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on Innovation |
| By: | Hanming Fang; Xian Gu; Hanyin Yan; Wu Zhu |
| Abstract: | We develop a high-precision classifier to measure artificial intelligence (AI) patents by fine-tuning PatentSBERTa on manually labeled data from the USPTO’s AI Patent Dataset. Our classifier substantially improves the existing USPTO approach, achieving 97.0% precision, 91.3% recall, and a 94.0% F1 score, and it generalizes well to Chinese patents based on citation and lexical validation. Applying it to granted U.S. patents (1976–2023) and Chinese patents (2010–2023), we document rapid growth in AI patenting in both countries and broad convergence in AI patenting intensity and subfield composition, even as China surpasses the United States in recent annual patent counts. The organization of AI innovation nevertheless differs sharply: U.S. AI patenting is concentrated among large private incumbents and established hubs, whereas Chinese AI patenting is more geographically diffuse and institutionally diverse, with larger roles for universities and state-owned enterprises. For listed firms, AI patents command a robust market-value premium in both countries. Cross-border citations show continued technological interdependence rather than decoupling, with Chinese AI inventors relying more heavily on U.S. frontier knowledge than vice versa. |
| JEL: | C55 G14 O31 O33 O34 O57 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35022 |
| By: | Dugoua, Eugenie; Noailly, Joëlle |
| Abstract: | This paper examines the patterns and mechanisms of global clean technology diffusion over the last two decades. We document four stylized facts: uneven sectoral progress favoring power and light transport; China’s dominance in innovation and manufacturing; the role of modularity in driving cost declines; and limited adoption in developing economies. Through case studies of solar, electric vehicles, and hydrogen, we analyze how policy and infrastructure enable scale. Finally, we assess emerging challenges for the next phase of diffusion, including critical mineral constraints, artificial intelligence, and geopolitical fragmentation. |
| Keywords: | clean technology diffusion; climate change mitigation; renewable energy; industrial policy; solar photovoltaics; electric vehicles; hydrogen |
| JEL: | O33 Q55 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:137824 |
| By: | Claudia Collodoro (Dipartimento di Politica Economica, DISCE, Università Cattolica del Sacro Cuore, Milano, Italy); Lucrezia Fanti (Dipartimento di Politica Economica, DISCE, Università Cattolica del Sacro Cuore, Milano, Italy - Instituto di Economia, Scuola Superiore Sant’Anna, Pisa, Italy); Jacopo Staccioli (Dipartimento di Politica Economica, DISCE, Università Cattolica del Sacro Cuore, Milano, Italy - Instituto di Economia, Scuola Superiore Sant’Anna, Pisa, Italy); Maria Enrica Virgillito (Dipartimento di Politica Economica, DISCE, Università Cattolica del Sacro Cuore, Milano, Italy - Instituto di Economia, Scuola Superiore Sant’Anna, Pisa, Italy) |
| Abstract: | This work provides a comprehensive large-scale analysis of artificial intelligence-based worker management (AIWM) systems from an industry-wide exposure perspective focusing on traditional industries. We begin by examining the knowledge production underlying these workforce management tools and leverage technology patent-classification to identify their dynamics and specific features. For this purpose, we use patent data retrieved from Orbis Intellectual Property covering the years 1975 to 2022, considering patents filed with both the EPO and the USPTO. Furthermore, to identify patents related to AIWM heuristics, we retrieve their full text from Google Patents and conduct a textual analysis using a dependency parsing algorithm. Finally, using the dictionary of human tasks provided by O*NET, we construct a measure of exposure to AIWM systems for individual human tasks and occupations. Linking the technological and labour market domains, we find that the professions most exposed to AIWM systems are those at the top of organisational hierarchies. |
| Keywords: | Artificial Intelligence Worker Management, Sector-level Analysis, Patenting Activity, Techno-organisational Change |
| JEL: | O14 O33 |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:ctc:serie5:dipe0056 |
| By: | Marta Ballatore (PSB - Paris School of Business - HESAM - HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université); Hélène Bussy-Socrate (CNAM Paris - Centre d'enseignement Cnam Paris - Cnam - Conservatoire National des Arts et Métiers [Cnam], LIRSA - Laboratoire interdisciplinaire de recherche en sciences de l'action - Cnam - Conservatoire National des Arts et Métiers [Cnam]) |
