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Supply chain analytics adoption: Determinants and impacts on organisational performance and competitive advantage

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  • Kalaitzi, Dimitra
  • Tsolakis, Naoum

Abstract

Despite manufacturing companies recognising the potential benefits associated with the adoption of Supply Chain Analytics (SCA), only a few firms adopt data-based decision-making processes due to fundamental technical, organisational and environmental challenges. In this regard, this research explores the determinants influencing SCA adoption and the impacts on firm performance and competitive advantage. Specifically, the Technological, Organisational, and Environmental (TOE) framework was applied to identify the key determinants influencing SCA adoption. Data was collected from 217 executives working in the UK manufacturing sector through a questionnaire-based survey. The research model was tested using a quantitative approach, i.e., Partial Least Squares Structural Equation Modelling. Surprisingly, none of the identified technological factors leads manufacturing companies to adopt SCA. On the contrary, organisational and environmental factors have a crucial role in influencing supply chain and logistics managers to adopt SCA. This research also emphasises and validates the importance of SCA adoption in improving firm performance and fostering competitive advantage. On evaluating SCA adoption, supply chain managers should concentrate on aspects other than technological competence. Manufacturing companies looking to make investment decisions regarding SCA adoption should mainly consider organisational and environmental factors; hence, SCA systems can be used effectively and efficiently. This study is the first to explore the TOE framework regarding the adoption determinants within an SCA context along with its implications on organisational performance and competitive edge.

Suggested Citation

  • Kalaitzi, Dimitra & Tsolakis, Naoum, 2022. "Supply chain analytics adoption: Determinants and impacts on organisational performance and competitive advantage," International Journal of Production Economics, Elsevier, vol. 248(C).
    • Handle: RePEc:eee:proeco:v:248:y:2022:i:c:s0925527322000597
    DOI: 10.1016/j.ijpe.2022.108466
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