Innovation and knowledge in sustainable supply chains: how ethical climate and training reduce greenwashing risk

Purpose The purpose of this study is to analyze how an organization’s ethical climate and the quality of its sustainability training initiatives contribute to innovation and knowledge while helping to minimize greenwashing risk in production-based supply chains. This study also explores employee skepticism about green claims as a mediating factor in influencing this relationship. Design/methodology/approach Data were gathered from 1,211 supply chain employees in production companies across the USA. This study used validated scales borrowed from earlier research studies. Structural Equation Modeling (SEM) via JASP was used to examine direct and indirect paths. To enhance the robustness of the results and advance knowledge in sustainability and innovation, Machine Learning (ML) algorithms – including Neural Network Regression and Support Vector Machine (SVM) Regression – were used to forecast greenwashing risk and identify influential variables. Findings SEM findings supported that ethical climate (ß = −0.36, p < 0.001) and training in sustainability (ß = −0.41, p < 0.001) decreased greenwashing risk significantly, with skepticism being a significant mediator (indirect effect for ethical climate = −0.18, p < 0.001; for training = −0.20, p < 0.001). Among ML models, Neural Network Regression achieved the highest accuracy at 93.5%, followed by SVM at 87.2%. These outcomes advance knowledge in the field by demonstrating how organizational ethics and training can foster innovation in sustainable initiatives while mitigating the risk of greenwashing. Originality/value This research offers a novel empirical integration of SEM and ML to assess ethical conduct in sustainability, thereby enhancing innovation and knowledge in greenwashing prevention within supply chains.