| Title: | Artificial intelligence in employee learning process : insights from Generation Z |
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| Authors: | ID Zolak Poljašević, Branka (Author)
ID Šarotar Žižek, Simona (Author)
ID Gričnik, Ana Marija (Author) |
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| Files: |  https://sciendo.com/article/10.2478/ngoe-2024-0014
 RAZ_Zolak_Poljasevic_Branka_2024.pdf (561,58 KB)
MD5: C490284D54C2773784E057C6365C63F4
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| Language: | English |
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| Work type: | Scientific work |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | EPF - Faculty of Business and Economics
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| Abstract: | Artificial intelligence, as a field of computer science focused on
developing technologies that simulate intelligent behaviours and
human cognitive functions, undoubtedly has huge potential to
transform all business activities, including the process of employee
learning. However, different generations have varying attitudes toward
the rapid advancement of technology and the increasing possibilities
offered by artificial intelligence. The general purpose of this research
is to gain insights into the attitudes of Generation Z regarding the use
of AI in the context of the employee learning process. Empirical
research was conducted on a sample of 264 respondents from Slovenia
and Bosnia and Herzegovina. In addition to descriptive statistics,
Cronbach's alpha, Shapiro-Wilk, and Mann-Whitney tests were used to
test hypotheses. Generally, the research findings indicate that the
upcoming generation of the workforce considers artificial intelligence
a significant factor in improving the employee learning process. The
study contributes to human resource management literature because it
brings new insights into Generation Z attitudes, whose participation in
the active workforce will significantly increase in the coming years. |
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| Keywords: | learning process, Artificial Intelligence, employees, generation Z, sociodemographic characteristic |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Publication date: | 01.10.2024 |
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| Year of publishing: | 2024 |
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| Number of pages: | str. 21-36 |
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| Numbering: | Vol. 70, no. 3 |
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| PID: | 20.500.12556/DKUM-92734-0b922230-cdd8-5788-9358-3714491ee6f1  |
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| UDC: | 004.8:658.3 |
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| ISSN on article: | 0547-3101 |
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| COBISS.SI-ID: | 219666947 |
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| DOI: | 10.2478/ngoe-2024-0014 |
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| Publication date in DKUM: | 27.05.2025 |
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| Views: | 212 |
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| Downloads: | 15 |
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| Metadata: |    |
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| Categories: | Misc.
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