literature review on credit risk management in banks

The effect of credit risk management and bank-specific factors on the financial performance of the South Asian commercial banks

Asian Journal of Accounting Research

ISSN : 2459-9700

Article publication date: 14 October 2021

Issue publication date: 27 May 2022

Among all of the world's continents, Asia is the most important continent and contributes 60% of world growth but facing the serving issue of high nonperforming loans (NPLs). Therefore, the current study aims to capture the effect of credit risk management and bank-specific factors on South Asian commercial banks' financial performance (FP). The credit risk measures used in this study were NPLs and capital adequacy ratio (CAR), while cost-efficiency ratio (CER), average lending rate (ALR) and liquidity ratio (LR) were used as bank-specific factors. On the other hand, return on equity (ROE) and return on the asset (ROA) were taken as a measure of FP.

Design/methodology/approach

Secondary data were collected from 19 commercial banks (10 commercial banks from Pakistan and 9 commercial banks from India) in the country for a period of 10 years from 2009 to 2018. The generalized method of moment (GMM) is used for the coefficient estimation to overcome the effects of some endogenous variables.

The results indicated that NPLs, CER and LR have significantly negatively related to FP (ROA and ROE), while CAR and ALR have significantly positively related to the FP of the Asian commercial banks.

Practical implications

The current study result recommends that policymakers of Asian countries should create a strong financial environment by implementing that monetary policy that stimulates interest rates in this way that automatically helps to lower down the high ratio of NPLs (tied monitoring system). Liquidity position should be well maintained so that even in a high competition environment, the commercial is able to survive in that environment.

Originality/value

The present paper contributes to the prevailing literature that this is a comparison study between developed and developing countries of Asia that is a unique comparison because the study targets only one region and then on the basis of income, the results of this study are compared. Moreover, the contribution of the study is to include some accounting-based measures and market-based measures of the FP of commercial banks at a time.

• South Asian countries

Credit risk

Bank-specific factors.

• Generalized method of moment

Siddique, A. , Khan, M.A. and Khan, Z. (2022), "The effect of credit risk management and bank-specific factors on the financial performance of the South Asian commercial banks", Asian Journal of Accounting Research , Vol. 7 No. 2, pp. 182-194. https://doi.org/10.1108/AJAR-08-2020-0071

Emerald Publishing Limited

Copyright © 2021, Asima Siddique, Muhammad Asif Khan and Zeeshan Khan

Published in Asian Journal of Accounting Research . Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

Introduction

Around the globe, depository institutions perform a crucial job in bringing financial stability and economic growth by mobilizing monetary resources across multiple regions ( Accornero et al. , 2018 ). The commercial plays an intermediary role by collecting the excessive amount from savers and issuing loans to the borrowers. In return, banks can earn a high interest rate ( Khan et al. , 2020 ; Ghosh, 2015 ). Banks tried to increase their financial performance (FP) by issuing loans while playing their intermediary role; banks have a high chance of facing credit risk. Accornero et al. (2018) found that the country's banking industry mostly collapses due to high credit risk. Sometimes, it leads to the failures of the whole financial system. Credit risk is expected to be arises when a borrower cannot meet their obligation about future cash flows. Commercial banks' FP is affected by two factors: one is external and the other is internal. Bank-specific factors are internal and able to control factors of the commercial banks. Ofori-Abebrese et al. (2016) pointed out that adverse selection and moral hazards were created due to mismanagement of internal factors. The abovementioned financial problems are turmoil period in the banking/financial sector.

Among the entire continent of the world, Asia is the most crucial continent and contributing 60% of world growth but facing the serving issue of high nonperforming loans (NPLs). It is well known that a high ratio of NPLs weakens the economy or country's financial position. The growth level in South Asia was the highest in 2015, and the ratio is 9.3%, which is the highest among all continents. According to the Asian Development Bank (2019), the NPLs in the south are approximately $518bn, which is relatively high compared to previous years. The soaring of NPLs in South Asian countries enforces a massive burden on commercial banks' financial position (mainly banks' lending process effected). The massive increase in NPL is observed after the global financial crisis (2007–2008). According to Masood and Ashraf (2012) , the credit risk high ratio of NPLs is the main reason for most of the financial crisis because NPLs alarmingly high during the Asian currency crisis in 1997 and subprime crises in 2007, and some loans are declared bad debts. The alarmingly high ratio of NPL resulted in an increasing depression in the financial market, unemployment and a slowdown of the intermediary process of banks (see Figure 1 ).

The World Bank statistics of different regions show that NPLs exist in almost all regions. Still, the ratio of NPLs is relatively high in the South Asian area compared to other regions. Therefore, the study is conducted in South Asia. Two proxies of credit risk are used in this study: NPLs and capital adequacy ratio (CAR). Moreover, the study also incorporates bank-specific factors to increase FP.

Various studies ( Louzis et al. , 2012 ; Ofori-Abebrese et al. , 2016 ; Hassan et al. , 2019 ) are conducted to address the issue, but literature shows that the results of these studies are inconclusive and also ignore the most important region of South Asia. Therefore, the study objective is to investigate that credit risk and banks specific factors affect FP of commercial banks in Asia or not? We have selected two from South Asia, Pakistan and India, as sample countries. In 2019, the NPLs were 13% and 10% in Pakistan and India, respectively. This ratio is relatively high as compared to the other countries of the world. Due to these reasons, we have mainly selected India and Pakistan from South Asian countries ( Siddique et al. , 2020 ). The present study uses secondary panel data set of 19 commercial banks from 2009 to 2018.

Two serious threats may exist: The first is autocorrelation and the second is endogeneity. If the data do not meet these CLRM assumptions, then the regression results are not best linear unbiased prediction (BLUE) ( Sekaran, 2006 ; Kusietal, 2017 ). And in this situation, apply pooled regression is applied, and then the results were biased because the coefficient results cannot give accurate meaning. After all, pool regression ignores year and cross section-wise variation. Therefore, in this study, an instrumental regression can be used that handle all these issues. Generalized method of moments (GMM) is used to analyze the data to overcome endogeneity. Our study is unique by addressing the autocorrelation and endogeneity issue at a time. Our study results show that credit risk measure NPLs decrease the FP due to having negative relation, while CAR has a positive relation with South Asian banks’ FP. The remainder of the research study is organized as follows: Section 2 consists of a detailed literature review; Section 3 consists of data and methodology. Sections 4 contains information about finding and suggestions. Finally, Section 5 discusses the conclusion.

Literature review

The Literature Review has mainly divided into two crucial sections; First part consists of the literature review related to credit risk and FP. The other part is related to the literature review of bank-specific variables and FP. In the hypothesis development, we have used commercial banks' profitability that represents the FP of commercial banks.

Credit risk and financial performance

While operating in the banking industry, three categories of risks that the bank has to face include environmental, financial and operational risks. Banks generate their incomes by issuing a massive amount of credit to borrowers. Still, this activity involves a significant amount of credit risk. When borrowers of the banking sector default cannot meet their debt obligation on time, it is called credit risk ( Accornero et al. , 2018 ). When there is a large amount of loan defaulter, then it adversely affects the profitability of the banking sector. Berger and DeYoung (1997) pointed out that the absence of effective credit risk management would lead to the incidence of banking turmoil and even the financial crisis. Siddique et al. (2020) explain that NPLs are related to information asymmetric theory, principal agency theory and credit default theory. When asymmetric information unequal distribution of information of high NPLs is spread, there is a chance that banks or financial declared bankrupt. According to Pickson and Opare (2016), the principal agency must separate corporate ownership from managerial interest. Because each management has its interest, they want more prestige, pay increment and want the stock options for management. Effective management of credit risk or nonperformance exposure in the banking sectors increases profitability. It enhances the development of banking sectors by adequate allotment of working capital in the economy ( Ghosh, 2015 ).

There is a growing literature ( Louzis et al. , 2012 ) on credit risk and its empirical relationship with the monetary benefits of the banking sector. Ekinci and Poyra (2019) investigate the relationship between credit risk and profitability of deposit banks in Turkey. The data sample used 26 commercial banks from 2005 to 2017. All data of this study are secondary and collected from annual reports of commercial Turkey banks. The proxies of profitability were taken as return on equity (ROE) and return on the asset (ROA), while NPLs of commercial banks were used as a proxy to measure credit risk. The research paper reveals that credit risk and ROA are negatively correlated as well as the relation between credit risk and ROE is also significantly negative relation. Therefore, the study suggests that the Turkey government tightly monitors and controls the alarmingly soaring ratio of NPLs. Upper management introduced some new measures to trim the credit risk.

There is a negative and significant relationship between NPLs and commercial banks' FP.

There is a positive and significant relationship between capital adequacy ratio and commercial banks' FP.

Bank-specific variables and financial performance

Bank-specific variables or internal factors are the product of business activity. Diversifiable risk is associated with these factors ( Louzis et al. , 2012 ) and can be reduced by efficient management. This risk is controllable compared to an external factor, which cannot be diversified because this risk is market risk ( Ghosh, 2015 ; Rachman et al. , 2018 ). If a firm can manage its internal factor effectively, then the firm can be high profitability, while, on the other hand, these factors are mismanaged. It would adversely affect the firm's balance sheet and income statement ( Ofori-Abebrese et al. , 2016 ). Different authors ( Akhtar et al. , 2011 ; Louzis et al. , 2012 ; Chimkono et al. , 2016 ; Hamza, 2017 ) discuss different bank-specific variables and firm performance in their studies. The bank-specific variables used in this study are cost-efficiency ratio (CER), average lending rate (ALR) and liquidity ratio (LR). Aspal et al. (2019) used two types of factors (macro and bank-specific factors) and inspected their connection with the FP of the commercial bank in India. Gross domestic product (GDP) and inflation are used as proxies of macroeconomic factors.

In contrast, a bank-specific variables’ proxy includes capital adequacy ratio, asset quality, management efficiency, liquidity and earnings quality. Data of 20 private banks have been used from 2008 to 2014. The panel data pointed out that one macroeconomic factor is significant (GDP), and another factor (Inflation) is insignificant. All bank's specific factors (earning quality, asset quality, management efficiency and liquidity) significantly affect the FP except the CAR (insignificant). Hasanov et al. (2018) conducted their study to explore the nature of the interrelation between bank-specific (BS) and macroeconomic determinants with the banking performance of Azerbaijan (oil-dependent economy). The study used the GMM to analyze the panel data set. The results show that bank loans, size, capital and some macro factors (inflation, oil prices) were positive and significantly interconnection with the FP of banks; on the other hand, liquidity risk, deposits and exchange rates are significantly affected negatively bonded with the FP.

There is a negative and significant relationship between the CER and commercial banks' FP.

There is a positive and significant relationship between the ALR and commercial banks' FP.

Francis et al. (2015) define liquidity in their study and, according to the liquidity of an asset, determined by how quickly this asset can be converted or transferred into cash. Liquidity is used to fulfill the short-term liabilities rather than the long term ( Siddique et al. , 2020 ; Raphael, 2013 ). Adebayo et al. (2011) mentioned in their study that when banks are unable to pay the required amount to their customers, it is considered bank failure. Sometimes liquidity risk affects the whole financial system of a country. Different studies are conducted on the issue of liquidity and performance, but different studies show different results. FP and liquidity, on the other hand, a chunk of studies ( Francis et al. , 2015 ; Hamza, 2017 ) revealed significant negative tie-up between liquidity and FP, while some other studies pointed out that there is no significant relationship between liquidity and FP. Therefore, the studies show a contradictory result, so the current study takes the bank-specific measures (LR, ALR study and CER) and checks its interconnection with commercial banks' FP.

There is a positive and significant relationship between the LR and commercial banks' FP.

Data and methodology

Our current study has one problem variable, financial performance (FP), while regressors variables are credit risk and bank-specific variables. Our model is consistent with Chimkono et al. (2016) , where ROA and ROE will be used as a measure of FP, while credit risk will be measured by NPL ratio, CAR and three specific variables: CER, LR and ALR.

Various studies ( Hamza, 2017 ; Belas, 2018 ) emphasize some macro and micro variables that need to be controlled when measuring FP because these factors are the influential factors. Three control variables: size of the bank, age of the banks and Inflation are used in this study and shown as yes in the tables. We have chosen these three control and most relevant variables because these variables represent both micro and economic situations. Data have been collected from two South Asian countries Pakistan and India. The nature of data is panel data and the number of banks from Pakistan (10 commercial banks) and India (9 commercial banks) is 19. The data have been collected from bank financial statements throughout 2009 to 2018, so the data of this study are a panel in nature. The final number of observations is 190 (19*10 = 190) for the analysis of this study (see Table 1 ).

Operational definition

The probability of lenders being the default, high credit risk higher FP of banks ( Louzis et al. , 2012 ).

Bank-specific factors are those which are under the control of the management of commercial banks ( Chimkono et al. , 2016 ).

Nonperforming loans

A loan becomes nonperforming when the duration of the loan has been passed, and after that duration, banks 90 days are passed unable to receive the principal amount of loan and interest payment ( Hamza, 2017 ).

Methodology

The current study investigates the interrelationship between credit risk, bank-specific factors and FP. Panel data set is used in our study, and two serious threats usually faced when using panel data set: (1) autocorrelation and (2) endogeneity. For this purpose, a GMM can be used. GMM model has many advantages on simple ordinary least square regression. And when in any study GMM model applies, it allows by adding the fixed effect model; this model can be able to tackle the problem of heterogeneity, and it also removes the problem of endogeneity by introducing some instrumental variables.

Model specification

The regression model is as follows:.

γ 0  = intercept; γ 1 - γ 8  = estimated coefficient of independent variables and control variables.

ε it represents error terms for those variables that are omitted or added intentionally/unintentionally.

According to Lassoued (2018) , panel data regression has two significant problems: autocorrelation and endogeneity, and this problem is existed due to the fixed effect. Therefore, our study checked the basic two assumptions of ordinary least squares.

Testing for autocorrelation

The fifth assumption of CLRM is that data should be free from autocorrelation. Sekaran (2006) pointed out the relationship between two different error terms should be zero; it means that there is no autocorrelation between error terms. There are different tests for testing autocorrelation, but the Wooldridge test is used in the present paper to test the autocorrelation.

Table 2 shows that the p -value of the Wooldridge test result is zero, so it means that all p -values are less than 0.05. It means that reject the null hypothesis. And the null hypothesis is that our data have no autocorrelation, but the results show that our data have autocorrelation problems.

Testing for endogeneity

The seventh assumption of CLRM is that data have no issue of endogeneity. Sekaran (2006) found that the relationship between the error term and explanatory or independent variable should be zero. If this relationship is not zero, then the problem of endogeneity exists. Brooks (2014) pointed out that Hausman test results probabilities can be used to test the endogeneity, and the null hypothesis of this test is that errors are uncorrelated. He also pointed out that if the probabilities are more than 0.10, then accept the null hypothesis. It means that there is no problem of endogeneity, and if the values are less than 0.1, then our data have the problem of endogeneity. Appendix 1 shows that some values of the Hausman test are less than 0.10, so it means that data have the problem of endogeneity. Our panel data results prove that our data have the problem of autocorrelation and endogeneity. Some CLRM model assumptions are not met, so ordinary least square regression results are not BLUE. And GMM model can be applied to any study because this model can be able to tackle the problem of autocorrelation, and it also removes the problem of endogeneity by introducing some instrumental variables.

Findings and discussion

The present research paper provides empirical evidence on the interconnection between credit risk and bank-specific/internal factors on FP commercial banks. To analyze the data set, first, the study applies the descriptive analysis to identify the big picture of the data, then the correlation section and at the end, regression results are discussed. Table 3 presents the descriptive statistics of the all variables used in the study: credit risk indicator which are the ratio of NPL, CAR; indicators of bank-specific factors (CER, ALR, LR); some control variables SIZE, AGE, INF and the measure of FP: ROA, ROE. The mean value of ROA and ROE is 0.986 and 7.964 with a standard deviation of 1.905 and 39.175, respectively, which shows that ROE has much higher variation than ROA. The standard deviation of NPL is 9.659, which is double that of CAR, whose standard deviation is 4.183 among all bank-specific factors (see Table 4 ).

Factor (CER, ALR, LR) LR has high dispersion (14.177) because there is a remarkable difference between minimum 25.027 and maximum value (107.179) of LR. ROA has 0.986 with a range between 10.408 and −6.234 with a standard deviation of 1.905, and it shows that there is a low level of dispersion in developed countries. The dispersion of ROE 39.175 is highest among all other variables, which means that some outliers exist in the ROE variable.

Correlation analysis is used to check the linear relationship between the two explanatory variables ( Brooks, 2014 ). If the sample size of any approaches to 100, greater than 100 and the correlation coefficient is 0.20, then the correlation is significant at 5% ( Lassoued, 2018 ). Most of the variables in the current study are significant at 5%.NPLs, and CER loans are negatively correlated with almost all independent variables, which supports the literature point that NPLs and CER are negatively associated with FP and bank-specific factors. The negative correlation of NPLs with ROE is loan −0.378, and this correlation is high as compared to other countries. At the same time, all bank-specific factors, CER, ALR and LR are mostly positively correlated with most of the other, almost all dependent and independent variables, while AGE and INF are mostly negatively correlated with the other variables of the study.

Regression results and discussion

Tables 5 and 6 have shown the regression results of pooled regression and GMM models. Tables include all independent, control variable coefficients, t -statistics, standard error and probability values. Additionally, tables have the values of R 2 , adjusted R 2 and Durbin Watson statistics. The adjusted R 2 under pooled regression are 0.250 and 0.231 in both models (ROA and ROE). While adjusted R 2 under the GMM are 0.358 and 0.249 in both models ROA and ROE.

It means the GMM more and better explains our model than pooled regression. Moreover, we also apply a Hausman test on both models. The p -value of both models is less than 0.05, so our data have the problem of endogeneity null hypothesis. To eliminate the endogeneity issue, the GMM coefficient was measured.

NPL has a significant and negative measure of FP: ROA and ROE. In contrast, CAR has significant and positive with all proxies of FP: ROE and ROA, which supports H1 and H2 of the paper. Our finding is consistent with Masood and Ashraf (2012) who conducted their study on credit risk and FP and found a significant negative relationship between NPL and FP, so NPLs hinder banks' profitability. Therefore, NPLs affect the whole financial system of a country especially in developing countries. The findings of CAR matched with Accornero et al. ’s (2018) study and pointed out that CAR has a significantly positive link with FP. CER has a significant negative relationship with ROA and ROE, which is consistent with the study of Francis et al. (2015) who pointed out a significant negative relationship between CER and ROE. Therefore, banks need to adapt strategies to control these costs and tried to increase their profitability. ALR had a significant and positive relationship with both measures of FP. ALR is significant at 1% with ROA and 10% significant with ROE. The result is supported by the study of Chimkono et al. (2016) who found a positive relationship between the ALR and FP of commercial banks.

LR has a significantly negative relationship with ROA and ROE. This finding is consistent with Siddique et al. (2020) who pointed out a significant negative relationship between LR and ROE; the more liquidity is maintained, the lesser the profitability level. In short, most of the independent variables are significant at 5% and 1%, and control variables are also significant in both models size of the bank and inflation except AGE. This result is matched with Ghenimi et al. ’s (2017) findings that prove that total assets or investment increment are directly proportional to the FP. Both variables of credit risk NPL and CAR are significant with the FP of commercial banks in both models. Banks try to reduce bank-specific factors risk, and by doing so, ultimately the amount of bad debt decreased, and another benefit is that it also reduces the amount of loan loss provision.

The current study empirically investigates the causal interrelation between credit risk, bank-specific factors and FP of commercial banks in two South Asian countries (Pakistan and India). The study's finding suggests that managers in South Asian countries should be focused on increasing capital adequacy to enhance the monetary gain (FP) while for the contraction of NPLs by implementing modern techniques and strategies for credit risk (NPLs) management. One indicator of the bank-specific variable (ALR) has a significant and positive interrelation with the FP of commercial banks. In contrast, CER and LR have a significant and positive relationship with the FP of commercial banks of South Asia. Control variables of the study (size of the bank and inflation) are also significant in both models except AGE. There are several policy implications that commercial banks of South Asian countries should be followed. NPLs are soaring due to the following reasons: less supervision and monitoring of customers, the problem of the market and lack of customer knowledge related to loans. Bank management should be efficient in judging that their customers have viable means of repayment or not. Moreover, banks can offer expert opinion to the professional loan take on feasible techniques of efficiently endow the borrowing to secure the required return on total firms investment is acquired. Liquidity position should be well maintained so that even in a high competition environment, the commercial can survive in that environment.

The scope of the study is only limited to commercial banks, but this model can also be applied to Islamic banks. And future researchers can also apply this model to a comparison-based study of commercial and Islamic banks. Data of this study have been collected only from 19 banks; future research can also increase the number of banks and increase the number of years to conduct their study. And if the number of banks and the number of the year increased, the results are a more reliable and accurate representation of the population. The data of this study have been taken only from two countries of South Asia, but this study can be extended by adding more countries in Asia. When we add the number of countries, the results are a better and accurate representation of developing and developed countries of Asia. This model can also be applied to some other continents because the macro environment and bank-specific factors are pretty different from continent to continent Appendix A1 .

NPLs-continent wise

Summary of explanatory variables and dependent variables

Results for autocorrelation for South Asia countries

Descriptive statistics

Correlation figures

ROA model (pooled regression and fixed effect GMM result)

Extra tables and figures in the Google drop box and available at: https://www.dropbox.com/sh/dro0gkowf3t542r/AAC3QQ5lKQTpLdke7UNxRUEea?dl=0

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• Open access
• Published: 09 December 2016

An empirical research on evaluating banks’ credit assessment of corporate customers

• Sang-Bing Tsai 1 , 2 , 3 , 4 , 5 ,
• Guodong Li 6 ,
• Chia-Huei Wu 7 ,
• Yuxiang Zheng 1 , 2 &
• Jiangtao Wang 3  

SpringerPlus volume  5 , Article number:  2088 ( 2016 ) Cite this article

15 Citations

Metrics details

Under the rapid change of the global financial environment, the risk control of the credit granting is viewed as the foremost task to each bank. With the impact one by one from financial crisis and European debt crisis, the steady bank business is also facing the severe challenge. Banks approve the credits for their customers and then make money from the interest.

Case presentation

Credit granting is not only the primary job but also the main source of income. The quality of credit granting concerns not just the reclaims of creditor’s rights; it also affects the successful running of banks.

