Search Results (4,112)

This study provides a systematic analysis of technological capabilities and their impact on the digital transformation of industrial small and medium-sized enterprises (SMEs) to develop an Industry 4.0 environment. A semi-systematic literature review was conducted, from which the authors identified and selected 117 articles for their relevance and contributions to be further studied, and framed to achieve the research objectives. The authors identified the following variables of analysis: technologies and digitalization, technological capabilities, infrastructure, and strategies. Furthermore, the study identified promoting and hindering factors to the digital transformation of industrial SMEs, along with the technological capabilities that drive it, thereby generating opportunities and challenges for improving their integration into Industry 4.0. As the promoting and inhibiting factors, the following were determined: culture, leadership and entrepreneurial orientation, market orientation, processes and operations, people and business environment. Government support, access to technology, leadership and strategy, and employees stand out as factors that, when limited or absent, restrict SMEs’ ability to manage and increase their maturity level to integrate into Industry 4.0. From a resource-based view and dynamic capability theory, this research advances theory by presenting four propositions that integrate the identified technological capabilities and driving factors into a capability-based framework. This framework will serve as the foundation for a capability-based roadmap to guide industrial SMEs toward an Industry 4.0 environment and achieve sustainability outcomes. Full article

This study aims to identify and evaluate the impact of operational and organizational activities on reducing energy consumption in selected stages of agricultural transport equipment component production. The research focused on manufacturing companies from the agricultural machinery sector, characterized by high energy intensity and operational complexity. The empirical study was conducted among 121 experts representing manufacturing enterprises. The results indicate that enterprises achieve the highest level of implementation in organizational and Lean Manufacturing-related activities, particularly in reducing downtime, optimizing production scheduling, limiting empty transport runs, and eliminating overproduction. In contrast, technological and investment-intensive solutions, such as heat recovery systems, advanced energy monitoring, and digital simulation tools, remain implemented to a significantly lower extent. The findings also reveal a moderate level of maturity in energy management practices and limited integration of energy-related data into strategic decision-making processes. The study confirms the multidimensional nature of energy efficiency and highlights the importance of integrating Lean Manufacturing principles with digital technologies and systemic energy management. The proposed research model may serve as a practical diagnostic tool supporting the identification of key improvement areas for sustainable production development in manufacturing enterprises. This study aims to identify and evaluate the impact of operational and organizational activities on reducing energy consumption at selected stages of agricultural transport equipment component manufacturing. The research focused on manufacturing companies operating in the agricultural machinery sector, which is characterized by high energy intensity and operational complexity. The empirical study involved 121 experts representing manufacturing enterprises. The results indicate that organizational and Lean Manufacturing-related practices exhibit the highest levels of implementation, particularly those aimed at reducing downtime, optimizing production scheduling, limiting empty transport runs, and eliminating overproduction. In contrast, technology-intensive and capital-intensive solutions, such as heat recovery systems, advanced energy monitoring, and digital simulation tools, show substantially lower implementation levels. The findings also reveal a moderate level of maturity in energy management practices and limited integration of energy-related data into strategic decision-making processes. The findings confirm the multidimensional nature of industrial energy efficiency and highlight the importance of integrating Lean Manufacturing principles with digital technologies and systematic energy management. The proposed research model may serve as a practical diagnostic tool for identifying key areas for improvement in the development of sustainable manufacturing. Full article

Artificial Intelligence (AI) is a transformative phenomenon for global economies. Corporate insolvency regimes, equally, are economically significant as they promote preventive measures, company rescue/restructuring, liquidation of inefficient enterprises, and actions against errant directors. However, corporate insolvency is typically stymied by high costs and complexities associated with tasks such as asset management, investigations, and resolving creditor claims. These challenges are acute for stakeholders, such as Insolvency Practitioners (IPs), who often manage corporate insolvencies under information, time, and liquidity constraints. This paper presents and analyses findings from a survey (the survey was conducted by the research team of Akin Business Constructs and led by the author. All Figures/images used in this paper were generated by Akin Business Constructs.) on the use of AI in corporate insolvency proceedings. The objectives of the survey are to determine the existence, volume, and type of/rationale for AI use in corporate insolvency. This paper will, therefore, highlight the following: there is evidence of some usage of AI for varying tasks to support corporate insolvency proceedings; such usage points towards the adaptability of AI for simple and more complex corporate insolvency tasks; while the rationale for AI use in this context is predicated on efficiency gains, arguments against its use are principally centred on the accuracy of AI outputs; and the preponderance of AI use in this context is based on generic/non-specialised generative AI tools. Full article

