Paper ID
2708
Description
Pre-employment screening of social media (SM) has become a common practice to assess prospective candidates’ fit to the job and gets increasingly automated with the application of people analytics. Besides some evidence that SM screening negatively affects applicants’ job pursuit intention in general, there is a lack of knowledge on the differential effects based on the type of the targeted SM network (private vs. professional) and the nature of the screening agent (human vs. self-learning algorithms). Drawing on signaling theory, we perform a vignette-based experimental study that aims at addressing this gap. The results indicate that only screening of private purpose SM profiles negatively affects job pursuit intention. Surprisingly, self-learning algorithms are perceived more negatively than humans. Our study contributes to IS literature by exploring the role of intelligent decision agents in the context of recruitment and the invasion of privacy in the digital world of work.
Recommended Citation
Schmoll, René and Bader, Verena, "Who or what screens which one of me? The differential effects of algorithmic social media screening on applicants’ job pursuit intention" (2019). ICIS 2019 Proceedings. 10.
https://aisel.aisnet.org/icis2019/future_of_work/future_work/10
Who or what screens which one of me? The differential effects of algorithmic social media screening on applicants’ job pursuit intention
Pre-employment screening of social media (SM) has become a common practice to assess prospective candidates’ fit to the job and gets increasingly automated with the application of people analytics. Besides some evidence that SM screening negatively affects applicants’ job pursuit intention in general, there is a lack of knowledge on the differential effects based on the type of the targeted SM network (private vs. professional) and the nature of the screening agent (human vs. self-learning algorithms). Drawing on signaling theory, we perform a vignette-based experimental study that aims at addressing this gap. The results indicate that only screening of private purpose SM profiles negatively affects job pursuit intention. Surprisingly, self-learning algorithms are perceived more negatively than humans. Our study contributes to IS literature by exploring the role of intelligent decision agents in the context of recruitment and the invasion of privacy in the digital world of work.