Discussion and Evaluation

To enhance the reliability and usefulness of bank credit risk assessment, we first will delve in the facets and indexes in the bank credit risk assessment. Then, we will examine the different dimensions of cause–effect relationships and correlations in the assessment process. Finally, the study focuses on how to raise the functions and benefits of the bank credit risk assessment.

Conclusions

In those five credit risk evaluation dimensions, A “optional capability” and D “competitiveness” are of high relation and high prominence among those dimensions, influencing other items obviously. By actively focusing on these two dimensions and improving their credit risk assessment ability will solve the foremost problems and also solve other facets of credit risk assessment problems at the same time.

Introduction

The so-called bank credit risk management is through the establishment of credit granting policies, instructions, and coordination between the different sections in the bank, such as the full supervision and control of customers’ credit investigation, choices of payment methods, confirmation of the credit limit, and reclaims of the sum of money, banks are guaranteed to retrieve the receivables back in time safely (Aebi et al. 2012 ; Benjamin and Charles 2014 ; Swami 2014 ).

However, there exists the phenomenon of “credit paradox” in the practice of credit risk management. This so called “credit paradox” is, on one hand, the risk management theory demands banks follow principles of the investment decentralization and diversification in bank credit risk management, to prevent the concentration of the credit authorization. Diversification is even more important and the golden rule to obey since particularly the traditional credit risk management model is lacking the effective credit risk hedge. On the other hand, in real practice, the bank loan business often shows that the diversification principle is not easy to put into practice because many banks do not abide by the diversification rule a lot on their loan business (Berger et al. 2009 ; Nuno and Manuela 2014 ; Mora 2014 ). There are several main reasons to cause “credit paradox” phenomenon, stated blow. (1) For most small–medium sized corporations without credit ratings, the credit situation is reveled by the long-term business partnership between the firms and banks. This way of partnership and information gained tends to make the banks execute loan business with the acquainted business clients. (2) Some banks would limit their loan business companies. Those firms whom the banks are familiar with in certain industry or in certain expertise are banks’ priorities. (3) Diversification of loan business tends to minimize the loan business to small-sized business, unfavorable to attain to the scale of benefits for banks on their loan business. (4) Sometimes the investment on the market would force banks to develop their loan business on certain limited sections or areas.

As to the credit risk assessment of banks, the accurate measurement of the risk is the basic premise. For the reasons stated above, it is extremely difficult to measure the credit risk accurately (Shipra and Yash 2014 ). So far some credit risk calculation models developed by JP Morgan and other institutes, such as Creditmetrics, CreditRlsk+, KMV models, are still disputable on their effectiveness and reliability. For the time being, there is still lacking effective measurement on credit risk (Aebi et al. 2012 ; Benjamin and Charles 2014 ; Shipra and Yash 2014 ).

Besides, for the related studies about the credit risk index in the past, there are two insufficient points. First, most studies are based on the hypothesis that indexes are independent, with no influences and cause–effect relationships on others. Second, some studies hold the same weight and hypothesis towards the assessment indexes. For solving the insufficiency in the previous studies and upgrading reliability and usefulness of the bank credit risk, this study adopts Decision Making Trial and Evaluation Laboratory (DEMATEL) to develop its theorizing. We first delve in the evaluation facets and indexes in the bank credit risk assessment. Then, we will examine the different dimensions of cause–effect relationships and correlations in the assessment process. Finally, the study focuses on how to raise the functions and benefits of the bank credit risk assessment.

Literature review

Bank credit risk and risk management.

The credit risk has been the most important management issue to banks. The quality of credit risk management, good or bad, matters a lot to banks which absorb the financial risks in exchange of benefits as their essence of business. The credit risk is like as follows: the borrower or the business counterparties are unable to fulfill the duty of their contracts out of the deterioration and other factors from the entrepreneurs (such as entanglement between firms); therefore this causes the risk of agreement violation and the loss of money. Generally, from different objects and behaviors, the credit risk could be further divided into two types: (1) lending risk, also called, issuer risk. This type of risk is duo to the violation of agreement when borrowers or bond issuers do not repay their debts or their credits get deteriorated, causing the money loss. Lending risk or issuer risk are often correlated to borrowers and bond issuers’ debt credit situations, and correlated to the risk sensitiveness degree of the financial products. (2) The second credit risk is counterparty risk; it could be further divided into two risks: settlement risk and pre-settlement risk. Settlement risk is the risk that counterparties do not fulfill their contract duties in the due settlement time and cause the loss of the equality principal to the bank. Pre-settlement risk is the risk that counterparties violate the agreement before the final settlement day and cause the risk of contract violation to the bank.

The bank credit risk management organizations and functions may appear in different forms. However, the bank should ensure the official positions and related authorities work independently and attributably, not just focusing on the superficial independency, to reach the goal of credit risk management and supervision, such as (Aebi et al. 2012 ; Jiang and Lo 2014 ; Nuno and Manuela 2014 ; Swami 2014 ):

Business functions should be independent from credit granting/verification functions to avoid the interest conflict.

Credit verification functions should be independent from credit granting functions to make sure the credit result report objective and just.

Accounting functions should be independent from credit granting/verification functions and business functions to avoid fraud and malpractice.

The unit responsible for designing, establishing, or executing the credit risk measurement system should be independent from the credit granting functions to keep this unit free of other interruptions.

The office worker in charge of verifying the credit risk measurement system should be different from the office worker responsible for designing or choosing the credit risk measurement system to lower the possibility of making errors from the credit risk measurement system.

The authorities should obey the regulations to restrict the interested parties in the bank.

Re-check the credit granting workers of interest in the bank, such as the credit granting of the general manager and the high-ranked officer.

Regularly (at least per year) check the strategies and related policies of the bank credit risk management to confirm that the high-ranked managers carry out the regulations successfully and to make sure the credit granting in accordance with those strategies and related policies. This is then to make the high-ranked managers ultimately responsible for establishing and maintaining the appropriate and effective credit risk management mechanism.

Make regular inspection on the bank management information and reflect on the correct credit risk strategies to guarantee the suitability and sufficiency of the bank capital.

The bank credit risk evaluation methods

For the past 20 years, the development of international bank credit risk management and evaluation has been through the several phases as follows:

Influenced by the debt crisis at 1980s, banks mostly began to focus on the preventative measures and management against the credit risk. Thus came out the result of the birth of “Basel Accord” which was a kind of vague analysis of the bank credit risk; through the adopting of different weights on different assets, this agreement quantified the risks.

Since 1990s some major banks acknowledged the fact that the credit risk was still the key factor in financial risks and they began to concern about the problems of the credit risk measurement, trying to establish the internal method and model for measuring the credit risk. Among those models, the credit risk management system “Credit Metrics” by J.P. Morgan obtained the widest attention.

After the outbreak of Asia financial crisis in 1997, some new phenomenon appeared in the global financial risk. The loss was not necessarily caused by single risk but by the mixture of the credit risk and the market risk etc. Financial crisis motivated people in the banking industry to value the mixture model of the market risk with the credit risk and to focus on the quantification problems of the operation risk. From this phase on, the comprehensive risk management model attained to people’s heed.

Within the traditional credit risk management, the main methods include the Expert System, Internal Ratings Grading Model for Loans, and Z Rating Model. Nevertheless, the modern development of banking makes those methods obsolete and inaccurate. With the advance of modern science and technology and with the enhancement of the management of the market risks plus other risks, modern credit risk management has also been lifted to the certain level. Therefore there appear some credit risk quantification management models such as “Creditmetrics”, “KMV”, “Creditrisk+” models. These models measuring the credit risk still arouse disputes over their effectiveness and reliability. Hence, in all respects, it is still lacking an effective calculating measure to assess the credit risk (Jiang and Lo 2014 ; Nuno and Manuela 2014 ; Swami 2014 ).

According to Dinh and Kleimeier ( 2007 ), the determination of loans does not depend on the borrower’s income or the amount of collateral, but rather on the qualitative analysis (of, for example, the borrower’s personality, reputation, or social status). Because the maintenance of social credit relationships is expensive, banks typically adopt the credit scoring model to quantitatively analyze a borrower’s credit situation to determine loans and identify whether a borrower can obey the contract. Banks’ credit assessment of corporate customers is a multiple-criteria decision-making problem in which various elements are comprehensively assessed. The construction of an effective credit assessment model requires that credit staff possess sufficient professional knowledge and practical experience. Previous credit assessment studies have mostly analyzed the opinion of a group of credit staff by using a single precise value, which cannot fully describe the actual distribution of credit staff opinions and tends to diminish minority and peculiar opinions. Therefore, precise values are inapplicable in actual decision environments and constructed credit assessment models do not possess the features of anti-catastrophism and sensitivity, which are the criteria of a superior assessment system (Hsieh 2003 ). Srinivasan and Kim ( 1988 ) stated that credit assessment can be conducted using theory-based scientific and objective methods. The experience of credit decision managers and senior credit staff responsible for credit assessment can be applied to credit assessment models for determining credit categorization and rating weights (Chiou and Shen 2011 ; Lee et al. 2016 ).

Research method

Based on the literature regarding the banks’ approaches and principles of corporate customer credit rating, this study developed five assessment dimensions and 25 criteria, with the definitions listed in Table  1 .

DEMATEL model

This study adopted the DEMATEL, which was proposed by Gabus and Fontela who were employed in the Battelle Memorial Institute of Geneva (Gabus and Fontela 1973 ; Fontela and Gabus 1976 ; Lee et al. 2014a , b ; Guo and Tsai 2015 ; Guo et al. 2015 ; Gandhi et al. 2016 ). At the initial stage, the DEMATEL was used to solve difficult and complex problems such as racial, hunger-related, environmental, and energy-related problems (Hu 2003 ; Huang 2013 ; Tsai and Xue 2013 ; Tsai et al. 2014 , 2015 , 2016a ; Qu et al. 2015 ). In this study, the DEMATEL was adopted to establish a relationship structure comprising elements used for banks’ credit assessment of corporate customers. When a bank assesses corporate customers, the relationship and degree of influence among the assessment elements are problems common to bank managers. In other words, when a bank manager intends to improve numerous decision-making elements, the optimal approach is to search for the most critical element that influences all other elements.

The DEMATEL structure and calculation steps are summarized and explained in the following sections (Yang and Tzeng 2011 ; Wu et al. 2013 ; Liu et al. 2015 ; Zhang et al. 2015 ; Tsai 2016 ; Tsai et al 2016b ; Zhou et al. 2016 ).

The six steps of DEMATEL analysis were implemented in this study:

Understanding and defining elements

Problems were thoroughly understood, and elements were determined and defined in a complex system through in-depth interviews, a literature review, brainstorming, or the collection of expert opinions.

Determining the correlation among elements and establishing measurement scales

Based on the relationship among elements, a scale of influence degree was developed for pair-wise comparisons. Specifically, each interviewee’s cognition of each aspect’s influence degree was assessed through the pair-wise comparison of aspects (elements). In the assessment scale, 0, 1, 2, 3, and 4 denoted no influence , low influence , moderate influence , high influence , and excessively high influence among the aspects (elements), respectively.

Constructing a direct-relation matrix

The number of elements was denoted as n . Expert opinions were collected by conducting a questionnaire survey. Elements were compared in pairs based on their relationship and degree of influence. Therefore, an n  ×  n direct-relation matrix (denoted as X ) was obtained, in which x ij indicated the influence degree of element i on element j , and the diagonal elements x ii were set as 0.

Direct-relation matrix X

The symbolic matrix S was established, representing the positive and negative influences (denoted as + and −, respectively).

Calculating a normalized direct-relation matrix

Through the calculation of Eqs. ( 2 ) and ( 3 ), the direct-relation matrix was multiplied by λ to generate the normalized direct-relation matrix N .

In addition, DEMATEL analysis assumes that the sum of at least one row of i must meet the requirement presented in Eq. ( 4 ).

Therefore, the substochastic matrix was computed using the normalized direct-relation matrix N .

where O represented a null matrix and I an identity matrix.

Calculating a direct/indirect relation matrix

When normalized direct-relation matrix N met the requirement of Eq. ( 5 ), the direct/ indirect relation matrix T , also named the total-relation matrix, was obtained using Eq. ( 6 ). The indirect relation matrix H , also called the total-indirect-relation matrix, was obtained using Eq. ( 7 ).

Let t ij be the assessment element in the direct/indirect relation matrix T , and i , \(j = 1,2, \ldots ,n\) . The sum of rows and that of columns of T were calculated using Eqs. ( 8 ) and ( 9 ). The sum of row i was denoted as D i , signifying that the assessment element i was the factor that influenced other assessment elements; R j represented the sum of column j , indicating that the assessment element i was the result influenced by other assessment elements. Both D i and R j , which were obtained using the direct/indirect relation matrix T , involved direct and indirect influences.

Illustrating the causal diagram

In the causal diagram, ( D k  +  R k , D k  −  R k ) represented the horizontal and vertical axes. The mean value and 0.0 were used as the dividing points on the horizontal axis ( D k  +  R k ) and vertical axis ( D k  −  R k ), respectively, dividing the causal diagram into four quadrants. The values of ( D k  +  R k ) on the horizontal axis were defined as prominence , and \(k = i = j = 1,2, \ldots ,n\) , indicated the total degree to which an element exerted influence on and was influenced by other elements. Therefore, ( D k  +  R k ) showed the degree to which element k was at the core of all problems. In addition, the values of ( D k  −  R k ) on the vertical axis were defined as relation , representing the difference in the degree to which an element exerted influence on and was influenced by other elements. Thus, ( D k  −  R k ) showed the causal degree of element k in all problems. If the value of ( D k  −  R k ) was positive, the element tended to be a cause; if the value was negative, the element tended to be a result (Hung 2011 ; Hsu et al. 2013 ; Ren et al. 2013 ; Gandhi et al. 2015 ).

Results and discussion

Questionnaires.

The five dimensions and 25 elements for banks’ credit assessment of corporate customers were used as items in the DEMATEL expert questionnaire. The questionnaire survey was administered to bank managers in Taiwan. The details are described as follows.

Questionnaires were distributed to 18 Taiwan bank credit managers with more than 20 years of work experience. The DEMATEL questionnaires were distributed between March 16, 2015, and April 30, 2015. The measurement scale was a 5-point scale, with 4 representing maximal influence and 0 representing no influence. The scores between these two values were sequential ratings based on value. The author visited each expert in person, explained the content of the questionnaire, and requested each expert to complete the questionnaire. Overall, 18 questionnaires were distributed and returned. The valid return rate was 100%.

This study used Matlab software to calculate. The scores from the 18 experts were averaged and rounded to one decimal place to create a table of five criteria, as shown in Table  2 .

Next, the normalized direct-relation matrix was calculated using column vectors and maximums as benchmarks for normalization. The reciprocal of the maximum value within the sum of each column was the λ value. Using Eqs. ( 2 ) and ( 3 ), the direct-relation matrix X was multiplied by the λ value to obtain the normalized direct-relation matrix N. The influence coefficient was rounded to two decimal places (Table  3 ).

Equations ( 4 ), ( 5 ), ( 6 ), ( 7 ) were then used to calculate the total-relation matrix T, as shown in Table  4 .

Equations ( 8 ) and ( 9 ) were used to calculate value Di of each column and value Rj of each row to obtain prominence (D + R) and relation (D − R), as shown in Table  4 . In addition, the five dimensions were drawn into a figure with prominence as the horizontal axis and relation as the vertical axis, as shown in Fig.  1 .

DEMATEL distribution diagram for the five dimensions

From the results of Table  5 and Fig.  1 , the cause–effect relationships and correlations among five evaluation dimensions are interpreted as follows.

High relation and high prominence: This category contained A “Operational capability” and D “Competitiveness”. These two dimensions were properties in the cause category and were core influences on the other dimensions. This indicates that these were driving factors and critical problem-solving factors.

Low relation and high prominence: This category contained B “Repayment ability” and C “Financing capacity”. These two dimensions were in the effect category and were influenced by the other properties. Although B, C were a property that required improvement, it could not be directly improved because it was in the effect class. Therefore, B, C was relatively irrelevant.

Low relation and low prominence: This category contained E “Response ability”. This dimension was influenced by other properties. However, the influences were small. This dimension that these properties were relatively independent.

All in all, in those five credit risk evaluation dimensions above, A “optional capability” and D “competitiveness” are of high relation and high prominence among those dimensions, influencing other items obviously. By actively focusing on these two dimensions and improving their credit risk assessment ability will solve the foremost problems and also solve other facets of credit risk assessment problems at the same time. Thus we suggest the bank corporations pay huge efforts to improve the credit risk assessment and censorship of these two facets, to upgrade the results of the credit risk assessment immediately.

What we will explain is A “optional capability” and D “competitiveness” are two base dimensions for corporation’s competitive ability, competitive advance, and profit gaining ability. If the corporations’ operational capability and competitive ability are the priority to get upgraded, then the corporation’s repayment ability and financing capacity would also arise naturally.

With the liberalization and globalization of financial development, innovative financial activities flourishing, and the banking business more and more complicated, the financial system risks also gradually increase with time. To effectively adjust to the rapid change of the financial environment, main countries in the world all devote to carrying out financial reforms. Through reflections on financial supervision system and financial regulations, through the improvement of financial credit risk assessment techniques, the banks are urged to sharpen their risk management and corporation administration, to derive a robust financial system and to enhance the country’s financial competitive advantage.

For resolving the insufficiency of the former studies, this study is developed with DEMATEL Model, to increase the reliability and usefulness of the bank credit risk. To enhance the reliability and usefulness of bank credit risk assessment, we first will delve in the facets and indexes in the bank credit risk assessment. Then, we will examine the different dimensions of cause–effect relationships and correlations in the assessment process. Finally, the study focuses on how to raise the functions and benefits of the bank credit risk assessment.

In those five credit risk evaluation dimensions above, A “optional capability” and D “competitiveness” are of high relation and high prominence among those dimensions, influencing other items obviously. By actively focusing on these two dimensions and improving their credit risk assessment ability will solve the foremost problems and also solve other facets of credit risk assessment problems at the same time. Thus we suggest the bank corporations pay huge efforts to improve the credit risk assessment and censorship of these two facets, to upgrade the results of the credit risk assessment immediately.

We suggest the follow-up studies could adopt DEMATEL model and study the cases from other different countries and areas, to discuss the bank credit risk assessment problems and make a comparative study over miscellaneous areas. Other research methods are also recommended to develop other evaluation index system and to make comparisons.

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Authors’ contributions

Writing: S-BT; providing case and idea: C-HW, S-BT; providing revised advice: GL, C-HW, YZ, JW. All authors read and approved the final manuscript.

Acknowledgements

This work was supported by National Social Science Fund of China (No. 12BYJ125), Provincial Nature Science Foundation of Guangdong (Nos. 2015A030310271 and 2015A030313679), Academic Scientific Research Foundation for High-level Researcher, University of Electronic Science Technology of China, Zhongshan Institute (No. 415YKQ08), Tianjin philosophy and social science planning project (No. TJGL-028), The Fundamental Research Funds for the Central Universities (No. ZXH2012N002).

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Sang-Bing Tsai

School of Business, Dalian University of Technology, Panjin, 124221, China

Economics and Management College, Civil Aviation University of China, Tianjin, 300300, China

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Tsai, SB., Li, G., Wu, CH. et al. An empirical research on evaluating banks’ credit assessment of corporate customers. SpringerPlus 5 , 2088 (2016). https://doi.org/10.1186/s40064-016-3774-0

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• Decision-Making Trial and Evaluation Laboratory (DEMATEL)
• Bank credit
• Credit assessment
• Decision making
• Bank credit risk
• Banking supervision law

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• Published: 03 September 2022

A literature review of risk, regulation, and profitability of banks using a scientometric study

• Shailesh Rastogi 1 ,
• Arpita Sharma 1 ,
• Geetanjali Pinto 2 &
• Venkata Mrudula Bhimavarapu   ORCID: orcid.org/0000-0002-9757-1904 1 , 3  

Future Business Journal volume  8 , Article number:  28 ( 2022 ) Cite this article

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Metrics details

This study presents a systematic literature review of regulation, profitability, and risk in the banking industry and explores the relationship between them. It proposes a policy initiative using a model that offers guidelines to establish the right mix among these variables. This is a systematic literature review study. Firstly, the necessary data are extracted using the relevant keywords from the Scopus database. The initial search results are then narrowed down, and the refined results are stored in a file. This file is finally used for data analysis. Data analysis is done using scientometrics tools, such as Table2net and Sciences cape software, and Gephi to conduct network, citation analysis, and page rank analysis. Additionally, content analysis of the relevant literature is done to construct a theoretical framework. The study identifies the prominent authors, keywords, and journals that researchers can use to understand the publication pattern in banking and the link between bank regulation, performance, and risk. It also finds that concentration banking, market power, large banks, and less competition significantly affect banks’ financial stability, profitability, and risk. Ownership structure and its impact on the performance of banks need to be investigated but have been inadequately explored in this study. This is an organized literature review exploring the relationship between regulation and bank performance. The limitations of the regulations and the importance of concentration banking are part of the findings.

Introduction

Globally, banks are under extreme pressure to enhance their performance and risk management. The financial industry still recalls the ignoble 2008 World Financial Crisis (WFC) as the worst economic disaster after the Great Depression of 1929. The regulatory mechanism before 2008 (mainly Basel II) was strongly criticized for its failure to address banks’ risks [ 47 , 87 ]. Thus, it is essential to investigate the regulation of banks [ 75 ]. This study systematically reviews the relevant literature on banks’ performance and risk management and proposes a probable solution.

Issues of performance and risk management of banks

Banks have always been hailed as engines of economic growth and have been the axis of the development of financial systems [ 70 , 85 ]. A vital parameter of a bank’s financial health is the volume of its non-performing assets (NPAs) on its balance sheet. NPAs are advances that delay in payment of interest or principal beyond a few quarters [ 108 , 118 ]. According to Ghosh [ 51 ], NPAs negatively affect the liquidity and profitability of banks, thus affecting credit growth and leading to financial instability in the economy. Hence, healthy banks translate into a healthy economy.

