Search results

Inspired by a food bank distribution operation, this paper aims to study synchronized vehicle routing for equitable and effective food allocation. The primary goal is to improve operational efficiency while ensuring equitable and effective food distribution among the partner agencies.

This study introduces a multiobjective Mixed Integer Programming (MIP) model aimed at addressing the complex challenge of effectively distributing food, particularly for food banks serving vulnerable populations in low-income urban and rural areas. The optimization approach described in this paper places a significant emphasis on social and economic considerations by fairly allocating food to food bank partner agencies while minimizing routing distance and waste. To assess the performance of the approach, this paper evaluates three distinct models, focusing on key performance measures such as effectiveness, equity and efficiency. The paper conducts a comprehensive numerical analysis using randomly generated data to gain insights into the trade-offs that arise and provide valuable managerial insights for food bank managers.

The results of the analysis highlight the models that perform better in terms of equity and effectiveness. Additionally, the results show that restocking the vehicles through the concept of synchronization improves the overall quantity of food allocation to partner agencies, thereby increasing accessibility.

This paper contributes significantly to the literature on optimization approaches in the field of humanitarian logistics.

This study provides food bank managers with three different models, each with a multifaceted nature of trade-offs, to better address the complex challenges of food insecurity.

This paper contributes significantly to social responsibility by enhancing the operational efficiency of food banks, ultimately improving their ability to serve communities in need.

To the best of the authors’ knowledge, this paper is the first to propose and analyze this new variant of vehicle routing problems in nonprofit settings.

The purpose of this paper is to evaluate the efficiency of a series network system with undesirable and unreturnable simultaneously.

The research was conducted by applying data envelopment analysis (DEA) approach to measure the efficiency score of a system and substages with an undesirable output of the second and third stages separately. For each case, new production technology was introduced, and based on them, novel DEA models were proposed.

One of the most important issues in the development of a country is the banking industry. In this study, 51 branches of commercial banks as a three-stage system with undesirable and unreturnable outputs in the second stage are considered. Then, the efficiency of each branch and substages is measured by using proposed models.

The efficiency of a three-stage network in the presence of undesirable and unreturnable outputs was assessed. In this model, Kousmanen’s technology was used.

This study aims to analyze the effect of cryptocurrency capitalization market development on bank deposits variability in the United Arab Emirates (UAE) spanning the period 2005M1–2020M4 using the novel nonlinear autoregressive distributive lag (NARDL).

The study employs the NARDL recently developed by Shin et al. (2014) to estimate the long and short-run relationships between the variables rather than the widely known ARDL (Pesaran et al., 2001), which suffers from a complex structure in the estimation equation that usually includes lags and differences in both short and long terms. The implementation of NARDL required several proceedings after plotting the descriptive data, commencing with unit root tests, selection of lag length, estimating the long-and-short variables coefficients, heteroscedasticity test and Wald test for symmetries.

The long-run estimations of the positive and negative asymmetric coefficients indicate that cryptocurrencies capitalization has a negative impact on bank deposits in the UAE. Further, the short-run estimations coefficients exhibit that both significant positive and negative partial sum squares of cryptocurrencies decrease bank deposits.

The study has applied to the UAE spanning the period 2005M1–2020M4 using the NARDL.

The short-run estimations coefficients exhibit that both significant positive and negative partial sum squares of cryptocurrencies decreases bank deposits, which means that the increase in the magnitude of cryptocurrencies capitalization stimulates depositors and speculators to adjust their portfolios towards contracting their deposits in banks to invest partially in cryptocurrencies, on the other hand, the decline in cryptocurrencies capitalization process spur depositors and speculators to reduce their deposits for purchasing cryptocurrencies at lower prices.

The study infers that individuals and businesses are cautious when investing in cryptocurrencies, and they need more certainty and trust to include these types of assets in their portfolios. The fluctuation in cryptocurrencies capitalization prompts speculators to change their deposits according to the cryptocurrencies' prices.

This study explores the short-and long-run asymmetric impacts of cryptocurrencies capitalization development on bank deposits volatility in the UAE, based on a NARDL, for providing a manifest depiction of whether the cryptocurrencies industry might be a threat to conventional banking system performance in the potential future.

Interconnections among banks are an essential feature of the banking system as it helps in an effective payment system and liquidity management. However, it can be a nightmare during a crisis when these interconnections can act as contagion channels. Therefore, it becomes essentially important to identify good links (non-contagious channels) and bad links (contagious channels).

The article estimated systemic risk using quantile regression through the ΔCoVaR approach. The interconnected phenomenon among banks has been analyzed through Granger causality, and the systemic network properties are evaluated. The authors have developed a fixed effect panel regression model to predict interconnectedness. Profitability-adjusted systemic index is framed to identify good (non-contagious) or bad (contagious) channels. The authors further developed a logit model to find the probability of a link being non-contagious. The study sample includes 36 listed Indian banks for the period 2012 to 2018.

