Search Results (794)

Adopting a circular economy approach requires new business models, multi-stakeholder engagement, and tailored financial models and mechanisms as core pillars. This paper examines the conditions needed to scale circular economy initiatives in Europe by analysing insights collected from the DECISO project and conducting a comparative analysis of 38 European projects. The study adopts a mixed methods approach that integrates an online stakeholder survey with inputs generated through participatory workshops and discussions of selected use cases. This combined approach is used to identify the main structural barriers limiting the maturity and investment readiness of circular economy projects, such as regulatory complexity, difficulties in accessing funding, and weak stakeholder dialogue mechanisms. The approach was also used for enabling factors that can support development of circular economy. Particular attention is given to the role of project development assistance, modular financing strategies, and de-risking tools, which are highlighted as crucial elements for supporting the technical and economic credibility of projects and attracting public and private investors. The article also identifies and addresses seven unresolved research gaps in the literature, including the lack of interoperable policy instruments, the absence of business models capable of integrating investor expectations, the paucity of integrated methodologies for assessing technical and economic regulatory feasibility, and the need for trust-building procedures. The findings suggest that the transition to a regenerative economy requires a systemic approach based on coherent policies, de-risking financial instruments, collaborative governance, and strategic technical support throughout the project development cycle. Full article

This study assesses liquidity, profitability, and working capital management (WCM) strategy in Polish manufacturing companies in the pressure-casting industry, drawing on evidence from the pre-COVID-19, COVID-19, and Russia–Ukraine war periods. Using panel data from 19 companies representing 90% of the Polish aluminum diecasting industry, we employ non-parametric tests (Mann–Whitney U and Kruskal–Wallis) to analyze the data. The period after the COVID-19 crisis coincides with the Russian–Ukrainian war. These countries are Poland’s neighbors. This period of uncertainty for Poland has led to supply chain disruptions and reduced investments. For manufacturing companies, this is dangerous because they have limited development opportunities. The results indicate the adoption of a conservative WCM strategy in Polish aluminum foundries during the pre-COVID-19, COVID-19, and Russia–Ukraine war periods, characterized by increased inventory levels, extended operating cycles in large firms. Additionally, the results showed reduced the level of receivables in large companies and visible decrease in the level of financial liquidity and profitability—however, these differences are not statistically significant. Polish aluminum foundries are adapting their WCM strategies toward an optimal, conservative approach that incorporates both safe and risky elements to ensure continued operations and profits. In addition, larger Polish aluminum foundries exhibit distinct liquidity patterns relative to smaller foundries, particularly in indicators of inventory, receivables, and fixed assets. In addition, the Russia–Ukraine war period exhibits distinct liquidity characteristics in Polish aluminum foundries compared with the COVID-19 and pre-COVID-19 periods, particularly in inventory turnover and operating cycle. The results of this study offer several novel contributions to the existing literature on financial security indicators by examining unexplored factors related to size and period. The results of this study have several practical implications for business leaders seeking to adopt an optimal liquidity, profitability, and WCM strategy. Full article

The widespread deployment of smart fault recorders (SFRs) in modern power grids faces critical bottlenecks: missing automatic discovery, low modeling efficiency, and incomplete validation coverage. Existing “one-key configuration” schemes and IEC 61850-based platforms still rely on manual intervention for device registration, model mapping, and rule verification, leading to configuration cycles of 2–3 days per substation. This work presents a system-level integration of existing mature techniques into a full-chain automated solution integrating multi-protocol active discovery, layered hierarchical modeling, and four-dimensional service validation. The main improvements include: (1) a link-to-application layer detection mechanism enabling plug-and-play device perception; (2) a dynamic parameter template adaptation algorithm that reduces manual adjustments by 85%; and (3) a four-dimensional rule library covering parameter legality, business logic rationality, cross-device coordination (including relay protection settings), and fault scenario adaptability. In provincial pilot substations, the proposed solution reduces single-device configuration time from 4.5 h to 1.2 h (73.3% improvement), lowers the error rate from 8.2% to 0.8%, and increases validation coverage from ~40% to 96.6%. The solution provides a feasible technical pathway for minute-level deployment and dynamic reconfiguration under flexible grid architectures. Full article

