Technology and Work: Three Essays

In recent decades, the world of work has experienced significant transformation due to technological advancements, economic fluctuations, and external crises, alongside growing societal and environmental concerns. This transformation mirrors the impact of the industrial revolutions of the early 20th century, which displaced certain jobs while creating new opportunities. The current technological revolution, characterized by digitalization, algorithms, automation, robotics, artificial intelligence, and machine learning, is reshaping the nature of work and employment. This study aims to deepen our understanding of the impacts and determinants of adopting automation technologies. It employs a multi-method approach to address three main objectives across three studies.

The first study provides an objective overview of the current research scope in the field of work and technology using bibliometric science mapping and text mining techniques. The analysis covers 1,250 articles from 2010 to August 2023, utilizing LDA and NMF algorithms to identify thematic clusters. The findings reveal the impact of advanced technologies on various domains, including the gig economy, HRM, trade unions, skill development, and remote work. The review highlights the multifaceted nature of technological integration in work, emphasizing the need for strategic approaches to manage these transformations effectively.

The second study examines the factors influencing automation adoption in manufacturing using the Technology-Organization-Environment framework and the resource-based view. Using firm-level data from the 2017 and 2019 Korean Workplace Panel Survey (669 manufacturing establishments), The empirical investigation identifies labor intensity and intelligence intensity as significant drivers of automation adoption. Market demand moderates the relationship between intelligence intensity and automation adoption negatively, while positively moderating the relationship between technology effectiveness and automation adoption. The findings highlight the need for context-specific automation strategies that consider both organizational capabilities and market conditions. The study contributes to the TOE framework by providing empirical evidence on the specific factors that drive automation adoption in manufacturing.

The third study explores the impact of automation adoption on workforce transformation, focusing on changes in workforce development and structure. Using firm-level data from the 2017 and 2019 waves of the Korean Workplace Panel Survey (849 manufacturing establishments), the study finds that while higher automation adoption is positively correlated with the perceived need for upskilling and training, the actual training coverage remains non-significant. The study also uncovers an inverted U-shaped relationship between automation adoption and the ratio of production workers, and a U-shaped relationship with the ratio of professionals. The study contributes to the literature by providing empirical evidence on the complex relationship between automation and workforce transformation. It highlights the need for continuous upskilling and reskilling to adapt to technological changes and underscores the role of unionization in moderating these dynamics.