Transversal competences constitute a set of the knowledge, skills, and attitudes required for various positions and in different professions. Such competences include: entrepreneurship, teamwork, creativity, and communicativeness; they are increasingly listed by employers in different countries as the key requirements in the labor market. The article presents the model of accelerating the process of acquiring transversal competences, developed based on the analysis of data collected in four countries of the European Union: Poland, Finland, Slovakia, and Slovenia. In the analysis, multivariate additive regression spline method was used, along with artificial neural networks, in order to create the best model describing the influence of different variables on the acceleration of acquiring transversal competences. Herewith, we demonstrated that by accelerating the acquisition of the transversal competence of entrepreneurship is influenced by the following factors: rank of the training method in the developed matrix, student numbers and the weighted average of the pace of acceleration regarding the acquisition of the remaining transversal competences, i.e., teamwork, communicativeness and creativity by the given student. The results validate our new method of the acceleration of acquiring transversal competences by students. Students may be from various higher education institutions in different countries. Developed results may be used in the course of education within the framework of the already planned vocational courses and for developing the skills required by employers for various positions and in different professions.

The study looks at developing a person-organization fit model based on the unique characteristics of the new generational cohort, Generation Z, now entering the workforce. Theory suggests competitive advantage may come to a firm based on its unique human capital, the human resources it employs and develops. Further, organizations will be more successful in attracting the valuable employees they seek if they can provide a workplace appealing to them in terms of organizational values, culture, and other aspects that may also include more familiar enticements such as pay and benefits. To address the gap, this pioneering study investigates the context of person-organizational culture for the Z Generation entering the labor market. The key questions answered by the authors when describing the Person-Organization Fit Framework for Generation Z include the differences in how organizational culture appeals to Gen Z men and Gen Z women. The research results are presented in three steps: an analysis of workplace environment elements for Gen Z, correlation analysis between the workplace environment elements required by Gen Z, and presentation of a Person-Organization Fit Framework for Gen Z. From a sample of 3393 students at technological secondary schools in the Wielkopolska Region, the survey results provided evidence of the workplace preferences for this cohort. In addition, results were further analyzed for differences in gender and intended profession. For this region, Generation Z has variable individual needs and wants, some of which can be easily identified (gender, profession) but some of which may be less clear. These research results may be used for designing appealing workplaces taking into account person-organization needs for young people. Based on this novel research, organizations employing the resulting work framework for Gen Z will be better prepared to consider the nature and communication of what they have to offer as well as how they can be flexible in adapting these offerings to unique individuals.

Challenges connected with neuroscience and the use of machine learning to support analytical processes encompass more and more areas, thus supporting practitioners and managerial decisions. These changes can also be seen in the area of human resource management and support for decisions on key future spending on the remuneration of future employees. The article presents an original spatial data enrichment and spatial data mining methodology used for the analysis of primary data based on a sample of 1149 young candidates from generation Z to measure the effectiveness of data mining learning methods. The studies used data collected directly from surveys that were “enriched” with spatial geolocation. The fact that the spatial context was taken into account in the studies made it possible to develop a model explaining the spatio-temporal differentiation of professional expectations of respondents from generation Z who were studying professions connected with broadly understood IT. The analyzes used modeling with linear polynomial regression, the neural network of a multi-layer perceptron type and the multivariate adaptive regression splines method in the variant with and without spatial data filtration. The use of different spatial data mining methods made it possible to compare the reliability of models of knowledge extraction from the data and to explain the significance of individual factors which affected the respondents' beliefs. The analysis shows that spatial filtering of the data generates twice lower mean squared error while effective application of machine learning methods requires the use of explanatory spatial data.

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.

There are many studies that address the topic of organizational development in the context of sustainability, but their results do not include a combined functional approach in the area of management as well as in the development of green organizations (GOs). Defining the development of GOs and their phases from a functional perspective in manufacturing companies has not yet been sufficiently studied. This refers to the process of organizational change including, but not limited to, production, human resources, marketing, environmental management, supply chain, resources, circular economy, zero-waste buildings or product design. The purpose of this paper is to characterize the criteria for describing GOs and to define the phases of their development against the background of the literature. Empirical research was conducted on a sample of 100 manufacturing companies. The development phases of GOs were determined using cluster analysis with the k-means method carried out in accordance with the Hartigan–Wong algorithm and compared with the publishing dates of scientific publications. The results indicate that it is possible to define organizations by their development phase, and thus, it is possible to identify criteria that, when refined, will make it possible to accelerate the development of GOs in accordance with the principles of sustainable development.