IDEAS home Printed from https://ideas.repec.org/a/inm/ororsc/v35y2024i4p1342-1362.html
   My bibliography  Save this article

Network Referrals and Self-Presentation in the High-Tech Labor Market

Author

Listed:
  • Santiago Campero

    (Center of Industrial Relations and Human Resources, University of Toronto, Toronto, Ontario M5S 2E8, Canada)

  • Aleksandra (Olenka) Kacperczyk

    (London Business School, Imperial College, London NW1 4SA, United Kingdom)

Abstract

The practice of recruiting job candidates sourced through social contacts (i.e., referrals) is pervasive in the labor market. One reason employers prefer to recruit through referrals is that these candidates often present resumes that are perceived to be a better fit for the role. Whereas existing research attributes this pattern to how individuals who make referrals (i.e., referrers) select individuals to refer, we propose a new mechanism: differences in self-presentation. We argue that referral ties increase the candidates’ propensity to engage in self-presentation work, motivating and assisting candidates in presenting their backgrounds to convey fit. We examine this claim by utilizing unique data from an applicant-tracking system containing job applications for positions at U.S.-based high-tech firms between 2008 and 2012. A candidate fixed-effects specification reveals that when a candidate applies to a firm via a referral, he or she tends to showcase a rendition of his or her career history that better matches the target job than when the candidate pursues positions without such ties. Several mechanism checks, combined with supplementary survey evidence, further indicate that the presence of referral ties to the target firm is associated with greater motivation to engage in self-presentation work as well as the provision of different forms of assistance in that work.

Suggested Citation

  • Santiago Campero & Aleksandra (Olenka) Kacperczyk, 2024. "Network Referrals and Self-Presentation in the High-Tech Labor Market," Organization Science, INFORMS, vol. 35(4), pages 1342-1362, July.
  • Handle: RePEc:inm:ororsc:v:35:y:2024:i:4:p:1342-1362
    DOI: 10.1287/orsc.2022.16674
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/orsc.2022.16674
    Download Restriction: no

    File URL: https://libkey.io/10.1287/orsc.2022.16674?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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:inm:ororsc:v:35:y:2024:i:4:p:1342-1362. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.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.