Understanding Network Effects on Firm Innovation Performance in Canada’s Emerging AI Industry

Keywords: AI clusters, AI industry, networks
Session Type: Virtual Paper Abstract
Day: Friday
Session Start / End Time: 2/25/2022 09:40 AM (Eastern Time (US & Canada)) - 2/25/2022 11:00 AM (Eastern Time (US & Canada))
Room: Virtual 16

Abstract

We develop a theoretical framework to explore how firms in emerging knowledge-intensive industries develop linkages within and across industrial clusters and what this means for their innovation performance. We apply our framework to Canada’s nascent artificial intelligence (AI) for which we have collected a unique network database of interfirm linkages within and across 4 Canadian clusters and combined it with firm-level AI patent and publication information. We find that a firm’s performance in terms of patents is dependent on both its local and inter-regional connectedness but that its performance in terms of publications primarily depends on cross-border connectedness. We also find differences in the effects of horizontal and vertical linkages: vertical linkages are only important with patents, while horizontal linkages are important both for patents and for scientific publications. Finally, the analysis reveals important differences in the social network structure of the 4 hubs, as well as highlights the important role of intermediaries and big multinational firms in maintaining cross-hub linkages. The paper concludes with prospects for the development of the AI industry and clusters in Canada, and for the creation of clusters in other emerging industries.