Title |
Need to Knowledge (NtK) Model: an evidence-based framework for generating technological innovations with socio-economic impacts
|
---|---|
Published in |
Implementation Science, February 2013
|
DOI | 10.1186/1748-5908-8-21 |
Pubmed ID | |
Authors |
Jennifer L Flagg, Joseph P Lane, Michelle M Lockett |
Abstract |
Traditional government policies suggest that upstream investment in scientific research is necessary and sufficient to generate technological innovations. The expected downstream beneficial socio-economic impacts are presumed to occur through non-government market mechanisms. However, there is little quantitative evidence for such a direct and formulaic relationship between public investment at the input end and marketplace benefits at the impact end. Instead, the literature demonstrates that the technological innovation process involves a complex interaction between multiple sectors, methods, and stakeholders. |
X Demographics
The data shown below were collected from the profiles of 10 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 3 | 30% |
United States | 1 | 10% |
Colombia | 1 | 10% |
Unknown | 5 | 50% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 6 | 60% |
Scientists | 2 | 20% |
Practitioners (doctors, other healthcare professionals) | 1 | 10% |
Science communicators (journalists, bloggers, editors) | 1 | 10% |
Mendeley readers
The data shown below were compiled from readership statistics for 85 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 3 | 4% |
Canada | 3 | 4% |
United States | 1 | 1% |
Brazil | 1 | 1% |
Unknown | 77 | 91% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Master | 17 | 20% |
Student > Ph. D. Student | 16 | 19% |
Researcher | 13 | 15% |
Student > Doctoral Student | 5 | 6% |
Other | 5 | 6% |
Other | 14 | 16% |
Unknown | 15 | 18% |
Readers by discipline | Count | As % |
---|---|---|
Engineering | 13 | 15% |
Social Sciences | 12 | 14% |
Business, Management and Accounting | 9 | 11% |
Medicine and Dentistry | 9 | 11% |
Computer Science | 6 | 7% |
Other | 19 | 22% |
Unknown | 17 | 20% |
Attention Score in Context
This research output has an Altmetric Attention Score of 7. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 September 2013.
All research outputs
#4,466,824
of 22,696,971 outputs
Outputs from Implementation Science
#870
of 1,719 outputs
Outputs of similar age
#53,934
of 307,673 outputs
Outputs of similar age from Implementation Science
#20
of 39 outputs
Altmetric has tracked 22,696,971 research outputs across all sources so far. Compared to these this one has done well and is in the 80th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,719 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.7. This one is in the 49th percentile – i.e., 49% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 307,673 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 82% of its contemporaries.
We're also able to compare this research output to 39 others from the same source and published within six weeks on either side of this one. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.