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This study seeks to examine the relationship between a firm’s effectiveness in managing working capital (WCM), as measured by the cash conversion cycle (CCC), and its exposure to product market competition (PMC).

Using 85,356 firm-year observations of 9,611 unique firms for the period 1990–2019, from the US, the baseline model assesses the CCC and PMC connection while controlling for multiple firm-level factors. Additional analyses are conducted to control for financial constraints, economic policy uncertainty, and endogeneity.

An inverse relationship is shown between PMC and CCC, indicating that firms facing increased competition tend to implement more efficient WCM strategies in order to free up scarce resources. In addition, we observe that increased PMC pushes companies to strategically adjust their credit policies, while also improving their administration of payables and inventories, resulting in improved efficiency. Our research highlights that CCC serves as a mediator between PMC and firm performance.

This study enhances comprehension of the impact of PMC on WCM, offering practical recommendations for companies seeking to optimize their strategy in competitive settings.

The study provides valuable insights for managers operating in competitive markets, highlighting the significant influence of working capital on business policies as a response to competition. This study contributes to the existing literature on WCM and PMC by providing guidance to organizations on how to improve their WCM practices, maintain competitiveness, and free up scarce resources.

The reduction of government expenditure in the healthcare system, the difficulty of finding new sources of funding and the reduction in disposable income per capita are the most important problems of the healthcare system in Greece over the last decade. Therefore, studying the profitability of health structures is a crucial factor in making decisions about their solvency and corporate sustainability. The aim of this study is to investigate the effect of economic liquidity, debt and business size on profitability for the Greek general hospitals (GHs) during the period 2016–2018.

Financial statements (balance sheets and income statements) of 84 general hospitals (GHs), 52 public and 32 private, over a three-year period (2016–2018), were analyzed. Spearman’s Rs correlation was carried out on two samples.

The results revealed that there is a positive relationship between the investigated determinants (liquidity, size) and profitability for both public and private GHs. It was also shown that debt has a negative effect on profitability only for private GHs.

Increasing the turnover of private hospitals through interventions such as expanding private health insurance and adopting modern financial management techniques in public hospitals would have a positive effect both on profitability and the efficient use of limited resources.

These results, in conjunction with the findings of the low profitability of private hospitals and the excess liquidity of public hospitals, can shape the appropriate framework to guide hospital administrators and government policymakers.

This study aims to determine how the applications of blockchain technology (BT) can play a crucial role in managing financial flows in the humanitarian supply chain (HSC) and what benefits and challenges are associated with BT in a humanitarian setting.

The present study used a qualitative research approach, incorporating a systematic literature review and conducting semi-structured interviews with 12 experts in the fields of humanitarian operations, supply chain management, fintech and information technology.

The findings show that the humanitarian sector has the potential to reap significant benefits from BT, including secure data exchange, efficient SCM, streamlined donor financing, cost-effective financial transactions, smooth digital cash flow management and the facilitation of cash programs and crowdfunding. Despite the promising prospects, this study also illuminated various challenges associated with the application of BT in the HSC. Key challenges identified include scalability issues, high cost and resource requirements, lack of network reliability, data privacy, supply chain integration, knowledge and training gaps, regulatory frameworks and ethical considerations. Moreover, the study highlighted the importance of implementing mitigation strategies to address the challenges effectively.

The present study is confined to exploring the benefits, challenges and corresponding mitigation strategies. The research uses a semi-structured interview method as the primary research approach.

This study adds to the existing body of knowledge concerning BT and HSC by explaining the pivotal role of BT in improving the financial flow within HSC. Moreover, it addresses a notable research gap, as there is a scarcity of studies that holistically cover the expert perspectives on benefits, challenges and strategies related to blockchain applications for effective financial flows within humanitarian settings. Consequently, this study seeks to bridge this knowledge gap and provide valuable insights into this critical area.

This study aims to optimize the levels of factors for a green supply chain (GSC) while concurrently gaining valuable insights into the dynamic interrelationships among several factors, leading to reductions in CO₂ emissions and the maximization of the average service level, thereby enhancing overall supply chain performance.

Response surface methodology (RSM) is employed as a technique for multiple response optimization. This study uses a supply chain simulation model that includes decision variables related to the level of inventory control parameters and vehicle capacity. The desirability approach is adopted to achieve optimization objectives by focusing on minimizing CO₂ emissions and maximizing service levels while simultaneously determining the optimum levels of considered decision variables.

The high R² values of 97.38% for CO₂ and 97.28% for service level, along with adjusted R² values reasonably close to predicted values, affirm the models' capability to predict responses accurately. Key significant model terms for CO₂ encompassed reorder point, order up to quantity, vehicle capacity, and their interaction effects, while service level is notably influenced by reorder point, order up to quantity, and their interaction effects. The study successfully achieved a high level of desirability value of %99.1 and the validated performance levels confirmed that the results fall within the prediction interval.

This study introduces a metamodel framework designed to optimize various design parameters for a GSC combining discrete event simulation (DES) and RSM in the form of a simulation optimization model. In contrast to the literature, the current study offers an exhaustive and in-depth analysis of the structural elements of the supply chain, particularly the inventory control parameters and vehicle capacity, which are crucial for comprehending its performance and environmental impact.

Agriculture commodity price forecasts have long been important for a variety of market players. The study we conducted aims to address this difficulty by examining the weekly wholesale price index of green grams in the Chinese market. The index covers a ten-year period, from January 1, 2010, to January 3, 2020, and has significant economic implications.

In order to address the nonlinear patterns present in the price time series, we investigate the nonlinear auto-regressive neural network as the forecast model. This modeling technique is able to combine a variety of basic nonlinear functions to approximate more complex nonlinear characteristics. Specifically, we examine prediction performance that corresponds to several configurations across data splitting ratios, hidden neuron and delay counts, and model estimation approaches.

Our model turns out to be rather simple and yields forecasts with good stability and accuracy. Relative root mean square errors throughout training, validation and testing are specifically 4.34, 4.71 and 3.98%, respectively. The results of benchmark research show that the neural network produces statistically considerably better performance when compared to other machine learning models and classic time-series econometric methods.

Utilizing our findings as independent technical price forecasts would be one use. Alternatively, policy research and fresh insights into price patterns might be achieved by combining them with other (basic) prediction outputs.