Sustainability knowledge and training: outcomes and firm performance

Abstract

Purpose

The purpose of this paper is to examine the impact of sustainability training and knowledge on sustainable supply chain practices (SSCP) and the resulting impact on sustainable supply chain outcomes (SSCO) and firm performance. It also provides a valid and reliable measure of SSCO.

Design/methodology/approach

Data collected from 129 manufacturing managers are analyzed using a partial least squares structural equation modeling methodology. Manufacturing managers provide data reflecting the degree to which their organizations improved sustainability training and knowledge, utilize SSCP, the degree to which SSCO result, and the subsequent operational performance (OPP) and environmental economic performance (EEP).

Findings

Organizational sustainability training and knowledge positively impacts SSCP, and the utilization of SSCP results in SSCO which favorably impact OPP and EEP.

Research limitations/implications

The study is limited to manufacturing organizations.

Practical implications

Practitioners are encouraged to improve organizational learning and training and are provided with a valid and reliable scale for measuring the outcomes of their sustainable practices. Combined with the work of others, this provides a framework for evaluating different aspects of sustainability with a firm.

Social implications

Improved green manufacturing practices improves the environment by eliminating all forms of waste and provides eco-friendly products and services.

Originality/value

A sustainable supply chain training and knowledge model is proposed and empirically assessed. The results of this investigation support the proposition that sustainability training and knowledge support the implementation of sustainability supply chain practices which, in turn, improve sustainability outcomes and operational and EEP.

Citation

Birou, L.M., Green, K.W. and Inman, R.A. (2019), "Sustainability knowledge and training: outcomes and firm performance", Journal of Manufacturing Technology Management, Vol. 30 No. 2, pp. 294-311. https://doi.org/10.1108/JMTM-05-2018-0148