Search Results (1,017)

The study examines economic and non-economic endeavors to explore the association between family involvement and financial decisions within family firms. The non-economic factors of a family drive the need to analyze the impact of socioemotional factors on the financial policies of the family firms. The study explores the impact of family ownership, family management, and family control drawn from agency theory and socioemotional wealth perspectives on the financial decisions of family firms. Our findings in support of the socioemotional wealth perspective show a positive relationship between family ownership and debt financing with a desire to finance growth and avoid control dilution, with an increase in the level of debt. However, the involvement of family members in management and the top management team leads to an adverse relationship between family ownership and debt level, exhibiting the risk-averse behavior of a firm, which drives firms to reduce debt levels. Overall, our findings suggest that the perceptions of the socioemotional wealth theoretical paradigm are important in determining capital structure decisions in family enterprises. The results are resilient to potential endogeneity and heterogeneity difficulties, which may assist scholars and practitioners in assessing capital structure decisions in emerging economies. Full article

This article analyzes statistics on the failure of technological equipment, assemblies, and mechanisms of agricultural (and other) machines associated with the breakdown or failure of gear pumps. It was found that the leading causes of gear pump failures are the opening of gear teeth contact during pump operation, poor assembly, wear of bushings, thrust washers, and gear teeth. It has also been found that there is a problem related to the restoration, repair, and manufacture of parts in the conditions of enterprises serving the agro-industrial complex of the Republic of Kazakhstan (AIC RK). This is due to the lack of necessary technological equipment, tools, and instruments, as well as centralized repair and restoration bases equipped with the required equipment. This work proposes to solve this problem by applying AM technologies to the repair and manufacture of parts for agricultural machinery and equipment. The study results on the stress–strain state of support bushings under various pressures are presented, showing that a fully filled bushing has the lowest stresses and strains. It was also found that bushings with 50% filling and fully filled bushings have similar stress and strain values under the same pressure. The difference between them is insignificant, especially when compared to bushings with lower filling. This means that filling the bushing by more than 50% does not provide a significant additional reduction in stresses. In terms of material and printing time savings, 50% filling may also be the optimal option. Full article

From the perspective of credit ratings, this paper studies the impact of air pollution on corporate credit costs and the impact mechanism. Based on 2007–2022 data on A-share listed companies in the Chinese capital market, this paper uses a two-way fixed effects model to examine the impact of air pollution on corporate credit costs and the impact mechanism. The results show that air pollution increases the credit costs for enterprises because air pollution affects the sentiment of rating analysts, leading them to give more pessimistic credit ratings to enterprises located in areas with severe air pollution. The moderating effect analysis reveals that the effect of air pollution on the increase in corporate credit costs is more pronounced for high-polluting industries, manufacturing industries, and regions with weaker bank competition. Further analysis reveals that in the face of rising credit costs caused by air pollution, enterprises tend to adopt a combination strategy of increasing commercial credit financing and reducing the commercial credit supply to cope. Although this response behavior alleviates corporations’ own financial pressure, it may have a negative effect on supply chain stability. This paper provides new evidence that reveals that air pollution is an implicit cost in the capital market, enriching research in the fields of environmental governance and capital markets. Full article

Highway accessibility plays a vital role in supporting local economic development, particularly in regions lacking access to sea or river ports. Recognizing the functional transformation of road infrastructure, the Chinese government has made substantial investments in its expansion. Nevertheless, a theoretical gap remains in justifying whether such investments yield significant economic returns. Drawing on the theory of economic externalities, this study investigates the causal relationship between highway development and regional economic growth, and assesses whether highway construction leads to an acceleration in growth rates. Utilizing panel data from 14 Chinese cities spanning 2000 to 2014, the synthetic control method (SCM) is employed to evaluate the economic externalities of highway investment. The results indicate a positive impact on surrounding industries. Furthermore, a growth rate forecasting analysis based on Back-Propagation Neural Networks (BPNNs) is conducted using industrial enterprise data from 2005 to 2014. The growth rate in the treated city is 1.144%, which is close to the real number 1.117%, higher than the number for the weighted control group, which is 1.000%. The findings suggest that the growth rate of total industrial output improved significantly, confirming the existence of positive spillover effects. This not only enriches the empirical literature on transport infrastructure but also provides targeted enlightenment for the sustainable development of urban economy in terms of policy guidance. Full article

