Mechanism of green and knowledge process toward minimizing innovation risks: A direct and configuration approach

Abstract

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.