At the heart of supply chain innovation lies the challenge of complexity, a pivotal force shaping the pathways to resilience and sustainable success in today’s business environment. Drawing from the resource-based view, dynamic capabilities, and contingency theories, this study examines the impact of green innovation strategies on supply chain resilience through the mediation role of green logistics management practices and the moderation effects of dynamic and structural supply chain complexity. Leveraging a quantitative approach, this study surveyed 404 managers from manufacturing firms in Turkey using a combination of physical and electronic questionnaires. Our analysis robustly supports the interconnected roles of green innovation strategy and logistics management practices in bolstering supply chain resilience. A green innovation strategy significantly enhances green logistics management practices and supply chain resilience. Further, green logistics practices contribute positively to supply chain resilience, acting as a crucial mediator in translating green innovation strategies into heightened supply chain resilience. Additionally, the effectiveness of green innovation strategies in improving green logistics management practices is amplified in less structurally complex supply chains. In contrast, the impact of green logistics practices on supply chain resilience becomes more pronounced in environments with lower dynamic complexity, highlighting the nuanced influence of supply chain complexity on sustainability efforts. The study’s findings contribute a novel perspective to the sustainability discourse, emphasizing complexity’s nuanced role as a determinant of supply chain resilience.

With the appearance of rising environmental challenges, businesses are increasingly recognizing the imperative of adopting sustainable practices. Within this context, the concept of green entrepreneurship has emerged as a pivotal force driving environmentally responsible innovation and economic growth. Employing Green Theory, this study examines the impact of green entrepreneurship (GEN) on business sustainability (BS) through the mediation role of green structural capital (GSC) and the moderation influence of environmental dynamism (ED). Using a quantitative research approach, 443 owners and managers of small and medium-sized enterprises (SMEs) (e.g., wholesale and retail trade, manufacturing, building materials, and scientific, professional, and technical activity enterprises) in Istanbul and Izmir, Turkey, were surveyed using both on site and electronic questionnaires. The data from the study were statistically analyzed using SPSS V.27 and structural equation modeling (SEM). The findings of this study show that green entrepreneurship (GEN) significantly enhances business sustainability (BS) and green structural capital (GSC), with GSC serving as a vital mediator in the GEN-BS relationship; however, environmental dynamism (ED) does negatively moderate the GEN-BS relationship, indicating that GEN’s positive impact on BS is accentuated under lower environmental uncertainties. Additionally, ED’s moderation effect is observed in the GEN-BS relationship via GSC, particularly under conditions of reduced dynamism, underscoring the nuanced role of environmental factors in sustainable entrepreneurship. The findings demonstrate green entrepreneurship and structural capital as key drivers for SME sustainability amidst environmental fluctuations.

The relationship between AI and management decision-making has received increasing attention in the literature, but the impact of AI on managerial decision-making through transformational leadership has not yet been thoroughly examined. Thus, this study investigates the impact of artificial intelligence on engineering management decision-making through transformational leadership. The participants include 385 employees drawn from manufacturing, construction, and information technology firms in Turkey. The data were processed using WarpPLS (7.0), and the estimation was conducted with the use of “partial least squares structural equation modeling (PLS-SEM)”. A positive and significant direct influence of “artificial intelligence” and “transformational leadership” on engineering management decision-making practices was demonstrated in this study, while transformational leadership was also found to have a significant mediating role in the relationship between artificial intelligence and engineering management decision-making practices. This study concluded with theoretical and practical implications for policymakers in the engineering industry by providing an integrated framework that allows for a nuanced examination of how AI impacts engineering management decision-making. It accounts for individual perceptions, leadership influences, and organizational adaptations, providing a comprehensive lens through which to analyze the complex interplay between AI technology, leadership, and decision-making processes in engineering management contexts. In addition, the findings of our study have significant implications for engineers and for governments creating standards to help preserve engineering businesses. Leaders and practitioners should research the instillation of values inherent to AI for an organization like engineering businesses to ensure that AI is being used to enable effective decision-making towards ensuring the accomplishment of their sustainable competitive advantage.

Modern workplaces increasingly use algorithmic management practices (AMPs), which shape task assignment, monitoring, and evaluation. Despite the potential benefits these practices offer, like increased efficiency and objectivity, their impact on workforce well-being (WFW) has raised concerns. Drawing on self-determination theory (SDT) and conservation of resources theory (COR), this study examines the relationship between algorithmic management practices and workforce well-being, incorporating job burnout (JBO) and perceived threat (PT) as parallel mediators and person–job fit (PJF) as a moderator. The research employed a cross-sectional survey design targeting 2450 KOSGEB-registered manufacturing SMEs in Istanbul, Turkey. A sample of 666 respondents participated, and the data were analyzed using Smart PLS 4, employing structural equation modeling to test the proposed model. The results indicated that algorithmic management practices significantly increased job burnout and perceived threat, both of which negatively impacted workforce well-being. However, the direct effect of algorithmic management practices on workforce well-being was non-significant. Person–job fit moderated the relationships between algorithmic management practices and both job burnout and perceived threat, further influencing workforce well-being. The findings underscore the critical need for organizations to balance algorithmic efficiency with human-centric practices. Prioritizing person–job fit and fostering transparency in algorithmic processes can mitigate negative impacts, enhance employee well-being, and drive sustainable organizational success in the digital age.

This study investigates how ethical leadership enhances environmental performance in manufacturing firms through the mediating role of green organizational identity (GOI) and the moderating role of green organizational culture (GOC). Grounded in the Resource-Based View (RBV) and Ecological Modernization Theory (EMT), the study develops an integrated framework that explains how leadership, identity, and culture jointly drive sustainability outcomes. Data were collected from 471 top management team members in Turkish manufacturing organizations and analyzed using structural equation modeling (SEM). The results indicate that ethical leadership positively affects environmental performance (β = 0.147, p = 0.009) and GOI (β = 0.381, p = 0.000). GOI, in turn, improves environmental performance (β = 0.359, p = 0.000) and mediates the leadership–performance link (β = 0.137, p = 0.000). The findings further demonstrate that GOC strengthens the impact of ethical leadership on GOI (β = 0.122, p = 0.000) and reinforces the GOI–performance relationship (β = 0.142, p = 0.000). Conversely, under low levels of GOC, the direct effect of ethical leadership on environmental performance weakens (β = −0.198, p = 0.000), underscoring culture as a boundary condition. These results advance RBV by highlighting ethical leadership as a strategic intangible resource and extend EMT by showing how identity and culture institutionalize ecological values. The study contributes theoretically by bridging RBV and EMT within a unified sustainability framework and provides practical guidance for managers to embed ethical leadership, identity, and culture into organizational practices to achieve superior environmental outcomes.

The construction industry faces significant challenges in ensuring worker safety, encompassing both physical hazards and mental health concerns. Drawing on Social Exchange Theory (SET), this study explores the impact of perceived leadership support (PLS) on employee safety behavior (ESB) and safety citizenship behavior (SCB), focusing on the mediating role of SCB and the moderating effect of safety learning (SL). A quantitative approach was employed, collecting a sample size of 410 construction workers from various companies within the Turkish construction sector. Data were collected through electronic questionnaires and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results reveal that PLS positively influences both ESB and SCB. Additionally, SCB mediates the relationship between PLS and ESB, while SL moderates the effect of PLS on both SCB and ESB, further strengthening the positive relationships. This study highlights the critical role of leadership support and safety learning in promoting safer behaviors within the construction industry, suggesting that organizations should cultivate a proactive safety culture to enhance safety outcomes.