Date of this Version
Although the existing theory predicts that a referral’s chances of being hired increase with the job performance of the referrer, no empirical evidence is available to support this claim. To address this discrepancy, we decompose the recruitment process into objective selection, subjective selection, and self-selection and theorize that the likelihood of passing a particular recruitment stage increases with the performance of the referrer under objective selection and self-selection, but remains undetermined at a stage of subjective selection. Our analysis of unique comprehensive data on online recruitment of sales agents in a virtual call center supports these arguments. The effectiveness of personnel as a recruitment channel varies with the type of the recruitment stage and performance of the referrer. When the firm evaluates candidates by an objective criterion, the advantage of a referral increases with the performance of his or her referrer; those referred by relatively high-performing workers are significantly better than the applicants who learned about the job from Internet ads. When job candidates self-select into the next stage of the online application process, the referral of any agent is more likely to continue than a nonreferral, and this likelihood increases with the performance of the referrer. On a subjective stage, the outcome is contingent on the intricacies of the recruitment process. In our case, an applicant’s chances of being hired increase with the performance of his or her referrer because the firm rejects the referrals of low-performing workers at a higher rate than it does nonreferrals, while it treats equally the referrals of high-performing workers and nonreferrals. The study’s contributions to the literature on social networks in labor markets are discussed.
labor markets, social networks, virtual recruitment, hiring through referrals, contingent workers
Yakubovich, V., & Lup, D. (2006). Stages of the Recruitment Process and the Referrer’s Performance Effect. Organization Science, 17 (6), 710-723. http://dx.doi.org/10.1287/orsc.1060.0214
Date Posted: 27 November 2017
This document has been peer reviewed.