Peer-reviewed publications and patents serve as important signatures of knowledge generation, and therefore the authors and their organizations can represent agents of intellectual transformation. Accurate tracking of these players enables scholars to follow knowledge evolution. However, while author name disambiguation has been discussed extensively, less is known about the impact of organization name on bibliometric studies. We expand here on the recently defined phenomenon of onomastic profusion, high-frequency words used in organization names for semantic reasons, and thus contributing a non-random source of error to bibliographic studies. We use the Small Business Innovation Research (SBIR) Phase I awardees of the National Aeronautics and Space Administration (NASA) as a use case in the field of engineering innovation. We find that firms in California or Massachusetts experience a six percent decrease in the likelihood of using the word Technologies in their names. Furthermore, use of the words Research and Science is linked to doubling the number of awards. We illustrate that, in aggregate, firms executing rational strategic naming decisions can create deterministic bibliometric challenges. @en

Bibliographic name disambiguation is an major semantic challenge, but critical to social sciences studies of important intellectual assets. Here we contribute to innovation research in several ways. We show a significant synonym problem in author names and discuss how a pre-processing heuristic step standardizing name variants helps, but homonyms generated with Chinese names are particularly difficult to resolve and manifest in an associated location list. Here we identify a new phenomenon of "onomastic profusion," the frequent use of certain words in firm names for semantic reasons that can confound disambiguation clustering algorithms. We illustrate these concerns with Patentopia, our customized platform accessing the PatentsView portal for the United States Patent and Trademark Office database and available for free academic use. This multi-stage system uses heuristics in concert with the PatentsView clustering process and reports meta-data to further assist analysis. As highly relevant use cases, we illustrate system performance with data derived from two important public innovation programs, I-Corps and Small Business Innovation Research (SBIR), and we close with implications for bibliometric analysis of current patent data.@en

The Small Business Innovation Research (SBIR) program provides federally funded research awards to companies with 500 or fewer employees. We explore the differential effects of the National Aeronautics and Space Administration SBIR program on firms of various sizes on their future patenting activity. Using propensity score matching, we construct comparable samples of selected and non-selected Phase II SBIR applicants by firm size. We then estimate the effect of selection for the matched sample on the probability of forward patent activity and conditional on any forward patenting, the count of patents within three years of the proposal. While firms with fewer than 10 employees, are least likely to patent, their probability of patenting is positively affected by receiving a Phase II award. We find sparse evidence of corresponding increase for larger firms. Nor do we find any evidence that a Phase II award impacts the conditional number of forward patents in the three years following the award. These data suggest that the Phase II award serves to advance the smallest teams "over the hump" to creating a potential source of competitive advantage.

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