Demand-driven innovation assessment in medical and university eco-systems – DInnAMICS

The project combined quantitative and qualitative approaches. The quantitative part of the project relied on a unique micro-level data infrastructure built by gathering information for the University of Strasbourg, one of the largest French research-oriented universities, surrounded by an eco-system of research, high-tech industrial and technology transfer activities together with its cutting-edge scientific equipment facilities. Several bibliometric data (i.e., Web of Science, arXiv, …) and indicators (impact, novelty, orginality, …) complemented the analysis of the diffusion of technology – e.g., AI/ML – in the sciences. A qualitative investigation, through two in-depth case studies, completed the empirical analysis adding coherence and substance to the results. A first case study focusing on the University of Strasbourg; a second case study on medical innovations in two leading institutes in the university’s eco-system: the Research Institute against Digestive Cancer (IRCAD) and the Institute of Image Guided Surgery (IHU). Both quantitative and qualitative parts were complemented by agent-based models (ABM) which allow theoretical modelling and simulations of the observed dynamics.

The project proposes a new view on the socio-economic role of academic research by focusing on (i) the impact of university procurement on innovation; (ii) the diffusion and impact of AI/ML in science. We show that innovation can emerge as the interplay between specific demands and solutions designed to overcome technology bottlenecks, and that university demand is particularly important for innovations. We also show that some AI/ML technologies are rapidly reshaping the scientific practices and such technologies classify as “general method of invention”, with strong impact on scientific discovery, especially in the health sciences. The project has led to several peer-reviewed journal articles, strengthened the partnership among members, and resulted in a number of outreach activities to the general public

The project opens the door for future research. For instance, regarding the impact of AI/ML, our studies do not allow us to generalize outside the medical sciences, but they lay the groundwork for an emerging socioeconomic literature. Also, our findings may re-open analyses on the value of labour, whose marginal utility, when combined with data and AI-based technologies, could become infinite if we adopted a neoclassical setting. Or questions related to the value and the storage of data: even if they are infinitely reproducible, their physical storage raises concerns on environmental issues such as energy consumption. In this respect, data might represent the “new land” as a means of production, with inherent diminishing returns in storage.

Bianchini, S., Llerena, P., & Patsali, S. (2019). Demand-pull innovation in science: Empirical evidence from a research university’s suppliers. Research Policy, 48, 100005.

Bianchini, S., Müller, M., & Pelletier, P. (2022). Artificial intelligence in science: An emerging general method of invention. Research Policy, 51(10), 104604.

Patsali, S., Pezzoni, M., & Krafft, J. (2023). Healthcare Procurement and Firm Innovation: Evidence from AI-powered Equipment (No. 2023-05). Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), Université Côte d'Azur, France.

Borsato, A., & Lorentz, A. (2023). The Kaldor–Verdoorn law at the age of robots and AI. Research Policy, 52(10), 104873.

Borsato, A., & Lorentz, A. (2023). Data production and the coevolving AI trajectories: an attempted evolutionary model. Journal of Evolutionary Economics, 33(5), 1427-1472.

Patsali, S. (2024). University procurement-led innovation: Sources, procedures, and effects. Some field-study evidence. Technovation, 130, 102901.

A consensus among economists and practitioners recognises the contribution of the university-generated knowledge to economic growth. Academic knowledge plays a central role in generating disruptive discoveries leading to radical innovations. The paths through which university-generated knowledge reaches final users and creates value are traditionally examined through the commercialisation and spin-off angles. This focus, however, provides a simplistic view and fails to account for the diversity of the diffusion mechanisms of academic activities. This project aims to complete the existing understanding of the contribution of universities to the innovation process, embracing the impacts of university demand on the innovative performance of firms constituting part of the scientific value chain. This novel perspective considers scientists as lead-users of technologies indirectly bearing the costs of learning and refining associated with their development.
The quantitative study at the heart of this project reverts to a unique micro-level data infrastructure built by gathering information for the University of Strasbourg. As one of the largest French research-oriented universities, surrounded by an eco-system of research, high-tech industrial and technology transfer activities together with its cutting-edge scientific equipment facilities, this university represents an ideal subject for this project. We retrieved granular information on all purchases made in all its laboratories over four years. Each supplier based in France could be matched with accounting and innovation-related data. Such a data infrastructure should allow us, first, to exploit a wide set of innovation variables to benchmark suppliers and other businesses in terms innovative performance, while controlling for a large number of firm-level attributes. Second, a cautious study of the regional economic impact of university demand should contribute to the increasing policy interest for the local effects of academic activities. Third, the data infrastructure should allow us to extend the scale of our results to further academic collaborations and French firms filing patents on research related instruments and equipment, and evaluate the impact of collaborations with academic users on innovative performance at the national scale.
A qualitative investigation, through two in-depth case studies, completes the empirical analysis adding coherence and substance to the results. In line with the previous exercise, a first case study focusing on the University of Strasbourg should isolate the channels through which the innovative activity of its suppliers could have been influenced. A second case study focuses on medical innovations and on two leading institutes in the university’s eco-system: the Research Institute against Digestive Cancer (IRCAD) and the Institute of Image Guided Surgery (IHU). These institutes are at the heart of the medical technology campus in which medical, research and education facilities interact with incubators. The particular legal statuses of both institutes free them from the public procurements the university is complying to, benefiting from less restrictive interactions with their suppliers. This comparative scenario is clearly a value added for our research and should allow us to identify further incentives for user-producer interactions.
A theoretical contribution in line with the above empirical analysis completes the project. Whilst overlooked in traditional innovation theories, an existing literature drawing economic systems as complex and evolving systems formalises the interplay between demand dynamics and innovation patterns. In such a frame, the nature and the structure of demand shapes the technological trajectories, on the one hand, whilst demand adapts to novelty selecting among technological innovations. Within such a frame, scenarios of institutional settings ruling the scientists demand as well as STI policies would be tested.