|
on Innovation |
| By: | Ralf Martin; Arjun Shah; Anna Valero; Dennis Verhoeven |
| Abstract: | Quantifying spillovers from scientific knowledge to technology is important for understanding the social returns to science and for designing policy. A key challenge is how to credit scientific work with the value generated in downstream technologies when ideas diffuse through chains of follow-on research. We propose a new measure - Science Rank - that uses the combined patent and paper citation network to assign a share of the private value of patented inventions to the scientific papers they directly or indirectly rely on. Validated against various types of scientific awards, the measure substantially outperforms direct patent-to-paper citation counts in identifying influential science. We document large heterogeneity in spillovers across countries, disciplines, and institutions. The US emerges from our analysis as a powerhouse of science spillovers, benefiting both domestic and foreign technology development. We apply our methodology to examine how different countries and individual institutions contribute to innovation that addresses global challenges such as climate change or more equal economic development. We find that a relatively large share of the total value generated by research in Lower and Middle Income Country (LMIC) feeds into climate change related innovation. We also highlight countries and institutions that are making particular contributions to LMIC innovation. |
| Keywords: | Technological change, growth, patents, spillovers, climate change, economic development |
| Date: | 2026–03–18 |
| URL: | https://d.repec.org/n?u=RePEc:cep:cepdps:dp2165 |
| By: | Giovanni Guidetti; Riccardo Leoncini; Mariele Macaluso |
| Abstract: | This paper studies patenting trends in artificial intelligence (AI) and robotics from 1980 to 2019. We introduce a novel distinction between traditional robotics and robotics embedding AI functionalities. Using patent data and a time-series econometric approach, we examine whether these domains share common long-run dynamics and how their trajectories differ across major innovation systems. Three main findings emerge. First, patenting activity in core AI, traditional robots, and AI-enhanced robots follows distinct trajectories, with AI-enhanced robotics accelerating sharply from the early 2010s. Second, structural breaks occur predominantly after 2010, indicating an acceleration in the technological dynamics associated with AI diffusion. Third, long-run relationships between AI and robotics vary systematically across countries: China exhibits strong integration between core AI and AI-enhanced robots, alongside a substantial contribution from universities and the public sector, whereas the United States displays a more market-oriented patenting structure and weaker integration between AI and robots. Europe, Japan, and South Korea show intermediate patterns. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.05034 |
| By: | Diego Ocampo-Corrales (AQR-IREA, Department of Econometrics, Statistics and Applied Economics, Universitat de Barcelona, Spain.); Rosina Moreno (AQR-IREA, Department of Econometrics, Statistics and Applied Economics, Universitat de Barcelona, Spain.) |
| Abstract: | This paper investigates the role of regions’ recombinatorial technological capacity in shaping the technological space. To do so, we identify novel combinations of technologies and track their evolution by tracing all subsequent inventions that incorporate the same combination. Building on the concepts of relatedness and geographical proximity, we focus on the relevance of the technological antecedents of a pair of technologies combined for the first time in determining their success. This is due through the estimation of the likelihood of a new technological combination eventually becoming embedded within the broader knowledge space. Using patent data from 1976 to 2022 in the case of the European regions, we find strong evidence that a higher degree of relatedness between the technological antecedents of the two combined technologies significantly increases the likelihood that the combination will be reused in future inventions. Additionally, we find that the success of a new combination also benefits from the presence of dissimilar knowledge—not directly involved in the combination’s antecedents but accessible within the surrounding technological environment. In these cases, the greater the relatedness between the new invention’s antecedents and the broader regional knowledge base, the more likely it is to generate a high number of follow-on inventions and contribute meaningfully to the formation of the technological space. |
| Keywords: | New Combination of Technologies; Regional Innovation; European Regions; Recombination Capacity; Knowledge Space; Technological Antecedents. JEL classification: O18; O31; O33; R11. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:ira:wpaper:202526 |
| By: | Julia Müller; Thorsten Upmann |
| Abstract: | This paper develops a dynamic model in which the productivity of joint research governs strategic investment timing in innovation races. Departing from the standard assumption that discovery rates scale proportionally with the number of active firms, we allow research to exhibit decreasing or increasing returns, thereby endogenizing the aggressiveness of innovation competition. We show that returns to joint research determine whether innovation races exhibit preemption or coordination. When research efforts are substitutes, follower entry is unattractive, generating a first-mover advantage and a preemption equilibrium. When complementarities are sufficiently strong, the gains from early investment vanish and firms invest simultaneously. The model thus identifies a regime shift in innovation races: competition accelerates investment under decreasing returns but promotes coordinated entry under increasing returns. These findings highlight the research technology as a central determinant of market dynamics and provide a unified perspective on heterogeneous patterns of innovation. |
