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Artificial Intelligence-Enabled Business Model Innovation: Competencies and Roles of Top Management

Overview of attention for article published in IEEE Transactions on Engineering Management, May 2023
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1 X user

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410 Mendeley
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Article details
Title
Artificial Intelligence-Enabled Business Model Innovation: Competencies and Roles of Top Management
Published in
IEEE Transactions on Engineering Management, May 2023
DOI 10.1109/tem.2023.3275643
URN
urn:issn:0018-9391
Authors

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Timeline Attention over time Attention Score history
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Activity
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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 demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 410 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
Unknown 410 100%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 40 10%
Lecturer 18 4%
Student > Master 13 3%
Researcher 12 3%
Student > Doctoral Student 11 3%
Other 53 13%
Unknown 263 64%
Readers by discipline
Readers by discipline Count As %
Business, Management and Accounting 76 19%
Computer Science 13 3%
Engineering 11 3%
Unspecified 10 2%
Social Sciences 9 2%
Other 21 5%
Unknown 270 66%
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 10 August 2023.
All research outputs
#20,674,485
of 25,394,764 outputs
Outputs from IEEE Transactions on Engineering Management
#378
of 418 outputs
Outputs of similar age
#286,583
of 389,224 outputs
Outputs of similar age from IEEE Transactions on Engineering Management
#3
of 3 outputs
Altmetric has tracked 25,394,764 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 418 research outputs from this source. They receive a mean Attention Score of 3.5. This one is in the 4th percentile – i.e., 4% 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 389,224 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 3 others from the same source and published within six weeks on either side of this one.