Search Results (656)

To address the complex and uncertain causes of safety accidents in chemical enterprises, this study applied text mining techniques to systematically extract 29 causative factors from 422 accident reports. These factors were classified into five categories: personnel issues, resource management deficiencies, adverse organizational atmosphere, organizational process flaws, and inadequate supervision. Based on the extracted factors, a complex network model of accident causation was constructed. Using degree centrality, betweenness centrality, and eigenvector centrality, seven core causative factors were identified, along with multiple peripheral factors closely linked to them. Bayesian network-based sensitivity analysis further revealed the factors that exert the greatest influence on accident occurrence, and subsequent path analysis uncovered several critical accident propagation paths. The findings reveal core causative factors and critical propagation paths, which may inform the prioritization of risk control measures under conditions of limited resources. Full article

This study examines the financial resilience of small enterprises in Ukraine during the wartime crisis, addressing the lack of quantitative evidence on how regional military risks and adaptive strategies jointly shape SME stability. The analysis is based on a sample of 30 small agricultural enterprises from the eastern, central, and western regions of Ukraine using annual data for 2022–2024. To capture multidimensional resilience patterns, the study applies factor analysis, cluster analysis, and taxonomic assessment methods to evaluate financial performance, operational adaptability, and access to external resources. The findings show that resilience variation across the sample is strongly associated with enterprises’ ability to sustain revenue flows, control operating costs, and maintain a balanced capital structure. Three distinct resilience profiles were identified: high resilience in western regions (KT = 0.89), moderate resilience in central regions (KT = 0.81), and low resilience in eastern frontline regions (KT = 0.49). These results indicate substantial regional asymmetry linked to differentiated exposure to military threats. Building on these empirical insights, the study proposes a hybrid risk-management approach that integrates digitalization of financial operations, diversification of funding sources, and enhanced social engagement as mechanisms supporting adaptation under prolonged instability. The novelty of the research lies in combining regional risk exposure with multidimensional financial indicators to develop an evidence-based framework for assessing SME resilience in wartime conditions. Full article

As China’s industrialization progresses, the transformation of site properties across various regions has become increasingly common. Concurrently, with the relocation and market exit of some enterprises, the land occupied by the original factory sites has been developed for other uses. This study provides a comprehensive evaluation of soil and groundwater contamination levels and the associated ecological and health risks in abandoned industrial lands. The investigation focused on analyzing heavy metal and polycyclic aromatic hydrocarbon (PAH) contamination using various assessment methods, including the single-factor pollution index, Nemerow composite pollution index, and potential ecological risk index. These methods were used to assess the contamination levels of 11 heavy metals in both soil and groundwater. Additionally, health risk assessments for PAHs were conducted using the Incremental Lifetime Cancer Risk (ILCR) and Carcinogenic Risk (CR) models, considering both direct and indirect exposure pathways. The results indicated that the average concentration of each heavy metal in the soil did not exceed the screening thresholds, with all Nemerow index values falling below 1, suggesting that the site is not significantly polluted. Ecological risk assessment further revealed that most heavy metals posed minor risks, while some localized areas showed slight enrichment. Health risk assessments for PAHs indicated that, although the risks for both adults and children were within acceptable limits, the ingestion pathway for children showed a slightly higher risk compared to adults. The groundwater quality met Class IV standards, indicating no significant pollution. These findings provide data support and reference for future land-use planning, environmental management, and remediation strategies for abandoned industrial sites. Full article

Growing global awareness of climate change and environmental protection has fueled the rapid expansion of the green bond market. Building upon a theoretical framework that links green bond issuance with corporate governance and green innovation effects, this study employs a sample of Chinese A-share listed firms from 2014 to 2022 and applies a staggered difference-in-differences (DID) approach to empirically examine the impact of green bond issuance on corporate risk-taking and the underlying mechanisms. The results indicate that green bond issuance significantly reduces firms’ risk-taking levels. This effect operates primarily through three channels: increasing agency costs, enhancing information transparency, and exacerbating structural imbalances in green innovation. Furthermore, the risk-mitigating effect of green bonds is more pronounced in state-owned enterprises, firms with low audit quality, and firms operating in heavily polluting industries. These findings offer important implications for accelerating the diversification of China’s green financial system, improving firms’ risk management capabilities, and fostering the development of green productivity. Full article

