JOURNAL ARTICLE
When Interpretations of Merit Thresholds Vary and Reproduce Inequality: Entering the Tech Industry Without Computer Science Credentials.
Published In: Organization Science (INFORMS), 2026, v. 37, n. 2. P. 490 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Eren, Dilan 3 of 3
Abstract
This article examines how aspiring software developers without computer science (CS) degrees respond to the ostensibly meritocratic opportunity of entering the tech industry through open-access coding skills, such as MOOCs and bootcamps. It identifies three distinct entry strategies—Early/Broad, Standard, and Late/Narrow—differing in timing and scope of job applications, which correspond to varying interpretations of the merit threshold (the coding competency believed necessary to secure a first tech job). Follow-up data show that the Early/Broad strategy, involving earlier market entry and broader job targeting, is associated with higher employment rates across all sociodemographic groups, particularly benefiting those historically underrepresented in tech; however, most aspirants from these groups did not adopt this strategy. The study attributes these variations to aspirants’ prior labor market experiences, specifically whether they perceived employers as having previously given or denied them chances, highlighting a supply-side mechanism that interacts with demand-side biases to reproduce labor market inequalities.
Additional Information
- Source:Organization Science (INFORMS). 2026/03, Vol. 37, Issue 2, p490
- Document Type:Article
- Subject Area:Computer Science
- Publication Date:2026
- ISSN:1047-7039
- DOI:10.1287/orsc.2023.17752
- Accession Number:192562409
- Copyright Statement:Copyright of Organization Science (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences 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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