JOURNAL ARTICLE
Effects of Entrepreneurship Education Components on Entrepreneurial Intentions in Oman.
Published In: Entrepreneurship Education & Pedagogy, 2025, v. 8, n. 4. P. 605 1 of 3
Database: Education Source Ultimate 2 of 3
Authored By: Kassim, Norizan Mohd; Alshukaili, Abdallah; Zain, Mohamed; Ravi, Anju; Muneerali, Mohammed; Sharif, Khurram 3 of 3
Abstract
This article investigates how specific components of entrepreneurship education (EE)—namely know-what (knowledge), know-why (values and motives), know-who (social interactions), and know-how (skills and abilities)—influence entrepreneurial intentions (EI) among undergraduate students in Oman, using Johannisson's framework integrated with Ajzen's Theory of Planned Behavior (TPB). The study finds that these EE components are interrelated, with know-what serving as the foundational knowledge that directly and indirectly affects EI through the TPB antecedents: entrepreneurial attitude (EA), subjective norms (SN), and perceived behavioral control (PBC). Additionally, the research reveals that business major students exhibit stronger positive effects of EE on EI compared to non-business majors. The findings suggest that effective entrepreneurship programs should holistically integrate theoretical knowledge, motivation, social networking, and practical skills to foster entrepreneurial intentions, with implications for curriculum design in higher education institutions in Oman and similar contexts.
Additional Information
- Source:Entrepreneurship Education & Pedagogy. 2025/10, Vol. 8, Issue 4, p605
- Document Type:Article
- Subject Area:Geography and Cartography
- Publication Date:2025
- ISSN:25151274
- DOI:10.1177/25151274241268283
- Accession Number:187649001
- Copyright Statement:Copyright of Entrepreneurship Education & Pedagogy 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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