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Need to Knowledge (NtK) Model: an evidence-based framework for generating technological innovations with socio-economic impacts

Overview of attention for article published in Implementation Science, February 2013
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (80th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
10 tweeters
googleplus
1 Google+ user

Citations

dimensions_citation
10 Dimensions

Readers on

mendeley
71 Mendeley
citeulike
1 CiteULike
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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.

Twitter Demographics

The data shown below were collected from the profiles of 10 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 71 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 63 89%

Demographic breakdown

Readers by professional status Count As %
Student > Master 17 24%
Student > Ph. D. Student 15 21%
Researcher 12 17%
Other 5 7%
Student > Bachelor 3 4%
Other 12 17%
Unknown 7 10%
Readers by discipline Count As %
Engineering 12 17%
Social Sciences 10 14%
Medicine and Dentistry 9 13%
Business, Management and Accounting 8 11%
Computer Science 6 8%
Other 18 25%
Unknown 8 11%

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
#2,021,990
of 12,013,283 outputs
Outputs from Implementation Science
#600
of 1,259 outputs
Outputs of similar age
#60,392
of 316,560 outputs
Outputs of similar age from Implementation Science
#17
of 33 outputs
Altmetric has tracked 12,013,283 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,259 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 12.6. This one has gotten more attention than average, scoring higher than 51% of its peers.
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 316,560 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 80% of its contemporaries.
We're also able to compare this research output to 33 others from the same source and published within six weeks on either side of this one. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.