J
John S. Liu
Researcher at National Taiwan University of Science and Technology
Publications - 61
Citations - 3016
John S. Liu is an academic researcher from National Taiwan University of Science and Technology. The author has contributed to research in topics: Data envelopment analysis & Literature survey. The author has an hindex of 23, co-authored 61 publications receiving 2432 citations. Previous affiliations of John S. Liu include Utrecht University & National Taiwan University.
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A survey of DEA applications
TL;DR: This study is the first literature survey that focuses on DEA applications, covering DEA papers published in journals indexed by the Web of Science database from 1978 through August 2010, and suggests that the two-step contextual analysis and network DEA are the recent trends across applications.
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Data envelopment analysis 1978-2010: A citation-based literature survey
TL;DR: The five most active DEA subareas in recent years are identified; among them the “two-stage contextual factor evaluation framework” is relatively more active.
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An integrated approach for main path analysis: Development of the Hirsch index as an example
John S. Liu,Louis Y.Y. Lu +1 more
TL;DR: This study enhances main path analysis by proposing several variants to the original approach that suggest new, complementary approaches to overcome limitations and hints at a divergence–convergence–divergence structure in the development of the Hirsch index.
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Research fronts in data envelopment analysis
TL;DR: This study applies a network clustering method to group the literature through a citation network established from the DEA literature over the period 2000 to 2014, and presents the research fronts, a coherent topic or issue addressed by a group of research articles in recent years.
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DEA and ranking with the network-based approach: a case of R&D performance
John S. Liu,Wen-Min Lu +1 more
TL;DR: This study enhances the network-based approach, which is a novel method to increase discrimination in data envelopment analysis, by removing the bias caused by a scale difference among organizations and highlighting the approach's ability to identify the strengths and weaknesses of each organization.