The construction industry faces severe safety challenges with over 80% of accidents stemming from unsafe behaviors, yet traditional management overlooks the role of individual differences, and existing research fails to address the specific psychological mechanisms operative in this high-risk, dynamic environment. To effectively curtail unsafe behaviors in such high-risk environments, this study aims to reveal the underlying mechanisms through which personality traits influence unsafe behaviors. Grounded in causal chain theory, the theory of planned behavior, and trait activation theory, this study constructs a hypothetical model of personality traits and unsafe behaviors, with fluke mentality serving as a mediating variable and safety climate as a moderating variable. A comprehensive approach combining questionnaire surveys, confirmatory factor analysis, correlation tests, and linear regression was employed to test the hypotheses. The results indicate that neuroticism, openness, and extraversion have significant positive effects on unsafe behaviors, while conscientiousness has a significant negative effect; agreeableness shows no significant influence. Fluke mentality plays a partial mediating role between personality traits and unsafe behaviors, while safety climate plays a negative moderating role. By clarifying the cognitive pathways of individual differences, this study enriches the theoretical framework of unsafe behavior research. The findings provide a theoretical basis for construction enterprises to optimize safety management from the perspective of individual differences, offering practical pathways to promote high-quality development in the construction industry. Full article

Predictive analytics for financial distress plays an important role in enterprise risk management and everyday business decisions. Most past studies mainly use accounting indicators that come from standard financial reports. This study adds analyst-forecast financial indicators and places them in a data-driven business intelligence setup to improve how companies predict financial distress. We work with seven real datasets to test several predictive models and run statistical checks to see how analyst forecasts work with historical financial data. The results show that analyst-forecast indicators can clearly improve prediction accuracy and make the results easier to understand. From an enterprise systems view, this study pushes traditional financial distress prediction toward a smarter analytics setup that supports real-time, explainable, and data-based risk assessment. The findings provide useful ideas for both the theory and practice of designing business intelligence systems and financial decision-support tools for companies. Full article

In this study, we develop a stochastic optimal control model for corporate carbon management that synergistically combines emission reduction initiatives with carbon trading mechanisms. The model incorporates two control variables: the autonomous emission reduction rate and initial carbon allowance purchases, while accounting for both deterministic and stochastic carbon pricing scenarios. The solution is obtained through a two-step optimization procedure that addresses each control variable sequentially. In the first step, the problem is transformed into a Hamilton–Jacobi–Bellman (HJB) equation in the sense of viscosity solution. A key aspect of the methodology is deriving the corresponding analytical solution based on this equation’s structure. The second-step optimization results are shown to depend on the relationship between the risk-free interest rate and carbon price dynamics. Furthermore, we employ daily closing prices from 16 July 2021, to 31 December 2024, as the sample dataset to calibrate the parameters governing carbon allowance price evolution. The marginal abatement cost (MAC) curve is calibrated using data derived from the Emissions Prediction and Policy Analysis (EPPA) model, enabling the estimation of the emission reduction efficiency parameter. Additional policy-related parameters are obtained from relevant regulatory documents. The numerical results demonstrate how enterprises can implement the model’s outputs to inform carbon emission reduction decisions in practice and offer enterprises a decision-support tool that integrates theoretical rigor and practical applicability for achieving emission targets in the carbon market. Full article

As enterprises increasingly depend on software systems, security defects such as vulnerability disclosures, exploitations, and misconfigurations have become economically relevant risk events. However, their short-term impacts on capital markets remain insufficiently understood. This study examines how different types of software security defects affect short-horizon stock market behavior. Using a multi-model event-study framework that integrates the Constant Mean Return Model (CMRM), Autoregressive Integrated Moving Average (ARIMA), and the Capital Asset Pricing Model (CAPM), we estimate abnormal returns and trading-activity responses around security-related events. The results show that vulnerability disclosures are associated with negative abnormal returns and reduced trading activity, while exploitation events lead to larger price declines accompanied by significant increases in trading activity. Misconfiguration incidents exhibit weaker price effects but persistent turnover increases, suggesting that markets interpret them primarily as governance-related issues. Further analyses reveal that market reactions vary with technical severity, exposure scope, industry context, and firm role, and that cyber shocks propagate through both price adjustment and liquidity migration channels. Overall, the findings indicate that software security defects act as short-term information shocks in financial markets, with heterogeneous effects depending on event type. This study contributes to the literature on cybersecurity economics and provides insights for firms, investors, and policymakers in managing software-related risks. Full article

