The purpose of this paper is to analyse the complex interrelationships among environmental drivers, Green Supply Chain Management (GSCM) approaches and performance.
A survey was sent to a sample of managers in the field of Purchasing and Supply Management in Spanish firms. Data were analysed using SmartPLS 2.0 to test a model that relates GSCM drivers, GSCM approaches and performance.
Results show that coercive and non-coercive drivers have different implications in terms of GSCM approaches. Moreover, monitoring itself is not sufficient to improve performance; firms need to adopt collaborative practices with their suppliers. Results show that whereas collaboration has a direct effect on performance, monitoring has only an indirect relationship through collaboration.
One of the main limitations of this study is the use of data from a single country (Spain). The main contribution of the paper is to show that coercive and non-coercive drivers have different effects on the GSCM approaches. Additionally, it quantifies the mediating effect of collaboration on the relationship between monitoring and environmental performance. As further research, the authors suggest the replication of this study in other countries (notably in emerging markets) and industrial sectors.
This study provides guidance to managers in the implementation of specific approaches of GSCM. For example, it shows that monitoring alone has no direct effect on performance whereas joint collaborative initiatives with suppliers have a significant effect on environmental performance.
This study analyses the implications in terms of drivers and performance for each GSCM approach (monitoring and collaboration), using a quantitative approach.
The authors acknowledge financial support from research grant No. ECO2010-16840 – sustainability through Supply Chain Management, Spanish Ministry of Science and Innovation.
Tachizawa, E.M., Gimenez, C. and Sierra, V. (2015), "Green supply chain management approaches: drivers and performance implications", International Journal of Operations & Production Management, Vol. 35 No. 11, pp. 1546-1566. https://doi.org/10.1108/IJOPM-01-2015-0023Download as .RIS
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