Evaluating and ranking the Indian private sector banks—A multi‐criteria decision‐making approach

• Published date: 01 May 2022
• Author: Hanumantha Rao Sama,Sri Venkateswara Kumar Kosuri,Sripathi Kalvakolanu
• Date: 01 May 2022
• DOI: http://doi.org/10.1002/pa.2419

ACADEMIC PAPER

Evaluating and ranking the Indian private sector banks—A

multi-criteria decision-making approach

Hanumantha Rao Sama

1

| Sri Venkateswara Kumar Kosuri

2

| Sripathi Kalvakolanu

2

1

Department of Statistics, Vignan's Foundation

for Science Technology and Research, Guntur,

India

2

Department of Management Studies, Gates

Institute of Management & Sciences,

Vijayawada, India

Correspondence

Sama Hanumantha Rao, Department of

Statistics, Vignan's Foundation for Science

Technology and Research, Vadlamudi, Guntur,

Andhra Pradesh-522213, India.

Email: sama.hanumantharao@gmail.com

The paper examines the performance of Indian private sector banks based on various

combinations of multi-criteria decision-making techniques. Annual reports of the

respective banks were used to capture the data and measure the performance by

considering multiple inputs and outputs. The data are analyzed through notable mul-

tiple criteria decision-making (MCDM) techniques, CRITIC, TOPSIS, and grey rela-

tional analysis (GRA). The study reveals that HDFC is the best performing bank

among other private sector banks and creates a benchmark. Applying the combina-

tion of MCDM techniques, namely CRITIC-TOPSIS and CRITIC-GRA, HDFC is ranked

first followed by Bandhan Bank as the second. Other banks are ranked differently

due to methodological differences. After obtaining the ranks by CRITIC-TOPSIS and

CRITIC-GRA, the ranks are tested using the Wilcoxon signed-rank test. Only private

sector banks are considered for the current study. Future studies on the performance

of banks can be taken up by comparing different types of banks, targeting an exten-

sion of the time frame studied. The findings suggest that the private sector banks

need to increase their performance by investing in income-generating areas. Improv-

ing the performance will help them in surviving in the market as well as competing

with the top public sector and foreign banks. The findings can also be useful to vari-

ous stakeholders and, in particular, to investors to know the value of the banks for

future investments. For the first time, researchers have used a combination of

MCDM techniques such as CRITIC-TOPSIS and CRITIC-GRA. Selected inputs and

outputs have been studied for the private sector banks using MCDM techniques to

measure the performance.

1|INTRODUCTION

Banking industry is the fulcrum of a country's economy. For a devel-

oping nation like India, banks play a huge role and help the economy

grow at a faster pace. Increase in population and growing disposable

income have led to robust demand for banking and related services. A

decade back, public sector banks dominated the banking industry. But

due to inefficiency and lack of technology, the door was left wide

open for private sector banks to operate and increase their share in

Indian economy. The advent of private banks led to the entry of tech-

nology into banks, which furthered the improvements in overall oper-

ations of the banking sector. Return on assets (RoA) and return on

equity (RoE) are considered as the important parameters for measur-

ing the performance of banks. If we compare private sector vis-a-vis

public sector banks for the year 2018–2019, it can be noted that the

RoA for the private sector was 0.63 and for the public sector it was

−0.65. The RoE for private sector was 5.45, whereas it was negative

for the public sector banks at −11.44 (Source: RBI). Therefore, it

shows that private sector banks have become an indispensable part of

the Indian economy's growth.

Given the complex and volatile scenario prevailing in the banking

sector, the present study attempts to establish methods to ascertain

the performance of banks. Since the performance of banks is depen-

dent on several variables, multiple variables are to be considered as

Received: 16 July 2020 Revised: 17 August 2020 Accepted: 19 August 2020

DOI: 10.1002/pa.2419

J Public Affairs. 2022;22:e2419. wileyonlinelibrary.com/journal/pa © 2020 John Wiley & Sons Ltd 1of16

https://doi.org/10.1002/pa.2419

the criteria to determine the performance. Moreover, all the variables

may not be assuming the same importance. Thus, any qualitative judg-

ment or prioritization based on subjective assessment would lead to a

biased decision outcome. In such an important context, the current

research examines the performance of private sector banks in India

using multiple criteria decision-making (MCDM) techniques.

MCDM is a branch of decision making and a part of the opera-

tions research. MCDM techniques are further divided into two cate-

gories, the first category is multi-objective decision making (MODM),

and the second category is multiple attribute decision making

(MADM). In recent days, MCDM is being regarded as one of the best

tools for solving complex problems. Using MCDM techniques, we can

improve the capability to decide among different alternatives on given

attributes/criteria for the best possible selection.

Several techniques are usable in MCDM to solve complicated

problems, but the same set of problems will provide different results

with different techniques. In the backdrop of measuring the perfor-

mance of private sector banks, we propose to use certain novel

MCDM techniques. Thus, the current research seeks to discuss the

research questions listed below.

