Predicting Indonesian academician turnover intention: validity and reliability analysis

Abstract

This study evaluates Indonesian academic turnover intention (TOI) by analyzing demographic and work-related factors through feature selection methods and utilizes random forest (RF) as a baseline classifier for TOI prediction, while applying statistical methods to ensure the reliability of the collected primary dataset. The main advantage of this approach is to find out the importance of these factors with statistical validation to reliably investigate Indonesian academicians’ TOI. Feature selection methods such as information gain (IG) and SelectKBest were used to find out feature importance, while the reliability of the dataset was assessed through statistical approaches such as Cronbach alpha, confirmatory factor analysis (CFA), average variance extracted (AVE), and consistency ratio (CR). To test the importance of demographic and work-related factors, Python was used as an implementation tool for the Indonesian academic TOI dataset (IRB reference: 19.12.4/UN32.14/PB/2024), comprising 527 samples. The superiority of the importance of work-related factors in contrast to demographic factors was consistently demonstrated by feature selection methods, and a statistical approach confirmed the reliability of the collected primary dataset, consequently ensuring the robustness of the findings. It is envisaged that this approach can be very useful for human resource (HR) departments to pay more attention to the important demographic factors for reducing Indonesian academic TOI.