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To improve the loan repayment performance (LRP) of microfinance banks (MFBs) in Pakistan, this study aims to look at the direct impact of multiple borrowing (MB) on LRP and client-business performance (CBP), as well as the direct impact of CBP on LRP. The moderating function of pandemic factors in the relationship between MB and CBP, as well as the mediating effect of CBP in the association between MB and LRP, was also investigated in this study.

A questionnaire was used to obtain data from 531 lower-level workers of microfinance institutions (MFIs) for the study. The respondents were chosen using stratified sampling, which divided the target population into four influential groups: lending officers in agriculture, lending officers in businesses, lending officers in gold loans and lending officers in salary loans. In this study, a two-stage structural equation modeling approach was used, including a measurement model (outer model) and a structural model (inner model). The validity and reliability of the questionnaire were investigated using the measurement model (outer model), whereas PLS-SEM bootstrapping was performed to test the hypothesis and find the relationship among different underpinning constructs by using the structural model (inner model).

The outcomes of this study demonstrate that MB has a direct impact on CBP, and that CBP has a direct impact on LRP. MB, on the contrary, had no direct and significant impact on LRP in this study. The idea that CBP mediates the relationship between MB and LRP, as well as the moderating effect of pandemic factors on the relationship between MB and CBP, is supported by this research.

Until now, the influence of MB on LRP via the mediating role of CBP and the moderating role of a pandemic factor in the setting of Pakistani MFBs has received little attention. During the COVID-19 pandemic, this research also aids MFBs in better understanding MB and its impact on LRP. Furthermore, based on the findings of this study, Pakistani MFIs can enhance their LRP by implementing new lending regulations, particularly with reference to MB and the COVID-19 pandemic.

This paper explores the effectiveness of using blockchain technology to solve financial constraints faced by small- and medium-sized suppliers in a capital-constrained supply chain.

To characterize the impact of blockchain on credit period and enterprise credit level, the study formulates a newsvendor model to analyze a supply chain in which a financially constrained supplier sells products to a financially sound manufacturer, subject to uncertain demand. The study investigates three repayment methods: the benchmark case without blockchain and two blockchain-enabled cases with the hybrid repayment mode and single repayment mode (SRM), respectively. The study derives and compares the equilibria under each repayment method to characterize their impact.

When the bank interest rate is low and the carbon cap is also low, choosing to implement blockchain technology leads to higher profitability for the manufacturer than not utilizing it. Within the framework of blockchain technology, when comparing the two repayment models, the manufacturer exhibits a preference for SRM. Furthermore, under specific conditions of the bank interest rate, blockchain technology can effectively facilitate consensus among supply chain members in terms of channel selection.

The results derived in this paper provide novel managerial implications to the capital-constrained members in terms of pricing decisions and order quantity under demand uncertainty considering blockchain technology, which transfers the creditor's rights to the bank and shortens the collection time. In addition, blockchain technology enables efficient and intelligent collaborative development of supply chains, which can reduce carbon emissions during the transportation of goods.

Few studies incorporate blockchain technology into supply chain finance, and this paper considers the credit period and capital's time value for a capital-constrained supplier facing the adoption of blockchain technology within a stochastic demand environment.

Providing loans, particularly to small-scale farmers, is one of the roles of formal financial institutions. Lending to small farmers is risky. An institution's health is closely related to the institution's ability to manage credit and portfolio risk. Expanding smallholder farmers' access to finance while maintaining a sustainable financial system is essential; however, pandemics present additional challenges. Accordingly, as reported in the literature, the pandemic's high loan default rates and decreases in return on assets (ROAs) call for further credit risk management research. There have been limited studies on credit risk management during coronavirus disease 2019 (COVID-19), so this article aims to provide useful information on its influences.

Researchers used data from formal financial institutions in 2018 (before COVID-19) and in 2021 (during COVID-19) to accomplish the study's broad objective. Descriptive and inferential statistics were the main analytical tools. The credit risk management indicators were categorized into collateral management, loan management, loan recovery management, governance and Information and Communication Technology (ICT). Weights were assigned to each category based on the importance to credit risk management. A binary logit model was employed in assessing the factors influencing credit risk management as proxied to loan repayment, while Ordinary Least Square (OLS) was used to examine factors that influence ROAs.

One of the most noteworthy findings is that credit risk management is affected by different factors and magnitudes before and during the COVID-19 era. Loan recovery and ICT management indicators were most influential during the pandemic. In addition, the study noted that low agricultural productivity during the pandemic contributed to an additional challenge in loan default rates because of various COVID-19-containing measures. Additionally, there was a lack of governance and ICT management capacity to drive credit and portfolio risk management during the epidemic.

