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Shiuh-Nan Hwang
Researcher at Ming Chuan University
Publications - 30
Citations - 3047
Shiuh-Nan Hwang is an academic researcher from Ming Chuan University. The author has contributed to research in topics: Data envelopment analysis & Inefficiency. The author has an hindex of 17, co-authored 29 publications receiving 2647 citations. Previous affiliations of Shiuh-Nan Hwang include National Cheng Kung University & Saint Petersburg State University.
Papers
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Efficiency decomposition in two-stage data envelopment analysis: an application to non-life insurance companies in taiwan
Chiang Kao,Shiuh-Nan Hwang +1 more
TL;DR: The relational model developed in this paper is more reliable in measuring the efficiencies and consequently is capable of identifying the causes of inefficiency more accurately.
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Using data envelopment analysis to measure hotel managerial efficiency change in Taiwan
Shiuh-Nan Hwang,Te-Yi Chang +1 more
TL;DR: In this paper, the authors used data envelopment analysis (DEA) to measure the managerial performance of 45 hotels in 1998 and the efficiency change of 45 Hotels from 1994 to 1998.
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The relationship among tourists’ involvement, place attachment and interpretation satisfaction in Taiwan’s national parks
TL;DR: In this article, a structural equation model is used for theory testing and development, and tourists' involvement has a positive significant effect on perceived interpretation service quality, as does place attachment.
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Efficiency measurement for network systems: IT impact on firm performance
Chiang Kao,Shiuh-Nan Hwang +1 more
TL;DR: A network DEA model is discussed which distributes the system inefficiency to its component processes and is applied to assess the impact of information technology (IT) on firm performance in a banking industry.
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Multi-period efficiency and Malmquist productivity index in two-stage production systems
Chiang Kao,Shiuh-Nan Hwang +1 more
TL;DR: A multi-period two-stage DEA model is developed, which is able to measure the overall and period efficiencies at the same time, with the former expressed as a weighted average of the latter.