With the widespread deployment of Industrial Cyber-Physical Systems (ICPS), their inherent vulnerabilities have increasingly exposed them to sophisticated cybersecurity threats. Although existing protective mechanisms can block attacks at runtime, the risk of defense failure remains. To proactively evaluate and harden ICPS security, we design a distributed crowdsourced testing platform tailored to the four-layer cloud ICPS architecture—spanning the workshop, factory, enterprise, and external network layers. Building on this architecture, we develop a Distributed Input–Output Testing and Verification Framework (DIOTVF) that models ICPS as systems with spatially separated injection and observation points, and supports controllable communication delays and multithreaded parallel execution. The framework incorporates a dynamic test–task management model, an asynchronous concurrent testing mechanism, and an optional LLM-assisted thread controller, enabling efficient scheduling of large testing workloads under asynchronous network conditions. We implement the proposed framework in a prototype platform and deploy it on a virtualized ICPS testbed with configurable delay characteristics. Through a series of experimental validations, we demonstrate that the proposed framework can improve testing and verification speed by approximately 2.6 times compared to Apache JMeter. Full article

High cybersecurity risks and attacks cause information theft, unauthorized access to data and information, reputational damage, and financial loss in small and medium enterprises (SMEs). This creates a need to adopt information security systems of SMEs through innovation and compliance with information security policies. This study seeks to develop an integrated research model assessing the adoption of InfoSec systems in SMEs based on three existing theories, namely the technology acceptance model (TAM), theory of reasoned action (TRA), and unified theory of acceptance and use of technology (UTAUT). A thorough review of literature identified prior experience, enjoyment of new InfoSec technology, top management support, IT infrastructure, security training, legal-governmental regulations, and attitude as potential determinants of adoption of InfoSec systems. A self-developed and self-administered questionnaire was distributed to 418 employees, mid-level managers, and top-level managers working in SMEs operating in Riyadh, Saudi Arabia. The study found that prior experience, top management support, IT infrastructure, security training, and legal-governmental regulations have a positive impact on attitude toward InfoSec systems, which in turn positively influences the adoption of InfoSec systems. Gender, education, and occupation significantly moderated the impact of some determinants on attitude and, consequently, adoption of InfoSec systems. Such an integrated framework offers actionable insights and recommendations, including enhancing information security awareness and compliance with information security policies, as well as increasing profitability within SMEs. The study findings make considerable theoretical contributions to the development of knowledge and deliver practical contributions towards the status of SMEs in Saudi Arabia. Full article

This study examines the impact of Environmental, Social, and Governance (ESG) strategies on reducing air pollution in the West Kazakhstan region, a major hub for Kazakhstan’s oil and gas industry. A spatial analysis of atmospheric emissions reveals an uneven distribution of emission sources, predominantly concentrated in the northern industrialized part of the region, where the Karachaganak oil and gas condensate field is located. The ESG model of Karachaganak Petroleum Operating b.v. (KPO), implemented as an integrated management system based on Global Reporting Initiative (GRI) standards, is compared with the ESG strategies of leading oil and gas companies in Kazakhstan and globally, aligning with current international research trends. The analysis underscores the interdependence of technological and social aspects in the transition to a low-carbon economy, confirming the importance of integrating the environmental, social, and governance components of ESG into a unified strategic planning framework for sustainable development. Using econometric modeling, the study establishes a relationship between ESG indicators and the reduction in atmospheric pollution and provides a forecast for emission reductions by 2030. The key measures proposed to improve regional air quality are linked to long-term decarbonization strategies within the context of the sustainable development of the entire region. The proposed algorithm for implementing ESG principles helps to identify the concentration of functions and associated risks at different management levels within Highly Polluting Enterprises (HPEs) and optimizes business processes by focusing efforts on air pollution mitigation. The findings are applicable to other countries, as oil and gas producers worldwide face a number of common air pollution challenges. Full article

To prevent swine fever transmission, swine farms in China adopt enclosed management, making strict farm personnel biosecurity essential for minimizing the risk of pathogen introduction. However, current shower-in procedures and personnel movement records on many farms still rely on manual logging, which is prone to omissions and cannot support enterprise-level supervision. To address these limitations, this study develops a digital personnel management system designed specifically for the changing-room environment that forms the core biosecurity barrier. The proposed three-tier architecture integrates distributed identification terminals, local central controllers, and a cloud-based data platform. The system ensures reliable identity verification, synchronizes templates across terminals, and maintains continuous data availability, even in unstable network conditions. Fingerprint-based identity validation and a lightweight CAN-based communication mechanism were implemented to ensure robust operation in electrically noisy livestock facilities. System performance was evaluated through recognition tests, multi-frame template transmission experiments, and high-load CAN/MQTT communication tests. The system achieved a 91.4% overall verification success rate, lossless transmission of multi-frame fingerprint templates, and stable end-to-end communication, with mean CAN-bus processing delays of 99.96 ms and cloud-processing delays below 70.7 ms. These results demonstrate that the proposed system provides a reliable digital alternative to manual personnel movement records and shower duration, offering a scalable foundation for biosecurity supervision. While the present implementation focuses on identity verification, data synchronization, and calculating shower duration based on the interval between check-ins, the system architecture can be extended to support movement path enforcement and integration with wider biosecurity infrastructures. Full article

