Search Results (57)

Background: The rapid digital transformation in logistics requires the adaptation of transport companies to electronic information management, particularly through the implementation of electronic consignment notes (e-CMR). This study examines how the digitization of transport documentation affects the competitiveness, operational efficiency, and environmental performance of road freight transport companies. Methods: A questionnaire survey was conducted among Slovak and Czech carriers to analyze their experience and readiness for adopting e-CMR. The collected data were evaluated using descriptive and comparative methods to quantify economic and ecological impacts, focusing mainly on invoicing efficiency and paper consumption. Results: The results show that only a small share of carriers currently use e-CMR, primarily due to high software costs and the lack of partner participation. Nevertheless, digitization can significantly shorten the average invoicing delay by approximately 11.5 days, releasing around 7% of tied-up working capital and improving cash flow. From an environmental perspective, the replacement of paper CMR forms could save millions of sheets annually, leading to a substantial reduction in CO₂ emissions and paper waste within the V4 region. Conclusions: The findings confirm that the adoption of e-CMR improves economic performance, increases transparency, and contributes to sustainability, representing a crucial step toward a more competitive and environmentally responsible road freight transport sector in Europe. Full article

Background: Women’s empowerment has been significantly associated with improved child health outcomes. Cambodia, amid a rapid socioeconomic transition, offers a critical setting to examine how advancements in women’s empowerment over the past decade have influenced child immunization completion within the first two years of life. Methods: Data from the Cambodia Demographic and Health Surveys conducted in 2010, 2014, and 2021–22, encompassing 9222 women with recent births, were analyzed. Empowerment was measured across literacy and information access, employment, and decision-making domains. Multinomial logistic regression assessed associations between empowerment factors and completion of oral polio (OPV), diphtheria–tetanus–pertussis (DTP), pneumococcal conjugate (PCV), and measles–rubella (MR) vaccines, adjusting for demographic and socioeconomic variables. Results: Between 2010 and 2022, women’s empowerment in Cambodia improved significantly, marked by higher literacy rates, nearly half of women completing primary education, and expanded digital access, with 82.4% owning mobile phones and approximately 50% using the internet daily. While non-working women slightly increased, agricultural employment declined by 20%, and cash earnings rose from 48.7% to 82.5%. Most women participated in major household decision-making, either independently or jointly. Completion rates for OPV, DTP, and PCV ranged from 79% to 83%, while just over half of children were fully vaccinated against measles. Higher maternal education and cash earnings were positively associated with OPV, DTP, and PCV completion but negatively associated with measles vaccination. Women in agricultural work were less likely to complete measles vaccination for their children than non-working women. Joint decision-making regarding the use of respondents’ income was associated with a higher likelihood of measles non-completion (OR = 2.26, 95% CI: 1.13–4.51), whereas joint decision-making about respondents’ health care was associated with a higher likelihood of measles completion (OR = 0.42, 95% CI: 0.21–0.83). Conclusions: Women’s empowerment remains a key determinant of vaccination outcomes in Cambodia. The distinct pattern observed for measles suggests that vaccines scheduled for older ages encounter greater structural and behavioral barriers. To overcome these challenges, strategies should focus on enhancing defaulter tracking, implementing reminder systems, expanding outreach and catch-up programs, and improving the convenience of vaccination services. Full article

Convertible bond financing has gained significant traction in China’s capital market, yet it poses financial risks, particularly for highly leveraged firms. This study investigates how corporate financial traits influence the decision to issue convertible bonds, challenging the direct applicability of Western theoretical frameworks in China’s unique institutional context. We employ a natural experiment design, constructing a binary logistic regression model to analyze data from Chinese A-share listed companies that issued convertible bonds, corporate bonds, seasoned equity offerings, or rights offerings between 2022 and 2023. Our results reveal a paradox: contrary to risk-transfer theory, firms with lower leverage exhibit a stronger propensity to issue convertible bonds. Instead, motives are driven by high profitability, operational inefficiencies, and robust operating cash flow generation—traits that align with signaling and backdoor equity theories. The study identifies China’s convertible bond market as a dual-track system where regulatory screening distorts classical motives while market frictions amplify the role of convertible bonds in resolving information asymmetry. We conclude with targeted policy implications for regulators and corporate treasurers to enhance market efficiency and governance. Full article

