Search results

This study focuses on the banking systems evaluation in Portugal, Italy, Ireland, Greece and Spain (known as the PIIGS) during the financial and post-financial crisis period from 2009 to 2018.

A conditional robust nonparametric frontier analysis (order-m estimators) is used to measure banking efficiency combined with variables highlighting the effects of Non-Performing Loans. Next, a truncated regression is used to examine if institutional, macroeconomic, and financial variables affect bank performance differently. Unlike earlier studies, we use the Corruption Perception Index (CPI) as an institutional variable that affects banking sector efficiency.

This research shows that the PIIGS crisis affects each bank/country differently due to their various efficiency levels. Most of the study variables — CPI, government debt to GDP ratio, inflation, bank size — significantly affect banking efficiency measures.

The contribution of this article to the relevant banking literature is two-fold. First, it analyses the efficiency of the PIIGS banking system from 2009 to 2018, focusing on NPLs. Second, this is the first empirical study to use probabilistic frontier analysis (order-m estimators) to evaluate PIIGS banking systems.

Accurately estimating the severity of derailment is a crucial step in quantifying train derailment consequences and, thereby, mitigating its impacts. The purpose of this paper is to propose a simplified approach aimed at addressing this research gap by developing a physics-informed 1-D model. The model is used to simulate train dynamics through a time-stepping algorithm, incorporating derailment data after the point of derailment.

In this study, a simplified approach is adopted that applies a 1-D kinematic analysis with data obtained from various derailments. These include the length and weight of the rail cars behind the point of derailment, the train braking effects, derailment blockage forces, the grade of the track and the train rolling and aerodynamic resistance. Since train braking/blockage effects and derailment blockage forces are not always available for historical or potential train derailment, it is also necessary to fit the historical data and find optimal parameters to estimate these two variables. Using these fitted parameters, a detailed comparison can be performed between the physics-informed 1-D model and previous statistical models to predict the derailment severity.

The results show that the proposed model outperforms the Truncated Geometric model (the latest statistical model used in prior research) in estimating derailment severity. The proposed model contributes to the understanding and prevention of train derailments and hazmat release consequences, offering improved accuracy for certain scenarios and train types

This paper presents a simplified physics-informed 1-D model, which could help understand the derailment mechanism and, thus, is expected to estimate train derailment severity more accurately for certain scenarios and train types compared with the latest statistical model. The performance of the braking response and the 1-D model is verified by comparing known ride-down profiles with estimated ones. This validation process ensures that both the braking response and the 1-D model accurately represent the expected behavior.

In the present study, using a novel fractional logit model, the link between R&D (Research & Development) investment and shareholder value-based CEO (Chief Executive Officer) compensation has been examined within the non-financial sector in the Euro area economies using a firm-level dataset for 2002–2019.

The fractional logit model is utilized to examine the effects of corporate payment on R&D investment. The fractional logit model can be considered the empirical approach that takes into account R&D non-performer firms to avoid reducing the sample size. The fractional logit model is superior to the censored or truncated models, like Tobit, since the fractional logit model is useful to address the econometric limitations that are found in the censored and truncated models in the non-linear models.

The findings obtained in this study showed a significant and negative effect of short-term aim-based CEO payment on R&D expenditures in the Euro area economies using firm-level data. These findings are robust to different robustness checks and modeling alternatives.

To the author's knowledge, there is no study that examines the effects of short-term shareholder value maximization-based CEO compensation on R&D in the European context in the literature.

Original equipment manufacturers and other manufacturing companies rely on the delivery performance of their upstream suppliers to maintain a steady production process. However, supplier capacity uncertainty and delayed delivery often poses a major concern to manufacturers to carry out their production plan as per the desired schedules. The purpose of this paper is to develop a decision model that can improve the delivery performance of suppliers to minimise fluctuations in the supply quantity and the delivery time and thus maximising the performance of the supply chain.

