and they have “food retail grocery store” as their main activity, and they had six cleared annual reports in the period 2012–2017. There was a total of 887 companies in the region examined, and 563 (63.5%) met the conditions. The analysis was made using the time-series data of companies for 2012–2017 based on their financial reports, and the authors dealt with various possibilities for extending DEA, which can support its more accurate use. Based on evaluating the retail food companies’ performance in the Northern Great Plain region, one can state that the efficiency of companies shows a very mixed picture over the years examined. The study suggests solutions to the indicated problem. The findings indicate that the application of extended DEA methods gives better results; that is, one can get better estimates of the efficiency of companies.

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    • Figure 1. Number of companies with the input efficiency value of 1
    • Figure 2. Number of companies with an output efficiency value of 1
    • Table 1. Main statistical characteristics of the efficiency analysis of the Northern Great Plain region’s food trading companies
    • Table 2. Main statistical characteristics of efficiency values based on the average of the years
    • Table 3. Empirical distribution of input and input-output efficiency values based on the average of the years
    • Table 4. Empirical distribution of output efficiency values based on the average of the years
    • Table 5. Main statistical characteristics of the confidence interval of input-output-oriented efficiency values calculated from the average of the years
    • Table 6. Main statistical characteristics of confidence interval values of input-output-oriented efficiency values calculated from the average of the years