The metalworking industry faces growing regulatory pressure to quantify and verify its product carbon footprint (PCF), driven by frameworks such as the European Union’s Carbon Border Adjustment Mechanism (CBAM) and emissions trading schemes (ETS). Traditional static accounting methods, based on generic emission factors and annual averages, are insufficient given the dynamic, multi-stakeholder nature of the sector’s supply chains. This study presents a systematic review conducted under the PRISMA 2020 protocol to identify, classify and critically evaluate the digital tools and technologies used to calculate, manage and verify PCF in this sector. From 495 records screened, 53 thematically relevant studies were analyzed and 5 sector-specific cases examined in depth. The results indicate that the Internet of Things (IoT) and smart sensor networks constitute the primary data-capture layer, while Machine Learning, Big Data and Digital Twins are the predominant processing technologies. Blockchain and verifiable digital credentials emerge as governance mechanisms that ensure the transparency, auditability and immutability of emissions inventories, enabling compliance through Digital Product Passports (DPPs). We conclude that digital decarbonization requires interoperable architectures integrating real-time capture, distributed traceability and common semantic standards; viability in small and medium-sized enterprises (SMEs) and interoperability across heterogeneous platforms remain the main research gaps. Full article

The study aims to identify and analyze the structural distribution of artificial intelligence-based solution (AIBS) application areas in modern enterprises, with particular emphasis on assessing the degree of concentration of reported AI application domains and their potential contribution to sustainable organizational development. The empirical study was conducted in 2025 using a questionnaire survey administered to 325 enterprises. Respondents were allowed to indicate multiple AI application areas, resulting in 668 valid AI application indications. Descriptive statistics, Pareto analysis, and the Herfindahl–Hirschman Index (HHI) were applied to examine the concentration of reported AI application areas. The results indicate that AI applications are unevenly distributed across organizational functions, with the highest concentration occurring in business and management, education, media and marketing, and information technology and cybersecurity. The calculated HHI value of 0.1639 indicates a moderate concentration of AI application areas, suggesting that organizations prioritize selected domains where AI can generate the greatest organizational and strategic value. From the perspective of sustainable development, the findings indicate that AI deployment supports more efficient resource allocation, organizational adaptability, data-driven decision-making, and digital transformation, which constitute important foundations of sustainable enterprise development. The HHI was used exclusively as a structural measure of concentration and does not assess the level, maturity, intensity, or quality of AI implementation within enterprises. Although the findings relate only to the analyzed sample, the study contributes to the literature by introducing a concentration-based analytical perspective on AI application areas and provides practical guidance for organizations seeking to implement AI in ways that support long-term sustainable development. Full article

This study investigates the asymmetric impact of Data Elements (DE) on Corporate Environmental Performance (CEP) in China, using a sample of 310 A-share listed firms from 2012 to 2021. The results show that DE significantly enhances CEP, with stronger effects observed in State-Owned Enterprises (SOEs) compared to non-SOEs. Additionally, heavily polluting firms are more responsive to DE than lightly polluting firms, indicating that DE has a stronger effect in industries with greater environmental challenges. The study also highlights regional differences, with firms located in areas with stricter environmental regulations experiencing a more substantial improvement in CEP. Mechanism analysis reveals that DE improves environmental performance through optimizing labor force structure, enhancing management efficiency, and alleviating financing constraints. These findings suggest that the impact of DE on CEP is not uniform, and firms should leverage DE more effectively, particularly in high-pollution industries, regulated regions, and state-owned enterprises, to support green development. The study provides valuable insights for policymakers and business leaders aiming to foster a green transformation in China’s economy. Full article

