Entrepreneurial technology networks: A systematic patent analysis using multi-method social network framework

In today's fast-changing business world, knowing how technology drives entrepreneurial innovation is key to staying competitive and ensuring long-term economic growth. Despite extensive research on patents and entrepreneurship separately, there remains a significant gap in comprehensively analyzing how different technologies interconnect and influence entrepreneurial innovation through patent classifications. The combination of Association Rule Mining (ARM), Social Network Analysis (SNA), and Fuzzy Cognitive Maps (FCM) methodologies enables a comprehensive understanding of these interconnections by leveraging ARM for identifying relationships, SNA for visualizing networks, and FCM for modeling dynamic influences, providing insights that single-method approaches often miss. This study aims to map and prioritize technological domains in entrepreneurship by analyzing patent classifications using an innovative multi-method approach involving data analysis and social network analysis. The research methodology integrates these three methods to analyze 14,358 business model patents, culminating in a TOPSIS-based prioritization framework. The findings reveal a significant surge in entrepreneurial patent activities from 2020 onwards, peaking in 2021 with 1,618 patents. The analysis identifies key technological clusters, with digital commerce, administration systems and sector-specific solutions emerging as central drivers of innovation. The ARM analysis uncovers 54 significant technological relationships, while FCM analysis reveals the dynamic influence patterns among different patent classifications.