Legitimating Illegitimate Practices: How Data Analysts Compromised Their Standards to Promote Quantification

Ryan Stice-Lusvardi (), Pamela J. Hinds () and Melissa Valentine ()
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Ryan Stice-Lusvardi: Department of Management Science and Engineering, Stanford University, Stanford, California 94305
Pamela J. Hinds: Department of Management Science and Engineering, Stanford University, Stanford, California 94305
Melissa Valentine: Department of Management Science and Engineering, Stanford University, Stanford, California 94305

Organization Science, 2024, vol. 35, issue 2, 432-452

Abstract: Prior studies that examine how new expertise becomes integrated into organizations have shown that different occupations work to legitimate their new expertise to develop credibility and deference from other organizational groups. In this study, we similarly examine the work that an expert occupation did to legitimate their expertise; however, in this case, they were legitimating practices that they actually considered illegitimate. We report findings from our 20-month ethnography of data analysts at a financial technology company to explain this process. We show that the company had structured data analytics in ways similar to Bechky’s idea of a captive occupation: They were dependent on their collaborators’ cooperation to demonstrate the value of data analytics and accomplish their work. The data analysts constantly encountered or were asked to provide what they deemed to be illegitimate data analysis practices such as hacking, peeking, and poor experimental design. In response, they sometimes resisted but more often reconciled themselves to the requests. Notably, they also explicitly lowered their stated standards and then worked to legitimate those now illegitimate versions of their expert practices through standardization, technology platforms, and evangelizing. Our findings articulate the relationship between captive occupations and conditions wherein experts work to legitimate what they consider illegitimate practices.

Keywords: digital technology; deviance; occupations and professions; research design and methods; qualitative research (search for similar items in EconPapers)
Date: 2024
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