The fashion industry faces growing pressure to demonstrate credible sustainability performance across environmental, social and economic dimensions, yet integrated approaches remain underdeveloped, particularly for small and medium-sized enterprises and social enterprises operating in circular textile systems. This article examines the case of INS3RTEGA, a Spanish textile social enterprise that combines post-consumer textile waste management with protected employment for people with disabilities. Using an exploratory single-case design informed by Triple Bottom Line thinking, the study develops a pragmatic assessment strategy that combines a partial Social Return on Investment (SROI)-style calculation, a simplified carbon-based estimation informed by life cycle thinking, and a set of non-monetised social inclusion and employment indicators. The results reveal a distinctive configuration of integrated sustainability performance: substantial avoided climate-related burden and high textile recovery rates, strong structural inclusion outcomes through disability-inclusive employment, and a modest partial monetised return under conservative and incomplete valuation assumptions. The case illustrates how socially inclusive circular fashion models may generate meaningful sustainability value that remains only partially visible through conventional financial or fully monetised metrics. The article discusses methodological implications for hybrid impact assessment in resource-constrained organisations and policy implications for the emerging European framework on sustainable and circular textiles. Full article

Artificial intelligence, digital payments and platform-based services are reshaping the operating conditions of Africa’s informal economy and micro-enterprise sector. While mobile money, digital marketplaces, app-mediated logistics and algorithmic scoring systems are commonly presented as instruments of financial inclusion and enterprise modernisation, their effects on informal traders remain uneven, conditional and under-governed. This systematic review synthesises recent peer-reviewed evidence on AI-mediated digitalisation pathways for informal and micro-enterprises in Africa, with particular attention to mobile money, platform payments, app-based logistics, digital credit, algorithmic management and platform governance. Following PRISMA-informed systematic review procedures, this review analyses 60 peer-reviewed, DOI-bearing articles published between April 2022 and June 2026 through a mechanism–outcome synthesis approach. The final corpus was selected through database searching, duplicate removal, title-and-abstract screening, full-text eligibility assessment, quality appraisal and mechanism–outcome coding. The findings show that digitalisation can expand market access, reduce cash-handling risks, create transaction histories, strengthen customer reach, improve operational continuity and support household resilience. However, the same digital infrastructures may also intensify livelihood vulnerability through opaque scoring, unexplained account freezes, fee shocks, exclusionary verification procedures, algorithmic ranking losses, data extraction and weak dispute-resolution mechanisms. The review therefore argues that informal enterprise digitalisation should not be understood only as a technology adoption issue, but as a socio-technical systems governance challenge. The article contributes a governance-and-risk framework linking local infrastructure, platform and payment design, AI (artificial intelligence) mediation, adoption conditions and livelihood outcomes. It further identifies minimum policy and design protections required for inclusive and resilient participation, including transparent fees, proportionate verification, human appeal channels, explainable restrictions, data-use consent, timely settlement and contingency mechanisms during digital outages or erroneous flags. The review concludes that sustainable digital inclusion for African informal micro-enterprises depends not merely on access to digital tools, but on the fairness, transparency, recoverability and accountability of the systems through which traders participate. Full article

