Automated Author ProfileWiseman, Thomas
Wiseman, Thomas
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 1.6 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
We develop a model of herds in which consumers observe only the aggregate purchase history, not the complete ordered history of search actions. We show that the purchasing information changes the conditions under which herds can occur for both low- and high-quality products. Inferior products are certain to be ignored; high quality products may be ignored, but complete learning may also occur. We obtain closed form solutions for the probabilities of these events and conduct comparative statics. We test the model's predictions using data from an online music market created by Salganik, Dodds, and Watts (2006). (JEL D11, D12, L82)
Authors
- Hendricks, Kenneth ;
- Sorensen, Alan ;
- Wiseman, Thomas
We develop a model of herds in which consumers observe only the aggregate purchase history, not the complete ordered history of search actions. We show that the purchasing information changes the conditions under which herds can occur for both low- and high-quality products. Inferior products are certain to be ignored; high quality products may be ignored, but complete learning may also occur. We obtain closed form solutions for the probabilities of these events and conduct comparative statics. We test the model's predictions using data from an online music market created by Salganik, Dodds, and Watts (2006). (JEL D11, D12, L82)
Authors
- Hendricks, Kenneth ;
- Sorensen, Alan ;
- Wiseman, Thomas