Automated Author ProfileLiegat, Marlen
Universität Hamburg (UHH)0000-0001-6517-1116
Liegat, Marlen
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: 4.0 (sum of 3 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Data for the following article: Vogel, R., Vogel, D., Liegat, M. C., & Hensel, D. (2024). From social categorization to implicit citizenship theories: Advancing the socio‐cognitive foundations of state–citizen interactions. Public Administration Review, Article puar.13844. Advance online publication. https://doi.org/10.1111/puar.13844
Authors
- Vogel, Rick ;
- Vogel, Dominik ;
- Liegat, Marlen ;
- Hensel, David
Data for the following article: Vogel, R., Vogel, D., Liegat, M. C., & Hensel, D. (2024). From social categorization to implicit citizenship theories: Advancing the socio‐cognitive foundations of state–citizen interactions. Public Administration Review, Article puar.13844. Advance online publication. https://doi.org/10.1111/puar.13844
Authors
- Vogel, Rick ;
- Vogel, Dominik ;
- Liegat, Marlen ;
- Hensel, David
This study draws attention to workplace aggression as critical incidents in state-citizen encounters and examines the traces they leave in employees’ subsequent thinking about citizens. Building on social cognition and affective events theory, we hypothesize that the more severe the aggressive incidents have been, the more negative employees’ associations with citizens become. Results of a free association task with subsequent sentiment analysis confirm this assumption. Type of work and the gender of the employees moderate the relationship between aggressions and associations. The findings raise awareness for the significance of workplace aggression and provide an outline and agenda of a socio-cognitive theory of public employees’ associative thinking about citizens.
Authors
- Liegat, Marlen