Body weight is one of the important phenotypic traits in sheep breeding for evaluating growth performance, planning flock management practices, and determining economic efficiency. In this study, biometric and machine learning approaches were jointly employed to predict body weight in Karayaka lambs using morphometric characteristics. The research material consisted of a total of 150 Karayaka lambs, including 75 males and 75 females, raised in a private enterprise located in the Erbaa district of Tokat province. The study evaluated body weight (BW), heart girth (HG), abdominal girth (AG), diagonal body length (DBL), body length (BL), withers height (WH), rump height (RH), hip width (HW), chest width (CW), and body condition score (BCS). Relationships among variables were examined using Pearson correlation analysis and principal component analysis (PCA). Multiple linear regression, Ridge Regression, LASSO regression, Random Forest, and Gradient Boosting algorithms were applied to predict body weight. Additionally, variable importance analysis and the SHAP (SHapley Additive Explanations) approach were utilized to enhance model interpretability. The results demonstrated significant differences between sexes in body weight and the majority of morphometric traits (p < 0.05). Correlation analysis revealed that abdominal girth and heart girth had the strongest associations with body weight. According to PCA results, the first principal component explained the majority of the total variance and represented overall body size. Among the evaluated machine learning models, the Gradient Boosting algorithm achieved the highest prediction performance, with a training R² of 0.962 and a test R² of 0.862, together with the lowest prediction errors (RMSE = 1.320 kg and MAE = 1.034 kg). The Random Forest model ranked second, achieving a test R² of 0.828. Variable importance and SHAP analyses indicated that heart girth and abdominal girth were the most influential features in predicting body weight. In conclusion, morphometric traits can be effectively utilized to predict body weight in Karayaka lambs, and the Gradient Boosting algorithm represents a robust approach offering high accuracy and interpretability. Full article

Artificial intelligence is increasingly integrated into physical products, industrial scenarios, and innovation ecosystems, yet management research has focused mainly on AI adoption rather than the depth of organizational integration. This study examines how AI embedding affects enterprise competitiveness in China’s embodied intelligence industry and whether competitive structure reconfiguration mediates this relationship. Using survey data from 266 firms and partial least squares structural equation modeling, the analysis shows that AI embedding positively affects both enterprise competitiveness and competitive structure reconfiguration. Competitive structure reconfiguration also improves enterprise competitiveness and partially mediates the focal relationship, with a variance accounted for value of 43.7%. By contrast, the moderating effects of data–computing foundation and scenario openness are not supported. These findings indicate that the competitive value of AI depends not only on adoption but also on its integration into R&D, decision-making, organizational coordination, and scenario development, as well as on the structural changes that follow. The study contributes by distinguishing AI embedding from AI adoption and by identifying competitive structure reconfiguration as a process mechanism linking embedded AI to technological, ecosystem, and rule-based competitiveness. Full article

To address the challenges of data security governance under new conditions and align with technological trends in data security, this paper proposes a graph algorithm-based method for identifying key personnel with critical permissions in the practical applications of large state-owned enterprises. This method can uncover potential permission influence factors within the system and evaluate the weight of influence from different perspectives, providing highly interpretable identification results. To tackle the issue of detecting anomalous user and entity behaviors in data security governance, a user and entity behavior anomaly detection method based on Generative Adversarial Networks (GAN) is introduced. Experimental results show that the proposed method achieves higher precision, recall, and F1-score averages compared to baseline models; specifically, an average F1-score of 0.75 versus 0.72 for LSTM-based TadGAN, 0.62 for ARIMA, and 0.65 for a commercial UEBA baseline across the three evaluation datasets. A data security platform was designed and developed, which plays a significant role in reducing data security risks, assisting enterprise compliance, and promoting data development and utilization. This platform has been applied in various centralized data management projects and meets the big data processing requirements in secure environments, demonstrating strong application and promotional value. Full article

This study examines the mediating role of financial decisions and the moderating role of digital transformation in the relationship between managerial characteristics and financial performance among small and medium-sized enterprises (SMEs) in Vietnam. Grounded in Upper Echelons Theory, Behavioral Theory of the Firm, Resource-Based View, and corporate finance theories, the study conceptualizes financial decisions as a formative higher-order construct comprising capital structure, investment, and working capital management decisions. The empirical analysis is based on survey data collected from 510 SMEs in Khanh Hoa Province, Vietnam. The data were analyzed using partial least squares structural equation modeling (PLS-SEM), including mediation and moderation tests. The results show that managerial characteristics have a significant positive effect on financial decisions, which in turn positively affect financial performance. Financial decisions are found to partially mediate the relationship between managerial characteristics and firm performance. In addition, digital transformation positively moderates the relationship between financial decisions and financial performance, indicating that firms with higher levels of digital capability derive greater performance benefits from their financial decisions. Overall, the findings highlight the importance of managerial attributes and digital transformation in shaping financial outcomes through effective financial decision-making in SMEs. Full article

