Unravelling Lifelong Learning in Multi-Generational Workforce Using Text Mining

Business Perspectives and Research, Ahead of Print.
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.

Competitive Effects of IPOs: Evidence from Chinese Listing Suspensions

Abstract

Theory suggests that initial public offerings (IPOs) can adversely impact listed firms, both directly by increasing intraindustry competition, and indirectly by completing related asset market spaces. However, the endogeneity of individual IPO activity hinders testing these channels. This paper examines listing suspensions in China in a panel specification that accounts for macro-economic and financial conditions, isolating the firm-level IPO impact. We identify the competitive impact of listing suspensions through the value share of postponed firms in the IPO queue in their industry, and asset-space competition by firms' historical covariance with a synthetic portfolio of listed firms with the IPO queue industry mix at the time of suspension. Our results support the predicted IPO effects through both channels. We also document heterogeneity in IPO effects. Stronger firms, measured through a variety of proxies, benefit less from the suspension news. These results are robust to a battery of sensitivity tests.