Digital transformation (DT) is a multidimensional and dynamic process that transcends the mere adoption of digital technologies, involving profound and interrelated changes in capabilities, behaviors, and institutional structures at the individual, or...
Digital transformation (DT) is a multidimensional and dynamic process that transcends the mere adoption of digital technologies, involving profound and interrelated changes in capabilities, behaviors, and institutional structures at the individual, organizational, and societal levels. While much of the existing research has focused on organizational strategies and macro-level implications, the growing importance of individual agency in digital ecosystems calls for a deeper understanding of how individuals perceive, adapt to, and participate in these transformations. Engagement with DT at the individual level has implications for economic participation, organizational agility, and digital inclusion. To address this gap, this dissertation conceptualizes digital transformation from an individual-level perspective and offers theoretical and empirical contributions through three interrelated essays that examine its structural, psychological, and societal dimensions.
The first essay, titled “Conceptualizing and Measuring Individual-Level Digital Transformation,” develops and validates a multidimensional framework for measuring digital transformation at the individual level. Grounded in the resources and appropriation theory, it identifies four core dimensions (i.e., attitude, access, digital skills, and usage) and introduces refined constructs such as software accessibility and device agility. The analysis uncovers variations across demographic groups (e.g., age, education, income), providing practical insights for advancing digital inclusion and informing targeted interventions.
The second essay, titled “Mitigating Psychological Reactance in Mandatory AI: The Roles of Perceived Controllability and Message Framing,” investigates the psychological mechanisms underlying resistance to mandatory AI adoption in organizational settings. Drawing on psychological reactance theory, the study demonstrates that perceived threats to autonomy and trait reactance trigger psychological reactance, which subsequently reduces perceived controllability and undermines AI adoption intentions. By incorporating prospect theory, the study further shows that loss-framed messages moderate this pathway, attenuating the negative impact of psychological reactance on controllability perceptions. The findings underscore perceived controllability and message framing as key factors in managing resistance in top-down AI initiatives.
The third essay, titled “Long-Term Trends in Digital Inequality among General and Marginalized Populations,” conducts a longitudinal analysis of digital inequality using nationally representative data from South Korea spanning 2004 to 2022. Although access has generally improved, disparities in digital skills and usage have persisted or worsened, especially among low-income individuals, older adults, and people with disabilities. The results suggest that digital divides are unlikely to diminish naturally over time, emphasizing the need to shift policy focus from mere access to enhancing digital skills and usage.