Cybervetting: A Common Antecedents Model
Date of Award
Doctor of Philosophy (Ph.D.)
Michael Zickar (Advisor)
Hanfeng Chen (Other)
Margaret Brooks (Committee Member)
Richard Anderson (Committee Member)
Cybervetting, defined as a practice of performing internet-based background checks on prospective employees including reviewing Social Networking Websites (SNWs; Mikkelson, 2010), is becoming widely used among hiring managers. Although prevalent in practice, the topic of cybervetting remains largely understudied by industrial and organizational psychologists. Lack of systematic research leaves cybervetters with little guidance on how to engage in psychometrically sound web-based searches. The aims of the current study were twofold: (1) to propose and empirically test a taxonomy of cyber-behavior, according to which SNW-based behaviors fall into four categories (professional, prosocial, antisocial, and job-irrelevant); and (2) to advance and test a common antecedents model of cybervetting, according to which (a) personality and general mental ability (GMA) serve as common antecedents of cyber-behavior and workplace criteria and (b) privacy settings usage and activity level serve as moderators of the relationship between cyber-behavior and cybervetters’ judgments of employability. Using a multitrait-multimethod approach, the data were collected from 200 full-time employees and 131 supervisors at several large Mid-Western universities. Ten trained research assistants rated the participants’ Facebook profiles using a standardized cybervetting form, developed specifically for this study. The results of the confirmatory factor analysis provided support for the taxonomy. Although personality and GMA were not found as common antecedents of cyber-behavior and workplace criteria, privacy settings usage and activity level were found to moderate the relationship between cyber-behavior and employability. Limitations and future directions are discussed.
Berger, Julia Lizabeth, "Cybervetting: A Common Antecedents Model" (2015). Psychology Ph.D. Dissertations. 190.