Despite regulations, such as high capital buffers and liquidity ratio requirements, during the second decade of the twenty-first century, the Indian banking sector still witnessed a substantial increase in NPAs. A recent report by the Indian central bank indicates that the gross NPA ratio reached an all-time peak of 11% in March 2018 and 12.2% in March 2019 [ 49 ]. Basel II has been criticized for several reasons [ 98 ]. Schwerter [ 116 ] and Pakravan [ 98 ] highlighted the systemic risk and gaps in Basel II, which could not address the systemic risk of WFC 2008. Basel III was designed to close the gaps in Basel II. However, Schwerter [ 116 ] criticized Basel III and suggested that more focus should have been on active risk management practices to avoid any impending financial crisis. Basel III was proposed to solve these issues, but it could not [ 3 , 116 ]. Samitas and Polyzos [ 113 ] found that Basel III had made banking challenging since it had reduced liquidity and failed to shield the contagion effect. Therefore, exploring some solutions to establish the right balance between regulation, performance, and risk management of banks is vital.

Keeley [ 67 ] introduced the idea of a balance among banks’ profitability, regulation, and NPA (risk-taking). This study presents the balancing act of profitability, regulation, and NPA (risk-taking) of banks as a probable solution to the issues of bank performance and risk management and calls it a triad . Figure  1 illustrates the concept of a triad. Several authors have discussed the triad in parts [ 32 , 96 , 110 , 112 ]. Triad was empirically tested in different countries by Agoraki et al. [ 1 ]. Though the idea of a triad is quite old, it is relevant in the current scenario. The spirit of the triad strongly and collectively admonishes the Basel Accord and exhibits new and exhaustive measures to take up and solve the issue of performance and risk management in banks [ 16 , 98 ]. The 2008 WFC may have caused an imbalance among profitability, regulation, and risk-taking of banks [ 57 ]. Less regulation , more competition (less profitability ), and incentive to take the risk were the cornerstones of the 2008 WFC [ 56 ]. Achieving a balance among the three elements of a triad is a real challenge for banks’ performance and risk management, which this study addresses.

Triad of Profitability, regulation, and NPA (risk-taking). Note The triad [ 131 ] of profitability, regulation, and NPA (risk-taking) is shown in Fig.  1

Triki et al. [ 130 ] revealed that a bank’s performance is a trade-off between the elements of the triad. Reduction in competition increases the profitability of banks. However, in the long run, reduction in competition leads to either the success or failure of banks. Flexible but well-expressed regulation and less competition add value to a bank’s performance. The current review paper is an attempt to explore the literature on this triad of bank performance, regulation, and risk management. This paper has the following objectives:

To systematically explore the existing literature on the triad: performance, regulation, and risk management of banks; and

To propose a model for effective bank performance and risk management of banks.

Literature is replete with discussion across the world on the triad. However, there is a lack of acceptance of the triad as a solution to the woes of bank performance and risk management. Therefore, the findings of the current papers significantly contribute to this regard. This paper collates all the previous studies on the triad systematically and presents a curated view to facilitate the policy makers and stakeholders to make more informed decisions on the issue of bank performance and risk management. This paper also contributes significantly by proposing a DBS (differential banking system) model to solve the problem of banks (Fig.  7 ). This paper examines studies worldwide and therefore ensures the wider applicability of its findings. Applicability of the DBS model is not only limited to one nation but can also be implemented worldwide. To the best of the authors’ knowledge, this is the first study to systematically evaluate the publication pattern in banking using a blend of scientometrics analysis tools, network analysis tools, and content analysis to understand the link between bank regulation, performance, and risk.

This paper is divided into five sections. “ Data and research methods ” section discusses the research methodology used for the study. The data analysis for this study is presented in two parts. “ Bibliometric and network analysis ” section presents the results obtained using bibliometric and network analysis tools, followed by “ Content Analysis ” section, which presents the content analysis of the selected literature. “ Discussion of the findings ” section discusses the results and explains the study’s conclusion, followed by limitations and scope for further research.

Data and research methods

A literature review is a systematic, reproducible, and explicit way of identifying, evaluating, and synthesizing relevant research produced and published by researchers [ 50 , 100 ]. Analyzing existing literature helps researchers generate new themes and ideas to justify the contribution made to literature. The knowledge obtained through evidence-based research also improves decision-making leading to better practical implementation in the real corporate world [ 100 , 129 ].

As Kumar et al. [ 77 , 78 ] and Rowley and Slack [ 111 ] recommended conducting an SLR, this study also employs a three-step approach to understand the publication pattern in the banking area and establish a link between bank performance, regulation, and risk.

Determining the appropriate keywords for exploring the data

Many databases such as Google Scholar, Web of Science, and Scopus are available to extract the relevant data. The quality of a publication is associated with listing a journal in a database. Scopus is a quality database as it has a wider coverage of data [ 100 , 137 ]. Hence, this study uses the Scopus database to extract the relevant data.

For conducting an SLR, there is a need to determine the most appropriate keywords to be used in the database search engine [ 26 ]. Since this study seeks to explore a link between regulation, performance, and risk management of banks, the keywords used were “risk,” “regulation,” “profitability,” “bank,” and “banking.”

Initial search results and limiting criteria

Using the keywords identified in step 1, the search for relevant literature was conducted in December 2020 in the Scopus database. This resulted in the search of 4525 documents from inception till December 2020. Further, we limited our search to include “article” publications only and included subject areas: “Economics, Econometrics and Finance,” “Business, Management and Accounting,” and “Social sciences” only. This resulted in a final search result of 3457 articles. These results were stored in a.csv file which is then used as an input to conduct the SLR.

Data analysis tools and techniques

This study uses bibliometric and network analysis tools to understand the publication pattern in the area of research [ 13 , 48 , 100 , 122 , 129 , 134 ]. Some sub-analyses of network analysis are keyword word, author, citation, and page rank analysis. Author analysis explains the author’s contribution to literature or research collaboration, national and international [ 59 , 99 ]. Citation analysis focuses on many researchers’ most cited research articles [ 100 , 102 , 131 ].

The.csv file consists of all bibliometric data for 3457 articles. Gephi and other scientometrics tools, such as Table2net and ScienceScape software, were used for the network analysis. This.csv file is directly used as an input for this software to obtain network diagrams for better data visualization [ 77 ]. To ensure the study’s quality, the articles with 50 or more citations (216 in number) are selected for content analysis [ 53 , 102 ]. The contents of these 216 articles are analyzed to develop a conceptual model of banks’ triad of risk, regulation, and profitability. Figure  2 explains the data retrieval process for SLR.

Data retrieval process for SLR. Note Stepwise SLR process and corresponding results obtained

Bibliometric and network analysis

Figure  3 [ 58 ] depicts the total number of studies that have been published on “risk,” “regulation,” “profitability,” “bank,” and “banking.” Figure  3 also depicts the pattern of the quality of the publications from the beginning till 2020. It undoubtedly shows an increasing trend in the number of articles published in the area of the triad: “risk” regulation” and “profitability.” Moreover, out of the 3457 articles published in the said area, 2098 were published recently in the last five years and contribute to 61% of total publications in this area.

Articles published from 1976 till 2020 . Note The graph shows the number of documents published from 1976 till 2020 obtained from the Scopus database

Source of publications

A total of 160 journals have contributed to the publication of 3457 articles extracted from Scopus on the triad of risk, regulation, and profitability. Table 1 shows the top 10 sources of the publications based on the citation measure. Table 1 considers two sets of data. One data set is the universe of 3457 articles, and another is the set of 216 articles used for content analysis along with their corresponding citations. The global citations are considered for the study from the Scopus dataset, and the local citations are considered for the articles in the nodes [ 53 , 135 ]. The top 10 journals with 50 or more citations resulted in 96 articles. This is almost 45% of the literature used for content analysis ( n  = 216). Table 1 also shows that the Journal of Banking and Finance is the most prominent in terms of the number of publications and citations. It has 46 articles published, which is about 21% of the literature used for content analysis. Table 1 also shows these core journals’ SCImago Journal Rank indicator and H index. SCImago Journal Rank indicator reflects the impact and prestige of the Journal. This indicator is calculated as the previous three years’ weighted average of the number of citations in the Journal since the year that the article was published. The h index is the number of articles (h) published in a journal and received at least h. The number explains the scientific impact and the scientific productivity of the Journal. Table 1 also explains the time span of the journals covering articles in the area of the triad of risk, regulation, and profitability [ 7 ].

Figure  4 depicts the network analysis, where the connections between the authors and source title (journals) are made. The network has 674 nodes and 911 edges. The network between the author and Journal is classified into 36 modularities. Sections of the graph with dense connections indicate high modularity. A modularity algorithm is a design that measures how strong the divided networks are grouped into modules; this means how well the nodes are connected through a denser route relative to other networks.

Network analysis between authors and journals. Note A node size explains the more linked authors to a journal

The size of the nodes is based on the rank of the degree. The degree explains the number of connections or edges linked to a node. In the current graph, a node represents the name of the Journal and authors; they are connected through the edges. Therefore, the more the authors are associated with the Journal, the higher the degree. The algorithm used for the layout is Yifan Hu’s.

Many authors are associated with the Journal of Banking and Finance, Journal of Accounting and Economics, Journal of Financial Economics, Journal of Financial Services Research, and Journal of Business Ethics. Therefore, they are the most relevant journals on banks’ risk, regulation, and profitability.

Location and affiliation analysis

Affiliation analysis helps to identify the top contributing countries and universities. Figure  5 shows the countries across the globe where articles have been published in the triad. The size of the circle in the map indicates the number of articles published in that country. Table 2 provides the details of the top contributing organizations.

Location of articles published on Triad of profitability, regulation, and risk

Figure  5 shows that the most significant number of articles is published in the USA, followed by the UK. Malaysia and China have also contributed many articles in this area. Table 2 shows that the top contributing universities are also from Malaysia, the UK, and the USA.

Key author analysis

Table 3 shows the number of articles written by the authors out of the 3457 articles. The table also shows the top 10 authors of bank risk, regulation, and profitability.

Fadzlan Sufian, affiliated with the Universiti Islam Malaysia, has the maximum number, with 33 articles. Philip Molyneux and M. Kabir Hassan are from the University of Sharjah and the University of New Orleans, respectively; they contributed significantly, with 20 and 18 articles, respectively.

However, when the quality of the article is selected based on 50 or more citations, Fadzlan Sufian has only 3 articles with more than 50 citations. At the same time, Philip Molyneux and Allen Berger contributed more quality articles, with 8 and 11 articles, respectively.

Keyword analysis

Table 4 shows the keyword analysis (times they appeared in the articles). The top 10 keywords are listed in Table 4 . Banking and banks appeared 324 and 194 times, respectively, which forms the scope of this study, covering articles from the beginning till 2020. The keyword analysis helps to determine the factors affecting banks, such as profitability (244), efficiency (129), performance (107, corporate governance (153), risk (90), and regulation (89).

The keywords also show that efficiency through data envelopment analysis is a determinant of the performance of banks. The other significant determinants that appeared as keywords are credit risk (73), competition (70), financial stability (69), ownership structure (57), capital (56), corporate social responsibility (56), liquidity (46), diversification (45), sustainability (44), credit provision (41), economic growth (41), capital structure (39), microfinance (39), Basel III (37), non-performing assets (37), cost efficiency (30), lending behavior (30), interest rate (29), mergers and acquisition (28), capital adequacy (26), developing countries (23), net interest margin (23), board of directors (21), disclosure (21), leverage (21), productivity (20), innovation (18), firm size (16), and firm value (16).

Keyword analysis also shows the theories of banking and their determinants. Some of the theories are agency theory (23), information asymmetry (21), moral hazard (17), and market efficiency (16), which can be used by researchers when building a theory. The analysis also helps to determine the methodology that was used in the published articles; some of them are data envelopment analysis (89), which measures technical efficiency, panel data analysis (61), DEA (32), Z scores (27), regression analysis (23), stochastic frontier analysis (20), event study (15), and literature review (15). The count for literature review is only 15, which confirms that very few studies have conducted an SLR on bank risk, regulation, and profitability.

Citation analysis

One of the parameters used in judging the quality of the article is its “citation.” Table 5 shows the top 10 published articles with the highest number of citations. Ding and Cronin [ 44 ] indicated that the popularity of an article depends on the number of times it has been cited.

Tahamtan et al. [ 126 ] explained that the journal’s quality also affects its published articles’ citations. A quality journal will have a high impact factor and, therefore, more citations. The citation analysis helps researchers to identify seminal articles. The title of an article with 5900 citations is “A survey of corporate governance.”

Page Rank analysis

Goyal and Kumar [ 53 ] explain that the citation analysis indicates the ‘popularity’ and ‘prestige’ of the published research article. Apart from the citation analysis, one more analysis is essential: Page rank analysis. PageRank is given by Page et al. [ 97 ]. The impact of an article can be measured with one indicator called PageRank [ 135 ]. Page rank analysis indicates how many times an article is cited by other highly cited articles. The method helps analyze the web pages, which get the priority during any search done on google. The analysis helps in understanding the citation networks. Equation  1 explains the page rank (PR) of a published paper, N refers to the number of articles.

T 1,… T n indicates the paper, which refers paper P . C ( Ti ) indicates the number of citations. The damping factor is denoted by a “ d ” which varies in the range of 0 and 1. The page rank of all the papers is equal to 1. Table 6 shows the top papers based on page rank. Tables 5 and 6 together show a contrast in the top ranked articles based on citations and page rank, respectively. Only one article “A survey of corporate governance” falls under the prestigious articles based on the page rank.

Content analysis

Content Analysis is a research technique for conducting qualitative and quantitative analyses [ 124 ]. The content analysis is a helpful technique that provides the required information in classifying the articles depending on their nature (empirical or conceptual) [ 76 ]. By adopting the content analysis method [ 53 , 102 ], the selected articles are examined to determine their content. The classification of available content from the selected set of sample articles that are categorized under different subheads. The themes identified in the relationship between banking regulation, risk, and profitability are as follows.

Regulation and profitability of banks

The performance indicators of the banking industry have always been a topic of interest to researchers and practitioners. This area of research has assumed a special interest after the 2008 WFC [ 25 , 51 , 86 , 114 , 127 , 132 ]. According to research, the causes of poor performance and risk management are lousy banking practices, ineffective monitoring, inadequate supervision, and weak regulatory mechanisms [ 94 ]. Increased competition, deregulation, and complex financial instruments have made banks, including Indian banks, more vulnerable to risks [ 18 , 93 , 119 , 123 ]. Hence, it is essential to investigate the present regulatory machinery for the performance of banks.

There are two schools of thought on regulation and its possible impact on profitability. The first asserts that regulation does not affect profitability. The second asserts that regulation adds significant value to banks’ profitability and other performance indicators. This supports the concept that Delis et al. [ 41 ] advocated that the capital adequacy requirement and supervisory power do not affect productivity or profitability unless there is a financial crisis. Laeven and Majnoni [ 81 ] insisted that provision for loan loss should be part of capital requirements. This will significantly improve active risk management practices and ensure banks’ profitability.

Lee and Hsieh [ 83 ] proposed ambiguous findings that do not support either school of thought. According to Nguyen and Nghiem [ 95 ], while regulation is beneficial, it has a negative impact on bank profitability. As a result, when proposing regulations, it is critical to consider bank performance and risk management. According to Erfani and Vasigh [ 46 ], Islamic banks maintained their efficiency between 2006 and 2013, while most commercial banks lost, furthermore claimed that the financial crisis had no significant impact on Islamic bank profitability.

Regulation and NPA (risk-taking of banks)

The regulatory mechanism of banks in any country must address the following issues: capital adequacy ratio, prudent provisioning, concentration banking, the ownership structure of banks, market discipline, regulatory devices, presence of foreign capital, bank competition, official supervisory power, independence of supervisory bodies, private monitoring, and NPAs [ 25 ].

Kanoujiya et al. [ 64 ] revealed through empirical evidence that Indian bank regulations lack a proper understanding of what banks require and propose reforming and transforming regulation in Indian banks so that responsive governance and regulation can occur to make banks safer, supported by Rastogi et al. [ 105 ]. The positive impact of regulation on NPAs is widely discussed in the literature. [ 94 ] argue that regulation has multiple effects on banks, including reducing NPAs. The influence is more powerful if the country’s banking system is fragile. Regulation, particularly capital regulation, is extremely effective in reducing risk-taking in banks [ 103 ].

Rastogi and Kanoujiya [ 106 ] discovered evidence that disclosure regulations do not affect the profitability of Indian banks, supported by Karyani et al. [ 65 ] for the banks located in Asia. Furthermore, Rastogi and Kanoujiya [ 106 ] explain that disclosure is a difficult task as a regulatory requirement. It is less sustainable due to the nature of the imposed regulations in banks and may thus be perceived as a burden and may be overcome by realizing the benefits associated with disclosure regulation [ 31 , 54 , 101 ]. Zheng et al. [ 138 ] empirically discovered that regulation has no impact on the banks’ profitability in Bangladesh.

Governments enforce banking regulations to achieve a stable and efficient financial system [ 20 , 94 ]. The existing literature is inconclusive on the effects of regulatory compliance on banks’ risks or the reduction of NPAs [ 10 , 11 ]. Boudriga et al. [ 25 ] concluded that the regulatory mechanism plays an insignificant role in reducing NPAs. This is especially true in weak institutions, which are susceptible to corruption. Gonzalez [ 52 ] reported that firm regulations have a positive relationship with banks’ risk-taking, increasing the probability of NPAs. However, Boudriga et al. [ 25 ], Samitas and Polyzos [ 113 ], and Allen et al. [ 3 ] strongly oppose the use of regulation as a tool to reduce banks’ risk-taking.

Kwan and Laderman [ 79 ] proposed three levels in regulating banks, which are lax, liberal, and strict. The liberal regulatory framework leads to more diversification in banks. By contrast, the strict regulatory framework forces the banks to take inappropriate risks to compensate for the loss of business; this is a global problem [ 73 ].

Capital regulation reduces banks’ risk-taking [ 103 , 110 ]. Capital regulation leads to cost escalation, but the benefits outweigh the cost [ 103 ]. The trade-off is worth striking. Altman Z score is used to predict banks’ bankruptcy, and it found that the regulation increased the Altman’s Z-score [ 4 , 46 , 63 , 68 , 72 , 120 ]. Jin et al. [ 62 ] report a negative relationship between regulation and banks’ risk-taking. Capital requirements empowered regulators, and competition significantly reduced banks’ risk-taking [ 1 , 122 ]. Capital regulation has a limited impact on banks’ risk-taking [ 90 , 103 ].

Maji and De [ 90 ] suggested that human capital is more effective in managing banks’ credit risks. Besanko and Kanatas [ 21 ] highlighted that regulation on capital requirements might not mitigate risks in all scenarios, especially when recapitalization has been enforced. Klomp and De Haan [ 72 ] proposed that capital requirements and supervision substantially reduce banks’ risks.

A third-party audit may impart more legitimacy to the banking system [ 23 ]. The absence of third-party intervention is conspicuous, and this may raise a doubt about the reliability and effectiveness of the impact of regulation on bank’s risk-taking.

NPA (risk-taking) in banks and profitability

Profitability affects NPAs, and NPAs, in turn, affect profitability. According to the bad management hypothesis [ 17 ], higher profits would negatively affect NPAs. By contrast, higher profits may lead management to resort to a liberal credit policy (high earnings), which may eventually lead to higher NPAs [ 104 ].

Balasubramaniam [ 8 ] demonstrated that NPA has double negative effects on banks. NPAs increase stressed assets, reducing banks’ productive assets [ 92 , 117 , 136 ]. This phenomenon is relatively underexplored and therefore renders itself for future research.

Triad and the performance of banks

Regulation and triad.

Regulations and their impact on banks have been a matter of debate for a long time. Barth et al. [ 12 ] demonstrated that countries with a central bank as the sole regulatory body are prone to high NPAs. Although countries with multiple regulatory bodies have high liquidity risks, they have low capital requirements [ 40 ]. Barth et al. [ 12 ] supported the following steps to rationalize the existing regulatory mechanism on banks: (1) mandatory information [ 22 ], (2) empowered management of banks, and (3) increased incentive for private agents to exert corporate control. They show that profitability has an inverse relationship with banks’ risk-taking [ 114 ]. Therefore, standard regulatory practices, such as capital requirements, are not beneficial. However, small domestic banks benefit from capital restrictions.

DeYoung and Jang [ 43 ] showed that Basel III-based policies of liquidity convergence ratio (LCR) and net stable funding ratio (NSFR) are not fully executed across the globe, including the US. Dahir et al. [ 39 ] found that a decrease in liquidity and funding increases banks’ risk-taking, making banks vulnerable and reducing stability. Therefore, any regulation on liquidity risk is more likely to create problems for banks.

Concentration banking and triad

Kiran and Jones [ 71 ] asserted that large banks are marginally affected by NPAs, whereas small banks are significantly affected by high NPAs. They added a new dimension to NPAs and their impact on profitability: concentration banking or banks’ market power. Market power leads to less cost and more profitability, which can easily counter the adverse impact of NPAs on profitability [ 6 , 15 ].

The connection between the huge volume of research on the performance of banks and competition is the underlying concept of market power. Competition reduces market power, whereas concentration banking increases market power [ 25 ]. Concentration banking reduces competition, increases market power, rationalizes the banks’ risk-taking, and ensures profitability.

Tabak et al. [ 125 ] advocated that market power incentivizes banks to become risk-averse, leading to lower costs and high profits. They explained that an increase in market power reduces the risk-taking requirement of banks. Reducing banks’ risks due to market power significantly increases when capital regulation is executed objectively. Ariss [ 6 ] suggested that increased market power decreases competition, and thus, NPAs reduce, leading to increased banks’ stability.

Competition, the performance of banks, and triad

Boyd and De Nicolo [ 27 ] supported that competition and concentration banking are inversely related, whereas competition increases risk, and concentration banking decreases risk. A mere shift toward concentration banking can lead to risk rationalization. This finding has significant policy implications. Risk reduction can also be achieved through stringent regulations. Bolt and Tieman [ 24 ] explained that stringent regulation coupled with intense competition does more harm than good, especially concerning banks’ risk-taking.

Market deregulation, as well as intensifying competition, would reduce the market power of large banks. Thus, the entire banking system might take inappropriate and irrational risks [ 112 ]. Maji and Hazarika [ 91 ] added more confusion to the existing policy by proposing that, often, there is no relationship between capital regulation and banks’ risk-taking. However, some cases have reported a positive relationship. This implies that banks’ risk-taking is neutral to regulation or leads to increased risk. Furthermore, Maji and Hazarika [ 91 ] revealed that competition reduces banks’ risk-taking, contrary to popular belief.

Claessens and Laeven [ 36 ] posited that concentration banking influences competition. However, this competition exists only within the restricted circle of banks, which are part of concentration banking. Kasman and Kasman [ 66 ] found that low concentration banking increases banks’ stability. However, they were silent on the impact of low concentration banking on banks’ risk-taking. Baselga-Pascual et al. [ 14 ] endorsed the earlier findings that concentration banking reduces banks’ risk-taking.

