Artificial Intelligence in Employee Learning Process: Insights from Generation Z

Poljašević Branka Zolak (), Žižek Simona Šarotar () and Gričnik Ana Marija ()
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Poljašević Branka Zolak: University of Banja Luka, Faculty of Economics, Bosnia and Herzegovina
Žižek Simona Šarotar: University of Maribor, Faculty of Economics and Business, Slovenia
Gričnik Ana Marija: University of Maribor, Faculty of Economics and Business, Slovenia

Naše gospodarstvo/Our economy, 2024, vol. 70, issue 3, 21-36

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.

Keywords: Learning process; Artificial intelligence; Employees; Generation Z; Sociodemographic characteristic (search for similar items in EconPapers)
JEL-codes: M14 M15 M53 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:ngooec:v:70:y:2024:i:3:p:21-36:n:1002

DOI: 10.2478/ngoe-2024-0014

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