# Spatiotemporal evolution and the multidimensional proximity mechanism of megaproject innovation networks

## TL;DR

This study examines how innovation networks in large-scale projects evolve over time and space, and how different types of proximity influence collaboration.

## Contribution

The study introduces a multidimensional proximity mechanism to explain city-level collaboration in megaproject innovation networks.

## Key findings

- Railway system units hold core positions and significant power in collaborative innovation networks.
- Nuclear cities in innovation networks shifted from first-tier to second-tier cities over time.
- Geographical, institutional, cognitive, and social proximity all promote city collaboration in megaprojects.

## Abstract

Megaprojects necessitate collaborative innovation that transcends organizational, departmental, industrial, and regional boundaries, culminating in the formation of innovation networks. Utilizing data from the megaprojects awarded in the 1st to 19th Zhan Tianyou Awards as a foundation, this study employed social network analysis, Ucinet, and ArcGIS to construct both the collaborative innovation network of participating units in megaprojects and the collaborative innovation network of cities involved in megaprojects. The study analyzed the spatiotemporal evolution characteristics and core organizations within the collaborative innovation network of participating units in megaprojects, and discussed the core-periphery structure and spatiotemporal evolution of the collaborative innovation network among cities engaged in megaprojects. ArcGIS and Spass software were employed, along with a negative binomial gravity model, to investigate the role of multidimensional proximity in the construction of collaborative innovation networks among cities for the 1st-5th, 6th-10th, 11th-15th, and 16th-19th sessions of megaprojects. The key conclusions were as follows. (1) Participating units of the railway system occupy core positions in the collaborative innovation networks of participating units and hold absolute ‘power’ in the networks. (2) Nuclear cities in the city collaborative innovation networks appear to transfer from the eastern first-tier cities, Guangzhou and Shenzhen, to the eastern second-tier cities, Taiyuan, Jinan, Hefei, and the central-western second-tier city, Chengdu. (3) Geographical proximity, institutional proximity, cognitive proximity and social proximity all promote cities’ collaborative innovation in megaprojects. This study still has some limitations. First, this study only uses a single measure of proximity and does not measure it from multiple perspectives. Second, this study used the number of megaproject collaborations as the dependent variable, without considering the relationship between network structure and actual innovation outcomes or project performance.