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Knowledge growth in university-industry innovation networks – Results from a simulation study

Chongfeng Mao, Xianyun Yu, Qing Zhou, Rainer Harms and Gang Fang

Technological Forecasting and Social Change, 2020, vol. 151, issue C

Abstract: University-industry innovation networks (UIINs) are important agents of innovation, as they bring together the unique profiles of higher education and industry partners. Knowledge growth in these networks does not happen automatically. We analyze the impact of network density and heterogeneity on knowledge growth in UIINs. Knowledge grows through knowledge transfer, spillover, and knowledge innovation. Knowledge growth is a function of each agent's initial knowledge level, network density, and agent heterogeneity. To analyze these correlates of knowledge growth, we use a knowledge growth model based on multiple agents and simulate knowledge growth in a UIIN. Our results show that network density positively influences knowledge growth. Initially, this positive impact increases and then disappears with a further increase in network density. We also find that heterogeneity moderates the relationship between density and knowledge growth. Through the positive moderating effect of its impact on knowledge innovation, it promotes new knowledge generation in the entire innovation network, thus providing a basis for subsequent knowledge transfer. Our study supports and enriches the contingency view of knowledge growth in innovation networks.

Keywords: Network density; Network heterogeneity; Knowledge growth; Knowledge transfer; Knowledge innovation; Agent-based modeling and simulation (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1016/j.techfore.2019.119746

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