Mu Yang

48 papers receiving 668 citations

Peers

Mu Yang
Comparison fields: 5 of 90
  • Marketing 123
  • Computer Science Applications 51
  • Communication 56
  • Business and International Management 15
  • Management Information Systems 60
Replace Jae-Min Lee with:
Jae-Min Lee South Korea
Paolo Spagnoletti Italy
Xin Tian United States
Yenni Tim Australia
Zhangxi Lin United States
Pengkun Wu China
Philipp Ebel Switzerland
Prabin Kumar Panigrahi India
Dominik Dellermann Germany
Ke‐Wei Huang Singapore
Mu Yang relative to Jae-Min Lee South Korea Jae-Min Lee's profile →
Citations per field
00.5×5.7×
Jae-Min Lee · 1×
Citations per year

Countries citing papers authored by Mu Yang

Since Specialization
Citations

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

Fields of papers citing papers by Mu Yang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 52 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201974
2 202157
3 201749
4 201845
5 202144
6 202138
7 202138
8 201734
9 200933
10 202331
11 201426
12 202422
13 202220
14 202016
15 201714
16 201914
17 202214
18 20249
19 20249
20 20239

About Mu Yang

Mu Yang is a scholar working on Sociology and Political Science, Artificial Intelligence, Marketing, Economics and Econometrics and Strategy and Management, having authored 52 papers that have together received 694 indexed citations. Recurring topics across this work include Digital Marketing and Social Media (16 papers), Consumer Behavior in Brand Consumption and Identification (6 papers), Privacy-Preserving Technologies in Data (5 papers), Cryptography and Data Security (4 papers), Blockchain Technology Applications and Security (4 papers), Sharing Economy and Platforms (4 papers), Open Source Software Innovations (4 papers) and AI in Service Interactions (4 papers). The work is most often cited by research in Marketing (123 citations), Computer Science Applications (51 citations), Communication (56 citations), Business and International Management (15 citations) and Management Information Systems (60 citations). Mu Yang has collaborated with scholars based in United Kingdom, China and Taiwan. Frequent co-authors include Chunjia Han, Andrea Margheri, Vladimiro Sassone, Yuanjun Zhao, Shizhen Bai, Stephen Thomas, Brij B. Gupta, Petros Ieromonachou, Hongru Zhang and Md Sadek Ferdous. Their work appears in journals such as IEEE Transactions on Engineering Management, British Food Journal, Technological Forecasting and Social Change, Journal of Consumer Behaviour and Frontiers in Psychology.

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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