Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
How online reviews affect purchase intention: a new model based on the stimulus-organism-response (S-O-R) framework
2020225 citationsLinlin Zhu, Li He et al.Aslib Journal of Information Managementprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
Countries citing papers authored by Feng‐Kwei Wang
Since
Specialization
Citations
This map shows the geographic impact of Feng‐Kwei 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 Feng‐Kwei Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Feng‐Kwei Wang more than expected).
This network shows the impact of papers produced by Feng‐Kwei 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 Feng‐Kwei Wang. The network helps show where Feng‐Kwei Wang may publish in the future.
Co-authorship network of co-authors of Feng‐Kwei Wang
This figure shows the co-authorship network connecting the top 25 collaborators of Feng‐Kwei Wang.
A scholar is included among the top collaborators of Feng‐Kwei Wang 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 Feng‐Kwei Wang. Feng‐Kwei Wang is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Zhu, Linlin, et al.. (2020). How online reviews affect purchase intention: a new model based on the stimulus-organism-response (S-O-R) framework. Aslib Journal of Information Management. 72(4). 463–488.225 indexed citations breakdown →
He, Wu & Feng‐Kwei Wang. (2004). Interface Design of Case-based Reasoning Systems. EdMedia: World Conference on Educational Media and Technology. 2004(1). 4452–4458.1 indexed citations
14.
Wang, Feng‐Kwei & John Wedman. (2003). Designing and Evaluating a Web-based Lesson Planning System. EdMedia: World Conference on Educational Media and Technology. 2003(1). 1875–1880.1 indexed citations
15.
He, Wu, et al.. (2003). Interface support for case retrieval in a Case-Based Reasoning knowledge repository-Knowledge Innovation for Technology in Education(KITE). Society for Information Technology & Teacher Education International Conference. 2003(1). 3598–3600.1 indexed citations
16.
Wang, Feng‐Kwei, et al.. (2003). KITE: A Knowledge Management Approach for Sharing Technology Integration Experiences in Education Communities. E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education. 2003(1). 2329–2336.1 indexed citations
Lin, Guan-Yu & Feng‐Kwei Wang. (2002). Using Technology to Improve Instructional Planning. Society for Information Technology & Teacher Education International Conference. 2002(1). 2317–2318.
19.
Jonassen, David H., et al.. (2002). Knowledge Innovation for Technology in Education (KITE). Society for Information Technology & Teacher Education International Conference. 2002(1). 1812–1813.1 indexed citations
20.
Wang, Feng‐Kwei. (2000). Constructing a Learning Environment for Knowledge Advancement. EdMedia: World Conference on Educational Media and Technology. 2000(1). 1835–1835.1 indexed citations
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.