IMPROVED COMMON WEIGHT DEA-BASED DECISION APPROACH FOR ECONOMIC AND FINANCIAL PERFORMANCE ASSESSMENT

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Karsak E. E., Göker N.

TECHNOLOGICAL AND ECONOMIC DEVELOPMENT OF ECONOMY, cilt.26, ss.430-448, 2020 (SSCI) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 26
  • Basım Tarihi: 2020
  • Doi Numarası: 10.3846/tede.2020.11870
  • Dergi Adı: TECHNOLOGICAL AND ECONOMIC DEVELOPMENT OF ECONOMY
  • Derginin Tarandığı İndeksler: Social Sciences Citation Index (SSCI), Scopus, Academic Search Premier, ABI/INFORM, Business Source Elite, Business Source Premier, Central & Eastern European Academic Source (CEEAS), ICONDA Bibliographic, Directory of Open Access Journals, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.430-448
  • Anahtar Kelimeler: common weight DEA-based models, discriminating power, decision analysis, performance evaluation, MSCI emerging markets, Turkish banking sector, DATA ENVELOPMENT ANALYSIS, EFFICIENCY, PRODUCTIVITY, RANKING, COST, INPUTS, UNITS, MODEL
  • Galatasaray Üniversitesi Adresli: Evet

Özet

Economic and financial performance assessment possesses an important role for efficient usage of available resources. In this study, a novel common weight multiple criteria decision making (MCDM) approach based on data envelopment analysis (DEA) is presented to identify the best performing decision making unit (DMU) accounting for multiple inputs as well as multiple outputs. The robustness of the developed model, which provides a rank-order with enhanced discriminatory characteristics and improved weight dispersion, is illustrated by two case studies that aim to provide economic and financial performance assessment. The first study presents an evaluation of Morgan Stanley Capital International emerging markets, whereas the second case study ranks the Turkish deposit banks using the proposed methodology as well as providing a comparative evaluation with several other approaches addressed in earlier works. The results indicate that the introduced approach guarantees to identify the best performing DMU without including a discriminating parameter requiring an arbitrary step size value in model formulation while also achieving an improved weight dispersion for inputs and outputs.