Factors Affecting Professional Pilots’ Intention to Leave Aviation Jobs: Supervised Machine Learning Algorithms

  • Pattarachat Maneechaeye Thai Aviation Services Limited Company, Thailand.
Keywords: Aviation, Intention to Leave, Pilot, Pilot Rank, Supervised Machine Learning Algorithms


The objective of this study is to gain knowledge of the factors affecting the likelihood of Thai pilots leaving aviation jobs and classify the intention to leave outcomes using supervised machine learning algorithms derived from data science disciplines. The focus is on career success and job demand as key factors contributing to the intention to leave or not leave the airline. This multidisciplinary study follows a quantitative approach and relies on a sample of 610 Thai pilots listed in Thai Pilot Association. The results indicate that pilots holding the rank of pilot in command and an air transport pilot license with no other extra responsibilities such as check airman and instructor pilot have a lessor chance to leave aviation jobs. Moreover, the overall binary classification model developed by this method fits with empirical data. It is recommended that airlines respond to these risks by providing the job resources needed to maintain their pilots’ morale and keep them on board. This research contributes to behavioral science disciplines by providing a classification model with moderate performance. Future research should broaden the sample to an international context and utilize a qualitative or a mixed methodology in order to obtain richer results.