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Employee Churn Prediction in Commercial Airlines in Portugal
Human Resources Development in a Digital Age
Year (definitive publication)
2025
Language
English
Country
Switzerland
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Abstract
Employee churn occurs when an employee leaves voluntarily from a
company. It’s essential to measure this indicator due to the financial costs a com-
pany deals with, especially in the commercial aviation sector. “Turnover inten-
tion” is the best antecedent to actual turnover. Job satisfaction encompasses many
dimensions and is the best antecedent to actual turnover. There are few studies
about employee churn in the commercial aviation sector, and no study has yet
been conducted to correlate employee satisfaction with turnover intentions in the
commercial aviation sector in Portugal. The methodology of this study is per-
forming a literature review on the factors commonly associated with employee
churn and a review of the principal concepts involved. The evaluation result re-
trieved the most common factors associated with employee churn. “Pay satisfac-
tion” (PS), “safety satisfaction” (SS), “work/ family satisfaction” (WFS), “career
satisfaction” (CS), “leadership satisfaction” (LS) and, finally, Job Performance
(JB), were validated to test the hypotheses about which factors are more associ-
ated with employee churn and the relative contribution of the variables for the
dependent variable “turnover intentions”. Multiple Linear Regression was used
to study a sample of 370 valid responses from people working in the commercial
aviation sector in Portugal. As for the effect of satisfaction on turnover intentions,
the dimensions of CS SS, LS, and PS, have a negative and significant effect on
turnover intentions. Other conclusions were drawn. Namely, relationships be-
tween demographic variables and the main variables were presented.
Acknowledgements
Citation: Brito, A. P., Sousa, M. J. & Moreira, A. (2025). Employee Churn Prediction in Commercial Airlines in Portugal. In M. J. Sousa (Ed.), Human Resources Development in a Digital Age (8, pp. 201-230). Springer;
Keywords
HR Analytics,AI,Predictive model,Airlines,Quantitative study
Português