IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i20p9234-d1774010.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/20/9234/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/17/20/9234/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rehman, Anisur & Behera, Rajat Kumar & Islam, Md Saiful & Elahi, Yasir Arafat & Abbasi, Faraz Ahmad & Imtiaz, Asma, 2024. "Drivers of metaverse adoption for enhancing marketing capabilities of retail SMEs," Technology in Society, Elsevier, vol. 79(C).
    2. Czarnitzki, Dirk & Fernández, Gastón P. & Rammer, Christian, 2023. "Artificial intelligence and firm-level productivity," Journal of Economic Behavior & Organization, Elsevier, vol. 211(C), pages 188-205.
    3. Vu, Khuong & Rohman, Ibrahim Kholilul & Bohlin, Erik, 2024. "Promoting the adoption of digital technology: Strategic policy insights from a network effects model," Telecommunications Policy, Elsevier, vol. 48(10).
    4. Guosong Wang & Huizhen Zhang, 2025. "FinTech-Driven Corporate Sustainability: A Technology–Organization–Environment Framework Analysis," Sustainability, MDPI, vol. 17(19), pages 1-28, September.
    5. Hiran, Kamal Kant & Dadhich, Manish, 2024. "Predicting the core determinants of cloud-edge computing adoption (CECA) for sustainable development in the higher education institutions of Africa: A high order SEM-ANN analytical approach," Technological Forecasting and Social Change, Elsevier, vol. 199(C).
    6. Fernando Almeida, 2024. "Contributions of Municipal Initiatives to Digital Health Equity," World, MDPI, vol. 5(4), pages 1-16, November.
    7. Pramanik, Paritosh & Jana, Rabin K. & Ghosh, Indranil, 2024. "AI readiness enablers in developed and developing economies: Findings from the XGBoost regression and explainable AI framework," Technological Forecasting and Social Change, Elsevier, vol. 205(C).
    8. Zhang, Jianhong & van Witteloostuijn, Arjen & Zhou, Chaohong & Zhou, Shengyang, 2024. "Cross-border acquisition completion by emerging market MNEs revisited: Inductive evidence from a machine learning analysis," Journal of World Business, Elsevier, vol. 59(2).
    9. Jun Chen & Asma-Qamaliah Abdul-Hamid & Suhaiza Zailani, 2024. "Blockchain Adoption for a Circular Economy in the Chinese Automotive Industry: Identification of Influencing Factors Using an Integrated TOE-TAM Model," Sustainability, MDPI, vol. 16(24), pages 1-28, December.
    10. Gerli, Paolo & Clement, Jessica & Esposito, Giovanni & Mora, Luca & Crutzen, Nathalie, 2022. "The hidden power of emotions: How psychological factors influence skill development in smart technology adoption," Technological Forecasting and Social Change, Elsevier, vol. 180(C).
    11. Yulia Treister-Goltzman, 2025. "Predicting Low Birth Weight in Big Cities in the United States Using a Machine Learning Approach," IJERPH, MDPI, vol. 22(6), pages 1-10, June.
    12. de Bruin, Sophie & Hoch, Jannis & de Bruijn, Jens & Hermans, Kathleen & Maharjan, Amina & Kummu, Matti & van Vliet, Jasper, 2024. "Scenario projections of South Asian migration patterns amidst environmental and socioeconomic change," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 88, pages 1-12.
    13. Shet, Sateesh V. & Pereira, Vijay, 2021. "Proposed managerial competencies for Industry 4.0 – Implications for social sustainability," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    14. Guanqiao Zhang & Tao Wang & Yuhan Wang & Shuai Zhang & Wenhao Lin & Zixin Dou & Haitao Du, 2023. "Study on the Influencing Factors of Digital Transformation of Construction Enterprises from the Perspective of Dual Effects—A Hybrid Approach Based on PLS-SEM and fsQCA," Sustainability, MDPI, vol. 15(7), pages 1-22, April.
    15. Cheng, Louis T.W. & Cheong, Tsun Se & Wojewodzki, Michal & Chui, David, 2025. "The effect of ESG divergence on the financial performance of Hong Kong-listed firms: An artificial neural network approach," Research in International Business and Finance, Elsevier, vol. 73(PA).
    16. Jang, Hyunmi & Haddoud, Mohamed Yacine & Roh, Saeyeon & Onjewu, Adah-Kole Emmanuel & Choi, Taeeun, 2023. "Implementing smart factory: A fuzzy-set analysis to uncover successful paths," Technological Forecasting and Social Change, Elsevier, vol. 195(C).
    17. Ruizhen Li & Kanwal Zahra & Hina Najam & Saitao Jia, 2025. "Examining the role of digitalization and ESG strategies in enhancing resilience and sustainable performance of SMEs," International Entrepreneurship and Management Journal, Springer, vol. 21(1), pages 1-23, December.
    18. Hülter, Svenja M. & Ertel, Christian & Heidemann, Ansgar, 2024. "Exploring the individual adoption of human resource analytics: Behavioural beliefs and the role of machine learning characteristics," Technological Forecasting and Social Change, Elsevier, vol. 208(C).
    19. Jessica Müller-Pérez & Viridiana Sarahí Garza-Muñiz & Ángel Acevedo-Duque & Elizabeth Emperatriz García-Salirrosas & Jorge Alberto Esponda-Pérez & Rina Álvarez-Becerra, 2022. "The Future of Tamaulipas MSMEs after COVID-19: Intention to Adopt Inbound Marketing Tools," Sustainability, MDPI, vol. 14(19), pages 1-18, October.
    20. Zhu, Tengteng & Lin, Zhibin & Liu, Xin, 2023. "The future is now? Consumers' paradoxical expectations of human-like service robots," Technological Forecasting and Social Change, Elsevier, vol. 196(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:17:y:2025:i:20:p:9234-:d:1774010. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.