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Adoption paths of digital transformation in manufacturing SME

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  • Battistoni, Elisa
  • Gitto, Simone
  • Murgia, Gianluca
  • Campisi, Domenico

Abstract

Digital transformation requires the implementation of different technologies that may improve the firms' capability in the collection, combination, processing, and use of business data. To guarantee an adequate combination of these technologies, several maturity models have been proposed in the literature, but only a few papers have investigated the actual implementation paths adopted by firms for digital transformation. In particular, no studies have investigated the implementation paths followed by SMEs, whose limited financial and human resources may prevent the adoption of the roadmaps developed for large firms. In this paper, we analyse the implementation paths for digital transformation adopted by a wide sample of Italian SMEs operating in different sectors. By combining Partial Least Squares Structural Equation Modelling with Necessity Condition Analysis, we clarify the specific enabler and enhancer roles played by different digital technologies. The study sheds further light on the relationship among these technologies and their contribution to the development of SMEs’ information processing capability. In particular, our analysis shows that digital technologies associated with Industry 4.0 can be classified into four hierarchical layers, Sensor, Integration, Intelligence, and Response, that are in charge of the collection, combination, processing and use of organizational data. Our results show that the implementation of these layers is not based on a standalone approach since the lower layers enable and enhance the adoption of the upper layers. The present paper may also offer useful insights to managers and policymakers, interested in improving the digital transformation of SMEs.

Suggested Citation

  • Battistoni, Elisa & Gitto, Simone & Murgia, Gianluca & Campisi, Domenico, 2023. "Adoption paths of digital transformation in manufacturing SME," International Journal of Production Economics, Elsevier, vol. 255(C).
    • Handle: RePEc:eee:proeco:v:255:y:2023:i:c:s0925527322002572
    DOI: 10.1016/j.ijpe.2022.108675
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