Concentration banking and competition are inversely related because of the inherent design of concentration banking. Market power increases when only a few large banks are operating; thus, reduced competition is an obvious outcome. Barra and Zotti [ 9 ] supported the idea that market power, coupled with competition between the given players, injects financial stability into banks. Market power and concentration banking affect each other. Therefore, concentration banking with a moderate level of regulation, instead of indiscriminate regulation, would serve the purpose better. Baselga-Pascual et al. [ 14 ] also showed that concentration banking addresses banks’ risk-taking.

Schaeck et al. [ 115 ], in a landmark study, presented that concentration banking and competition reduce banks’ risk-taking. However, they did not address the relationship between concentration banking and competition, which are usually inversely related. This could be a subject for future research. Research on the relationship between concentration banking and competition is scant, identified as a research gap (“ Research Implications of the study ” section).

Transparency, corporate governance, and triad

One of the big problems with NPAs is the lack of transparency in both the regulatory bodies and banks [ 25 ]. Boudriga et al. [ 25 ] preferred to view NPAs as a governance issue and thus, recommended viewing it from a governance perspective. Ahmad and Ariff [ 2 ] concluded that regulatory capital and top-management quality determine banks’ credit risk. Furthermore, they asserted that credit risk in emerging economies is higher than that of developed economies.

Bad management practices and moral vulnerabilities are the key determinants of insolvency risks of Indian banks [ 95 ]. Banks are an integral part of the economy and engines of social growth. Therefore, banks enjoy liberal insolvency protection in India, especially public sector banks, which is a critical issue. Such a benevolent insolvency cover encourages a bank to be indifferent to its capital requirements. This indifference takes its toll on insolvency risk and profit efficiency. Insolvency protection makes the bank operationally inefficient and complacent.

Foreign equity and corporate governance practices help manage the adverse impact of banks’ risk-taking to ensure the profitability and stability of banks [ 33 , 34 ]. Eastburn and Sharland [ 45 ] advocated that sound management and a risk management system that can anticipate any impending risk are essential. A pragmatic risk mechanism should replace the existing conceptual risk management system.

Lo [ 87 ] found and advocated that the existing legislation and regulations are outdated. He insisted on a new perspective and asserted that giving equal importance to behavioral aspects and the rational expectations of customers of banks is vital. Buston [ 29 ] critiqued the balance sheet risk management practices prevailing globally. He proposed active risk management practices that provided risk protection measures to contain banks’ liquidity and solvency risks.

Klomp and De Haan [ 72 ] championed the cause of giving more autonomy to central banks of countries to provide stability in the banking system. Louzis et al. [ 88 ] showed that macroeconomic variables and the quality of bank management determine banks’ level of NPAs. Regulatory authorities are striving hard to make regulatory frameworks more structured and stringent. However, the recent increase in loan defaults (NPAs), scams, frauds, and cyber-attacks raise concerns about the effectiveness [ 19 ] of the existing banking regulations in India as well as globally.

Discussion of the findings

The findings of this study are based on the bibliometric and content analysis of the sample published articles.

The bibliometric study concludes that there is a growing demand for researchers and good quality research

The keyword analysis suggests that risk regulation, competition, profitability, and performance are key elements in understanding the banking system. The main authors, keywords, and journals are grouped in a Sankey diagram in Fig.  6 . Researchers can use the following information to understand the publication pattern on banking and its determinants.

Sankey Diagram of main authors, keywords, and journals. Note Authors contribution using scientometrics tools

Research Implications of the study

The study also concludes that a balance among the three components of triad is the solution to the challenges of banks worldwide, including India. We propose the following recommendations and implications for banks:

This study found that “the lesser the better,” that is, less regulation enhances the performance and risk management of banks. However, less regulation does not imply the absence of regulation. Less regulation means the following:

Flexible but full enforcement of the regulations

Customization, instead of a one-size-fits-all regulatory system rooted in a nation’s indigenous requirements, is needed. Basel or generic regulation can never achieve what a customized compliance system can.

A third-party audit, which is above the country's central bank, should be mandatory, and this would ensure that all three aspects of audit (policy formulation, execution, and audit) are handled by different entities.

Competition

This study asserts that the existing literature is replete with poor performance and risk management due to excessive competition. Banking is an industry of a different genre, and it would be unfair to compare it with the fast-moving consumer goods (FMCG) or telecommunication industry, where competition injects efficiency into the system, leading to customer empowerment and satisfaction. By contrast, competition is a deterrent to the basic tenets of safe banking. Concentration banking is more effective in handling the multi-pronged balance between the elements of the triad. Concentration banking reduces competition to lower and manageable levels, reduces banks’ risk-taking, and enhances profitability.

No incentive to take risks

It is found that unless banks’ risk-taking is discouraged, the problem of high NPA (risk-taking) cannot be addressed. Concentration banking is a disincentive to risk-taking and can be a game-changer in handling banks’ performance and risk management.

Research on the risk and performance of banks reveals that the existing regulatory and policy arrangement is not a sustainable proposition, especially for a country where half of the people are unbanked [ 37 ]. Further, the triad presented by Keeley [ 67 ] is a formidable real challenge to bankers. The balance among profitability, risk-taking, and regulation is very subtle and becomes harder to strike, just as the banks globally have tried hard to achieve it. A pragmatic intervention is needed; hence, this study proposes a change in the banking structure by having two types of banks functioning simultaneously to solve the problems of risk and performance of banks. The proposed two-tier banking system explained in Fig.  7 can be a great solution. This arrangement will help achieve the much-needed balance among the elements of triad as presented by Keeley [ 67 ].

Conceptual Framework. Note Fig.  7 describes the conceptual framework of the study

The first set of banks could be conventional in terms of their structure and should primarily be large-sized. The number of such banks should be moderate. There is a logic in having only a few such banks to restrict competition; thus, reasonable market power could be assigned to them [ 55 ]. However, a reduction in competition cannot be over-assumed, and banks cannot become complacent. As customary, lending would be the main source of revenue and income for these banks (fund based activities) [ 82 ]. The proposed two-tier system can be successful only when regulation especially for risk is objectively executed [ 29 ]. The second set of banks could be smaller in size and more in number. Since they are more in number, they would encounter intense competition for survival and for generating more business. Small is beautiful, and thus, this set of banks would be more agile and adaptable and consequently more efficient and profitable. The main source of revenue for this set of banks would not be loans and advances. However, non-funding and non-interest-bearing activities would be the major revenue source. Unlike their traditional and large-sized counterparts, since these banks are smaller in size, they are less likely to face risk-taking and NPAs [ 74 ].

Sarmiento and Galán [ 114 ] presented the concerns of large and small banks and their relative ability and appetite for risk-taking. High risk could threaten the existence of small-sized banks; thus, they need robust risk shielding. Small size makes them prone to failure, and they cannot convert their risk into profitability. However, large banks benefit from their size and are thus less vulnerable and can convert risk into profitable opportunities.

India has experimented with this Differential Banking System (DBS) (two-tier system) only at the policy planning level. The execution is impending, and it highly depends on the political will, which does not appear to be strong now. The current agenda behind the DBS model is not to ensure the long-term sustainability of banks. However, it is currently being directed to support the agenda of financial inclusion by extending the formal credit system to the unbanked masses [ 107 ]. A shift in goal is needed to employ the DBS as a strategic decision, but not merely a tool for financial inclusion. Thus, the proposed two-tier banking system (DBS) can solve the issue of profitability through proper regulation and less risk-taking.

The findings of Triki et al. [ 130 ] support the proposed DBS model, in this study. Triki et al. [ 130 ] advocated that different component of regulations affect banks based on their size, risk-taking, and concentration banking (or market power). Large size, more concentration banking with high market power, and high risk-taking coupled with stringent regulation make the most efficient banks in African countries. Sharifi et al. [ 119 ] confirmed that size advantage offers better risk management to large banks than small banks. The banks should modify and work according to the economic environment in the country [ 69 ], and therefore, the proposed model could help in solving the current economic problems.

This is a fact that DBS is running across the world, including in India [ 60 ] and other countries [ 133 ]. India experimented with DBS in the form of not only regional rural banks (RRBs) but payments banks [ 109 ] and small finance banks as well [ 61 ]. However, the purpose of all the existing DBS models, whether RRBs [ 60 ], payment banks, or small finance banks, is financial inclusion, not bank performance and risk management. Hence, they are unable to sustain and are failing because their model is only social instead of a much-needed dual business-cum-social model. The two-tier model of DBS proposed in the current paper can help serve the dual purpose. It may not only be able to ensure bank performance and risk management but also serve the purpose of inclusive growth of the economy.

Conclusion of the study

The study’s conclusions have some significant ramifications. This study can assist researchers in determining their study plan on the current topic by using a scientific approach. Citation analysis has aided in the objective identification of essential papers and scholars. More collaboration between authors from various countries/universities may help countries/universities better understand risk regulation, competition, profitability, and performance, which are critical elements in understanding the banking system. The regulatory mechanism in place prior to 2008 failed to address the risk associated with banks [ 47 , 87 ]. There arises a necessity and motivates authors to investigate the current topic. The present study systematically explores the existing literature on banks’ triad: performance, regulation, and risk management and proposes a probable solution.

To conclude the bibliometric results obtained from the current study, from the number of articles published from 1976 to 2020, it is evident that most of the articles were published from the year 2010, and the highest number of articles were published in the last five years, i.e., is from 2015. The authors discovered that researchers evaluate articles based on the scope of critical journals within the subject area based on the detailed review. Most risk, regulation, and profitability articles are published in peer-reviewed journals like; “Journal of Banking and Finance,” “Journal of Accounting and Economics,” and “Journal of Financial Economics.” The rest of the journals are presented in Table 1 . From the affiliation statistics, it is clear that most of the research conducted was affiliated with developed countries such as Malaysia, the USA, and the UK. The researchers perform content analysis and Citation analysis to access the type of content where the research on the current field of knowledge is focused, and citation analysis helps the academicians understand the highest cited articles that have more impact in the current research area.

Practical implications of the study

The current study is unique in that it is the first to systematically evaluate the publication pattern in banking using a combination of scientometrics analysis tools, network analysis tools, and content analysis to understand the relationship between bank regulation, performance, and risk. The study’s practical implications are that analyzing existing literature helps researchers generate new themes and ideas to justify their contribution to literature. Evidence-based research knowledge also improves decision-making, resulting in better practical implementation in the real corporate world [ 100 , 129 ].

Limitations and scope for future research

The current study only considers a single database Scopus to conduct the study, and this is one of the limitations of the study spanning around the multiple databases can provide diverse results. The proposed DBS model is a conceptual framework that requires empirical testing, which is a limitation of this study. As a result, empirical testing of the proposed DBS model could be a future research topic.

Availability of data and materials

SCOPUS database.

Abbreviations

Systematic literature review

World Financial Crisis

Non-performing assets

Differential banking system

SCImago Journal Rank Indicator

Liquidity convergence ratio

Net stable funding ratio

Fast moving consumer goods

Regional rural banks

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Rastogi, S., Sharma, A., Pinto, G. et al. A literature review of risk, regulation, and profitability of banks using a scientometric study. Futur Bus J 8 , 28 (2022). https://doi.org/10.1186/s43093-022-00146-4

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Non-performing loans (NPLs, henceforth) represent the main challenge that jeopardizes the steadiness of the banking sector. The purpose of this study is to explore the main determinants of banks' non-performing loans in emerging markets. To better understand the hidden aspects of these determinants, the current research employs a panel approach and dynamic data estimates through Generalized Methods of Moments (GMM) using data of 53 banks listed in five Middle East and North African (MENA) emerging markets between 2000 and 2019. This study documents that GDP growth, unemployment, bank capitalization, bank performance, bank operating inefficiency, bank ownership concentration, inflation, sovereign debt and bank size are the main determinants of NPLs, whereas, loan growth, bank diversification and interbank competition were found to have an insignificant impact on NPLs. This analysis is motivated by the recent economic changes surrounding the financial systems in emerging countries with the aim to provide new evidence and insights. The results show that non-performing loans can be explained mainly by macroeconomic variables and bank-specific factors with interesting differences in their quantitative impacts. This study has substantial theoretical and practical contributions. It shows strong evidence on the leading indicators of future problematic loans. The identification of these factors would help regulators address appropriate interventions, design ample credit policies and adopt adjusted prudential regulations. Further, it empowers the regulatory authorities with an in-depth understanding of credit risk determinants, allowing them to place emphasis on risk management systems and procedures that minimize borrowers' default in order to avert future financial instability. Our findings underscore the necessity of closely monitoring bank-specific factors along with reinforcing country level mechanisms to reduce banks’ credit risk.

Credit risk; Non-performing loans; Bank risk management; Emerging markets.

1. Introduction

Considered as the backbone of the economy and the primary contributor to its survival and growth, the banking sector plays a vital role in the economic stability of any country. It is the sinew that keeps the economy working as it grants credits and allows businesses and households to save, invest and increase their spending, which ultimately support the economic growth ( Naili and Lahrichi, 2020 ). This sector, nevertheless, faces numerous kinds of risks that, not only destabilize its financial well-being, but jeopardize the stability of the whole country. In particular, one of the risks that erodes banks' profitability and marks the onset of a crisis is bank credit risk ( Berger and Deyoung, 1997 ). Central bankers agreed that the distress of the financial sector during financial crises was predominantly due to banks' credit risk, mostly conveyed by the level of banks' non-performing loans. 1 By definition, a loan is considered non-performing when its payment is past due by at least 90 days ( IMF, 2005 , p 8), mirroring the banks’ assets quality and serving as a signpost of their well-being ( Cucinelli et al., 2018 ; Partovi and Matousek, 2019 ; Reinhart and Rogoff, 2011 ; Salas and Saurina, 2002 ; Tarchouna et al., 2017 ).

In view of the lessons learned from the past financial downturns, the accumulation of non-performing loans is regarded as a red flag and a potential sign of deeper troubles. Their consequences are not only harmful for the creditors, but for the whole economy. It was documented by various scholars that the level of NPLs is significantly associated with the banking sector vulnerability and crisis occurrence ( Calomiris et al., 2007 ; Desmet, 2000 ; Laeven and Valencia, 2008 ; Reinhart and Rogoff, 2011 ; Salas and Saurina, 2002 ; Samad, 2012 ). For instance, a research piloted by Salas and Saurina (2002) documents that large levels of NPLs harm the liquidity and profitability of credit institutions. Besides, Reinhart and Rogoff (2011) argued that a NPLs ratio reflects an alarming indicator that mark the beginning of a financial slump. Other scholars categorized NPLs as “financial pollution” due to the scars it leaves on banks’ balance sheets ( Barseghyan, 2010 ; Makri et al., 2014 ; Zeng, 2012 ).

The issue of NPLs came to dominate the economic debate especially in the aftermath of the financial crises. For instance, the crisis of the subprime left profound scars on the United States affecting both, developed and emerging countries due to global integrated financial systems ( Shahin and El-achkar, 2018 ). Billions of NPLs were recorded, making the strongest economies look fragile ( Jabbouri and Naili, 2019a ). With regards to the recent European crisis, various countries recorded a huge pile of NPLs as well. EU banks recorded approximately €1 trillion worth of NPLs by 2013, which distressed the economy ( ECB, 2017 ). In 2016, Greece and Cyprus held alone an NPLs ratio of 46% and 45%, respectively. The European Central Bank and the European Banking Authority spare no efforts to reduce the level of NPLs. In this sense, a special taskforce has been created, fully dedicated to tackle NPLs issue. 2 Aside from the economic conditions, the ECB provides strong evidence that the immense volume of NPLs is a result of banks' internal drivers including, among others, improper credit screening and insufficient internal governance ( ECB, 2017 ). As a result, the level of NPLs has been reduced considerably, from €958 billions in December 2014 to €688 billions in 2018. Yet, the remaining stock still poses a considerable issue and requires further efforts ( ECB, 2017 ). Asset quality has been as well the concern of economic policymakers in emerging countries. Emerging markets have been buffeted by a number of economic shocks including non-performing loans ( Farooq and Jabbouri, 2015a ). A recent and comprehensive review of literature showed that despite the significant number of studies that explored the determinants banks' credit risk, the issue of NPLs remains unsolved ( Naili and Lahrichi, 2020 ). In addition to that, given the importance of banks' credit risk and the little attention given to emerging markets in terms of research 3 , this paper aims to extend knowledge on the main determinants of banks’ NPLs in a sample of MENA emerging markets. To meet this objective, the present paper aims to answer the following questions:

• 1. What are the macroeconomic determinants of banks' credit risk in emerging markets?
• 2. What are the bank-specific determinants of banks' credit risk in emerging markets?
• 3. What are the industry-specific determinants of banks' credit risk in emerging markets?

To answer these questions, a sample of 53 banks listed in five emerging markets was employed, with the hope of providing new and significant insights on the aforementioned relationships. The scarcity of empirical studies in emerging markets and the importance of banks’ credit risk in the survival and growth of economies, make this research an appealing substance of research.

The rest of this research is arranged as follows. Section 2 introduces the review of literature and hypotheses development. Sections 3 presents data collection and variables measurement. Section 4 illustrates the empirical procedure and Section 5 discusses the results. Section 6 exhibits the robustness test. Finally, Section 7 concludes.

2. Review of literature and hypothesis development

There is an extensive body of research that shed lights on the determinants of credit risk. There are three kinds of studies regarding these determinants. There are research that explore the macroeconomic determinants ( Louzis et al., 2012 ; Nkusu, 2011 ; Radivojevic and Jovovic, 2017 ; Salas and Saurina, 2002 ; Vouldis and Louzis, 2017 ), the ones that investigate the bank-specific variables ( Beaton et al., 2016 ; Iannotta et al., 2007 ; Jabbouri and Naili, 2019a ; Messai and Jouini, 2013 ) and the studies that explore the banking-industry specific variables ( Natsir et al., 2019 ; Schaeck et al., 2009 ).

2.1. Bank-specific factors

The unique features of the banking system along with the different policy choices of each bank are expected to have an impact on the emergence and evolution of NPLs. There is ample evidence that examines the relationship between bank internal factors and their credit risk.

2.1.1. Bank size

There is an abundant amount of literature that addresses the association between bank size and the level of NPLs. Yet, no clear-cut evidence was found in the literature regarding this relationship.

On one hand, scholars document that larger banks are more likely to incur lower loan losses ( Alhassan et al., 2014 ; Louzis et al., 2012 ; Salas and Saurina, 2002 ; Solttila and Vihriâlâ, 1994 ). This negative link was explained by the fact that larger banks are more able to conduct proper loan screening given their sophisticated risk management techniques ( Salas and Saurina, 2002 ). In the same context, larger banks are in a better position to devote adequate resources to loan analysis and assessments which prevent them from granting loans to low-quality borrowers ( Louzis et al., 2012 ).

On the other hand, literature shed lights on the “too big to fail” hypothesis, implying that larger banks consider themselves indispensable and thus, engage in riskier lending practices. These banks usually suppose that they will be bailed out by the government in case of financial breakdowns ( Louzis et al., 2012 ; Stern and Feldman, 2004 ). On a sample of 15 European countries, Haq and Heaney (2012) tested and confirmed the aforementioned hypothesis. The authors report that large-sized banks, whose role in the nation's financial system is vital, usually take excessive risk as they do not shoulder the burden of their lending decisions. Given the contrasting evidence, it is worthwhile to investigate this relationship further through the following hypothesis:

Bank size has an impact on banks' credit risk, conveyed by the level of NPLs.

2.1.2. Bank capitalization

The literature presented evidence that capital adequacy ratio (CAR, henceforth) has a strong effect on loan loss rates ( Sinkey and Greenawalt, 1991 ). This impinged capital is usually used as a buffer against excessive risks.

A strand of literature presents evidence that banks holding a large capital as a proportion of their risk-weighted assets experience lower loan losses ( Shrieves and Dahl, 1992 ). The rationale behind this negative link is that banks with high capital adequacy are more likely to engage in thoughtful lending to sustain the capital set aside ( Shrieves and Dahl, 1992 ). This relationship was further explained by the moral hazard hypothesis, insinuating that thinly capitalized banks will more likely take excessive risk given the limited loss they may incur in a potential breakdown ( Berger and Deyoung, 1997 ; Keeton and Morris, 1987 ).

In rebuttal, other scholars documents a negative link regarding the CAR-NPLs relationship ( Ghosh, 2017 ; Koehn and Santomero, 1980 ; Rime, 2001 ). On a sample of US commercial banks spanning the period between 1992 and 2016 Ghosh (2017) documents that loan losses usually coincide with high capital adequacy. This latter leads banks to involve in riskier lending with insufficient risk evaluation and assessments ( Ghosh, 2017 ). To confirm or reject the previous empirical evidence, we formulate the following hypothesis:

Capital adequacy ratio (CAR) has an impact on banks' credit risk, conveyed by the level of NPLs.

2.1.3. Bank Performance

There is an important number of literature that tackles the link between NPLs and banks' performance ( Garcí a-Marco and Dolores Robles-Fernández, 2008 ; Louzis et al., 2012 ; Rajan, 1994 ). Researchers have investigated the impact of the lagged profitability on banks' bad loans. For instance, Louzis et al. (2012) formulated the bad management hypothesis, explaining the negative link between banks’ profitability and NPLs. He documents that low profitable banks incur higher loan losses due to their poor management skills and inefficient lending strategies. In this line of research, a study analyzed 50 US banks between 1984 and 2013, corroborates the prior findings and suggests that, on the other hand, profitable banks are less likely to take higher risks which improves their credit quality ( Ghosh, 2015 ; Makri et al., 2014 ).

Rajan (1994) contends that banks may boost up-front fees to escalate their current earnings through concealing the level of their NPLs, which results in heavy future loan losses. 4 Further, a study conducted on a sample of Spanish banks spanning the period between 1993 and 2000, provides evidence that a positive relationship exists between lagged profitability and NPLs. The authors argue that poorly performing banks are more likely to engage in thoughtful lending through the adoption of conservative credit policy in order to limit further losses ( Garcí a-Marco and Dolores Robles-Fernández, 2008 ).

These conflicting arguments make the question of whether bank's profitability impacts the level of NPLs, a heatedly debated issue that requires a deeper analysis:

Bank profitability has an impact on banks' credit risk, conveyed by the level of NPLs.