Macroeconomic volatility remains a major conundrum in SSA countries despite financial market liberalization reforms aimed at global financial market integration and unconstrained external capital flows to foster macroeconomic stability. Therefore, this study examines the impact of financial globalization on macroeconomic volatility by comparing how the overall and sub-component indicators of financial globalization have explained macroeconomic volatility in 39 Sub-Saharan African (SSA) countries from 2000 to 2023, using the panel-corrected standard error (PCSE) and two-step system generalized method of moments (2SGMM) for econometric modeling. The empirical results show that macroeconomic volatility responds differently to overall, de facto, and de jure measures of financial globalization: the overall and de facto measures are positively associated with macroeconomic volatility, whereas the de jure indicator is negatively related to it. These findings provide strong evidence in support of the real business cycle theory but against the external capital theory. Additionally, the study demonstrates that fiscal balance, central government debt, population growth rate, changes in leading export commodity prices, and institutional quality are significantly associated with macroeconomic volatility in Sub-Saharan African countries. Resultantly, the study recommends that Sub-Saharan countries create the right conditions, such as strengthening governance architecture and deepening financial market liberalization, to ensure the volatility-reducing benefits of global financial integration are achieved and sustained in SSA countries. Full article

Against the backdrop of the rapid expansion of rural tourism, an increasing number of migrant tourism entrepreneurs (MTEs) are moving from urban to rural areas. The integration of MTEs is crucial for both business sustainability and rural development. Using Hongcun village as a case study, this study adopts qualitative methods, including participant observation and semi-structured interviews, to explore the dynamic process of MTEs’ place integration from a social capital perspective. The findings suggest that MTEs’ place integration is manifested through four dimensions, namely economic embeddedness, social interaction, cultural acceptance, and emotional integration. Moreover, place integration and social capital form a mutually reinforcing cycle: integration practices generate localized social capital, which in turn facilitates deeper integration. The study also identifies that selective interaction leads to network stratification, highlighting the potential “dark side” of social capital. These findings suggest that effective integration depends not only on the accumulation of social capital but also on maintaining a dynamic balance among different types of social relationships. Full article

Estimating long-term prices for base metals is central to the financial viability of mining investments, yet prices remain highly volatile and difficult to forecast. This study systematizes the determinants of base metal prices and evaluates their empirical influence using daily and weekly data from the London Metal Exchange (LME) for aluminium, copper, nickel, and zinc between April 2005 and May 2017. In this context, the study aims to identify and evaluate the key economic, financial, and physical drivers of base metal prices, with particular emphasis on distinguishing between short-run predictive factors and long-run equilibrium determinants. After aligning metal prices with candidate explanatory variables, linear associations are quantified through Pearson correlations and alternative functional forms are explored for price modelling, including linear, log-linear, and selected nonlinear transformations. The methodology is complemented with econometric diagnostics. Explanatory variables are grouped into four categories: (i) supply–demand metrics (inventories, production–consumption balances, sales aggregates, and LME position data), (ii) business cycle and income proxies (global GDP growth, China Caixin PMI, the U.S. S&P 500 index, and China steel rebar futures), (iii) investment variables (cross-metal prices and Brent crude), and (iv) monetary indicators (U.S. and the U.S. 10-year yield). Results show that short-run price movements are mainly driven by business cycle indicators and inventory dynamics, while long-run trends reflect structural supply conditions. Monetary variables generate temporary price impulses, and prices tend to lead speculative positioning rather than the reverse. Full article