Driven by the urgent need to decarbonize the logistics sector—where conventional vehicles exhibit high energy consumption and emissions, posing significant environmental sustainability challenges—electrification represents a pivotal strategy for reducing emissions and achieving sustainable urban freight transport. Despite rising global electric vehicle sales, the penetration rate of electric logistics vehicles (ELVs) remains comparatively low, impeding progress toward sustainable logistics objectives. Battery-swapping mode (BSM) has emerged as a potential solution to enhance operational efficiency and economic viability, thereby accelerating sustainable adoption. This model improves ELV operational efficiency through rapid battery swaps at centralized stations. This study constructs a tripartite evolutionary game model involving government, consumers, and BSM-ELV manufacturers to analyze market dynamics under diverse strategies. Key considerations include market scale, government environmental benefits, battery leasing/purchasing costs, lifecycle cost analysis (via discount rates), and resource efficiency (reserve battery ratio $\lambda$). MATLAB-2021b-based simulations predict participant strategy evolution paths. Findings reveal that market size and manufacturer expectations significantly influence governmental and manufacturing strategies. Crucially, incorporating discount rates demonstrates that battery leasing reduces consumer enterprises’ initial investment, enhancing economic sustainability and cash flow while offering superior total cost of ownership. Furthermore, gradual reduction of government subsidies effectively stimulates market self-regulation, incentivizes leasing adoption, and bolsters long-term economic/operational sustainability. Market feedback can guide policy adjustments toward fiscally sustainable support mechanisms. This study proposes the following management implications for advancing sustainable logistics: 1. Governments should phase out subsidies systematically to foster market resilience; 2. Manufacturers must invest in BSM R&D to improve efficiency and resource circularity; 3. Consumer enterprises can achieve economic benefits and emission reductions by adopting BSM-ELVs. Full article

This article presents an innovative model for managing cash logistics, grounded in the 4P concept of supply chain management. The 4P framework encompasses four interconnected elements: Product, Players, Processes and Policies. Developed with a focus on sustainability the 4P Cash Logistics Model is based on empirical research conducted in Poland, involving key participants in the cash supply chain—the central bank, commercial banks and cash handling companies. It also incorporates, albeit less explicitly, the perspectives of merchants and consumers as end-users of cash, offering a comprehensive view of the cash cycle management. The 4P Cash Logistics Model has been designed in a country-agnostic manner, employing the concept of a control tower, with the central bank positioned as the integrator of the cash supply chain. This paper proposes several improvements to cash logistics, including the introduction of a standardised electronic bank deposit slip and a multilateral platform for exchanging information on cash stocks and flows and for trading monetary value between banks and cash handling companies. Full article

Despite the rapid advancements in payment technologies, cash continues to play a significant role in modern society. This phenomenon presents a unique area of analysis, particularly within metropolitan societies such as those in the Jakarta metropolitan area in Indonesia. The present study aimed to investigate cash usage in the Jakarta metropolitan area by analyzing two cases: (1) cash usage in physical stores, and (2) intention to continue to use cash in daily activities. To this end, two analytical techniques were employed: logistic regression and structural equation modeling (PLS-SEM). These techniques were implemented using data from 400 respondents residing in the Jakarta metropolitan area. The results of the study indicate a preference for cash over digital payments in transactions among a significant proportion of the respondents. The determinant analysis further identified several factors influencing cash usage in physical stores, including education, employment status, and the number of bank accounts. Furthermore, this study identified attitudes toward behavior, subjective norms, and satisfaction as variables affecting the intention to continue using cash in Indonesian society. Full article

The present study focuses on how various firm characteristics influence their dividend payout policies. The study finds empirical evidence with regard to primarily two aspects of corporate dividend decisions—dividend increase and decrease, whose exploration is inadequate in the past literature. The random effect logistic regression has been considered in order to analyze the panel dataset from 2001–2002 to 2021–2022 including 3739 listed Indian firms. The empirical models are formatted based on the relevant dividend-related theories in the Indian context such as the residual theory, transaction cost theory, signalling theory, etc. Further, additional tests are conducted regarding the robustness of the reported results. The empirical results document that firm size, profitability, promoter holdings, cash holdings, and life cycle have a favourable influence on the propensity of both increasing and decreasing dividend payouts. In contrast, earnings volatility, leverage, and free cash flow reduce firms’ tendency to increase and decrease dividend payments. These results indicate that higher liquidity and ownership concentration provide firms with greater financial flexibility to adjust their dividend policies as per their prevailing opportunities. The findings of the study offer insightful information about how to arrange dividend policies with firm-specific traits which will be helpful for managers and investors to make better decisions. Full article

