Abstract:

In today´s competitive environment and a global world, employees and their attitudes and behaviours are becoming increasingly important. Employers try to understand the complexity of their employees and how best to maximize their potential. Employee requirements for employee benefits are different and employers have to respond flexibly not only to their requirements but also to new trends in the benefits provided. The motivation process, therefore, represents a challenge for employers as well as an opportunity to use new technologies. In the field of human resources, techniques such as machine learning are increasingly being implemented, which can be used to improve and streamline human resource management processes. The article aims to point out the possibility of using machine learning techniques in the segmentation of university teachers. Using the clustering method, which belongs to unsupervised learning, it will be possible to segment university teachers into clusters according to their employee benefit preferences. This way of segmenting can simplify the process of motivation of university teachers by using of the new forms of employee motivation (e. g. Cafeteria system). The motivation of university teachers can be considered an important task because the work of university teachers is very demanding and has a great impact on students, both lay and professional public and employers. In terms of the motivation process, Slovak organizations also face challenges such as the unbalanced application of the incentive program, which causes some employees to feel neglected and thus become demotivated. Improper use of motivational tools can be just as counterproductive. The motivation of university teachers therefore plays an important role in society.

Keywords:

Higher education, motivation, machine learning.