Data mining the MNC like internal co-opetition duality in a university context

TL;DR

This paper investigates the internal coopetition duality within universities by analyzing specialization competition and cooperation through data mining, graph theory, and community detection, revealing insights into student profiles and organizational dynamics.

Contribution

It introduces the concept of internal coopetition duality in universities and applies graph theory and data mining to quantify competition and cooperation among academic specializations.

Findings

Distinct community structures among university specializations

Quantitative measure of cooperation via conductance ratios

Correlation between community structure and academic performance

Abstract

The goal of the paper is to quantify the simultaneous competition and cooperation that takes place in organizations. As the concepts seem to be dichotomous opposites at first, the term internal coopetition duality is put forth. Parallels are drawn between coopetitive processes in big multinational corporations (MNCs) and these taking place in universities, also the structural solutions used in both are analyzed. Data mining is used while looking at how specializations inside the university are in competition for better students. We look at the profiles that students have and find natural divisions between the specializations, by applying graph theory and modularity algorithms for community detection. The competitive position of the specializations is evident in the average grades of the detected communities. The ratio of intercommunity ties to intracommunity ties (conductance) quantifies the cooperative stance, though, as students with similar profiles express linkages in the curricula; and the choices regarding career development undertaken become evident. Managerial implications discussed include the imperative for actively managing and financially rewarding the outcomes of the coopetitive duality.