The cosmetic industry represents a major sector of the global economy and is expected to significantly grow in the coming years. To enhance consumer acceptance and address increasing sustainability concerns, cosmetic companies are actively seeking innovative solutions to mitigate their environmental, economic, and social impacts. In accordance with this, several scientific studies focus on the development, scale-up, and life cycle assessment of sustainable cosmetic products, especially those derived from side streams in accordance with circular economy principles. Various reviews have addressed this topic; however, they typically cover one or two of these dimensions, providing only a partial perspective. In particular, existing studies mainly analyze the types of side streams used and the resulting products, often lacking a comprehensive framework that can effectively support the translation of these approaches into industrial-scale production. The aim of the present review is to address this gap by providing a comprehensive analysis of the maturity level of development, scale-up processes, and life cycle assessment of cosmetic products based on agro-industrial side streams. This analysis is intended to support companies in the transition towards more sustainable practices by reducing carbon footprint and limiting the intensive extraction of virgin raw materials. The different approaches and methodologies proposed for the development and scale-up of sustainable cosmetic products from agro-industrial side streams are also analyzed, considering whether life cycle assessment has been performed. Furthermore, the most suitable business models will be selected as innovative and sustainable value chains capable of generating economic benefits, fostering local development, and enhancing resource efficiency and supply security. Full article

Carbon is an essential component in the regulation of climate systems through the global biogeochemical cycle. However, changes in land use/land cover (LULC) have reduced the capacity of terrestrial ecosystems like watershed to store carbon. This shows the need for a policy framework that balances conservative objectives with agricultural demands, as watersheds are required to support carbon storage and food production. Previous studies have generally assessed carbon dynamics or LULC change separately, with limited integration of policy-driven scenarios. Therefore, this study aimed to conduct multi-scenario carbon storage modeling to evaluate LULC protection strategies in the Cimanuk Watershed, Indonesia, an area experiencing significant LULC pressures. The method used consisted of Support Vector Machine (SVM)–Markov, the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST), Geodetector, and Getis-Ord Gi*. A total of four scenarios were used to project LULC and carbon storage in 2042, which included Business as Usual (BAU), Paddy Field Protection (PFP), Forest Protection (FOP), and Paddy Field and Forest Protection (PFFOP). The results showed that forest area declined by 39,400 ha between 2015 and 2025, thereby reducing carbon storage. The PFFOP scenario was identified as the most viable, combining the protection of paddy fields and forests to balance agricultural production and carbon sequestration. Among the factors analyzed, slope exerted the greatest influence on carbon storage. Spatial cluster analysis showed that carbon hotspots were predominantly located in the upper Cimanuk sub-watershed. These results offered valuable insights into scenario-based sustainable watershed management to optimize carbon storage and maintain agricultural function. Furthermore, the proposed framework showed promising potential for application in other tropical watersheds, serving as a reference for decision-makers in sustainable watershed management. Full article

Social media engagement drives both individual behavior and content dissemination, yet traditional analytics often reduce interactions to simple counts, obscuring the complex structures underlying user activity. In the highly competitive digital landscape, understanding how users interact with content is crucial for businesses aiming to optimize social media campaigns and maximize return on investment (ROI). Traditional engagement metrics, such as likes and shares, fail to capture the underlying structure and dynamics of user behavior. This study investigates the latent patterns of engagement by combining topological data analysis (TDA) with behavioral clustering across 100,000 posts on multiple platforms. Using persistent homology and k-nearest neighbour graphs, we reveal a primary bifurcation between Active (validation-focused) and Passive (consumption/propagation) users, nested four-strain substructures, and over 650 significant $H_1$ loops indicating recurring feedback cycles. Active users exhibit strong cluster cohesion and high engagement rates, while Passive users contribute broadly to content diffusion with slightly higher loop counts, highlighting distinct functional roles in social media dynamics. These findings provide a principled framework for targeting content, reinforcing feedback loops, and leveraging hub posts to amplify engagement. By linking topological structure to behavioral patterns, this work advances both the theoretical understanding of digital interaction and the practical design of more effective social media campaigns. Full article

