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Factors AffectingProfessional Pilots' Intentionto Leave Aviation Jobs: Supervised Machine Learning Algorithms |
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| รหัสดีโอไอ | |
| Creator | Pattarachat Maneechaeye |
| Title | Factors AffectingProfessional Pilots' Intentionto Leave Aviation Jobs: Supervised Machine Learning Algorithms |
| Publisher | Thai Aviation Services Limited Company, Thailand |
| Publication Year | 2564 |
| Journal Title | ASEAN Journal of Management & Innovation |
| Journal Vol. | 8 |
| Journal No. | 2 |
| Page no. | 84 - 94 |
| Keyword | Aviation, Intention to Leave, Pilot, Pilot Rank, Supervised Machine Learning Algorithms |
| URL Website | http://ajmi.stamford.edu |
| Website title | AJMI -ASEAN Journal of Management & Innovation |
| ISSN | 2351-0307 |
| Abstract | The objective of this study is to gain knowledge of the factors affecting the likelihood of Thai pilotsleaving aviationjobs and classify the intention to leaveoutcomesusingsupervised machine learning algorithms derived from data science disciplines. The focus is on career success and job demand as key factorscontributing to the intention to leave or not leavetheairline. This multidisciplinary study follows aquantitative approach and relieson asampleof 610Thai pilots listed in Thai Pilot Association. The results indicate thatpilots holding therank of pilot in command and anair transport pilot license with no other extra responsibilities such as check airman and instructor pilothave a lessor chance to leave aviation jobs. Moreover, theoverallbinary classification model developed by this method fitswith empirical data. It is recommended that airlinesrespondto these risks by providing thejob resources needed to maintain their pilots'morale and keep them on board.This research contributesto behavioral science disciplinesby providing a classification model with moderate performance. Future research should broaden the sample to an international context and utilizea qualitative or a mixed methodology in order to obtain richer results. |
