The influence factors of innovation networking formation based on ERGM: Evidence from the smart medical industry

Abstract

With the growth of the social economy and technology, innovation networks have emerged as one of the most significant methods for analyzing the evolution of industrial innovation. Yet, there is a shortage of studies analyzing the components that influence network creation. By highly integrating digital technology with the traditional medical industry chain, the smart medical industry has become one of the important sectors of the digital economy. With the advent of internet-based diagnosis and treatment technologies, innovation inside the smart medical industry has taken the form of a network. This study aims to construct an innovation network by organizing and analyzing patent data from China's smart medical industry cooperation, covering the period from 2005 to 2022. The data is sourced from the IncoPat database. The analysis utilizes the Exponential Random Graph Model (ERGM) approach to conduct regression analysis on various factors. These factors include endogenous structural characteristics, node feature variables such as node emergence time and institutional attributes, as well as the distance network and IPC attribute network. By examining the driving mechanism and influence mechanism that influence the innovation network, this study contributes to the smart medical industry research by gaining a better understanding of the current status of innovation network, which can be advantageous for businesses in this field to accurately recognize and actively promote their innovation practices.