2.1.4. Loan growth

Prior literature suggests that loan growth has a significant impact on banks’ NPLs ( Boudriga et al., 2010 ; Foos et al., 2010 ; Keeton and Morris, 1987 ). In fact, loan growth was one of the main reasons triggering the recent financial crisis ( Naili and Lahrichi, 2020 ). One of the earliest studies examining the link between credit growth and NPLs, contends that rapid loan growth leads to higher loan losses ( Keeton and Morris, 1987 ). The authors argue that when banks increase their lending, loan screening and credit standards deteriorate. A study conducted on a sample of 16 countries between 1997 and 2009 presents compelling results, arguing that banks will more likely ease their credits standards in order to achieve a targeted loan growth which, in turn, results in heavy future losses ( Foos et al., 2010 ). The positive association between loan growth and NPLs was as well supported by Salas and Saurina (2002) and Alhassan et al. (2014) .

Other studies contradict prior findings and report a negative relationship between loan growth and NPLs. For instance, Boudriga et al. (2010) found that banks who aim at increasing their lending, are more likely to perform proper loan and screening in order to cope with defaulters. Furthermore, a negative link between NPLs and credit risk was further reported in study conducted in the MENA region between 2003 and 2016. Thus, it comes as no surprise that loan growth is a significant determinant of NPLs, but with inconclusive points of view in the literature about its impact on credit risk.

Loan growth has an impact on banks' credit risk, conveyed by the level of NPLs.

2.1.5. Bank inefficiency

A large body of literature attempted to address the link between bank inefficiency and bank credit risk, yet the results are vague. Berger and Deyoung (1997) investigated a sample of US bank spanning the period between 1985-1994 and formulated three main hypotheses. The bad management hypothesis suggests that due to the poor managerial skills of banks' managers, low-cost efficient banks incur high levels of NPLs, through inadequate collateral evaluation, poor credit scoring and low borrower monitoring. This hypothesis was further validated by Podpiera and Weill (2008) who investigated Czech banks between 1994 and 2005. The link between bank inefficiency and NPLs was further explained by the bad luck hypothesis, indicating that unpredicted events such as an economic slowdown lead to an increase in NPLs. During these economic crises, managerial efforts are doubled resulting in extra operating costs, which in turn, impacts banks’ cost efficiency ( Berger and Deyoung, 1997 ).

On the other hand, the skimping hypothesis provides opposing views and supports a negative link between bank inefficiency and NPLs. This latter insinuates that cost-efficient banks who devote insufficient resources to credit underwriting and loan quality to the costs devoted to underwriting and clients’ evaluation ( Louzis et al., 2012 ; Rossi et al., 2009 ).

Bank inefficiency has an impact on banks' credit risk, conveyed by the level of NPLs.

2.1.6. Ownership concentration

The importance of ownership concentration within financial institutions amplifies given the nature of financial institutions and the unique characteristics of banks. As a matter of fact, banks operate in a highly regulated environment, distinguished by special corporate governance mechanisms and information opacity ( Barth et al., 2004 ; Berger and Deyoung, 1997 ; Macey and O'Hara, 2003 ; Jabbouri and AlMustafa, 2021 ; Prowse, 1997 ).

There is ample evidence on the role of concentrated ownership in reducing the level of banks' NPLs. For instance, Iannotta et al. (2007) conducted a study on a sample of 181 banks from 15 European countries between 1999 and 2004 and concluded that ownership concentration is positively associated with NPLs. Other researchers confirm prior findings, arguing that concentrated ownership leads to a significant decrease in banks' non-performing loans ( Shehzad et al., 2010 ). They concluded that ownership concentration contributes to lowering the level of NPLs by enhancing the supervisory control and investors’ protection within banks, improving the monitoring of management and the capital adequacy ratio, which is considered as a buffer against excessive risk-taking ( Leech and Leahy, 1991 ; Shehzad et al., 2010 ). In parallel to the above arguments, it is documented that the absence of monitoring within widely dispersed banks increases agency problems and encourages managerial self-serving behaviors, which upsurges the level of NPLs.

In rebuttal, an opposing strand of literature documents a positive relationship between ownership concentration and banks’ NPLs ( Berle and Means, 1933 ; Haw et al., 2010 ; Louzis et al., 2012 ). Due to the potential conflicts of interests between controlling and minority shareholders, agency problems intensify in concentrated banks, which leads to higher loan losses. Furthermore, controlling shareholders are tempted to take part in expropriating and tunneling activities and transferring the firm resources to serve their own agendas, which exacerbates agency problems and increases the level of bad loans ( Barclay and Holderness, 1989 ; Stulz, 1988 ). These studies conclude that large shareholders have the power to influence bank risk-taking, by coercing bank managers to undertake risky investments and conduct unthoughtful lending, which burgeons the level of NPLs.

Given this theoretical and empirical contention, it is worthwhile to investigate the impact of concentrated ownership on banks’ NPLs.

Ownership concentration has an impact on banks' credit risk, conveyed by the level of NPLs.

2.1.7. Diversification

Researchers have documented that banks' diversification has a significant impact on banks' risk taking. Louzis et al. (2012) claim that diversification has a negative impact on the level of banks’ NPLs. The authors explain their finding by the “dark side” of diversification. This latter claims that banks who expand into new businesses are more likely to incur higher loan losses due to the increased risk ( Louzis et al., 2012 ; Stiroh, 2004 ). Another study analyzed a sample of Chinese banks and confirmed the prior findings and claims that diversification increases the probability of banks' collapses particularly during deregulation periods ( Boyd and Graham, 1986 ; Zhou, 2014 ). Given the scarcity of research tackling the relationship between diversification and NPL, future research could yield valuable insights. We, thus, hypothesize that:

Banks' diversification has a negative impact on banks' credit risk, conveyed by the level of NPLs.

2.2. Macroeconomic factors

2.2.1. gdp growth.

There is ample empirical evidence that links the macroeconomic environment to credit quality. It has been reported that under the expansionary phase of economic growth, individuals and firms face sufficient stream of revenues to repay their financial obligations ( Louzis et al., 2012 ). During challenged times, firms and households are more likely to default on their loans due to the decrease in asset values that serve as collaterals, which lead to the increase of NPLs. Several empirical studies confirm that the level of NPLs decreased during economic booms and the opposite happens during economic slowdowns ( Jabbouri and Naili, 2019a ; Nkusu, 2011 ; Salas and Saurina, 2002 ). Based on these arguments, we expect inverse relationship between GDP growth and banks’ credit risk. We hypothesize that:

GDP growth has a positive impact on banks' credit risk, conveyed by the level of NPLs.

2.2.2. Inflation

Prior literature provides sufficient evidence on the casual relationship between inflation and NPLs ( Ghosh, 2015 ; Naili and Lahrichi, 2020 ; Nkusu, 2011 ). However, the literature remains ambiguous about the direction of this relationship. Rinaldi and Sanchis-Arellano (2006) documents that higher inflation aggravates bank credit risk. They claim in a study conducted on a sample of European countries, that high inflation rates erode the real value of borrowers' revenue which then restricts their capacity to reimburse their debts. Other studies confirm prior findings, reporting that under inflationary conditions, borrowers’ probability to default upsurges, especially in case of variable interest rates loans ( Amuakwa-Mensah et al., 2017 ; Klein, 2013 ). In emerging markets, inflation is one of the biggest concerns of central bankers as wages are in most cases sticky, which increase the level of NPLs and make firms and households more challenged to repay their debts.

Conversely, other pieces of literature support opposing views. Nkusu (2011) They document that inflation decreases the value of outstanding debts which, in turn, improves the repayment capacity of borrowers. In the same line, Khemraj and Pasha (2009) examined banks in Guyana and argued that labor wages are more likely to adjust to the increase of prices, which ensures borrowers’ sustainability of repayments. These findings were confirmed as well in an empirical study conducted on a sample of Indian banks, reporting that inflation has a negative relationship with NPLs ( Gulati et al., 2019 ). Given the contracting evidence, it is important to investigate this relationship further. We formulate the following hypothesis:

Inflation has an impact on banks' credit risk, conveyed by the level of NPLs.

2.2.3. Public debt

Prior literature provides compelling evidence about the relationship between public debt or the so-called sovereign debt and the level of NPLs – a relationship that, at first sight, does not appear evident. In fact, prior empirical research points that public debt plays a significant role in triggering financial crises ( Laeven and Valencia, 2013 ). In this sense, 290 banking crises and 209 sovereign default episodes were investigated in 70 countries ( Reinhart and Rogoff, 2011 ). The authors of this study report that a significant relationship was detected between banking downturns and public debt crises. Yet, the relationship is still ambiguous. A school of thought argues that public debt cuts the public spending which leads to a downswing in households income and social expenditure ( Reinhart and Rogoff, 2011 ). A high public debt impacts the creditworthiness of banks as well by putting a sovereign ceiling on their solvency. As a result, banks face higher difficulties to raise market financing and borrowers become more challenged to refinance their debts ( Reinhart and Rogoff, 2011 ). The sovereign debt hypothesis was formulated to stipulate that higher public debt leads to higher NPLs ( Louzis et al., 2012 ). Several authors tested this hypothesis reporting that an increase in fiscal deficit leads to a deterioration of loan quality ( Ghosh, 2015 ; Makri et al., 2014 ). These findings are in accordance with the role of public debt as an important determinant of NPLs. Accordingly, we formulate the following hypothesis:

Public debt has an impact on banks' credit risk, conveyed by the level of NPLs.

2.2.4. Unemployment

Unemployment is a key macroeconomic determinant of banks' credit risk. It has been hypothesized that as the country's unemployment rate increases, banks' loan quality deteriorates ( Salas and Saurina, 2002 ). In this line of research, studies stipulate that borrowers with low income face higher chances of unemployment, which in turn, limits their reimbursement capacity ( Ghosh, 2015 ). In addition to that, individuals with low-income levels are considered as risky clients. This lead banks to charge them higher interest rates due to the uncertainty of their employment status, which worsen their repayment ability ( Lawrence, 1995 ). To empirically test previous findings, we formulate the following hypothesis:

Unemployment has a negative impact on banks' credit risk, conveyed by the level of NPLs.

2.3. Industry-specific factors

2.3.1. interbank competition/concentration.

Another impact determinant of credit risk is bank competition. This latter has gained keen interest especially after the global financial crisis, providing countless lessons to bank regulators on how banks’ competition and concentration can, either, harm or coarsen the financial sector ( Naili and Lahrichi, 2020 ).

Keeley (1990) demonstrated a direct association between interbank competition and the number of banks' collapses in the US during the 1980s. In this sense and in line with the competition-fragility paradigm, the author developed the “franchise value hypothesis” to explain this relationship. This latter asserts that banks risk exposure increases as interbank competition increases. In the same vein, Hellmann et al. (2000) argued that interbank competition influences banks’ franchise value as it reduces their profitability. This will more likely induce banks to engage in riskier lending. The positive relationship between competition/concentration and credit risk was further explained by the adverse-selection problems. On the other hand, in a concentrated interbank market where large banks monopolize, low quality borrowers cannot easily access to credits, which decreases the probability of default ( Boudriga et al., 2009 ). Other scholars confirm the aforementioned findings such as Wang (2018) , Turk Ariss (2010) and De Haan and Poghosyan (2012) .

Contrariwise, other scholars criticized the above hypothesis, contending that the financial system stability can be enhanced by interbank competition ( Jiménez and Saurina, 2005 ; Ozili, 2019 ). The authors assert that competition drives banks to lower their lending rates, reducing the profitability of defaults ( Boyd and Nicolo, 2005 ). In the same line, competition would press bank managers to minimize their credit risk through careful lending decisions and adequate borrowers screening in order to gain advantageous risk management perception from their bank regulators and investors ( Jiménez and Saurina, 2005 ; Ozili, 2019 ). The opposing views regarding the impact of interbank competition/concentration on banks’ NPLs, make this relationship of a particular interest. New evidence on this association would offer bank regulators, managers, and academicians new enriching insights.

Interbank competition/concentration has an impact on banks' credit risk, conveyed by the level of NPLs.

3. Methodology

3.1. sample and data sources.

The sample used in this research consists of 53 banks in the following MENA non-GCC countries: Morocco, Tunisia, Egypt, Jordan and Turkey. The time period covers between 2000 and 2019. The sample covers conventional banks and excludes saving and investment banks as they have different business models. The aggregate data used in this research is annual given that data for the majority of variables is available on a yearly basis. The data is obtained from two major sources: Bank Scope and Thomson Reuters database. The data on non-performing loans were extracted from the Thomson Reuters database and verified in the official annual report of each bank. For data unavailability, the ratio of NPLs to total gross loans is employed instead of the sectorial NPLs’ ratio. The bank-specific data as well as the ownership structure data were extracted from the Thomson Reuters Database. The macroeconomic data was extracted from the World Bank official database. Our final sample comprises 1060 bank-year observations from 53 banks spanning the period between 2000 and 2019.

3.2. Variable definition

The variables used in this research are divided into three major categories: bank-specific, macroeconomic and industry-specific factors.

3.2.1. Dependent variable

Our dependent variable is the bank's credit risk which is conveyed by the ratio of non-performing loans (NPL) calculated as the level of the bank total level of NPLs over the total gross loans ( Louzis et al., 2012 ; Salas and Saurina, 2002 ; Vouldis and Louzis, 2017 ; Zhang et al., 2016 ) (see Table 1 ).

Table 1

Description of the determinants of NPLs, their proxies, symbols and a representative sample of their use in the literature.

3.2.2. Independent variables

3.2.2.1. bank-specific variables.

• - Bank size (SIZE): Natural logarithm of a bank total assets is used to estimate bank size ( Khemraj and Pasha, 2009 ; Mensah and Adjei, 2014 ).
• - Capital adequacy ratio (CAR): This ratio refers to the level of capital a bank should set aside as a proportion of its risky assets. The capital adequacy ratio is measured as total capital to total risk-weighted assets ( Naili and Lahrichi, 2020 ; Salas and Saurina, 2002 ).
• - Bank Performance (ROE): Profitability is estimated using ROE of previous year. ROE is computed as the ratio of net income to total equity ( Louzis et al., 2012 ; Us, 2017 ).
• - Loan growth (GROWTH): The current year's gross loans as a percentage of the previous year's is used to proxy for loan growth ( Shehzad et al., 2010 ).
• - Bank inefficiency (INEFF): The ratio of operating expense to operating profit ratio is employed to measure bank's inefficiency ( Shehzad et al., 2010 ).
• - Ownership concentration (OC): Bank's ownership concentration, is measured by the fraction of the closely-held shares ( Berle and Means, 1933 ; Jabbouri and Naili, 2019b ; Jabbouri et al., 2019 ). The fraction of closely held shares is measured as the total number of shares held by stake insiders over the total number of shares outstanding. According to the World Scope, the stake insiders includes “shares held by officers, directors and their immediate families, shares held by shareholders who hold more than 5% of the total outstanding shares” ( Worldscope, 2007 ).
• - Diversification (DIV): Bank diversification will be proxied by the ratio of noninterest income to total income ( Naili and Lahrichi, 2020 ).

3.2.2.2. Macroeconomic variables

• - GDP growth (GDP): GDP growth is defined as the yearly change in the natural logarithm of real GDP in each country ( Louzis et al., 2012 ; Naili and Lahrichi, 2020 ).
• - Inflation (INF): The annual inflation rate in a country is used to capture this variable ( Nkusu, 2011 ).
• - Public debt (DEBT): Public debt is proxied by the gross government debt as percentage of GDP ( Amuakwa-Mensah et al., 2017 ; Makri et al., 2014 ).
• - Unemployment (UNEM): Unemployment is measured by the unemployment rate in a country ( Ćurak et al., 2013 ; Ghosh, 2015 ).

3.2.2.3. Industry-specific variables

• - Concentration (CR3): The banking sector related variables comprise mainly interbank competition and concentration. According to the literature, these factors can be proxied by different measures including Lerner Index, the Boone indicator or using the concentration ratio. Due to data availability, we will capture this variable using the share of the three largest banks' total assets ( Boudriga et al., 2010 ; Srairi, 2013 ).

3.3. Descriptive statistics

Table 2 shows the descriptive statistics for all banks for the study period, 2000–2019. 5 The mean NPL ratio is 8.9, which is high compared to the world's average estimated at 5.03 during the same period ( World Bank, 2019 ). Banks had an average loan growth of 13.95, and an inefficiency of 48.18, a relatively high capital adequacy ratio of 11.46 and an overall positive profitability during the period analyzed. Regarding the macroeconomic indicators used in our study, the statistics indicate an average economic growth of 4.10, an inflation of 8.29, a relatively high public debt of 72.14 and an unemployment ratio close to 12 percent. Concerning the banking sector related variables, the average demonstrates that the banking sector in the selected countries has a considerably high concentration of 59.77.

Table 2

Descriptive statistics for the whole sample period 2000–2019.

Value are expressed in percentage except for SIZE which captures bank size as the logarithm of a bank's total assets.

To have an in depth understanding of the determinants used in the sample, descriptive statistics of each factor year wise and country wise are provided in Table 3 . Panel A exhibits the descriptive statistics of our variables country-wise. Among the countries included in our study, Egypt has the highest level of NPLs. As a matter of fact, during the recent years and aside from the Arab revolutions, Egypt faced numerous political and economic struggles. The country faced a remarkable depreciation in the Egyptian pound against the US dollar, which defied the banking sector. In addition to that, a slump in real estate prices was witnessed during recent years, which impaired the performance of the overall banking sector given that these loans amounts to 30% of total banking loans assets ( BMI, 2017 ). In the same context, Egypt, Morocco, and Jordan have a mean NPL ratio of 8.30, 11.06 and 8.52, respectively, which is higher than the region's mean, demonstrating the immense threat of credit risk in these countries. On the other hand, Turkey has the lowest NPL ratio of 4.5 and a rapid economic growth during the recent years as the mean GDP ratio stands at 16.30, which might explain the low level of impaired loans compared to the other sampled countries. The banks' profitability and loans' growth in the selected countries remain positive. Besides, the sampled banks demonstrate an adequate level of capital adequacy, which is usually used as a buffer against risks. A high concentration ratio of 79.36 is witnessed in Tunisia, while Jordan has recorded the lowest concentration ratio of 41.69. Concerning the diversification of the banking sector of the selected countries, the descriptive statistics show a relatively small diversification ratio in almost all countries except for Tunisia which has a mean banks' diversification of 60. In addition to that, Panel B shows that the ratio of NPLs is increasing from 6.41 in 2000 to 9.03 in 2019. A remarkable increase in NPLs is witnessed as well between the period 2007–2010, from 6.69 to 9.52, respectively. This increase can be explained by the dire consequences of the global financial crisis. As the crisis emerged in the US, the global banking sector has been affected including the non-GCC countries. However, compared to European countries, the impact is less pronounced in our sample, given that the selected countries were less integrated in the global financial markets as they focus mainly on traditional lending. The level of non-performing loans continued its upward trend to reach 9.03 in 2019, which casts attention to NPLs as red flags. Figure 1 exhibits the evolution of NPLs during the period of the study. 6

Table 3

Descriptive statistics country and year-wise.

The evolution of NPLs ratio year wise for the sampled countries.

Besides, before addressing the empirical procedure and running the descriptive statistics for our sample, correlation analysis should be introduced. In this sense, to assess the correlation and multicollinearity among our variables, the Pearson's pair-wise correlation matrix and variance inflation factor (VIF) were produced. 7 The pair-wise correlation matrix is exhibited in Table 5 , demonstrating a relatively small pairwise correlation among all the explanatory variables.

Table 5

Pearson's pair-wise correlation matrix.

The numbers 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 refers to Credit risk, Bank size, Bank capitalization, Bank performance, Loan growth, Bank inefficiency, Ownership concentration, GDP growth, Inflation, Public debt, Unemployment, Bank diversification and Concentration, respectively.

To further assess whether the sample suffers from multicollinearity, the variance inflation factor (VIF) was produced. According to Table 6 , the VIF values of all the explanatory variables are relatively very small and are within the permissible range as none of the values exceed 3. Therefore, the absence of multicollinearity in our dataset can be concluded, which is consistent with the precedent analysis based on the correlation matrix (see Tables  7 and ​ and8 8 ).

Table 6

Variance inflation factor (VIF).

Table 7

The impact of the macroeconomic, bank-specific and industry-specific determinants on banks’ NPLs. The fixed effects results.

Notes: Table 7 presents the fixed effects results of the relationship between NPLs and explanatory variables. The standard errors are reported in parentheses. They represent robust standard errors corrected for potential heteroscedasticity and time-series autocorrelation within each bank using the robust cluster option in STATA. Dummies for time and country effects are used. The bold coefficients denote the statistically significant values. Asterisks indicate the significance at the 1 percent (∗), 5 percent (∗∗) and 10 percent (∗∗∗) level.

Table 8

The impact of the macroeconomic, bank-specific and industry-specific determinants on banks’ NPLs. The random effects results.

Notes: Table 8 presents the random effects results of the relationship between NPLs and the explanatory variables. The standard errors are reported in parentheses. They represent robust standard errors corrected for potential heteroscedasticity and time-series autocorrelation within each bank using the robust cluster option in STATA. Dummies for time and country effects are used. The bold coefficients denote the statistically significant values. Asterisks indicate significance at the 1 percent (∗), 5 percent (∗∗) and 10 percent (∗∗∗) level. Based on Hausman test, the results of the random effects model will be considered in the current study.

Besides, to test the reliability of our variables, the Cronbach's α was used. This latter is one of the most common methods for assessing scale score reliability in a data sample. The test indicates that all variables are reliable given that the estimated αs were all equal or greater than 0.7 (see Table 4 ).

Table 4

Reliability statistics.

4. Empirical procedure

In the current research, a panel data analysis will be conducted. We will examine the impact of macroeconomic, bank-specific and industry-specific variables on the level of NPLs (Eq. 1 ). The regression investigating the impact of macroeconomic, bank-specific and industry-specific variables on the level of NPLs takes the following form:

• - NPL it denotes the model's dependent variable,
• - J denotes the number of bank-specific variables (J = 7 in our study),
• - K denotes the number of macroeconomic variables (K = 4 in our study),
• - N denotes the number of industry-specific variables (N = 1 in our study)
• - (BANK j it ) denotes a vector of bank-specific variables,
• - (MACRO k t ) represents the vector of macroeconomic variables,
• - (INDUSTRY n t ) denotes the industry-specific variables,
• - β are the coefficients of vectors,
• - α i is the constant term,
• - μ i are the unobservable bank-specific effects,
• - ε it is the error term.

