The potential applications of blockchain technology across various business functions and industries have generated significant interest. However, its underlying knowledge structure remains unclear. This study aimed to gain a deeper understanding of the technological domain and knowledge structure of blockchain technology by analyzing 4753 USPTO patent data from 2008 to 2019. We used multiple approaches, such as analyzing patent filing volumes, constructing co-citation networks, and examining text (patent abstract) data with a variant of bidirectional encoder representations from transformers (BERT). The results demonstrate the advantages of using an NLP-based BERT text analysis approach for examining technological knowledge and relationships within the blockchain technology field. Our findings reveal that the field of blockchain technology is expanding and diversifying, with increasing patent filings in both cryptocurrency and distributed ledger technologies and growing knowledge similarity between these two subdomains. We also found that patent assignees (companies) engage differently in innovative activities within the blockchain technology domain based on their prior experience in the field. These results hold potential for informing future research in emerging technology studies and guiding industry and policy decisions related to blockchain technology.

This paper investigates how historical inter-firm syndication networks influence venture capitalists’ (VCs) propensity to invest in startups pursuing novel, uncertain technologies, with a focus on artificial intelligence (AI). We theorize that VCs’ positional attributes within cumulative syndication networks determine their access to external expertise and intelligence that aid AI investment decisions amidst informational opacity. Specifically, reachability to prior AI investors provides referrals and insights transmitted across short network paths to reduce ambiguity. Additionally, VC brokerage between disconnected industry clusters furnishes expansive, non-redundant information that is pivotal for discovering and assessing AI opportunities. Through hypotheses grounded in social network theory, we posit network-based mechanisms that equip VCs to navigate uncertainty when engaging with ambiguous innovations like AI. We test our framework, utilizing comprehensive historical records of global venture capital investments. Analyzing the location information of VC firms in this database, we uncovered a history of 14,751 investments made by Korean and Japanese firms. Using these data, we assembled an imbalanced panel dataset from 1984 to 2022 spanning 230 Korean and 413 Japanese VCs, with 4508 firm-year observations. Negative binomial regression analysis of this dataset reveals how historical relational patterns among venture capital firms foster readiness to evaluate unfamiliar innovations.