Patent Citation Data in Social Science Research: Overview and Best Practices

What are the main applications and best practices for using patent citation data in social science research?

The use of patent citation data has surged in social science research over the last two decades, providing critical insights into the diffusion of knowledge, the impact of innovations, and the structure of innovation networks. Understanding and utilizing patent citation effectively is essential for researchers aiming to draw accurate conclusions about knowledge flows and technological impact. However, there are also several pitfalls and methodological challenges in interpreting these data, necessitating a careful approach to ensure valid results.

We systematically reviewed the applications of patent citation data in research by examining a variety of articles on the subject and discussing the pitfalls associated with these data types. We used a structured keyword search on Google Scholar complemented with a manual review of influential articles, ultimately curating a comprehensive overview of the usage and implications of patent citation metrics.

We identify four major uses of patent citation data in social science research: assessing innovation attributes, tracing knowledge flows, mapping innovation networks, and evaluating economic impact. We also discuss key pitfalls, such as office-specific citation practices, the influence of temporal and technological factors on citation patterns, and biases introduced by strategic citation behaviors. Furthermore, we highlight the necessity of ongoing validation and methodological development in the field of citation analysis.

• No primary empirical data

• Citation patterns reflect underlying technological and economic factors
• Citations are indicative of the knowledge flow between patents and innovations
• Differences in citation practices across jurisdictions affect the interpretation of citation data