Analyzing tourists' satisfaction: A multivariate ordered probit approach
Analyzing tourists' satisfaction: A multivariate ordered probit approach
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DOI：
10.1016/j.tourman.2009.01.008
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发表时间：
2010-02
影响因子：
12.7
通讯作者：
Hikaru Hasegawa
中科院分区：
文献类型：
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作者：
Hikaru Hasegawa
本文用马尔可夫链蒙特卡罗(MCMC)方法研究了多元有序概率模型的贝叶斯估计。将该方法应用于游客满意度的单位记录数据。这些数据来自北海道政府经济部进行的《2002年游客满意度调查年度报告》。此外，利用贝叶斯分析的后验结果，构建了出行总体满意度与出行具体方面满意度之间的关系指数。结果表明，从风景和餐饮两方面获得的满意度对总体满意度的影响最大。
This article considers a Bayesian estimation of the multivariate ordered probit model using a Markov chain Monte Carlo (MCMC) method. The method is applied to unit record data on the satisfaction experienced by tourists. The data were obtained from the Annual Report on the Survey of Tourists' Satisfaction 2002, conducted by the Department of Economic Affairs of the Hokkaido government. Furthermore, using the posterior results of the Bayesian analysis, indices of the relationship between the overall satisfaction derived from the trip and the satisfaction derived from specific aspects of the trip are constructed. The results revealed that the satisfaction derived from the scenery and meals has the largest influence on the overall satisfaction.