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Combining Big Data and Artificial Intelligence for Managing Collective Knowledge in Unpredictable Environment—Insights from the Chinese Case in Facing COVID-19

Author

Listed:
  • Francesca Iandolo

    (University of Rome ‘La Sapienza’)

  • Francesca Loia

    (University of Naples ‘Federico II’)

  • Irene Fulco

    (University of Tuscia of Viterbo)

  • Chiara Nespoli

    (University of Campania ‘L. Vanvitelli’)

  • Francesco Caputo

    (University of Naples ‘Federico II’)

Abstract

The increasing fluidity of social and business configurations made possible by the opportunities provided by the World Wide Web and the new technologies is questioning the validity of consolidated business models and managerial approaches. New rules are emerging and multiple changes are required to both individuals and organizations engaged in dynamic and unpredictable paths. In such a scenario, the paper aims at describing the potential role of big data and artificial intelligence in the path toward a collective approach to knowledge management. Thanks to the interpretative lens provided by systems thinking, a framework able to explain human-machine interaction is depicted and its contribution to the definition of a collective approach to knowledge management in unpredictable environment is traced. Reflections herein are briefly discussed with reference to the Chinese governmental approach for managing COVID-19 spread to emphasise the support that a technology-based collective approach to knowledge management can provide to decision-making processes in unpredictable environments.

Suggested Citation

  • Francesca Iandolo & Francesca Loia & Irene Fulco & Chiara Nespoli & Francesco Caputo, 2021. "Combining Big Data and Artificial Intelligence for Managing Collective Knowledge in Unpredictable Environment—Insights from the Chinese Case in Facing COVID-19," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 12(4), pages 1982-1996, December.
    • Handle: RePEc:spr:jknowl:v:12:y:2021:i:4:d:10.1007_s13132-020-00703-8
    DOI: 10.1007/s13132-020-00703-8
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    1. Armenia, Stefano & Franco, Eduardo & Iandolo, Francesca & Maielli, Giuliano & Vito, Pietro, 2024. "Zooming in and out the landscape: Artificial intelligence and system dynamics in business and management," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    2. Perotti, Francesco Antonio & Ferraris, Alberto & Candelo, Elena & Busso, Donatella, 2022. "The dark side of knowledge sharing: Exploring “knowledge sabotage” and its antecedents," Journal of Business Research, Elsevier, vol. 141(C), pages 422-432.
    3. Yong Qin & Zeshui Xu & Xinxin Wang & Marinko Skare, 2024. "Artificial Intelligence and Economic Development: An Evolutionary Investigation and Systematic Review," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(1), pages 1736-1770, March.
    4. Ziqing Xie & Dickson K. W. Chiu & Kevin K. W. Ho, 2024. "The Role of Social Media as Aids for Accounting Education and Knowledge Sharing: Learning Effectiveness and Knowledge Management Perspectives in Mainland China," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(1), pages 2628-2655, March.
    5. Giacomoni Gilbert, 2024. "The Need for Products Interchangeability: An Unsolved Problem of Semantic Conflicts No Product Definition System Can Support Perfectly," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(2), pages 7672-7701, June.

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