Date of Award
Open Access Dissertation
School of Social Science, Politics, and Evaluation
Ronald E. Riggio
Dissertation or Thesis Committee Member
Michelle C. Bligh
Dissertation or Thesis Committee Member
© 2020 Timothy C Lisky
bias, personality, privacy, selection, social media, transformational leadership
Organizational Behavior and Theory
A thorough assessment of privacy concerns, reviewer bias, and applicant computer familiarity informs this longitudinal study incorporating features derived from social media, personality, leadership, traditional selection methodology, and objective measures of employee performance to build an empirical foundation for future research. To date, limited research has embarked upon an in-depth examination of the organizational implications of using social media data to assess job applicants. This dissertation addresses the question of whether social media data matters in the practical context of talent selection. I begin with a review of pertinent online communication theories, including media richness, cues filtered out, and social information processing theories before applying their concepts to social media. I review accumulated evidence that signals from social media use can predict personality and explore less-studied links between social media and full leadership behavior, with a focus on transformational leadership. The review also integrates privacy behavior. A survey covering personality, leadership, and privacy behavior, was completed by 107 call center agents who were subsequently invited to share their public Facebook profile. Of those, 48 volunteered to share quantitative and qualitative data from their public profile. A group of trained raters further coded profiles. The participants' employer also provided performance and retention data. This study found mixed support for previously reported links between social media use and personality. An interaction of conscientiousness and computer skills predicted privacy skills and profile completeness, such that participants either high in both or low in both were more likely to have higher self-rated privacy skills and completed social media profiles. Raters were easily able to deduce demographic information from social media profiles, including gender, age, and ethnicity. Worryingly, evidence of bias in pass rates was detected based on raters' hire vs no-hire recommendations, though the degree of bias varied by pass rate threshold. Finally, the various predictors were combined alongside scores from participants' original pre-hire selection assessments to determine whether there was incremental value in including them as part of a holistic selection process. Some support was found for the incremental utility of the entire battery, as personality, social media activity, human ratings of social media profiles, and self-reported transformational leadership behavior uniquely contributed to a Cox regression model predicting retention. Support for the battery approach was much weaker when predicting efficiency (average handle time) as only transformational leadership provided statistically significant predictiveness beyond the pre-hire assessment. Altogether, this dissertation underscores the importance of relying on defensible selection methods to predict retention and performance outcomes. If social media is used in screening, it is best done in the context of other selection methods and should be based on computer-based automated screening rather than individual human ratings to reduce bias. This dissertation demonstrates that social media and leadership can add incremental prediction to selection decisions for entry-level jobs and makes recommendations for further research.
Lisk, Timothy Charles. (2020). Social Media, Personality, and Leadership as Predictors of Job Performance. CGU Theses & Dissertations, 625. https://scholarship.claremont.edu/cgu_etd/625.