The authors studied a single manufacturer – single supplier supply chain considering supplier uncertain capacity allocation and uncertain time of delivery. Mathematical models are developed to capture expected profit of manufacturer and supplier under this uncertain allocation and delivery behaviour of supplier. A reward–penalty mechanism is proposed to minimise delivery quantity and time of delivery fluctuations from the supplier. Further, an order-fulfilment heuristic based on delivery probability is developed to modify the order quantity which can maximise the probability of a successful deliveries from the supplier.

Analytical results reveal that the proposed reward–penalty mechanism improves the supplier delivery consistency. This consistent delivery performance helps the manufacturer to maintain a steady production schedule and high market share. Modified ordering schedule developed using proposed probability-based heuristic improves the success probability of delivery from the supplier.

Practitioners can benefit from the findings of this study to comprehend how contracts and ordering policy can improve the supplier delivery performance in a manufacturing supply chain.

This paper improves the supplier delivery performance considering both the uncertain capacity allocation and uncertain time of delivery.

The novel coronavirus (COVID-19) has caused financial stress and limited their lending agility, resulting in more non-performing loans (NPLs) and lower performance during the II wave of the coronavirus crisis. Therefore, it is essential to identify the risky factors influencing the financial performance of Indian banks spanning 2018–2022.

Our sample consists of a balanced panel dataset of 75 scheduled commercial banks from three different ownership groups, including public, private and foreign banks, that were actively engaged in their operations during 2018–2022. Factor identification is performed via a fixed-effects model (FEM) that solves the issue of heterogeneity across different with banks over time. Additionally, to ensure the robustness of our findings, we also identify the risky drivers of the financial performance of Indian banks using an alternative measure, the pooled ordinary least squares (OLS) model.

Empirical evidence indicates that default risk, solvency risk and COVAR reduce financial performance in India. However, high liquidity, Z-score and the COVID-19 crisis enhance the financial performance of Indian banks. Unsystematic risk and systemic risk factors play an important role in determining the prognosis of COVID-19. The study supports the “bad-management,” “moral hazard” and “tail risk spillover of a single bank to the system” hypotheses. Public sector banks (PSBs) have considerable potential to achieve financial performance while controlling unsystematic risk and exogenous shocks relative to their peer group. Finally, robustness check estimates confirm the coefficients of the main model.

This study contributes to the knowledge in the banking literature by identifying risk factors that may affect financial performance during a crisis nexus and providing information about preventive measures. These insights are valuable to bankers, academics, managers and regulators for policy formulation. The findings of this paper provide important insights by considering all the risk factors that may be responsible for reducing the probability of financial performance in the banking system of an emerging market economy.

The empirical analysis has been done with a fresh perspective to consider unsystematic risk, systemic risk and exogenous risk (COVID-19) with the financial performance of Indian banks. Furthermore, none of the existing banking literature explicitly explores the drivers of the I and II waves of COVID-19 while considering COVID-19 as a dependent variable. Therefore, the aim of the present study is to make efforts in this direction.

The purpose of this paper is to measure the high-quality entrepreneurial efficiency of family-owned small- and medium-sized enterprises (SMEs) while exploring the potential determinants of their performance. This study places particular emphasis on the firms' technological competencies and internationalization efforts. The authors aim to shed light on the internal and external characteristics that impact the efficiency of family SMEs.

This study adopts a two-stage approach. In the first stage, a data envelopment analysis model is utilized to measure the high-quality entrepreneurial efficiency of family SMEs. To achieve this, this study considered as outputs three key quality aspects of entrepreneurship, namely innovativeness, export orientation and turnover rate, while the inputs were the number of employees and the business environment. Then, in the second stage, the efficiency scores are regressed against a set of environmental factors that may affect the efficiency. The proposed efficiency measurement models are utilized with a particularly rich dataset of 1,910 family SMEs from 35 developed countries.

The results demonstrated that the efficiency of family SMEs primarily engaged in the production of goods was significantly higher than those providing services. Importantly, the presence of barriers related to innovation and digitalization had a pronounced negative impact on efficiency. Additionally, scale-up firms exhibited higher levels of efficiency. When examining family SMEs within their national context, it was observed that non-EU countries and countries with a higher gross domestic product displayed significantly higher efficiencies.

