Abstract
Although the contribution of small and medium-sized enterprises (SMEs) to economic growth is beyond doubt, they collectively affect the environment and society negatively. As SMEs have to perform in a very competitive environment, they often find it difficult to achieve their environmental and social targets. Therefore, making SMEs sustainable is one of the most daunting tasks for both policy makers and SME owners/managers alike. Prior research argues that through measuring SMEs’ supply chain sustainability performance and deriving means of improvement one can make SMEs’ business more viable, not only from an economic perspective, but also from the environmental and social point of view. Prior studies apply data envelopment analysis (DEA) for measuring the performance of groups of SMEs using multiple criteria (inputs and outputs) by segregating efficient and inefficient SMEs and suggesting improvement measures for each inefficient SME through benchmarking it against the most successful one. However, DEA is limited to recommending means of improvement solely for inefficient SMEs. To bridge this gap, the use of structural equation modelling (SEM) enables developing relationships between the criteria and sub-criteria for sustainability performance measurement that facilitates to identify improvement measures for every SME within a region through a statistical modelling approach. As SEM suggests improvements not from the perspective of individual SMEs but for the totality of SMEs involved, this tool is more suitable for policy makers than for individual company owners/managers. However, a performance measurement heuristic that combines DEA and SEM could make use of the best of each technique, and thereby could be the most appropriate tool for both policy makers and individual SME owners/managers. Additionally, SEM results can be utilized by DEA as inputs and outputs for more effective and robust results since the latter are based on more objective measurements. Although DEA and SEM have been applied separately to study the sustainability of organisations, according to the authors’ knowledge, there is no published research that has combined both the methods for sustainable supply chain performance measurement. The framework proposed in the present study has been applied in two different geographical locations—Normandy in France and Midlands in the UK—to demonstrate the effectiveness of sustainable supply chain performance measurement using the combined DEA and SEM approach. Additionally, the state of the companies’ sustainability in both regions is revealed with a number of comparative analyses.
Highlights
Climate change presents one of the most serious environmental challenges faced by humanity today (Dey et al 2018)
data envelopment analysis (DEA) and structural equation modelling (SEM) have been applied separately to study the sustainability of organisations, according to the authors’ knowledge, there is no published research that has combined both the methods for sustainable supply chain performance measurement
In view of the above, the objective of this study is to develop a framework to measure the supply chain sustainability of small and medium-sized enterprises (SMEs) using a combined DEA and SEM approach
Summary
Climate change presents one of the most serious environmental challenges faced by humanity today (Dey et al 2018). The achievement of sustainability is a major issue for organizations worldwide. Enterprises need to both maintain and improve their market position and fulfill their environmental and social responsibilities (Halkos and Evangelinos 2002). The focus of most studies up to date, has been on the activities of large-scale companies, while less is known about the operations of small and medium-sized enterprises (SMEs) (Johnson and Schaltegger 2016), which the majority of published studies on sustainability have largely ignored. While SMEs are of crucial importance in the economic growth across the world, they impose collectively considerable pressures on the environment (Mollenkopf 2008; Johnson and Schaltegger 2016)
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