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A Conceptual Framework for Sustainable AI-ERP Integration in Dark Factories: Synthesising TOE, TAM, and IS Success Models for Autonomous Industrial Environments

Author

Listed:
  • Md Samirul Islam

    (Department of Management Information Systems, International American University, 3440 Wilshire Blvd, STE 1000, Los Angeles, CA 90010, USA)

  • Md Iftakhayrul Islam

    (Department of Management Information Systems, International American University, 3440 Wilshire Blvd, STE 1000, Los Angeles, CA 90010, USA)

  • Abdul Quddus Mozumder

    (Department of Information System Management, Stanton University, 888 Disneyland Dr., Suite 400, Anaheim, CA 92802, USA)

  • Md Tamjidul Haq Khan

    (Department of Management Information Systems, International American University, 3440 Wilshire Blvd, STE 1000, Los Angeles, CA 90010, USA)

  • Niropam Das

    (Department of Management Information Systems, International American University, 3440 Wilshire Blvd, STE 1000, Los Angeles, CA 90010, USA)

  • Nur Mohammad

    (College of Technology & Engineering, Westcliff University, Irvine, CA 92614, USA)

Abstract

This study explores a conceptual framework for integrating Artificial Intelligence (AI) into Enterprise Resource Planning (ERP) systems, emphasising its transformative potential in highly automated industrial environments, often referred to as ‘dark factories’, where operations are carried out with minimal human intervention using robotics, AI, and IoT. These lights-out manufacturing environments demand intelligent, autonomous systems that go beyond traditional ERP functionalities to deliver sustainable enterprise operations and supply chain management. Drawing from secondary data and a comprehensive review of existing literature, the study identifies significant gaps in current AI-ERP research and practice, namely, the absence of a unified adoption framework, limited focus on AI-specific implementation challenges, and a lack of structured post-adoption evaluation metrics. In response, this paper proposes a novel integrated conceptual framework that combines the Technology–Organisation–Environment (TOE) framework, the Technology Acceptance Model (TAM), and the Information Systems (IS) Success Model. The model incorporates industry-specific dark factors, such as AI autonomy, human–machine collaboration, operational agility, and sustainability, by optimising resource efficiency, enabling predictive maintenance, enhancing supply chain resilience, and supporting circular economy practices. The primary research aim of the current study is to provide a theoretical foundation for further empirical research on the input of AI-ERP systems into autonomous industry settings. The framework provides a robust theoretical foundation and actionable guidance for researchers, technology leaders, and policy-makers navigating the integration of AI and ERP in sustainable enterprise operations and supply chain management.

Suggested Citation

  • Md Samirul Islam & Md Iftakhayrul Islam & Abdul Quddus Mozumder & Md Tamjidul Haq Khan & Niropam Das & Nur Mohammad, 2025. "A Conceptual Framework for Sustainable AI-ERP Integration in Dark Factories: Synthesising TOE, TAM, and IS Success Models for Autonomous Industrial Environments," Sustainability, MDPI, vol. 17(20), pages 1-23, October.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:20:p:9234-:d:1774010
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    References listed on IDEAS

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    1. Muhammad Zafar Yaqub & Abdullah Alsabban, 2023. "Industry-4.0-Enabled Digital Transformation: Prospects, Instruments, Challenges, and Implications for Business Strategies," Sustainability, MDPI, vol. 15(11), pages 1-33, May.
    2. Jireh Yi-Le Chan & Steven Mun Hong Leow & Khean Thye Bea & Wai Khuen Cheng & Seuk Wai Phoong & Zeng-Wei Hong & Yen-Lin Chen, 2022. "Mitigating the Multicollinearity Problem and Its Machine Learning Approach: A Review," Mathematics, MDPI, vol. 10(8), pages 1-17, April.
    3. Chatterjee, Sheshadri & Rana, Nripendra P. & Dwivedi, Yogesh K. & Baabdullah, Abdullah M., 2021. "Understanding AI adoption in manufacturing and production firms using an integrated TAM-TOE model," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
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