Journal of Korean Institute of Industrial Engineers (대한산업공학회지)

Korean Institute of Industrial Engineers (대한산업공학회)
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Design of DEA/(AR-I, ARGM) Models and Sensitivity Analysis for Performance Evaluation on Governmental Funding Projects for IT Small and Medium-sized Enterprises

IT중소기업 정부자금 지원정책 성과 평가를 위한 DEA/(AR-I, ARGM) 모형 설계 및 민감도 분석

  • Park, Sungmin (Dept. of Business Administration, Baekseok University) ;
  • Kim, Heon (Dept. of Business Administration, Baekseok University) ;
  • Baek, Donghyun (Dept. of Business Administration, Hanyang University)
  • 박성민 (백석대학교 경상학부) ;
  • 김헌 (백석대학교 경상학부) ;
  • 백동현 (한양대학교 경상대학 경영학부)
  • Published : 2008.06.30
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Abstract

Recently, it has been strongly required to establish a systematic and sustainable performance investigation and evaluation framework on governmental funding projects for IT small and medium-sized enterprises. In this paper, Data Envelopment Analysis (DEA) models are adopted for performance evaluation on governmental funding projects for IT small and medium-sized enterprises. A new data structure is proposed for the DEA performance evaluation. Generally, in using DEA models, DEA multipliers restriction is critical to achieve the reliability of DEA optimal solutions. Based on the outputs and inputs considered in this study, Acceptance Region (AR) constraints are generated and incorporated into the DEA models so as to improve the reliability of DEA efficiency scores. Associated with AR Type I (AR-I), AR Global Model (ARGM) constraints, DEA/ (AR-I, ARGM) models are designed and then sensitivity analysis follows investigating the robustness of DEA efficiency scores relating to AR constraints adjustment. Finally, a performance evaluation is illustrated regarding governmental direct funding projects from Ministry of Information and Communication (MIC) in Korea where each project unit (i.e. Decision Making Unit (DMU)) is determined whether it is efficient or not. By using DEA/(AR-I, ARGM) models designed in this paper, robustly efficient DMUs are gradually identified according to the successive AR constraints adjustment. Among 25 DMUs, results show that 6 DMUs such as B, E, G, Q, S, Y are determined as robustly efficient against AR constraints intermediate adjustment.

Keywords

References

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