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Knowledge-based Management of IT Projects with Methods of Artificial Intelligence to Increase Efficiency

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  • Sascha Bruggen

    (UCAM Universidad Católica San Antonio de Murcia, Spain)

  • Alexander Holland

    (FOM University of Applied Sciences, Germany)

Abstract

The success of IT projects in companies is increasingly becoming a competitive factor. This study aims to analyze whether selected artificial intelligence (AI) methods and knowledge-based management (KBM) can provide benefits in the management of IT projects. The approach of knowledge management is of particular importance for companies, as the continuous and unpredictable changes are nowadays the standard in many business sectors. Consequently, knowledge has become an essential corporate resource. The deductive research approach in this paper has a qualitative exploratory design based on semi-structured interviews followed by Qualitative Data Analysis (QDA). The categories and coding used within the QDA were generated through an interview guide. The participants surveyed are proven experts in their field and hold high-ranking positions in industry, retail, consulting, and the public sector. This study shows that using artificial intelligence in conjunction with knowledge management is neglected in companies. AI is perceived and pursued, but knowledge management approaches are not consistently carried out. The limitation of this study is not limited to a specific type of IT project but focuses on classic project procedure models that are frequently found in practice. The study results can aid organizations in making their project management processes more efficient while benefiting from the resource of available implicit and explicit knowledge inside their organizations. No comprehensive framework for using AI methods and knowledge management in IT projects was found in the existing literature. Therefore, this study fills the research gap.

Suggested Citation

  • Sascha Bruggen & Alexander Holland, 2022. "Knowledge-based Management of IT Projects with Methods of Artificial Intelligence to Increase Efficiency," Scientia Moralitas Journal, Scientia Moralitas, Research Institute, vol. 7(1), pages 1-17, July.
    • Handle: RePEc:smo:journl:v:7:y:2022:i:1:p:1-17
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    References listed on IDEAS

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    1. Wang, Eric T.G. & Chia-Lin Lin, Cathy & Jiang, James J. & Klein, Gary, 2007. "Improving enterprise resource planning (ERP) fit to organizational process through knowledge transfer," International Journal of Information Management, Elsevier, vol. 27(3), pages 200-212.
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