Model for Assessing the Level of Knowledge Convergence in Multinational Projects


  • Olena Sharovara
  • Mariia Dorosh
  • Olena Trunova
  • Mariia Voitsekhovska
  • Olena Verenych



Knowledge Transfer, Multinational Projects, Convergence, Fuzzy Logic


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.


J. P. Girard and J. L. Girard, “Defining knowledge management: Toward an applied compendium,” Online Journal of Applied Knowledge Management, vol. 3, issue 1, pp. 1–20, 2015.

T. H. Davenport, “Saving IT’s soul: Human centered information management,” Harvard Business Review, vol. 72, issue 2, pp. 119–131, 1994.

S. Geng, K. B. Chuah, K. M. Y. Law, C. K. Cheung, Y. C. Chau, & C. Rui, “Knowledge contribution as a factor in project selection,” Project Management Journal, vol. 49, issue 1, pp. 25–41, 2018.

D. Shrikant, “The use of knowledge in projects: A discourse on planning,” Proceedings of the 7th Annual University of Maryland Project Management Symposium, College Park, Maryland, USA, May, 2020, PM World Journal, vol. IX, issue IX, September 2020.

R. S. Narazaki, C. M. Silveira, P. C. Drebes, “A project knowledge management framework grounded in design science research,” Knowl Process Manag, vol. 27, pp. 197–210, 2020.

P. Edwards, P. Vaz‐Serra, and M. Edwards, Project Risk Knowledge Management, in: Edwards, P., Vaz‐Serra, P. and Edwards, M. (eds.) Managing Project Risks, 2019.

M. Dülgerler and M. Negri, “Lessons (Really) Learned? How to Retain Project Knowledge and Avoid Recurring Nightmares: Knowledge Management and Lessons Learned,” Proceedings of the PMI® Global Congress 2016, EMEA, Barcelona.

X. Ren, X. Deng, and L. Liang, “Knowledge transfer between projects within project-based organizations: the project nature perspective,” Journal of Knowledge Management, vol. 22, no. 5, pp. 1082-1103, 2018.

V. d. A. A. d. Araujo, I. C. Scafuto, F. R. Serra, L. Vils, and F. Bizarrias, “The effects of internal stickiness on the success of projects,” International Journal of Managing Projects in Business, vol. 15, issue 1, pp. 175-191, 2022.

L. Pereira, J. Santos, Á. Dias, & R. Costa, “Knowledge management in projects,” International Journal of Knowledge Management (IJKM), vol. 17, issue 1, pp. 1-14, 2021.

Z. Zhang, and M. Min, “Project manager knowledge hiding, subordinates’ work-related stress and turnover intentions: empirical evidence from Chinese NPD projects,” Journal of Knowledge Management, 2021.

H. Solli-Sæther, J. T. Karlsen, and K. van Oorschot, “Strategic and cultural misalignment: Knowledge sharing barriers in project networks,” Project Management Journal, vol. 46, issue 3, pp. 49–60, 2015.

X. Ren, Z. Yan, Z. Wang, and J. He, “Inter-project knowledge transfer in project-based organizations: an organizational context perspective,” Management Decision, vol. 58, no. 5, pp. 844-863, 2019.

J. T. Karlsen, and P. Gottschalk, “A study of factors affecting knowledge transfer in IT projects,” Engineering Management Journal, vol. 16, no. 1, pp. 3-10, 2004.

S. Spalek, “How to facilitate the knowledge transfer,” Proceedings of the PMI® Global Congress 2014, EMEA, Dubai, United Arab Emirates, Newtown Square, PA: Project Management Institute, Project Management Journal, 2014.

Van C. Waveren, L. Oerlemans, and T. Pretorius, “Refining the classification of knowledge transfer mechanisms for project-to-project knowledge sharing,” South African Journal of Economic and Management Sciences, vol. 20, issue 1, pp. 1-16, 2017.

M. Takahashi, M. Indulska, J. Steen, “Collaborative research project networks: Knowledge transfer at the fuzzy front end of innovation,” Project Management Journal, vol. 49, issue 4, pp. 36-52, 2018.

A. Terhorst, D. Lusher, D. Bolton, I. Elsum, P. Wang, “Tacit knowledge sharing in open innovation projects,” Project Management Journal, vol. 49, issue 4, pp. 5-19, 2018.

H. Wu, X. Xue, Z. Zhao, Z. Wang, G.Q. Shen, X. Luo, “Major knowledge diffusion paths of megaproject management: A citation-based analysis,” Project Management Journal, vol. 51, issue 3, pp. 242-261, 2020.

M. Dorosh, O. Trunova, D. Itchenko, M. Voitsekhovska, M. Dvoieglazova, “The study of participants’ values convergence on the example of international scientific project on cyber security,” Eastern-European Journal of Enterprise Technologies, vol. 6/3 (84), pp. 4-10, 2016.

R. E. Bellman, L. A. Zadeh, “Decision-making in a fuzzy environment, Management Science, vol. 17, issue 4, pp. 141-164, 1970.

Robert J. Barro, “Convergence and modernization,” The Economic Journal, vol. 125 (585), pp. 911– 942, 2015.

T. L. Saaty, “Relative measurement and its generalization in decision making: Why pairwise comparisons are central in mathematics for the measurement of intangible factors – The Analytic Hierarchy/Network Process,” Review of the Royal Spanish Academy of Sciences RACSAM, Series A, Mathematics, vol. 102, issue 2, pp. 251–318, 2008.

P. C. Fishburn, “Utility theory,” Management Science (Theory Series), vol. 14, no. 5, pp. 335-378, 1968.

D. Chicco, M. J. Warrens, G. Jurman, “The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation,” PeerJ Comput. Sci., vol. 7, e623. 2021,

I. Kononenko, S. Lutsenko, “The information system for a project management approach selection and formation,” Radioelectronic and Computer Systems, no. 2(94), pp. 109-118, 2020. (in Russian)




How to Cite

Sharovara, O., Dorosh, M., Trunova, O., Voitsekhovska, M., & Verenych, O. (2022). Model for Assessing the Level of Knowledge Convergence in Multinational Projects. International Journal of Computing, 21(2), 169-176.