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A Policy Category Analysis Model for Tourism Promotion in China During the COVID-19 Pandemic Based on Data Mining and Binary Regression

BACKGROUND AND AIM: At the end of 2019, the outbreak of COVID-19 had a significant impact on China’s tourism industry, which was almost at a standstill in the short-term. After reaching the preliminarily stable state, the government and the scenic area management department implemented a series of i...

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Autores principales: Chen, Tinggui, Peng, Lijuan, Yin, Xiaohua, Jing, Bailu, Yang, Jianjun, Cong, Guodong, Li, Gongfa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7781111/
https://www.ncbi.nlm.nih.gov/pubmed/33408543
http://dx.doi.org/10.2147/RMHP.S284564
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author Chen, Tinggui
Peng, Lijuan
Yin, Xiaohua
Jing, Bailu
Yang, Jianjun
Cong, Guodong
Li, Gongfa
author_facet Chen, Tinggui
Peng, Lijuan
Yin, Xiaohua
Jing, Bailu
Yang, Jianjun
Cong, Guodong
Li, Gongfa
author_sort Chen, Tinggui
collection PubMed
description BACKGROUND AND AIM: At the end of 2019, the outbreak of COVID-19 had a significant impact on China’s tourism industry, which was almost at a standstill in the short-term. After reaching the preliminarily stable state, the government and the scenic area management department implemented a series of incentive policies in order to speed up the recovery of the tourism industry. Therefore, analyzing all sorts of social effects after policy implementation is of guiding significance for the government and the scenic areas. METHODS: Targeted as the social effect with the implementation of tourism promotion policy during the COVID-19 pandemic, this paper briefly analyzes the impact of COVID-19 on the national cultural and tourism industry and selects several representative types of tourism policies, crawls the comment data of Weibo users, analyzes users’ perception and emotional preference to the policy, and thus mines the social effect of various policies. Subsequently, by identifying the social effects of various policies as dependent variables, a binary logistic regression model is constructed to obtain the best combination of tourism promotion policies and promote the rapid revitalization of the cultural and tourism industry. RESULTS: The results show that from the single policy, the social effect of the “safety” policy is the best. From the perspective of combination policies, the simultaneous release of “safety” policies and “economy” policies have the greatest social impact, which can dramatically accelerate the recovery of the cultural and tourism industry. Finally, this paper proposes suggestions for policy formulation to improve the ability of the cultural tourism industry to cope with crisis events. CONCLUSION: These results explain the perceived effects of the public on the government policies and can be used to judge whether the policies have been released in place. Based on the above results, corresponding suggestions are proposed as follows: 1) the combination of economic policies and security policies can achieve better results; and 2) the role of “opinion leaders” can be played to improve the perceived effect of policies.
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spelling pubmed-77811112021-01-05 A Policy Category Analysis Model for Tourism Promotion in China During the COVID-19 Pandemic Based on Data Mining and Binary Regression Chen, Tinggui Peng, Lijuan Yin, Xiaohua Jing, Bailu Yang, Jianjun Cong, Guodong Li, Gongfa Risk Manag Healthc Policy Original Research BACKGROUND AND AIM: At the end of 2019, the outbreak of COVID-19 had a significant impact on China’s tourism industry, which was almost at a standstill in the short-term. After reaching the preliminarily stable state, the government and the scenic area management department implemented a series of incentive policies in order to speed up the recovery of the tourism industry. Therefore, analyzing all sorts of social effects after policy implementation is of guiding significance for the government and the scenic areas. METHODS: Targeted as the social effect with the implementation of tourism promotion policy during the COVID-19 pandemic, this paper briefly analyzes the impact of COVID-19 on the national cultural and tourism industry and selects several representative types of tourism policies, crawls the comment data of Weibo users, analyzes users’ perception and emotional preference to the policy, and thus mines the social effect of various policies. Subsequently, by identifying the social effects of various policies as dependent variables, a binary logistic regression model is constructed to obtain the best combination of tourism promotion policies and promote the rapid revitalization of the cultural and tourism industry. RESULTS: The results show that from the single policy, the social effect of the “safety” policy is the best. From the perspective of combination policies, the simultaneous release of “safety” policies and “economy” policies have the greatest social impact, which can dramatically accelerate the recovery of the cultural and tourism industry. Finally, this paper proposes suggestions for policy formulation to improve the ability of the cultural tourism industry to cope with crisis events. CONCLUSION: These results explain the perceived effects of the public on the government policies and can be used to judge whether the policies have been released in place. Based on the above results, corresponding suggestions are proposed as follows: 1) the combination of economic policies and security policies can achieve better results; and 2) the role of “opinion leaders” can be played to improve the perceived effect of policies. Dove 2020-12-31 /pmc/articles/PMC7781111/ /pubmed/33408543 http://dx.doi.org/10.2147/RMHP.S284564 Text en © 2020 Chen et al. http://creativecommons.org/licenses/by-nc/3.0/ This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php).
spellingShingle Original Research
Chen, Tinggui
Peng, Lijuan
Yin, Xiaohua
Jing, Bailu
Yang, Jianjun
Cong, Guodong
Li, Gongfa
A Policy Category Analysis Model for Tourism Promotion in China During the COVID-19 Pandemic Based on Data Mining and Binary Regression
title A Policy Category Analysis Model for Tourism Promotion in China During the COVID-19 Pandemic Based on Data Mining and Binary Regression
title_full A Policy Category Analysis Model for Tourism Promotion in China During the COVID-19 Pandemic Based on Data Mining and Binary Regression
title_fullStr A Policy Category Analysis Model for Tourism Promotion in China During the COVID-19 Pandemic Based on Data Mining and Binary Regression
title_full_unstemmed A Policy Category Analysis Model for Tourism Promotion in China During the COVID-19 Pandemic Based on Data Mining and Binary Regression
title_short A Policy Category Analysis Model for Tourism Promotion in China During the COVID-19 Pandemic Based on Data Mining and Binary Regression
title_sort policy category analysis model for tourism promotion in china during the covid-19 pandemic based on data mining and binary regression
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7781111/
https://www.ncbi.nlm.nih.gov/pubmed/33408543
http://dx.doi.org/10.2147/RMHP.S284564
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