@article{Sharovara_Dorosh_Trunova_Voitsekhovska_Verenych_2022, title={Model for Assessing the Level of Knowledge Convergence in Multinational Projects}, volume={21}, url={https://computingonline.net/computing/article/view/2585}, DOI={10.47839/ijc.21.2.2585}, abstractNote={<p>In modern conditions, knowledge management acquires a new meaning and becomes one of the decisive factors for success in the project implementation. Knowledge transfer is significantly complicated in international projects. This requires an in-depth analysis of different participants` project management systems, identifying their differences and determining the ability to converge (convergence) through knowledge transfer. The paper proposes the model for assessing the convergence level of project management systems, which includes a fuzzy assessment of the factors influencing the ability of the system to transfer knowledge, as well as assessing the rate of convergence (approximation) in projects. The results of the study shows that the proposed methods allow identifying “bottlenecks” of knowledge transfer processes in multinational projects and determining a strategy to increase the level of knowledge systems convergence at the project initial stage. Evaluation of the accuracy and reliability of the proposed methods prove the adequacy of their applications for forecasting new project convergence level.</p>}, number={2}, journal={International Journal of Computing}, author={Sharovara, Olena and Dorosh, Mariia and Trunova, Olena and Voitsekhovska, Mariia and Verenych, Olena}, year={2022}, month={Jun.}, pages={169-176} }