This systematic review analyzes and compares the predictive power between traditional financial ratios and cash flow-based ratios in estimating performance. Although traditional ratios of return on assets and debt to equity have received extensive application, cash flow ratios are increasingly valued by their dynamic insights into both liquidity and financial health. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, this review systematically analyzes 21 studies spread across various industries and regions. The results reveal that cash flow ratios usually dominate the traditional metrics during forecasting financial performance, especially in the presence of the use of machine learning models. Among the identified variables of the logistic regression model and gradient boosting model predictors, key indicators are those showing the return on investment, the current ratio, and the debt-to-asset ratio. The bottom line of the findings is that a combination of cash flow and traditional ratios gives a better understanding of a company’s financial stability. These results may serve as a starting point for investors, regulators, and entrepreneurs and may further facilitate informed decisions with a reduced chance of miscalculations that enhance proactive financial planning. In addition, future prediction models should integrate non-financial factors such as governance quality and market conditions to enhance financial health assessments. Additionally, longitudinal studies examining the evolution of financial ratios over time, along with hybrid statistical and machine learning approaches, can improve forecasting accuracy. Integrating cutting-edge analytical tools with the strength of financial metrics gives this study actionable insights that allow stakeholders to understand financial performance in a more nuanced sense. Full article

Despite the fact that a decade has elapsed since the establishment of the Eurasian Economic Union (EAEU), the impact of the EAEU on the economic development of its member states remains a subject of ongoing debate. This article examines the mutual trade in services between the Eurasian Economic Union (EAEU) countries, with the aim of assessing the impact of membership on it. The difference-in-difference model has been applied for impact assessment. The model utilizes data from five EAEU member countries—Armenia, Belarus, Kazakhstan, Kyrgyzstan, and Russia—capturing periods both before and after their EAEU membership, spanning 17 years in total. The results show that membership in the EAEU has significantly affected the exports of services from Russia and Belarus and has a less significant impact on the exports of services from Kazakhstan to the EAEU. At the same time, it has no significant effect on the exports of services from Kyrgyzstan and Armenia to other EAEU countries. In order to ascertain the challenges that exist, expert surveys among service exporters from Armenia have been conducted. Representatives of companies exporting various services to the EAEU have been selected as experts. The survey results indicate the presence of various barriers, including legal, logistical (for cargo transportation companies), and cultural challenges. These barriers encompass licensing difficulties, technical obstacles related to VAT refunds, a ban on cash payments, and difficulties with financial transfers due to sanctions against Russia. The findings of this research are of practical importance and can serve as a guideline for policymakers in the EAEU. Full article

Accounting account codes are created within a specific logic framework to systematically and accurately record a company’s financial transactions. Currently, accounting reports are processed manually, which increases the likelihood of errors and slows down the process. This study aims to use image processing techniques to predict cash codes in accounting reports, automate accounting processes, improve accuracy, and save time. Deep learning embeddings from Inception V3, SqueezeNet, VGG-19, VGG-16, Painters, and DeepLoc networks were utilized in the feature extraction phase. A total of six learning algorithms, namely Logistic Regression, Gradient Boosting, Neural Network, kNN, Naive Bayes, and Stochastic Gradient Descent were employed to classify the images. The highest accuracy rate of 99.2% was achieved with the combination of the Inception V3 feature extractor and the Neural Network classifier. The results demonstrate that image processing methods significantly reduce error rates in accounting records, accelerate processes, and support sustainable accounting practices. This indicates that image processing techniques have substantial potential to contribute to digital transformation in accounting, helping businesses achieve their sustainability goals. Full article

We examine the key drivers behind management decisions on share repurchase from various theoretical perspectives, including the free cash flow theory and the signaling theory/hypothesis. Specifically, we investigate the relationship between share repurchase and three key drivers: surplus cash, undervaluation, and leverage, along with several control variables. Using a sample of UK-listed non-financial companies from 2012 to 2022, we apply logistic regression, standard OLS regression, and Tobit regression to identify the factors influencing share repurchase. Our findings reveal that firms repurchase shares to distribute cash to shareholders with surplus cash and Surplus investing cash flow. This study also finds that undervalued smaller firms with lower market-to-book ratios and lower leverage are more likely to repurchase shares. Our study highlights the key factors motivating companies’ share repurchases, such as undervaluation, surplus cash, and leverage, examined from various theoretical perspectives, including the free cash flow theory and signaling theory. Focusing on the UK context, as well as adding a new angle in regard to applying logistic regression, standard OLS regression, and Tobit regression in combination, this research contributes to the existing body of knowledge in corporate finance. The outcome of the study has plausible implications for financial managers and investors in selecting stocks. Its practical implications will help investors gain a better understanding of the factors and forces influencing share repurchase decisions. Full article

