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2022, Volume 18, Issue 6: 4093-4110. Doi: 10.3934/jimo.2021149
This issue Previous Article The evolution mechanism of the multi-value chain network ecosystem supported by the third-party platform Next Article A new smoothing spectral conjugate gradient method for solving tensor complementarity problems

Human resources optimization with MARS and ANN: Innovation geolocation model for generation Z

  • 1.

    Poznan University of Technology, Faculty of Engineering Management, Poznan, Poland

  • 2.

    Warsaw University of Technology, Faculty of Geodesy and Cartography, Warsaw, Poland

*ORCID IDs: Graczyk-Kucharska [0000-0002-4241-8216]; Robert Olszewski [0000-0003-1697- 9367]; Marek Golinski [0000-0002-0170-2835]; Ma lgorzata Spycha la [0000-0003-1471-5536]; Maciej Szafranski [0000-0002-4281-9845]; Gerhard Wilhelm Weber [0000-0003-0849-7771].

Received:  March 2021
Revised:  June 2021
Early access:  September 2021
Published:  November 2022
Abstract / Introduction Full Text(HTML) Figure(4) / Table(1) Related Papers Cited by
    Abstract

  • 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.

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  • Figure 1.  Distance zones from large cities (red circles – 30 km, pink circles – 60 km)

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    Figure 2.  Model MLP for students graduating in 2018-2022

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    Figure 3.  Model MLP for students graduating in 2018-2019

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    Figure 4.  Model MLP for students graduating in 2020-2022

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    Table 1.  Dependent variable and independent variables selected for the innovation geolocation model

    Willingness of representatives of the Z generation to work in an innovative company $Y$
    Gender: $X_1$
    Year of graduation: $X_2$
    Place of residence (close, average, far away from a large city): $X_3$
    Unemployment in the region: $X_4$
    Balance of migration in the region: $X_5$
    Expected salary (high, medium, low): $X_6$
    Willingness to continue university education: $X_7$
    Willingness to leave the place of residence: $X_8$
    Individual work vs. group work: $X_9$
    Remote work vs. traditional work in the company: $X_{10}$
    Work with passion vs. just work: $X_{11}$
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