Aktuálne číslo: 1/2026
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DETERMINANTS OF ARTIFICIAL INTELLIGENCE ADOPTION IN HIGHER EDUCATION: AN IMPORTANCE–PERFORMANCE MAP ANALYSIS APPROACH
Rapid advancements in artificial intelligence (AI), particularly Generative AI, are fundamentally transforming higher education. The aim of this study is to identify the determinants of AI acceptance among Generation Z students and to extend existing technology adoption models by integrating the methods of Partial Least Squares Structural Equation Modeling and Importance–Performance Map Analysis. Empirical data were collected through a questionnaire survey conducted among university students. The model included Perceived Informedness, Perceived Knowledge, Perceived Usefulness, and Perceived Risk. The Partial Least Squares Structural Equation Modeling results confirmed the significance of Perceived Knowledge and Perceived Usefulness, while Perceived Risk was not found to be significant. The Importance–Performance Map Analysis revealed that Perceived Knowledge represents the most important and best-developed factor, whereas Perceived Usefulness constitutes the key area for improvement. Perceived Informedness shows a limited impact despite its relatively high level of performance. The findings highlight the need to focus on the practical applicability of AI as the primary driver of its adoption in the higher education environment.
Vydanie: 2026/1 Strany: 15-26 Klasifikácia JEL: O33, C83, I23
DOI: https://doi.org/10.52665/ser20260102
Kľúčové slová: Generative Artificial Intelligence, Higher Education, Adoption models, PLS-SEM, IPMA
Sekcia:
Kontakty:
Dana Jašková, RNDr., PhD..
Faculty of Social and Economic Relations
Alexander Dubček University in Trenčín
Študentská 3
911 50 Trenčín Slovak Republic
e-mail: dana.jaskova@tnuni.sk
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