Determinants of Length of Stay for Domestic Tourists: Case Study of Yixing
TL;DR: In this paper, the authors employed data from a tourist survey in Yixing, China, to investigate potential factors influencing a tourist's length of stay, and found that distance, age, organized tour, transportation, motivation, past visits and assessment of accommodation are some of the major determinants of a tourists' stay.
Abstract: The length of stay of a tourist is one of the most important factors indicating consumption levels and revenue generation for certain tourist destinations. This study employs data from a tourist survey in Yixing, China, to investigate potential factors influencing a tourist's length of stay. Applying an ordered logit model, it is found that distance, age, organized tour, transportation, motivation, past visits and assessment of accommodation are some of the major determinants of a tourist's length of stay. The results indicate that traveling distance and the assessment of accommodation are positively associated with the length of stay. In addition, tourists with different modes of transportation, motivations and past visits have different durations of stay. Based on the estimation results from subsamples, it is also found that there are differences in determinants of length of stay between organized tourists and individual tourists, and among different age groups.
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TL;DR: In this article, the authors identify how the use of peer-to-peer accommodation leads to changes in travelers' behavior and identify how these changes can affect expansion in destination selection, increase in travel frequency, length of stay, and range of activities participated in tourism destinations.
Abstract: As a result of the phenomenal growth of the sharing economy in the travel industry, investigating its potential impacts on travelers and tourism destinations is of paramount importance. The goal of this study was to identify how the use of peer-to-peer accommodation leads to changes in travelers’ behavior. Based on two online surveys targeting travelers from the United States and Finland, it was identified that the social and economic appeals of peer-to-peer accommodation significantly affect expansion in destination selection, increase in travel frequency, length of stay, and range of activities participated in tourism destinations. Travelers’ desires for more meaningful social interactions with locals and unique experiences in authentic settings drive them to travel more often, stay longer, and participate in more activities. Also, the reduction in accommodation cost allows travelers to consider and select destinations, trips, and tourism activities that are otherwise cost-prohibitive. Implications for tourism planning and management are provided.
597 citations
TL;DR: In this paper, the authors investigate potential factors contributing to the hotel location choice by an ordered logit model incorporating both hotel and location characteristics, and they show that, downscale hotels tend not to actively seek the benefits of agglomeration effects while upscale ones are more sensitive to accessibility.
148 citations
TL;DR: In this paper, a shared heterogeneity duration model was applied to tourists' length of stay at different locations of multidestination trips to understand tourists' behaviors and to predict their length-of-stay according to relevant variables.
Abstract: This study applied a shared heterogeneity duration model to tourists’ length of stay at different locations of multidestination trips. This analysis helps to understand tourists’ behaviors and to predict their length of stay according to relevant variables. Such information can be applied to the development of efficient marketing strategies aiming to push the average length of stay to the desired direction, and to develop “on the fly” service provision and revenue management strategies. The focus on multiple destination trips offers an innovative analytical perspective. A large data set of 309,000 visits to Brazilian destinations was analyzed. Several empirical findings regarding determinants of tourists’ length of stay were obtained. Positively skewed distributions for duration and hazard functions were found to best fit observed data. Shared heterogeneity was found to statistically improve the explanatory capacity of duration models when multidestination tourism trips data are analyzed.
68 citations
Cites background or methods or result from "Determinants of Length of Stay for ..."
...On the contrary, Yang, Wong, and Zhang (2011) found that tourists on vacation at Yixing, China, have a shorter average stay than business tourists, while those visiting friends and relatives display the longest average length of stay....
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...Yang, Wong, and Zhang (2011) found that expected length of stay increases according to the mean of transport following the sequence self-driving, coach and bus, airplane, and train....
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...This result is contradictory to all previous studies (Alegre, Mateo, and Pou 2011; Alegre and Pou 2006; Barros and Machado 2010; Gokovali, Bahar, and Kozak 2007; Mak, Moncur, and Yonamine 1977; Yang, Wong, and Zhang 2011)....
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...…by binary logit (Alegre and Pou 2006), multinomial logit (Grigolon et al. 2014; Nicolau and Más 2009), ordered logit (Ferrer-Rosell, Martínez-Garcia, and Coenders 2014; Yang, Wong, and Zhang 2011) and count data models (Alegre, Mateo, and Pou 2011; Brida, Meleddu, and Pulina 2013; Hellström 2006)....
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...…at the destination, which is consistent with direct analysis of the effect of distance conducted by other studies (Blaine, Mohammad, and Var 1993; Nicolau and Más 2009; Paul and Rimmawi 1992; Silberman 1985; Yang, Wong, and Zhang 2011; Walsh and Davitt 1983; Wang, Little, and DelHomme-Little 2012)....
