Te-Min Chang

28 papers receiving 278 citations

Peers

Te-Min Chang
Comparison fields: 5 of 71
  • Industrial and Manufacturing Engineering 99
  • Management Information Systems 42
  • Management Science and Operations Research 50
  • Artificial Intelligence 97
  • Information Systems 62
Replace Alan G. Merten with:
Alan G. Merten United States
Kamalendu Pal United Kingdom
Herwig Mannaert Belgium
Filip Caron Belgium
Rua‐Huan Tsaih Taiwan
P. Miliotis Greece
Viara Popova Netherlands
Özden Gür Ali Türkiye
Dilpreet Singh India
Suriadi Suriadi Indonesia
Te-Min Chang relative to Alan G. Merten United States Alan G. Merten's profile →
Citations per field
00.5×8.5×
Alan G. Merten · 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 18 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 29 papers — load more, or switch the sort, to bring in the rest.

#Work
1 199462
2 200745
3 201729
4 199425
5 200816
6 201715
7 200712
8 201611
9 199811
10 201910
11 201510
12 19989
13 20197
14 20117
15 20235
16 20215
17 20154
18 19793
19 20143
20 20233

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 29 papers that have together received 309 indexed citations. Recurring topics across this work include Stock Market Forecasting Methods (6 papers), Financial Distress and Bankruptcy Prediction (4 papers), Advanced Manufacturing and Logistics Optimization (3 papers), Supply Chain Resilience and Risk Management (3 papers), Recommender Systems and Techniques (3 papers), Neural Networks and Applications (3 papers), Rough Sets and Fuzzy Logic (3 papers) and Scheduling and Optimization Algorithms (3 papers). The work is most often cited by research in Industrial and Manufacturing Engineering (99 citations), Management Information Systems (42 citations), Management Science and Operations Research (50 citations), Artificial Intelligence (97 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, Chih-Ya Shen, Jen‐Her Wu, Hao-Yun Kao, Hsin‐Hui Lin, Yu‐Feng Su and Rex K. Kincaid. Their work appears in journals such as International Journal of Production Research, Expert Systems with Applications, Computers in Human Behavior, Program electronic library and information systems and Annals of Operations Research.

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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