Bing Wu

59 papers receiving 1.8k citations

Hit Papers

Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model 2016 · 837 citations
8370+3+6Years since publication250500750

Peers

Bing Wu
Comparison fields: 5 of 153
  • Information Systems and Management 650
  • Computer Science Applications 311
  • Communication 136
  • Marketing 145
  • Organizational Behavior and Human Resource Management 115
Replace Sevgi Özkan with:
Sevgi Özkan Türkiye
Cheng‐Min Chao Taiwan
Wen-Shan Lin Taiwan
Concepción S. Wilson Australia
Ejaz Ahmed Pakistan
Kenneth David Strang United States
Omar El-Gayar United States
Elfi Furtmueller Netherlands
Sangno Lee South Korea
Nicholas Roberts United States
Bing Wu relative to Sevgi Özkan Türkiye Sevgi Özkan's profile →
Citations per field
00.5×1.5×1.9×
Sevgi Özkan · 1×
Citations per year

Countries citing papers authored by Bing Wu

Since Specialization
Citations

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

Fields of papers citing papers by Bing Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Bing 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 Bing Wu. The network helps show where Bing Wu may publish in the future.

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 69 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model
Hit paper breakdown →
2016837
2 2014152
3 201877
4 201877
5 201857
6 201753
7 201648
8 201943
9 201840
10 201339
11 202136
12 201335
13 201930
14 201729
15 202124
16 201523
17 201318
18 202213
19 202313
20 201613

About Bing Wu

Bing Wu is a scholar working on Computer Science Applications, Molecular Biology, Communication, Sociology and Political Science and Information Systems, having authored 69 papers that have together received 1.8k indexed citations. Recurring topics across this work include Online Learning and Analytics (11 papers), Knowledge Management and Sharing (9 papers), Technology Adoption and User Behaviour (6 papers), Transportation Planning and Optimization (5 papers), Online and Blended Learning (5 papers), Recommender Systems and Techniques (4 papers), Digital Marketing and Social Media (3 papers) and Fibroblast Growth Factor Research (3 papers). The work is most often cited by research in Information Systems and Management (650 citations), Computer Science Applications (311 citations), Communication (136 citations), Marketing (145 citations) and Organizational Behavior and Human Resource Management (115 citations). Bing Wu has collaborated with scholars based in China, United States and Malaysia. Frequent co-authors include Chenyan Zhang, Linbo Li, Ziqi Song, Anthony Chen, Yuanchao Tu, Zhe Cao, Xiaowei Wu, Yi Ma, Gang Li and Shengsheng Zhang. Their work appears in journals such as IEEE Access, Computers & Education, Behaviour and Information Technology, Biochemical and Biophysical Research Communications and Acta Physico-Chimica Sinica.

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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