Academic Journal
When Interpretations of Merit Thresholds Vary and Reproduce Inequality: Entering the Tech Industry Without Computer Science Credentials.
| Τίτλος: | When Interpretations of Merit Thresholds Vary and Reproduce Inequality: Entering the Tech Industry Without Computer Science Credentials. |
|---|---|
| Συγγραφείς: | Eren, Dilan1 (AUTHOR) deren@ivey.ca |
| Πηγή: | Organization Science (INFORMS). Mar/Apr2026, Vol. 37 Issue 2, p490-515. 26p. |
| Θεματικοί όροι: | *Information technology industry, *Job hunting, *Labor market, *Income inequality, Computer programming education, Meritocracy |
| Περίληψη: | Meritocracy is widely believed to be a fair system. Although extant literature focuses on managers' implementation of meritocratic decisions, less attention has been paid to jobseekers' responses to meritocratic opportunities. This study addresses this gap by examining how aspiring software developers without computer science (CS) degrees respond to the ostensibly meritocratic promise of entering tech through open-access coding skills. Drawing on interview, social media, and ethnographic data, I find that although all aspirants agreed coding skills were the key meritocratic criteria for entering tech without CS degrees, they interpreted the merit threshold (the level of coding competency needed to get their first job) differently and adopted three distinct entry strategies that varied in timing and scope—Early/Broad, Standard, and Late/Narrow. Follow-up data collected three years later revealed that one strategy (Early/Broad) was associated with high employment rates across all subgroups of aspirants and substantially increased employment chances for those historically underrepresented in tech. Yet, most did not opt for it. Indeed, only one group (White men with white-collar/professional backgrounds) clustered in strategies with higher employment rates, resulting in this population securing jobs at a higher rate than others. To explain the variation in merit thresholds and accompanying entry strategies, this study highlights aspirants' previous encounters with demand-side actors—particularly, their perceptions of whether, and to what extent, employers had previously been willing to give them a chance. These findings contribute to research on meritocracy and labor markets and offer insights into building a more diverse workforce. Funding: This work was supported by the American Sociological Association Doctoral Dissertation Research Improvement [Grant 55209764] and the Washington Center for Equitable Growth [Grant 5510223]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2023.17752. [ABSTRACT FROM AUTHOR] |
| 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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| Items | – Name: Title Label: Title Group: Ti Data: When Interpretations of Merit Thresholds Vary and Reproduce Inequality: Entering the Tech Industry Without Computer Science Credentials. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Eren%2C+Dilan%22">Eren, Dilan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> deren@ivey.ca</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Organization+Science+%28INFORMS%29%22">Organization Science (INFORMS)</searchLink>. Mar/Apr2026, Vol. 37 Issue 2, p490-515. 26p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Information+technology+industry%22">Information technology industry</searchLink><br />*<searchLink fieldCode="DE" term="%22Job+hunting%22">Job hunting</searchLink><br />*<searchLink fieldCode="DE" term="%22Labor+market%22">Labor market</searchLink><br />*<searchLink fieldCode="DE" term="%22Income+inequality%22">Income inequality</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+programming+education%22">Computer programming education</searchLink><br /><searchLink fieldCode="DE" term="%22Meritocracy%22">Meritocracy</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Meritocracy is widely believed to be a fair system. Although extant literature focuses on managers' implementation of meritocratic decisions, less attention has been paid to jobseekers' responses to meritocratic opportunities. This study addresses this gap by examining how aspiring software developers without computer science (CS) degrees respond to the ostensibly meritocratic promise of entering tech through open-access coding skills. Drawing on interview, social media, and ethnographic data, I find that although all aspirants agreed coding skills were the key meritocratic criteria for entering tech without CS degrees, they interpreted the merit threshold (the level of coding competency needed to get their first job) differently and adopted three distinct entry strategies that varied in timing and scope—Early/Broad, Standard, and Late/Narrow. Follow-up data collected three years later revealed that one strategy (Early/Broad) was associated with high employment rates across all subgroups of aspirants and substantially increased employment chances for those historically underrepresented in tech. Yet, most did not opt for it. Indeed, only one group (White men with white-collar/professional backgrounds) clustered in strategies with higher employment rates, resulting in this population securing jobs at a higher rate than others. To explain the variation in merit thresholds and accompanying entry strategies, this study highlights aspirants' previous encounters with demand-side actors—particularly, their perceptions of whether, and to what extent, employers had previously been willing to give them a chance. These findings contribute to research on meritocracy and labor markets and offer insights into building a more diverse workforce. Funding: This work was supported by the American Sociological Association Doctoral Dissertation Research Improvement [Grant 55209764] and the Washington Center for Equitable Growth [Grant 5510223]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2023.17752. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>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.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1287/orsc.2023.17752 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 26 StartPage: 490 Subjects: – SubjectFull: Information technology industry Type: general – SubjectFull: Job hunting Type: general – SubjectFull: Labor market Type: general – SubjectFull: Income inequality Type: general – SubjectFull: Computer programming education Type: general – SubjectFull: Meritocracy Type: general Titles: – TitleFull: When Interpretations of Merit Thresholds Vary and Reproduce Inequality: Entering the Tech Industry Without Computer Science Credentials. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Eren, Dilan IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar/Apr2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 10477039 Numbering: – Type: volume Value: 37 – Type: issue Value: 2 Titles: – TitleFull: Organization Science (INFORMS) Type: main |
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