In a business environment with rapidly growing digital technologies, knowledge management (KM) capability is an indispensable source for enterprise innovation activities. Nevertheless, there is limited understanding of the specific KM capability that leads to sustainable business model innovation (SBMI). This study therefore aimed to investigate the internal relationship between KM capability and SBMI by leveraging dynamic capability theory. A hierarchical regression analysis (HRA) and a fuzzy set qualitative comparative analysis (fsQCA) are used to analyze a sample of 115 Chinese innovative enterprises. The results indicate that organizational structure promotes information technology by improving human capital, and that information technology then stimulates collaboration depth by expanding collaboration breadth, thereby driving SBMI. Specifically, human capital, information technology, collaboration breadth, and collaboration depth play significant chain-mediating roles in the relationship between organizational structure and SBMI. This study contributes to the literature on KM and innovation management, extends the use of low-order and high-order dynamic capabilities in DCT, and assists managers in developing SBMI effectively. Full article

As digital transformation accelerates across Europe, cloud computing plays an increasingly central role in modernizing public services and private enterprises. Yet adoption rates vary markedly among EU member states, reflecting deeper structural differences in digital capacity. This study employs explainable machine learning to uncover the drivers of national cloud adoption across 27 EU countries using harmonized panel datasets spanning 2014–2021 and 2014–2024. A methodological pipeline combining Random Forests (RF), XGBoost, Support Vector Machines (SVM), and Elastic Net regression is implemented, with model tuning conducted via nested cross-validation. Among individual models, Elastic Net and SVM delivered superior predictive performance, while a stacked ensemble achieved the best overall accuracy (MAE = 0.214, R² = 0.948). The most interpretable model, a standardized RF with country fixed effects, attained MAE = 0.321, and R² = 0.864, making it well-suited for policy analysis. Variable importance analysis reveals that the density of ICT specialists is the strongest predictor of adoption, followed by broadband access and higher education. Fixed-effect modeling confirms significant national heterogeneity, with countries like Finland and Luxembourg consistently leading adoption, while Bulgaria and Romania exhibit structural barriers. Partial dependence and SHAP analyses reveal nonlinear complementarities between digital skills and infrastructure. A hierarchical clustering of countries reveals three distinct digital maturity profiles, offering tailored policy pathways. These results directly support the EU Digital Decade’s strategic targets and provide actionable insights for advancing inclusive and resilient digital transformation across the Union. Full article

Achieving high-level green technological innovation in heavily polluting enterprises is critical for advancing sustainable development, particularly in the context of both organizational and regional digitalization. This study adopts a configurational perspective grounded in the Technology–Organization–Environment (TOE) framework and integrates theoretical insights from resource orchestration, resource dependence, and IT capability theories. It investigates how different types of skilled talent, such as production, technical, sales, and managerial employees, contribute to green innovation under varying digital conditions. By applying fuzzy-set qualitative comparative analysis (fsQCA) to a sample of 96 publicly listed firms from China’s heavily polluting industries, this study identifies four distinct talent-based configurations that can lead to high levels of green innovation: production-centric, management-led, technical talent driven, and regionally enabled models. Each configuration reflects a specific system state in which a core group of skilled employees plays a leading role, supported by complementary functions, and shaped by the interaction between internal digital transformation and the external digital environment. This study contributes to the systems literature by elucidating the combinational roles of digital resources and talent deployment within the systemic TOE framework, and offers practical guidance for enterprises aiming to strategically utilize human capital to enhance green innovation performance amid ongoing digital transformations. Full article

