Due to a significant reduction in the availability and standard of natural resources, numerous firms are claiming to implement environmentally sustainable practices. This research constructs and validates green variables within the knowledge management (KM) process, drawing on resource‐based views (RBV) and organizational learning theory. It aims to explain how manufacturing firms minimize innovation risk. The author followed a combined methodology of Smart partial least squares structural equation modeling (PLS‐SEM) and fuzzy set qualitative comparative analysis (fsQCA). Primary response data were collected from industry experts and literature studies to develop items for the knowledge aptitude model to decrease innovation risk (KMIR). The mixed variables of the KM and green process were validated through the fsQCA technique. The outcome of PLS‐SEM showed a positive connection between certain green variables to minimize innovation risk. fsQCA examines the combined approach of green implementation and KM practice; the finding indicated significant connections between green variables and the KM process to KMIR. This study can be measured as innovative in the KMIR field, as it has validated and developed its constructs based on primary data. It can help scholars and industry experts acquire a head start in the KMIR field, and this mechanism will assist with the investigation of the green variables and knowledge domain, providing an outline for future studies.

中文翻译：

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最小化创新风险的绿色和知识流程机制：直接和配置方法


由于自然资源的可用性和标准显着下降，许多公司声称实施环境可持续的做法。本研究借鉴基于资源的观点（RBV）和组织学习理论，构建并验证了知识管理（KM）流程中的绿色变量。它旨在解释制造企业如何最大限度地降低创新风险。作者采用了智能偏最小二乘结构方程建模（PLS-SEM）和模糊集定性比较分析（fsQCA）的组合方法。主要反应数据是从行业专家和文献研究中收集的，以开发用于降低创新风险的知识能力模型的项目（KMIR）。知识管理和绿色流程的混合变量通过 fsQCA 技术进行了验证。 PLS-SEM 的结果显示某些绿色变量之间存在正相关关系，可最大限度地降低创新风险。 fsQCA 审查绿色实施和知识管理实践的结合方法；这一发现表明绿色变量与 KMIR 的 KM 过程之间存在显着联系。这项研究可以被视为 KMIR 领域的创新，因为它基于原始数据验证并开发了其结构。它可以帮助学者和行业专家在KMIR领域取得先机，该机制将有助于绿色变量和知识领域的调查，为未来的研究提供纲要。