Infrastructure projects of significant scale face persistent challenges in data coordination, scheduling, and cost control. Although individual digital tools are widely adopted in the construction sector, empirical evidence on their coherent systemic integration within a unified management cycle remains limited. This explanatory case study addresses that gap by examining Section 3 of the Sibiu–Făgăraș Motorway (17.61 km, 27 structures) in Romania—an ongoing TEN-T project. Evidence was collected during the active construction phase (January 2022–December 2024) from Common Data Environment (CDE) logs, BIM/BrIM model outputs, drone photogrammetry datasets, schedule and payment records, and Business Intelligence (BI) dashboards. The study demonstrates how six digital applications—CDE, model-based fabrication (LOD 400), 3D coordination, 4D/5D simulation, reality capture, and BI dashboards—were operationalized as a closed-loop Plan–Do–Check–Act (PDCA) cycle, functioning as a human-in-the-loop digital twin for project delivery. Illustrative operational indicators observed during implementation include an estimated 20% reduction in coordination-related RFIs, a 15% reduction in steel fabrication material waste, a reduction in payment validation cycle time from 15 days to approximately 2 days, and a 40% improvement in cash flow stability through data-driven activity re-sequencing. These findings suggest that systemic digital integration, rather than isolated tool adoption, supports more proactive and sustainability-aligned infrastructure project control. Full article

Amid rapid digital transformation in emerging economies, firms face increasing pressure to modernize accounting information systems (AIS) to enhance transparency and decision usefulness. Limited evidence exists on when IT investment yields differential returns across firm life-cycle stages, particularly in transition economies. Vietnam, as a transition economy with a state-led digital transformation agenda, provides a relevant context to examine whether firm age moderates the relationship between information technology (IT) investment and AIS quality. AIS quality is conceptualized through two dimensions: system quality and information quality. Using survey data from 649 Vietnamese enterprises, the study employs SPSS and the PROCESS macro (version 4.2) to test moderation effects. The results show that IT investment positively affects both system quality and information quality, while firm age also has a positive direct effect on AIS quality. However, the interaction between IT investment and firm age is negative, indicating that the marginal benefits of IT investment are weaker in mature firms than in younger firms, as structural rigidity and legacy system constraints limit the effective leverage of additional IT investment despite higher baseline AIS quality. The study contributes by linking the IS Success Model with an Organizational information processing theory-based contingency perspective, identifying firm age as a structural boundary condition that explains heterogeneous IT returns within an institutional digital governance context. The findings suggest that managers and policymakers should align digital investment and regulatory strategies with organizational maturity to support sustainable digital business ecosystems. Full article

The construction industry is one of the major sources of carbon emissions, and green retrofitting of buildings is an effective pathway to promoting sustainable development in the sector. However, existing research and implementation strategies often struggle to reconcile the needs of governments, businesses, and residents. Therefore, this study proposes a comprehensive research framework that employs bibliometric and text analysis methods to examine implementation barriers in retrofitting projects across four dimensions: policy, cost, technology, and resident satisfaction. The results indicate that retrofitting costs are the primary factor, while technology is a secondary factor. Furthermore, existing policies feature vague technical standards, insufficient incentives, and a lack of differentiation. Conflicts of interest and challenges regarding cost allocation persist throughout the renovation life cycle. Decision-support tools and renovation technologies face limitations and issues regarding applicability. Residents face constraints from multiple factors, including their knowledge base and economic capacity. Based on these findings, the government urgently needs to improve a differentiated policy system and encourage technological R&D and knowledge dissemination. Enterprises must actively respond to policies and optimize their technologies and management practices. Residents need to enhance their energy-saving awareness, participate in retrofitting efforts, and improve their energy consumption behaviors. Full article

