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Transition towards the artificial intelligence via re-engineering of digital platforms: comparing European Member States

Author

Listed:
  • Alfonso Marino

    (University of Campania Luigi Vanvitelli, Italy)

  • Paolo Pariso

    (University of Campania Luigi Vanvitelli, Italy)

  • Michele Picariello

    (University of Campania Luigi Vanvitelli, Italy)

Abstract

t. The present study is directed at comparing the performances of the 27 European Member States in the period 2012 – 2021 with the choice of assessing their improvement in the transition toward Artificial Intelligence. The study, opening from an analysis of the key factors with strategic rate in the countries’ transition, is centered on society, law, physiology, and trust impacts. The valuation of the effects was evaluated by seeing forty - two official European Commission rapports in order to create a composite analysis and assessment of the state of the transition in Europe. The study pointed at understanding the degree of transition related to 27 European Member States in the realization of AI targets. The outcomes of the study highpoint that the transition, evaluated with the support of the composite analysis, is current with a different degree between the Member State and only some elements could be evaluated as suitable in the transition toward an artificial intelligence system in the Europe.

Suggested Citation

  • Alfonso Marino & Paolo Pariso & Michele Picariello, 2022. "Transition towards the artificial intelligence via re-engineering of digital platforms: comparing European Member States," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 9(3), pages 350-368, March.
    • Handle: RePEc:ssi:jouesi:v:9:y:2022:i:3:p:350-368
    DOI: 10.9770/jesi.2022.9.3(21)
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    References listed on IDEAS

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    1. Sára Csillag, 2019. "Ethical Dilemmas and Moral Muteness in the HRM Profession," Society and Economy, Akadémiai Kiadó, Hungary, vol. 41(1), pages 125-144, March.
    2. Phillips, Robert A. & Margolis, Joshua D., 1999. "Toward an Ethics of Organizations," Business Ethics Quarterly, Cambridge University Press, vol. 9(4), pages 619-638, October.
    3. Zoubin Ghahramani, 2015. "Probabilistic machine learning and artificial intelligence," Nature, Nature, vol. 521(7553), pages 452-459, May.
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    Cited by:

    1. Yue Cheng & Dong Zheng, 2023. "Does the Digital Economy Promote Coordinated Urban–Rural Development? Evidence from China," Sustainability, MDPI, vol. 15(6), pages 1-20, March.

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    JEL classification:

    • L20 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - General
    • L26 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Entrepreneurship

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