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on Project, Program and Portfolio Management |
| By: | Chowdhury, Farhat (University of North Carolina at Greensboro, Department of Economics); Link, Albert (University of North Carolina at Greensboro, Department of Economics); van Hasselt, Martijn (University of North Carolina at Greensboro, Department of Economics) |
| Abstract: | We describe public support for AI research in small firms using data from U.S. Department of Defense-funded SBIR projects. Ours is the first collection of firm-level project information on publicly funded R&D investments in AI. We find that the likelihood of an SBIR funded research project being focused on AI is greater the larger the amount of the SBIR award. AI-focused research projects are associated with a 7.6 percent increase in average award amounts. We also find suggestive evidence that the likelihood of an SBIR project being AI-focused is greater in smaller-sized firms. Finally, we find that SBIR-funded AI research is more likely to occur in states with complementary university research resources. |
| Keywords: | Artificial intelligence; machine learning; Department of Defense; Small Business Innovation Research program; agglomeration; |
| JEL: | O31 O38 |
| Date: | 2022–06–07 |
| URL: | http://d.repec.org/n?u=RePEc:ris:uncgec:2022_003&r= |
| By: | Chowdhury, Farhat (University of North Carolina at Greensboro, Department of Economics); Link, Albert (University of North Carolina at Greensboro, Department of Economics); van Hasselt, Martijn (University of North Carolina at Greensboro, Department of Economics) |
| Abstract: | A spatial distributional analysis of the population of Phase II research projects funded by the U.S. SBIR program in FY 2020 shows differences across states in projects focused on Artificial Intelligence (AI). AI is a relatively new research field, and this paper contributes to a better understanding of government support for such research. We find that AI projects are concentrated in states with complementary AI research resources available from universities nationally ranked in terms of their own AI research. To achieve a more diverse spatial distribution of AI-related technology development, the availability of complementary AI research resources must be expanded. We suggest that the National Science Foundation’s National AI Research Institutes represents an important step in this direction. |
| Keywords: | Artificial intelligence (AI); Public sector program management; Small Business Innovation Research (SBIR); Agglomeration; University research; |
| JEL: | H54 O31 O38 R11 |
| Date: | 2022–06–07 |
| URL: | http://d.repec.org/n?u=RePEc:ris:uncgec:2022_004&r= |
| By: | Fletcher, Joshua (RTI International); Howard, Eric (University of North Carolina at Greensboro, Department of Economics); Link, Albert (University of North Carolina at Greensboro, Department of Economics); O'Connor, Alan (University of North Carolina at Greensboro, Department of Economics) |
| Abstract: | This paper explores the impact that external sources of information have on the effectiveness of R&D in small, entrepreneurial firms. The effectiveness of R&D is measured in terms of two probabilities; the probability that a firm that received and completed a Phase I SBIR-funded research project is invited to submit a proposal for a Phase II award, and given such an invitation, the probability that a firm receives the Phase II award. Information from competitors is an important, in a statistical sense, covariate with the probability of being asked to submit a Phase II proposal whereas information from suppliers and customers in an important covariate with the probability of receiving a Phase II award. |
| Keywords: | Small Business Innovation Research (SBIR) program; small firms; entrepreneurial firms; R&D; knowledge sources; program evaluation; |
| JEL: | H43 L26 O31 O32 O38 |
| Date: | 2022–06–07 |
| URL: | http://d.repec.org/n?u=RePEc:ris:uncgec:2022_002&r= |
| By: | M. Hermse; I. Nijland; M. Picari; Mark Sanders |
| Abstract: | In this paper we present an index for coding new ventures, projects and firms as “smart-city†or not. The index is based on a systematic assessment of some 70+ definitions of the concept from the literature. Based on this analysis, we propose a 7-item coding scheme based on venture descriptions that are commonly available from public data sources. We identified two necessary and 5 “intensity†items and propose an algorithm that translates these items into a single smartc-city index (SCI) that expresses the degree to which an activity is contributing to smart city development in a score between 1 and 5. We then show the results of coding 759 new ventures in different datasets to illustrate that our index gives sensible results. Some 90 (11%) of these ventures could be classified as “smart city†in our sample, scoring an average of about 3.3, with significant variation around these averages that make intuitive sense. Our index can be used in a broad range of applications. |
| Keywords: | Urban Development, Smart City, Entrepreneurship, Innovation, DataCollection |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:use:tkiwps:2107&r= |
| By: | Vogel, Dominic |
| Abstract: | The development of the Future Combat Air System (FCAS) is Europe's most important defence project. Both technologically and militarily, the project has the potential to set new standards and revolutionise the use of air power. Politically, the multinational project is a litmus test for the extent to which Europe is capable of cooperating on security policy, developing its own capabilities and putting national interests to one side for this purpose. The success of the project rides to a great extent on Germany and France. However, the different perspectives and procedures of these two countries place FCAS at risk of collapse - a failure that would have serious disadvantages for all involved. |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:swpcom:22021&r= |