Predicting patent transaction behaviour based on embedded features of knowledge search networks

Abstract

Purpose

This study aims to analyse the relationship between a patent’s network position in a knowledge search network and the likelihood and speed of patent transactions. Additionally, it explores whether patent scope moderates these relationships.

Design/methodology/approach

In this empirical study, the authors collected a sample of patents in the artificial intelligence industry over the period of 1985–2018. Then, the authors examined the direct roles of degree centrality, betweenness centrality and closeness centrality on the likelihood and speed of patent transactions and the moderating role of patent scope in the knowledge search network using the logit and accelerated failure time models.

Findings

The findings reveal that degree centrality positively affects both the likelihood and speed of patent transactions, while betweenness centrality enhances the likelihood, and closeness centrality significantly boosts both. However, regarding the speed of patent transactions, closeness centrality is the most impactful, followed by degree centrality, with no significant influence of betweenness centrality. Additionally, the patent scope moderates how betweenness centrality affects the likelihood of transactions.

Research limitations/implications

This study has limitations owing to its exclusive use of data from the Chinese Intellectual Property Office, lack of visibility of the confidential terms of most patent transactions, omission of transaction directionality and focus on a single industry, potentially restricting the breadth and applicability of the findings. In the future, expanding the data set and industries and combining qualitative research methods may be considered to further explore the content of this study.

Practical implications

This study has practical implications for developing a better understanding of how network structure in the knowledge search network affects the likelihood and speed of patent transactions as well as the identification of high-value patents. These findings suggest future directions for patent holders and policymakers to manage and optimise patent portfolios.

Originality/value

This study expands the application boundaries of social network theory and the knowledge-based view by conducting an in-depth analysis of how the position characteristics of patents within the knowledge search network influence their potential and speed of transactions in the technology market. Moreover, it provides a theoretical reference for evaluating patent value and identifying high-quality patents by quantifying network positions. Furthermore, the authors construct three centrality measures and explore the development of patent transactions, particularly within the context of the developing country.

Citation

Zhang, Q., Yu, C., Yang, X. and Gu, X. (2024), "Predicting patent transaction behaviour based on embedded features of knowledge search networks", Journal of Knowledge Management, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/JKM-12-2023-1220