Search Results (88)

This paper examines the effectiveness of government subsidies in fostering innovation among small and medium-sized enterprises (SMEs), with a particular focus on additionality, crowding-out, and cherry-picking effects. Using the latest national survey data on Chinese high-tech SMEs, we apply robust econometric techniques—including the Heckman selection model, structural equation modeling (SEM), and propensity score matching (PSM)—to address potential selection bias and endogeneity. Our findings reveal that government subsidies positively influence both innovation inputs and outputs, suggesting a predominant additionality effect rather than a crowding-out effect, at least within high-tech SMEs. However, subsidies do not appear to alleviate the financial constraints faced by most SMEs, indicating that they are insufficient as a standalone solution to financing challenges. Furthermore, state ownership enhances input additionality but does not significantly impact output additionality. We also find evidence of cherry-picking in subsidy allocation, with loans exhibiting stronger additionality effects on innovation compared to grants and tax credits, which are more prone to selective intervention. These findings highlight the need for more targeted subsidy policies that prioritize financially constrained firms with high innovation potential while mitigating government selectivity. Our study offers valuable insights for policymakers seeking to design more effective innovation support mechanisms for high-tech SMEs. Full article

We investigate whether China’s Value-Added Tax (VAT) Credit Refund policy influences firms’ sustainable development capability (SDC), which reflects innovation-driven growth and green development. Exploiting the 2018 implementation of the VAT Credit Refund policy as a quasi-natural experiment, we employ a difference-in-differences (DID) approach and find causal evidence that the policy significantly enhances firms’ SDC. This suggests that fiscal instruments like VAT refunds are valued by firms as drivers of long-term sustainable and high-quality development. Our mediating analyses further reveal that the policy promotes firms’ SDC by strengthening artificial intelligence (AI) capabilities and facilitating intelligent transformation. This mechanism “AI Capability Building—Intelligent Transformation” aligns with the socio-technical systems theory (STST), highlighting the interactive evolution of technological and social subsystems in shaping firm capabilities. The heterogeneity analyses indicate that the positive effect of VAT Credit Refund policy on SDC is more pronounced among small-scale and non-high-tech firms, firms with lower perceived economic policy uncertainty, higher operational diversification, lower reputational capital, and those located in regions with a higher level of marketization. We also find that the policy has persistent long-term effects, with improved SDC associated with enhanced ESG performance and green innovation outcomes. Our findings have important implications for understanding the SDC through the lens of STST and offer policy insights for deepening VAT reform and promoting intelligent and green transformation in China’s enterprises. Full article

This research aims to investigate financing decisions of capital-constrained small and medium-sized enterprise (SME) manufacturers and distributors under a Green Supply Chain (GSC) framework. By evaluating the impact of Supply Chain Finance (SCF) instruments, this study utilizes Stackelberg game model to explore a decentralized decision-making system. To our knowledge, this investigation represents the first exploration of game models that uniquely compares financing through trade credit, where the manufacturer offers zero-interest credit without discounts with reverse factoring, while also considering distributor’s efforts on sustainable marketing under the impact of supportive government policies. Our study suggests that manufacturers should adopt reverse factoring for optimal profits and actively participate in distributors’ financing decisions to address inefficiencies in decentralized systems. Furthermore, the distributor’s demand quantity, profits and sustainable marketing efforts show significant increase under reverse factoring, aided by favorable policies. Finally, the results are validated through Python 3.8.8 simulations in the Anaconda distribution, offering meaningful insights for policymakers and supply chain managers. Full article

This study reframes closure intention in small- and medium-sized enterprises (SMEs) as an ex ante diagnostic signal rather than a post-mortem symptom of failure. The survey evidence from 385 Ecuadorian SMEs was analyzed in two stages; confirmatory factor analysis validated the scales capturing environmental pessimism and personal pressures, and a structural equation model confirmed that both latent constructs directly heighten exit propensity. A binomial logistic regression model correctly classified 71% of the cases and explained 30% of variance. Five variables proved decisive: low-level liquidity (OR = 0.84), a high debt-to-equity ratio (1.41), weak profitability (0.14), negative environmental perceptions (1.72), and a shorter operating tenure (0.91); the sector and the firm size were non-significant. The combined CFA-SEM-logit sequence yields practical early warning thresholds—debt-to-equity ratio > 1.4, current ratio < 1.0, and ROA < 0.15—that lenders, advisers, and entrepreneurs can embed in dashboards or credit screens. Recognizing closure intention as a rational, strategic step challenges the stigma surrounding exit and links financial distress and the strategic exit theory. Policymakers can use the findings to pair debt relief and liquidity programs with cognitive bias training that helps owners interpret risk signals realistically. For scholars, the results highlight closure intention as a dynamic learning process, especially pertinent in emerging economies characterized by informality and institutional fragility. Full article

