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on Knowledge Management and Knowledge Economy |
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Issue of 2018‒05‒07
two papers chosen by Laura Ştefănescu Centrul European de Studii Manageriale în Administrarea Afacerilor |
| By: | Lee Branstetter; Britta Glennon; J. Bradford Jensen |
| Abstract: | The location of US multinational foreign R&D has shifted significantly to include emerging markets in addition to traditional Western R&D hubs, resulting in two challenges for multinationals: (1) how to transfer knowledge across geographic distances, and (2) how to facilitate learning when local knowledge sources in given technological areas are inadequate. This paper argues that to overcome these challenges, multinationals utilize home country inventors on foreign affiliate inventor teams – and in particular on teams in locations with insufficiently specialized local knowledge stocks – to facilitate knowledge transfer. Empirical analysis of a comprehensive dataset of US multinational R&D and patenting activity provides robust support for this argument. The findings have important implications for understanding how countries can gain expertise in technical areas and how poor countries can escape the knowledge trap, and they provide insight into management of increasingly dispersed multinational global R&D networks, particularly in locations with relatively unspecialized local inventors. |
| JEL: | O31 O32 O57 |
| Date: | 2018–03 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:24453&r=knm |
| By: | Iain M. Cockburn; Rebecca Henderson; Scott Stern |
| Abstract: | Artificial intelligence may greatly increase the efficiency of the existing economy. But it may have an even larger impact by serving as a new general-purpose “method of invention” that can reshape the nature of the innovation process and the organization of R&D. We distinguish between automation-oriented applications such as robotics and the potential for recent developments in “deep learning” to serve as a general-purpose method of invention, finding strong evidence of a “shift” in the importance of application-oriented learning research since 2009. We suggest that this is likely to lead to a significant substitution away from more routinized labor-intensive research towards research that takes advantage of the interplay between passively generated large datasets and enhanced prediction algorithms. At the same time, the potential commercial rewards from mastering this mode of research are likely to usher in a period of racing, driven by powerful incentives for individual companies to acquire and control critical large datasets and application-specific algorithms. We suggest that policies which encourage transparency and sharing of core datasets across both public and private actors may be critical tools for stimulating research productivity and innovation-oriented competition going forward. |
| JEL: | L1 |
| Date: | 2018–03 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:24449&r=knm |