Distinguishing Round from Square Pegs: Predicting Hiring Based on Pre-hire Language Use

Sarah K. Stein, Amir Goldberg and Sameer B. Srivastava
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Sarah K. Stein: ?
Amir Goldberg: Stanford University
Sameer B. Srivastava: ?

Research Papers from Stanford University, Graduate School of Business

Abstract: This article examines how cultural matching relates to a job applicant's likelihood of getting hired into an organization and identifies the components of cultural similarity that matter most for hiring success. Cultural compatibility at the hiring stage can forecast an individual's post-hire productivity but is difficult to reliably measure in the selection process. As a consequence, cultural matching is often subject to various informational and identity-based biases. We develop a language-based model that provides a means for directly assessing job candidates' cultural similarity. Based on variegated data from a mid-sized technology firm--including job applicants' free text responses at the pre-hire stage, applicant characteristics, applicant-interviewer assignments, and hiring outcomes--we find that linguistic similarity with previously hired employees increases a job candidate's chances of being hired, even after controlling for the applicant's human and social capital. We further find that, although all three forms of cultural fit that we assess--fit based on work preferences, lifestyles, and ideology--predict hiring in between-interviewer models, only work preferences fit predicts hiring in within-interviewer models. Supplemental analyses indicate that pre-hire cultural fit is also predictive of successful enculturation in the firm over the first six months of employment. Together, these results indicate that cultural matching leads to sorting on attributes that are both relevant and potentially irrelevant for job success.

Date: 2018-01
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Persistent link: https://EconPapers.repec.org/RePEc:ecl:stabus:repec:ecl:stabus:3627

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