The construction industry’s small and medium-sized enterprises (SMEs) face significant financial difficulties, exacerbated by disruptions such as COVID-19. Traditional supply chain finance models, relying on core enterprise credit, fail to address the dynamic nature of this sector. This study proposes a novel approach to value co-creation among stakeholders (core enterprises, suppliers, and financial institutions) through an evolutionary game theory framework. A stochastic model was developed to examine the strategic decisions of these parties, considering risk, penalty, and incentive coefficients. The results reveal that higher incentives encourage faster participation, while financial institutions are less sensitive to risk and penalty changes. This study provides new insights into promoting cooperative behavior and enhancing the sustainability of small and medium-sized enterprises (SMEs) in the construction industry through platform-based models. Full article

The study of carbon stocks in organic compounds within terrestrial ecosystems allows us to create a pool of potential carbon farming projects. At present, it is essential to assess the economic viability of natural-based solutions in order to develop strategies to encourage small and medium enterprises (SME) and governments to address climate change through specific measures. This article is devoted to the study of the economic efficiency of afforestation projects. The purpose of this study is to evaluate the economic efficiency of the project and, based on NPV sensitivity analysis, to identify the factors affecting economic efficiency. This will make it possible to formulate directions for stimulating the development of afforestation projects using tools to improve their economic efficiency. Based on data on the number of carbon credits issued, their price, and the costs and other revenue associated with the implementation of the afforestation project, a sensitivity analysis of economic efficiency was conducted, highlighting the most significant factors. Given that different tree species are characterized by variable seedling values, planting costs, and sequestration potentials, an afforestation project with the most carbon efficient tree species was selected as a pilot project. Black alder exhibits the most optimal proportion between the volume of carbon units released and the cost of planting trees. A sensitivity analysis of the project’s net present value was conducted in order to ascertain the factors that have the most significant impact on the project’s economic efficiency. These include the discount rate based on the cost of capital and the cost of tree planting. As a result, this article makes recommendations for improving the economic efficiency of afforestation projects for SME. The government’s role in enhancing the economic efficiency of such initiatives entails reducing the cost of capital through a reduction in the key rate or the provision of subsidies for the interest rate on bank credits. An alternative approach involves the granting of subsidies for the cost of tree planting, since the effects can be seen as a series of public goods, such as the creation of recreational areas and increased biodiversity of the ecosystem. Full article

Risk-taking is a critical driver of sustainable development and financial performance for firms, especially under environmental degradation constraints. Despite the increasing implementation of green credit policies, their impact on corporate risk-taking remains underexplored in the existing literature. This study investigates the effects and underlying mechanisms of green credit policies on risk-taking behaviors among Chinese listed companies from 2009 to 2019. Utilizing econometric methodologies, including Difference-in-Differences, mediation analysis, and moderation analysis, the findings reveal that green credit policies significantly enhance the risk-taking activities of polluting enterprises. These results are robust across various sensitivity tests. Additionally, the relationship between green credit policies and corporate risk-taking is mediated by debt maturity mismatch and moderated by ESG and executive compensation. Subgroup analyses indicate that large and state-owned polluting enterprises experience greater increases in risk-taking compared to their small, medium-sized, and private counterparts. Furthermore, executive remuneration notably amplifies risk-taking in private firms. This research provides essential micro-level insights to optimize the effectiveness of green credit policies in promoting corporate risk-taking and advancing sustainable development. Full article

China’s retail industry faces unique challenges in supply chain financing, particularly for small and medium-sized enterprises (SMEs) that often struggle to secure loans due to insufficient credit ratings and collateral in the business environment of China. This paper presents a groundbreaking approach that integrates real-time data elements into financing models, addressing the critical issue of information asymmetry between financial institutions and retail SMEs. By leveraging dynamic data such as orders, receivables, and project progress, our novel framework moves beyond the limitations of traditional asset-based lending, employing advanced data analytics for enhanced credit assessment and risk management. Applying the Stackelberg game theory, we explore the strategic interactions between suppliers and purchasers in the retail supply chain, identifying optimal financing strategies that improve capital flow efficiency and reduce overall costs. Our comprehensive data-driven model incorporates various scenarios, including the traditional supply chain financing model (Model T) and the innovative data-element secured financing model (Model G). The latter further considers risk assessment, risk appetite, volume, and schedule factors, providing a holistic approach to financial decision-making. Through rigorous mathematical modeling and numerical analysis, we demonstrate the effectiveness of our proposed framework in optimizing supply chain financing strategies. The results highlight the potential for data-driven approaches to unlock new financing opportunities for SMEs, fostering a more collaborative and efficient ecosystem within the retail industry. This study presents comprehensive data-driven strategies that unlock new financing opportunities for SMEs, providing a practical roadmap for stakeholders to foster a more collaborative and efficient supply chain financing ecosystem. The significance of studying supply chain finance for small and medium-sized enterprises (SMEs) lies in optimizing financing models to address the financing difficulties faced by SMEs. This helps improve their market competitiveness and promotes resource sharing and collaboration among all parties in the supply chain, thereby achieving sustainable economic development. Full article

