This study examines how Industry 4.0 (I4.0) technologies enhance supply chain resilience (SCR) in manufacturing firms by testing the mediating roles of supply chain agility (SCAG), supply chain adaptability (SCAD) and the moderating effect of customer integration (CI). Grounded in the Resource-Based View (RBV) and Dynamic Capabilities View (DCV), the research conceptualizes digital technologies—such as the Internet of Things (IoT), big data analytics, and artificial intelligence (AI)—as both strategic resources and enablers of dynamic capabilities in turbulent environments. Survey data were collected from 273 manufacturing firms in Turkey, a context shaped by geopolitical and economic disruptions, and analyzed using structural equation modeling (SEM). The results indicate that I4.0 technologies positively affect SCR directly and indirectly through SCAG and SCAD. However, while agility consistently strengthens resilience, adaptability shows a negative mediating effect, suggesting context-specific constraints. CI significantly amplifies the positive impact of I4.0 on SCR, underscoring the importance of external relational capabilities. Theoretically, this research advances supply chain literature by integrating RBV and DCV to explain how digital transformation drives resilience through distinct dynamic capabilities. Practically, it offers guidance for managers to combine digital infrastructure with collaborative customer relationships to mitigate disruptions and secure long-term performance. Overall, the study provides an integrated framework for building resilient supply chains in the digital era.

Educational data mining (EDM) plays a crucial role in developing intelligent early warning systems that enable timely interventions to improve student outcomes. This study presents a novel approach to student performance prediction by integrating metaheuristic hyperparameter optimization with explainable artificial intelligence for enhanced learning analytics. While Light Gradient Boosting Machine (LightGBM) demonstrates efficiency in educational prediction tasks, achieving optimal performance requires sophisticated hyperparameter tuning, particularly for complex educational datasets where accuracy, interpretability, and actionable insights are paramount. This research addressed these challenges by implementing and evaluating five nature-inspired metaheuristic algorithms: Fox Algorithm (FOX), Giant Trevally Optimizer (GTO), Particle Swarm Optimization (PSO), Sand Cat Swarm Optimization (SCSO), and Salp Swarm Algorithm (SSA) for automated hyperparameter optimization. Using rigorous experimental methodology with 5-fold cross-validation and 20 independent runs, we assessed predictive performance through comprehensive metrics including Coefficient of Determination (R²), Root Mean Squared Error (RMSE), Mean Squared Error (MSE), Relative Absolute Error (RAE), and Mean Error (ME). Results demonstrate that metaheuristic optimization significantly enhances educational prediction accuracy, with SCSO-LightGBM achieving superior performance with R² of 0.941. SHapley Additive exPlanations (SHAP) analysis provides crucial interpretability, identifying Attendance, Hours Studied, Previous Scores, and Parental Involvement as dominant predictive factors, offering evidence-based insights for educational stakeholders. The proposed SCSO-LightGBM framework establishes an intelligent, interpretable system that supports data-driven decision-making in educational environments, enabling proactive interventions to enhance student success.

The banking sector faces increasing pressure to balance financial performance with sustainability goals amid ongoing digital transformation, regulatory reform, and societal expectations for ethical responsibility. Entrepreneurial leadership has emerged as a pivotal approach for addressing these challenges; however, the behavioral and contextual mechanisms through which it shapes sustainability remain insufficiently understood. Drawing on Social Learning Theory (SLT), this study investigates how and when entrepreneurial leadership enhances sustainable bank performance through the mediating role of employee creativity and the moderating influence of an innovation-oriented climate. A two-wave multi-source survey was conducted among 459 employees and managers from Turkish banks, and the hypothesized model was tested using structural equation modeling to ensure robust empirical validation. The results indicate that entrepreneurial leadership significantly fosters employee creativity, which serves as a critical behavioral mechanism linking leadership behaviors to sustainability-oriented outcomes. Moreover, an innovation-oriented climate strengthens both the direct effect of entrepreneurial leadership on creativity and its indirect effect on sustainable bank performance, emphasizing the contextual importance of supportive organizational environments. Theoretically, this study extends the leadership and sustainability literature by illustrating how learning and behavioral modeling processes translate leadership vision into sustainable performance. Practically, it offers actionable guidance for bank executives to develop innovation-oriented climates, empower employees’ creative engagement, and design incentive systems that align leadership behavior with sustainability imperatives, thereby enhancing resilience and long-term competitiveness.

Leaders play a crucial role in shaping employees’ safety behaviors (SBs). However, research on broader leadership styles has yielded inconsistent findings, emphasizing the need for a more tailored leadership approach, especially in high-risk industries, such as construction. Applying the social exchange theory and the positive organizational behavior framework, this study examined the impact of safety-specific transformational leadership (SSTL) on SB. This study uses a quantitative research design to collect data from employees of Turkish construction firms in Ankara and Istanbul. A cross-sectional research design was employed, with purposive sampling of data collected from 706 construction workers in Türkiye. The findings indicate that SSTL positively influences both SB and knowledge sharing, whereas knowledge sharing enhances SB. Knowledge sharing mediates the relationship between SSTL and SB. This study’s findings suggest that implementing safety-specific transformational leadership (SSTL) can significantly improve safety behavior among construction workers by promoting knowledge sharing and psychological safety.

This study unpacks how supply chain management, knowledge management, and green innovations act as critical levers in driving energy transition while safeguarding environmental sustainability in an era of escalating climate challenges. Focusing on the G7 nations and using data from 2000 to 2022, this study addresses two central research questions: (i) What are the key determinants of energy transition (ET)? And (ii) what are the key determinants of environmental degradation (ED)? To answer these questions, the study applied Lewbel IV-2SLS and FGLS estimators, revealing that in G7 economies, supply chain performance reduces environmental degradation but slows energy transition. Digital transformation also hinders transition in the short run, though at higher maturity it helps curb degradation. Trade openness supports transition but increases degradation, while urbanization promotes transition. Knowledge management and green innovation follow an inverted-U pattern, and control of corruption shows mixed effects. Energy transition itself strongly reduces environmental degradation, whereas economic growth generally increases it. Based on these results, the study formulates a set of policy recommendations to align economic growth with long-term sustainability goals.

By weaving together cutting-edge AI robotics, resilient global supply chains, universal school enrollment, and dynamic public–private energy investments, this study unveils a powerful, integrated blueprint for driving environmental sustainability in the 21st century. In doing so, the study employed advanced machine-learning techniques—specifically, it introduced an ANN-enhanced wavelet quantile regression framework to uncover the multiscale determinants of China’s ecological footprint. Leveraging quarterly data from 2011/Q1 through 2024/Q4, it reveals dynamic, quantile-specific relationships that conventional approaches often miss. The result from the study demonstrates that robotics, supply-chain integration, public–private energy investments, gender-parity enrolment, and economic growth each exert a positive—and often escalating—upward pressure on the nation’s ecological footprint over short, medium, and long horizons, with the strongest effects in high ecological footprint contexts. The study proposes a significant, tailor-made policy based on these findings.