Tracing technological development trajectories: A genetic knowledge persistence-based main path approach

TL;DR

This paper introduces a new method for identifying main technological development paths using patent citations and genetic knowledge persistence, resulting in clearer, more comprehensive trajectories.

Contribution

The paper presents a novel main path analysis method based on genetic knowledge persistence, reducing network complexity and capturing more relevant patents.

Findings

Main paths are nearly 10 times less complex.

The new method captures more relevant knowledge.

It outperforms previous approaches in clarity and comprehensiveness.

Abstract

The aim of this paper is to propose a new method to identify main paths in a technological domain using patent citations. Previous approaches for using main path analysis have greatly improved our understanding of actual technological trajectories but nonetheless have some limitations. They have high potential to miss some dominant patents from the identified main paths; nonetheless, the high network complexity of their main paths makes qualitative tracing of trajectories problematic. The proposed method searches backward and forward paths from the high-persistence patents which are identified based on a standard genetic knowledge persistence algorithm. We tested the new method by applying it to the desalination and the solar photovoltaic domains and compared the results to output from the same domains using a prior method. The empirical results show that the proposed method overcomes the aforementioned drawbacks defining main paths that are almost 10x less complex while containing more of the relevant important knowledge than the main path networks defined by the existing method.