The study indicated interconnections increased drastically during the Indian non-performing asset crisis. The study highlighted that contagion channels are higher than non-contagious channels for the studied periods. Interbank bad distance dominates good distance, highlighting the systemic importance of banking network. It is also found that network characteristics can act as an indicator of a crisis.

The study is the first to differentiate the systemic contagious and non-contagious channels in the interbank network. The uniqueness also lies in developing the normalized systemic index, where systemic risk is adjusted to profitability.

This study examines the impacts of the Ethiopian developmental state model on the competition, efficiency and profitability of banks.

The competition, efficiency and profitability of the Ethiopian bank are measured using Panzar Rose, data envelopment analysis and financial ratio. Fixed-effect panel regression methods are applied to test the direction and strength of association between the Ethiopian developmental state model and the competition, efficiency and profitability of the country's banks while controlling bank-specific market structure and macroeconomic factors.

The Ethiopian developmental state model embeds the state-directed financial system, which affects the banking industry using a range of credit allocation instruments. Of which, directed credit schemes, interest rate control and the lack of financial freedom reduce the competition and efficiency of banks. The National Bank of Ethiopia (NBE) advances to the government and the sale of Treasury bills to a captive market enhances banking competition while negatively affecting banking efficiency. Interest rate control and the lack of financial freedom lower banking profitability. Unexpectedly, directed credit schemes improve banking profitability.

As with any study, this one has limitations. The intra-period comparison of efficiency is based on balanced data. Future studies can use methods that can measure the efficiency of banks using unbalanced data. The computation of the yearly H-statistic is constrained by the small sample size. The use of high-frequency data for measuring competition can provide us with better insights into banking competition in Ethiopia. Furthermore, there are a number of methods for measuring banking competition, efficiency and profitability with different assumptions. Approaching the subject of this study by applying different methods will offer different insights.

The contributions of this study to practice are at two levels. First, at the policy level, it enhances our understanding of the impacts of developmental state model policies, as implemented in Ethiopia, on the banking industry and therefore provides suggestions to policymakers to reform the sector's policies. Second, it offers input to the management of banks regarding the factors that impact the industry.

The banking industry is often studied in the context of financial liberalisation. The originality of this study lies in investigating how the competition, efficiency and profitability of banks are affected when operating in the context of significant state interventions in the industry.

The primary purpose is to empower financial institutions in AI integration decisions. By combining QSFS and the Golden Cut technique, the study establishes a robust foundation for assessing AI progress effects, aligning implementation with performance goals, and promoting technical innovation. Dimensions explored include AI-related workforce competency, technological adaption, and ethical AI practices, crucial components within the BSC framework for technological innovation.

This study employs a distinctive approach, integrating the Balanced Scorecard (BSC) framework with Quantum Spherical Fuzzy Sets (QSFS) and the Golden Cut approach to explore the dynamic landscape of AI deployment. The integration addresses uncertainties, enhancing impact assessment accuracy amid ambiguity associated with AI outcomes. QSFS and the Golden Cut technique together facilitate precise identification of thresholds and crucial values.

The research delves into the intricate relationship between enduring financial stability and AI progress, recognizing technology's crucial influence on financial decision-making. Findings underscore technology's significant impact on financial institutions' AI integration decisions. This novel approach provides a strong quantitative basis, offering insights into workforce competency, technological adaption, and ethical AI practices.

Despite valuable contributions, the study acknowledges limitations, such as potential biases and generalizability concerns, emphasizing the need for cautious interpretation and suggesting future research directions. Recognizing the research's boundaries and complexities in studying AI deployment in financial institutions underscores the need for ongoing exploration.

The research's originality lies in presenting an innovative methodology, integrating BSC, QSFS, and the Golden Cut, providing a unique perspective for decision-making. Contributions extend beyond academia, offering practical insights to enhance AI strategic implementation in the financial industry. This novel approach enriches the technology and finance discourse, fostering theoretical and practical advancements.

This study aims to investigate the relationship between stock markets, environmental, social and governance (ESG) factors and Shariah-compliant in an integrated framework.

The authors employ the multivariate factor stochastic volatility (mvFSV) framework to extract the volatility of the different sectoral indices. Based on this evidence, the authors employ the quantile vector autoregressive (QVAR) approach to examine the dynamic spillover connectedness among the aforementioned indices.

