by Anita Mezzetti, Loïc Maréchal, Dimitri Percia David, William Blonay, Sébastien Gillard, Michael Tsesmelis, Thomas Maillart, Alain Mermoud | Sep 20, 2023 | Publications, Scientific Articles
Abstract We introduce a novel recursive algorithm that analyzes and ranks the relative influence that companies and technologies have in a technology landscape. The algorithm also incorporates exogenous variables that reflect investor preferences. The results provide...
by Dimitri Percia David, William Blonay, Sébastien Gillard, Thomas Maillart, Alain Mermoud, Loïc Maréchal, Michael Tsesmelis | Sep 20, 2023 | Publications, Scientific Articles
Abstract We propose a reproducible, automated, scalable, and free method for automated bibliometric analysis that requires little computing power. We explain how firms can use this method with open source data from public repositories to generate unbiased insights...
by Ciarán Bryce, Daniel Celeny, André Charneca, Tugrul Daim, Marc Greuter, Andrei Kucharavy, Loïc Maréchal, Natalia Ostapuk, Dimitri Percia David, Alon Shahak, Maxime Würsch, Haydar Yalcin | Jun 23, 2023 | Podcasts
About Members of the Swiss Technology Observatory community met in Sachseln (Obwalden) in end of June 2023 and gave presentations on key topics such as scouting and technology and market monitoring. The event brought together many participants from the DDPS,...
by Sarah Ismail, Alain Mermoud, Loïc Maréchal, Samuel Orso, Dimitri Percia David | Apr 20, 2023 | Publications, Scientific Articles
Abstract This paper presents a novel science indicator to identify, analyze, and capture technology trends based on Wikipedia page views and OpenAlex presented at STI2022. Our webometric methodology is grounded in open science practices and applied to crowd-sourced,...
by Dimitri Percia David, Santiago Anton Moreno, Loïc Maréchal, Thomas Maillart, Alain Mermoud | Mar 28, 2023 | Publications, Scientific Articles
Abstract We use a unique database of digital, and cybersecurity hires from Swiss organizations and develop a method based on a temporal bi-partite network, which combines local and global indices through a Support Vector Machine. We predict the appearance and...
