1286. sredin seminar: Marjan Cugmas: Global network structure and local network mechanisms of knowledge-flow networks

Understanding patterns and underlaying mechanisms of exchanging knowledge among employees is crucial to ensure their professional development and consequently competitiveness of a company. Exchanging knowledge of different types can be operationalized by so called knowledge-flow networks. In such networks, nodes represent employees and links among them are measured by the flow of different kinds of knowledge.

 

The presentation will consist of two parts. In the first part, the analysis of the data, collected among the employees of a middle size Slovenian company, will be presented (Miha Škerlavaj, 2007). The data were collected at three time points (2004, 2006 and 2007). The generalized blockmodeling (Doreian, Batagelj, & Ferligoj, 2005) was used to reveal the global network structures of the knowledge-flow networks. Then, different types of indices for measuring the similarity of two partitions (Cugmas & Ferligoj, 2018) were used to explain the stability of the obtained blockmodels in time and finally, different attributes (characteristics of the employees, e.g., gender, tenure, …) were considered to describe the mechanisms that might cause the observed dynamics of the global network structure.

 

The algorithm for generating knowledge-flow networks (by considering different local network mechanisms) will be presented in the second part of the presentation. The algorithm and the corresponding mechanisms are defined based on the theory proposed by Nebus (2006). Here, advice-seekers consider the cost of obtaining advice from a given employee on one hand and the potential value of the employees’ advice on the other hand. Different types of the perceived costs and the perceived values are operationalized by different local network mechanisms. The Monte Carlo simulations were used to test which local network mechanisms are necessary to obtain a hierarchical blockmodel with complete blocks on the diagonal.

References:

• Cugmas, M., & Ferligoj, A. (2018). Comparing two partitions of non-equal sets of units. Advances in Methodology and Statistics, 15(1), 1–21.
• Doreian, P., Batagelj, V., & Ferligoj, A. (2005). Generalized blockmodeling (Vol. 25). Cambridge: Cambridge university press.
• Nebus, J. (2006). Building collegial information networks: A theory of advice network generation. Academy of Management Review, 31(3), 615–637.
• Škerlavaj, Miha. (2007). The network perspective and performance of organizational learning: Theoretical and empirical analysis. Ljubljana: University of Ljubljana.