Te-Min Chang

409 citations
28 papers · 299 · h-index 11

Impact in

Papers in

Te-Min Chang

27 papers receiving 271 citations

Peers

Te-Min Chang
Comparison fields: 5 of 71
  • Industrial and Manufacturing Engineering 91
  • Management Information Systems 40
  • Management Science and Operations Research 46
  • Artificial Intelligence 96
  • Information Systems 62
Replace Kamalendu Pal with:
Kamalendu Pal United Kingdom
Alan G. Merten United States
Herwig Mannaert Belgium
Shuning Wu China
Stefan Weß Germany
Viara Popova Netherlands
Özden Gür Ali Türkiye
Dilpreet Singh India
Marjan Krisper Slovenia
Suriadi Suriadi Indonesia
Te-Min Chang relative to Kamalendu Pal United Kingdom Kamalendu Pal's profile →
Citations per field
00.5×6.3×
Kamalendu Pal · 1×
Citations per year

Countries citing papers authored by Te-Min Chang

Since Specialization
Citations

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

Fields of papers citing papers by Te-Min Chang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Te-Min 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 Te-Min Chang. The network helps show where Te-Min Chang may publish in the future.

Co-authors

The 16 scholars most cited alongside Te-Min 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 Te-Min Chang Line = papers co-authored together Te-Min Chang links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 28 papers — load more, or switch the sort, to bring in the rest.

#Work
1 199462
2 200745
3 201728
4 199425
5 200816
6 201715
7 200712
8 199811
9 201611
10 201910
11 201510
12 20197
13 20117
14 20235
15 20215
16 20154
17 19793
18 20143
19 20233
20 20023

About Te-Min Chang

Te-Min Chang is a scholar working on Artificial Intelligence, Management Science and Operations Research, Information Systems, Accounting and Strategy and Management, having authored 28 papers that have together received 299 indexed citations. Recurring topics across this work include Stock Market Forecasting Methods (6 papers), Financial Distress and Bankruptcy Prediction (4 papers), Supply Chain Resilience and Risk Management (3 papers), Neural Networks and Applications (3 papers), Rough Sets and Fuzzy Logic (3 papers), Recommender Systems and Techniques (3 papers), Sentiment Analysis and Opinion Mining (2 papers) and Text and Document Classification Technologies (2 papers). The work is most often cited by research in Industrial and Manufacturing Engineering (91 citations), Management Information Systems (40 citations), Management Science and Operations Research (46 citations), Artificial Intelligence (96 citations) and Information Systems (62 citations). Te-Min Chang has collaborated with scholars based in Taiwan and United States. Frequent co-authors include Yuehwern Yih, Sin‐Jin Lin, Ming-Fu Hsu, Keng‐Pei Lin, Hsin‐Hui Lin, Chih-Ya Shen, Jen‐Her Wu, Hao-Yun Kao, Yu‐Feng Su and Jhih‐Hong Zeng. Their work appears in journals such as International Journal of Production Research, Expert Systems with Applications, Computers in Human Behavior, Information Sciences and Journal of the Association for 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.

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