In order to investigate these regressions, this study will focus on two main approaches: fixed effects model (FE) and random effects model (RE).

4.1. The fixed effects model

• - i and t represent cross-section dimension and time indicator, respectively,
• - α i denotes the unknown intercept for each bank,
• - μ it is the error term.

4.2. The random effects model

• - μ i is the between-entity error,
• - ε it is the within-entity error.

The main reason behind the use of these two approaches, is to unhidden the differences and similarities across models. The analysis of the two different regressions will enhance the robustness of our results in case these latter are consistent in terms of significance and correlation. In addition to that, due to the time persistence of the credit risk structure and the importance to include lags of the dependent variable in the model, a dynamic approach would be advised.

4.3. Empirical results

This section presents the empirical results. To identify the main determinants of credit risk in our sampled countries, a panel data technique is employed. This empirical analysis is conducted using the fixed and random effects models 8 . These models allow for variable intercepts as well as heterogeneity across firms, which ensure the consistency and efficiency of estimates (see Tables  7 and ​ and8 8 ).

Prior to documenting the empirical results, heteroscedasticity and serial correlation tests are of vital importance to confirm the validity and efficiency of the analysis.

In order to test for heteroscedasticity in our model, White General test is employed. 9 The results of the test indicate the presence of heteroscedasticity in our model. To correct for heteroscedasticity, we employed the Eicker–Huber–White standard errors estimator that is, mainly known as Huber/White sandwich estimator 10 . Besides, a serial correlation test was conducted, referring to the Wooldridge test 11 ( Jabbouri and El Attar, 2017 ). The test indicates that our model's estimates are robust to serial correlation.

In order to decide which model is the most suitable for our analysis, Hausman test is conducted. This common approach points if the parameters estimates differ in the two models, fixed and random, and if there is any correlation between the unit effects and the independent variables ( Hausman, 2015 ). In our case, the Hausman test indicates that the random effect is most appropriate for the current analysis as the difference in coefficients is systematic and the error terms are correlated with the regressors. 12

5. Discussion of the results

The results exhibited in the tables above are interesting and will shed a better light on the factors influencing NPLs. Despite the similarities between the two models, Hausman test indicates that the random effects model is the most appropriate for the current analysis. Thus, the empirical results of random effects will be considered. Indeed, the analysis reports an adjusted R-square value of 61.1 percent.

5.1. The impact of bank-specific determinants of banks’ credit risk

The literature is inconclusive about the relationship between bank size and NPLs. Our current research found that, in MENA emerging markets, bank size is negatively associated with the level of NPLs, at a significance level of 1 percent. This result confirms prior findings, arguing that larger banks are in a better position to conduct proper loan screening and successfully cope with defaulters thanks to their sophisticated risk management mechanisms, lessening the level of NPLs ( Alhassan et al., 2014 ; Hu et al., 2004 ; Louzis et al., 2012 ; Salas and Saurina, 2002 ; Solttila and Vihriâlâ, 1994 ). As a matter of fact, small-sized banks are more likely to devote limited resources to risk management procedures, which causes the upsurge of NPLs. Further, our results provide strong evidence in favor of the diversification hypothesis, postulating that larger banks tend to be more diversified compared to their smaller counterparts, which reduces their risk exposure and, thus, the level of bad loans ( Louzis et al., 2012 ).

Regarding the CAR-NPLs relationship, our analysis documents striking results. We report a significant and negative relationship between banks' capital adequacy and the level of NPLs, significant at the 1 percent level. This result corroborates the results documented by; Sinkey and Greenawalt (1991) , Barth et al. (2004) and Boudriga et al. (2009) and contradicts the finding of Ghosh (2017) and Rime (2001) . In fact, our findings support the notion that undercapitalized banks usually raise their capital in response to additional risk exposure. This negative relationship can be explained by the fact that banks with high CARs, are more likely to avoid imprudent lending to sustain the capital set aside, usually used as a buffer against excessive risks ( Salas and Saurina, 2002 ; Us, 2017 ). This research confirms the moral hazard hypothesis, contending that thinly capitalized banks tend to engage in irresponsible lending with inadequate risk screening given the limited loss they may incur in a potential financial slump, which justifies a higher level of NPLs ( Berger and Deyoung, 1997 ). In order to monitor banks' risk, major regulatory changes have been adopted by central banks in MENA emerging markets during the last decade ( BMI, 2017 ). The regulatory measures include the reinforcement of the level of banks’ CAR, used as an instrument to control excessive risk taking. In this sense, MENA banks with a low level of CAR were requested to comply with the new Basel accords and adjust their balance sheet to comply with the regulatory requirements, either by raising more capital or reducing risk-weighted assets ( BMI, 2017 ).

In contrast to Garcí a-Marco and Dolores Robles-Fern á ndez, 2008 who have demonstrated that bank profitability is positively linked to NPLs, the current research reports a negative relationship between bank profitability and credit risk in the considered emerging markets, significant at the 1 percent level. Our analysis supports the bad management hypothesis, which suggests that low profitability denotes poor management skills with regards to lending strategies, and thus, a high the level of NPLs ( Louzis et al., 2012 ). In fact, banks incurring a low profitability are more likely to increase their risk exposure and adopt a liberal credit policy to recover the preceding losses and maintain a decent current profitability, which may be achieved, at the expense of higher future NPLs. Since they are less pressed to generate more income compared to their counterparts, highly profitable banks are less likely to grant risky loans, which minimizes their credit risk ( Ghosh, 2015 ; Louzis et al., 2012 ).

The current study documents a positive relationship between credit growth and NPLs. This finding associates rapid loans growth to riskier lending behaviors and supports the results documented by Salas and Saurina (2002) , Foos et al. (2010) and Keeton and Morris (1987) . This can be approached through different perspectives. First, as banks shift their supply, loan screening and analysis deteriorate, which inflates the level of NPLs. Put differently, a rapid credit growth could overwhelm the resources dedicated to loan screening and monitoring, leading to insufficient risk analysis and thus, a higher level of NPLs. Further, the current research supports the notion that in search to expand their credit portfolios, banks may be interested in increasing short-term profits through easing their credit standards, often, at the expense of heavy future bad loans ( Solttila and Vihriâlâ, 1994 ). Nevertheless, the impact of loan growth is less pronounced in the sampled MENA emerging markets. Our current state of knowledge on this suggests that, compared to the other macroeconomic variables, the slight and inconsequential increase (decrease) of loan growth in the sampled countries may explain the insignificance of this relationship.

The current analysis reports a negative relationship between bank inefficiency and NPLs, which is significant at the 1 percent level. This result supports the findings reported by Louzis et al. (2012) and Rossi et al. (2009) . We provide a strong evidence in favor of the skimming hypothesis, while we present opposing evidence to the bad management and bad luck hypotheses. The finding of this research links the costs devoted to credit assessment and evaluation processes to the quality of banks' loan portfolios. This implies that banks devoting insufficient resources to conduct adequate loan analysis and underwriting are cost efficient in the short run, yet they will incur higher loan losses in the long run. On the other hand, banks dedicating necessary resources to loan assessment have better chances to minimize their NPLs ( Rossi et al., 2009 ). In the aftermath of the global financial crisis, most MENA emerging countries adopted transformation programs, which aim at decreasing banks’ credit risk by dedicating sufficient capital and resources to loans underwriting and monitoring ( BMI, 2017 ). 13

The literature exposes contracting arguments about the effect of ownership concentration on banks’ credit risk. The finding of this research is in line with the school of thought that suggests a positive relationship between ownership concentration and NPLs ( Berle and Means, 1933 ; Dong et al., 2014 ; Haw et al., 2010 ; Louzis et al., 2012 ). Our analysis supports the notion that ownership concentration increases agency problems which might results in an increased level of NPLs. In fact, the result of this finding confirms that agency problems intensify in the presence of strong ownership concentration due to potential conflicts of interests between controlling and minority shareholders ( Shleifer and Vishny, 1986 ). We contend that shareholders in concentrated banks have the power to influence bank risk-taking, by coercing bank managers to undertake risky investments with high expected returns, which in turn, increases the level of loan problems ( Jensen and Meckling, 1976 ). The results of this research draw a particular attention to the severity of agency problems within concentrated banks in the MENA emerging markets. These banks are more likely to exhibit an increase level of credit risk and suffer from massive NPLs due to the unbalance of power between controlling and minority shareholders.

Furthermore, this research contends that banks' diversification is negatively linked to NPLs and confirms our initial hypothesis. This rejects the notion that banks entering new businesses in which they have little experience face excessive risks ( Louzis et al., 2012 ; Stiroh, 2004 ). In contrast, we argue that when banks extend and diversify their activities, their focus on credits shifts and their loan lending might decrease, which may result in a decreased level of NPLs. Yet, the negative relationship between diversification and banks' credit risk is found to be insignificant, implying that diversification does not necessary impact banks’ risk behaviors in the sampled MENA emerging countries. As a matter of fact, banks in the MENA region remain less diversified compared to their counterparts in developed countries. The noninterest income, which is the proxy for bank diversification, includes mainly income from investment banking, insurance brokerage commissions, venture capital, gains on non-hedging derivatives and income from trading and securitization. These activities are still underdeveloped in the sampled countries. In this sphere, the core activities of MENA banks are, predominately, issuing loans and collecting the interest payments which may explain the insignificance of the diversification-credit risk relationship.

5.2. The impact of macroeconomic determinants of banks’ credit risk

The analysis confirms our initial hypothesis and reports a negative relationship between GDP growth and banks' NPLs, significant at the 1 percent level. As expected, this result is in line with prior findings, arguing that the economic conditions impact significantly the level of banks' NPLs ( Anastasiou et al., 2019 ; Beck et al., 2015 ; Carey, 1998 ; Ghosh, 2017 ; Jabbouri and Naili, 2019a ; Nkusu, 2011 ; Salas and Saurina, 2002 ). Furthermore, GDP growth has always been used as the primary indicator that mirrors the status of the country's business cycle. In this sense, we argue that under good economic conditions, households and businesses are more likely to service their debts, which lessens the level of bad loans. In rebuttal, during economic abysses, creditors will struggle to honor their debt obligations, which weakens banks' credit quality.

In contrast to the findings of Khemraj and Pasha (2009) , Nkusu (2011) and Gulati et al. (2019) who argue that inflation is negatively related to banks' credit risk, the current research contends that as inflation increases, the level of NPLs escalates, especially in case of floating rates loans. Given that inflation has an adverse impact of household's income, high inflation rates erode the real value of household's revenues, which limits their capacity to reimburse their debts. As a matter of fact, in MENA emerging countries, inflation is considered as one of the main concerns of financial regulators as wages are often sticky. That is said, households are more challenged to repay their debts under inflationary conditions, which worsens banks' loan quality.

Consistent with the previous findings of Reinhart and Rogoff (2011) and Louzis et al. (2012) , public debt was found to be positively linked to NPLs, at a significance level of 1 percent. We argue that as public debt increases, the creditworthiness of banks becomes doubtful. This puts a sovereign ceiling on their solvency, making banks hard pressed to raise market financing, which makes refinancing loans a tedious task for borrowers. Further, our findings are in accordance with the sovereign debt hypothesis, stipulating that higher public debt leads to higher NPLs ( Louzis et al., 2012 ). Indeed, in the MENA emerging countries, the social demands triggered by the Arab spring at the beginning of 2011 have pushed regulators and policy makers to increase their spending in order to finance the structural reforms aiming to tackle the social unrest. 14 As a result, banks' liquidity and credit growth were impacted, pushing banks to reduce their lending. In addition to that, as public debt upsurges, governments are more likely to raise taxes and reduce subsidies, which adversely impact households’ income and purchasing power and, thus, their capacity to honor their debt obligations.

Another important finding of the study is the significant and positive relationship between unemployment and NPLs, at the 1 percent level. This result confirms our hypothesis, documenting that unemployment is one of the major determinants of NPLs in emerging countries. This finding is consistent with prior studies of Ghosh (2015) , Nkusu (2011) and Salas and Saurina (2002) . This outcome stipulates that when jobs are scarce, borrowers are less likely willing to repay their debts, which escalates the level of NPLs. In addition to that, due to the uncertainty of their employment status, individuals with low-income levels are charged ballooned interest rates which, impairs their capacity to service their loans.

5.3. The impact of industry-specific determinants of banks’ credit risk

Contrary to the findings reported by Keeley (1990) , Broecker (1990) and Hellmann et al. (2000) who contend that competition impacts banks' franchise value, which decreases their incentives to grant thoughtful loans; this study asserts that interbank competition leads to an enhanced loan quality portfolio. As a matter of fact, the finding of this research rejects the so-called “franchise value hypothesis” and supports the notion that interbank competition tends to lessen banks' charged interest rates which, therefore, reduces the number of defaulters. Also, we argue that banks’ competition would press managers to ensure solid loan quality portfolios through adequate loan screening and monitoring in order to gain advantageous risk management perception from their bank regulators and investors ( Jiménez and Saurina, 2005 ; Ozili, 2019 ).

5.4. The impact of GDP components on banks’ credit risk

The current paper pursues innovation by analyzing the impact of GDP components on banks' credit risk. Given the significant impact of GDP on the evolution of banks’ NPLs, it would be interesting to investigate the impact of its components. In this sense, the previous regressions will be re-conducted by a GDP breakdown of three main sectors: agricultural GDP growth (GDPagri), services (GDPservices) and industrial GDP growth (GDPindustry).

In order to decide which model is more suitable for this analysis, we conducted the Hausman test. The test indicates that the random effects model is the most suitable. Table 9 exhibits the results using the random effects model through four main models.

Table 9

Random effects results by a GDP Breakdown: GDPagriculture, GDPindustry and GDPservices.

Notes: Table 9 presents the random effects results of the relationship between NPLs and the explanatory variables using GDP components. The standard errors are reported in parentheses. They represent robust standard errors corrected for potential heteroscedasticity and time-series autocorrelation within each bank using the robust cluster option in STATA. Dummies for time and country effects are used. The bold coefficients denote the statistically significant values. Asterisks indicate significance at the 1 percent (∗), 5 percent (∗∗) and 10 percent (∗∗∗) level.

The regression exhibits striking results. All GDP components were found to be significant at the 1 percent level. Interestingly, agricultural GDP was found to have the greatest impact on banks' NPLs. This finding contends that an increase (decrease) in agricultural GDP is more likely to decrease (increase) the level of NPLs, compared to an increase (decrease) in the other GDP components. Put differently, fluctuations in the agricultural sector would have a prenominal impact on the quality of banks' loan portfolios. As a matter of fact, in the considered MENA emerging countries, the agricultural sector remains the backbone of the economy, employing a high percentage of its workforce and massively contributing to export revenues. Given that the sector is documented to be one of the main occupations of the majority of citizens in our sample of countries, its collapse would harm borrowers’ capacity to service their debt obligations. Thus, these findings emphasize the necessity of closely monitoring and reinforcing country level mechanisms by providing an environment conducive to economic growth.

6. Robustness test

To deal with endogeneity and due to the time persistence of the credit risk structure, a Generalized Method of Moments (GMM) is applied as a robustness check. This latter allows the introduction of more instruments that can massively improve the model's efficiency ( Roodman, 2009 ) 15 . In addition to that, in our dynamic model, we ensure to control for the three types of endogeneity, namely, unobserved heterogeneity, simultaneity and dynamic endogeneity. The estimated models take the following general forms:

• - The subscripts i and t represent the cross-sectional and the time dimensions of our data, respectively,
• - α is the constant term,
• - y i,t-1 is the one-period lagged independent variable and ϒ j is the coefficient of persistence;
• - X ' it is the k × 1 for the dependent variables,
• - β is a k×1, vector of coefficients,
• - η i are the firm fixed effect,

The introduction of the lag of the dependent variable might influence the traditional models such as the pooled OLS, the fixed and random effects models due to the correlation between the lagged dependent variable y i,t-1 and the bank-specific factors η i . Sargan test of over-identifying restrictions is used to test for the overall validity of our instruments and to ensure the consistency of our model ( Arellano and Bond, 1991 ; Arellano and Bover, 1995 ). Second, the Arellano-Bond autocorrelation tests were conducted to evaluate the assumption of serially uncorrelated errors, ε it .

In Table 10 , we present the estimated coefficients of the system GMM and difference GMM, the Sargan test and the autocorrelation tests; AR (1) and AR (2). The Sargan over-identification test indicates that all instruments are valid. The AR (1) test rejects the null hypothesis of no first-order serial correlation, yet it does not reject the null hypothesis that there is no second-order serial correlation. Hence, all requirements of the tests are met as suggested by p-values, which confirms the consistency of our dynamic model. The analysis of our dynamic model indicates that NPLs are time persistent. The results of the different estimation techniques are quite similar, and the coefficient estimates are fairly stable across models. The results of the GMM analysis are consistent with the previous findings in terms of significance and correlation between NPLs and the independent variables, which confirms the robustness of our findings to changes in empirical models.

Table 10

Regression analyses using GMM estimation techniques.

Notes: Table 10 illustrates the regressions' results using GMM estimation techniques; system and difference GMM. Dummies for time and country effects are used. The bold coefficients denote the statistically significant values, standard errors are reported between parentheses and, corrected for potential heteroscedasticity and time-series autocorrelation within each bank using the robust option. The one-step system GMM are estimated using the collapse option to address the instruments proliferation problem. The p-values of the Sargan test, the Hansen test are reported in brackets. The p-value of the Arellano-Bond serial correlation tests AR(1) and AR(2) are reported in brackets. Asterisks indicate significance at the 1 percent (∗), 5 percent (∗∗) and 10 percent (∗∗∗) level.

7. Conclusion

Banks' credit risk, considered as a prominent threat to the stability of the banking sector, has been widely discussed among researchers and policymakers. Yet, research on MENA emerging economies continues to lag far behind many regions. In order to lessen this void in the literature, this research aims to explore the main determinants of banks' NPLs in a sample of five MENA emerging markets between 2000 and 2019. Using a panel approach and a dynamic data estimation technique through system GMM, this research documents that GDP growth, unemployment, bank capitalization, bank profitability, bank operating inefficiency, bank ownership concentration, inflation, sovereign debt and bank size are the main determinants of NPLs, whereas, loan growth, bank diversification and interbank competition were found to have an insignificant impact on NPLs. More precisely, it appears that the systematic determinants i.e., macroeconomic ones, are preponderant compared to bank's specific factors. This implies that in terms of exposure to credit risk, banks are strongly dependent on the economic context and cannot offset or avoid the impact of the latter even through an effective management of bank specific factors. Thus, reinforcing country level regulations and mechanisms is of vital importance to control banks' credit risk.

The findings of this research have substantial implications. This research provides new evidence on the determinants of banks' credit risk in MENA emerging markets which will empowers regulators and policymakers with a comprehensive understating of credit risk in MENA emerging markets. In fact, the identification of these factors would help regulators address appropriate interventions, design appropriate credit policies and adopt adjusted prudential regulations. More specifically, economic and fiscal policies should be directed towards the creation of an environment conducive to economic growth with less unemployment and an effective management of public debt. Likewise, this study pursue innovation by exploring the effect of bank ownership concentration, as an important internal mechanism of corporate governance, on credit risk. Given that central banks acquired the role of forestalling bank panics, integrating ownership concentration as a fundamental determinant of NPLs is crucial to anticipate future financial calamities. In this sense, it is necessary for policymakers and bank regulators to develop adapted regulatory and supervisory frameworks and reforms for banks in the MENA region regarding the degree of ownership of the ultimate shareholders, as this region differs from other regions in term cultural and institutional environment. Furthermore, practitioners are likely to benefit from the results of this research as it sheds lights on the relationships between a wide range of variables, which will help address prejudices and improve collaboration between market participants. Prior studies have been largely silent on how industry-related factors can shape banks' credit risk. Hence, to have a holistic understating of NPLs' determinants, this paper adds more to the existing finance and banking literature by exploring industry-related variables and their impact on banks' risk taking. Finally, this research provides academicians and researchers with rich findings related to the key determinants of NPLs, as an extension of the existing literature, covering a large sample of bank and a more recent period. In the same vein, this study can be of significance to scholars as it offers important information and data related to banks’ credit risk from another market, MENA region, in which research is infrequent.

Qualitative research on this topic using structure questionnaires and interviews could yield profound understanding of the main determinants of NPLs. Thus, future research can further examine the factors shaping banks' credit risk from the perspectives of banks’ managers and regulators.

Declarations

Author contribution statement.

Maryem Naili: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Wrote the paper.

Younes Lahrichi: Conceived and designed the experiments; Contributed reagents, materials, analysis tools or data; Analyzed and interpreted the data.

Funding statement

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Data availability statement

Declaration of interests statement.

The authors declare no conflict of interest.

Additional information

No additional information is available for this paper.

Acknowledgements

The authors would like to thank Professor Tkiouat Mohamed and Professor Imad Jabbouri for their valuable comments.

1 Central bankers agreed that banks' NPLs were the reason behind the onset of several financial crises including the slump of the US financial system in the 1980s, the Asian crisis during the 1990s and 2000s, the US subprime crisis and, more recently, the loans debacle of the European credit crisis.

2 A comprehensive strategy set by the European Central Bank to tackle of the NPL issue, since 2015. Several reports have been published in this sense: the NPL guidance on 2017, the Addendum to the NPL guidance on 2018 and the ECB press release on supervisory expectations for NPL stock in July 2018. These reports are a result of comprehensive research on the main drivers of NPLs in the EU countries. All reports are published on the ECB website.

3 It was reported that over the past 33 years, only 7% of conducted research on the determinants of banks' NPLs focused on emerging markets ( Naili and Lahrichi, 2020 ).

4 This can be achieved through the extension of credits' terms, renewal of borrowers' credit lines and weakening covenants.

5 The descriptive statistics were obtained using the command xt sum and describe in STATA.

6 To measure the reliability of our dataset, the alpha command in STATA was used. An alpha higher than 0.7 indicates that all variables are reliable and thus, confirms the reliability of our data.

7 The variance inflation factor (VIF) quantifies the severity of multicollinearity in an ordinary least squares regression. The Variance Inflation Factor is defined as: VIF (β k ) = 1/(1-R 2 k ), where R 2 k is the coefficient of multiple determination, R 2 -value obtained by regressing the k th predictor on the remaining predictors. The VIF can be interpreted as the ratio of the actual variance of the estimated coefficient, β k , to what it would have been if there was no multicollinearity and R 2 k = 0.

