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This study aims to determine the effect of bank ownership on the credit risk of Indonesian Islamic local banks (ILBs).

This study uses the system generalized method of moments (GMM) estimation technique with a sample of 155 Islamic local banks in Indonesia from 2012 to 2019.

The results show that commissioner board (D.COW) ownership has a negative effect on credit risk. This indicates that an increase in the number of shares of Islamic local banks owned by the commissioner board reduces credit risk. On the other hand, government ownership (D.GOW), the Sharia supervisory board (D.SOW) and the director board (D.DOW) do not affect credit risk.

The government, Sharia supervisory board and director board need opportunities to easily own more Islamic local bank shares. Therefore, the provisions regarding the share ownership rights of the government, Sharia supervisory board and director board need to be improved to increase their role in reducing credit risk.

Previous researchers have not studied the effect of government ownership, the commissioner board, the Sharia supervisory board and the ownership of directors on credit risk at the ILB in Indonesia.

This study investigates the association between debt overhang and carbon emissions (both direct and indirect emissions) using a sample of US publicly listed firms.

The study applies generalized least squares (GLS) regression analyses to a sample of 2,043 US firm-year observations over a period of 14 years from 2007 to 2020. The methods include contemporaneous effect, lagged effect, alternative measures of carbon emissions and debt overhang, intensive versus non-intensive analysis, channel analysis, firm fixed effects, change analysis, controlling for credit rating analysis, propensity score matching approach, instrumental variable analysis with industry and year fixed effect.

This study's findings reveal that the debt overhang problem increases carbon emissions. This finding holds when the authors use alternative measures of carbon emissions and debt overhang. The authors find that carbon abatement investment is a channel that is negatively impacted by debt overhang, which in turn increases carbon emissions. This study's results are robust for several endogeneity tests, including firm fixed effects, change analysis, propensity score matching approach and two-stage least squares (2SLS) instrumental variable analysis.

The outcome of this research has policy implications for several stakeholders, including investors, firms, market participants and regulators. This study's findings offer insights for investors and firms, helping them allocate resources effectively and make financing decisions aimed at reducing carbon emissions. Regulators and policymakers can also use the findings to formulate policies that promote alternative sustainable finance practices.

The outcome of this research is likely to help firms develop their understanding of the debt overhang problem and undertake strategies that yield a significant amount of funding to invest in reducing carbon emissions.

Corporate social responsibility (CSR) and fintech have emerged as critical megatrends in the banking industry. This study aims to examine the impact of financial technology on the relationship between CSR and banks' financial stability. Specifically, it investigates the moderating effect of fintech on the association between CSR and the financial stability of conventional banks operating in Qatar, UAE, Saudi Arabia, Kuwait, Bahrain, Jordan, Pakistan and Turkey from 2010 to 2021.

To achieve the authors’ objective, the authors apply Baron and Kenny's three-link model, tested with fixed and random effects regression models.

The results reveal that the development of fintech decreases banks' financial stability, whereas it promotes banks' involvement in CSR strategies. Furthermore, the findings indicate that fintech plays a moderating role in the relationship between CSR and financial stability. It positively moderates the impact of CSR on financial stability. The robustness analysis highlights the mutual reinforcement of fintech and CSR dimensions in improving the financial stability of banks. Thus, by fostering community and product responsibility, fintech could enhance the financial stability of banks.

Finally, the authors recommend that banks focus more on developing technological and environmentally friendly financial products.

This study contributes significantly by providing valuable insights for managers and policymakers seeking to improve banks' financial stability through the simultaneous adoption of new financial technology products and the strong commitment to CSR practices.

The credit of agricultural product quality and safety reflects the ability of the main actors involved in the supply chain to provide reliable agricultural products to consumers. To fundamentally solve the problem of agricultural product quality and safety, it is worth studying how to make the credit awareness and integrity self-discipline of the supply chain agriculture-related subjects strengthened and the role and value of credit supervision given full play. Starting from the application of blockchain in the agricultural product supply chain, this paper aims to investigate the main factors affecting the credit regulation of agricultural product quality.

