IDEAS home Printed from https://ideas.repec.org/p/ecl/stabus/repececlstabus3627.html
   My bibliography  Save this paper

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

Author

Listed:
  • Stein, Sarah K.
  • Goldberg, Amir

    (Stanford University)

  • Srivastava, Sameer B.

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.

Suggested Citation

  • Stein, Sarah K. & Goldberg, Amir & Srivastava, Sameer B., 2018. "Distinguishing Round from Square Pegs: Predicting Hiring Based on Pre-hire Language Use," Research Papers repec:ecl:stabus:3627, Stanford University, Graduate School of Business.
  • Handle: RePEc:ecl:stabus:repec:ecl:stabus:3627
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ecl:stabus:repec:ecl:stabus:3627. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/gsstaus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.