Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Advertisement

Springer Nature Link
Log in

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

  • Published:
Information Systems Frontiers Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Notes

  1. The Project made possible the study of the behavior of prehistoric man who lived in a cave in southern France between 600,000 and 90,000 years ago.

References

  • Alavi, M., & Leidner, D. E. (2001). Knowledge Management and Knowledge Management Systems: Conceptual Foundations and Research Issues. MIS Quarterly, 25, 107–136.

    Article  Google Scholar 

  • Anderson, J. R. (1983). The Architecture of cognition. Harvard University Press.

    Google Scholar 

  • Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S. A., Barbado, S., García, S., Gil-López, D. M., & Benjamins, R. (2020). Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities, and challenges toward responsible AI. Information Fusion, 58, 82–115.

    Article  Google Scholar 

  • Bailey, D. E., & Barley, S. R. (2019). Beyond design and use: How scholars should study intelligent technologies. Information and Organization. https://doi.org/10.1016/j.infoandorg.2019.100286

    Article  Google Scholar 

  • Baskerville, R. L., & Wood-Harper, A. T. (1996). A critical perspective on action research as a method for information systems research. Journal of Information Technology, 11(3), 235–246. https://doi.org/10.1080/026839696345289

    Article  Google Scholar 

  • Cardenas, A. F. (1977). Technology for Automatic Generation of Application Programs -A Pragmatic View. MIS Quarterly, 1(3), 49–72.

    Article  Google Scholar 

  • Cockburn, I., Henderson, R., & Stern, S. (2019). The Impact of Artificial Intelligence on Innovation: An Exploratory Analysis. Chap. 4 In The Economics of Artificial Intelligence, edited by Ajay K. Agrawal, Joshua Gans, and Avi Goldfarb. University of Chicago Press, 2019.

  • Collins, C., Dennehy, D., Conboy, K., & Mikalef, P. (2021). Artificial intelligence in information systems research: A systematic literature review and research agenda. International Journal of Information Management, 60, 102383. https://doi.org/10.1016/j.ijinfomgt.2021.102383

    Article  Google Scholar 

  • Davenport, T. H., & Prusak, L. (1998). Working Knowledge: How Organizations Manage What They Know. Harvard Business School Press.

  • Davis, E., & Marcus, G. (2015). Common-sense reasoning and common-sense knowledge in artificial intelligence. Communications of the ACM, 58(9), 92–103.

    Article  Google Scholar 

  • Davison, R., Martinsons, M. G., & Ou, C. X. J. (2012). The Roles of Theory in Canonical Action Research. MIS Quarterly, 36(3), 763–786.

    Article  Google Scholar 

  • Davison., R.M., Martinsons., M. G., & Kock, N., (2004). Principles of canonical action research. Information Systems Journal, 14(1), 65-86.https://doi.org/10.1111/J.1365-2575.2004.00162.X.

  • Dennehy, D., Griva, A., Pouloudi, N., Mäntymäki, M., & Pappas, I. (2022). Artificial intelligence for decision-making and the future of work. International Journal of Information Management. https://doi.org/10.1016/j.ijinfomgt.2022.102574

    Article  Google Scholar 

  • Dennehy, D., Griva, A., Pouloudi, N., Dwivedi, Y. K., Pappas, I., & Mäntymäki, M. (2021). Responsible AI and Analytics for an Ethical and Inclusive Digitized Society. In Proceedings of the 20th IFIP WG 6.11 Conference on e-Business, e-Services and e-Society, I3E 2021, Galway, Ireland, September 1–3, 2021 (vol. 12896). Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-030-85447-8

  • Dewhurst, F. W., & Gwinnett, E. A. (1990). Artificial Intelligence and Decision Analysis. The Journal of the Operational Research Society, 41(8), 693–701.

    Article  Google Scholar 

  • Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda. International Journal of Information Management, 48, 63–71.

    Article  Google Scholar 

  • Enholm, I.M., Papagiannidis, E., Mikalef, Krogstie, J. (2021). Artificial Intelligence and Business Value: a Literature Review. Information Systems Frontiers.https://doi.org/10.1007/s10796-021-10186-w.

  • Faghmous, J. A., Banerjee, S. S., Steinbach, M., Kumar, V., Ganguly, A. R., & Samatova, N. (2014). Theory-Guided Data Science for Climate Change. Computer, 47(11), 74–78.

    Article  Google Scholar 

  • Faraj, S., Pachidi, S., & Sayegh, K. (2018). Working and organizing in the age of the learning algorithm. Information and Organization, 28(1), 62–70.