Using the DEMATEL-ISM (decision-making trial and evaluation laboratory–interpretative structural modeling) method, we analyze the credit influencing factors of agricultural quality and safety empowered by blockchain technology, find the causal relationship between the crucial influencing factors and deeply explore the hierarchical transmission relationship between the influencing factors. Then, the path analysis in structural equation modeling is utilized to verify and measure the significance and effect value of the transmission relationship among the crucial influencing factors of credit regulation.

The results show that the quality and safety credit regulation of agricultural products is influenced by a combination of direct and deep influencing factors. Long-term stable cooperative relationship, Quality and safety credit evaluation, Supply chain risk control ability, Quality and safety testing, Constraints of the smart contract are the main influence path of blockchain embedded in agricultural product supply chain quality and safety credit supervision.

Credit supervision is an important means to improve the ability and level of social governance and standardize the market order. From the perspective of blockchain embedded in the agricultural supply chain, the regulatory body is transformed from the product body to the supply chain body. Take the credit supervision of supply chain subjects as the basis of agricultural product quality supervision. With the help of blockchain technology to improve the effectiveness of agricultural product quality and safety credit supervision, credit supervision is used to constrain and incentivize the behavior of agricultural subjects.

The world order is experiencing unremitting changes. With this, the national governance of emerging economies is also becoming robust. Therefore, the current study examines the efficacy of national governance in the context of emerging economies by investigating its effects on the profitability of the microfinancing sector. Further, the study inspects if national governance mitigates the impact of credit risks to protect profitability.

The study considers panel data from 224 microfinancing institutions from five economies of world importance: Brazil, Russia, India, China and South Africa (BRICS). The study uses dynamic panel data modeling, particularly the generalized method of moments, alongside multiple univariate and multivariate techniques.

The findings indicate that credit risks negatively impact profitability. In addition, the study documents a significant positive linkage between national governance and profitability. However, national governance fails to restrict the adverse effects of credit risks. National governance is found to be effective in reducing internal agency problems; the monitoring effects successfully limit the moral hazards due to managers' actions. Conversely, the national governance in these economies misses the mark in regulating the moral hazards due to borrowers' behavior.

The current study provides fresh perspectives on the efficacy of national governance in microfinancing in the setting of emerging economies.

Consumer culture is promoting immediate gratification, and the rise of digital financial services is increasing the risk of indebtedness while debt reduces well-being and affects mental health. The authors assess the effects of consumer information provision, debt literacy, chronic debt and attitudes toward debt on the intent to purchase on credit.

An online survey including an experiment with a credit offer vignette was conducted in a representative sample of Estonia (n = 1204). Treatment conditions depicted either the total cost and duration of the credit agreement or the annual percentage rate.

Receiving modified information resulted in a 26 to 30 percentage points decrease in propensity to purchase on credit. Purchasing on credit was associated with attitudes towards credit and chronic debt, but not with debt literacy.

The findings reveal large effects of information provision and highlight the limited effects of debt literacy on credit decisions. Limitations may emerge from differences in financial regulation across countries.

The authors' results highlight the importance of applying behavioural insights in consumer credit information provision, both in the financial sector and policy. Testing the messages allows having evidence-based solutions that promote responsible purchasing on credit.

The findings call for changes in credit information provision requirements. Their effect is significantly larger compared to the literature, emphasizing the role of credit information provision in less regulated online markets.

This work examines the impact of institutional agricultural credit on crop productivity of some major crops such as paddy, cotton, wheat and pulses for small and marginal farmers across various social groups.

The cross-sectional field data on socio economic variables was collected from three Indian states from about 400 small and marginal farmers across various social groups using multi-stage stratified random and purposive sampling through a structured questionnaire by interviewing. The method of propensity score matching (PSM) was employed to calculate average treatment effect (ATE) and average treatment effect on the treated (ATET) by categorising sample farmers as treatment group and control group where crop productivity was considered as outcome variable and access to institutional credit was considered as treatment variable.

The PSM estimates reveal that ATE and ATET for all the selected crops are found to be significantly higher for the treated group vis-à-vis non-treated group suggesting that institutional agricultural credit has a statistically and significant positive impact on the crop productivity.

Similar study can be extended for more crops and across regions in India for a universal coverage.