A long-term strategy for China’s national development is to foster the growth of “Specialized, Refined, Niche, and Innovative (SRNI)” small and medium-sized enterprises (SMEs). However, these enterprises often face significant financing constraints due to their high technological input, high human capital input, light asset characteristics, and lack of effective collateral. Supply chain finance, as an important way to combine production and financing, could provide financial services in the real economy by alleviating these constraints of SMEs and improving the quality of credit so as to revitalize supply chain funds. This paper empirically examines the relationship between supply chain finance, fintech development, and financing efficiency using a sample of 757 “SRNI” SMEs in Shanghai and Shenzhen A-shares from 2013 to 2023. The findings reveal that supply chain finance significantly enhances the financing efficiency of “SRNI” SMEs. Moreover, the development of financial technology further amplifies such positive effects. This research contributes to the theoretical understanding of how supply chain finance and fintech impacts the financing efficiency of SRNI SMEs and provides valuable insights for evaluating SME financing efficiency. Full article

As an important pillar of the national economy, the green transformation of SMEs is the key to promoting sustainable economic development. However, SMEs generally face issues such as information opacity and high operational risks, which make it difficult for them to obtain traditional financing support, thereby hindering green development. Green Supply Chain Finance has opened up new financing channels for SMEs, but the accuracy of credit risk evaluation remains a bottleneck that limits its widespread application. This paper constructs a credit risk evaluation index system that integrates multiple sources of information, covering factors such as the situations of SMEs themselves, stakeholder feedback, and expert ratings. It compares and analyzes the performance of the genetic algorithm-optimized random forest model (GA-RF), the BP neural network, the support vector machine, and the logistic regression model in credit risk evaluation. The empirical results indicate that the GA-RF model is significantly better than the other models in terms of accuracy, precision, and F1 score, and has the highest AUC value, making it more effective in identifying credit risk. In addition, the GA-RF model reveals that the asset–liability ratio, the time of establishment, the growth rate of operating revenue, the time of collection of accounts receivable, the return on net assets, and daily shipments are the key indicators affecting the credit risk assessment. Full article

Technology-based small and micro enterprises play a crucial role in national economic and social development. Managing their credit risk effectively is key to ensuring their healthy growth. This study is based on corporate credit management theory and Wu’s three-dimensional credit theory. It clarifies the credit concept and measurement logic of these enterprises, considering their unique development characteristics in China. A credit evaluation system is constructed, and an innovative method combining machine learning with comprehensive evaluation is proposed. This approach aims to assess the credit status of technology-based small and micro enterprises in a thorough and objective manner. The study finds that, first, the credit level of these enterprises is currently moderate, with little variation. Second, financial information remains a key factor in credit evaluation. Third, the ML-AHP (Machine Learning-Analytic Hierarchy Process) combined weighting method effectively integrates subjective experience with objective data, providing a more rational assessment. The findings provide theoretical references and practical guidance for the healthy development of technology-based small and micro enterprises, early credit risk warning, and improved financing efficiency. Full article

This study explores how Myanmar’s sustainable business environment influences entrepreneurs’ performance, focusing on the mediating role of knowledge spillover effects of foreign direct investment (KFDI). Data were gathered from 308 entrepreneurs across micro, small, medium, and large enterprises in Myanmar using online surveys via Google Forms and Microsoft Forms. The analysis employed partial least squares structural equation modeling (PLS-SEM) with SPSS 29 and SmartPLS 4. The results reveal that (i) the economic environment exerts a substantial positive influence on entrepreneurs’ performance; (ii) access to credit and the social environment show no discernible impact on entrepreneurs’ performance; (iii) both economic and social environments positively influence KFDI; (iv) access to credit has no effect on KFDI; and (v) KFDI partially mediates the relationship between the economic environment and entrepreneurs’ performance while fully mediating the relationship between the social environment and entrepreneurs’ performance. However, KFDI does not mediate the effect of access to credit on entrepreneurs’ performance. These findings underscore the critical role of sustainable economic and social environments in enhancing entrepreneurs’ performance and attracting foreign firms. Policymakers should prioritize these dimensions of the business environment to foster growth, maximize KFDI, and support long-term entrepreneurial success. This approach will not only ensure the economic vitality of Myanmar’s entrepreneurial ecosystem but also contribute to broader social and environmental sustainability. Full article

