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on Economics of Strategic Management |
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Issue of 2023‒06‒12
five papers chosen by João José de Matos Ferreira Universidade da Beira Interior |
| By: | Stefan Pauly (ECON - Département d'économie (Sciences Po) - Sciences Po - Sciences Po - CNRS - Centre National de la Recherche Scientifique); Fernando Stipanicic (UC Berkeley - University of California [Berkeley] - UC - University of California) |
| Abstract: | Click here for the latest version This paper provides new causal evidence of the impact of air travel time on the creation and diffusion of knowledge. We exploit the beginning of the Jet Age as a quasi-natural experiment. We digitize airlines' historical flight schedules and construct a novel data set of the flight network in the United States. Between 1951 and 1966, travel time between locations more than 2, 000 km apart decreased on average by 41%. The reduction in travel time explains 33% of the increase in knowledge diffusion as measured by patent citations. The increase in knowledge diffusion further caused an increase in the creation of new knowledge. The results provide evidence that jet airplanes led to innovation convergence across locations and contributed to the shift in innovation activity towards the South and the West of the United States. |
| Date: | 2022–10–21 |
| URL: | http://d.repec.org/n?u=RePEc:hal:wpaper:hal-04067326&r=cse |
| By: | Foreman-Peck, James (Cardiff Business School); Zhou, Peng (Cardiff Business School) |
| Abstract: | We model how R&D enters the innovation system in four ways (intramural, extramural, cooperative, and spillover). Despite measuring three different spillovers together, for a very large sample of European enterprises we conclude that the productivity effects of spillovers were at best smaller than intramural R&D productivity effects. We also find that building on the greater skills and experience of enterprises already undertaking R&D (intensity) raised labour productivity more than providing support for those beginning R&D (extensity). Optimal extramural R&D intensity was higher than the actual level; sample firms could boost productivity either by abandoning extramural R&D or by doing much more. There were substantial differences in our sample between enterprises and countries in terms of R&D spillovers. Greater multinational corporation incidence in new EU members accounted for these countries’ high direct R&D intensity productivity, regardless of their generally low overall labour productivity. Absorptive capacity made little difference to the utilisation of spillovers. |
| Keywords: | R&D; innovation; knowledge spillover |
| JEL: | L53 L21 H71 H25 |
| Date: | 2023–05 |
| URL: | http://d.repec.org/n?u=RePEc:cdf:wpaper:2023/16&r=cse |
| By: | Xian Gong; Claire McFarland; Paul McCarthy; Colin Griffith; Marian-Andrei Rizoiu |
| Abstract: | Understanding the relationship between emerging technology and research and development has long been of interest to companies, policy makers and researchers. In this paper new sources of data and tools are combined with a novel technique to construct a model linking a defined set of emerging technologies with the global leading R&D spending companies. The result is a new map of this landscape. This map reveals the proximity of technologies and companies in the knowledge embedded in their corresponding Wikipedia profiles, enabling analysis of the closest associations between the companies and emerging technologies. A significant positive correlation for a related set of patent data validates the approach. Finally, a set of Circular Economy Emerging Technologies are matched to their closest leading R&D spending company, prompting future research ideas in broader or narrower application of the model to specific technology themes, company competitor landscapes and national interest concerns. |
| Date: | 2023–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2305.02476&r=cse |
| By: | Dam, John; Rickon, Henry |
| Abstract: | This literature review aims to elucidate the nuanced relationship between data openness and innovation within the field of Artificial Intelligence (AI). As the significance of AI continues to expand across various sectors, understanding the role of open data in fostering innovation becomes increasingly critical. Through this review, we systematically explore and analyze the wealth of existing literature on the topic. We address key concepts, theoretical perspectives, and empirical findings, shedding light on the multi-dimensional facets of data openness, including accessibility and usability, and their impact on AI innovation. Furthermore, the review highlights the practical implications and potential strategies to leverage data openness in propelling AI innovation. We also identify existing gaps and limitations in current literature, suggesting avenues for future research. This comprehensive review contributes to the evolving discourse in AI studies, offering valuable insights to researchers, data managers, and AI practitioners alike. |
| Date: | 2023–05–15 |
| URL: | http://d.repec.org/n?u=RePEc:osf:thesis:a3zwu&r=cse |
| By: | Maria Balta (Kent Business School, University of Kent); Konstantina Spanaki (Audencia Business School); Thanos Papadopoulos (Kent Business School, University of Kent); M.N. Ravishankar (Queens University Belfast) |
| Date: | 2023–04 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-04053205&r=cse |