Differentiating artificial intelligence activity clusters in Australia

Alexandra Bratanova, Hien Pham, Claire Mason, Stefan Hajkowicz, Claire Naughtin, Emma Schleiger, Conrad Sanderson, Caron Chen and Sarvnaz Karimi

Technology in Society, 2022, vol. 71, issue C

Abstract: We demonstrate how cluster analysis underpinned by analysis of revealed technology advantage can be used to differentiate geographic regions by activity in artificial intelligence (AI). Our analysis uses novel datasets on Australian AI businesses, intellectual property patents and labour markets to explore location, concentration and intensity of AI activities across 333 geographical regions. We find that Australia's AI business and innovation activity is clustered in geographic locations with higher investment in research and development. Through cluster analysis we identify three tiers of AI capability regions that are developing across the economy: ‘AI hotspots’ (10 regions), ‘Emerging AI regions’ (85 regions) and ‘Nascent AI regions’ (238 regions). While the AI hotspots are mainly concentrated in central business district (CBD) locations, there are examples when they also appear outside CBD in areas where there has been significant investment in innovation and technology hubs. Policy makers and investors can use these results to learn about the current landscape of AI business and innovation activities in Australia, identify potential growth opportunities in AI capabilities and to guide future policy and business decisions.

Keywords: Artificial intelligence; Cluster; Revealed technology advantage; Regional innovation; Australia (search for similar items in EconPapers)
JEL-codes: O31 O33 O38 R12 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:teinso:v:71:y:2022:i:c:s0160791x22002457

DOI: 10.1016/j.techsoc.2022.102104

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