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on Innovation |
| By: | Johann Kortsch; Stefan Raff-Heinen; David Bendig; Martin Murmann; Colin Schulz; Fiona Murray |
| Abstract: | The term “deep-tech innovation” has attracted growing attention in research, policy, and practice, but it is applied inconsistently and lacks an agreed-upon definition. This limits cumulative knowledge building and blurs how deep-tech innovation relates to adjacent concepts. We address this gap by developing a framework that treats deep-tech innovation as a distinct object of inquiry. Using a multi-method design that combines a systematic, integrative, concept-centric literature review and semi-structured interviews with deep-tech founders, we identify twelve defining attributes structured across three levels: invention, venture, and ecosystem. At the invention level (the conceptual core), we specify six attributes: three foundational attributes that capture the scientific and technological basis of the invention, and three attributes that describe its characteristic exposure profile. The remaining six attributes capture recurring implications at the venture level (staged financing strategies, dual scientific and commercial maturation, and the multidisciplinary broadening of teams) and the ecosystem level (multi-actor interactions, specialized incubation support, and industrial de-risking and scaling partnerships). We use this framework to delineate the boundaries of deep-tech innovation, distinguish it from adjacent concepts, and propose an agenda for future research. |
| JEL: | O33 |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35255 |
| By: | Jongduk KIM (KOREA INSTITUTE FOR INTERNATIONAL ECONOMIC POLICY (KIEP)) |
| Abstract: | As U.S.–China competition intensifies, rivalry is also sharpening within the global knowledge system. This raises a central question: how does deepening U.S.–China tension reshape the global innovation network (GIN)—the cross-border web through which knowledge is created, shared, and recombined?<p> Governments worldwide have long emphasized the strategic importance of science, technology, and knowledge. Yet science and technology policy has often been approached primarily as a domestic agenda. In today’s global economic environment—marked by heightened strategic competition—this perspective is increasingly incomplete. The key issues are not only how much a country invests in innovation, but also how its position within global knowledge networks is changing, how geopolitical frictions affect knowledge flows, and what strategic direction policy should take in response.<p> This study therefore focuses on the cross-country and cross-industry structure of technology and innovation. Traditional policy approaches have tended to prioritize expanding total R&D spending. However, innovation cannot be generated by the R&D efforts of a single country, industry, or firm alone. A defining feature of innovation—often underappreciated in policy debate—is its networked and cumulative nature: advances in one sector shape the trajectory of innovation in others, often with long-run effects. Semiconductor progress, for example, does not remain within the semiconductor industry; it underpins innovation in telecommunications, computing, and automobiles. Conversely, knowledge developed in these downstream industries feeds back into semiconductor advances. These interactions occur not only within national borders but also across them, as knowledge diffuses through multiple channels linking countries and sectors. Accordingly, effective R&D allocation and innovation strategy require explicit attention to international and intersectoral “network effects.”<p> Against this backdrop, this World Economic Brief traces how Korea and other major economies—including the United States, China, Japan, and Europe—have evolved within the global innovation network over 2000–2020. Using this network perspective, we examine (i) changing patterns of interdependence and (ii) shifts in countries’ relative positions across key technologies. |
| Keywords: | global innovation network; technology; interdependence; US-China rivalry |
| Date: | 2026–03–04 |
| URL: | https://d.repec.org/n?u=RePEc:ris:kiepwe:022509 |
| By: | Robert, Marc |
| Abstract: | Artificial intelligence has moved from a specialist computational field into a general organisational, industrial and political question. It reshapes production, coordination, learning, and competition, while also unsettling the conceptual boundaries that have long separated innovation policy from business model analysis. This chapter argues that AI-enabled transformation should be understood through a combined lens that links mission-oriented innovation policy, dynamic capabilities, and business model change. The key claim is straightforward. AI does not matter only because it automates tasks or improves prediction. It matters because it reorganises how firms sense opportunities, create and distribute value, capture returns, and position themselves within wider ecosystems. At the same time, it reopens a foundational policy question about direction. If AI is treated as a neutral productivity tool, policy remains trapped within a narrow market-failure logic. If AI is treated as an infrastructural and strategic technology, policy must confront the harder issues of purpose, capability, coordination, and public value. Building on the arguments of mission-oriented innovation policy and the literature on digital transformation of business models, this chapter develops a critical review of how AI changes firms and how policy should respond. It argues for a framework in which public institutions shape directions of change, crowd in experimentation, discipline concentration, and build the collective capabilities required for broad-based adoption. The chapter concludes that effective AI transformation depends less on diffusion alone and more on the alignment between public missions, organisational capabilities, business model redesign, and democratic governance. |
