JOURNAL ARTICLE
ADDRESSING ECONOMIC INEQUALITY THROUGH MANAGEMENT EDUCATION: DISRUPTING STUDENT ATTRACTION TO THE MYTH OF NEOLIBERAL MERITOCRACY.
Published In: Academy of Management Learning & Education, 2024, v. 23, n. 3. P. 432 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: EISENMAN, MICKI; FOROUGHI, HAMID; FOSTER, WILLIAM M. 3 of 3
Abstract
In this essay, we argue that economic inequality is reproduced because business students uncritically accept the neoliberal myth of meritocracy. This myth advances values and beliefs suggesting that hard work and innate talent lead to equally accessible opportunities and corresponding rewards. These ideas are embedded in the narratives (e.g., stories, exercises, cases, or guest speakers) prevalent throughout the business school but remain “hidden” to students because they are implicit rather than surfaced. We explain that these narratives are attractive to students and, because they are implicit within the curriculum, they limit business students’ abilities to make the systemic changes needed to address economic inequality. In our call to action, we propose a set of tools—literary analysis, plural vocality, and historical learning—that can disrupt this attraction and enable students to critically engage with the myth of neoliberal meritocracy. It is our opinion that a more critical outlook will raise students’ awareness to economic inequality and encourage them to ameliorate this type of inequality as they move through their professional lives. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Academy of Management Learning & Education. 2024/09, Vol. 23, Issue 3, p432
- Document Type:Article
- Subject Area:Political Science
- Publication Date:2024
- ISSN:1537-260X
- DOI:10.5465/amle.2023.0015
- Accession Number:179623260
- Copyright Statement:Copyright of Academy of Management Learning & Education is the property of Academy of Management 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.)
Looking to go deeper into this topic? Look for more articles on EBSCOhost.