|
on Project, Program and Portfolio Management |
| By: | Link, Albert (University of North Carolina at Greensboro, Department of Economics) |
| Abstract: | Homophily studies have tended to focus on gender and race. Albeit that these comparisons are important, a focus on ethnic group relationships is conspicuously absent in the literature. In an effort to begin to fill this void, homophilic ethnic relationships among firm owners and publicly funded research project principal investigators is considered in this paper. Using data on Small Business Innovation Research (SBIR) program funded projects and Small Business Technology Transfer (STTR) program-funded projects, we find that the performance enhancing benefit of a homophilic relationship is dependent on the area of technology research. To the extent that the area of technology research is reflected in terms of the federal agency funding the research project, Department of Defense-funded projects are less enhanced by homophilic relationships than are research projects funded by other federal agencies. |
| Keywords: | homophily; SBIR; STTR; project R&D; program performance; |
| JEL: | H41 O22 O31 |
| Date: | 2023–08–22 |
| URL: | http://d.repec.org/n?u=RePEc:ris:uncgec:2023_006&r=ppm |
| By: | Chowdhury, Farhat (University of North Carolina at Greensboro, Department of Economics); Link, Albert (University of North Carolina at Greensboro, Department of Economics); Royalty, Anne (University of North Carolina at Greensboro, Department of Economics) |
| Abstract: | This paper presents a systematic empirical study of covariates associated with the success of NIH Phase I SBIR-funded research projects, where success is defined in terms of the small, entrepreneurial firm conducting the Phase I research subsequently receiving a follow-on Phase II research award. We find that women-owned firms are especially disadvantaged in this regard. Our findings suggest that SBIR program managers consider recommendations to overcome these disadvantages. Our recommendations could enhance the rate at which follow-on Phase II research projects are funded and possibly the rate at which the developed technologies are commercialized. |
| Keywords: | Small Business Innovation Research (SBIR) program; entrepreneurship; gender; program management; public sector; Phase I and Phase II research; technology development |
| JEL: | L38 O32 O38 |
| Date: | 2023–08–22 |
| URL: | http://d.repec.org/n?u=RePEc:ris:uncgec:2023_005&r=ppm |
| By: | Ajay K. Agrawal; John McHale; Alexander Oettl |
| Abstract: | We model a key step in the innovation process, hypothesis generation, as the making of predictions over a vast combinatorial space. Traditionally, scientists and innovators use theory or intuition to guide their search. Increasingly, however, they use artificial intelligence (AI) instead. We model innovation as resulting from sequential search over a combinatorial design space, where the prioritization of costly tests is achieved using a predictive model. We represent the ranked output of the predictive model in the form of a hazard function. We then use discrete survival analysis to obtain the main innovation outcomes of interest – the probability of innovation, expected search duration, and expected profit. We describe conditions under which shifting from the traditional method of hypothesis generation, using theory or intuition, to instead using AI that generates higher fidelity predictions, results in a higher likelihood of successful innovation, shorter search durations, and higher expected profits. We then explore the complementarity between hypothesis generation and hypothesis testing; potential gains from AI may not be realized without significant investment in testing capacity. We discuss the policy implications. |
| JEL: | O31 O33 |
| Date: | 2023–08 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:31558&r=ppm |