Modelling Accelerating Acquisition of Teamwork Competences with Transversal Competences and Artificial Intelligence - Graczyk-Kucharska, Magdalena; Olszewski, Robert; Przybyła, Joanna; Łuszkiewicz, Julia; Hojka, Klaudia; Spychała, Małgorzata (Krakow Review of Economics and Management/Zeszyty Naukowe Uniwersytetu Ekonomicznego w Krakowie, 2024)
Objective: The purpose of this paper was to develop a model for accelerating the acquisition of the selected transversal competence of teamwork. Based on data from four EU countries, four models were developed and the best of them was selected, describing the results and variables relevant to that model. Research Design & Methods: Data on improving transversal competences were collected from students in four countries, i.e. Poland, Slovakia, Slovenia and Finland. 26 variables were taken into account in the modelling which was based on four methods. They included the Multiple Linear Regression Model, Multivariate Adaptive Regression Splines, Support Vector Machine and two Artificial Neural Network methods. Findings: The analyses show that the method of educating students and young employees, e.g. during training courses, can be a catalyst for accelerating teamwork competence acquisition. Other transversal competences including creativity, communicativeness and entrepreneurship correlate positively with growth in teamwork competence. Implications / Recommendations: The study was conducted on an international group, also taking into account cross-cultural variables. However, to deepen the results, it is suggested that the sample size be increased and the research updated. The ranking of the education method is indicated to have an impact on the growth of transversal competences, including teamwork. Contribution: New approaches in the paper include the analytical approach to modelling the growth in teamwork competence in relation to many variables describing students and young workers in the labour market in the UE. The use of multiple analytical and statistical methods allows the most fitting model to be selected and the error to be minimised.