Embedding Knowledge: How Scientists Shape the Landscape of Firm Innovation Competition

This repository contains the dataset and replication codes for the study "Embedding Knowledge: How Scientists Shape the Landscape of Firm Innovation Competition." The dataset provides a comprehensive panel of Chinese listed firms, integrating social network metrics with patent-level competitive indicators. The primary focus is to examine how scientist embeddedness within innovation networks triggers strategic interfirm competition, specifically measured through meritorious patent litigation and patent invalidation challenges.
The data covers several key dimensions:（1）Scientist Network Metrics: Firm-level measures of scientist embeddedness and betweenness centrality. （2）Innovation Competition Indicators: Detailed records of patent-related legal disputes and challenges.（3）Mechanism Variables: Proxies for R&D Breadth (technological complexity) and R&D Depth (basic research outputs). （4）Heterogeneity Contexts: Information on regional IPR protection, high-tech certifications, and executive academic backgrounds.
The datasets provided in this repository are compiled from authoritative public platforms (including but not limited to CNIPA and CSMAR), supplemented by extensive data collection efforts—utilizing both automated technical extraction and manual verification—conducted by the research team. We affirm that all data is derived from legitimate sources and remains consistent with the original records. The research team assumes full responsibility for the integrity and provenance of the compiled information presented here.