The paper presents new empirical findings on credit risk management during the COVID-19 era. The study used a methodology which has not been used previously in credit risk management in Nigerian financial institutions. Therefore, this research could become the cornerstone of further academic research in other developing countries using this methodology.

This study investigates important factors that should be used by lenders in risk‐rating their farm customers. These factors predict actual farm performance and debt repayment ability. Linear and logistic regression models are used to identify the debt‐to‐asset ratio as a major predictor of repayment ability. In addition, the rate of asset turnover and family living expenses are strong predictors of farm performance. The results are tested over several time periods to verify the robustness of the predictors.

US decoupled direct payments, paid to farm operators based on historic yields and base acreage under the 2002 Farm Bill, may alter a farmer's access to credit or his ability to meet debt servicing obligations. More specifically, direct payments might improve the farmer's liquidity position or repayment capacity enabling the farmer to obtain more favorable credit terms. In turn, more favorable credit terms might allow a farm to remain in business or expand production, leading to current production distortions. Since direct payments are based on historic production, beginning farmers tend to receive lower levels of direct payments and hence these payments might impact beginning farmers differently than more experienced farmers. The purpose of this paper is to investigate the effects of direct payments on liquidity and repayment capacity for experienced and beginning farmers.

Given the manner in which direct payments are calculated and administered, it is likely that direct payments affect beginning farmers and more experienced farmers differently; hence the authors analyze the impacts of direct payments on the current and term debt coverage ratios for these two groups separately. In the analysis, the authors control for farm financial characteristics, farm operator characteristics, and other factors. Data from the US Department of Agriculture (USDA) Agricultural Resource Management Survey (ARMS) for the years 2005, 2006, and 2007 were used in the weighted regression analysis and jackknifed standard errors computed.

A positive significant relationship was found between the level of direct payments (in dollars) and the term debt coverage ratio for experienced farmers, suggesting that direct payments improve repayment capacity. However, this relationship is not significant for beginning farmers. Also, a negative significant relationship was found between the number of base acres and the current ratio for experienced farmers, while this relationship lacks significance for beginning farmers.

The paper provides evidence that decoupled direct payments impact a farmer's liquidity and repayment capacity. Furthermore, direct payments impact beginning and experienced farmers differently. This paper also contributes to the growing body of research investigating the mechanisms by which decoupled payments have the potential to distort current production.

The purpose of this paper is to estimate the effectiveness of the credit evaluation system using the borrowing data from China’s leading P2P lending platform, Renrendai.com.

The current credit valuation systems are classified into the forward-looking mechanism, which judges the borrowers’ credit levels based on their uploaded information, and the backward-looking mechanism, which judges the borrowers’ credit levels based on their historical repayment performance. Probit models and Tobit models are used to examine the effectiveness of credit evaluation mechanisms.

The results show that only the “hard” information reflecting borrowers’ credit ability can explain the default risk on the platform under the forward-looking credit evaluation mechanism. The backward-looking credit evaluation mechanism (BCEM) based on the repeated borrowings produces both promise-enhancing and “fishing” incentives and thus fails to explain the default risk, and weakens the effectiveness of forward-looking credit indicators in explaining the default risk because it encourages borrowers to invest in forging forward-looking credit indicators. Additional information such as the interest rate and the repayment periods reveals borrowers’ credit and thus can also be used as a predictor of borrowers’ default risk.

The findings suggest that current ex ante screening based on the information collected from the borrowers or repeated borrowings is inadequate to control the default risk in P2P lending markets and thus needs be improved. Ex post monitoring and sharing on defaulter’s information should be strengthened to increase the default cost and thus to deter potential bad borrowers.

To the authors’ knowledge, this is the first paper classifying the credit evaluation system in online P2P lending market into the forward-looking type and the backward-looking type, which is important since they provide different incentives to borrowers. The paper also investigates and provides evidence on the promise-enhancing and “fishing” incentives of BCEMs.

The purpose of this paper is to explore the implementation of a mobile network system for an Islamic microfinance institution, made in collaboration with a commercial bank in Malaysia. It also intends to highlight any emerging issues pertaining to the implementation of technology into the disbursement and repayment system of an Islamic microfinance institution from their clients’ perspectives. As Islamic microfinance industry is still growing in Malaysia, findings gathered throughout the course of study are also intended to boost further knowledge relating to this area.