| Abstract: | Digital transformation is no longer confined to industrial and technology-intensive ecosystems; it is now impacting traditional and non-technology-intensive industries as well. Current literature often overlooks these contexts and fails to adequately explain the disruptive processes involved, particularly the role of complementors in driving digital transformation alongside traditional orchestrators who are typically resistant to technology. Based on an emerging conceptual framework that combines innovation and digital transformation to explain the latter's effects and changes, this paper explores the role of tech companies in the wine industry, with a focus on the interconnection between green-oriented innovation and digital transformation. Our preliminary findings identify three main strategies adopted by tech companies in wine ecosystems to foster digital transformation: adopt new dynamics to favour relationship between the actors, opt for shared values, and orchestrate a technologydriven community to joint efforts in transforming the ecosystem. |
| Keywords: | Wine, Servitization, Innovation, Digital Transformation, Ecosystem, Ecosystem Digital Transformation Innovation Servitization Wine |
| Date: | 2024–10–10 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05466936 |
| By: | Pulito, Giuseppe (ROCKWOOL Foundation Berlin); Pytlikova, Mariola (CERGE-EI Prague); Schroeder, Sarah (Aarhus University and Ratio Institute); Lodefalk, Magnus (Orebro University, Ratio Institute, GLO) |
| Abstract: | Using surveys of Danish firms and individuals linked to employer–employee administrative data, we analyze AI adoption across technologies, business functions, and workers. We show that AI adoption is driven primarily by firm capacities rather than performance. Adoption is strongly associated with firm size, digital infrastructure, and workforce composition, particularly education and STEM intensity, while productivity and capital intensity explain little of the variation. Conditional on AI adoption, larger and more digitally mature firms deploy advanced technologies more broadly. Moreover, AI technologies diffuse across multiple business functions while other advanced technologies remain function-specific. Individual-level evidence mirrors these patterns and points towards workforce readiness as a key determinant of AI adoption. Finally, commonly used occupational AI exposure measures vary substantially in their ability to predict actual adoption, with benchmark-based measures outperforming patent-based and LLM-focused alternatives. These findings show that treating AI as a monolithic category obscures economically meaningful variation in who adopts, what they deploy, and how well existing measures capture it. |
| Keywords: | Artificial Intelligence, technology adoption, digitalisation, human capital, AI exposure measures |
| JEL: | D24 J23 J62 O33 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18515 |
| By: | Lodefalk, Magnus (The Ratio Institute); Löthman, Lydia (The Ratio Institute); Koch, Michael (The Ratio Institute); Engberg, Erik (The Ratio Institute) |
| Abstract: | We show that the age composition of employment within Swedish employers shifts after the arrival of generative AI, with no corresponding reduction in aggregate labour demand. Using 4.6 million job advertisements from Sweden's largest recruitment platform, we find that the broad decline in postings since 2022 aligns with monetary tightening rather than AI, exploiting Sweden's seven-month gap between the Riksbank's first rate hike and the launch of ChatGPT as a timing test. We then use full-population employer–employee register data and an employer-level difference-in-differences design to estimate how AI exposure affects employment composition across six age groups. An event study documents an accelerating decline in employment of 22–25-year-olds in high-AI-exposure occupations, reaching 5.5 per cent by early 2025 relative to less exposed occupations within the same employers, while employment of workers over 50 rose by 1.3 per cent. The widening age gradient suggests that generative AI reshapes hiring composition rather than aggregate demand, with the adjustment burden falling disproportionately on entry-level workers. |
| Keywords: | Generative artificial intelligence; Job postings; Labour demand; Employment composition; Monetary policy |
| JEL: | J23 J24 O33 |
| Date: | 2026–03–16 |
| URL: | https://d.repec.org/n?u=RePEc:hhs:ratioi:0388 |
| By: | Wagner, Joachim (Leuphana University Lüneburg) |
| Abstract: | The use of advanced technologies like artificial intelligence, robotics, or smart devices will go hand in hand with, among others, higher productivity, higher product quality, more exports and better chances to survive any crisis. Better firms tend to use advanced technologies. Information on firm level determinants of adoption of these technologies, therefore, is important to inform industrial policies. This paper uses firm level data for manufacturing enterprises from 38 countries collected in 2025 to shed further light on this issue by investigating the link between the use of advanced technologies and firm characteristics. Applying a new machine-learning estimator, Kernel-Regularized Least Squares (KRLS), which does not impose any restrictive assumptions for the functional form of the relation between use of advanced technologies, firm characteristics and any control variables, we find that firms which use advanced technologies tend to be larger and more innovation orientated, while firm age does not matter. |
| Keywords: | advanced technologies, firm characteristics, Flash Eurobarometer 559, kernel-regularized least squares (KRLS) |
| JEL: | D22 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18499 |