How Embeddedness Affects the Evolution of Collaboration: The Role of Knowledge Stock and Social Interactions

TL;DR

This paper investigates how social and knowledge embeddedness influence the evolution of collaborative innovation networks, using longitudinal data and stochastic modeling to analyze network dynamics and the impact of various factors.

Contribution

It introduces a novel approach by extracting knowledge elements to better represent scientific information and applies a stochastic actor-oriented model to analyze collaboration dynamics over time.

Findings

Network embeddedness significantly affects collaboration evolution.

Knowledge stock and social interactions influence network changes.

Proximity dimensions impact collaboration formation and stability.

Abstract

Science and technology are becoming increasingly collaborative. This paper aims to explore the factors and mechanisms that impact the dynamic changes of collaborative innovation networks. We consider both collaborative interactions of organizations and their knowledge element exchanges to reveal how social and knowledge network embeddedness affects the collaboration dynamics. Knowledge elements are extracted to present the core concepts of scientific and technical information, overcoming the limitations of using predefined categorizations such as IPC when representing the content. Based on multiple collaboration and knowledge networks, we then conduct a longitudinal analysis and apply a stochastic actor-oriented model (SAOM) to model network dynamics over different periods. The influence of network features and structures, individual node characteristics, and various dimensions of proximity on collaboration dynamics is tested and analyzed.