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2022 | OriginalPaper | Buchkapitel

Platforms, AI and the Spillover Effect

verfasst von : Ahmed Bounfour, Alberto Nonnis, Clément Sternberger, Nguyen Minh Phuong Le

Erschienen in: Platforms and Artificial Intelligence

Verlag: Springer International Publishing

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Abstract

Due to the central role played by platforms in the development of new technologies such as AI, the study concentrates on patents applicants that collaborate with platforms, considering that collaborations create an essential link for the transmission of knowledge. Using patent data and a panel of 22 platforms and 207 worldwide applicants that share the characteristics of having collaborated at least once with platforms in their life, indicators of knowledge creation, knowledge stock and knowledge spillovers are constructed. Distinguishing applicants by organisation type (platforms, large firms, SMEs and universities), the effect of different types of spillovers on each of the different categories’ knowledge is studied via negative binomial regressions. While we found evidence in favour of knowledge diffusion between firms in the whole sample, not every category benefits from these spillovers: large firms are found to be those that benefit more and from a greater number of spillovers, followed by SMEs, while universities receive very little benefits from spillovers in terms of knowledge creation, and more importantly platforms, despite being the main creators of knowledge in the AI sector, do not benefit of spillovers, which attests to their internalised knowledge strategy with regard to AI investment.

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The names of the platforms in the sample are listed in Appendix.

The stock of patents of each firm is computed as the cumulative sum of all patents published by each firm from the early 1900–2020. We use the total stock of patents rather than new patents published as more representative of the technological category of a firm. In fact, there are many SMEs in our sample that in many years did not publish any patents, for which it would be difficult to identify the category if we used new patents published in each year.

Given the difficulty of pairing non-harmonised firm names with other databases, we follow Kim et al. (2014) in ignoring possible controls variables and focusing on average relationships. We assume that the effect of controls is captured by the constant terms.

In this case, also the Jaffe matrix of coefficients is re-computed using citation weighted measures rather than the simple count.

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Titel: Platforms, AI and the Spillover Effect
verfasst von: Ahmed Bounfour
Alberto Nonnis
Clément Sternberger
Nguyen Minh Phuong Le
Verlag: Springer International Publishing
Buch: Platforms and Artificial Intelligence
Print ISBN: 978-3-030-90191-2

Electronic ISBN: 978-3-030-90192-9

Copyright-Jahr: 2022
DOI: https://doi.org/10.1007/978-3-030-90192-9_3

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