The purpose of this study was to examine the influence of green supply chain management on organizational sustainability, where technology innovation and environmental performance are taken as mediating factors. The focus of this study was on the increasing need for better protection of the environment and the increasing demands of the sustainable development of industries, especially in developing countries, where there are limited empirical data available on sustainability activities. The collected sample comprised 387 managers and technologists from Libyan manufacturing companies using a survey instrument and a stratified random sampling technique. Validity scales were used, and the measurements were done using the five-point Likert scale. The data analysis involved the use of PLS-SEM, where SmartPLS 4.0 was used. It entailed the analysis of the measurement model as well as the structural model, with the bootstrapping being done for 5000 samples. The findings show that GSCM significantly affects the OS. Specifically, it has a positive impact on environmental performance (EP) and technological innovation (TI), which, in turn, enhance the OS. Among the mediating factors, the EP was found to be the strongest predictor and mediator of the OS, followed by TI. The developed model proved to have a high explanatory power, with a coefficient of determination (R²) of 0.660. Thus, the integration of GSCM with innovation and environmental activities can lead to the successful achievement of sustainability objectives among manufacturing enterprises. The study’s limitations are associated with its cross-sectional design and its focus on managers. Future research could employ longitudinal designs and examine other contexts, such as digital transformation, the institutional environment, and the organizational culture. Full article

Background: Logistics and supply chain resilience research typically assumes access to formal infrastructure, including contracted fleets, warehouses, inventory systems, and institutionalized contingency mechanisms. Yet many micro and small enterprises (MSEs) in emerging economies operate without these resources, leaving their logistics practices underexplored. This study examines how informal MSEs sustain operational continuity and recover from disruptions through tacit relational support. Methods: Fuzzy-set Qualitative Comparative Analysis (fsQCA) was applied to 400 informal MSEs in Ecuador’s manufacturing, services, and commerce sectors. Tacit network structure was measured using four function-specific name generators covering transport, storage, contingency management, and informal financing. Operational resilience was assessed through order fulfillment rate and recovery time after disruption. Results: No relational-support domain was individually necessary for resilience. Six sufficient configurations were identified, with consistency values from 0.817 to 0.978 and solution coverage of 0.433. One configuration combined contingency-management and informal-financing ties despite low membership in transport and storage networks, indicating a compensatory relational pathway. Conclusions: Tacit personal networks are better understood as context-dependent relational logistics support systems than as substitutes for formal infrastructure. The findings offer bounded, exploratory configurational evidence on selected pathways associated with operational resilience in informal MSEs. Full article

The growing complexity of cryptocurrency ecosystems has created substantial difficulties for anti-money laundering (AML) enforcement across jurisdictions. Existing AML information-sharing mechanisms are fragmented, slow, and constrained by privacy regulations. This paper presents CAHE-AML, a risk-adaptive architecture built on a context-aware hybrid encryption framework designed to enable secure and privacy-preserving cross-border AML data exchange between regulatory authorities and virtual asset service providers (VASPs). CAHE-AML integrates symmetric encryption, attribute-based encryption (ABE), a decentralized key governance model, and heuristic risk-scoring mechanisms for dynamic policy adaptation. Using an Ethereum Fraud Dataset, we compute risk scores to dynamically assign context-aware access policies across three hierarchical tiers: local, regional, and global. Experimental evaluation on an enterprise-grade multi-core system demonstrates high computational efficiency, achieving substantial throughput and low processing latency. The system identifies integrity breaches with high accuracy while maintaining a manageable data expansion ratio. These results highlight the computational feasibility of CAHE-AML’s cryptographic layer for real-time, high-frequency AML data processing in decentralized financial systems. Full article