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on Innovation |
| 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: | https://d.repec.org/n?u=RePEc:osf:thesis:a3zwu_v1 |
| By: | David Dekker; Dimitirs Christopoulos; Heather McGregor |
| Abstract: | We explore a dynamic patent citation network model to explain the established link between network structure and technological improvement rate. This model, a type of survival model, posits that the *dynamic* network structure determines the *constant* improvement rate, requiring consistent structural reproduction over time. The model's hazard rate, the probability of a patent being cited, represents "knowledge production, " reflecting the output of new patents given existing ones. Analyzing hydrogen technology patents, we find distinct subdomain knowledge production rates, but consistent development across subdomains. "Distribution" patents show the lowest production rate, suggesting dominant "distribution" costs in $H_2$ pricing. Further modeling shows Katz-centrality predicts knowledge production, outperforming subdomain classification. Lower Katz centrality in "distribution" suggests inherent organizational differences in invention. Exploitative learning (within-subdomain citations) correlates with higher patenting opportunity costs, potentially explaining slower "distribution" development, as high investment needs may incentivize monopolization over knowledge sharing. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.00797 |
| By: | Vivarelli, Marco; Arenas Díaz, Guillermo |
| Abstract: | The relationship between technology and employment has long been a topic of debate. This issue is even more pertinent today as the global economy undergoes a technological revolution driven by automation and the widespread adoption of Artificial Intelligence. The primary objective of this paper is to provide insights into the relationship between innovation and employment by proposing a conceptual framework and by discussing the state of the art of the debates and analyses surrounding this topic. |
| JEL: | O33 O32 O15 |
| Date: | 2025–02–10 |
| URL: | https://d.repec.org/n?u=RePEc:unm:unumer:2025005 |
| By: | Vivarelli, Marco; Arenas Díaz, Guillermo |
| Abstract: | The relationship between technology and employment has long been a topic of debate. This issue is even more pertinent today as the global economy undergoes a technological revolution driven by automation and the widespread adoption of Artificial Intelligence. The primary objective of this paper is to provide insights into the relationship between innovation and employment by proposing a conceptual framework and by discussing the state of the art of the debates and analyses surrounding this topic. |
| Keywords: | Technology, employment, compensation theory, AI, robot |
| JEL: | O33 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:glodps:1565 |
| By: | Mohnen, Pierre (RS: GSBE other - not theme-related research, QE Econometrics); Mairesse, Jacques (Quantitative Economics); Notten, Ad (Mt Economic Research Inst on Innov/Techn) |
| Abstract: | This paper reviews the empirical work that has been done over the period 2013-2023 on the topic of innovation and productivity. A visual graph based on keywords shows the main areas that have been investigated. The literature review is organized around the way the link between innovation and productivity has been analyzed, the data that have been used, and the evidence that has been obtained. The paper ends with suggestions of future research on the topic. |
| JEL: | D24 O30 O31 O32 |
| Date: | 2025–02–03 |
| URL: | https://d.repec.org/n?u=RePEc:unm:unumer:2025003 |