Title |
Modeling technology innovation: How science, engineering, and industry methods can combine to generate beneficial socioeconomic impacts
|
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Published in |
Implementation Science, May 2012
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DOI | 10.1186/1748-5908-7-44 |
Pubmed ID | |
Authors |
Vathsala I Stone, Joseph P Lane |
Abstract |
Government-sponsored science, technology, and innovation (STI) programs support the socioeconomic aspects of public policies, in addition to expanding the knowledge base. For example, beneficial healthcare services and devices are expected to result from investments in research and development (R&D) programs, which assume a causal link to commercial innovation. Such programs are increasingly held accountable for evidence of impact-that is, innovative goods and services resulting from R&D activity. However, the absence of comprehensive models and metrics skews evidence gathering toward bibliometrics about research outputs (published discoveries), with less focus on transfer metrics about development outputs (patented prototypes) and almost none on econometrics related to production outputs (commercial innovations). This disparity is particularly problematic for the expressed intent of such programs, as most measurable socioeconomic benefits result from the last category of outputs. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 1 | 13% |
Mexico | 1 | 13% |
Unknown | 6 | 75% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 4 | 50% |
Science communicators (journalists, bloggers, editors) | 2 | 25% |
Practitioners (doctors, other healthcare professionals) | 1 | 13% |
Scientists | 1 | 13% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 2 | <1% |
Brazil | 2 | <1% |
Colombia | 1 | <1% |
Germany | 1 | <1% |
Ireland | 1 | <1% |
Canada | 1 | <1% |
United Kingdom | 1 | <1% |
Unknown | 202 | 96% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Master | 39 | 18% |
Student > Ph. D. Student | 28 | 13% |
Researcher | 23 | 11% |
Student > Doctoral Student | 17 | 8% |
Other | 13 | 6% |
Other | 45 | 21% |
Unknown | 46 | 22% |
Readers by discipline | Count | As % |
---|---|---|
Business, Management and Accounting | 30 | 14% |
Engineering | 28 | 13% |
Medicine and Dentistry | 25 | 12% |
Social Sciences | 23 | 11% |
Computer Science | 12 | 6% |
Other | 39 | 18% |
Unknown | 54 | 26% |