Yu-Lung Wu
- Information Systems top 2%
- Information Systems and Management top 2%
- Artificial Intelligence top 5%
- Sociology and Political Science top 10%
- Computational Theory and Mathematics top 5%
- Co-authors
- Yu‐Hui TaoTzung‐Pei HongPei‐Chi YangChun-Hao ChenYeong-Chyi LeeBin‐Shyan JongJerry Chun‐Wei LinTsong‐Wuu Lin
- Topics
- Technology Adoption and User Behaviour (14 papers)Data Mining Algorithms and Applications (10 papers)Digital Marketing and Social Media (9 papers)
In The Last Decade
Yu-Lung Wu
50 papers receiving 679 citations
Peers
Comparison fields: 5 of 83
- Information Systems 276
- Information Systems and Management 264
- Artificial Intelligence 240
- Sociology and Political Science 169
- Computational Theory and Mathematics 154
Countries citing papers authored by Yu-Lung Wu
This map shows the geographic impact of Yu-Lung Wu'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 Yu-Lung Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yu-Lung Wu more than expected).
Fields of papers citing papers by Yu-Lung Wu
This network shows the impact of papers produced by Yu-Lung Wu. 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 Yu-Lung Wu. The network helps show where Yu-Lung Wu may publish in the future.
Co-authorship network of co-authors of Yu-Lung Wu
This figure shows the co-authorship network connecting the top 25 collaborators of Yu-Lung Wu. A scholar is included among the top collaborators of Yu-Lung Wu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Yu-Lung Wu. Yu-Lung Wu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 0 | |
| 4 | 6 | |
| 5 | 0 | |
| 6 | 6 | |
| 7 | 57 | |
| 8 | 16 | |
| 9 | 2 | |
| 10 | 26 | |
| 11 | 10 | |
| 12 | 22 | |
| 13 | A Practical Computer Adaptive Testing Model for Small-Scale Scenarios | 8 |
| 14 | 5 | |
| 15 | 2 | |
| 16 | Critical success factors for online C2C auction websites: a consumer's perspective | 6 |
| 17 | 23 | |
| 18 | 2 | |
| 19 | 15 | |
| 20 | 4 |
About Yu-Lung Wu
Yu-Lung Wu is a scholar working on Information Systems and Management, Organizational Behavior and Human Resource Management and Information Systems, having authored 56 papers that have together received 750 indexed citations. Recurring topics across this work include Technology Adoption and User Behaviour (14 papers), Data Mining Algorithms and Applications (10 papers) and Digital Marketing and Social Media (9 papers). The work is most often cited by research in Information Systems and Management (264 citations), Information Systems (276 citations) and Computer Science Applications (58 citations). Yu-Lung Wu has collaborated with scholars based in Taiwan, China and Japan. Frequent co-authors include Yu‐Hui Tao, Tzung‐Pei Hong, Pei‐Chi Yang, Chun-Hao Chen, Yeong-Chyi Lee, Bin‐Shyan Jong, Jerry Chun‐Wei Lin, Tsong‐Wuu Lin, Shyue-Liang Wang and Yen‐Teh Hsia. Their work appears in journals such as PLoS ONE, Computers in Human Behavior and Expert Systems with Applications.
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.