From Detection to Prioritisation A Computational Framework for Identifying Commercially Relevant White Spaces in the Science–Technology Nexus

Details

Supervisors

Cassiman, Bruno

Faculty

Louvain School of Management

Degree label

Master [120] : ingénieur de gestion, à finalité spécialisée

Abstract

enScience guides technological search, but it does not dictate where invention will take root. Some scientific domains cross rapidly into the patent landscape; others remain structurally disconnected from it, even when they carry knowledge of plausible commercial value. These uncovered regions, referred to as white spaces, matter to firms scouting external science, to investors positioning ahead of technological consolidation, and to policymakers directing innovation funding. Yet existing approaches either detect such gaps without judging their commercial promise, or score individual papers without locating them within the broader topology of the science-technology nexus. This thesis asks whether commercially relevant white spaces can be detected, ranked, and validated within a single framework, and tests the answer on the alternative-fuels sector, a domain in which multiple pathways compete without a dominant design. Working with roughly 58,600 scientific papers and 34,900 patents, the framework first identifies which scientific clusters sit far enough from the patent frontier to qualify as genuine white spaces, then ranks them by the concentration of commercially promising research they contain. Twenty white spaces emerge from this process, and a strategic matrix positions each along both dimensions. The result reveals an unexpected pattern: structural absence from the patent frontier tells us little about where commercial promise concentrates, and only one cluster scores high on both. A historical test, in which the framework was rerun on data available before 2019, shows that its ranking anticipated where patent-citation attention would subsequently strengthen, an outcome drawn from evidence outside the framework’s own construction. The thesis therefore reframes white spaces as a two-dimensional phenomenon, defined by structural absence and characterised by commercial concentration, and shows that distinguishing the two dimensions is what makes the resulting prioritisation strategically informative. The framework does not replace the judgment of firms, investors, or policymakers; it narrows the field within which such judgment becomes worthwhile.