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Use of web mining in studying innovation

Overview of attention for article published in Scientometrics, September 2014
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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 (92nd percentile)
  • High Attention Score compared to outputs of the same age and source (92nd percentile)

Mentioned by

policy
2 policy sources
twitter
16 X users

Citations

dimensions_citation
87 Dimensions

Readers on

mendeley
182 Mendeley
Title
Use of web mining in studying innovation
Published in
Scientometrics, September 2014
DOI 10.1007/s11192-014-1434-0
Pubmed ID
Authors

Abdullah Gök, Alec Waterworth, Philip Shapira

Abstract

As enterprises expand and post increasing information about their business activities on their websites, website data promises to be a valuable source for investigating innovation. This article examines the practicalities and effectiveness of web mining as a research method for innovation studies. We use web mining to explore the R&D activities of 296 UK-based green goods small and mid-size enterprises. We find that website data offers additional insights when compared with other traditional unobtrusive research methods, such as patent and publication analysis. We examine the strengths and limitations of enterprise innovation web mining in terms of a wide range of data quality dimensions, including accuracy, completeness, currency, quantity, flexibility and accessibility. We observe that far more companies in our sample report undertaking R&D activities on their web sites than would be suggested by looking only at conventional data sources. While traditional methods offer information about the early phases of R&D and invention through publications and patents, web mining offers insights that are more downstream in the innovation process. Handling website data is not as easy as alternative data sources, and care needs to be taken in executing search strategies. Website information is also self-reported and companies may vary in their motivations for posting (or not posting) information about their activities on websites. Nonetheless, we find that web mining is a significant and useful complement to current methods, as well as offering novel insights not easily obtained from other unobtrusive sources.

X Demographics

X Demographics

The data shown below were collected from the profiles of 16 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 182 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 <1%
India 1 <1%
Mexico 1 <1%
Russia 1 <1%
United States 1 <1%
Unknown 177 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 39 21%
Student > Master 29 16%
Researcher 25 14%
Student > Doctoral Student 16 9%
Other 11 6%
Other 33 18%
Unknown 29 16%
Readers by discipline Count As %
Computer Science 44 24%
Social Sciences 24 13%
Business, Management and Accounting 23 13%
Engineering 12 7%
Economics, Econometrics and Finance 10 5%
Other 30 16%
Unknown 39 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 19. 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 18 October 2021.
All research outputs
#1,733,475
of 23,289,753 outputs
Outputs from Scientometrics
#342
of 2,716 outputs
Outputs of similar age
#19,445
of 244,660 outputs
Outputs of similar age from Scientometrics
#4
of 41 outputs
Altmetric has tracked 23,289,753 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,716 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.7. This one has done well, scoring higher than 87% 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 244,660 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 41 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 92% of its contemporaries.