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.

本仓库包含研究《嵌入性知识：科学家如何塑造企业创新竞争格局》的配套数据集与复现代码。本数据集涵盖一套全面的中国上市公司面板数据，整合了社会网络指标与专利层面的竞争度量指标。本研究核心旨在探讨创新网络内的科学家嵌入性如何引发企业间战略竞争，具体通过优质专利诉讼与专利无效宣告挑战两类场景进行量化测度。
本数据集涵盖四大核心维度：
（1）科学家网络指标：包含企业层面的科学家嵌入性与中间中心性（Betweenness Centrality）度量值；
（2）创新竞争指标：收录专利相关法律纠纷与挑战的详细记录；
（3）机制变量：作为研发广度（技术复杂度）与研发深度（基础研究产出）的代理变量；
（4）异质性情境：涵盖区域知识产权（Intellectual Property Rights, IPR）保护水平、高新技术企业认证以及高管学术背景相关信息。
本仓库提供的数据集以权威公开平台（包括但不限于中国国家知识产权局（CNIPA）与国泰安数据库（CSMAR））的数据为基础，由研究团队通过自动化技术提取与人工核验相结合的方式完成大规模数据采集与补充工作。本研究团队声明，所有数据均来源于合法渠道，且与原始记录保持一致。研究团队对本仓库所呈现的汇编信息的完整性与来源真实性承担全部责任。