Sheng‐Kai Chang

480 citations
26 papers · 318 · h-index 11

Impact in

Papers in

Sheng‐Kai Chang

24 papers receiving 312 citations

Peers

Sheng‐Kai Chang
Comparison fields: 5 of 88
  • Immunology 57
  • Endocrine and Autonomic Systems 17
  • Behavioral Neuroscience 8
  • Finance 23
  • Economics and Econometrics 59
Replace Yun-Jung Lee with:
Yun-Jung Lee South Korea
Emma Greenwood United Kingdom
Wenjing Zhao China
Haiyue Zhang China
Min Liang China
Veronika Novotná Czechia
Yi‐Hsuan Chen Taiwan
Tieli Li Japan
Sheng‐Kai Chang relative to Yun-Jung Lee South Korea Yun-Jung Lee's profile →
Citations per field
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Yun-Jung Lee · 1×
Citations per year

Countries citing papers authored by Sheng‐Kai Chang

Since Specialization
Citations

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

Fields of papers citing papers by Sheng‐Kai Chang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sheng‐Kai Chang. 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 Sheng‐Kai Chang. The network helps show where Sheng‐Kai Chang may publish in the future.

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 26 papers — load more, or switch the sort, to bring in the rest.

#Work
1 197944
2 201140
3 201424
4 202124
5 200924
6 200623
7 201422
8 201317
9 201416
10 200915
11 201314
12 201410
13 201210
14 20168
15 19977
16 20236
17 20115
18 20113
19 20111
20 20081

About Sheng‐Kai Chang

Sheng‐Kai Chang is a scholar working on Economics and Econometrics, Electrical and Electronic Engineering, Molecular Biology, Immunology and Cancer Research, having authored 26 papers that have together received 318 indexed citations. Recurring topics across this work include Spatial and Panel Data Analysis (4 papers), Perovskite Materials and Applications (3 papers), MicroRNA in disease regulation (3 papers), Economic Growth and Productivity (3 papers), Organic Electronics and Photovoltaics (2 papers), Advanced DC-DC Converters (2 papers), Fiscal Policy and Economic Growth (2 papers) and Multilevel Inverters and Converters (2 papers). The work is most often cited by research in Immunology (57 citations), Endocrine and Autonomic Systems (17 citations), Behavioral Neuroscience (8 citations), Finance (23 citations) and Economics and Econometrics (59 citations). Sheng‐Kai Chang has collaborated with scholars based in Taiwan, United States and China. Frequent co-authors include Chung‐Yi Hu, Susan Henning, Erika G. Gisel, Ping‐Ning Hsu, Chien‐Sheng Wu, Hurng‐Liahng Jou, Kuen‐Der Wu, Jinn‐Chang Wu, Ching‐Fuh Lin and Shu‐Wha Lin. Their work appears in journals such as Studies in Nonlinear Dynamics and Econometrics, Clinical Rheumatology, Scientific Reports, Journal of Productivity Analysis and The FASEB Journal.

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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