By using a case study method, interview sessions were conducted separately with clients of the Islamic microfinance institution. The purpose of interview sessions is to identify the benefits and problem that surrounds the usage of mobile banking into the repayment system for Islamic microfinance products. Data triangulation with various public documents was conducted to enhance the credibility and reliability of data, also to support the claims that were made by the respondents.

It was found that clients are quite satisfied with the disbursement process through a mobile solution. However, the same cannot be said with the repayment process. The difficulties in using the mobile solution pose a major threat to its success. As most clients are not born to be technological savvy, the lack of easiness in methods for the usage of a mobile solution for their transactions pushes them away from further exploring the benefits that can be brought in by the function. Other risks which were highlighted include concerns towards breach of trust and risks of robbery. Clients of the case study, on the other hand, are concerned that the transfer of cash between their meeting venues to the bank would expose them to the public who might try to take advantage from the situation.

As the current study had only focusses on mobile banking aspect of the repayment system for one Islamic microfinance institution, a multiple case study could be adapted to investigate various banking channels being implemented by different Islamic microfinance institutions in Malaysia and their current success.

By highlighting several issues through this study, it is hoped that this Islamic microfinance institution would consider applying other means of payment that are available in the market that is not only cost-efficient, but also beneficial for clients of the institution.

This study highlights the setbacks in the usage of technology by clients of Islamic microfinance institution in Malaysia. Although many approved to the diffusion of innovation in Malaysian banking sector, the same has yet to be achieved in the Islamic microfinance industry, which clients are mostly technology illiterate.

This study seeks to provide a systematic analysis of bounded rationality expressed by individual lenders in a Peer-to-peer (P2P) lending market.

26,383 personal loan listings collected from Moneyauction in Korea, were analyzed with binary logit regression. 6 hypothesis based on bounded rationality theory were constructed and tested. Binary logit regression was employed as both dependent variables have binary characteristics and can thus be assigned values equal to 0 or 1.

The results confirm that individual P2P lenders make their funding decisions based on bounded rationality, arousing from cognitive limitations, incomplete information, and time constraints.

By adopting the theory of bounded rationality, this study attempts to prepare the theoretical background for an explanation of the decision behavior of individual lenders in a P2P lending market.

The findings of this research emphasize the importance of the platform provider's role to facilitate the sustainable market growth of P2P lending as an alternative form of finance. As the rationality of individual lenders is bounded during their decision-making process according to the research findings, the platform provider must continuously adjust their decision criteria by referencing the cumulative loan repayment data.

This study attempts to identify for the first time the suboptimal decision making by individual lenders in a P2P lending market on the basis of bounded rationality theory.

The purpose of this paper is to provide a method that can better evaluate the credit risk (CR) under PPP project finance.

The principle to evaluate the CR of PPP projects is to calculate three critical indicators: the default probability (DP), the recovery rate (RR) and the exposure at default (EAD). The RR is determined by qualitative analysis according to Standard & Poor’s Recovery Scale, and the EAD is estimated by NPV analysis. The estimation of the DP is the focus of CR assessment because the future cash flow is not certain, and there are no trading records and market data that can be used to evaluate the credit condition of PPP projects before financial close. The modified CreditMetrics model and Monte Carlo simulation are applied to evaluate the DP, and the application is illustrated by a PPP project finance case.

First, the proposed method can evaluate the influence of the project’s cash flow uncertainty on the potential loss of the bank. Second, instead of outputting a certain default loss value, the method can derive an interval of the potential loss for the bank. Third, the method can effectively analyze how different repayment schedules and risk preference of banks influence the evaluating result.

The proposed method offers an approach for the bank to value the CR under PPP project finance. The method took into consideration of the uncertainty and other characteristics of PPP project finance, adopted and improved the CreditMetrics model, and provided a possible loss range under different project cash flow volatilities through interval estimation under certain confident level. In addition, the bank’s risk preference is considered in the CR evaluating method proposed in this study where the bank’s risk preference is first investigated in the CR evaluating process of PPP project finance.

Microcredit schemes fashioned after the Grameen Bank model are widely acclaimed for their potential for empowering the poor through access to credit based on social collateral. However, women market vendors in Ibadan refer to microcredit loans as owo komulelanta, a term which translates as “resting the breasts on a hot kerosene lantern,” a plain critique of the stringent conditions of loan repayment. This paper presents the lived experience of borrowers based upon ethnographic fieldwork conducted between 2017 and 2019. It reflects on the Nigerian state's neoliberal policies of microfinance and the experience of women borrowers. The paper argues that social–emotional vulnerability of women borrowers is exacerbated by the acceptance of a loan due to the rigid system of repayment and harassment from providers.