Branching career pipelines: a latent class analysis of PHD students' career intentions

Purpose - This paper aims to identify patterns in the career intentions of PhD students and explore factors influencing these patterns. Design/methodology/approach - Based on data from the Nature 2019 global PhD survey, the paper uses latent class analysis to identify the number and types of patterns in PhD students' career intentions. Multinomial logistic regressions are used to analyse the influential factors, and means comparisons are used to describe differences in study experiences among career intention patterns. Findings - The paper reveals distinct career intentions among PhD students: Pure Academic Enthusiasts (25.60%), Research-Driven Flexibles (28.64%), Neutralists to Non-research (16.27%), Uncertain Career Explorers (13.63%) and Non-academia Pursuers (15.86%). Research-Driven Flexibles, inclusive of researching roles beyond academia, demonstrate similar engagement and academic skills but have more transferable skills compared to Pure Academic Enthusiasts. Uncertain Career Explorers express positivity but show relatively lower engagement and academic skills. Non-academia Pursuers spend above-average time on learning but have the weakest relationship with supervisors, participation in academic activities, campus environmental support and transferable skills. Older doctoral students with dual degrees are less likely to be Uncertain Career Explorers, while those motivated by academic interests are more likely to be Research- Driven Flexibles or Pure Academic Enthusiasts. Originality/value - This study provides a more accurate multi-dimensional perspective of PhD students' career intentions, extending previous research that focused solely on the type of work PhD students sought or the sector in which they desired to work.