Politics speak louder than skills: Political similarity effects in hireability judgments in multiparty contexts and the role of political interest.

Mönke W. Franz , Lievens Filip , Hess Ursula , Schäpers Philipp

Abstract

Recruiters increasingly cybervet job applicants by checking their social media profiles. Theory (i.e., the political affiliation model, PAM) and research show that during cybervetting, recruiters are exposed to job-unrelated information such as political affiliation, which might trigger similarity-attraction effects and bias hireability judgments. However, as the PAM was developed in a more polarized two-party political system, it is pivotal to test and refine the PAM in a multiparty context. Therefore, we asked working professionals from the United States (two-party context, N = 266) and Germany (multiparty context, N = 747) to rate an applicant's hireability after cybervetting a LinkedIn profile that was manipulated in a between-subjects design (party affiliation by individuating information). Key tenets of the PAM could be transferred to multiparty contexts: The political similarity-attraction effect predicted hireability judgments beyond job-related individuating information, especially regarding organizational citizenship behavior. In addition, in a multiparty context, these biasing effects of political similarity and liking were not attenuated. Yet, there were also differences: In a multiparty context, political similarity had to be operationalized in terms of political value similarity and recruiters' political interest emerged as a significant moderator of the effects. So, this study refines the PAM by showing in multiparty contexts the importance of (a) a values-based perspective (instead of a behavioral political affiliation perspective) and (b) political interest (instead of identification). Accordingly, we provide a more nuanced understanding of when political affiliation similarity contributes to perceived overall similarity in affecting liking and hireability judgments in cybervetting.