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Navigating Digital Transformation: A Risk-Based Approach for Industry 4.0 Innovation

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  • Zhi Li

    (Ningxia University)

Abstract

This study addresses the critical gap in understanding the risks associated with digital transformation, particularly focusing on their impact on business innovation and growth within Industry 4.0. While the transformative potential of digital technologies is well-documented, the inherent challenges remain underexplored. This research introduces an innovative decision-support model designed to evaluate and prioritize risks unique to digital transformation in the industrial sector. Utilizing Pythagorean fuzzy sets (PFSs) and multicriteria decision-making (MCDM) techniques, the model systematically assesses and ranks risks to enhance informed decision-making processes. An extensive case study reveals that key risks include a lack of commitment from top management and unstable market environments, which significantly jeopardize the digital transformation journey. The study’s findings underscore the importance of a strategic approach in mitigating these risks, facilitating a smoother transition to the digital economy. The proposed model offers actionable insights for organizations to optimize their digital transformation strategies by integrating advanced analytics and machine learning. This research contributes to the knowledge economy by providing a robust framework for managing the complexities of digital transformation, promoting sustainable innovation, and enhancing overall business performance. The study’s strengths are further reinforced through sensitivity and comparison analyses, highlighting the resilience and practical applicability of the decision-support model. These insights are invaluable for policymakers, industry leaders, and scholars focused on leveraging technology to drive economic growth and societal progress in the era of Industry 4.0.

Suggested Citation

  • Zhi Li, 2025. "Navigating Digital Transformation: A Risk-Based Approach for Industry 4.0 Innovation," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 16(2), pages 9715-9751, June.
    • Handle: RePEc:spr:jknowl:v:16:y:2025:i:2:d:10.1007_s13132-024-02264-6
    DOI: 10.1007/s13132-024-02264-6
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