Eric S. Vorm
Impact in
- Health Informatics top 5%
- Artificial Intelligence in Healthcare and Education
- Safety Research top 5%
- Ethics and Social Impacts of AI
Papers in
-
- Ethics and Social Impacts of AI 3
-
- Human-Automation Interaction and Safety 3
- Co-authors
- David J. Combs (2 shared papers)David Gunning (1 shared paper)Yunyan Wang (1 shared paper)Andrew Miller (2 shared papers)Joseph T. Coyne (1 shared paper)J. Gregory Trafton (1 shared paper)Anthony Mark Harrison (1 shared paper)Cyrus K. Foroughi (1 shared paper)
- Journals
- IEEE Intelligent Systems (1 paper)International Journal of Human-Computer Interaction (1 paper)Lecture notes in computer science (2 papers)Military Medicine (1 paper)PubMed Central (1 paper)
- Partner nations
- United StatesAustralia
In The Last Decade
Eric S. Vorm
12 papers receiving 303 citations
Peers
Comparison fields: 5 of 92
- Health Informatics 58
- Safety Research 69
- Artificial Intelligence 163
- Information Systems and Management 24
- Social Psychology 47
Countries citing papers authored by Eric S. Vorm
This map shows the geographic impact of Eric S. Vorm's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Eric S. Vorm with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Eric S. Vorm more than expected).
Fields of papers citing papers by Eric S. Vorm
This network shows the impact of papers produced by Eric S. Vorm. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Eric S. Vorm. The network helps show where Eric S. Vorm may publish in the future.
Co-authors
The 9 scholars most cited alongside Eric S. Vorm, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2021 | 130 | |
| 2 | 2022 | 127 | |
| 3 | 2018 | 21 | |
| 4 | 2020 | 9 | |
| 5 | 2020 | 9 | |
| 6 | 2017 | 8 | |
| 7 | 2021 | 6 | |
| 8 | Assessing the Value of Transparency in Recommender Systems: An End-User Perspective | 2018 | 5 |
| 9 | Assessing Demand for Transparency in Intelligent Systems Using Machine Learning | 2018 | 3 |
| 10 | 2016 | 2 | |
| 11 | 2020 | 2 | |
| 12 | 2016 | 1 |
About Eric S. Vorm
Eric S. Vorm is a scholar working on Safety Research, Social Psychology, Human-Computer Interaction, Management Information Systems and Artificial Intelligence, having authored 12 papers that have together received 323 indexed citations. Recurring topics across this work include Explainable Artificial Intelligence (XAI) (4 papers), Ethics and Social Impacts of AI (3 papers), Human-Automation Interaction and Safety (3 papers), Big Data and Business Intelligence (2 papers), Gaze Tracking and Assistive Technology (2 papers), AI-based Problem Solving and Planning (1 paper), Suicide and Self-Harm Studies (1 paper) and Economic and Environmental Valuation (1 paper). The work is most often cited by research in Health Informatics (58 citations), Safety Research (69 citations), Artificial Intelligence (163 citations), Information Systems and Management (24 citations) and Social Psychology (47 citations). Eric S. Vorm has collaborated with scholars based in United States and Australia. Frequent co-authors include David J. Combs, David Gunning, Yunyan Wang, Andrew Miller, Joseph T. Coyne, J. Gregory Trafton, Anthony Mark Harrison, Cyrus K. Foroughi and Ciara M. Sibley. Their work appears in journals such as IEEE Intelligent Systems, International Journal of Human-Computer Interaction, Lecture notes in computer science, Military Medicine and PubMed Central.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.