Artificial Intelligence (AI) startups possess four key attributes; being small enterprises, adopting AI technology, undergoing digital transformation, and using big data systems to enhance their competitiveness. This study aims to identify the key influencing factors needed to enhance the competitiveness of AI technology-based startups and to suggest a decision-making model to improve the technology and business competitiveness of AI startups in the digital era. To achieve this, the hierarchy concept framework was built with four evaluation areas based on the mechanism-based view theory, and the 16 evaluation factors that can influence were identified through existing literature, combining factors related to the digital transformation, technological application, and business competitiveness of the startups. These factors were analyzed using the Analytic Hierarchy Process (AHP) by the survey, targeting experts in South Korea. The analysis results indicate that the subject area was the most crucial for the business competitiveness of AI startups. It was also revealed that the subject’s strategic mind is the most significant factor to AI startups’ success. In the case of two control groups, categorized as ‘AI experts’ and ‘startup experts’, AI experts chose the subject as the most important area, whereas startup experts selected the environment, and significant differences were observed in all other factors. The results of this study will provide implications for strengthening the business competitiveness of AI startups and factors important for the growth of AI startups in this era.

Decentralized Autonomous Organizations (DAOs) have gained widespread attention in academia and industry as potential future models for decentralized governance and organization. In order to understand the trends and future potential of this rapidly growing technology, it is crucial to conduct research in the field. This research aims at a data-driven approach for the objective content analysis of big data related to DAOs, using text mining and Latent Dirichlet Allocation (LDA)-based topic modeling. The study analyzed tweets with the hashtag #DAO and all Reddit data with “DAO”. The results were from the identification of the top 100 frequently appearing keywords, as well as the top 20 keywords with high network centrality, and key topics related to finance, gaming, and fundraising, from both Twitter and Reddit. The analysis revealed twelve topics from Twitter and eight topics from Reddit, with the term “community” frequently appearing across many of these topics. The findings provide valuable insights into the current trend and future potential of DAOs, and should be used by researchers to guide further research in the field and by decision makers to explore innovative ways to govern the organizations.

This study defines the intrinsic and extrinsic motivational factors that influence the prevention of industrial technology leakage by high-tech company employees. It also investigates how these factors affect the employees’ intention to prevent leakage. Based on the TPB (theory of planned behavior), this study analyzes the relationship between “attitude toward behavior”, “subjective norm”, and “perceived behavioral control”, which in turn influences the behavioral intention to prevent such leakage. Specifically, an online survey was conducted among office workers in South Korea’s high-tech industry. A total of 200 questionnaires were collected and analyzed. As the analysis results show, intrinsic motivation has a positive effect on attitude toward behavior, subjective norms, and perceived behavioral control. Extrinsic motivation has a positive effect on subjective norms and perceived behavioral control but a negative effect on attitudes toward behavior. This study also proved, based on the TPB, that the three variables impact the behavioral intention to prevent technology leakage. These results confirm that, in the high-tech sector, where employees are highly specialized and autonomous, technical security behaviors are primarily influenced by individual professional ethics and judgment rather than by organizational regulation or extrinsic motivation.

As both investment attraction and mergers and acquisitions targeting information technology and platform companies are becoming more important in the digital-centric economic environment, interest in valuing corporate data assets is increasing. Accordingly, among the income approaches used in business valuation, this study presents a data valuation model based on discounted cash flow. This model is expected to be useful for corporate investment decision-making. The assumptions used in this study for the estimation of data income include intangible asset value, exclude net asset value, and data attribution is centered on technology, human resources, and market factors. In particular, data attribution accounts comprise ordinary data research and development, data labor costs, and data advertising expenses. Data costs were divided into those incurred during collection, storage, curation, analysis, and utilization. Financial statements and related data from a real estate information platform operator over three years were collected and used to simulate the data valuation model. The simulation reveals that the operator possesses KRW 472.6 billion in data assets. Ultimately, the data valuation model developed in this study can contribute to strengthening platform operators’ investment attraction, guaranteeing financial sustainability, and transparency and data assetization.

The importance of data assets as intangible corporate assets is being emphasized as more business activities based on digital technology are being carried out. This study proposes the development of a data valuation model that can enable companies to use data assets as collateral for loans in financial transactions. To this end, a model was designed with a focus on the cost approach, which is less likely to involve arbitrariness and error among other valuation model approaches. Furthermore, a model simulation was conducted after securing transaction data of a Korean secondhand marketplace provider. Among the total costs of this marketplace provider, the cost of using data reflecting the ratio of data activities was derived, focusing on financial statements and tangible and intangible assets for the last five years. The data asset acquisition costs were derived, and the data replacement costs were calculated by reflecting the past price and wage growth rates. The results revealed that simulation companies could use a total of KRW 26.8 billion worth of data as collateral for a loan. Accordingly, the data valuation model developed in this study will contribute to reinforcing the value of corporate data assets and proposing a new means of corporate financing.

Decentralization holds a significant role in the context of decentralized autonomous organizations (DAOs), with its nature being not a fixed value but a comparative spectrum. Prior research investigating the measurement of decentralization in nations’ governance system provides a foundation for our current study. This research aims to integrate these insights to define dimensions and indicators, tailored explicitly for assessing decentralization levels within DAOs. Then, the article undertakes an examination of the suitability of traditional decentralization measurement approaches within the unique DAO context, employing confirmatory factor analysis (CFA) as our analytical tool based on a total of 44 DAOs. Hence, the results suggest that DAOs have three dimensions for measuring decentralization, ‘political decentralization as a participatory engagement’, ‘economic decentralization as a resource distribution’, and ‘administrative decentralization as the self-governing execution of decisions’. By substantiating the applicability of established decentralization measurement frameworks within the unique context of DAOs, the findings not only enhance the understanding of this emergent governance paradigm but also provide DAO practitioners, policymakers, and researchers with invaluable insights.