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@article{Rikap2024,

title = {Varieties of corporate innovation systems and their interplay with global and national systems: Amazon, Facebook, Google and Microsoft’s strategies to produce and appropriate artificial intelligence},

author = {Cecilia Rikap},

url = {https://www.tandfonline.com/doi/pdf/10.1080/09692290.2024.2365757},

doi = {/10.1080/09692290.2024.2365757},

year  = {2024},

date = {2024-06-24},

journal = {Review of inteRnational Political economy},

abstract = {The widely accepted globalization of innovation entails two interrelated undertheorized aspects: (1) the capacity of certain firms to orchestrate transnational innovation systems appropriating successful results, which some have explained with the concept of corporate innovation systems (CIS), and (2) the co-existence of such globalization with those CIS and national innovation systems. I address these matters analysing US Big Tech artificial intelligence (AI) CIS showing that they combine multiple mechanisms to co-produce and appropriate AI. I propose ‘frenemy’ to describe Microsoft’s strategy because many Chinese organizations and even direct competitors integrate its CIS. ‘University’ symbolises Google’s strategy, given its focus on fundamental AI, its central place in the AI research field and appropriation mechanisms that are not translating into clear business advantages. ‘Secrecy’ defines Amazon’s strategy, maximizing knowledge inflows while minimizing outflows. Facebook, with the narrowest AI CIS, exhibits an ‘application-centred’ strategy. Ultimately, this paper contributes to understanding the multiple mechanisms used by leading corporations for controlling and shaping frontier transnational knowledge production and appropriation. By doing so, it advances our knowledge of the interplay between different innovation spheres (national, global and corporate) and highlights the dangers of CIS’s encroachment of national and global systems.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Rikap2019,

title = {Asymmetric Power of the Core: Technological Cooperation and Technological Competition in the Transnational Innovation Networks of Big Pharma},

author = {Cecilia Rikap},

url = {https://www.tandfonline.com/doi/abs/10.1080/09692290.2019.1620309},

doi = {10.1080/09692290.2019.1620309},

year  = {2019},

date = {2019-06-26},

urldate = {2019-06-26},

journal = {Review of International Political Economy},

volume = {26},

number = {5},

pages = {987-1021},

abstract = {This article theoretically and empirically analyzes leader corporations’ innovation processes in contemporary capitalism. We highlight three characteristics: their transnational scope, the primacy of power or asymmetric relations exercised by leaders over the participants of their innovation circuits or networks, and the relevance of what we called technological competition and technological cooperation between leaders. Focusing on the latter, our theoretical contribution integrates the concepts of innovation circuit, global innovation network and modularity of knowledge production in order to elaborate a preliminary model for synthesizing leader’s technological competition and collaboration behaviors. This model is the general framework used for studying three big pharma’s innovation networks (Roche, Novartis and Pfizer). In particular, we study those networks by considering two outputs: scientific publications and patents. Network maps are constructed based on institutions’ co-occurrences, thus looking at who is co-authoring their publications and co-owning these corporations’ patents. We find that big pharmaceuticals co-produce together mainly generic knowledge modules, thus develop a strong technological cooperation. Simultaneously, to succeed in their technological competition they outsource stages of their innovation networks to subordinate institutions that, even if they contribute to achieve the innovation, will not be co-owners of the resulting patents, while big pharmaceuticals enjoy associated innovation rents.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}