1. How can we assign objective weights to different attributes using

CRITIC tool?

2. How can the performance of private banks be measured using dif-

ferent MCDM tools?

3. How can the similarity of the ranks, obtained by different MCDM

tools, be compared?

For a clear flow of understanding, the paper is presented in differ-

ent sections. The second and third sections discuss the previous

scholarly literature and the methods applied in the present work.

While the fourth section involves the results and discussions, the last

section describes the conclusion and implications.

2|LITERATURE REVIEW

The literaturein the corresponding area is reviewedto comprehend the

studies previously done by other researchers in the same field to find

out the research gap leading to the present study. The views on the

performance of organizations have been studied and analyzed by vari-

ous researchers over the years (Adeusi, Akeke, Adebisi, &

Oladunjoye, 2014; Bikker, 2010;Kwan,2003; Mondal & Ghosh,2012).

In the academic writings, “performance”is understood as “profitability”

and “efficiency.”These studies have utilized several kinds of decision-

making techniques to arrive at the performance-related judgments

(Abor et al., 2019; Beheshtinia & Omidi, 2017; Hemmati, Dalghandi, &

Nazari, 2013;Ho&Wu,2006;Ongore & Kusa, 2013).

The preliminary review of the literature shows that the studies

were carried out for assigning the weights using AHP (Dincer, 2015;

Ecer, 2014; Hunjak & Jakovcˇevi

c, 2001; Kaya & Kahraman, 2011). The

drawback of such a method is that only subjective weights are mea-

sured, where theresponse may be biased, which affects the weights of

the attributes. In a few studies, entropy is used as a technique for find-

ing the weights.Entropy has a drawbackof not allowing negative values

and zero. In order to overcome such limitations, the CRITIC (Criteria

ImportanceThrough Inter criteria Correlation) method has been applied

in the current study. First put forward by Diakoulaki, Mavrotas, and

Papayannakis (1995), CRITIC is used to evaluate the objective weights

of the criteria/attributes. In CRITIC, objective weights are evaluated

based on the data. CRITICalso allows negative values to be considered

for the analysis. A critical review of the literature is provided (Table 1)

with a view to develop a more practical framework toward the perfor-

mance of privatesector banks in India.

2.1 |Applications of CRITIC

CRITIC has been applied in various areas like in logistics (Çakir & Per-

çin, 2013), in ICT (Lu, Li, & Wu, 2015), in renewable energy systems

(Babatunde & Ighravwe, 2019), and in manufacturing (Adalı&

Is¸ık, 2017). In a very recent study, Abdel-Basset and Mohamed (2020)

estimated a sustainable supply chain model using TOPSIS and CRITIC

methods. Aznar Bellver, Cervelló Royo, and García (2011) applied

MCDM technique, CRITIC and valuation ratio, to assess the value of

savings bank in Spain. Raikar (2019) studied a few food processing

firms' performance by employing the SAW and TOPSIS methods after

ascertaining the weights by the CRITIC method, which resulted in

ranking the top five firms for investments in portfolios.

2.2 |Applications of TOPSIS

In the current paper, Technique for Order Preference by Similarity to

an Ideal Solution (TOPSIS)—a multi-criteria model in making decisions

has been used. TOPSIS was developed in 1981 by Hwang and Yoon

with further modifications in 1987 by Yoon and Hwang, Lai, and Liu in

1993. TOPSIS is applied to provide solution to the decision-making

problems. TOPSIS has its own merits of ease of calculation, rationality,

unambiguity, and ability to assess the relative performance for each

alternative. Several authors in various fields have applied TOPSIS.

Chang, Lin, Lin, and Chiang (2010) utilized the TOPSIS technique

with two distance approaches to assess the performance of 82 mutual

funds in Taiwan. Bulgurcu (2012) proposed the MCDM technique

(TOPSIS) and evaluated 13 technology companies' performances, which

were traded in the Istanbul Stock market. Behzadian, Otaghsara, Yazdani,

and Ignatius (2012) attempted to propose an assessment model using

FAHP and Fuzzy TOPSIS for evaluating performance in a fuzzy environ-

ment. Opricovic and Tzeng (2004) presented the comparison and vari-

ances between two MCDM techniques, VIKOR and TOPSIS. Bilbao-

Terol, Arenas-Parra, Cañal-Fernández, and Antomil-Ibias (2014) success-

fullyappliedandmeasuredtheinvestment sustainability in Govt. bonds

using TOPSIS. Hsu, Ou, and Ou (2015)useds(GRA)andmodified

TOPSIS to develop a workable performance evaluation model for Tai-

wanese companies and ranked accordingly. Shaverdi, Ramezani,

Tahmasebi, and Rostamy (2016) studied seven petrochemical companies

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