Kaiyu Wang
Impact in
- Marketing top 2%
- Consumer Behavior in Brand Consumption and Identification
- Consumer Retail Behavior Studies
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- Technology Adoption and User Behaviour
Papers in
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- Digital Marketing and Social Media 18
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- Metaheuristic Optimization Algorithms Research 15
- Evolutionary Algorithms and Applications 10
- Co-authors
- Wen‐Hai Chih (17 shared papers)Li‐Chun Hsu (9 shared papers)Shangce Gao (18 shared papers)Sichen Tao (9 shared papers)Shu-Hao Chang (4 shared papers)Jiujun Cheng (3 shared papers)Ting Jin (1 shared paper)Barry J. Dickson (6 shared papers)
In The Last Decade
Kaiyu Wang
68 papers receiving 1.5k citations
Peers
Comparison fields: 5 of 121
- Marketing 404
- Information Systems and Management 224
- Organizational Behavior and Human Resource Management 248
- Cellular and Molecular Neuroscience 217
- Sociology and Political Science 487
Countries citing papers authored by Kaiyu Wang
This map shows the geographic impact of Kaiyu Wang'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 Kaiyu Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kaiyu Wang more than expected).
Fields of papers citing papers by Kaiyu Wang
This network shows the impact of papers produced by Kaiyu Wang. 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 Kaiyu Wang. The network helps show where Kaiyu Wang may publish in the future.
Co-authors
The 25 scholars most cited alongside Kaiyu Wang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 74 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2021 | 185 | |
| 2 | 2016 | 115 | |
| 3 | 2020 | 93 | |
| 4 | 2020 | 66 | |
| 5 | 2011 | 63 | |
| 6 | 2013 | 58 | |
| 7 | 2022 | 56 | |
| 8 | 2021 | 47 | |
| 9 | 2017 | 45 | |
| 10 | 2011 | 45 | |
| 11 | 2014 | 43 | |
| 12 | 2020 | 41 | |
| 13 | 2012 | 39 | |
| 14 | 2011 | 39 | |
| 15 | 2022 | 38 | |
| 16 | 2012 | 37 | |
| 17 | 2019 | 35 | |
| 18 | 2021 | 34 | |
| 19 | 2015 | 31 | |
| 20 | 2020 | 28 |
About Kaiyu Wang
Kaiyu Wang is a scholar working on Sociology and Political Science, Artificial Intelligence, Information Systems and Management, Marketing and Computational Theory and Mathematics, having authored 74 papers that have together received 1.5k indexed citations. Recurring topics across this work include Digital Marketing and Social Media (18 papers), Metaheuristic Optimization Algorithms Research (15 papers), Technology Adoption and User Behaviour (14 papers), Advanced Multi-Objective Optimization Algorithms (10 papers), Evolutionary Algorithms and Applications (10 papers), Consumer Behavior in Brand Consumption and Identification (9 papers), Customer Service Quality and Loyalty (7 papers) and Neurobiology and Insect Physiology Research (6 papers). The work is most often cited by research in Marketing (404 citations), Information Systems and Management (224 citations), Organizational Behavior and Human Resource Management (248 citations), Cellular and Molecular Neuroscience (217 citations) and Sociology and Political Science (487 citations). Kaiyu Wang has collaborated with scholars based in Canada, China and Taiwan. Frequent co-authors include Wen‐Hai Chih, Li‐Chun Hsu, Shangce Gao, Sichen Tao, Shu-Hao Chang, Jiujun Cheng, Ting Jin, Barry J. Dickson, Hongwei Dai and Xuemei Bian. Their work appears in journals such as Journal of Business Research, International Journal of Computational Intelligence Systems, Nature, Service Industries Journal and Industrial Marketing Management.
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.