8 The fixed effect model is estimated using the command areg, absorb () in STATA 14, while the random effect model is estimated using the STATA 14 command; xtreg re ( Baker et al., 2017 ; Jabbouri and El Attar, 2018a ; Jabbouri and Jabbouri, 2020 ; Jabbouri and Farooq, 2021 ).

9 whitetst computes the White (1980) general test for heteroscedasticity in the error distribution by regressing the squared residuals on all distinct regressors, cross-products, and squares of regressors. The test statistic, a Lagrange multiplier measure, is distributed Chi-squared(p) under the null hypothesis of homoscedasticity ( Greene, 2005 ). In order to test for heteroscedasticity, we employed the estat imtest, white command in STATA 14. The results of the test indicate that the p value is lower than 0.05, meaning that we reject the null hypothesis H 0 , leading to the presence of heteroscedasticity.

10 The command vce(robust) STATA 14 to correct for heteroscedasticity.

11 We test the serial correlation using the Wooldridge's test through the command xtserial in STATA 14. The results of the test indicate that autocorrelation does not exist in the model as the p-value was greater than 0.05, indicating that we fail to reject the null hypothesis (H0: no first order autocorrelation).

12 The Hausman test confirms the appropriateness of the random model. The command Hausman was employed in STATA 14 after storing the estimates of the two models using the commands estimates store re and estimates store fe . The result shows that Prob>chi2 = 0.0000, which means we reject the null hypothesis; stating that difference in coefficients is not systematic. The random effects model is more appropriate. In fact, the error terms are correlated with regressors which makes the random effect more appropriate for our sample.

13 Modernized mechanism have been adopted as part of the regulatory frameworks initiated by central banks in MENA emerging countries, mainly Morocco, Jordan and Tunisia. These latter's purpose is to dedicate adequate resources to face the threat of credit risk through a rigorous credit assessment and loan evaluation ( BMI, 2017 ).

14 During the Arab spring, public revenues have declined due to the economic conditions in major countries.

15 The xtabond2 command can be used to implement these estimators in STATA ( Roodman, 2009 ). This command, compared to the previous xtabond command, implements the two estimators and makes available a finite sample correction to the two-step covariance matrix ( Roodman, 2009 ). Besides, it makes two-step robust more efficient than one-step robust along with addressing the instruments proliferation problem. We used the collapse option to address these problems and ensure the validity of our instruments.

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Impact of the quality of credit risk management practices on financial performance of commercial banks in Tanzania

• Published: 02 March 2024
• Volume 4 , article number  38 , ( 2024 )

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• Grace Isidor Temba   ORCID: orcid.org/0000-0002-2281-0448 1 ,
• Pendo Shukrani Kasoga   ORCID: orcid.org/0000-0001-6634-3020 1 &
• Chirongo Moses Keregero 1  

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Commercial banks’ roles in economic development make it paramount for their sustainable financial performance, hence a need for effective credit risk management as credits are the main source of revenue creation, yet are the main threat to the bank’s asset quality and, if not effectively managed, will jeopardize performance. The study investigated the influence of the quality of credit risk management practices on the financial performance of commercial banks in Tanzania. Balanced panel data of fifteen commercial banks with 255 observations from 2003 to 2019 have been used for the analysis. Results revealed that risk assessment and approval, the quality of credit processes and controls, adequacy of the recovery process, and risk supervision & monitoring positively influence banks’ performance through their capital adequacy, efficient use of equity and asset quality, respectively. Further, banks’ earning ability and liquidity are negatively affected by risk assessment and approval as well as risk supervision and monitoring. The study recommends that credit risk management practices be central to bank operations due to their positive effect on financial performance. However, caution should be taken and strike a balance on mix and concertation in the facilitation of all studied variables, as credit risk assessment & approval and credit risk monitoring and supervision negatively affect banks’ liquidity.

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Machine learning-driven credit risk: a systemic review

Acknowledgements

Authors acknowledge the public relations / legal officers of TPB Bank Plc, DCB Commercial Bank Plc, Diamond Trust Bank (Tanzania) Ltd and Akiba Commercial Bank Plc, who made efforts to provide hard-bound books of annual reports for a few years which were missing in their banks’ websites.

The authors did not receive support from any organization for the submitted work. Further, all authors certify that they have no affiliations with or involvement in any organization or entity with any financial or non-financial interest in the subject matter or materials discussed in this manuscript.

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Grace Isidor Temba, Pendo Shukrani Kasoga & Chirongo Moses Keregero

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GIT conceptualized the introduction, reviewed the literature, developed study hypotheses, collected and analyzed data, discussed findings, drew the conclusion, and was a major contributor to writing the manuscript. PSK sharpened the conceptualized introduction, hypotheses, findings discussion and conclusion. CMK worked on the grammar issues and manuscript editing.

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Financial/Non-Financial Interests: All authors have no relevant financial or non-financial interests to disclose. Affiliations: All authors certify that they have no affiliations with or involvement in any organization or entity with any financial or non-financial interest in the subject matter or materials discussed in this manuscript. Editorial Board Members and Editors: All authors certify that they make no part in the editorial part and/or Editors in which this manuscript will be processed/edited/published. Employment: This research work has not been conducted for any financial gain to any organization. The work is purely for academic requirements. Nevertheless, none of the authors is employed by any institution whose data were used for this research work.

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This research work was approved by the Vice Chancellor of the University of Dodoma through the Directorate of Research, Publication and Consultancy ([email protected]) of the University of Dodoma—Tanzania as it is part of the requirement for the attainment of PhD studies by the corresponding Author pursued at the University of Dodoma. This research used published secondary data of Institutions (commercial banks), Audited and Published under the Tanzania Central Bank—Bank of Tanzania requirements. No part of the collected and utilized information was obtained from human participants, nor did the research involve human participants.

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Temba, G.I., Kasoga, P.S. & Keregero, C.M. Impact of the quality of credit risk management practices on financial performance of commercial banks in Tanzania. SN Bus Econ 4 , 38 (2024). https://doi.org/10.1007/s43546-024-00636-3

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Private Credit

Private equity, key differences, the bottom line.

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Private Credit vs. Private Equity: What's the Difference?

Michael Bromberg is a finance editor with a decade of experience. He is an expert at elucidating complex financial topics in clear, concise language. Michael received a Bachelor of Arts in literature from the University of Wisconsin-Madison and a master's in linguistics from the Universidad de Antioquia in Medellin, Colombia.

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Private Credit vs. Private Equity: An Overview

Securities traded on the public markets, like stocks and bonds, may be the backbone of most everyday investors’ portfolios. But there are also plenty of alternative investments that aren’t publicly available, like private credit and private equity.

These assets can be quite profitable, but because they’re also risky and tend to tie up capital for a long time, trading generally takes place among institutional investors and accredited investors.

In the private credit market, investors make loans to businesses and sometimes individuals who may have trouble accessing credit from banks or the public market. Because there is often a heightened risk that the borrower may be unable to repay the loan, private credit investors can collect higher interest rates than they would earn on bonds or other debt investments.

Private equity investing, meanwhile, involves taking an ownership share in a company that isn’t currently traded on the public markets. Unlike a stock, which can be easily bought and sold on a public exchange, private equity investments require investors to make a longer-term commitment with their capital . In exchange for this lack of liquidity, private equity investors also look for elevated returns.

The chance for outsized gains might make private credit and private equity attractive to investors who have access to these private markets.

Key Takeaways

• Private credit investors lend money to borrowers who may have trouble accessing loans elsewhere, while private equity involves buying ownership shares in a nonpublic company.
• These investments may offer attractive returns, but their riskiness and illiquidity make them more suitable for institutional investors and accredited investors.
• Private credit offers more predictable and stable returns, while the higher upside potential of private equity comes with the risk of significant losses.

When you invest in private credit, you lend out your money—mainly to companies, but occasionally to individuals—and then generate returns by collecting interest payments.

Private credit plays an important role in the financial system by making loans available to businesses that may not be able to secure them through banks or the public debt markets.

The borrowers seeking these private, non-bank loans often have credit ratings that are below investment grade , suggesting a heightened risk that they may not be able to pay their debts. To compensate for the greater risk of default , they generally have to pay higher interest rates. This means the potential for higher profits for investors willing and able to stomach the risk.

Private credit investing is much like buying a bond, with the main difference being that private credit isn’t traded on the public markets and typically isn’t available to the general investing public.

As investors in private credit are making loans, rather than acquiring an ownership stake, they are more likely to be repaid if the borrower faces bankruptcy. In addition, there is a chance for diversification, with the flexibility to invest in different types of loans with distinct risk/return profiles. Private loans often have floating interest rates , which can benefit investors when rates increase.

However, given the risks involved, private credit firms often require investors to meet strict accreditation standards and start with a high minimum investment. Private credit firms also make loans for extended terms, requiring investors to commit their capital over long time frames.

Large institutional investors are central players in the private credit industry because they have the scope and expertise to manage these potential drawbacks.

Pros and Cons of Private Credit Investing

Rapid growth of industry

Predictable returns outperforming other fixed-income options

Diversification and low correlation with public markets

Priority for repayment (as creditor) in case of bankruptcy

Flexibility to manage risk by selecting different types of loans

Stringent accreditation requirements and high minimum investments

Illiquidity

Increased default risk

Management fees

Lack of transparency and regulatory protections

Rather than making a loan, investors in private equity are acquiring an ownership stake in a company. Private equity firms typically pool together assets from institutional investors and accredited investors into large investment funds.

Then they use this money to acquire companies. This may include purchasing businesses that are already privately owned or taking control of public companies in their entirety. Firms often form consortiums with other investors to complete these buyouts .

Once a private equity firm has taken control of a target company, it will carry out a strategy to increase the value of its investment. That could include significant restructuring or cost cutting. The goal is to add value and then exit the investment, which could be done through a sale to another owner or by taking the company public through an initial public offering (IPO) .

Private equity firms typically invest in more mature companies. This stands in contrast with venture capital , another type of alternative investment that acquires stakes in startups and early-stage companies before they offer their shares to the public.

A successful private equity transaction can be very profitable for investors. However, because these exit strategies take time to develop, private equity investors also tend to have their investments tied up for extended periods.

In addition, along with the chance for stellar gains from private equity comes the risk of painful losses. As shareholders, private equity investors would be among the last to be compensated in the event of bankruptcy, meaning they could lose 100% of their investment.

Given the lack of liquidity and heightened risk levels, private equity firms also limit participation to institutions and individuals with significant wealth and financial sophistication. However, these high barriers to entry haven’t restricted the growth of the private equity market.

Pros and Cons of Private Equity Investing

Possibility for huge returns

Increased control over management decisions

Potential to benefit from expertise of private equity firm

Lack of transparency and disclosure requirements

Limited recourse in case of bankruptcy, with chance of losing entire investment

Private credit and private equity share some key similarities. They both represent alternative investments available only on a private basis. In addition, they typically have strict accreditation standards and require lofty minimum investments, leading to a concentration of institutional investors in both areas.

Management fees also tend to be high for these private investments, but investors are rewarded with the potential for outsized returns. This helps explain why both asset classes have experienced tremendous growth in recent decades.

There are also some important differences to keep in mind. For one thing, private equity involves taking an ownership stake, while private credit represents a loan. This makes the two types of investment quite different in terms of their risk-reward profile.

Private equity investors could earn huge profits if and when the company they invested in is sold or brought public. Conversely, they could lose their investment entirely if the company is unsuccessful. Meanwhile, returns for private credit investors are more predictable—established by the terms of the loan and relatively stable (provided the borrower doesn’t default).

Which Is Better: Private Credit or Private Equity?

Private credit and private equity are both alternative assets that could be attractive to investors looking for different benefits for their portfolios. Private credit may be appropriate for investors seeking relatively stable and predictable returns that often exceed those of bonds and other fixed-income assets. Private equity could be suitable for those in search of high potential returns, although this also means elevated risks.

What Types of Investors Typically Invest in Private Equity?

Private equity often requires a high minimum investment and a commitment of capital for years or even decades. Given these characteristics, private equity firms typically vet investors based on strict accreditation standards. For this reason, institutional investors and individuals with a high net worth or strong financial expertise dominate the private equity space.

Why Is an Investor Likely to Opt for Private Credit Over Private Equity?

Investors may choose private credit over private equity if they are seeking more predictable and stable returns. Because they are acting as creditors rather than equity holders, private credit investors assume lower levels of risk, but their potential profits are limited to the interest generated by the loan.

Investing in private credit involves making loans to companies or individuals and collecting interest payments, while private equity investors acquire an ownership stake in a company whose shares don’t currently trade on the public markets.

Both of these investment classes may offer higher potential returns than their publicly traded counterparts, but they also tend to be highly expensive, less liquid, and less transparent, making them more suitable for institutional investors and accredited investors.

U.S. Code, via GovInfo. “ 11 USC § 507 .”

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• Published: 25 May 2024

Examining consumer behavior towards adoption of quick response code mobile payment systems: transforming mobile payment in the fintech industry

• Mohammad Ali Yousef Yamin   ORCID: orcid.org/0000-0002-6944-6866 1 &
• Omima Abdalla Abass Abdalatif 1  

Humanities and Social Sciences Communications volume  11 , Article number:  675 ( 2024 ) Cite this article

Metrics details

• Business and management
• Information systems and information technology

The quick response (QR) code-enabled mobile payment has gained large attention from academicians and policymakers due to its fast, convenience, and usefulness. However, acceptance of this technology among consumers is limited and rare in a few cases. Therefore, current research attempts to gain insight into factors that influence consumer behavior to adopt QR code-enabled mobile payment. This research has developed an integrated research framework that combines the technology acceptance model, theory of reasoned action, transaction speed, convenience, and innovativeness to investigate consumer behavior to adopt QR code mobile payment. In addition to that moderating effect of transaction speed is tested between consumer attitude and intention to adopt QR code-driven mobile payment. This study empirically investigates consumer attitudes and intention to adopt the QR mobile payment system with 216 responses. Findings of the statistical analysis have revealed that perceived usefulness, perceived ease of use, convenience, subjective norms, and innovativeness explained a substantial variance \({R}^{2}\) 52.3% in measuring user attitude to adopt QR code-enabled mobile payment. Practically, this study suggests that policymakers should pay attention to perceived convenience, transaction speed, subjective norm, and perceived usefulness to boost consumer attitude and intention to adopt QR-enabled mobile payment. This study is unique as it integrates the technology acceptance model and theory of reasoned action to investigate consumer behavior towards the adoption of QR code mobile payment. This study is also valuable as it examines the moderating effect of transaction speed between consumer attitude to accept QR code and their intention to adopt QR code-driven mobile payment and adds a new dimension to information system literature.

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Introduction.

The rapid acceptance of mobile phone devices has transformed individual personal and professional lives. Aside from communication tools mobile devices are now being used in the fintech industry for mobile payments. Mobile payment denotes to payment process wherein business transactions are performed through mobile devices (Liu and Zheng, 2023 ; Nicoletti et al., 2017 ). A recent development in mobile payment systems is the advent of QR code technology, which has gained large attention from academicians and policymakers due to its fast, convenient, and useful service (N. Singh et al., 2020a ). The QR code is a storage system that comprises a dot matrix kind of bar code and can be shown or printed on the screen, interpreted by a special reader, and reveals extensive information that could not be explained by traditional bar code (Denso, 2000 ). Although the QR code-enabled mobile payment method is easy, convenient, and enjoyable (Chang et al., 2021 ) however, the acceptance of quick response payment system is at its initial stage (Bhat et al., 2023 ; Boden et al., 2020 ; Yan et al., 2021 ). Consistently, the focus of this research is to understand factors that influence user behavior to adopt quick response (QR) codes for mobile payments.

Authors like Tew et al. ( 2021 ) asserted that QR code mobile payment systems have gained less attention when compared to other mobile-empowered financial services like Internet or mobile banking. Nevertheless, current research fills the research gap in this context and integrates the technology acceptance model with the theory of reasoned action to investigate consumer attitudes and behavioral intention to adopt the QR code mobile payment system in Saudi Arabia. This study is significant as it integrates the technology acceptance model and theory of reasoned action toward consumer adoption of the QR code mobile payment system in Saudi Arabia. In addition to that this research examines how perceived enjoyment, convenience, and innovativeness impact consumer behavior to accept QR code-enabled payment systems in the Saudi region. This study is unique as it conceptualizes the moderating effect of transaction speed between consumer attitude to accept QR code and their intention to adopt a QR code-based mobile payment system. The following section demonstrates the definition of the constructs and supporting literature to generate hypotheses.

Research model and hypotheses development

Qr code-enabled mobile payment.

With the emergence of 4th industrial revolution fintech industry has evolved several fintech services including the Internet of things (IOT), artificial intelligence, near-field communication, smartphone applications, and quick response code payment systems (Liu and Zheng, 2023 ; Mi Alnaser et al., 2023 ; Nicoletti et al., 2017 ; Shang et al., 2023 ). Similarly, the mobile payment system is also updated with the latest innovative applications and software. Traditionally, mobile payments have been done with mobile banking applications or web-based internet banking also known as e-banking (Rahi et al., 2023 ; Rahi et al., 2021b ). Nevertheless, the use of these applications for payments was found complex when compared with the quick response (QR) payment system. Therefore, an alternative service namely a quick response (QR) payment system has gained consumer attention in a cashless society (Alamoudi, 2022 ). With the advent of quick response (QR)-enabled payment systems consumers are no longer constrained to credit/debit card services (Chang et al., 2021 ). The use of QR codes is easy and enjoyable. For instance, the consumer just needs to scan a QR code with their mobile phone camera and payment will be completed quickly (Gutiérrez et al., 2013 ). In the context of Saudi Arabia, the Saudi payment authority has recently made a contract with the High-tech payment systems authority to introduce a QR code scheme. Nevertheless, consumers are reluctant to adopt QR code-enabled mobile payment systems in the Saudi retail industry. Therefore, it is essential to understand factors that impact consumer behavioral intention to adopt a QR code-enabled mobile payment system.

Technology acceptance model

The technology acceptance model was invented by Davis ( 1993 ) and investigated consumer attitudes and behavior toward adopting technology. The technology acceptance framework comprises two main exogenous constructs namely usefulness and ease of use. The perceived usefulness is the degree to wherein individuals perceive technology as useful and improves task performance (Ahn and Park, 2023 ; S. Singh et al., 2020b ). Therefore, ease of use is identified as the extent to wherein technology is perceived by users as easy and simple to use and enhances individual task performance (Davis et al., 1989 ). It is assumed that QR code mobile payment is new technology and hence usefulness and ease of use would encourage users to adopt mobile payment driven by QR code. Prior literature on information systems has long established that usefulness and ease of use influence consumer intention to adopt QR code-enabled payment systems (Cao et al., 2016 ; de Luna et al., 2019 ; Kaatz, 2020 ; Ooi and Tan, 2016 ; Shankar and Datta, 2018 ). Therefore, the following hypotheses are proposed:

H1: Perceived usefulness impacts consumer attitude towards QR code mobile payment .

H2: Perceived ease of use impacts consumer attitude towards QR code mobile payment .

Theory of reasoned action

The theory of reasoned action was introduced by Ajzen and Fishbein and has been widely used in information system studies (Ajzen and Fishbein, 1977 ). TRA determines consumer attitudes and subjective norms to adopt a specific technology (Flavian et al., 2020 ). Subjective norm is the extent that measures individual values and beliefs whether important people encourage or discourage them from adopting technology (Flavian et al., 2020 ). Nevertheless, the subjective norm in the QR code context is conceptualized as the degree wherein individual belief is affected by surrounding people’s opinions including family and peers, and brings the feeling that QR code-based payment is attractive and must be used for financial activities (Flavian et al., 2020 ; Shang et al., 2023 ; Wu and Gong, 2023 ). Prior studies have established the positive impact of the subjective norm in measuring consumer attitudes toward adopting mobile payment (Al Nawayseh, 2020 ; Baptista and Oliveira, 2015 ; Liu and Zheng, 2023 ). Therefore, the subjective norm is hypothesized as:

H3: Subjective norm is related to consumer attitude towards QR code-based mobile payment .

Perceived convenience and innovativeness

The role of perceived convenience and innovation is found critical in determining consumer behavior to adopt QR codes (Boden et al., 2020 ; Nguyen and Dao, 2024 ). Perceived convenience is the extent to wherein users believe that payment through a QR code is easy and convenient (Boden et al., 2020 ; Chen, 2008 ). It is argued that digital consumers expect the use of technology to be time-saving and convenient and hence they prefer to use innovative technology (Boden et al., 2020 ). The term innovativeness is the degree to wherein consumer perceives that technology is new and amusing (Liu and Zheng, 2023 ; Rahi and Abd Ghani, 2021 ). Therefore, it is assumed that innovation will motivate consumers to try new mobile technology which is QR code-enabled mobile payment (Boden et al., 2020 ; Kim et al., 2010 ). Both convenience and innovativeness have substantial support from literature to predict consumer behavior to adopt QR code-based mobile payment (Chen and Nath, 2008 ; Cowart et al., 2008 ; De Kerviler et al., 2016 ; Kim et al., 2010 ; Makki et al., 2016 ; Ooi and Tan, 2016 ; Rampton, 2017 ; Slade et al., 2015 ; Zhang et al., 2018 ). Therefore, convenience and innovativeness are hypothesized as:

H4: Convenience determines consumer attitude towards QR code mobile payment .

H5: Perceived innovativeness positively determines consumer attitude towards QR code-based mobile payment .

The main attractiveness of QR codes is speed during financial transactions. For instance, users do not need to login and they just scan and pay their bills by using a QR code (Yan et al., 2021 ). Therefore, the importance of transaction speed cannot be neglected in measuring user attitude and intention to adopt QR code-based payment. The term transaction speed is the extent to wherein consumer feels that the use of QR code mobile payment increases transaction speed that is not possible in traditional mobile payment methods (Chen, 2008 ; Yan et al., 2021 ). Therefore, it is assumed that transaction speed will boost consumer confidence resulting higher acceptance rate of QR code-enabled mobile payment. In literature prior studies have established that transaction speed is the core factor that influences consumer behavior and encourages to adoption of QR code mobile payment systems (Chen, 2008 ; Norton and Hall, 2006 ; Teo et al., 2015b ; Yang, 2009 ). Nevertheless, this study advances the body of knowledge and conceptualizes transaction speed as a moderating factor in the relationship between consumer attitude and intention to adopt QR code-enabled mobile payment. Therefore, the following hypotheses are proposed:

H6: Attitude towards QR code influences consumer intention to adopt QR code mobile payment .

