Unravelling Lifelong Learning in Multi-Generational Workforce Using Text Mining

Abstract

Accelerated structural transformations characterize the workplace post-pandemic. A lifelong learning ecosystem must be built to ensure a smooth and inclusive transition since generational diversity is ubiquitous in contemporary organizations. The present phenomenological qualitative study attempts to explore generational diversity from the lens of lifelong learning construct across the three prominent generations, X, Y, and Z, present in the workforce. The data collected from 24 semi-structured telephonic interviews were analyzed using text mining and topic modeling. The results suggest differences and similarities among the members of different generations. The topics derived waxed and waned across generations. While the drive to engage in continuous learning varied across generational cohorts, the preferred mode for engaging in it was similar. The study provides insights that could help enhance the effectiveness of human resource management practices and firms’ competitiveness during tough times. Further, the findings contribute to the existing literature by adopting machine learning as a tool to extract and decode the latent topics across the three generational cohorts.