This paper focuses on regeneration projects in ‘first-generation’ seaside resorts in England from the perspective of those leading and managing such projects. There have been numerous recent initiatives intended to revive seaside resorts and enable them to regain competitiveness, but limited analysis of what is necessary for such regeneration projects to be successful. This paper contributes to debates about the role of critical success factors (CSFs) in regeneration by identifying issues that apply to the specific context of seaside resorts. In-depth interviews were undertaken with ten managers responsible for individual projects focusing on the CSFs necessary for regeneration projects to succeed. Four such factors were identified: (1) the need to secure appropriate funding (and associated difficulties); (2) the importance of involving stakeholders (particularly the local authority and local community); (3) the need for a strong business plan (which must evolve as the project progresses); and (4) the importance of considering best practices elsewhere. The importance of each success factor varied by the sector (public/commercial/third) leading the regeneration initiative and varied at different stages of a regeneration project. These findings have practical implications for local authorities, commercial enterprises, and third-sector bodies in seaside destinations. Full article

As environmental issues become increasingly prominent, the sustainable practices of enterprises, especially measures at the product level, have garnered widespread attention from scholars. Although numerous studies have explored suppliers’ sales strategies for green products, they often overlook the scenario where suppliers simultaneously sell both green and non-green products.This study focuses on the sales mode selection strategies of suppliers when providing green and non-green products through e-commerce platforms. Utilizing a game model, we analyze the equilibrium strategies between suppliers and e-commerce platforms, and conduct sensitivity analyses to evaluate the impact of key parameters on decision-making. The results reveal that there are significant differences in the strategic preferences of suppliers and e-commerce platforms. However, when commission rates are moderate and green products incur high production costs, these preferences tend to align, leading to Pareto optimal outcomes. Additionally, our findings demonstrate that adopting differentiated sales modes for the two product types can effectively mitigate the problem of double marginalization, thereby enhancing the efficiencyof supply chains. These insights provide valuable guidance for e-commerce platform managers and suppliers in making decisions on sales models for managing multiple types of products. Full article

Predicting workload for tasks in manufacturing is a complex challenge due to the numerous variables involved. In small- and medium-sized enterprises (SMEs), this process is often experience-based, leading to inaccurate predictions that significantly impact production planning, order management, and consequently the ability to meet customer deadlines. This paper presents an approach that leverages machine learning to enhance workload prediction with minimal data collection, making it particularly suitable for SMEs. A case study application using supervised machine learning models for regression, trained in an open-source data analytics, reporting, and integration platform (KNIME Analytics Platform), has been carried out. An Automated Machine Learning (AutoML) regression approach was employed to identify the most suitable model for task workload prediction based on minimising the Mean Absolute Error (MAE) scores. Specifically, the Regression Tree (RT) model demonstrated superior accuracy compared to more traditional simple averaging and manual predictions when modelling data for a single product type. When incorporating all available product data, despite a slight performance decrease, the XGBoost Tree Ensemble still outperformed the traditional approaches. These findings highlight the potential of machine learning to improve workload forecasting in manufacturing, offering a practical and easily implementable solution for SMEs. Full article

This study examines the awareness and implementation of Circular Economy (CE) principles within Chile’s mining sector, which represents the world’s leading copper producer. We employed a mixed-methods approach, combining quantitative surveys with qualitative semi-structured interviews, to evaluate perceptions and implementation levels of CE practices across diverse organizational contexts. Our findings reveal a pronounced knowledge gap: while 73.3% of mining professionals reported familiarity with CE concepts, only 57.3% could provide accurate definitions. State-owned mining companies demonstrated substantially higher CE implementation rates, with 36.5% participating in eco-industrial collaborations and 51% conducting environmental audits, compared to their private counterparts. Small enterprises (1–100 employees) exhibited particularly limited engagement, as demonstrated by 71.8% lacking established sustainability reporting mechanisms. A considerable implementation gap was also identified; although 94.8% of respondents considered CE principles integral to business ethics and 89.6% recognized CE as essential for securing a social license to operate, only 20.8% reported that their organizations maintained dedicated CE units. The research presents actionable recommendations for policymakers, including targeted financial incentives and training programs for small- and medium-sized enterprises (SMEs) in mining services, the establishment of standardized CE performance metrics for the sector, and the integration of CE principles into strategic management education to accelerate sustainable transformation within Chile’s critical mining industry. Full article

