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on Innovation |
| By: | Jeannerat, Hugues; Butzin, Anna; Carvalho, Luís; Manniche, Jesper |
| Abstract: | While knowledge has long been central to theories of innovation-led regional development, its conceptualization within the emerging transformative innovation paradigm has remained largely implicit and undertheorized. This paper draws on insights from sustainability transitions, organizational learning, and higher education studies to develop a perspective on the action-oriented nature of knowledge, as it increasingly associates with the matters of directionality, materiality and structuration. Based on this, we articulate an idea of transformative knowledge through a triple lens, emphasising interdependencies between knowledge for action (goal- and mission-oriented), knowledge by action (generated through experimentation), and knowledge as action (situated in practice and everyday life). We apply this lens to discuss the outlines of transformative knowledge regions, proposing an expansion in the repertoire of regional innovation interventions. In doing so, the paper broadens the epistemic contours of knowledge in regional development and contribute to current debates on challenge- and mission-oriented regional innovation policy. |
| Keywords: | transformative learning, sustainability transitions, regional innovation policy, mission innovation, valuation |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:iatdps:324867 |
| By: | Philippe Aghion; Antonin Bergeaud; Timo Boppart; Peter J. Klenow; Huiyu Li |
| Abstract: | Firm price-cost markups may reflect (a) bigger step sizes from quality innovations that confer significant knowledge spillovers onto other firms, and/or (b) higher process efficiency than competing firms or other factors which bear no obvious knowledge externality. We write down an endogenous growth model with innovation step size and process efficiency as alternative sources of markup heterogeneity. Compared with the laissez-faire equilibrium, the social planner wants to reallocate research towards high step size firms but not high process efficiency firms. We then use price and productivity data across firms in French manufacturing to infer firm step sizes and process efficiency. We find that the planner could achieve faster growth by reallocating research toward high step size firms, and more so if high step size firms could freely license their innovations to high process efficiency firms. |
| JEL: | O31 O38 O41 O52 |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34190 |
| By: | Gavin Wang; Lynn Wu |
| Abstract: | Although AI has the potential to drive significant business innovation, many firms struggle to realize its benefits. We examine how the Lean Startup Method (LSM) influences the impact of AI on product innovation in startups. Analyzing data from 1, 800 Chinese startups between 2011 and 2020, alongside policy shifts by the Chinese government in encouraging AI adoption, we find that companies with strong AI capabilities produce more innovative products. Moreover, our study reveals that AI investments complement LSM in innovation, with effectiveness varying by the type of innovation and AI capability. We differentiate between discovery-oriented AI, which reduces uncertainty in novel areas of innovation, and optimization-oriented AI, which refines and optimizes existing processes. Within the framework of LSM, we further distinguish between prototyping focused on developing minimum viable products, and controlled experimentation, focused on rigorous testing such as AB testing. We find that LSM complements discovery oriented AI by utilizing AI to expand the search for market opportunities and employing prototyping to validate these opportunities, thereby reducing uncertainties and facilitating the development of the first release of products. Conversely, LSM complements optimization-oriented AI by using AB testing to experiment with the universe of input features and using AI to streamline iterative refinement processes, thereby accelerating the improvement of iterative releases of products. As a result, when firms use AI and LSM for product development, they are able to generate more high quality product in less time. These findings, applicable to both software and hardware development, underscore the importance of treating AI as a heterogeneous construct, as different AI capabilities require distinct organizational processes to achieve optimal outcomes. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.16334 |
| By: | Nicholas A. Carollo; Elior Cohen; Jingyi Huang |
| Abstract: | Using novel occupational data from the United States between 1860 and 1940, we evaluate Adam Smith’s core propositions regarding the division of labor, market size, innovation, and productivity. We document significant growth in occupational diversity during this period using new measures of labor specialization that we construct from workers’ self-reported job titles in the decennial census. Consistent with Smith’s hypotheses, we find strong empirical evidence that labor specialization increases with the extent of the market, is facilitated by technological innovation, and is ultimately associated with higher manufacturing productivity. Our findings also extend Smith’s narrative by highlighting the role of organizational changes and innovation spillovers during the Second Industrial Revolution. These results speak to the enduring relevance of Smith’s insights in the context of an industrializing economy characterized by large firms, complex organizational structures, and rapid technological change. |
| Keywords: | division of labor; occupations; productivity growth; technological change |
| JEL: | N11 O14 J24 D24 |
| Date: | 2025–09–03 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedkrw:101725 |