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on Innovation |
| By: | Francesco D'Alessandro (Dipartimento di Politica Economica, DISCE, Università Cattolica del Sacro Cuore, Milano, Italy); Enrico Santarelli (, Department of Economics, University of Bologna, Italy - Global Labor Organization (GLO), Essen, Germany); Marco Vivarelli (Dipartimento di Politica Economica, DISCE, Università Cattolica del Sacro Cuore, Milano, Italy – UNU-MERIT, Maastricht, The Netherlands – IZA, Bonn, Germany) |
| Abstract: | In this paper we integrate the insights of the Knowledge Spillover Theory of Entrepreneurship and Innovation (KSTE+I) with Schumpeter's idea that innovative entrepreneurs creatively apply available local knowledge, possibly mediated by Marshallian, Jacobian and Porter spillovers. In more detail, in this study we assess the degree of pervasiveness and the level of opportunities brought about by AI technologies by testing the possible correlation between the regional AI knowledge stock and the number of new innovative ventures (that is startups patenting in any technological field in the year of their foundation). Empirically, by focusing on 287 Nuts-2 European regions, we test whether the local AI stock of knowledge exerts an enabling role in fostering innovative entry within AI-related local industries (AI technologies as focused enablers) and within non AI-related local industries, as well (AI technologies as generalised enablers). Results from Negative Binomial fixed-effect and Poisson fixed-effect regressions (controlled for a variety of concurrent drivers of entrepreneurship) reveal that the local AI knowledge stock does promote the spread of innovative startups, so supporting both the KSTE+I approach and the enabling role of AI technologies; however, this relationship is confirmed only with regard to the sole high-tech/AI-related industries. |
| Keywords: | KSTE+I, Artificial Intelligence, innovative entry, enabling technologies |
| JEL: | O33 L26 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:ctc:serie5:dipe0039 |
| By: | Sabien Dobbelaere (Vrije Universiteit Amsterdam); Michael D. König (Vrije Universiteit Amsterdam); Andrin Spescha (ETH Zurich); Martin Wörter (ETH Zurich) |
| Abstract: | The fraction of R&D active firms decreased in Switzerland but increased in the Netherlands from 2000-2016. This paper examines reasons for this divergence and its impact on productivity growth. Our micro-data reveal R&D concentration among high-productivity firms in Switzerland. Innovation support sustains firms’ R&D activities in both countries. Our structural growth model identifies the impact of innovation, imitation and R&D costs on firms’ R&D decisions. R&D costs gained importance in Switzerland but not in the Netherlands, explaining the diverging R&D trends. Yet, counterfactual analyses show that policies should prioritize enhancing innovation and imitation success over cost reduction to boost productivity growth. |
| Keywords: | R&D, innovation, imitation, R&D costs, policy, productivity growth, traveling wave. |
| Date: | 2023–12–22 |
| URL: | https://d.repec.org/n?u=RePEc:tin:wpaper:20230080 |
| By: | Anders Broström (School of Industrial Engineering and Management, KTH Royal Institute of Technology); Cornelia Lawson (Manchester Institute of Innovation Research, The University of Manchester); Mabel Sanchez Barrioluengo (Manchester Institute of Innovation Research, The University of Manchester) |
| Abstract: | Science is widely embraced as an important prerequisite for innovation, and there is widespread support for public investment in science on that basis. It remains less clear to what extent the general public also perceives science as a relevant source of expertise on technological development and innovation. Drawing on representative panels from two European countries (the United Kingdom and Sweden), we investigate whether scientists are perceived as credible senders of messages regarding future technological development and its consequences. We apply a conjoint analysis methodology. Specifically, we estimate the credibility of scientists by comparing how respondents’ assessments of societal challenges statements change with the attribution of that statement to scientists, compared with attribution to other type of expert groups (government, businesspersons, and issue advocates). While our study identifies positively framed predictions about new technology and innovation as a domain where scientific expertise is perceived as enjoying relatively high credibility, actors representing business and special interest groups are overall perceived as more credible conveyors of ‘bad news’, of negatively framed messages about the future. Implications for our understanding of the social contract of science are discussed. |
| Keywords: | Scientific Experts, Expertise, Trust in Science, SDGs, Emerging Technologies |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:bdj:smioir:2024-04 |
| By: | Philippe Aghion; Lint Barrage; David Hémous; Ernest Liu |