    Article  Google Scholar 

  • FossoWamba, S., & Queiroz, M. M. (2021). Responsible Artificial Intelligence as a Secret Ingredient for Digital Health: Bibliometric Analysis, Insights, and Research Directions. Information Systems Frontiers. https://doi.org/10.1007/s10796-021-10142-8

    Article  Google Scholar 

  • Furman, J. L., & Teodoridis, F. (2019). Automation, research technology, and researchers’ trajectories: Evidence from computer science and electrical engineering. Organization Science, 31(2), 245–534.

    Google Scholar 

  • Grønsund, T., & Aanestad, M. (2020). Augmenting the algorithm: Emerging human-in-the-loop work configurations, The Journal of Strategic Information Systems, 29(2020), 101614.

  • Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., & Chen, T. (2018). Recent advances in convolutional neural networks. Pattern Recognition, 77, 354–377.

    Article  Google Scholar 

  • Haibe-Kains, B., Adam, G. A., Hosny, A., Khodakarami, F., Waldron, L., Wang, B., McIntosh, C., Goldenberg, A., Kundaje, A., Greene, C. S., Broderick, T., Hoffman, M. M., Leek, J. T., Korthauer, K., Huber, W., Brazma, A., Pineau, J., Tibshirani, R., Hastie, T., … Aerts, H. J. W. L. (2020). Transparency and reproducibility in artificial intelligence. Nature, 586(7829), E14–E16.

    Article  Google Scholar 

  • Harfouche, A., Quinio, B., Skandrani, S., Marciniak, R. (2017), A Framework for Artificial Knowledge Creation in Organizations, Thirty eighth International Conference on Information Systems ICIS2017, Seoul.

  • Horvatić, D., Lipić, T. (2021). Human-Centric AI: The Symbiosis of Human and Artificial Intelligence. Entropy, 23, 332. https://doi.org/10.3390/e23030332.

  • Johnson, M., Albizri, A., & Harfouche, A. (2021a). Responsible Artificial Intelligence in Healthcare: Predicting and Preventing Insurance Claim Denials for Economic and Social Wellbeing. Information Systems Frontiers. https://doi.org/10.1007/s10796-021-10137-

    Article  Google Scholar 

  • Johnson, M., Albizri, A., Harfouche A., & Fosso-Wamba, S., (2022). Integrating Human Domain Knowledge into Artificial Intelligence: Informed Artificial Intelligence. International Journal of Information Management, 64.https://doi.org/10.1016/j.ijinfomgt.2022.102479.

  • Johnson, M., Albizri, A., Harfouche, A., & Tutun, S. (2021b). Digital Transformation to Mitigate Emergency Situations: Increasing Opioid Overdose Survival Rates through Explainable Artificial Intelligence. Industrial Management & Data Systems. https://doi.org/10.1108/IMDS-04-2021-0248

    Article  Google Scholar 

  • Karpatne, A., Atluri, G., Faghmous, J. H., Steinbach, M., Banerjee, A., Ganguly, A., Shekhar, S., Samatova, N., & Kumar, V. (2017). Theory-guided Data Science: A New Paradigm for Scientific Discovery from Data. IEEE Transactions Knowledge Data Engineering, 29, 2318–2331.

    Article  Google Scholar 

  • Lawrence, T. (1991). Impacts of artificial intelligence on organizational decision-making. Journal of Behavioral Decision Making, 4(3), 195–214.

    Article  Google Scholar 

  • Lepri, B., Oliver, N., Pentland, A. (2021). Ethical machines: The human-centric use of artificial intelligence. iScience, 24(3), https://doi.org/10.1016/j.isci.2021.102249.

  • Lipton, Z. C. (2018). The Mythos of Model Interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue, 16(3), 31–57.

    Article  Google Scholar 

  • Liu, R., Gupta, S., & Patel, P. (2021). ApplyingThe Application of the Principles of Responsible AI on Social Media Marketing for Digital Health. Information Systems Frontiers, 1–25. 10.1007/s10796–021–10191-z.

  • Markus, M. L., (2017). Datification organizational strategy and IS research: What’s the Score?, The Journal of Strategic Information Systems, 26 (3), 233–241. https://doi.org/10.1016/j.jsis.2017.08.003

  • Merhi, M. I. (2022). An Assessment of the Barriers Impacting Responsible Artifcial Intelligence. Information Systems Frontiers. https://doi.org/10.1007/s10796-022-10276-3.

  • Nonaka, I., Toyama, R., & Konno, N. (2000). SECI, Ba and leadership, a unified model of dynamic knowledge creation. Long Range Planning, 33, 1–31.

    Article  Google Scholar 

  • Nonaka, I. (1994). A Dynamic Theory of Organizational Knowledge Creation. Orgnization Science, 5(1), 14–37.

