Digital Transition and Technological Change

The adoption of digital technologies is the fastest technological change in history. The list of digital technologies today is not limited to artificial intelligence (AI), but includes Big Data, the internet of things (IoT), robotics, additive manufacturing (3D printing, etc.), virtual and augmented reality, cloud computing, and 5G networks.
These technologies act as powerful magnifiers of economic, social, and cultural connectivity, merging physical and virtual realities. Digital technologies create immense opportunities but also come with numerous challenges. How can we maximize the benefits and minimize the risks of digital transformation? What is the role of the different actors in our economic, political, and social systems?

Focus on...

Science and Artificial Intelligence. Our research seeks to understand the impact of AI-related technologies on
(i) the speed and novelty of scientific discoveries, (ii) the topology of scientific networks and their dynamics, and (iii) the causes and potential consequences of the privatization of AI research.
We develop real-time indicators for science, technology, and innovation (STI) by analyzing publicly available data from the websites of researchers, universities, and PROs, as well as social media. We provide new information to a broad audience in the scientific community and also contribute to better informed policy measures in the field of science, technology, and innovation.

Twin transition. Our research aims to understand the implications of the digital and green (twin) transition in terms of environmental impacts. We are constructing a database of both digital and green scientific (publications) and technological knowledge (patents), as well as on greenhouse gas (GHG) emissions in urban areas in the European Union (EU28), Norway, and Switzerland. We assess the environmental impact of a subset of digital technologies. Can the digital transition and the green transition work in tandem to reduce global greenhouse gas emissions or, conversely, will they engender competing trends?

New indicators. We are developing novel indicators for scientific research via the science of networks and machine learning, for example by taking into account the collaborative networks of authors, their stocks of knowledge and skills, and other elements. These indicators will complement studies of AI in science.

Latest publications

Local Ecosystem Open Innovation Intermediaries as Key Enablers for the Development of Incumbents’ Digital Technology Partnerships

BERTIN Clarice, SCHAEFFER Véronique (à paraître)

Proceedings of the 2022 IEEE 28th International Conference on Engineering, Technology and Innovation (ICE/ITMC) & 31st International Association For Management of Technology (IAMOT) Joint Conference, June 19-23, 2022

Blockchain technology and open innovationf or SMEs

GUICHARDAZ Rémy, PÉNIN Julien, SCHENK Eric (à paraître)

Systèmes d'Information et Management

Structural Transformations and Cumulative Causation: Towards an Evolutionary Micro-foundation of the Kaldorian Growth Model

LORENTZ André, CIARLI Tommaso, SAVONA Maria, VALENTE Marco (à paraître)

Handbook of Research Methods and Applications in Industrial Dynamics and Evolutionary Economics, U. Cantner, M. Guerzoni, S. Vannuccini (eds.), Edgar Elgar.

Chap. 14 - Finance through a social science lens

TADJEDDINE Yamina (à paraître)

Ecological Money and Finance. Exploring Sustainable Monetary and Financial Systems. New York, Palgrave Macmillan

Les transformations de la banque dans le temps long, une approche régulationniste

TADJEDDINE Yamina (2023)

S. Ringler, B. Séjourné et R. Bigot (eds.), Le banquier du XXIème siècle, CEPRISCA, collection colloques.