Analyzing tourists' satisfaction: A multivariate ordered probit approach

Hikaru Hasegawa

Tourism Management, 2010, vol. 31, issue 1, 86-97

Abstract: 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.

Keywords: Bayesian analysis; Gibbs sampling; Markov chain Monte Carlo (MCMC); Metropolis–Hastings (M–H) algorithm (search for similar items in EconPapers)
JEL-codes: C11 C35 (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (12)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:touman:v:31:y:2010:i:1:p:86-97

DOI: 10.1016/j.tourman.2009.01.008

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