A non-radial directional distance method on classifying inputs and outputs in DEA: Application to banking industry. (February 2018)

• Record Type: Journal Article
• Authors: Toloo, Mehdi; Allahyar, Maryam; Hančlová, Jana
• Is Part Of: Expert systems with applications. Volume 92(2018)
• Page Start: 495
• Page End: 506
• Publication Date: 2018-02
• Subjects: Data envelopment analysis -- Directional distance function -- Non-radial non-oriented models -- Mixed integer linear programming -- Flexible measure; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2017.09.034
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A non-radial nor-oriented method is developed to deal with flexible measures. Two optimistic and pessimistic approaches are proposed. Each approach contains two individual and integrated models. A case study of 61 banks in the Visegrad Four region validates the new models. Abstract: The original Data Envelopment Analysis (DEA) models have required an assumption that the status of all inputs and outputs be known exactly, whilst we may face a case with some flexible performance measures whose status is unknown. Some classifier approaches have been proposed in order to deal with flexible measures. This contribution develops a new classifier non-radial directional distance method with the aim of taking into account input contraction and output expansion, simultaneously, in the presence of flexible measures. To make the most appropriate decision for flexible measures, we suggest two pessimistic and optimistic approaches from both individual and summative points of view. Finally, a numerical real example in the banking system in the countries of the Visegrad Four (i.e. Czech Republic, Hungary, Poland, and Slovakia) is presented to elaborate applicability of the proposed method.