Efficiency ranking method using SFA and TOPSIS (ERM-ST): case of Indian banks

Abstract

Purpose The purpose of this paper is to propose a performance evaluation framework using an integrated approach of stochastic frontier analysis (SFA) and technique of order preference with similarity to ideal solution (TOPSIS) called efficiency ranking method using SFA and TOPSIS (ERM-ST) specifically in the banking sector where service excellence is of prime importance for business growth. Design/methodology/approach The proposed approach ERM-ST measures the performance of a DMU in the SFA framework by considering multiple outputs and multiple inputs. It is a non-parametric tool which does not need any prior model assumptions which enhances its applicability in real-life business scenarios. Moreover, the efficiency score obtained using the proposed model ERM-ST lies between 0 and 1, unlike in case of super efficiency data envelopment analysis (DEA) which may go well above 1. Findings The proposed framework is evaluated for its applicability using two various data sets and is further used to evaluate the performance of a group of 26 public sector banks in India. The results obtained by the proposed method ERM-ST are compared with those obtained by super efficiency DEA using Friedman’s test. Originality/value The proposed approach ERM-ST is developed to evaluate the performance of a service unit with multiple outputs and multiple inputs in the SFA framework.

A merged approach for modeling qualitative characteristics of agile arena under grey domain

“…∂m|1 ∂m|2 ∂m|3 ∂m|4 ∂m|5 that AHP possesses the capability to recognize the inter-associational bonding among criteria, measures, metrics, etc. Moreover; the authors investigated TOPSIS as, due to the ease of computation of linked distances from positive ideal and negative ideal alternatives, it is one of the renowned and often-used multicriteria decision-making techniques favored by researchers (Zakeri and Keramati, 2015;Bali and Gumus, 2014;Chitnis and Vaidya, 2018;Bianchini, 2018;Khan and Maity, 2017;Prascevic and Prascevic, 2013). Thus, the authors have scrutinized TOPSIS techniques to evaluate the supplier's positional ranking.…”

“…Thus, the authors have scrutinized TOPSIS techniques to evaluate the supplier's positional ranking. Consequently, the authors have inclined toward a merged approach based on AHP and TOPSIS techniques, accompanying multiple agile performance measures due to their inbuilt sensitivity for yielding valid results as adapted and approved by the body of researchers (Prascevic and Prascevic, 2013;Chow and Luk, 2006;Chitnis and Vaidya, 2018;Costa and Evangelista, 2008;Vaidya and Kumar, 2006;Zakeri and Keramati, 2015;Sipahi and Timor, 2010;Bali and Gumus, 2014;Bianchini, 2018;Parthasarathy and Daneva, 2014…”

Performance of TV programs: a robust MCDM approach

“…Researchers have been recently integrating TOPSIS with other approaches. For instance, Chitnis and Vaidya (2018) integrated SFA and TOPSIS to evaluate Indians banks; Bianchini (2018) provided a two-phase analytical hierarchy process and a TOPSIS methodology to evaluate a third-party logistics provider; Lau et al (2018) proposed a model embracing hierarchical holographic modeling, fuzzy analytic hierarchy process, and TOPSIS to evaluate the competitiveness level of companies. Similarly, the present research integrates SVD, PCA, GRA, and Tobit regression to evaluate the performance of TV programs and understand the contextual variables that affect them.…”

Sahi̇pli̇k, Yöntem Ve Banka Ti̇pi̇ Ne Ölçüde Önemli̇di̇r? Karşilaştirmali Banka Performansi Ve Etki̇nli̇ği̇ Üzeri̇ne Bi̇r Li̇teratür Araştirmasi

“…To benchmark alternative methodologies, the measurements should be based on the same frontiers. Chitnis and Vaidya (2017) compare the effect of SFA-TOPSIS combined methodology with super-efficiency DEA. Although the ranks of Indian banks differ slightly from each other, both approaches can be used successfully according to the Wilcoxon test.…”

“…(2008), Liadaki and Gaganis (2010), Lin et al (2016), Mamatzakis (2008), Manlagnit (2011), Margono (2010), Miah and Sharmeen (2015), Miah and Uddin (2017), Mobarek (2014), Mohanty et al (2016), Nitoi an Spulbar (2015), Othman et al (2017), Perea and Skully (2012), Sokic (2015), Staikouras (2008), Sturm and Williams (2010), Sun and Chang (2011), Tabak et al (2013), Thoraneenitiyan and Avkiran (2009), Yin et al (2013), Zuhroh et al (2015). REGRESSION ANALYSIS Assaf et al (2011), Batir et al (2017), Beck et al (2013), Behr (2010), Ben Slama Zouari and Taktak (2014), Berger et al (2009), Caporale et al (2017), Chotareas et al (2012 & 2013),Curi et al (2015),Daly and Zhang (2014),Djennas (2016),Fernandes et al (2017),Gardener et al (2011), Hardianto andWulandari (2016),Ismail et al (2013), Jawadi et al (2017), Jose et al (2014,Lin and Zhang (2009),Rashid and Jabeen (2016),Rosman et al(2014),Salim et al (2016), Ioanni Schiniotakis (2012),Shah and Jan (2015),Sufian et al (2016),Wanke et al (2016aWanke et al ( & b, 2017,Wasiuzzaman and Gunasegavan (2013).TOPSISBayyurt (2013),Beheshtinia and Omidi (2017),Chitnis and Vaidya (2017),Mandic et al (2014),Seçme et al (2009),Wanke et al (2016a, 2016b, 2016c) Wu et al (2009.AHP Beheshtinia and Omidi (2017), Dinçer and Hacıoğlu (2013), Mandic et al (2014), Seçme et al (2009), Stankevicine and Mencaite (2012), Wanke et al (2016b), Wu et al (2009).…”