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Feasibility analysis of machine learning for performance-related attributional statements

Overview of attention for article published in International Journal of Accounting Information Systems, March 2023
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Mentioned by

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1 X user

Citations

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3 Dimensions

Readers on

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33 Mendeley
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Title
Feasibility analysis of machine learning for performance-related attributional statements
Published in
International Journal of Accounting Information Systems, March 2023
DOI 10.1016/j.accinf.2022.100597
Authors

Anil Berkin, Walter Aerts, Tom Van Caneghem

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user 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 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 33 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 12%
Unspecified 3 9%
Researcher 3 9%
Student > Master 3 9%
Lecturer 2 6%
Other 8 24%
Unknown 10 30%
Readers by discipline Count As %
Business, Management and Accounting 14 42%
Unspecified 3 9%
Computer Science 2 6%
Economics, Econometrics and Finance 1 3%
Psychology 1 3%
Other 1 3%
Unknown 11 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 05 December 2022.
All research outputs
#17,301,727
of 25,392,582 outputs
Outputs from International Journal of Accounting Information Systems
#40
of 79 outputs
Outputs of similar age
#248,009
of 422,415 outputs
Outputs of similar age from International Journal of Accounting Information Systems
#2
of 2 outputs
Altmetric has tracked 25,392,582 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 79 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 18th percentile – i.e., 18% 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 422,415 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 29th percentile – i.e., 29% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 2 others from the same source and published within six weeks on either side of this one.