R&D cluster quality measures and technology maturity

“Innovation indicators” strive to track the maturation of an emerging technology to help forecast its prospective development. One rich source of information is the changing content of discourse of R&D, as the technology progresses. We analyze the content of research paper abstracts obtained by searching large databases on a given topic. We then map the evolution of that topic's emphasis areas.The present research seeks to validate a process that creates factors (clusters) based on term usage in technical papers. Three composite quality measures—cohesion, entropy, and F measure—are computed. Using these measures, we create standard factor groupings that optimize the composite term sets and facilitate comparisons of the R&D emphasis areas (i.e., clusters) over time.The conceptual foundation for this approach lies in the presumption that domain knowledge expands and becomes more application specific in nature as a technology matures. We hypothesize implications for this knowledge expansion in terms of the three factor measures, then observe these empirically for the case of a particular technology—autonomous navigation. These metrics can provide indicators of technological maturation.