Decision-making in the context of leadership, has received scant attention in the management literature, which has traditionally centered on general mental ability and personality as predictors of effectiveness. This research effort bridges the neuroscientific and management literatures to offer an alternative, neuropsychological profile of effective leadership by proposing prefrontal brain processes (executive function) as a key component and predictor of complex decision-making and leadership effectiveness. While the management literature has largely viewed decision-making as a cognitive ability, neuroscience informs us that this complex function emerges from the integration of affective and cognitive signals in the prefrontal cortex. In an attempt to identify the neural predictors of effective leadership decision-making, 105 corporate leaders were assessed on a robust array of neuropsychological indices of prefrontal brain function. These were in turn correlated with their leadership and decision-making abilities after controlling for general mental ability and personality, utilizing structural equation modeling. Executive function incrementally predicts complex decision-making and transformational leadership effectiveness, above and beyond general mental ability. Complex decision-making does not appear to be central to leadership effectiveness, while extraversion emerges as the strongest predictor of transformational leadership followed by executive function. Executive function, extraversion and general mental ability do not predict transactional leadership. These results would need replication in a larger dataset to establish their validity, especially in the case of executive function. While the heritability of leadership ability has emerged as fairly significant, this opens the field to unearthing the biological variables and predictors of leadership ability. Neuroscience thus has the potential to offer biomarkers and metrics of leadership that can further not only our foundational knowledge of organizational behavior, but can also find useful applications in recruitment, training and development practice, though this cross-disciplinary initiative is in its infancy. Based on the preliminary results from this study, executive function (which has so far remained in the domain of neurology) has the potential to inform and measure leadership effectiveness.

Automated video interviews (AVIs) use machine learning algorithms to predict interviewee personality traits and social skills, and they are increasingly being used in industry. The present study examines the possibility of expanding the scope and utility of these approaches by developing and testing AVIs that score ability from interviewee verbal, paraverbal, and nonverbal behavior in video interviews. To advance our understanding of whether AVI ability assessments are useful, I develop AVIs that predict ability (GMA, verbal ability, and interviewer-rated intellect) and investigate their reliability (i.e., inter-algorithm reliability, internal consistency across interview questions, and test retest reliability). Then, I investigate the convergent and discriminant-related validity evidence as well as potential ethnic and gender bias of such predictions. Finally, based on the Brunswik lens model, I compare how ability test scores, AVI ability assessments, and interviewer ratings of ability relate to interviewee behavior. By exploring how ability relates to behavior and how ability ratings from both AVIs and interviewers relate to behavior, the study advances our understanding of how ability affects interview performance and the cues that interviewers use to judge ability.

Identifying and hiring the highest performers is essential for organizations to remain competitive. Research has provided effective guidelines for this but important aspects of these evidence-based processes have yet to gain acceptance among practitioners. The general aim of this thesis was to help narrowing the gap between research and practice concerning personnel selection decisions. The first study compared the validity estimates of general mental ability (GMA) and the five factor model of personality traits as predictors of job performance, finding that, when the recently developed indirect correction for range restriction was applied, GMA was an even stronger predictor of job performance than previously found, while the predictive validity of the personality traits remained at similar levels. The approach used for data collection and combination is crucial to forming an overall assessment of applicants for selection decisions and has a great impact on the validity of the decision. The second study compared the financial outcomes of applying a mechanical or clinical approach to combining predictor scores. The results showed that the mechanical approach can result in a substantial increase in overall utility. The third study examined the potential influences that practitioners’ cognitive decision-making style, accountability for the assessment process, and responsibility for the selection decision had on their hiring approach preferences. The results showed that practitioners scoring high on intuitive decision-making style preferred a clinical hiring approach, while the contextual aspects did not impact practitioners’ preferences. While more research may be needed on practitioner preferences for a particular approach, the overall results of this thesis support and strengthen the predictive validity of GMA and personality traits, and indicate that the mechanical approach to data combination provides increased utility for organizations. / <p>At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 2: Manuscript. Paper 3: Manuscript.</p>