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Improving the accuracy of DEA efficiency analysis: a bootstrap application to the health care foodservice industry

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  • A. Assaf
  • K. M. Matawie

Abstract

This article analyses the efficiency of health care foodservice operations and its determinants using a Data Envelopment Analysis (DEA) bootstrapping approach. The purpose of using the bootstrapping approach is two-fold: first, to obtain the bias corrected estimates and the confidence intervals of DEA-efficiency scores and second, to overcome the correlation problem of DEA-efficiency scores and to provide consistent inferences in explaining the determinants of health care foodservice efficiency. The approach was implemented on a sample of 89 health care foodservice operations. The results showed the presence of inefficiency in the sample, with an average efficiency level of 72.6%. Further, the results from analysing the determinants of health care foodservice operations provided policy implication regarding the factors that might improve the efficiency of health care foodservice operations.

Suggested Citation

  • A. Assaf & K. M. Matawie, 2010. "Improving the accuracy of DEA efficiency analysis: a bootstrap application to the health care foodservice industry," Applied Economics, Taylor & Francis Journals, vol. 42(27), pages 3547-3558.
    • Handle: RePEc:taf:applec:v:42:y:2010:i:27:p:3547-3558
    DOI: 10.1080/00036840802112497
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    References listed on IDEAS

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    1. Freshwater, John F., 1980. "Least-Cost Hospital Food Service Systems," Marketing Research Reports 313791, United States Department of Agriculture, Agricultural Marketing Service, Transportation and Marketing Program.
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    Cited by:

    1. ChuangLin Fang & XingLiang Guan & ShaSha Lu & Min Zhou & Yu Deng, 2013. "Input–Output Efficiency of Urban Agglomerations in China: An Application of Data Envelopment Analysis (DEA)," Urban Studies, Urban Studies Journal Limited, vol. 50(13), pages 2766-2790, October.
    2. Yongrok Choi & Fan Yang & Hyoungsuk Lee, 2020. "On the Unbalanced Atmospheric Environmental Performance of Major Cities in China," Sustainability, MDPI, vol. 12(13), pages 1-14, July.
    3. Mehdi Rhaiem & Nabil Amara, 2020. "Determinants of research efficiency in Canadian business schools: evidence from scholar-level data," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 53-99, October.
    4. Shahsuzan Zakaria, 2023. "The Need to go Beyond Deterministic Data Envelopment Analysis (DEA): A Comparative Analysis with Bootstrapping DEA in Risk Management Efficiency Measurements," Information Management and Business Review, AMH International, vol. 15(4), pages 433-446.
    5. Sandrine A. Kablan & Jean-phillipe Boussemart, 2015. "Bootstrapping efficiency scores of microfinance institutions in West African economic and monetary union," Economics Bulletin, AccessEcon, vol. 35(4), pages 2220-2232.
    6. Tomas Baležentis & Irena Kriščiukaitienė & Alvydas Baležentis, 2014. "A nonparametric analysis of the determinants of family farm efficiency dynamics in Lithuania," Agricultural Economics, International Association of Agricultural Economists, vol. 45(5), pages 589-599, September.
    7. Lingxiang Jian & Xueqing Yin & Minglou Zhao, 2024. "Research on the Level of Synergistic Development of Supply and Demand in China’s Health Industry," Sustainability, MDPI, vol. 16(9), pages 1-24, April.

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