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The Recursive Theory of Knowledge Augmentation: Integrating human intuition and knowledge in Artificial Intelligence to augment organizational knowledge

Author

Listed:
  • Antoine Harfouche

    (Paris Nanterre University)

  • Bernard Quinio

    (Paris Nanterre University)

  • Mario Saba

    (Business School Lausanne)

  • Peter Bou Saba

    (Léonard de Vinci Pôle Universitaire)

Abstract

Artificial intelligence (AI) has increased the ability of organizations to accumulate tacit and explicit knowledge to inform management decision-making. Despite the hype and popularity of AI, there is a noticeable scarcity of research focusing on AI's potential role in enriching and augmenting organizational knowledge. This paper develops a recursive theory of knowledge augmentation in organizations (the KAM model) based on a synthesis of extant literature and a four-year revised canonical action research project. The project aimed to design and implement a human-centric AI (called Project) to solve the lack of integration of tacit and explicit knowledge in a scientific research center (SRC). To explore the patterns of knowledge augmentation in organizations, this study extends Nonaka's SECI (socialization, externalization, combination, and internalization) model by incorporating the human-in-the-loop Informed Artificial Intelligence (IAI) approach. The proposed design offers the possibility to integrate experts' intuition and domain knowledge in AI in an explainable way. The findings show that organizational knowledge can be augmented through a recursive process enabled by the design and implementation of human-in-the-loop IAI. The study has important implications for research and practice.

Suggested Citation

  • Antoine Harfouche & Bernard Quinio & Mario Saba & Peter Bou Saba, 2023. "The Recursive Theory of Knowledge Augmentation: Integrating human intuition and knowledge in Artificial Intelligence to augment organizational knowledge," Information Systems Frontiers, Springer, vol. 25(1), pages 55-70, February.
    • Handle: RePEc:spr:infosf:v:25:y:2023:i:1:d:10.1007_s10796-022-10352-8
    DOI: 10.1007/s10796-022-10352-8
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    References listed on IDEAS

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    1. Duan, Yanqing & Edwards, John S. & Dwivedi, Yogesh K, 2019. "Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda," International Journal of Information Management, Elsevier, vol. 48(C), pages 63-71.
    2. Sanjay K. Sahay & Nihita Goel & Murtuza Jadliwala & Shambhu Upadhyaya, 2021. "Advances in Secure Knowledge Management in the Artificial Intelligence Era," Information Systems Frontiers, Springer, vol. 23(4), pages 807-810, August.
    3. Popadiuk, Silvio & Choo, Chun Wei, 2006. "Innovation and knowledge creation: How are these concepts related?," International Journal of Information Management, Elsevier, vol. 26(4), pages 302-312.
    4. Shrestha, Yash Raj & Krishna, Vaibhav & von Krogh, Georg, 2021. "Augmenting organizational decision-making with deep learning algorithms: Principles, promises, and challenges," Journal of Business Research, Elsevier, vol. 123(C), pages 588-603.
    5. Ikujiro Nonaka, 1994. "A Dynamic Theory of Organizational Knowledge Creation," Organization Science, INFORMS, vol. 5(1), pages 14-37, February.
    6. Collins, Christopher & Dennehy, Denis & Conboy, Kieran & Mikalef, Patrick, 2021. "Artificial intelligence in information systems research: A systematic literature review and research agenda," International Journal of Information Management, Elsevier, vol. 60(C).
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    Cited by:

    1. Denis Dennehy & Anastasia Griva & Nancy Pouloudi & Yogesh K. Dwivedi & Matti Mäntymäki & Ilias O. Pappas, 2023. "Artificial Intelligence (AI) and Information Systems: Perspectives to Responsible AI," Information Systems Frontiers, Springer, vol. 25(1), pages 1-7, February.

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