In recent years, the intensifying frequency of natural disasters such as floods and typhoons has brought severe disruptions to the global supply chain system, making supply chain resilience an important academic research and practical application topic. This study explores the influencing factors and allocation effects of supply chain resilience under the risk of natural disasters, with a particular focus on its impact on sustainability. This paper conducts an empirical study on supply chain resilience in the context of natural disasters by using the Structural Equation Model (SEM) and Fuzzy Set Qualitative Comparative Analysis (fsQCA). Based on 407 valid questionnaires, the study found that supply chain flexibility, foresight, visibility, cooperation, and support significantly positively affected the enhancement of supply chain resilience. Furthermore, through the fsQCA method, this study identified a single configuration approach that leads to high supply chain resilience and clarified the complexity of resilience formation under different conditions. This research not only enriches the theoretical framework of supply chain resilience but also provides targeted strategies for enterprises and governments to enhance their resilience to natural disasters, thereby suggesting potential pathways to support economic stability, social well-being, and environmental protection, though further empirical validation is needed. Full article

Eliminating all forms of poverty is a core component of the United Nations’ Sustainable Development Goals. At the household level, poverty and income inequality significantly threaten farmers’ sustainable development and food security. Based on a sample of 1234 kiwi farmers from the Shaanxi and Sichuan provinces in China, this paper empirically examines the impact of participation in agricultural industry organizations (AIOs) on household income and income inequality, as well as the underlying mechanisms. The results indicate the following: (1) Participation in AIOs increased farmers’ average household income by approximately 19,570 yuan while simultaneously reducing the income inequality index by an average of 4.1%. (2) Participation increases household income and mitigates income inequality through three mechanisms: promoting agricultural production, enhancing sales premiums, and improving human capital. (3) After addressing endogeneity concerns, farmers participating in leading agribusiness enterprises experienced an additional average income increase of 21,700 yuan compared to those participating in agricultural cooperatives. Therefore, it is recommended to optimize the farmer–enterprise linkage mechanisms within agricultural industry organizations, enhance technical training programs, and strengthen production–marketing integration and market connection systems, aiming to achieve both increased farmer income and improved income distribution. Full article

Specialized and special new “little giant” enterprises are characterized by specialization, refinement, uniqueness, and innovation. They have relatively strong innovation capabilities and enterprise vitality. However, they also face problems such as high innovation costs, long investment recovery cycles, and high risks of investment returns, which lead to information asymmetry and financing difficulties. Government venture capital is a policy fund provided by the government and established with the participation of local governments, financial institutions, and private capital. They can utilize fiscal policies to attract market funds and support the development of key industries. Therefore, in this study, the first through sixth batches of specialized and special new “little giant” enterprises listed on the A-share and New Third Board from 2013 to 2023 were taken as samples, and their investment behavior and investment effects were empirically studied using the multiple linear regression method. The investment behavior of government venture capital tends to target strategic emerging industries. The intervention of government venture capital can enhance the innovation of “little giant” enterprises and has an impact through the intermediary mechanism of R&D investment. This paper draws conclusions and puts forward relevant policy suggestions for supporting the development of “little giant” enterprises. Full article

Hyperledger Fabric (HLF) is a leading permissioned blockchain platform designed for enterprise applications. However, it faces significant security risks from Denial-of-Service (DoS) attacks targeting its core components. This study systematically investigated network-level DoS attack vectors against HLF, with a focus on threats to its ordering service, Membership Service Provider (MSP), peer nodes, consensus protocols, and architectural dependencies. In this research, we performed experiments on an HLF test bed to demonstrate how compromised components can be exploited to launch DoS attacks and degrade the performance and availability of the blockchain network. Key attack scenarios included manipulating block sizes to induce latency, discarding blocks to disrupt consensus, issuing malicious certificates via MSP, colluding peers to sabotage validation, flooding external clients to overwhelm resources, misconfiguring Raft consensus parameters, and disabling CouchDB to cripple data access. The experimental results reveal severe impacts on the availability, including increased latency, decreased throughput, and inaccessibility of the ledger. Our findings emphasize the need for proactive monitoring and robust defense mechanisms to detect and mitigate DoS threats. Finally, we discuss some future research directions, including lightweight machine learning tailored to HLF, enhanced monitoring by aggregating logs from multiple sources, and collaboration with industry stakeholders to deploy pilot studies of security-enhanced HLF in operational environments. Full article