The findings of this research provide guidance for the development of entrepreneurship-oriented policies that consider both the internal characteristics of family SMEs and the diverse socioeconomic contexts in which they operate.

This paper aims to investigate the efficiency and performance of the Islamic banking industry amid the COVID-19 pandemic.

The authors used a two-stage data envelopment analysis to first estimate the efficiency of 78 Islamic banks (IBs) across 15 countries for the 2005–2020 period (a total of 782 bank-year observations) and then to examine their determinants, including the COVID-19 pandemic.

The authors found that the Islamic banking industry performed at a moderate level during the 2005–2020 period, providing evidence that IBs are resilient to the financial shocks created by COVID-19. The authors also found that bank-level characteristics (such as bank size) and country-level characteristics (such as inflation) can contribute to the bank’s operational efficiency.

The results of this study suggested that banking management and government macroeconomic policy, especially in terms of precautions and continuous support, are important for IBs to improve their performance.

To the best of the authors’ knowledge, this is the first study to examine the efficiency and performance of IBs amid COVID-19.

Outgrower scheme as a contractual agreement between farmers and some funding entities has in recent times found proliferation among resource poor farmers in Ghana, especially in northern Ghana. This contractual arrangement, which involves the provision of farm inputs, and in some cases, technical support by the implementing company and the repayment by farmers with portions of their harvest, is often regarded as an effective way to mutually improve the outcomes of both smallholder farmers and outgrower companies. The study aims to analyse. the level of awareness, nature of input package, determinants of participation and intensity of participation in input credit scheme by smallholder rice farmers in the Mamprugu Moagduri District of Ghana’s North East Region, using the Integrated Water Management and Agriculture Development (IWAD) scheme as a case.

Using a quantitative analytical approach, the study gathers information from 233 randomly selected smallholder rice farmers consisting of 150 participants and 83 non-participants using a structured questionnaire. Descriptive statistics, as well as the Tobit model, are the methods used in the analysis.

The results show that while factors such as age, marital status, number of dependents and farming experience only influenced participation in the scheme, religion, age, sex, number of dependents and farming experience influenced intensity of participation.

This study calls for the adoption of sustainable approaches by input credit companies in their credit support to smallholder farmers rather than the current ad hoc support during each cropping season.

This research analyzes borrowers' credit utilization through prepayment behavior in peer-to-peer (P2P) lending. The authors investigate factors influencing the decision to prepay and assess the role of P2P lending as an alternative source of consumer credits.

The authors use individual loan-level data from the LendingClub, one of the largest P2P platforms in the USA. The authors use a Logit model and a sample selection model estimated by the two-stage Heckman method. The empirical analysis considers borrower-specific and loan-specific characteristics as well as macroeconomic factors.

The authors present a number of significant findings that can enhance understanding consumers' financing decisions. The authors offer evidence that borrowers are able to take advantage of cheaper loans offered by P2P lending to better manage credit card balance and consolidate debt. The authors find that borrowers tend to prepay P2P loans quickly when the aggregate cost of borrowing is low, suggesting that P2P lending offers an efficient alternative to obtain credit. This is particularly true for creditworthy borrowers that are able to take advantage of competing sources of finance. The authors' results provide evidence that P2P lending can improve consumers' optimal credit utilization.

P2P lending has grown exponentially and has become a significant credit supplier to consumers and small businesses. While the existing literature mostly focuses on default risks, prepayment has received much less attention. This research fills in the gap and investigates borrowers' prepayment behavior in P2P loans and the role of P2P lending as an alternative source of consumer credits.

This study investigates the connection between corporate governance and zombie firm’s exit time.

With a sample of 2,794 French zombie firms, the analysis focuses on four aspects of corporate governance: board size (BS), managerial ownership (MO), director turnover (DT) and ownership concentration, using tobit regression.

Dimensions of corporate governance have an important role in determining zombie firms’ exit time. MO and ownership concentration increase zombie firm exit time, whereas larger BSs and DT reduce it.

To the best of the authors’ knowledge, this study is the first to include corporate governance as a characteristic relevant to zombie firms’ exit time. It provides new insights on why some zombie firms remain in the market longer than expected.