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Virtual Reality as a Tool for Sustainable Training and Education of Employees in Industrial Enterprises

Author

Listed:
  • Věroslav Holuša

    (Department of Economics and Control Systems, Faculty of Mining and Geology, VSB-Technical University of Ostrava, 17. Listopadu 2172/15, 708 00 Ostrava, Czech Republic)

  • Michal Vaněk

    (Department of Economics and Control Systems, Faculty of Mining and Geology, VSB-Technical University of Ostrava, 17. Listopadu 2172/15, 708 00 Ostrava, Czech Republic)

  • Filip Beneš

    (Department of Economics and Control Systems, Faculty of Mining and Geology, VSB-Technical University of Ostrava, 17. Listopadu 2172/15, 708 00 Ostrava, Czech Republic)

  • Jiří Švub

    (Department of Economics and Control Systems, Faculty of Mining and Geology, VSB-Technical University of Ostrava, 17. Listopadu 2172/15, 708 00 Ostrava, Czech Republic)

  • Pavel Staša

    (Department of Economics and Control Systems, Faculty of Mining and Geology, VSB-Technical University of Ostrava, 17. Listopadu 2172/15, 708 00 Ostrava, Czech Republic)

Abstract

The paper deals with the possibilities of using Virtual Reality in the training and safety of enterprises active in the raw materials industry. It examines the influence and impact on their employees. The main impetus for starting research in this area has been a need for more use of the full potential of Virtual Reality in the industrial sector. Virtual Reality (VR) has become a promising education and employee training tool. It provides an immersive and interactive learning environment, allowing users to engage with simulations, scenarios, and simulations in real time. VR can facilitate the acquisition of practical skills, help learners retain information better, and foster the development of soft skills, such as communication, teamwork, and leadership. The paper is divided into the following sections. The first two are devoted to the introduction to the issue and a review of the literature. The materials and methods section describes the possibilities of using photogrammetry to create virtual scenes and 3D models usable in Virtual Reality. This section also describes the research methods used to evaluate the approach for teaching and training employees. The last two sections evaluate and discuss the results achieved. Having regarded the research realized, it was found that our approach to researching the education of employees and the development of their skills brings excellent benefits and, compared to the traditional educational approach, is much more time-efficient so that employees can improve their work habits and behavior in a relatively short period. In employee training, VR can simulate real-life scenarios, providing workers with hands-on experience in a safe, controlled environment. This technology can also help companies save time and resources, eliminating the need for travel and reducing expenditure on expensive equipment. However, despite its many benefits, VR in education and training can be cost-demanding and requires specialized hardware and software, which may limit its widespread adoption.

Suggested Citation

  • Věroslav Holuša & Michal Vaněk & Filip Beneš & Jiří Švub & Pavel Staša, 2023. "Virtual Reality as a Tool for Sustainable Training and Education of Employees in Industrial Enterprises," Sustainability, MDPI, vol. 15(17), pages 1-24, August.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:17:p:12886-:d:1225472
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    References listed on IDEAS

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    1. Holman Ospina-Mateus & Leonardo Augusto Quintana Jiménez & Francisco J. Lopez-Valdes & Katherinne Salas-Navarro, 2019. "Bibliometric analysis in motorcycle accident research: a global overview," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(2), pages 793-815, November.
    2. Adela Rueda Márquez de la Plata & Pablo Alejandro Cruz Franco & Jorge Alberto Ramos Sánchez, 2023. "Applications of Virtual and Augmented Reality Technology to Teaching and Research in Construction and Its Graphic Expression," Sustainability, MDPI, vol. 15(12), pages 1-19, June.
    3. Cuevas, Antonio & Febrero, Manuel & Fraiman, Ricardo, 2004. "An anova test for functional data," Computational Statistics & Data Analysis, Elsevier, vol. 47(1), pages 111-122, August.
    4. Martin Krajčovič & Gabriela Gabajová & Marián Matys & Beáta Furmannová & Ľuboslav Dulina, 2022. "Virtual Reality as an Immersive Teaching Aid to Enhance the Connection between Education and Practice," Sustainability, MDPI, vol. 14(15), pages 1-14, August.
    5. Adriana AnaMaria Davidescu & Simona-Andreea Apostu & Andreea Paul & Ionut Casuneanu, 2020. "Work Flexibility, Job Satisfaction, and Job Performance among Romanian Employees—Implications for Sustainable Human Resource Management," Sustainability, MDPI, vol. 12(15), pages 1-53, July.
    6. Gabriela Gabajová & Beáta Furmannová & Iveta Medvecká & Patrik Grznár & Martin Krajčovič & Radovan Furmann, 2019. "Virtual Training Application by Use of Augmented and Virtual Reality under University Technology Enhanced Learning in Slovakia," Sustainability, MDPI, vol. 11(23), pages 1-16, November.
    7. Yifan Li & Shufan Ying & Qu Chen & Jueqi Guan, 2022. "An Experiential Learning-Based Virtual Reality Approach to Foster Students’ Vocabulary Acquisition and Learning Engagement in English for Geography," Sustainability, MDPI, vol. 14(22), pages 1-14, November.
    8. Andrzej Paszkiewicz & Mateusz Salach & Paweł Dymora & Marek Bolanowski & Grzegorz Budzik & Przemysław Kubiak, 2021. "Methodology of Implementing Virtual Reality in Education for Industry 4.0," Sustainability, MDPI, vol. 13(9), pages 1-25, April.
    9. Ziwen Wei & Man Yuan, 2023. "Research on the Current Situation and Future Development Trend of Immersive Virtual Reality in the Field of Education," Sustainability, MDPI, vol. 15(9), pages 1-18, May.
    10. Martin Krajčovič & Gabriela Gabajová & Marián Matys & Patrik Grznár & Ľuboslav Dulina & Róbert Kohár, 2021. "3D Interactive Learning Environment as a Tool for Knowledge Transfer and Retention," Sustainability, MDPI, vol. 13(14), pages 1-22, July.
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    2. Sumitra Nuanmeesri, 2024. "The Affordable Virtual Learning Technology of Sea Salt Farming across Multigenerational Users through Improving Fitts’ Law," Sustainability, MDPI, vol. 16(17), pages 1-21, September.
    3. Konrad Lewczuk & Patryk Żuchowicz, 2024. "Virtual Reality Application for the Safety Improvement of Intralogistics Systems," Sustainability, MDPI, vol. 16(14), pages 1-26, July.

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