| Keywords: | innovation races, R&D competition, strategic investment timing, preemption and coordination, research complementarities |
| JEL: | O31 D81 C73 L13 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12552 |
| By: | Konstantin Remke (Métis Lab EM Normandie - EM Normandie - École de Management de Normandie = EM Normandie Business School); Sönke Mestwerdt (Alliance MBS - Alliance Manchester Business School - University of Manchester [Manchester]); Matthias Mrożewski (ESCP Europe Campus Berlin - ESCP Europe - Ecole Supérieure de Commerce de Paris); Maximilian Tigges (ESCP Europe Campus Berlin - ESCP Europe - Ecole Supérieure de Commerce de Paris); René Mauer (ESCP Europe Campus Berlin - ESCP Europe - Ecole Supérieure de Commerce de Paris) |
| Abstract: | The circular economy (CE) has emerged as a promising paradigm to address environmental challenges through resource efficiency, product life cycle transformation, and business model innovation. Yet, implementing circular business models remains challenging due to the complexity of coordinating stakeholders and managing large volumes of data, especially across the different life cycle phases. Artificial intelligence (AI) offers great potential to address these challenges. However, prior research has predominantly focused on a generic and undifferentiated application of AI within the CE, neglecting the complexity and diversity across life cycle phases. Our study seeks to address this. Drawing on the external enablement framework, we conduct a qualitative study comprising 57 semi‐structured interviews with domain experts and AI‐based ventures operating in the CE. In demonstrating how AI facilitates business model innovation by enabling navigation through circular product life cycles, our findings advance three key areas. We extend the external enablement framework by showing that external enablers can interact synergistically. Specifically, we develop a model that illustrates how the underlying dynamics of the CE and AI jointly enable business model innovation and subsequent new venture creation through complementary mechanisms, a dynamic we term dual enablement . We show how AI enhances life cycle efficiency, fosters systems interconnectivity, and supports holistic decision‐making, engendering two novel categories of business models, which we coin AI‐based business models for complexity navigation and dual enablement. |
| Keywords: | Artificial intelligence, Circular economy, Business model innovation, External enablement, Grand challenges |
| Date: | 2026–01–22 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05547705 |
| By: | Drydakis, Nick |
| Abstract: | Artificial intelligence (AI) is increasingly recognised as a key driver of business innovation, yet its adoption among small and medium-sized enterprises (SMEs) varies considerably. This study examines whether AI Capital, defined as AI-related knowledge, skills and capabilities, is associated with business innovation among SMEs in England. Using a two-wave longitudinal panel dataset comprising 504 observations from SMEs collected in 2024 and 2025, the study develops and validates a 45-item AI Capital of Business scale. Business innovation is measured across five dimensions: product and service innovation, process innovation, technology adoption, market and customer engagement, and organisational culture and strategy. Regression models, including pooled OLS, Random Effects, and Fixed Effects specifications, are employed. The findings reveal a robust positive association between AI Capital and business innovation across all model specifications. This association holds across all business innovation dimensions and remains consistent for SMEs with differing levels of financial performance, size, and operational maturity. Each component of AI Capital independently exhibits a positive association with business innovation outcomes. The results highlight the central role of AI Capital in enabling SMEs to translate AI adoption into tangible business innovation. From a policy perspective, the findings indicate the value of targeted interventions that prioritise AI upskilling, organisational capability development, and accessible support mechanisms to promote inclusive and sustainable AI-driven business innovation among SMEs. |
| Keywords: | Artificial Intelligence, Artificial Intelligence Capital, Business Innovation, Innovation, SMEs |
| JEL: | O31 O33 O32 L26 L25 M15 D83 J24 O14 O39 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:glodps:1723 |
| By: | Dubey, Rohan (National Institute of Public Finance and Policy); Chakraborty, Lekha (National Institute of Public Finance and Policy) |
