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Andrea L. Zavakos, Ph.D. is a 2006 graduate of the PHD Program in Leadership and Change at Antioch University.

Dissertation Committee

  • Jon Wergin Ph.D., Committee Chair
  • Elizabeth Holloway, Ph.D., Committee Member
  • Carol Baron, Ph.D., Committee Member
  • Marc Wenzel, Ph.D., Committee Member
  • Dale Rose, Ph.D., External Reader

Keywords

Leadership, Multiple Regression, Assessment, Big Five Personality Model, Five Factor Personality Model, Hospitals

Document Type

Dissertation

Publication Date

2006

Abstract

The purpose of this study was to identify predictors of leadership using a newly developed assessment for leadership selection within the healthcare industry by comparing assessment scores to supervisor rankings of the subjects. The study population consisted of 195 employees of 11 different hospitals. Each of the participants completed the Healthcare Leadership Inventory (HLI) assessment; their immediate supervisors completed performance ratings for them. None of the instruments were designed by the researcher. The dependent variable of the study was the supervisor-provided factor of Promotion Potential. Stepwise multiple regression was the main analytical approach. The analysis yielded two predictors of leadership success from the HLI assessment (Achievement Orientation and Openness to Change) and five from the Supervisor Ratings (Multi-Tasking, Drive for Results, Self Confidence, Openness to Change, and Customer Orientation). The identified predictors from each instrument had construct symmetry, although they were not statistically duplicative. The predictors from Supervisor Ratings provide some insight into the implicit leadership theories shared by management personnel in the healthcare industry. The HLI assessment factors of Achievement, Conscientiousness, Innovative, and Customer Focus had significant correlations with their counterparts from Supervisor Ratings. The Critical Thinking factor surprisingly did not significantly predict leadership potential or correlate with any of the other factors.

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