In typical executive compensation structures, higher corporate ranks are associated with greater pay. However, the reform of state-owned enterprises (SOEs) in China introduced strict salary caps for top executives, while lower-tier managers continued to receive market-based compensation, resulting in a phenomenon of pay-rank inversion—where subordinates earn more than their superiors. Leveraging this anomaly as a quasi-natural experiment, this study investigates the specific impact and underlying mechanism of pay-rank inversion on mergers and acquisitions (M&A) decisions and subsequent value realization within Chinese SOEs, thereby addressing the broad academic discourse on optimal executive compensation design. Employing a difference-in-differences (DID) approach with panel data spanning from 2007 to 2022, our analysis reveals that pay-rank inversion significantly reduces firms’ M&A intentions. Mechanistic analysis suggests that this negative effect arises primarily from diminished executive risk-taking. Furthermore, we find that the adverse impact is attenuated when CEOs possess longer tenures or receive equity-based incentives, but it ultimately undermines the realization of value post-M&A. These findings highlight the unintended consequences of high-level compensation reforms and emphasize the critical role of a well-structured pay hierarchy in sustaining executive incentives for strategic decision-making. Despite providing robust evidence, this study is subject to limitations, including its focus on measuring inversion only between the first and second management tiers. Future research should extend the analysis to the pay inversion between the listed firm and its controlling SOE group and explore alternative causal pathways beyond risk-taking, such as CEO work motivation, to deepen the understanding of high-level executive behavior. Full article

This article examines the growing relevance of photovoltaic (PV) energy amid rising electricity demand, sustainability goals, and the need for flexible energy management in households and enterprises. It analyzes six PV business models, ownership, leasing, Power Purchase Agreement (PPA), energy communities/peer-to-peer (P2P), crowdfunding, and subscription-based Solar-as-a-Service, using the Business Model Canvas (BMC) framework. A systematic literature review was combined with a unified BMC for each model, enabling structured comparison of value propositions, customer segments, cost structures, revenue streams, and risk allocation. The results show that no single universal model exists; each addresses different financial capacities, risk preferences, and strategic needs of households, SMEs, large enterprises, and energy communities. Significant differences were found in investment requirements, operational involvement, scalability, and potential for energy independence. The study’s novelty lies in providing a coherent, cross-model comparison using a standardized BMC approach, offering insights not systematically explored in previous research. These findings support informed decision-making for organizations considering PV adoption and provide a basis for further research on innovative energy management strategies. The topic is highly relevant in the context of the accelerating global energy transition, technological advances, regulatory changes, and increasingly diverse customer profiles, highlighting the need for comprehensive comparative analyses to guide flexible photovoltaic deployment. Full article

The importance of safety culture in high-risk sectors such as railway transport has gained increasing prominence, particularly within the evolving European regulatory landscape. Commission Delegated Regulation (EU) 2018/762 requires railway organisations to establish strategies for the continuous improvement of safety culture, emphasizing both behavioural and systemic dimensions of safety. This paper presents a structured literature review and proposes a conceptual model of performance indicators designed to support the implementation of these strategies in railway enterprises. Drawing on established continuous improvement methodologies—Kaizen, Six Sigma, and the DMAIC (Define–Measure–Analyse–Improve–Control) framework—the model aligns with Safety Management System (SMS) and Maintenance Management System (MMS) processes. The proposed indicators encompass domains such as risk assessment, change management, employee competence, incident reporting, and system monitoring. The model aims to transform railway organisations into learning systems capable of proactively adapting to emerging risks, including those related to cybersecurity as addressed by the NIS2 Directive. Through a structured literature review and conceptual synthesis, this study provides a theoretical foundation for the integration of continuous improvement and sustainability in safety management. The findings offer practical guidance for policymakers and railway operators seeking to strengthen data-driven, resilient, and sustainable transport safety governance in the European context. Full article