This study investigates the prediction of small and medium-sized enterprise (SME) default rates in Republic of Korea by comparing the performance of three prominent time-series forecasting models: ARIMA, SARIMA, and Prophet. The research utilizes a comprehensive dataset provided by the Korea Credit Guarantee Fund (KODIT), which covers regional and monthly default rates from January 2012 to December 2023, spanning 12 years. By focusing on Republic of Korea’s 17 major cities, the study aims to identify regional and seasonal patterns in default rates, highlighting the critical role that regional economic conditions and seasonality play in risk management. The proposed methodology includes an exploratory analysis of default rate trends and seasonal patterns, followed by a comparative evaluation of ARIMA, SARIMA, and Prophet models. ARIMA serves as a baseline model for capturing non-seasonal trends, while SARIMA incorporates seasonal components to handle recurring patterns. Prophet is uniquely suited for dynamic datasets, offering the ability to include external factors such as holidays or economic shocks. This work distinguishes itself from others by combining these three models to provide a comprehensive approach to regional and seasonal default risk forecasting, offering insights specific to Republic of Korea’s economic landscape. Each model is evaluated based on its ability to capture trends, seasonality, and irregularities in the data. The ARIMA model shows strong performance in stable economic environments, while SARIMA proves effective in modeling seasonal patterns. The Prophet model, however, demonstrates superior flexibility in handling irregular trends and external events, making it the most accurate model for predicting default rates across varied economic regions. The study concludes that Prophet’s adaptability to irregularities and external factors positions it as the most suitable model for dynamic economic conditions. These findings emphasize the importance of region-specific and seasonal factors in tailoring risk forecasting models. Future research will validate these predictions by comparing forecasted default rates with actual data from 2024, providing actionable insights into the long-term effectiveness of the proposed methods. This comparison aims to refine the models further, ensuring robust financial stability and enhanced SME support strategies for institutions like KODIT. Full article

This study addresses a significant gap in the literature by comparing the effectiveness of traditional statistical methods with artificial intelligence (AI) techniques in predicting bankruptcy among small and medium-sized enterprises (SMEs). Traditional bankruptcy prediction models often fail to account for the unique characteristics of SMEs, such as their vulnerability due to lean structures and reliance on short-term credit. This research utilizes a comprehensive database of 7104 Belgian SMEs to evaluate these models. Belgium was selected due to its unique regulatory and economic environment, which presents specific challenges and opportunities for bankruptcy prediction in SMEs. Our findings reveal that AI techniques significantly outperform traditional statistical methods in predicting bankruptcy, demonstrating superior predictive accuracy. Furthermore, our analysis highlights that a firm’s position within the Global Value Chain (GVC) impacts prediction accuracy. Specifically, firms operating upstream in the production process show lower prediction performance, suggesting that bankruptcy risk may propagate upward along the value chain. This effect was measured by analyzing the firm’s GVC position as a variable in the prediction models, with upstream firms exhibiting greater vulnerability to the financial distress of downstream partners. These insights are valuable for practitioners, emphasizing the need to consider specific performance factors based on the firm’s position within the GVC when assessing bankruptcy risk. By integrating both AI techniques and GVC positioning into bankruptcy prediction models, this study provides a more nuanced understanding of bankruptcy risks for SMEs and offers practical guidance for managing and mitigating these risks. Full article

Climate change is a significant and urgent threat, gaining traction in the scientific community around the globe and requiring immediate action across many sectors. In this context, the digital economy could provide a mutually beneficial solution by utilizing innovation and technical breakthroughs to establish a sustainable future that addresses environmental deterioration, promotes economic growth, and encourages energy conservation. Against this background, this study examined the diffusion of innovation modeling-based factors affecting small and medium-sized firms’ (SMFs) adoption of the Internet of Things (IoT) technology and its impact on SMFs’ sustainability performance related to environmental, economic, innovation, and energy conservation perspectives. The key findings revealed that (i) the relative advantage, trialability, and observability drive IoT adoption. However, compatibility and complexity hinder IoT adoption. (ii) When prioritizing the adoption factors, the relative benefit is the strongest driver, and compatibility is the most significant barrier to IoT adoption. (iii) IoT technology adopter SMFs spent less on natural resources and more on renewable energy and environmental monitoring systems than non-adopter firms, boosting their environmental sustainability. (iv) IoT technology adopter firms had greater revenue, profits, and credit access than non-adopters and lower input costs, improving their economic sustainability. (v) IoT adopter firms spent more on innovative products than non-adopter enterprises, demonstrating innovation performance. (vi) Compared to non-adopter firms, IoT technology adopter SMFs had lower utility expenses and spent more on energy-efficient technologies. (vii) To realize the full potential of the IoT for a more sustainable and inventive future, authorities may pursue a variety of policy actions involving the strengthening and implementation of IoT technology standards and regulations, securing the incentivization of financial resources to SMFs, diverting the allocation of resources to research and development avenues, prioritizing the capacity development and environmental awareness, and focusing on IoT infrastructure development. Full article