Hee Rin Lee

1.3k citations
39 papers · 854 indexed · h-index 14

Hee Rin Lee

37 papers receiving 840 citations

Peers

Hee Rin Lee
Comparison fields: 5 of 86
  • Human-Computer Interaction 339
  • Social Psychology 437
  • Neuropsychology and Physiological Psychology 30
  • Demography 161
  • Applied Psychology 57
Replace Alisha Pradhan with:
Alisha Pradhan United States
Kellie Morrissey United Kingdom
Jörn Hurtienne Germany
Maurizio Caon Switzerland
Karyn Moffatt Canada
Hadas Erel Israel
Petr Slovák United Kingdom
Caroline Hummels Netherlands
Michal Luria United States
Vincent Koenig Luxembourg
Hee Rin Lee relative to Alisha Pradhan United States Alisha Pradhan's profile →
Citations per field
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Citations per year

Countries citing papers authored by Hee Rin Lee

Since Specialization
Citations

This map shows the geographic impact of Hee Rin Lee'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 Hee Rin Lee with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hee Rin Lee more than expected).

Fields of papers citing papers by Hee Rin Lee

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Hee Rin Lee. 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 Hee Rin Lee. The network helps show where Hee Rin Lee may publish in the future.

Co-authorship network

The 25 scholars most cited alongside Hee Rin Lee, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Hee Rin Lee Line = papers co-authored together Hee Rin Lee links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20253
2 20250
3 20242
4 20242
5 20242
6 20248
7 20230
8 202310
9 20226
10 202111
11 201911
12 201964
13 201940
14 20183
15 20179
16
How Humanlike Should a Social Robot Be: A User-Centered Exploration.
20169
17 201628
18 201413
19 201321
20 201013

About Hee Rin Lee

Hee Rin Lee is a scholar working on Human-Computer Interaction, Neuropsychology and Physiological Psychology and Applied Psychology, having authored 39 papers that have together received 854 indexed citations. Recurring topics across this work include Innovative Human-Technology Interaction (21 papers), Social Robot Interaction and HRI (13 papers), Technology Use by Older Adults (8 papers), AI in Service Interactions (7 papers), Digital Mental Health Interventions (6 papers), Teaching and Learning Programming (3 papers), Human-Automation Interaction and Safety (3 papers) and Persona Design and Applications (3 papers). The work is most often cited by research in Human-Computer Interaction (339 citations), Social Psychology (437 citations) and Neuropsychology and Physiological Psychology (30 citations). Hee Rin Lee has collaborated with scholars based in United States, Netherlands and United Kingdom. Frequent co-authors include Selma Šabanović, Laurel D. Riek, Casey C. Bennett, Jennifer Piatt, David Hakken, Shinichi Nagata, Wan-Ling Chang, Ja-Young Sung, Andrea G. Parker and Rebecca E. Grinter. Their work appears in journals such as Proceedings of the ACM on Human-Computer Interaction, ACM Transactions on Human-Robot Interaction, JMIR Formative Research, Proceedings of the Association for Information Science and Technology and University of Birmingham Research Portal (University of Birmingham).

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.

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