The agricultural credit policy of India has been to increase the access and availability of institutional farm credit. This has led to in general increase in the flow of formal farm credit to agricultural sector. However, the impact of institutional credit and crop productivity especially for small and marginal farmers across social groups is not well recognized in India using field data. Accordingly, this field data study contributes to the existing research by providing fresh evidence from field across social groups for both kharif and rabi crops using recent survey data from small and marginal farmers which has important policy implications.

With the rapid growth of the domestic lending industry, assessing whether the borrower of each loan is at risk of default is a pressing issue for financial institutions. Although there are some models that can handle such problems well, there are still some shortcomings in some aspects. The purpose of this paper is to improve the accuracy of credit assessment models.

In this paper, three different stages are used to improve the classification performance of LSTM, so that financial institutions can more accurately identify borrowers at risk of default. The first approach is to use the K-Means-SMOTE algorithm to eliminate the imbalance within the class. In the second step, ResNet is used for feature extraction, and then two-layer LSTM is used for learning to strengthen the ability of neural networks to mine and utilize deep information. Finally, the model performance is improved by using the IDWPSO algorithm for optimization when debugging the neural network.

On two unbalanced datasets (category ratios of 700:1 and 3:1 respectively), the multi-stage improved model was compared with ten other models using accuracy, precision, specificity, recall, G-measure, F-measure and the nonparametric Wilcoxon test. It was demonstrated that the multi-stage improved model showed a more significant advantage in evaluating the imbalanced credit dataset.

In this paper, the parameters of the ResNet-LSTM hybrid neural network, which can fully mine and utilize the deep information, are tuned by an innovative intelligent optimization algorithm to strengthen the classification performance of the model.

This study aims to test the validity of the Rajan theory in South Africa and other selected emerging markets (Chile, Peru and Brazil) during the period 1975–2019.

In this study, the researchers used time-series data to estimate a Bayesian Vector Autoregression (BVAR) model with hierarchical priors. The BVAR technique has the advantage of being able to accommodate a wide cross-section of variables without running out of degrees of freedom. It is also able to deal with dense parameterization by imposing structure on model coefficients via prior information and optimal choice of the degree of formativeness.

The results for all countries except Peru confirmed the Rajan hypotheses, indicating that inequality contributes to high indebtedness, resulting in financial fragility. However, for Peru, this study finds it contradicts the theory. This study controlled for monetary policy shock and found the results differing country-specific.

The findings suggest that an escalating level of inequality leads to financial fragility, which implies that policymakers ought to be cautious of excessive inequality when endeavouring to contain the risk of financial fragility, by implementing sound structural reform policies that aim to attract investments consistent with job creation, development and growth in these countries. Policymakers should also be cautious when implementing policy tools (redistributive policies, a sound monetary policy), as they seem to increase the risk of excessive credit growth and financial fragility, and they need to treat income inequality as an important factor relevant to macroeconomic aggregates and financial fragility.

This study examines the impact of agricultural credit on rice farmers' technical efficiency (TE) in Côte d'Ivoire by considering the heterogeneity among credit sources.

A multistage sampling technique was used to collect data from 588 randomly sampled rice farmers in seven rice areas of the country. The authors use the endogenous stochastic frontier production (ESFP) model to account for the endogeneity of access to agricultural credit.

On the one hand, agricultural credit has a significant and positive impact on rice farmers' TE. Rice farmers receiving agricultural credit have an average of 5% increase in their TE, confirming the positive impact of agricultural credit on TE. On the other hand, the study provides evidence that the impact of credit on rice production efficiency differs depending on the source of credit. Borrowing from agricultural cooperatives and paddy rice buyers/processors positively and significantly influences the TE, while borrowing from microfinance institutions (MFIs) negatively and significantly influences the TE. Moreover, borrowing from relatives/friends does not significantly influence TE.

Future research can further explore the contribution of agricultural credit by including several agricultural productions and using panel data.

The study provides evidence that the impact of agricultural credit on agricultural production efficiency depends on the source of credit. This study contributes to the literature on the impact of agricultural credit and enlightens policymakers in the design of agricultural credit models in developing countries, particularly Côte d'Ivoire.