The development trajectories of tourism literature and a study of Taiwan’s tourism industry
博士 === 元智大學 === 管理學院博士班 === 105 === This study retrieves 11,063 tourism relevant articles from Scopus database and applies main path analysis and edge-betweenness clustering technique to identifying the knowledge development trajectory and the major research themes in tourism. The time span of data...
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ndltd-TW-105YZU056270082017-09-19T04:29:38Z http://ndltd.ncl.edu.tw/handle/91127743157760920711 The development trajectories of tourism literature and a study of Taiwan’s tourism industry 觀光文獻發展軌跡與台灣觀光產業之探討 Ching-Chen Lee 李靜貞 博士 元智大學 管理學院博士班 105 This study retrieves 11,063 tourism relevant articles from Scopus database and applies main path analysis and edge-betweenness clustering technique to identifying the knowledge development trajectory and the major research themes in tourism. The time span of data ranges from 1953 to 2014. A citation network of the relevant articles is built and then two methods are applied on the network. First, the main path analysis is used to identify the knowledge diffusion path and the important articles. Second, the edge-betweenness clustering technique is applied to grouping and finding the major research themes. Six research themes are identified: tourism destination choice, sustainable tourism development, authenticity of heritage tourism, tourism demand forecasting, the impact of tourism on climate change, and medical tourism. Among them, tourism demand forecasting is the most relevant to Taiwan’s tourism development. This study uses the major inbound tourists from Mainland china , Hong Kong and Macaw, Japan, Korea and United States to build the tourism demand forecasting model. Final, the forecasting model is applied to estimate future inbound tourists from five main countries or regions in Taiwan. Yu-Yang Lu 盧煜煬 2017 學位論文 ; thesis 90 zh-TW |
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博士 === 元智大學 === 管理學院博士班 === 105 === This study retrieves 11,063 tourism relevant articles from Scopus database and applies main path analysis and edge-betweenness clustering technique to identifying the knowledge development trajectory and the major research themes in tourism. The time span of data ranges from 1953 to 2014. A citation network of the relevant articles is built and then two methods are applied on the network. First, the main path analysis is used to identify the knowledge diffusion path and the important articles. Second, the edge-betweenness clustering technique is applied to grouping and finding the major research themes. Six research themes are identified: tourism destination choice, sustainable tourism development, authenticity of heritage tourism, tourism demand forecasting, the impact of tourism on climate change, and medical tourism. Among them, tourism demand forecasting is the most relevant to Taiwan’s tourism development. This study uses the major inbound tourists from Mainland china , Hong Kong and Macaw, Japan, Korea and United States to build the tourism demand forecasting model. Final, the forecasting model is applied to estimate future inbound tourists from five main countries or regions in Taiwan.
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Yu-Yang Lu |
author_facet |
Yu-Yang Lu Ching-Chen Lee 李靜貞 |
author |
Ching-Chen Lee 李靜貞 |
spellingShingle |
Ching-Chen Lee 李靜貞 The development trajectories of tourism literature and a study of Taiwan’s tourism industry |
author_sort |
Ching-Chen Lee |
title |
The development trajectories of tourism literature and a study of Taiwan’s tourism industry |
title_short |
The development trajectories of tourism literature and a study of Taiwan’s tourism industry |
title_full |
The development trajectories of tourism literature and a study of Taiwan’s tourism industry |
title_fullStr |
The development trajectories of tourism literature and a study of Taiwan’s tourism industry |
title_full_unstemmed |
The development trajectories of tourism literature and a study of Taiwan’s tourism industry |
title_sort |
development trajectories of tourism literature and a study of taiwan’s tourism industry |
publishDate |
2017 |
url |
http://ndltd.ncl.edu.tw/handle/91127743157760920711 |
work_keys_str_mv |
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