This study aims to find out how sub-factors of digital literacy capabilities affect entrepreneurial intention for middle-aged people aged 40 to 64 who are not currently starting a business. In particular, we analyzed whether online network competency have a mediating effect on the relationship between digital literacy competency and entrepreneurial intention. Based on previous research, a hypothesis and research model were established, and the results of a survey of 280 middle-aged people nationwide who were not currently starting a business were empirically analyzed using SPSS 29.0. Among the digital literacy competency sub-variables, digital device utilization competency and content utilization competency, only content utilization competency was found to have a significant positive effect on the dependent variable, entreprenurial intention. Online network capabilities were analyzed to mediate effect between content utilization capabilities and entrepreneurial intention. This study provides practical implications for the need and importance of education to improve digital literacy capabilities in planning educational programs to support stable and successful start-ups for middle-aged people aged 40 to 64.

Acknowledging that the emergence of a new population (or an industry) unfolds through the knowledge-based innovation processes, we examine the roles of science and technology. In this study, we focus on the early stage of the knowledge-based innovation processes, investigating the process in which creation of scientific knowledge is associated with technological development. While counting the number of the artifacts of the innovation processes (i.e. research articles or patents) is useful to capture the innovation processes, it is limited to specify what topics have been newly developed, advanced, or proliferated. To reconcile the limitations of the count-based analysis of innovation, we employ a topic modeling approach, as well as the conventiaonl approach (i.e. frequency-based analyses and network analyses). Specifically, we apply the Latent Dirichlet Allocation (LDA) method to the U.S research grant programs and patents filed to the USPTO to figure out the topics which appear collectively and frequently. Then, we attempt to explain how the emergence of the new industries which unfolds in our society (such as the platform businesses, the mobility field, etc.) can be explained by the topics developed from scientific areas and technological areas.

Credit risk management has recently been emphasized through the advancement of the corporate credit evaluation model. Representative models used in studies related to corporate credit evaluation have various statistics and machine learning techniques, and recently, deep learning-based algorithms that show good performance in various fields have been studied to show high predictive performance. However, in corporate credit evaluation research, not only performance but also the content to focus on together is an interpretation of results and the processing of imbalanced data. In this work, we propose a preprocessing method that generates new variables for performance, sampling techniques for handling unbalanced data, and a new framework utilizing TabNet, an explainable artificial intelligence (XAI) based on deep learning. As a result of the study, the high performance of the new framework was confirmed compared to logistic regression and multilayer perceptron. In addition, as a result of analyzing explanatory power, it was confirmed that stability and profitability indicators had a significant effect on prediction, and growth and activity indicators had a negligible effect.