JOURNAL ARTICLE
Emotional Intelligence as a Crucial Skill for the Next Generation: Cluster Analysis of Czech University Students.
Published In: New Horizons in Adult Education & Human Resource Development, 2025, v. 37, n. 3. P. 191 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Kašparová, Petra; Trávníčková, Hana 3 of 3
Abstract
This article investigates the level of emotional intelligence (EI) among Czech university students using Daniel Goleman's five-component EI framework—self-awareness, managing emotions, motivating oneself, empathy, and social skills—and compares these competencies with employer demands outlined in the World Economic Forum's 2023 Future of Jobs Report. A survey of 161 students in business-related bachelor programs was analyzed via hierarchical cluster analysis, revealing three distinct student groups differing in EI profiles: one with strengths in self-awareness, empathy, and social skills; a second with balanced but moderate competencies; and a third showing strong self-awareness but weaker social skills, emotional management, and motivation. The study highlights that while students generally perceive self-awareness and empathy as strengths, managing emotions, self-motivation, and social skills require development to meet labor market expectations. These findings offer insights for educators, employers, and policymakers aiming to enhance Generation Z's readiness for evolving workplace demands, though the study's generalizability is limited by its sample size and scope.
Additional Information
- Source:New Horizons in Adult Education & Human Resource Development. 2025/09, Vol. 37, Issue 3, p191
- Document Type:Article
- Subject Area:Education
- Publication Date:2025
- ISSN:1939-4225
- DOI:10.1177/19394225251356894
- Accession Number:187236957
- Copyright Statement:Copyright of New Horizons in Adult Education & Human Resource Development is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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