    Article  Google Scholar 

  • Pan, Y.H. (2016). Heading toward artificial intelligence 2.0. Engineering, 2(4), 409–413.

  • Polanyi, M. (1966). The Tacit Dimension. Routledge & Kegan Paul.

    Google Scholar 

  • Ramchurn, S. D, Stein, S., & Jennings, N. R, (2021), Trustworthy human-AI partnerships, iScience, 24(8), 102891. https://doi.org/10.1016/j.isci.2021.102891.

  • Rokach, L. (2010). Ensemble-based classifiers. Artificial Intelligence Review, 33(1), 1–39.

    Article  Google Scholar 

  • Russell, S., & Norvig, P. (2010). Artificial Intelligence, A Modern Approach. Third Edition. Pearson Education.

  • Saba, P. B., & Meissonier, R. (2020). Fighting fire with fire: Action research on the inoculation technique to limit the resistances related to an IT project. Systèmes D’information Et Management, 25(3), 87–126.

    Google Scholar 

  • Sahay, S. K., Goel, N., Jadliwala, M., et al. (2021). Advances in Secure Knowledge Management in the Artificial Intelligence Era. Information Systems Frontiers, 23, 807–810. https://doi.org/10.1007/s10796-021-10179-9

    Article  Google Scholar 

  • Shollo, A., & Galliers, R. D. (2016). Towards an understanding of the role of business intelligence systems in organisational knowing. Information Systems Journal, 26(4), 339–367.

    Article  Google Scholar 

  • Shrestha, Y., Vaibhav, K., & von Krogh, G. (2021). Augmenting organizational decision-making with deep learning algorithms: Principles, promises, and challenges. Journal of Business Research, 123, 588–603. https://doi.org/10.1016/j.jbusres.2020.09.068

    Article  Google Scholar 

  • Shrestha, Y. R., Ben-Menahem, S. M., & von Krogh, G. (2019). Organizational Decision-Making Structures in the Age of Artificial Intelligence. California Management Review, 61(4), 66–83.

    Article  Google Scholar 

  • Susman, G. I., & Evered, R. D. (1978). An Assessment of the Scientific Merits of Action Research. Administrative Science Quarterly, 23(4), 582. https://doi.org/10.2307/2392581

    Article  Google Scholar 

  • Tutun, S., Harfouche, A., Albizri, A., & Johnson, M. (2022). A Responsible AI Framework for Mitigating the Ramifications of the Organ Donation Crisis. Information Systems Frontiers. https://doi.org/10.1007/s10796-022-10340-y

    Article  Google Scholar 

  • von Krogh, G. (2018). Artificial Intelligence in organizations: New opportunities for phenomenon-based theorizing. Academy of Management Discoveries, 4(4), 404–409.

    Article  Google Scholar 

  • Von Rueden, L., Sebastian M., Jochen, G., Christian B., & Jannis, S. (2019). Informed Machine Learning - Towards a Taxonomy of Explicit Integration of Knowledge into Machine Learning, arXiv preprint, arXiv:1903.12394.

  • Zhuang, Y.T., Wu, F, Chen, C., & Pan. Y.H (2017). Challenges and opportunities: from big data to knowledge in AI 2.0. Frontiers of Information Technology & Electronic Engineering, 18(1), 3–14.

  • Wilson, H. J., & Daugherty, P. R. (2018). Collaborative intelligence: Humans and AI are joining forces. Harvard Business Review, 96(4), 114–123.

    Google Scholar 

  • Popadiuk, S., & Choo, C. W. (2006). Innovation and knowledge creation: How are these concepts related? International Journal of Information Management, 26(4), 302–312. https://doi.org/10.1016/j.ijinfomgt.2006.03.011

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Paris Nanterre University, 200 Av. de La République, 92000, Nanterre, France

    Antoine Harfouche & Bernard Quinio

  2. Business School Lausanne, Rte de La Maladière 21, 1022, Chavannes-Près-Renens, Switzerland

    Mario Saba

  3. Research Center, Léonard de Vinci Pôle Universitaire, 92 916, Paris La Défense, France

    Peter Bou Saba

Authors
  1. Antoine Harfouche
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bernard Quinio
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mario Saba
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Peter Bou Saba
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Antoine Harfouche.

Ethics declarations

Conflicts of Interest

The authors declare that there are no conflicts of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Harfouche, A., Quinio, B., Saba, M. et al. The Recursive Theory of Knowledge Augmentation: Integrating human intuition and knowledge in Artificial Intelligence to augment organizational knowledge. Inf Syst Front 25, 55–70 (2023). https://doi.org/10.1007/s10796-022-10352-8

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10796-022-10352-8

Keywords

Search

Navigation