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.
Investigating students’ perceived satisfaction, behavioral intention, and effectiveness of e-learning: A case study of the Blackboard system
Countries citing papers authored by Shu-Sheng Liaw
Since
Specialization
Citations
This map shows the geographic impact of Shu-Sheng Liaw'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 Shu-Sheng Liaw with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shu-Sheng Liaw more than expected).
This network shows the impact of papers produced by Shu-Sheng Liaw. 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 Shu-Sheng Liaw. The network helps show where Shu-Sheng Liaw may publish in the future.
Co-authorship network of co-authors of Shu-Sheng Liaw
This figure shows the co-authorship network connecting the top 25 collaborators of Shu-Sheng Liaw.
A scholar is included among the top collaborators of Shu-Sheng Liaw 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 Shu-Sheng Liaw. Shu-Sheng Liaw is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Liaw, Shu-Sheng, et al.. (2016). Exploring learners attitudes toward a social e-learning system: A case study of the Edmodo. 764–769.2 indexed citations
Liaw, Shu-Sheng & Hsiu‐Mei Huang. (2012). Perceived satisfaction, perceived usefulness and interactive learning environments as predictors to self-regulation in e-learning environments. Computers & Education. 60(1). 14–24.421 indexed citations breakdown →
6.
Liaw, Shu-Sheng & Hsiu‐Mei Huang. (2011). A study of investigating learners attitudes toward e-learning.79 indexed citations
7.
Liaw, Shu-Sheng & Hsiu‐Mei Huang. (2009). Gender difference, computer experience, self-efficacy, motivation and intention toward e-learning: a case study of the Blackboard system. E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education. 2009(1). 1762–1770.1 indexed citations
8.
Huang, Hsiu‐Mei, et al.. (2009). Developing a Virtual Reality Learning Environment for Medical Education. E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education. 2009(1). 1320–1329.3 indexed citations
9.
Huang, Hsiu‐Mei & Shu-Sheng Liaw. (2007). Exploring Learners' motivation on knowledge sharing for e-learning. EdMedia: World Conference on Educational Media and Technology. 2007(1). 2539–2545.1 indexed citations
10.
Liaw, Shu-Sheng. (2007). Investigating students’ perceived satisfaction, behavioral intention, and effectiveness of e-learning: A case study of the Blackboard system. Computers & Education. 51(2). 864–873.834 indexed citations breakdown →
11.
Liaw, Shu-Sheng & Hsiu‐Mei Huang. (2007). Investigating Motivation, Enjoyment, Usefulness toward Video on Demand. 14(2). 355–358.1 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.