Human resources (HR) have a key impact on the creation and implementation of modern products, solutions and concepts. Relatively new and rarely undertaken research challenge in enterprise is optimization of HR in the context of their location and requirements for working conditions. A great challenge here is the transparency and reliability of the collected data. In the article, we present a modern approach to knowledge extraction based on Artificial Intelligence (AI) and Multivariate Adaptive Regression Splines optimizing the availability of HR with a high innovation rate, taking into account their availability time and location. This study was conducted on a group of 5095 young people from the Z generation. A total of 11 variables were analyzed in the context of innovation and presented in this article. The effect of research using machine learning methods is the analysis of the characteristics of generation Z representatives, whose desire is to work in innovative companies. Research results indicate that some regions offer candidates with a higher level and commitment to innovation, and thus make HR more available for the development of innovative products. Chosen models designed by using AI and Operational Research Analytics were presented in the graphic visualization, which is a novelty in the presentation of similar issues in relation to HR.

Purpose: We aimed to identify factors that influence student engagement in distance learning.Design/methodology/approach: The research involved a group of 671 students from economic and technical higher education institutions in Poland. We collected the data with the CAWI technique and an original survey. Next, we processed the data using principal component analysis and then used the extracted components as predictors in the induced smoothing LASSO regression model.Findings: The components of the students’ attitude toward remote classes learning conditions are: satisfaction with teachers’ approach, attitude to distance learning, the system of students’ values and motivation, IT infrastructure of the university, building a network of contacts and communication skills. The final model consisted of seven statistically significant variables, encompassing the student’s sex, level of studies and the first five extracted PCs. Student’s system of values and motivation as well as attitude toward distance learning, were those variables that had the biggest influence on student engagement.Practical implications: The research result suggests that in addition to students’ system of values and motivation and their attitude toward distance learning, the satisfaction level of teachers’ attitude is one of the three most important factors that influence student engagement during the distance learning process.Originality/value: The main value of this article is the statistical model of student engagement during distance learning. The article fills the research gap in identifying and evaluating the impact of various factors determining student engagement in the distance learning process.

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.