The study emphasizes the following major findings: (1) significant time-varying spillover connectedness across quantiles, (2) bidirectional and asymmetric spillover effect among the ESG index and the other sectoral indices, (3) the strength of spillover connectedness is time-varying across quantiles, (4) based on the perspective of portfolio optimization, ESG market is a significant strong forecasting contributor to conventional and Shariah-compliant markets, (5) overall, the findings point out serious quantile pass-through effect among ESG index and the other sectoral indices during the COVID-19 health crisis.

This study extends the previous literature in the following ways. First, to the best of the researchers’ knowledge, none of the existing studies have investigated the relationship between stock markets, ESG factors and Shariah-compliant in an integrated framework. Second, this study extends the previous scholarships by applying the mvFSV. Third, the authors propose a new rolling version to estimate dynamic spillovers, namely the rolling-window quantile VAR method. This approach provides a great advantage in computing the dynamics of return and variance spillover between variables in terms not only of the overall factor but also of the net (pairwise) aspect.

This paper aims to explore the impact of cycle superposition on bank liquidity risk under different levels of financial openness so that banks can better manage their liquidity risk. Meanwhile, it can also provide some ideas for banks in other emerging economies to better cope with the shocks of the global financial cycle.

Employing the monthly data of 16 commercial banks in China from 2005 to 2021 and based on the time-varying parameter vector autoregressive model with stochastic volatility (TVP-SV-VAR) model, the authors first examine whether the cycle superposition can magnify the impact of China's financial cycle on bank liquidity risk. Subsequently, the authors investigate the impact of different levels of financial openness on cycle superposition amplification. Finally, the shock of the financial cycle of the world's major economies on the liquidity risk of Chinese banks is also empirically analyzed.

Cycle superposition can magnify the impact of China's financial cycle on bank liquidity risk. However, there are significant differences under different levels of financial openness. Compared with low financial openness, in the period of high financial openness, the magnifying effect of cycle superposition is strengthened in the short term but obviously weakened in the long run. In addition, the authors' findings also demonstrate that although the United States is the main shock country, the influence of other developed economies, such as Japan and Eurozone countries, cannot be ignored.

Firstly, the cycle superposition index is constructed. Secondly, the authors supplement the literature by providing evidence that the association between cycle superposition and bank liquidity risk also depends on financial openness. Finally, the dominant countries of the global financial cycle have been rejudged.

This study aims to provide empirical evidence on the return and volatility spillover structures between Bitcoin, Fintech stocks and Asian-Pacific equity markets over time and during different market conditions, and their implications for portfolio management.

We use Time-varying parameter vector autoregressive and quantile frequency connectedness approach models for the connectedness framework, in conjunction with Diebold and Yilmaz’s connectivity approach. Additionally, we use the minimum connectedness portfolio model to highlight implications for portfolio management.

Regarding the uncertainty of the whole system, we show a small contribution from Bitcoin and Fintech, with a higher contribution from the four Asian Tigers (Taiwan, Singapore, Hong Kong and Thailand). The quantile and frequency analyses also demonstrate that the link among assets is symmetric, with short-term spillovers having the largest influence. Finally, Bitcoins and Fintech stocks are excellent diversification and hedging instruments for Asian equity investors.

There is an instantaneous, symmetric and dynamic return and volatility spillover between Asian stock markets, Fintech and Bitcoin. This conclusion should be considered by investors and portfolio managers when creating risk diversification strategies, as well as by policymakers when implementing their financial stability policies.

The study’s major contribution is to analyze the volatility spillover between Bitcoin, Fintech and Asian stock markets, which is dynamic, symmetric and immediate.

The financial system has witnessed the substantial growth of financial technology (fintech) firms. One of the strategies that banks have adopted to cope with this emergence is to cooperate with fintech firms. This study empirically investigated whether cooperation between banks and fintech companies would improve banks’ risk-adjusted returns.

We developed a novel index of bank–fintech cooperation across various fintech sectors. A system generalized method of moments (GMM) was used to examine this relationship using a sample of Vietnamese banks from 2007 to 2019.

The findings show that the diversity of bank–fintech cooperation across seven sectors tends to enhance banks’ risk-adjusted returns. The results also highlight that this relationship may depend on the types of fintech sectors and bank ownership. More specifically, the positive association between this cooperation and banks’ risk-adjusted returns only holds in the comparison sector of fintech, whereas there is a negative relationship between them in the payments and mobile wallets sector. Furthermore, state-owned commercial banks that engage in more bank–fintech cooperation tend to generate greater earnings. If we look at listed banks, the positive effect of bank–fintech partnerships on risk-adjusted returns still holds. A similar result was also found in the case of large banks.

Our empirical evidence provides motivations for incumbent banks to implement appropriate strategies toward diversity in bank–fintech partnerships when fintech firms have engaged in various financial segments.

This study adds more evidence to the existing literature on the relationship between bank–fintech cooperation and bank performance.