Adaptation of knowledge management models in project and operational activities of an organization for software implementation

Authors

DOI:

https://doi.org/10.15587/1729-4061.2026.365174

Keywords:

knowledge management, automation of personnel management, software engineering, knowledge retention, IT project knowledge monitoring, forgetting curve

Abstract

This study investigates knowledge management processes involving highly qualified personnel at enterprises engaged in project and operational activities. The task addressed is to adapt knowledge management models for further implementation in software modules of an information system. Special attention has been paid to intellectual projects, which include software development at software engineering enterprises and knowledge management during their project and operational activities.

At the meso level, a dynamic model was built, based on a system of differential equations, which describes the rate of change in the integrated level of knowledge by the project team. At the microlevel, a model for assessing the effectiveness of corporate training was constructed. A special feature of the models is the transition from a descriptive description (such as the SECI model) to an analytical calculation of cognitive processes by integrating pure rates of knowledge exchange and formalized digital traces of specialists.

The results of model construction for assessing learning outcomes are attributed to the combination of the classical Ebbinghaus forgetting exponent and a linear function of the level of practical activity intensity, in which the cognitive memory fading indicator decreases inversely proportionally through performing verified operations in various instrumental environments.

Conditions for the practical application of models are their implementation in specialized HR analytics software modules with REST API support for automated metric collection. Analytical solution of models based on open industry data confirmed their adequacy. Thus, the calculation results indicate that under the condition of active internal learning, the developer approaches the target expert level in less than a year. Experimental modeling of learning outcomes for conditions of lack of practice recorded a degradation of skills up to 22% after 6 months, while regular performance of operations ensures the preservation of competencies at the level of 98%.

The constructed mathematical and visual models of knowledge management ready for implementation could lay the groundwork for developing special software and practical cases for knowledge management specialists

Author Biographies

Denys Robotko, Vinnytsia National Technical University

PhD Student

Department of Information Technologies and Computer Engineering

Olena Kovalenko, Vinnytsia National Technical University

Candidate of Technical Sciences, Associate Professor

Department of Information Technologies and Computer Engineering

References

  1. King, W. R. (2009). Knowledge Management and Organizational Learning. Springer. https://doi.org/10.1007/978-1-4419-0011-1
  2. Nonaka, I., Takeuchi, H. (1995). The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation. Oxford University Press.
  3. Bratianu, C., Bejinaru, R. (2020). Knowledge dynamics: a thermodynamics approach. Kybernetes, 49 (1), 6–21. https://doi.org/10.1108/k-02-2019-0122
  4. Huang, J.-J., Chen, C.-Y. (2025). Knowledge Flow Dynamics in Organizations: A Stochastic Multi-Scale Analysis of Learning Barriers. Mathematics, 13 (2), 294. https://doi.org/10.3390/math13020294
  5. Cress, U., Kimmerle, J. (2008). A systemic and cognitive view on collaborative knowledge building with wikis. International Journal of Computer-Supported Collaborative Learning, 3 (2). https://doi.org/10.1007/s11412-007-9035-z
  6. Alvarenga, A., Matos, F., Godina, R., C. O. Matias, J. (2020). Digital Transformation and Knowledge Management in the Public Sector. Sustainability, 12 (14), 5824. https://doi.org/10.3390/su12145824
  7. Di Vaio, A., Palladino, R., Pezzi, A., Kalisz, D. E. (2021). The role of digital innovation in knowledge management systems: A systematic literature review. Journal of Business Research, 123, 220–231. https://doi.org/10.1016/j.jbusres.2020.09.042
  8. Gurusinghe, R. N., Arachchige, B. J. H., Dayarathna, D. (2021). Predictive HR analytics and talent management: a conceptual framework. Journal of Management Analytics, 8 (2), 195–221. https://doi.org/10.1080/23270012.2021.1899857
  9. Yadav, R. K. (2025). Modeling Memory Retention with Ebbinghaus’s Forgetting Curve and Interpretable Machine Learning on Behavioral Factors. https://doi.org/10.36227/techrxiv.174495325.58680708/v1
  10. Hake, R. R. (1998). Interactive-engagement versus traditional methods: A six-thousand-student survey of mechanics test data for introductory physics courses. American Journal of Physics, 66 (1), 64–74. https://doi.org/10.1119/1.18809
  11. Pelánek, R. (2016). Applications of the Elo rating system in adaptive educational systems. Computers & Education, 98, 169–179. https://doi.org/10.1016/j.compedu.2016.03.017
  12. Tyndale, P. (2002). A taxonomy of knowledge management software tools: origins and applications. Evaluation and Program Planning, 25 (2), 183–190. https://doi.org/10.1016/s0149-7189(02)00012-5
  13. Training Industry Report. Available at: https://trainingmag.com/2023-training-industry-report
Adaptation of knowledge management models in project and operational activities of an organization for software implementation

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Published

2026-06-30

How to Cite

Robotko, D., & Kovalenko, O. (2026). Adaptation of knowledge management models in project and operational activities of an organization for software implementation. Eastern-European Journal of Enterprise Technologies, 3(3 (141), 21–28. https://doi.org/10.15587/1729-4061.2026.365174

Issue

Section

Control processes