| Keywords: | artificial intelligence, innovation policy, mission-oriented policy, dynamic capabilities, business models, digital transformation, ecosystems, public value |
| JEL: | O3 O32 O33 O38 |
| Date: | 2026–02–08 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:128691 |
| By: | Ursel Baumann; Zoë B. Cullen; Ester Faia; Annalisa Ferrando; Ricardo Perez-Truglia; Judit Rariga |
| Abstract: | How well does innovation diffuse across geographic boundaries? To shed light on this question, we present a large-scale field experiment involving 3, 300 firms across twelve European Union countries. We elicit firms' perceptions of the share of similar firms in their own country that had invested in artificial intelligence (AI), as well as the corresponding share among similar firms in Germany, France, and Italy. We randomly provide half of the sample with accurate information about both domestic and foreign AI investment. We show that firms substantially underestimate competitors' current AI investment, both domestically and abroad, and that they update their expectations about competitors' future AI investment in response to the information treatment. The treatment also causes a statistically significant increase in firms' own expected AI investment rate (p-value |
| JEL: | C93 D22 L21 O33 |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35314 |
| By: | Hung-Ju Chen (National Taiwan University); Hao Guo (Liaoning University); Chien-Yu Huang (International University of Japan); Yibai Yang (University of Macau) |
| Abstract: | This study explores the cross-country effects of inflation on innovation and technology transfer in a North-South variety-expansion model. We find that higher southern inflation causes a permanent increase in the North-South relative wage ratio, a temporary decrease in the northern innovation rate, and a permanent decrease in technology transfer. Higher northern inflation causes a permanent decrease in the North-South relative wage ratio, a temporary decrease in the northern innovation rate, and a permanent decrease (increase) in technology transfer if the southern population is sufficiently small (large). We calculate the model to the China-US data to justify the model implications. |
| Keywords: | Inflation; Innovation; North-South product cycles; R&D; Technology transfer |
| JEL: | E41 F43 O30 O40 |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:iuj:wpaper:ems_2026_10 |
| By: | Tugrul Temel, Tugrul |
| Abstract: | This paper develops a co-evolutionary network model to analyze how micro-level interactions among heterogeneous organizations generate macro-level structural patterns and performance outcomes in innovation systems. Organizations possess knowledge stocks, absorptive and distributive capacities, and adaptively rewire their connections. We integrate six key mechanisms---capacity-constrained knowledge flows, endogenous capacity accumulation, resource-based collaboration costs, innovation as a growth-structure interaction, strategic repositioning, and adaptive network rewiring---into a formal simulation framework. The model is calibrated using Approximate Bayesian Computation to match stylized facts from the innovation literature and employed in a structured scenario analysis spanning alternative policy-relevant regimes. Results reveal systematic trade-offs with important policy implications. Expanding connectivity without parallel capacity development yields limited gains; isolated capacity building amplifies inequality. In contrast, coordinated interventions targeting both network structure and organizational capabilities produce the most robust and equitable growth. Comparative analysis across four distinct economic environments demonstrates that intervention effectiveness is highly contingent on underlying frictions. The findings underscore the need for innovation policy that is explicitly network-aware and systemic, emphasizing bundled, context-sensitive interventions rather than isolated levers. The model provides a computational laboratory for exploring such policy design principles. |
| Keywords: | innovation systems; policy design; network analysis; graph-theoretic concepts; |
| JEL: | O31 O32 O38 |
| Date: | 2026–02–11 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:128018 |
| By: | Ouakil, Hicham; Liouaeddine, Mariem; Hosni, Mohamed; Saadi, Ayoub |
| Abstract: | The allocation and effectiveness of public subsidies for R&D and innovation are crucial issues for firms and policymakers. This study has two main objectives: first, to identify the determinants of access to public funding for R&D and innovation within firms; second, to quantitatively assess the causal impact of this support on firms’ R&D and innovation activities. We used data from the 2019 World Bank survey of 1, 096 Moroccan firms (www. enterprise surveys and applied two econometric approaches. For the first objective, we resorted to logistic regression based on a probit model. The results show that competitive firms, those investing in ICT, and those that employ graduates are more likely to receive public financial support. For the second objective, we used Propensity Score Matching (PSM) to control for selection bias and endogeneity. The results show that government financial support significantly favors the innovation inputs and outputs of Moroccan firms. |
| Keywords: | Public Support, Firm R&D and Innovation, Impact Assessment, Probit Model, Propensity Score Matching (PSM). |
| JEL: | D2 D22 O3 |
| Date: | 2026–01–30 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:128707 |
| By: | Ozili, Peterson K |
