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Article


Title

The use of spatial data mining methods for modeling HR challenges of generation Z in greater Poland Region

Authors

[ 1 ] Instytut Inżynierii Bezpieczeństwa i Jakości, Wydział Inżynierii Zarządzania, Politechnika Poznańska | [ P ] employee

Scientific discipline (Law 2.0)

[5.6] Management and quality studies

Year of publication

2022

Published in

Central European Journal of Operations Research

Journal year: 2022 | Journal volume: 2022

Article type

scientific article

Publication language

english

Keywords
EN
  • Human resources
  • Modeling
  • Spatial data mining
  • Multivariate adaptive regression splines
  • Artificial neutral networks
  • Geolocalization
Abstract

EN 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 spatiotemporal 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.

Date of online publication

12.06.2022

Pages (from - to)

1 - 33

DOI

10.1007/s10100-022-00805-5

URL

https://link.springer.com/article/10.1007/s10100-022-00805-5

Points of MNiSW / journal

70.0

Impact Factor

2.345 [List 2020]

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