Data envelopment analysis for measuring performance in a competitive market
-
DOIhttp://dx.doi.org/10.21511/ppm.18(1).2020.27
-
Article InfoVolume 18 2020, Issue #1, pp. 315-325
- Cited by
- 1086 Views
-
883 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
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.
- Keywords
-
JEL Classification (Paper profile tab)C44, M20
-
References35
-
Tables6
-
Figures2
-
- 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
-
- Ablanedo-Rosas, J. H., Gao, H., Zheng, X., Alidaee, B., & Wang, H. (2010). A study of the relative efficiency of Chinese ports: a financial ratio-based data envelopment analysis approach. Expert Systems, 27(5).
- Allahyar, M., & Rostamy-Malkhalifeh, M. (2015). Negative data in data envelopment analysis: Efficiency analysis and estimating returns to scale. Computers & Industrial Engineering, 82, 78-81.
- Bogetoft, P., & Otto, L. (2011). Benchmarking with DEA, SFA, and R. Springer Science + Business Media, LLC.
- Bogetoft, P., & Otto, L. (2015). Package ’Benchmarking’, Benchmark and frontier analysis using DEA and SFA. Version: 0.27.
- Charnes, A., Cooper, W. W., Lewin, A. Y., & Seiford, L. M. (1994). Data Envelopment Analysis: Theory, Methodology, and Application. Springer Science + Business Media, LLC.
- Cooper, W. W., Seiford, L. M., & Tone, K. (2007). Data envelopment analysis. A Comprehensive Text with Models, Applications, References and DEA-Solver Software. Springer Science + Business Media, LLC.
- Daraio, C., & Simar, L. (2011). Advanced robust and nonparametric methods in efficiency analysis. Methodology and applications. Springer, New York.
- Ehrbar, A. (2000). EVA: Economic Value Added: Gazdasági hozzáadott érték: Kulcs az értékteremtéshez. Panem-Wiley, Budapest.
- Emrouznejad, A., & Yang, G-I. (2018). A survey and analysis of the first 40 years of scholarly literature in DEA: 1978–2016. Socio-Economic Planning Sciences, 61.
- Farantos, G. I. (2015). The Data Envelopment Analysis Method and the influence of a phenomenon in organizational Efficiency: A literature review and the Data Envelopment Contrast Analysis new application. Data Envelopment Analysis and Decision Science, 2, 101-117.
- Feroz, E. H., Kim, S., & Raab, R. L. (2003). Financial Statement Analysis: A Data Envelopment Analysis Approach. Journal of the Operational Research Society, 54(1), 48-58.
- Ferrier, D. G., & Hirschberg, J. G. (1999). Can We Bootstrap DEA Scores? Journal of Productivity Analysis, 11, 81-92.
- Figurek, A., Goncharuk, A., Shynkarenko, L., & Kovalenko, O. (2019). Measuring the efficiency of higher education: case of Bosnia and Herzegovina. Problems and Perspectives in Management, 17(2), 177-192.
- Forsund, F. R., & Hjalmarsson, L. (2004). Are all Scales Optimal in DEA? Theory and Empirical Evidence. Journal of Productivity Analysis, 21, 25-48.
- Halkos, G. E., & Tzeremes, N. G. (2012). Industry performance evaluation with the use of financial ratios: An application of bootstrapped DEA. Expert Systems with Applications, 39(5), 5872-5880.
- Hamzeh, R., & Xu, X. (2019). Technology selection methods and applications in manufacturing: A review from 1990 to 2017. Computers and Industrial Engineering, 138, 1-12.
- Heiberger, R. M., & Neuwirth, E. (2009). R Through Excel. A Spreadsheet Interface for Statistics, Data Analysis, and Graphics. Springer Science + Business Media, LLC.
- Huang, C. C. (2019). Does rapid market growth enhance efficiency? An evaluation of the Chinese mutual fund market. Investment Management and Financial Innovations, 16(2), 383-394.
- Huang, Z., & Li, S. X. (2011). Stochastic DEA Models with Different Types of Input-Output Disturbances. Journal of Productivity Analysis, 15, 95-113.
- Jordá, P., Cascajo, R., & Monzón, A. (2012): Analysis of the Technical Efficiency of Urban Bus Services in Spain Based on SBM Models. ISRN Civil Engineering, 1-13.
- Kaplan, R. S., & Norton, D. P. (2004). Balanced Scorecard. Complex Kiadó. Budapest.
- Khezrimotlagh, D., Salleh, S., & Mohsenpour, Z. (2014). A new method for evaluating decision making units in DEA. Journal of the Operational Research Society, 65, 694-707.
- Ko, K., Chang, M., Bae, E.-S., & Kim, D. (2017). Efficiency Analysis of Retail Chain Stores in Korea. Sustainability, 9(9), 1629.
- Neely, A., Adams, C., & Kennerley, M. (2004). Teljesítményprizma: Az üzleti siker mérése és menedzselése. Alinea Kiadó, Budapest.
- Nguyen, N. T., Vu, L. T., & Dinh, L. H. (2019). Measuring banking efficiency in Vietnam: parametric and nonparametric methods. Banks and Bank Systems, 14(1).
- Pendharkar, P. C., & Pai, D. R. (2013). A Bayesian Approach for Estimating Confidence Intervals for DEA Efficiency Scores under Certain Inefficiency Distribution Assumptions. In Proceedings for the Northeast Region Decision Sciences Institute (pp. 897-916).
- Ramanathan, R. (2003). An Introduction to Data Envelopment Analysis. A Tool for Performance Measurement. Sage Publications India Pvt Ltd.
- Ray, S. C. (2004). Data Envelopment Analysis. Theory and Techniques for Economics and Operations Research. New York: Cambridge University Press.
- Shewell, P., & Migiro, S. (2016). Data envelopment analysis in performance measurement: a critical analysis of the literature. Problems and Perspectives in Management, 14(3-3), 705-713.
- Stapenhurst, T. (2009). The Benchmarking Book: A How-to-Guide to Best Practice for Managers and Practitioners. Elsevier Ltd.
- Stavárek, D., & Řepková, I. (2012). Efficiency in the Czech Banking Industry: A non-parametric approach. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, LX(2), 357-366.
- Sturtz, S., Ligges, U., & Gelman, A. (2005). R2WinBUGS: A Package for Running WinBUGS from R. Journal of Statistical Software, 12(3), 1-16.
- Tehrani, R., Mehragan, M. R., & Golkani, M. R. (2012). A Model for Evaluating Financial Performance of Companies by Data Envelopment Analysis. A Case Study of 36 Corporations Affiliated with a Private Organization. International Business Research, 5(8).
- Thomas, R. R., Barr, R. S., William, L., Cron, W. L., & Slocum Jr., J. W. (1998). A process for evaluating retail store efficiency: a restricted DEA approach. International Journal of Research in Marketing, 15, 487-503.
- Zhu, J. (2009). Quantitative Models for Performance Evaluation and Benchmarking: Data Envelopment Analysis with Spreadsheets. Springer Science + Business Media, LLC.