Ibrahim Alnafrah

The capacity to coordinate human capital and knowledge resources to sustainable innovation has emerged as the final boundary to organizational survival in the transition to a global green economy. This research paper explains the strategic design of Green Human Resource Management (GHRM), Green Knowledge Sharing (GKS), and Green Innovation (GI) as an integrated architecture towards the optimal Organizational Environmental Performance (OEP). Based on the Knowledge-Based View (KBV) and the Natural Resource-Based View (NRBV), the study outlines how manufacturing companies develop path-dependent capabilities in which human capital and knowledge intangibles are integrated into ecological excellence. The empirical data were collected via a multi-respondent approach from an initial sample of 500 manufacturing SMEs from China, yielding 309 final valid and complete responses. The outcomes of Covariance-Based Structural Equation Modeling (CB-SEM) conducted via AMOS v.26 demonstrate strong evidence that GHRM positively influences GKS and leads to enhanced green innovation, which subsequently improves OEP. In particular, the results support the view that green knowledge is a strategic-level resource, concomitant with KBV, that enables the smooth incorporation of idiosyncratic environmental knowledge into routine operations. This study provides a clear strategic road map, which holds that the harmonization of human resource systems with knowledge-based resources is the most plausible path to achieving high levels of environmental performance in an ever-more institutionalized, environmentally conscious global supply chain.

Customer returns are an unavoidable and increasingly costly challenge in business operations, especially in online marketplaces. This study addresses this issue by introducing a practical multi-supplier closed-loop location-inventory problem (CLLIP) that incorporates customer returns. The objective of the CLLIP is to minimize overall supply chain costs by optimizing facility location and inventory management strategies. To solve this complex problem, an improved hybrid artificial bee colony algorithm (IHABC) is proposed, which integrates two novel search equations to generate candidate solutions during the employed bee and onlooker bee phases, effectively balancing exploration and exploitation. The performance of IHABC is evaluated against various artificial bee colony variants as well as the commercial solver Lingo. The results of numerical experiments demonstrate that IHABC consistently outperforms competing methods, achieving superior solutions with the lowest mean values and optimal total cost results, while also requiring less computation time. The results of numerical experiments demonstrate that IHABC consistently outperforms competing methods, achieving up to 29.97% improvement in solution quality over the standard ABC algorithm. These findings confirm that IHABC is a highly effective and efficient tool for solving the proposed CLLIP. Furthermore, a sensitivity analysis is conducted to provide actionable insights, enabling managers to make informed and strategic decisions in real-world supply chain operations.

This study examines patenting patterns to identify inefficiencies in innovation systems and proposes a blockchain‐based solution to advance sustainable development goals (SDGs) 4, 8, 9, and 17. Using a dataset of 13,910 patent documents, the research employs a multi‐step methodology: a patent landscape analysis, network analysis, and structural equation modeling (SEM). Our findings indicate that patents linked to non‐competitive technologies and low originality scores are more likely to become inactive. Furthermore, the study identifies several factors contributing to the low efficiency of the innovation system, including closed technology transfer processes and weak collaborative synergies among stakeholders. To address these challenges, a blockchain‐based rewards system is proposed to foster beneficial relationships among innovation stakeholders, supporting sustainable innovation practices. This study enhances our understanding of innovation theory and provides insights for policymakers, linking the proposed solution to achieving SDGs 4, 8, 9, and 17 through sustainable innovation.