| Abstract: | This study examines the asymmetric effect of artificial intelligence (AI) innovation on economic growth in 50 countries from 2000 to 2020 using the quantile regression method. The findings reveal that AI innovation stimulates economic growth at low and middle tail of the economic growth distribution. Interaction analyses reveal that the use of AI innovation in the stock market stimulates economic growth while the use of AI innovation to support financial stability and international trade activities diminish economic growth. Asymmetric interaction analyses reveal that: AI innovation stimulates economic growth when countries are experiencing low growth rates; the use of AI innovation in the stock market stimulates economic growth when countries are experiencing high growth rates and in mid-growth emerging market and developing countries; the use of AI innovation to support financial stability activities diminish economic growth when countries are experiencing low growth rates and the use of AI innovation to support international trade activities diminish economic growth when countries are experiencing high growth rates. |
| Keywords: | Quantile regression, asymmetry, economic growth, artificial intelligence, innovation, internet, financial stability, unemployment, trade openness, endogenous growth theory |
| JEL: | O30 O31 O33 O47 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:128950 |
| By: | Fabbri Emanuele (European Commission - JRC); Spalazzi Annalisa |
| Abstract: | This technical report offers a preliminary exploration of how the design and implementation of Smart Specialisation Strategies (S3) might contribute to rural development in the Western Balkans. Although in the Western Balkans rural areas account for nearly 50% of the region's population they face critical challenges, such as economic stagnation, depopulation, and infrastructure deficits. As a key pillar for EU accession, S3 represents a place-based policy framework for boosting innovation ecosystems and European Research Area (ERA) integration. Here, S3 is also examined as a strategic framework to potentially mitigate the main challenges in rural areas by discovering untapped potential and promoting policy integration. As a result, this report assesses the degree to which S3 frameworks are tailored to rural needs and sheds a light on potential impact on regional development, specifically within the agriculture, tourism, and energy sectors. Based on a mixed-method approach of stakeholder interviews and document analysis, this research investigates the integration of rural areas within Western Balkan S3 strategies at both the national and macro-regional levels. The study evaluates the gaps and opportunities in policy design and implementation, specifically addressing the depth of rural integration and the identification of sectors capable of driving innovation and competitiveness. Furthermore, it examines how S3 facilitates territorial cohesion between urban and rural dimensions and fosters cross-border collaboration across the Western Balkan economies. Findings aim to provide actionable policy recommendations to enhance the effectiveness of S3 strategies in fostering development and innovation for rural development, through a place-based approach on the Western Balkans economies. By identifying opportunities, sectoral synergies, and areas requiring further attention, this report contributes to the broader goal of integrating rural development more effectively into national and regional innovation policies in the region, particularly with reference to the Green Agenda for the Western Balkans and the Innovation Agenda. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:ipt:iptwpa:jrc145560 |
| By: | Aries Eric (European Commission - JRC); Fereres Sonia (European Commission - JRC); Bellomo Nicolas (European Commission - JRC); Gonzalez Cuenca Jose (European Commission - JRC); Ferreira De Almeida Vanessa (European Commission - JRC); Tejedor Sanz Sara (European Commission - JRC); Chronopoulos Georgios (European Commission - JRC); Retsoulis Ioannis (European Commission - JRC); Roudier Serge (European Commission - JRC); Lambert Caroline (European Commission - JRC) |
| Abstract: | The objective of this publication, the Technical Report on Innovative Techniques (TRIT) of 2026 by the INCITE, is to serve as a strategic technological compass for Europe’s industrial transition. The report maps a comprehensive dataset of 563 demonstrator projects across Europe identified be-tween 2020 and 2025. The content focuses on energy-intensive industries (EIIs) and prioritises sectors with the highest environmental impact and strategic relevance for the Clean Industrial Deal. It was found that innovation in industry is highly concentrated in three ‘hard-to-abate’ sectors: Iron & Steel, Chemicals, and Cement, Lime, and Magnesia. Together, these sectors account for approximately 65% of all identified demonstrators. The report focuses on techniques that have reached a Technology Readiness Level (TRL) of 6–7 or higher. Currently, TRL 7 (33%) and TRL 9 (28%) represent the largest shares of the dataset, indicating a strong pipeline of solutions demonstrated in operational environments or ready for market. While decarbonisation is the dominant driver (present in 71% of projects), industrial pilots are increasingly engineered for synergistic benefits. These include depollution (42%) and circularity (30%), such as waste-to-feedstock conversion and the valorisation of industrial by-products. Several barriers to imple-mentation were identified, and were associated to challenges in permitting, infrastructure availa-bility, and financing risks with first-of-a-kind installations. The current dataset is strongly influenced by EU-funded projects (> 70% of the projects) due to robust reporting obligations. Private and national sector investments remain less visible and fragmented. Overall, the findings of this report serve as a foundation for future Best Available Techniques (BAT). By identifying mature, high-performance technologies now, INCITE ensures that upcoming EU norms accelerate the deployment of cleaner technologies. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:ipt:iptwpa:jrc146559 |