Javier Lerch
Impact in
- General Decision Sciences top 5%
- Decision-Making and Behavioral Economics
-
- Complex Systems and Decision Making
Papers in
-
- Cognitive Science and Mapping 2
- Bayesian Modeling and Causal Inference 1
-
- Knowledge Management and Sharing 1
- Co-authors
- Cleotilde González (1 shared paper)Christian Lebière (1 shared paper)James D. Herbsleb (1 shared paper)Sandra A. Slaughter (1 shared paper)J. Alberto Espinosa (1 shared paper)Audris Mockus (1 shared paper)Robert E. Kraut (1 shared paper)Donald E. Harter (1 shared paper)
- Journals
- Journal of the Association for Information Systems (1 paper)Cognitive Science (1 paper)International Conference on Information Systems (1 paper)
- Partner nations
- United States
In The Last Decade
Javier Lerch
3 papers receiving 423 citations
Peers
Comparison fields: 5 of 78
- General Decision Sciences 77
- Management Science and Operations Research 107
- Communication 41
- Artificial Intelligence 170
- Social Psychology 107
Countries citing papers authored by Javier Lerch
This map shows the geographic impact of Javier Lerch'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 Javier Lerch with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Javier Lerch more than expected).
Fields of papers citing papers by Javier Lerch
This network shows the impact of papers produced by Javier Lerch. 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 Javier Lerch. The network helps show where Javier Lerch may publish in the future.
Co-authors
The 8 scholars most cited alongside Javier Lerch, 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 | 2003 | 376 | |
| 2 | Shared Mental Models, Familiarity and Coordination: A Multi-Method Study of Distributed Software Teams | 2002 | 88 |
| 3 | Time Pressure in Real-Time Dynamic Decision Making | 1998 | 2 |
About Javier Lerch
Javier Lerch is a scholar working on Artificial Intelligence, Communication, Social Psychology, Information Systems and Experimental and Cognitive Psychology, having authored 3 papers that have together received 466 indexed citations. Recurring topics across this work include Cognitive Science and Mapping (2 papers), Software Engineering Techniques and Practices (1 paper), Team Dynamics and Performance (1 paper), Bayesian Modeling and Causal Inference (1 paper), Educational and Psychological Assessments (1 paper), Multi-Criteria Decision Making (1 paper), Cognitive Abilities and Testing (1 paper) and Knowledge Management and Sharing (1 paper). The work is most often cited by research in General Decision Sciences (77 citations), Management Science and Operations Research (107 citations), Communication (41 citations), Artificial Intelligence (170 citations) and Social Psychology (107 citations). Javier Lerch has collaborated with scholars based in United States. Frequent co-authors include Cleotilde González, Christian Lebière, James D. Herbsleb, Sandra A. Slaughter, J. Alberto Espinosa, Audris Mockus, Robert E. Kraut and Donald E. Harter. Their work appears in journals such as Journal of the Association for Information Systems, Cognitive Science and International Conference on Information Systems.
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.