Jun Ye

542 citations
37 papers · 419 · h-index 10

Impact in

Papers in

Jun Ye

33 papers receiving 398 citations

Peers

Jun Ye
Comparison fields: 5 of 105
  • Organizational Behavior and Human Resource Management 105
  • Marketing 91
  • Developmental Neuroscience 14
  • Strategy and Management 49
  • Information Systems and Management 21
Replace Jing Long with:
Jing Long China
Tobias Keim Germany
Rajendra Sahu India
Huaying Shu China
Alrence Halibas Vietnam
Ludovico Solima Italy
Yiming Ma China
Alan French United Kingdom
Jeeyeon Kim South Korea
Jun Ye relative to Jing Long China Jing Long's profile →
Citations per field
00.5×3.8×
Jing Long · 1×
Citations per year

Countries citing papers authored by Jun Ye

Since Specialization
Citations

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

Fields of papers citing papers by Jun Ye

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jun Ye. 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 Jun Ye. The network helps show where Jun Ye may publish in the future.

Co-authors

The 25 scholars most cited alongside Jun Ye, 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 Jun Ye Line = papers co-authored together Jun Ye links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 37 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2008101
2 200539
3 201035
4 201732
5 201631
6 201829
7 200721
8 201616
9 201214
10 202111
11 20239
12 20199
13 20149
14 20208
15 20057
16 20166
17 20046
18 20214
19 20124
20 20214

About Jun Ye

Jun Ye is a scholar working on Artificial Intelligence, Molecular Biology, Sociology and Political Science, Marketing and Social Psychology, having authored 37 papers that have together received 419 indexed citations. Recurring topics across this work include Customer Service Quality and Loyalty (5 papers), Cryptography and Data Security (4 papers), Angiogenesis and VEGF in Cancer (4 papers), Consumer Behavior in Brand Consumption and Identification (4 papers), Privacy-Preserving Technologies in Data (3 papers), Axon Guidance and Neuronal Signaling (3 papers), Chaos-based Image/Signal Encryption (3 papers) and Advanced Steganography and Watermarking Techniques (2 papers). The work is most often cited by research in Organizational Behavior and Human Resource Management (105 citations), Marketing (91 citations), Developmental Neuroscience (14 citations), Strategy and Management (49 citations) and Information Systems and Management (21 citations). Jun Ye has collaborated with scholars based in China, United States and Japan. Frequent co-authors include Jagdip Singh, Detelina Marinova, Xuemin Shen, J.W. Mark, Yuan Li, Ju-Yeon Lee, Beibei Dong, Nagesh N. Murthy, Lan Jiang and Sara Hanson. Their work appears in journals such as Marketing Letters, International Journal of Oncology, Journal of Business Research, International Journal of Hospitality Management and Journal of Brand Management.

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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