The fallacy of profitable green supply chains: The role of green information systems (GIS) in attenuating the sustainability trade-offs

Abstract

While green supply chain management (GSCM) has been studied extensively, a lack of a clear view on performance improvements arising from the adoption of GSCM practices obstructs a full understanding of resultant consequences. Moreover, there are still limited efforts to understand the contingent nature of how performance is improved in this context. This study aims to ascertain whether the GSCM implementation yields sustainability–profitability trade-offs and examine the moderating effects of green information systems (GIS) on performance improvements. Survey data were collected from 189 firms operating in the UK automotive industry and analyzed using moderated hierarchical regression. The results suggest that pursuing GSCM can bring trade-offs into play, demonstrating a paradoxical view of enhanced sustainability versus less profitability. The authors call this phenomenon the fallacy of profitable GSCM. Interestingly, high levels of GIS were found to positively moderate the relationships between GSCM practices and economic performance. This study contributes to the knowledge bank of GSCM by elucidating the mixed views about the GSCM adoption and its economic effects and refutes the fallacy that “low-hanging fruits” of GSCM are readily available. Second, this study offers new directions to balance the trade-offs between sustainability and profitability, contributing to the development of a more robust GSCM theory. Two important managerial contributions can be drawn from this study: (1) managers need to prioritize GSCM practices on the basis of having the most significant performance improvement; (2) they are encouraged to develop more robust GIS and exploit the capabilities of information sharing, supply chain traceability, and monitoring as a new pathway to attenuate sustainability trade-offs. Future studies are recommended to explore wider sectors and employ longitudinal or quasi-experimental designs to capture the effects of GSCM practices on performance over time.