Role of technological dimensions of green supply chain management practices on firm performance

Abstract

Purpose

The research study aims to investigate green supply chain management (GSCM) elements as part of a complete system. It aims to understand the special properties of the GSCM system under the moderating effects of product complexity and purchasing structure.

Design/methodology/approach

A thorough literature review led to the building of the conceptual framework. Six constructs were identified using systems theory. These constructs include green supply chain technological dimensions (particularly, Artificial Intelligence (AI) based), green supply chain strategy, green supply chain process, product complexity, purchasing structure, and firm performance. The instrument was scientifically developed for gathering survey responses using complete design test methods. The conceptual model was eventually tested based on survey data collected from 250 automotive components and allied manufacturers in the emerging economy of South Africa.

Findings

The results indicate that GSCM technological dimensions (AI-based) positively influence GSCM strategy. Further, GSCM strategy was found to positively influence the GSCM process. The GSCM processes have significant effects on environmental performance, social performance, and financial performance. The product complexity has a significant moderation effect on the paths GSCM strategy and GSCM process.

Originality/value

The findings from multivariate data analysis provide a better understanding of GSCM system dynamics and are helpful to key decision-makers. This unique model has elevated GSCM theory to a new level. There are limited studies available in the existing GSCM literature using systems theory. This study will offer an advanced/comprehensive understanding to readers in this relatively new concept.

Citation

Bag, S., Gupta, S., Kumar, S. and Sivarajah, U. (2021), "Role of technological dimensions of green supply chain management practices on firm performance", Journal of Enterprise Information Management, Vol. 34 No. 1, pp. 1-27. https://doi.org/10.1108/JEIM-10-2019-0324