Eunji Choi

Generative AI is reshaping how workers form digital commerce entrepreneurial intentions, yet how AI-related fear of missing out (AI-FoMO) converts into proactive entrepreneurial behavior remains unclear. Drawing on social cognitive theory, this study tests a model in which AI-FoMO drives AI reliance, which in turn shapes two distinct cognitive routes: a tool-referenced route through perceived task automation (PTA) and an agent-referenced route through borrowed entrepreneurial competence (BEC), a newly proposed construct capturing how human–AI coupling reshapes workers’ self-perceived capability for solo digital entrepreneurship. We further test whether digital self-efficacy (DSE) moderates the conversion of these perceptions into intention. Survey data from 328 Korean workers were analyzed using PLS-SEM. AI-FoMO predicted AI reliance (β = 0.391), which shaped both PTA (β = 0.531) and BEC (β = 0.291); both routes predicted entrepreneurial intention, and both serial mediation paths were significant, with the PTA route approximately 2.6 times stronger than the BEC route. DSE moderated only the BEC route (β = 0.105, p = 0.025), not the PTA route (β = −0.077, p = 0.109), an asymmetry confirmed by a formal coefficient-difference test. The findings reframe AI-FoMO as a potential driver of approach-oriented entrepreneurial behavior, introduce BEC as an extension of vicarious experience to the human–AI context, and show that digital self-efficacy selectively regulates how AI-induced perceptions convert into entrepreneurial intention.

This study investigates the strategic responses associated with small- and medium-sized enterprise (SME) recovery under crisis conditions. Rather than assuming that maintaining ambidexterity is universally beneficial, it questions whether the balance between exploration and exploitation remains effective when firms face severe resource constraints and heightened uncertainty. Using panel data on Korean SMEs from 2019 to 2023, this study compares the performance implications of ambidexterity balance and strategic switching, identifies which firms are more likely to undertake strategic switching, and examines how post-switch recovery patterns unfold over time. The findings indicate that the performance implications of ambidexterity balance weaken during the crisis period and, in some cases, become negative, whereas strategic switching is associated with more favorable recovery patterns during the crisis period. In addition, the benefits of strategic switching do not emerge immediately but instead become gradually visible over two to three years following the initial switch, highlighting the importance of post-switch adaptation time.

This study examines how the resource configurations of early-stage startups are associated with intellectual property (IP) management capability. To achieve this objective, a dual analytical framework integrating hierarchical regression analysis (OLS) with machine learning techniques (XGBoost and SHAP) is employed. Because conventional linear models may not capture complex associations, the analysis also explores potential nonlinear patterns among key variables, which are interpreted as exploratory, model-based tendencies rather than as causal or temporal effects. The empirical findings reveal several important insights. First, the results of the linear regression analysis indicate that the main effects of simple quantitative indicators—such as firm age and organizational size—are not statistically significant. The interaction between the startup period and pre-startup education (H1) is the only relationship to approach statistical significance, although it is borderline and not robust to alternative variable coding. This pattern suggests that IP management capability is associated not with the quantity of inputs but with the preparedness of the entrepreneur’s knowledge resources. Second, the explainable artificial intelligence (XAI)-based analysis surfaces nonlinear patterns that are not captured by conventional linear models. Specifically, the model-estimated contribution of entrepreneurial education is comparatively small among firms in their first two years and larger among firms around the third year, and the model-estimated contribution of organizational size diminishes once the firm reaches roughly thirty employees. These inflections are model-based tendencies observed in SHAP dependence plots and are corroborated by formal segmented (breakpoint) regressions (spline terms p = 0.010 and p = 0.002). Methodologically, the study shows how integrating hierarchical regression with explainable machine learning (XGBoost and SHAP) can reveal nonlinear and threshold patterns that conventional linear models overlook. Building on this, it proposes resource latency as an interpretive lens, rather than an established construct, for age-related patterns in startup resource utilization, to be examined in future longitudinal research. From a practical perspective, the findings suggest the value of sustained support during the early scale-up period and of more systematic management structures as firms grow, while recognizing that these patterns are cross-sectional associations.