H7: Transaction speed has a moderating effect on consumer attitude and intention to adopt QR code-based mobile payment .

After reviewing detailed literature this study has summarized that perceived usefulness, ease of use, subjective norm, transaction convenience, enjoyment, and innovativeness have a positive impact on consumer attitude and intention to adopt QR code-enabled mobile payment. In addition to that transaction speed has been conceptualized as a moderating factor in the relationship between consumer attitude and intention to adopt QR code-enabled mobile payment. The theoretical framework is exhibited in Fig. 1 .

Theoretical framework.

Methodology

Scale measurement.

Scale development is the process of retaining relevant research items for construct assessment (Rahi et al., 2019 ). Rahi et al. ( 2019 ) asserted that developing a new scale is not essential if theory exists. Therefore, in the current study scale exists and hence scale items are adopted from past studies and then adapted into the current research setting. Construct items of usefulness and ease of use were adopted from Yan et al. ( 2021 ) and Rahi et al. ( 2018 ). Scale items for subjective norms were adopted from Liébana-Cabanillas et al. ( 2015 ). Perceived convenience items were adopted from Teo et al. ( 2015a ). Moving further innovativeness items were adopted from Slade et al. ( 2015 ) and Rahi and Abd. Ghani ( 2018 ). Scale items for attitude were adopted from Rahi, Khan, et al. ( 2021a ). Scale items for intention to adopt QR code mobile payment were adopted from Yan et al. ( 2021 ) and (Rahi, 2022 ). Next to this transaction speed items were adopted from Yan et al. ( 2021 ) and Teo et al. ( 2015a ). Scale items are presented in Table 1 . Concerning with Likert scale type researchers have found substantial support against the 7-point Likert scale (Rahi et al., 2019 ; Rowley, 2014 ). Therefore, 7 point Likert scale is used wherein 7 indicates strongly agree and 1 represents strongly disagree.

Research approach, sample size calculation, and data collection

The current research investigates user behavior towards the adoption of quick response code-enabled mobile payment. Consequently, an integrated research framework is established and empirically investigated with numerical data. Nevertheless, before data collection, it is essential to select a population and adequate sample size. The population of this study is smart mobile phone users all around Saudi Arabia. Therefore, sample computation is done with the prior power method (Rahi, 2018 ; Rahi, 2023 ). The priori power method estimates sample size through predictors. There are seven predictors in the research framework and requires a sample of 153 respondents for empirical analysis. The face validity issue is addressed through a pilot study (Hair et al., 2015 ). For the pilot survey, 20 smart mobile phone users were approached physically and requested to fill out the questionnaire. Overall, respondents have faced no difficulty in filling out the questionnaire, however, they have suggested adding preliminary questions to screen out relevant respondents. Thus, a preliminary question is added to the main research questionnaire having contents that whether respondents are smartphone users or not. The purpose of this preliminary question is to identify relevant respondents. Thus, respondents having smart mobile phones were allowed to participate in the research survey.

Concerning with sampling approach researcher has selected a purposive sampling approach for data collection (Hair et al., 2015 ). According to Samar Rahi ( 2017a , 2017b ) purposive sampling approach could be selected if the objective of the research is to collect data only from selective respondents. As the population of this study is smartphone mobile banking users therefore purposive sampling approach was most appropriate. For data collection, a research questionnaire was distributed among 243 mobile banking users who requested to fill out the survey questionnaire. These respondents were approached by visiting different retail stores located in Jeddah city of Saudi Arabia. As the research design of this study is based on cross-sectional thus data were collected at once. In addition to that participation in this research survey was voluntary instead of mandatory. Among 243 respondents 216 respondents participated in the QR code mobile payment research survey with an attractive response rate of 88%. Thus, these numerical responses were further analyzed with a structural equation model.

A descriptive analysis is conducted to disclose the respondent’s characteristics. In the data set, there are 69% male therefore 31% of respondents are counted as female participants. Respondent’s age is measured and descriptive analysis has revealed that a total of 135 respondents are aged between 21 to 30 years. Therefore, a total of 35 respondents were found aged between 31 to 40 years. Next 36 respondents are found aged between 41 to 50 years. Therefore, only 10 respondents are found aged between 51 to 60 years. Aside from age comparison education level of the respondents is measured following three categories including high school education, i.e., equivalent to 10th grade, graduation level, i.e., equivalent to 14th years of education and master level of education, i.e., equivalent to 16 years of education. Results indicate that 42 respondents have a high school education. Therefore, 78 respondents have shown graduation level of study. Similarly, 96 respondents have shown master-level education and participated in the research survey.

Data analysis

Addressing common method biases issue.

The quantitative research that is based on a research survey may be affected due to common method issues. In addition to that in this study structured survey questionnaire has been used for data collection. Nevertheless, authors like Hair et al. ( 2015 ) have stated that common method variance issues could arise if data have been collected at one point in time against all predictor and criterion variables. Thus, the CMV issue is addressed through procedural and statistical remedies. Following procedural remedies questionnaires were jumbled up and then distributed among respondents. Therefore, among statistical remedies common method variance bias is assessed through Harman’s single-factor solution (Fornell and Larcker, 1981 ; Rahi, 2022 ). Harman’s single-factor solution suggests that the value of the first un-rotated factor must be less than 40% (Fornell and Larcker, 1981 ). Result of the Harman’s single-factor solution suggests that the value of the first un-rotated factor was 11% less than 40% and hence confirmed the validity of the data.

Structural equation modeling

The structural equation modeling approach is incorporated for data estimation. This approach is based on two stages namely structural model and measurement model. Initially, data were analyzed with a measurement model and established factors reliability, indicator reliability, discriminant, and convergent validity of the instruments. Therefore, in the second stage hypotheses were tested. For data estimation, Smart-PLS software v.3.39 has been used (Rahi et al., 2022 ).

Measurement model

In measurement model estimation indicator reliability was assessed first following the criterion that the loading of the indicator must be higher than 0.60 (Rahi, 2018 ; Rahi et al., 2022 ). Therefore, factor reliability was tested with composite reliability and Cronbach alpha following the criterion that the value of CR and CA must be greater than 0.70 (Podsakoff et al., 2003 ; Rahi et al., 2022 ). Similarly, convergent validity is assessed with average variance extracted following a threshold value of 0.50 (S. Rahi, 2017a , 2017b ; Rahi et al., 2022 ). Table 1 exhibits the results of the measurement model.

Another analysis in the measurement model is identified by Fornell and Larcker which measures the discriminant validity of the factors (Podsakoff et al., 2003 ; M. Yamin, 2020a ). Discriminant validity is established that constructs measure distinct concepts and discriminant. Therefore, the Fornell and Larcker analysis is employed to test discriminant validity (Fornell and Larcker, 1981 ; M. Yamin, 2020a ). It is suggested that for adequate discriminant validity square root of AVE must be higher in the correlation table (Fornell and Larcker, 1981 ; M. A. Y. Yamin, 2020b ). Results of the Fornell and Larcker analysis are shown in Table 2 indicating satisfactory discriminant validity of the factors.

The cross-loading method is an alternative method to measure the discriminant validity of the constructs. This method has suggested that the loading of the indicator must be higher when comparing corresponding construct indicator loadings (Fornell, 1992 ; Yamin, 2019 ). Table 3 depicts that cross-loadings are higher and demonstrates that constructs are discriminant and measure distinct concepts.

Measuring the discriminant validity of the constructs is critical and therefore Heterotrait-Monotrait ratio analysis is recommended to measure the discriminant validity of the constructs (Kline, 2011 ). According to Kline ( 2011 ), cross-loading and Fornell and Larcker analysis have shown some deficiencies and hence HTMT is the most appropriate analysis to be taken into consideration. This analysis suggests that values of HTMT ratios must be ≤0.90 indicating adequate discriminant validity of the constructs (Gold et al., 2001 ; Kline, 2011 ). The results of the HTMT ratio are shown in Table 4 .

Structural assessment

The structural model tests the hypotheses relationship using the bootstrapping method (Hair Jr et al., 2016 ). Nevertheless, prior to structural model assessment multi-collinearity issue is addressed through the variance inflation factor (VIF). Results of the VIF analysis indicate that values of the VIF were lower than the threshold value, i.e., 3.3 hence confirming that multi-collinearity is not likely an issue in this study. Finally, data were bootstrapped for structural assessment. Results of the bootstrapping have revealed values of path coefficient, t -statistics, standard error, and significance level. The findings of the hypotheses analysis are shown in Table 5 .

Table 5 demonstrates that perceived usefulness has a positive impact on user attitude and statistically confirmed H1: β  = 0.158 path, significance p  = 0.005 and t -statistics of 2.901. Therefore, perceived ease of use has a positive impact on user attitude and is supported by β  = 0.059 path, significance p  = 0.046, and t -statistics of = 1.790, and hence H2 is confirmed. Concerning subjective norms results have shown that subjective norms have a positive impact on user attitude and are statistically backed by β  = 0.149 path coefficient, significance level p = 0.000, and t -statistics of 4.969. Perceived convenience has shown a positive impact on user attitude to adopt QR code-enabled payment and is supported by H4: β  = 0.493 path, significance p  = 0.000, and t -statistics of 15.306. Innovativeness has shown a significant influence on user attitude and hence confirmed H5: β  = 0.104 path coefficient, significance level p  = 0.002, and t -values of 3.236. Consumer attitude has shown a positive influence on user behavioral intention to adopt QR code mobile payment and is supported by H6: β  = 0.668 path, significance p = 0.000, and t -statistics of 18.515. These results are shown in Appendix 1 with path coefficient and significance level. Overall, results have shown a positive influence of exogenous factors in measuring endogenous factors. The variance explained by exogenous factors in measuring endogenous factors and the effect size of the factors is given in the following section.

Factors affect size, predictive power, and R 2

The structural model assessment has established the impact of exogenous factors in determining endogenous variables. Nevertheless, the variance explained by these factors was assessed with the coefficient of determination R 2 . Results indicate that user attitude is jointly measured by perceived usefulness, perceived ease of use, convenience, subjective norms, and innovativeness and explained substantial variance R 2 52.3% in measuring user attitude to adopt QR code-enabled mobile payment. Therefore, user intention to adopt QR code-enabled mobile payment is measured by transaction speed and attitude and explains a large variance in user intention R 2 55% hence confirming the validity of the research model. Next to this effect size analysis is incorporated to disclose the impact of each factor in measuring user attitude and intention. Results indicate that perceived convenience has a substantial effect size in measuring user attitude when compared with other exogenous factors. On the flip side, the attitude has shown a large effect size in measuring user intention to adopt QR-enabled mobile payment. Nevertheless, transaction speed has shown a small effect size when compared with attitude. Finally, predictive power was tested with Q 2 as recommended by earlier studies (Rahi and Abd Ghani, 2021 ; M. Yamin, 2020a ). Results as depicted in Table 6 revealed substantial predictive power to measure consumer attitude Q 2 37.3% and intention to adopt QR-enabled mobile payment Q 2 41.4%. Therefore, it is confirmed that the research model is theoretically and statistically valid to measure consumer attitude and intention to adopt QR-enabled mobile payment.

Importance-performance analysis

The current study has analyzed data with importance-performance analysis to reveal the importance and performance of the factors. Rahi ( 2022 ) stated that before incorporating IPMA analysis it is essential to select a single outcome variable. Consistently user intention to adopt QR mobile payment is selected as an outcome variable. Results of the IPMA analysis revealed that user attitude has the highest importance in determining mobile banking users’ intention to adopt QR-enabled payment. Therefore, perceived convenience is found second most important factor in measuring user intention. Moving further transaction speed is found third most important factor to determine user intention. In addition to that the importance of perceived usefulness and subjective norm was also notable. However, factors like ease of use and innovativeness have shown less importance and hence weak influence on user intention to adopt QR-enabled mobile payment. Table 7 comprises the outcome of the IPMA analysis.

Aside from statistical values trend of the importance and performance is tested with an importance-performance map. IPMA map as shown in Fig. 2 indicates that factors like perceived convenience, transaction speed, subjective norm, and perceived usefulness are the most influential factors in determining user attitude and intention to adopt QR-enabled mobile payment. Therefore, policymakers should pay attention to perceived convenience, transaction speed, subjective norm, and perceived usefulness to boost user attitude which in turn will enhance consumer intention to adopt QR-enabled payment.

IPMA analysis map.

Transaction speed

As this study discusses QR code-enabled mobile payment consequently, speed is being considered as the main factor that influences user attitude and intention to adopt mobile payment derived by quick response code. As a result, the moderating effect of transaction speed is tested between consumer attitude and intention to adopt QR code-enabled payment. The moderating analysis is calculated with a product indicator approach (Rahi, 2022 ). Findings of the moderating analysis are exhibited in Fig. 3 demonstrating significant values of path coefficient β  = 0.138, adequate significance p  < 0.05, and t -statistics 3.995 and hence established H7.

Moderating effect of transaction speed.

Although statistical findings have established that transaction speed positively moderates the relationship between user attitude and intention to adopt QR code-enabled mobile payment nevertheless, the power of the moderating effect is analyzed with a simple slope graph. A simple slope graph basically displays the trend of the relationship at positive and negative gradients. A simple slope graph given in Fig. 4 illustrates that TRS at +1 SD is sharply moving upwards when compared with a negative trend at TRS at -1SD. This trend explains that with transaction speed user attitude and intention will be higher towards the adoption of QR code-enabled mobile payment.

Trend of the moderating effect.

The quick response code-enabled mobile payments have revolutionized the fintech industry around the globe (Yuan et al., 2023 ). Now consumers don’t need to remember long passwords and they can pay by scanning a QR code anywhere in the world. Despite the widespread use of mobile devices, the adoption of QR code-enabled mobile payment systems is limited among the Saudi population (Bhat et al., 2023 ). Therefore, factors underpinned theory of reasoned action and the technology acceptance model were conceptualized to investigate user attitudes and intentions to adopt QR code-enabled mobile payment. Underpinned factors have shown a positive impact in predicting consumer attitude and behavioral intention to adopt QR code-enabled payment. Refereeing to technology acceptance model results have confirmed that both perceived usefulness and ease of use have a positive influence on user attitude and are consistent with prior studies (de Luna et al., 2019 ; Shankar and Jebarajakirthy, 2019 ). Concerning with factors underpinned theory of reasoned action results have confirmed that the positive impact of subjective norms in determining consumer attitude and consistent with prior studies (Al Nawayseh, 2020 ; Baptista and Oliveira, 2015 ). This study has added some additional factors namely convenience and innovativeness to determine consumer attitude and behavioral intention. Therefore, statistical findings have confirmed that perceived convenience positively impacts consumer attitude and is consistent with prior studies (Boden et al., 2020 ; Chen, 2008 ). Likewise, innovativeness is positively related to consumer attitude and is similar to prior research findings (Boden et al., 2020 ; Kim et al., 2010 ). Aside from predicting consumer attitude, this research has tested the impact of consumer attitude toward behavioral intention to adopt QR code-enabled mobile payment. Findings have confirmed that consumer attitude positively impacts consumer behavioral intention to adopt QR code-driven mobile payment hence supporting to argument developed by prior studies (Chen, 2008 ; Yan et al., 2021 ).

Although the newly developed integrative model has confirmed statistical validity to measure consumer attitude however the impact of underpinned factors was tested with a coefficient of determination and effect size analysis. Overall, the research framework demonstrates that consumer attitude is jointly measured by perceived usefulness, perceived ease of use, convenience, subjective norms, and innovativeness and explained substantial variance R 2 52.3% in measuring user attitude to adopt QR code-enabled mobile payment which is higher than previous study (Yan et al., 2021 ). In the extended model consumer intention to adopt QR code-enabled mobile payment is measured by transaction speed and attitude and explained by a large variance in consumer intention R 2 55%, i.e., greater than prior studies (Chang et al., 2021 ; Yan et al., 2021 ). Similarly, effect size f 2 analysis has revealed a substantial impact of perceived convenience in measuring consumer attitude. These findings indicate that convenience is the most important factor behind the selection of a QR code-based payment option. Nevertheless, this study has also revealed that if the objective is to determine consumer behavioral intention to adopt QR code payment it is mandatory to have a positive attitude. This is also confirmed with effect size analysis wherein results have revealed that consumer attitude has a substantial impact in measuring consumer intention to adopt a QR code payment system. The following sub-section demonstrates the theoretical and practical contributions of this study.

Contribution to theory and method

In terms of theoretical contributions this study has developed an integrative research model with the help of the theory of reasoned action and technology acceptance model and hence contributes to information system literature. In addition to that this study has outlined innovativeness as exogenous factor in the research model and examined consumer attitudes to adopt QR codes and hence enrich information systems in the context of innovativeness. Similarly, examining perceived convenience impact to investigate consumer attitude also contributes to the literature. Aside from the direct relationship, this study has confirmed the moderating impact of transaction speed between consumer attitude and intention to adopt QR code-enabled payment and added a new dimension to the research model. This study widely contributes to methods. For instance, the research methodology is designed under the positivist paradigm. Additionally, a survey was administered at a large scale, and over 216 respondents participated in this research. These responses were analyzed with the structural equation modeling approach which is the latest statistical approach for financial analysis.

Contribution to practice

Practically this study directs that policymakers should pay attention to factors underpinned theory of reasoned action, the technology acceptance model, perceived convenience, and innovativeness. More precisely this study recommends that perceived convenience is the most important factor in determining consumer attitude to accept QR-based payment. Nevertheless, a unified perspective is taken from the importance-performance analysis. Results of the importance-performance analysis indicate that factors like perceived convenience, transaction speed, subjective norm, and perceived usefulness are the most influential factors in determining user attitude and intention to adopt QR-enabled mobile payment. Consequently, policymakers should pay attention to perceived convenience, transaction speed, subjective norm, and perceived usefulness to boost user attitude and intention to adopt QR-enabled mobile payment. This study has also concluded that transaction speed moderates the relationship between consumer attitude and behavioral intention to adopt QR code-enabled mobile payment. Therefore, if policymakers highlight the transaction speed character of the QR code it will impact positively both consumer attitude and intention to adopt a QR code-based payment system.

The current research develops an integrated research framework that combines the technology acceptance model, theory of reasoned action, transaction speed, convenience, and innovativeness to investigate consumer behavior toward the adoption of a QR code mobile payment system. For empirical investigation, responses were collected through a research survey that was administered to smart mobile phone users. Results of this study have concluded that perceived usefulness, perceived ease of use, convenience, subjective norms, and innovativeness positively impact consumer attitude and explained a substantial variance \({R}^{2}\) 52.3% in determining user attitude to adopt QR code-enabled mobile payment. Moreover, consumer intention to adopt QR code-enabled mobile payment is predicted by transaction speed and consumer attitude and disclosed a large variance \({R}^{2}\) 55% in consumer behavioral intention to adopt QR code mobile payment. Aside from the overall impact of exogenous factors in predicting endogenous factor effect size analysis was conducted. Similarly, effect size f 2 analysis has revealed the substantial effect of perceived convenience in determining consumer attitude to adopt QR code-driven payment. The model is further extended with the moderating effect of transaction speed and reveals a significant moderating effect between consumer attitude and intention to adopt QR code-driven mobile payment. Theoretically, this study has developed an integrative research model with the help of the theory of reasoned action and technology acceptance model and hence contributes to information system literature. Therefore, for practitioners, this study suggests that policymakers should pay attention to perceived convenience, transaction speed, subjective norm, and perceived usefulness to boost consumer attitude and intention to adopt QR-enabled mobile payment. Moreover, this study has suggested if policymakers visualize the transaction speed character of QR codes it will impact positively on consumer attitude and behavior to adopt QR code-based payment. To conclude this research provides useful findings to policymakers to understand factors that influence consumer behavior to adopt quick response code mobile payment systems. Quick response code is an emerging technology and hence it will bring transformation in the Saudi fintech industry.

Limitations and future research directions

This research has some limitations and therefore it is important to acknowledge these limitations for future research directions. First, the integrative research model has combined the theory of reasoned action and the technology acceptance model altogether to investigate consumer intention to adopt QR code-based mobile payment. However, this study has not claimed to include all factors that impact consumer attitudes and behavioral intention to adopt QR code-enabled mobile payment. Therefore, future researchers are suggested to extend the current research model with service quality factors such as system quality, and information quality. Second, this study has outlined innovativeness as a single factor to investigate consumer attitudes toward adopting QR code-based payment. Aside from innovativeness diffusion of innovation theory also comprises compatibility. Therefore, future researchers may add compatibility in the current research framework to understand how it impacts consumer attitudes and intention to adopt QR code-based payment. Similarly, the perceived convenience factor has been extracted from self-determination theory. Nevertheless adding some other factors underpinned by self-determination could strengthen the research framework. Moreover, security is identified as an important concern for technology users. Therefore, future researchers are suggested to extend the current research model with perceived security to get deep insight into consumer behavior toward the adoption of QR codes. Another limitation of this study is that it collects data at one point in time and is based on a cross-sectional research design. Nevertheless, future researchers may investigate consumer adoption behavior under a longitudinal research design. “Descriptive analysis has shown that the majority of the respondents were young and expected that young people would have more incline towards adoption of innovative technology when compared with old age people. Thus, future researchers are suggested to examine the behavior of old age people and observe their attitude towards the adoption of QR code-enabled mobile payment. Another limitation of this research is to assess data biases through Harman’s single-factor analysis. However, testing common method bias through marker variables could reveal robust results. Finally, this study is conducted in a developing country context like Saudi Arabia. Nevertheless, a comparative study may be conducted to reveal how consumer attitudes and behavior toward the adoption of QR codes vary in different regions.

Data availability

Data used for this study are publicly available at the following link: https://github.com/mohammadyamin1978/data .

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Acknowledgements

This work was funded by the University of Jeddah, Jeddah, Saudi Arabia under grant No. (UJ-23-SHR-65). Therefore, the authors thank the University of Jeddah for its technical and financial support.

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Yamin MAY and Abdalatif OAA: conceived and designed the study. Abdalatif OAA: conducted the experiments and collected the data. Yamin MAY: analyzed and interpreted the data. All authors critically revised the manuscript for important intellectual content and approved the final version for publication.

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Yamin, M.A.Y., Abdalatif, O.A.A. Examining consumer behavior towards adoption of quick response code mobile payment systems: transforming mobile payment in the fintech industry. Humanit Soc Sci Commun 11 , 675 (2024). https://doi.org/10.1057/s41599-024-03189-w

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DOI : https://doi.org/10.1057/s41599-024-03189-w

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