Implicit Biases in Evaluating Information Gathered during Social Network Screenings



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In recent years, an increasing amount (60%; Grasz, 2016) of human resource professionals have been turning to social media as a means to screen potential employees due to the vast amounts of information available on these sites (Brown & Vaughn, 2011; Grasz, 2006; Landers & Schmidt, 2016; Roth, Bobko, Van Iddekinge & Thatcher, 2013; Schmidt & O’Connor, 2016; Van Iddekinge, Lanivich, Roth, & Junco, 2016). Despite the rapid increase in the use of social networking sites as a screening tool, very little research has been done regarding how the information available on these sites is used to make decisions. I analyze how four specific types of information (health, family, social and political information) available about an applicant on these sites influences the likelihood that the applicant will be recommended to be hired. I hypothesize that implicit biases surrounding these four types of information will lead to a decrease in the hireability ratings of applicants that provide these types of information on their social networking sites. Implicit bias is an umbrella term commonly used in research on discrimination and employment law that encompasses both implicit attitudes and implicit stereotypes (Faigman, Dasgupta & Ridgeway, 2007; Greenwald & Kriefer, 2006; Jolls & Sunstein; 2006). Implicit biases are especially troublesome in employment practices because they can lead to unintentional discrimination (Bodensteiner, 2008; Macan & Merritt, 2011).I found that applicants that provide these types of information are social media are less likely to be recommended to be hired than applicants that do not post these types of information. This contributes empirically driven findings to the currently scarce literature on social networking sites as a screening tool by identifying how certain information is assessed during these screenings. Additionally, these results may help inform best practices regarding the use of social networking sites as screening tools and adhering to fair employment practices.



Social media, Selection, Bias, Policy capturing