Exploring the influence of critical parameters on green supply chain management performance of small and medium-sized enterprise: An integrated multivariate analysis-robust design approach

Abstract

The implementation of green supply chain management (GSCM) practices is still in its rudimentary stage in small and medium-sized enterprises (SMEs) of emerging economies. So far, too little research has shown clear empirical evidence on the actual impact of various parameters on GSCM performance of SMEs. In order to fill this knowledge gap, this research attempts to identify the critical GSCM parameter and quantitively measure its influence on the GSCM performance of an India-based SME. To this end, twenty important GSCM parameters are identified from an extant literature and verified by the experts. Data is collected through questionnaire development, expert committee formation, and conducting interviews. This research employs an integrated multivariate analysis-robust design approach, in which item analysis is used to check the consistency of the data and principal component analysis (PCA) is used to determine the weights of the parameters. The Taguchi’s L9 orthogonal array analysis is used in which, qualitative data, quantitative data, and attitude of decision maker are taken as input parameters and a GSCM performance index is taken as a response parameter. The result reveals that, ‘Use of emission control system’ is the most critical GSCM parameter and the optimal combination of input parameters are: use of the emission control system (0.08); total carbon emission (0.37); attitude of decision maker (0.6), and the corresponding GSCM performance index is 0.196. The proposed framework may help decision makers identify and control a set of influential parameters for the best GSCM performance.