The aviation industry faces several workforce related challenges as fleet expansion, technological complexity, and rising demand for skilled maintenance professionals outpace supply, particularly in India. Despite its safety-critical nature, turnover intention formation in aviation maintenance workforces remains underexplored. This study addresses that gap by examining how significant predictors in traditional turnover literature influence turnover intention across 661 demographically diverse aviation maintenance professionals, using regression, mediation–moderation modelling, and person-centred segmentation via Proximal Withdrawal States Theory and Latent Profile Analysis. The study introduces a concept of Contextual Conditioning Flux (CCF), a framework proposing that turnover mechanisms are contingent on latent workforce states, with constructs varying in role, effect sign, and mechanistic form across classes. Findings identify distinct profiles, like opportunity-driven leavers, reluctant stayers, and a novel 'escape quitter' substate and demonstrate that aggregate models systematically obscure this heterogeneity, producing suppression effects, competitive mediation, and sign reversals that generate misleading inferences. Traditional predictors such as job satisfaction lose explanatory power in certain states entirely. The CCF advances turnover theory by establishing that predictor roles and effect signs are not universal. It extends Proximal Withdrawal States Theory to accommodate dynamic substates, and provides practitioners with diagnostic tools for targeted, segment-specific retention strategies.

Keywords

Aviation Maintenance Workforce, Turnover Intention, Proximal Withdrawal States Theory, Latent Profile Analysis