Template-type: ReDIF-Paper 1.0
Author-Name: Colombelli, Alessandra    
Author-Email: alessandra.colombelli@polito.it 
Author-Name: Belitski, Maksim 
Author-Email: m.belitski@reading.ac.uk 
Author-Name: D’Amico, Elettra  
Author-Email: elettra.damico@polito.it 
Author-Workplace-Name: University of Turin
Author-Workplace-Homepage: http://www.est.unito.it/
Title: Artificial Intelligence and Firm Innovation: The Resource-Allocation Perspective.
Abstract: Extant research has established that firms increase absorptive capacity to engage in knowledge
sourcing and technology adoption and to create knowledge internally to achieve two strategically
important objectives: to become more innovative and commercialize innovation. We shift this
conversation to a new direction by asking the question of how the adoption of Artificial Intelligence (AI)
technology changes firm’s choice on resource allocation and shapes innovation performance. Using
novel data on 14,143 UK firms over 2004-2020 and the two-step procedure to deal with endogeneity in
innovation function, we find an inverted U-shape relationship between internal and external resource
allocation and firm innovation and AI reduces the cost of knowledge investment, when these costs are
high. Taken together, these results call for a fundamental rethinking of the resource allocation
mechanisms and strategies used for firm innovation.
Length: pages 57
Creation-Date: 2023-10
File-URL:https://www.est.unito.it/do/home.pl/Download?doc=/allegati/wp2023dip/wp_16_2023.pdf
File-Format: Application/PDF
Handle: RePEc:uto:labeco:202304