Abstract

Despite advances in subjective personality questionnaires and one-dimensional approaches to predicting decision making (DM) potential, there is room for improvement. Most traditional selection methods tap present level of DM skill, or use past DM performance to inform future DM potential. An extensive review of 53 studies investigating the use of personality tests that determined that effect sizes were small, inconsistent across studies, and task dependent (Koelega, 1992). The present effort sought to extend this approach to the skill of DM. The goal was to establish a multidimensional approach to predicting DM potential by determining if physiological measures obtained during a short battery of tasks are predictive of DM performance in a real-world task. From a cognitive task analysis of managerial DM, three distinct components were identified and these formed the basis of the short task battery. After performing the necessary manipulation checks, results showed that the physiological responses during the short task battery significantly improved prediction of composite DM performance above that using traditional selection methods (ΔR2 = 0.432, p = 0.003). Future research is needed to examine the underlying potential tapped by the physiological measures, as well as the physiological bases or correlates of competencies and skills. Implications of this effort include developing an approach to assessment for selection that would transcend the individual's current achievement and skill level, to provide information about potential for training and development. This would be a step towards assessments that are more talent-focused rather than job-focused, allowing the right person to craft the right job.

Graduation Date

2016

Semester

Fall

Advisor

Reinerman, Lauren

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Degree Program

Modeling & Simulation; Engineering

Format

application/pdf

Identifier

CFE0006834

URL

http://purl.fcla.edu/fcla/etd/CFE0006834

Language

English

Release Date

June 2022

Length of Campus-only Access

5 years

Access Status

Doctoral Dissertation (Campus-only Access)

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