<b>Impact of Green Supply Chain Management on Sustainable performance: A dual mediated-moderated analysis of Green Technology Innovation and Big Data Analytics Capability powered by Artificial Intelligence</b>

<b>Background</b>This paper aims to empirically test a comprehensive interrelationship among green supply chain management (GSCM), green technology innovation (GTI), waste management (WM), big data analytics capability powered by artificial intelligence (BDAC-AI), and their collective impact on sustainable performance (SP) in organizational contexts.<br><b>Method</b>This study has been conducted in food processing sector of Pakistan. The respondents include 495 managers working in the food processing industry. A structural equation modeling (SEM) approach is used to examine direct and indirect relationships between the variables. Originality occurs when the integration of TAM and DCT is looked at to understand sustainable practices in the context of the provided model.<br><b>Results</b>The research of this paper highlights that GSCM, GTI, WM, and BDAC-AI have a positive, strong, and direct impact on SP. Furthermore, GTI and WM only partially mediate the link between GSCM and SP, whereby the two moderate the link. Also, BDAC-AI shows the moderation effect on the relationship between GTI and SP.<br><b>Conclusion</b>This study has managerial implications, including strategies that involve using theoretical frameworks in technological acceptance and dynamic capabilities to support sustainable initiatives. However, it is worth noting that the findings provide a practical contingency for managers and businesses interested in implementing green studies effectively, improving technologies, and strengthening capabilities for sustainable performance. This research adds value in that the paper first integrates TAM and DCT to explain sustainable operations and their impact on organizations. Further, it extends the existing literature by establishing the relationship between GSCM and SC. It offers a model through which GSCM can be operationalized in the context of the FS sector.<br><b>研究背景</b>本研究旨在实证检验绿色供应链管理（Green Supply Chain Management, GSCM）、绿色技术创新（Green Technology Innovation, GTI）、废物管理（Waste Management, WM）、人工智能赋能的大数据分析能力（Big Data Analytics Capability Powered by Artificial Intelligence, BDAC-AI）之间的复杂内在关联，以及上述要素在组织场景下对可持续绩效（Sustainable Performance, SP）的综合影响。<br><b>研究方法</b>本研究以巴基斯坦食品加工行业为研究场景，调研对象为该行业内的495名管理人员。研究采用结构方程模型（Structural Equation Modeling, SEM）方法，检验各变量间的直接与间接关联。本研究的创新之处在于，整合技术接受模型（Technology Acceptance Model, TAM）与动态能力理论（Dynamic Capabilities Theory, DCT），以解析所提模型框架下的可持续实践路径。<br><b>研究结果</b>本研究结果表明，绿色供应链管理、绿色技术创新、废物管理及人工智能赋能的大数据分析能力均对可持续绩效产生显著正向直接影响。进一步来看，绿色技术创新与废物管理仅在绿色供应链管理与可持续绩效的关联中发挥部分中介作用，同时二者还对该关联存在调节效应。此外，人工智能赋能的大数据分析能力对绿色技术创新与可持续绩效的关联亦存在显著调节效应。<br><b>研究结论</b>本研究具有明确的管理启示：可依托技术接受与动态能力相关理论框架，制定支持可持续发展举措的相关策略。值得注意的是，本研究结论可为有意向有效推进绿色实践、优化技术应用并强化可持续绩效提升能力的管理人员与企业提供切实的权变参考。本研究的学术贡献主要体现在两方面：其一，首次整合技术接受模型与动态能力理论，用以阐释可持续运营及其对组织的影响；其二，拓展了现有文献中关于绿色供应链管理与供应链（Supply Chain, SC）之间关联的研究，并构建了可在FS领域内实现操作化的绿色供应链管理模型。