| Abstract: | The rapid diffusion of artificial intelligence (AI) has generated widespread expectations of substantial productivity gains, yet empirical evidence on its macroeconomic effects remains limited. This paper provides across-country empirical assessment of the relationship between AI adoption and labour productivity using a newly constructed panel dataset covering G20 over the period 2012–2023. We develop two composite indices of AI adoption that capture both relative cross-country positioning and within-country evolution overtime, drawing on indicators of investment, innovation, computational capacity, and scientific output. Employing panel regressions with country and time fixed effects and a rich set of macroeconomic controls, we find evidence of a statistically significant short-run effect of AI diffusion on aggregate labour productivity. These results are robust across alternative index constructions and model specifications. We then extend our analysis to human development indicators and find that AI diffusion is positively associated with UNDP the Human Development Index (HDI). At the sametime, the magnitude and dynamics of the estimated effects suggest that productivity gains from AI are likely to materialize gradually and depend on complementary investments and structural conditions. Beyond the regression results, the indices developed in this paper provide a transparent framework for tracking AI diffusion and identifying areas of AI preparedness and technological lag, offering useful insights for future research and policy design. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:npf:wpaper:26/445 |
| By: | Panle Jia Barwick; Hongyuan Xia; Tianli Xia |
| Abstract: | This paper examines China’s transition from pharmaceutical “free rider” to global innovator over the last decade. In 2010, China accounted for less than 8% of global clinical trials; by 2020, it had surpassed the US in annual registered clinical trial volume. To study this transformation, we compile a comprehensive, synchronized database spanning the pharmaceutical drug development supply chain, covering scientific publications, clinical trials, drug development milestones for China, the U.S., and Europe, alongside drug sales and government policies over the same period. We provide strong evidence that China’s rise was primarily driven by the National Reimbursement Drug List (NRDL) reform, which dramatically expanded the effective market size for innovative drugs. We document a sharp rise in both the quantity (86% increase) and novelty of drug trials post reform, with growth concentrated in reform-exposed disease categories, first- or best-in-class drugs, and among domestic firms. A decomposition exercise reveals that the NRDL reform accounts for 43% of the growth in oncology trial activity, nearly doubling the combined contribution of upstream knowledge accumulation and talent flows (24%), while other government policies play a minor role. Finally, dynamic gains from induced innovation exceed the reform’s static gains in consumer access to innovative drugs by threefold, underscoring the importance of accounting for the reform’s long-run effects on innovation incentives in addition to near-term improvements in drug affordability. |
| JEL: | I18 L65 O31 O38 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34977 |
| By: | Davide Antonioli; Elisa Chioatto; Giovanni Guidetti; Riccardo Leoncini; Mariele Macaluso |
| Abstract: | This paper analyses how firms' skill development strategies affect their propensity to introduce innovation. We develop an adjustment-cost framework that links human capital theory and institutionalist and evolutionary approaches, considering innovation as an activity that entails costs in labour adjustment arising either from the training activities of workers or the recruitment of skilled employees. Using a two-wave panel of Italian manufacturing firms observed in 2017-2018 and 2019-2020, we analyse firms' adoption of total, product, process, and circular innovation as a function of internal training practices and of external skills acquisition. Overall, the empirical analysis confirms the expected positive relationship between training and innovation, while also revealing important nuances in the workforce upskilling strategies required for different types of innovation. Moreover, while training activities and skills development are essential across all forms of innovation, our findings indicate that internal training is particularly effective in supporting the implementation of circular innovations. By contrast, external recruitment appears to be consistently necessary whenever innovations are introduced, regardless of their type. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.05153 |
| By: | Lerner, Josh (Harvard University and NBER); Liu, Junxi (University of Warwick); Moscona, Jacob (MIT); Yang, David Y. (Harvard University, BREAD, J-PAL and NBER.) |
| Abstract: | Global innovation and entrepreneurship have traditionally been dominated by a handful of high-income countries, especially the US. This paper investigates the international consequences of the rise of a new hub for innovation, focusing on the dramatic ascent of high-potential entrepreneurship and venture capital in China. First, using comprehensive global data, we show that as the Chinese venture industry rose in importance in certain sectors, entrepreneurship increased substantially in other emerging markets. Using a broad set of country-level economic indicators, we find that this effect was driven by country-sector pairs most similar to their counterparts in China. The estimates are similar when exploiting Chinese sector-specific policies that affected the likelihood of entrepreneurship. Second, turning to mechanisms, we show that the baseline findings are driven by local investors and by new firms that more closely resemble existing Chinese companies. Third, we find that this growth in emerging market investment had wide-ranging economic consequences, including a rise in serial entrepreneurship, cross-sector spillovers, innovation, and broader measures of socioeconomic well-being. Together, our findings suggest that many developing countries benefited from the more “appropriate” businesses and technology that resulted from a rise of an innovation hub in an emerging economy. |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:wrk:warwec:1608 |