Exploring the Impact of COVID-19 on Job Satisfaction Trends: A Text Mining Analysis of Employee Reviews Using the DMR Topic Model

Abstract

Job satisfaction is a critical determinant in talent acquisition and corporate value enhancement. The COVID-19 pandemic has triggered a significant increase in online-based non-face-to-face services and consumption, leading to sustained growth in ICT industry job demand. Given the ICT sector’s heavy reliance on human capital and its growing workforce demands, understanding the evolving factors of job satisfaction in this sector has become increasingly crucial. This study analyzed job satisfaction factors derived from employee reviews on an online job review platform using the Dirichlet Multinomial Regression (DMR) topic model, examining temporal changes in these factors before and after the COVID-19 pandemic. As a result, 25 distinct job satisfaction-related topics were identified, and their temporal distribution patterns were categorized into three trajectories: ascending, descending, and stable. Topics exhibiting ascending patterns included work–life balance, organizational systems, corporate culture, employee benefits, work environment, and software development practices. Conversely, factors demonstrating descending patterns encompassed annual compensation, task characteristics, supervisory relationships, employee treatment, commuting conditions, work-related stress, and welfare programs. The remaining topics maintained relatively stable patterns throughout the observation period. These findings contribute to both academic literature and industry practice by elucidating the evolutionary trends in job satisfaction determinants during the COVID-19 pandemic, thereby facilitating more informed strategic human resource management decisions in the ICT sector.