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Analysis of the adoption of emergent technologies for risk management in the era of digital manufacturing

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  • Rodríguez-Espíndola, Oscar
  • Chowdhury, Soumyadeb
  • Dey, Prasanta Kumar
  • Albores, Pavel
  • Emrouznejad, Ali

Abstract

The Industry 4.0 (I4.0) revolution has led to rapid digital transformation, automation of manufacturing processes and efficient decision-making in business operations. Despite the potential benefits of I4.0 technologies in operations management reported in the extant literature, there has been a paucity of empirical research examining the intention to adopt I4.0 technologies for managing risks. Risk management identifies, assesses, and introduces responses for risks to avert crises. This study combines institutional theory, the resource-based view and the technology acceptance model to develop a novel behavioural model examining the adoption of big data, artificial intelligence, cloud computing, and blockchain for risk management from the operations manager's perspective, which has never been examined in the literature. The model was tested for each I4.0 technology using data collected from 117 operations managers in the UK manufacturing industry which were analysed using structural equation modelling. We contribute to the theory on I4.0 in digital manufacturing by showing the impact of digital transformation maturity, market pressure, regulations, and resilience on the perceived usefulness and adoption of these technologies for managing risks in business operations. Based on the findings, we discuss implications for operations managers effectively and efficiently to adopt I4.0 technologies aiming to boost operational productivity.

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  • Rodríguez-Espíndola, Oscar & Chowdhury, Soumyadeb & Dey, Prasanta Kumar & Albores, Pavel & Emrouznejad, Ali, 2022. "Analysis of the adoption of emergent technologies for risk management in the era of digital manufacturing," Technological Forecasting and Social Change, Elsevier, vol. 178(C).
    • Handle: RePEc:eee:tefoso:v:178:y:2022:i:c:s0040162522000944
    DOI: 10.1016/j.techfore.2022.121562
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