| Abstract: | We analyze a model of green technological transition along a supply chain. In each layer, a good is produced with a dirty technology, or, if the required “electrification” innovation has occurred, with a clean technology which uses the immediate upstream good. We show that the economy is characterized by a single equilibrium but multiple steady-states, and that even in the presence of Pigouvian environmental taxation, a targeted industrial policy is generally necessary to implement the social optimum. We also show that: (i) small, targeted, industrial policy may bring large welfare gains; (ii) a government which is constrained to focus its subsidies to electrification on one particular sector, should primarily target downstream sectors; (iii) when extending the model so as to allow for supply chains also for the dirty technology, overinvesting in electrication in the wrong upstream branch may derail the overall transition towards electrication downstream. Finally, we illustrate our model with a calibration to decarbonization of global iron and steel production via hydrogen direct reduction, and show that, absent industrial policy, the economy can get stuck in a “wrong” steady-state with CO2 emissions vastly above the social optimum even with a carbon price in place. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:zur:econwp:450 |
| By: | Talia Bar (University of Connecticut); Heshan Zhang (PNC Bank) |
| Abstract: | We examine gender differences in US patent outcomes -- forward citations, triadic grants (related patents in EU and Japan), and renewals. We find that differences in workplace explain a significant part of the gap. After accounting for technology, application years, examiners and patent assignees, we show that while on average, patent teams with at least one woman-inventor have slightly weaker outcomes, for solo-inventor patents there are no significant gender differences in any of the outcomes. But men-lead mixed gender teams have on average slightly weaker outcomes than men-only teams, even when we control for the identity of the first inventor. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:uct:uconnp:2024-04 |
| By: | Alexandra Bykova (The Vienna Institute for International Economic Studies, wiiw); Viktrória Döme; Richard Grieveson (The Vienna Institute for International Economic Studies, wiiw); Francesca Guadagno (The Vienna Institute for International Economic Studies, wiiw); Doris Hanzl-Weiss (The Vienna Institute for International Economic Studies, wiiw); Nadya Heger (The Vienna Institute for International Economic Studies, wiiw); Niko Korpar (The Vienna Institute for International Economic Studies, wiiw); Sebastian Leitner (The Vienna Institute for International Economic Studies, wiiw); Jan Muś; Magdolna Sass; Bernd Christoph Ströhm (The Vienna Institute for International Economic Studies, wiiw); Andrea Szalavetz; Maryna Tverdostup (The Vienna Institute for International Economic Studies, wiiw); Zuzana Zavarská (The Vienna Institute for International Economic Studies, wiiw) |
| Abstract: | This study builds on our previous analyses of a new growth model for the EU member states of Central and Eastern European (CEE), focusing on fostering innovation-driven development. We aim to explain the types of innovation systems and policies that enhance domestic innovation capabilities, drawing on global best practices. A critical evaluation of the current innovation landscape in EU-CEE countries is conducted, particularly in the context of the green and digital transitions. The study assesses the strengths and weaknesses of both national innovation initiatives and opportunities provided by EU industrial and technology policy frameworks. Based on these insights, we offer actionable policy recommendations to promote innovation-driven growth, enhance productivity, and boost economic convergence over the medium term, taking into account the unique political and historical contexts of the EU-CEE countries. Additionally, we prepare country-specific briefing notes tailored to the individual development needs and opportunities of each nation. |
| Keywords: | Innovation policy, technological development, Central Eastern Europe, convergence |
| JEL: | O14 O31 O38 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:wii:rpaper:rr:476 |
| By: | Martin Obschonka; Moren Levesque |
| Abstract: | The rapid expansion of AI adoption (e.g., using machine learning, deep learning, or large language models as research methods) and the increasing availability of big data have the potential to bring about the most significant transformation in entrepreneurship scholarship the field has ever witnessed. This article makes a pressing meta-contribution by highlighting a significant risk of unproductive knowledge exchanges in entrepreneurship research amid the AI revolution. It offers strategies to mitigate this risk and provides guidance for future AI-based studies to enhance their collective impact and relevance. Drawing on Akerlof's renowned market-for-lemons concept, we identify the potential for significant knowledge asymmetries emerging from the field's evolution into its current landscape (e.g., complexities around construct validity, theory building, and research relevance). Such asymmetries are particularly deeply ingrained due to what we term the double-black-box puzzle, where the widely recognized black box nature of AI methods intersects with the black box nature of the entrepreneurship phenomenon driven by inherent uncertainty. As a result, these asymmetries could lead to an increase in suboptimal research products that go undetected, collectively creating a market for lemons that undermines the field's well-being, reputation, and impact. However, importantly, if these risks can be mitigated, the AI revolution could herald a new golden era for entrepreneurship research. We discuss the necessary actions to elevate the field to a higher level of AI resilience while steadfastly maintaining its foundational principles and core values. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.08890 |