Business sectors face disruptive challenges such as cash flow problems in finance and material flow problems in supply chain and logistics processes in today’s rapidly evolving and uncertain environment. Given these challenges, effective management of resource and material flows by managers has become increasingly complex. Supply chain management is crucial for businesses to sustain competitive market positioning. This study distinctively explores the interplay between supply chain management and the financial performance of manufacturing companies, highlighting the increasingly dynamic and competitive global markets. It scrutinizes the moderating roles of supply chain agility and flexibility in this relationship, offering diverse analytical perspectives. The research methodology involved surveying white-collar employees within these companies. Factor analysis was employed to affirm the scale’s validity, and the Hayes model 3 method was utilized to test hypotheses. Our research uncovered intricate interactions between supply chain management, agility, and resilience, underscoring their collective impact on financial performance. The thesis that supply chain management has a substantial impact on financial performance was corroborated by the study’s results. The study also emphasizes the moderating impact of supply chain agility in the relationship between financial performance and supply chain management. The results of the study that supply chain resilience moderates the moderating effect of supply chain agility indicate that the interaction between supply chain resilience and supply chain agility may affect the relationship between supply chain management and financial performance if supply chain resilience enhances the resilience of organizations to external challenges. These insights suggest organizations must integrate agility, management, and resilience considerations in their supply chains to optimize performance. This study contributes a novel viewpoint to the literature, providing strategic guidance for managerial decision making. Full article

In this paper, we used data from publicly traded restaurant firms between 2000 and 2019 to test the effectiveness of multiple discriminant analysis (MDA) and logistic regression (logit) in predicting the probability of bankruptcy in the restaurant industry. We constructed various financial ratios extracted from the financial information and analyzed them to determine the optimal models. Our results show that liquid ratios (particularly the quick ratio), operating cash flow, and working capital emerge as the most crucial indicators of potential bankruptcy filings for restaurant firms. The results also show that the logit model performs better within the sample. However, both models exhibit similar predictive capacities with out-of-sample data. Full article

As enterprises face increasing competitive pressures, financial crises can significantly impact on their capital operations, potentially leading to operational difficulties and, ultimately, market exclusion. Consequently, many enterprises have begun to utilize financial early-warning systems to guide and control risks. Currently, there is neither a universal nor comprehensive enterprise financial risk management model in China, nor a unified classification standard for enterprise financial risk management levels. This article takes financial data on A-share listed companies in 2020 as the data sample, including those with special treatment (represented by ST) or non-ST status. We establish an independent indicator system within the framework of profitability, solvency, operational capability, development potential, shareholders’ retained earnings, cash flow, and asset growth. The model is constructed employing the factor–logistic fusion algorithm. The factor part addresses the issue of collinearity among risk indicators, and the logistic part presents the results in probabilistic form, enhancing the interpretability of the model. The prediction accuracy of this model exceeds 89%. Finally, by applying the principles of interval estimation theory to statistical hypothesis testing, we categorize the risk levels into Grade A, representing significant risk; Grade B, representing moderate risk; Grade C, representing minor risk; and Grade D, representing no risk. This article aims to provide a comprehensive definition of a universal financial risk management early-warning model applicable to all enterprises in China. Full article

The rise of the e-commerce industry has markedly changed the global economy, providing customers with unparalleled access to goods and services. This study empirically examines online shoppers’ perceptions and preferences, focusing on their experiences with last-mile delivery (LMD) services and its impact on their shopping behaviour. This research employs machine learning classification and regression models for a large-scale analysis of customers’ responses, collected using an online survey in the main cities in Saudi Arabia, which is experiencing rapid e-commerce growth amidst a broader digital transformation. The findings highlight a strong consumer preference for timely LMD services, typically within a day of purchase, while noting dissatisfaction with exceedingly early delivery windows. The research emphasises the need to address customer dissatisfaction with delivery services to retain clientele, as many may switch retailers without informing the retailers. Additionally, a considerable trend towards preferring digital over cash-on-delivery payment methods was observed among online shoppers. Overall, this study provides valuable insights into the significant influence of LMD services on customer satisfaction and behaviour in the e-commerce sector. The use of robust machine learning models has revealed critical factors that can guide retailers and LMD providers in enhancing service delivery and customer experience, contributing to the broader discourse on e-commerce logistics efficiency and customer satisfaction. Full article