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Reconfiguring the global factory: The synergistic role of additive manufacturing, explainable AI, and knowledge acquisition in MNC operations

Author

Listed:
  • Femi Olan

    (Essex Business School - University of Essex)

  • Konstantina Spanaki

    (Audencia Business School)

  • Uchitha Jayawickrama

    (Loughborough University)

Abstract

Additive Manufacturing (AM) is reshaping multinational corporations' (MNCs) global production strategies through the intertwined roles of knowledge acquisition (KA) and explainable artificial intelligence (XAI). The study draws on survey data from international managers and AI/manufacturing specialists in MNCs and uses a dual empirical approach that combines (SEM) with XAI framework. The analysis shows that KA capabilities significantly enhance firms' ability to identify, absorb and integrate AM-relevant knowledge across borders, while XAI capabilities strengthen transparency and trust in data-driven decisions about AM deployment. Together, these capabilities support more adaptive "global factory" configurations, including greater localization of production and more sustainable manufacturing footprints. The findings suggest that AM's disruptive potential for global value chains depends less on the technology in isolation and more on how it is embedded in knowledge-based and AI-enabled organizational processes. The study contributes to theory by linking AM to knowledge-based and global factory perspectives on the MNC, by conceptualizing XAI as a strategic capability in international operations rather than a purely technical feature, and by demonstrating how integrating traditional modelling with XAI can illuminate complex capability interactions in international business.

Suggested Citation

  • Femi Olan & Konstantina Spanaki & Uchitha Jayawickrama, 2026. "Reconfiguring the global factory: The synergistic role of additive manufacturing, explainable AI, and knowledge acquisition in MNC operations," Post-Print hal-05740895, HAL.
  • Handle: RePEc:hal:journl:hal-05740895
    DOI: 10.1016/j.techsoc.2026.103478
    Note: View the original document on HAL open archive server: https://hal.science/hal-05740895v1
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