Predicting Escape and Advancement Job-Hopping Motives Among Generation Z BPO Employees Using Multiple Regression and Artificial Neural Networks

Abstract

This study investigates the predictors of escape and advancement motives for job-hopping among Generation Z employees within the Philippine business process outsourcing (BPO) sector. Employing a cross-sectional predictive design, data were gathered from a purposive sample of 250 BPO employees (M age = 24.47 years; SD = 2.02) in the National Capital Region. Key constructs included work engagement, measured by vigor, dedication, and absorption, and organizational commitment, assessed through affective, continuance, and normative dimensions. Analyses used multiple regression and multilayer perceptron artificial neural networks (ANNs) to determine relationships among these variables. The escape model revealed significant predictors F(9, 240) = 10.09, p < 0.001, adjusted r2 = .247), highlighting continuance commitment, absorption, and gender as influence factors. Similarly, the advancement model demonstrated significance, F(9,240) = 9.61, p < .001, adjusted r2 = .238, with continuance commitment and vigor emerging as key predictors. In the ANN analysis, continuance commitment had a strong influence on both job-hopping motives, while vigor was a crucial secondary predictor of advancement-oriented. These findings emphasize the necessity for practitioners to differentiate between withdrawal-oriented and career-oriented job-hopping motives, suggesting that continuance commitment reflects a strategic evaluation of personal and organizational costs and benefits across both pathways. Results are interpreted as predictive correlations rather than causal connections. This study has implications for human resources management.