JOURNAL ARTICLE
The AICPA Core Competency Framework: The Perceived Importance and Perceived Preparedness of Entry Level Accountants (ELAs).
Published In: Issues in Accounting Education, 2025, v. 40, n. 4. P. 65 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Kinsley Wright, Jessie; Morlino, Stasia H.; Lamb, Laura Bea; Mahoney, Daniel P. 3 of 3
Abstract
From the perspective of entry-level accountants (ELAs), experienced professionals, and accounting educators, this study of ELAs examines the perceived importance and perceived level of possession of competencies with the AICPA Pre-certification Core Competency Framework. We surveyed 67 ELAs, 72 experienced professionals, and 207 educators. Results show that participants perceive the competencies as important and relevant to the skills required of ELAs in the workplace. Overwhelmingly, internships/co-ops were selected as the best form of entry-level preparation for students. Our findings suggest value and relevance in leveraging the AICPA Core Competencies within an accounting curriculum. The majority of faculty indicated that they have implemented at least some of the framework into the curriculum. Accounting educators should note these results as they demonstrate the benefits of incorporating these competencies into accounting education and improving the preparedness of accounting graduates for greater success as entry-level accountants in the profession. Data Availability: Data will be made available on request. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Issues in Accounting Education. 2025/11, Vol. 40, Issue 4, p65
- Document Type:Article
- Subject Area:Business and Management
- Publication Date:2025
- ISSN:0739-3172
- DOI:10.2308/ISSUES-2023-128
- Accession Number:189037555
- Copyright Statement:Copyright of Issues in Accounting Education is the property of American Accounting Association 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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