Date of Award

Spring 2024

Access Type

Dissertation - Open Access

Degree Name

Doctor of Philosophy in Aviation


College of Aviation

Committee Chair

Dothang Truong, Ph.D.

First Committee Member

Alan J. Stolzer, Ph.D.

Second Committee Member

Steven Hampton, Ed.D.

Third Committee Member

Rafael Echevarne, Ph.D.

College Dean

Alan J. Stolzer, Ph.D.


States and their respective national civil aviation authorities promote the improvement of the global civil aviation system by developing and enforcing safety regulations for aircraft design, operations, personnel training, infrastructure, and air traffic management, among other topics. For many years, the International Civil Aviation Organization (ICAO) has driven the continuous improvement of states’ safety oversight functions, supporting them in evaluating the effectiveness of these functions by conducting audits under the Organization’s Universal Safety Oversight Audit Program (USOAP). While the benefits of these assessments have been broadly recognized, scholarly literature on the factors commonly associated with safety oversight effectiveness across states is minimal. This study aimed to bridge this gap by drawing knowledge and understanding from the scholarly literature on public administration and the evolving governance theory and testing the relationship between state governance measures and aviation safety oversight effectiveness. Two variables were selected from the Worldwide Governance Indicators to reflect state governance measures, namely Regulatory Quality (RQ) and Government Effectiveness (GE), and the result of the ICAO safety oversight audits, as expressed in the Effective Implementation (EI) metric, is selected as a measure of safety oversight v effectiveness. The research methodology included exploratory and quantitative approaches. Various multivariate quantitative analytical models, including multiple linear regression, structural equation modeling, and data mining, were developed in the exploratory dimension. The non-linear data mining approaches included random forest, deep learning, and decision tree models. The analysis supported the validation of RQ and GE's factor structure and tested the relationships between these constructs and EI. These models were compared with respect to model fit and predictive performance. This approach was complemented by quantitative analyses of the association between states’ RQ and GE dimensions of governance and EI and the evaluation of their relative importance. All three approaches presented relevant insights into the association under study. The findings indicate a statistically significant association between governance and aviation safety oversight effectiveness. Government effectiveness explained a notable portion of the variation in safety oversight effective implementation. Among the predictive data mining models, random forest showed better performance when compared with deep learning and decision tree models. Some of the theoretical contributions of the study include the added support for the factorial validity of the WGI structure for RQ and GE and the establishment of a framework linking broader dimensions of governance and aviation safety oversight, particularly between GE and EI. Practical contributions include recommendations for aviation safety oversight organizations to consider governance measures in safety oversight assessment mechanisms and to promote additional research on common factors associated with safety oversight effectiveness. The study also outlines recommendations for future research, such as exploring additional types and sources of governance metrics and expanding the tested theoretical framework to other safety-relevant industries beyond aviation.