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Measuring adoption of industry 4.0 technologies via international trade data: insights from European countries

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  • Davide Castellani

    (University of Reading
    University of Perugia)

  • Fabio Lamperti

    (University of Reading)

  • Katiuscia Lavoratori

    (University of Reading)

Abstract

The investigation of the adoption of Industry 4.0 (I4.0) technologies and its implications, both at the macro and micro level, has attracted growing interest in the recent literature. Most studies have looked at the production and diffusion of related innovations and knowledge, but what do we know about the adoption of these technologies over time and across countries? In this paper, we look at three I4.0 technologies and present a new empirical perspective able to overcome the limitations of existing attempts at measuring their adoption, generally based on small-scale and country-specific studies. Our study provides a methodology that allows measuring adoption across countries for a relatively long time period. In so doing, we build on the well-established idea in the international economics literature that trade of capital goods captures technology diffusion, and so adoption across countries. We provide preliminary and comprehensive evidence on the adoption of these I4.0 technologies in Europe and set the premise for monitoring its evolution and implications on a large scale and over time.

Suggested Citation

  • Davide Castellani & Fabio Lamperti & Katiuscia Lavoratori, 2022. "Measuring adoption of industry 4.0 technologies via international trade data: insights from European countries," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 49(1), pages 51-93, March.
  • Handle: RePEc:spr:epolin:v:49:y:2022:i:1:d:10.1007_s40812-021-00204-y
    DOI: 10.1007/s40812-021-00204-y
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    1. Arindam Das, 2023. "The Relationship between International Trade in Industry 4.0 Products and National-Level Sustainability Performance: An Empirical Investigation," Sustainability, MDPI, vol. 15(2), pages 1-15, January.
    2. Parteka, Aleksandra & Kordalska, Aleksandra, 2023. "Artificial intelligence and productivity: global evidence from AI patent and bibliometric data," Technovation, Elsevier, vol. 125(C).

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