Artificial Intelligence (AI) systems based on traditional Deep Learning (DL) are expected to play a leading role in the early detection of various diseases in healthcare applications. However, there are two major drawbacks of these systems: protecting patient privacy and obtaining sufficiently large, high-quality datasets to train reliable models. In traditional DL, collecting data from different sources on a single central server increases system complexity and raises serious privacy and security concerns. Federated Learning (FL) makes it possible to train models locally at multiple data locations while collaboratively improving a global model without exposing raw data, making it a promising architectural solution for privacy preservation. Although previous studies have reported that FL can achieve performance comparable to centralized DL approaches, traditional FL approaches often struggle to maintain consistent performance across different settings. This limitation becomes more noticeable when heterogeneous data distributions, modalities, and domains are involved. In these situations, client drift, overfitting, and generalization capability of the global model arise as major challenges. Thus, this study presents FedCycle as an incremental improvement of the FedAvg algorithm. It modifies the aggregation frequency. It aims to overcome these drawbacks and make the global model more stable and efficient. The FedCycle eliminates centralized data collection, enhances data security, and effectively reduces client drift and overfitting by supporting model training across heterogeneous data distributions, modalities, and domains. The performance evaluation involves extensive experiments using various real-world breast cancer image datasets, namely BREAKHIS, ROBOFLOW, RSNA, BUSI, and BCFPP. The presented method is evaluated against both traditional DL and FL approaches using accuracy, precision, recall, F1-score, and AUC. The findings confirm that applying fine-tuning within FedCycle reduces overfitting during training. As a result, FedCycle achieves performance improvements of 7.75% and 4.65% in accuracy and F1-score on the RSNA and BCFPP datasets compared to traditional DL approaches, while also providing an average improvement of approximately 1.5% in accuracy and F1-score across BREAKHIS, ROBOFLOW, and BUSI datasets compared to FedAvg. Full article

This study develops a Life-Cycle Sustainability Framework for circular business models in the context of post-war economic reconstruction and sustainable value chain transformation. Ukraine is used as the main case study due to its post-war reconstruction context and the need for resource-efficient economic recovery strategies. Under conditions of disrupted supply systems, resource constraints, and structural economic change, circular economy principles are conceptualized as strategic mechanisms for enhancing resilience, resource efficiency, and long-term competitiveness rather than solely as environmental policy instruments. Building on a structured hierarchy of circular business models aligned with product life-cycle stages, the framework emphasizes value retention through functional and usage extension beyond material recovery. The framework includes a hierarchical classification of 12 circular business models and a sustainability evaluation approach based on four criteria (K1–K4), which allow for the comparative assessment of circular business models and their combinations across life-cycle stages. Using secondary statistical data and policy review as analytical inputs, the study identifies sectors with high potential for circular transformation and sustainable investment, including agriculture, energy, industry, construction, and logistics. The results indicate that circular business models applied at early life-cycle stages, such as reuse, repair, and remanufacturing, provide the highest potential for reducing resource intensity and improving long-term economic sustainability, while recycling and energy recovery play a supporting role. These findings highlight how life-cycle-oriented circular strategies can support sustainable reconstruction pathways, strengthen international cooperation, and inform policy and managerial decision-making in transitional economic contexts. Full article

Robotic Process Automation (RPA) has emerged as a significant technology within Business Process Management (BPM), yet the academic literature remains fragmented. This paper presents a systematic literature review (SLR) of RPA in BPM, conducted following PRISMA 2020 guidelines and synthesizing 83 peer-reviewed journal articles published between 2015 and 2025. Systematic content analysis identifies six thematic dimensions: (1) process identification and selection; (2) implementation and life cycle management; (3) benefits and performance outcomes; (4) challenges and barriers; (5) technology integration; and (6) governance and organizational impact. These dimensions are synthesized into a conceptual integrated framework comprising three pillars—strategic alignment, operational execution, and continuous improvement—grounded in BPM theory and dynamic capabilities. The framework, while not empirically validated in this study, provides a theoretical foundation for future research and practice. Emerging themes include expanding sectoral breadth, empirical adoption model validation, and heightened focus on governance and sociotechnical work transformation. This review contributes a comprehensive thematic synthesis of RPA within BPM and offers a theory-grounded conceptual framework to guide researchers and practitioners in navigating intelligent process automation. Full article