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TL;DR: This paper analyzes the subsequent decisions of tourists with respect to sequential destinations: why they visit a given destination after visiting a previous one and where they are most likely to go.
65 citations
TL;DR: Length-of-stay (LOS) is a key parameter in destination management that determines the number of guest nights relative to arrival numbers, with concomitant repercussions for revenue generation and o...
Abstract: Length-of-stay (LOS) is a key parameter in destination management that determines the number of guest nights relative to arrival numbers, with concomitant repercussions for revenue generation and o...
64 citations
Cites background from "Determinants of Length of Stay for ..."
...Walsh and Davitt (1983) as well as Silberman (1985) suggested that distance travelled positively influences LOS, a relationship more recently confirmed by Nicolau and M as (2009) and Yang, Wong, and Zhang (2011)....
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References
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09 May 2005
TL;DR: This chapter discusses models for making pseudo-random draw, which combines asymptotic theory, Bayesian methods, and ML and NLS estimation with real-time data structures.
Abstract: This book provides the most comprehensive treatment to date of microeconometrics, the analysis of individual-level data on the economic behavior of individuals or firms using regression methods for cross section and panel data. The book is oriented to the practitioner. A basic understanding of the linear regression model with matrix algebra is assumed. The text can be used for a microeconometrics course, typically a second-year economics PhD course; for data-oriented applied microeconometrics field courses; and as a reference work for graduate students and applied researchers who wish to fill in gaps in their toolkit. Distinguishing features of the book include emphasis on nonlinear models and robust inference, simulation-based estimation, and problems of complex survey data. The book makes frequent use of numerical examples based on generated data to illustrate the key models and methods. More substantially, it systematically integrates into the text empirical illustrations based on seven large and exceptionally rich data sets.
8,189 citations
Book•
10 Sep 2014
TL;DR: In this article, the authors present a brief tutorial for estimating, testing, fit, and interpretation of ordinal and binary outcomes using Stata. But they do not discuss how to apply these models to other estimation commands, such as post-estimation analysis.
Abstract: Preface PART I GENERAL INFORMATION Introduction What is this book about? Which models are considered? Whom is this book for? How is the book organized? What software do you need? Where can I learn more about the models? Introduction to Stata The Stata interface Abbreviations How to get help The working directory Stata file types Saving output to log files Using and saving datasets Size limitations on datasets Do-files Using Stata for serious data analysis Syntax of Stata commands Managing data Creating new variables Labeling variables and values Global and local macros Graphics A brief tutorial Estimation, Testing, Fit, and Interpretation Estimation Postestimation analysis Testing estat command Measures of fit Interpretation Confidence intervals for prediction Next steps PART II MODELS FOR SPECIFIC KINDS OF OUTCOMES Models for Binary Outcomes The statistical model Estimation using logit and probit Hypothesis testing with test and lrtest Residuals and influence using predict Measuring fit Interpretation using predicted values Interpretation using odds ratios with listcoef Other commands for binary outcomes Models for Ordinal Outcomes The statistical model Estimation using ologit and oprobit Hypothesis testing with test and lrtest Scalar measures of fit using fitstat Converting to a different parameterization The parallel regression assumption Residuals and outliers using predict Interpretation Less common models for ordinal outcomes Models for Nominal Outcomes with Case-Specific Data The multinomial logit model Estimation using mlogit Hypothesis testing of coefficients Independence of irrelevant alternatives Measures of fit Interpretation Multinomial probit model with IIA Stereotype logistic regression Models for Nominal Outcomes with Alternative-Specific Data Alternative-specific data organization The conditional logit model Alternative-specific multinomial probit The sturctural covariance matrix Rank-ordered logistic regression Conclusions Models for Count Outcomes The Poisson distribution The Poisson regression model The negative binomial regression model Models for truncated counts The hurdle regression model Zero-inflated count models Comparisons among count models Using countfit to compare count models More Topics Ordinal and nominal independent variables Interactions Nonlinear models Using praccum and forvalues to plot predictions Extending SPost to other estimation commands Using Stata more efficiently Conclusions Appendix A Syntax for SPost Commands Appendix B Description of Datasets References Author Index Subject Index
4,703 citations
Journal Article•
2,947 citations
"Determinants of Length of Stay for ..." refers background or methods in this paper
...Defining y* as a latent variable ranging from 21 to +1, the structural model is y∗i = a+ bXi + 1i where i is the observation (individual tourist) and 1 is a random error (Long & Fresse, 2003)....
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...This would suggest that a generalized ordered logit model may be applied in future research (Long & Fresse, 2003)....
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TL;DR: Gologit2 as discussed by the authors is a generalized ordered logit model inspired by Vincent Fu's gologit routine (Stata Technical Bulletin Reprints 8: 160-164).
Abstract: This article describes the gologit2 program for generalized ordered logit models. gologit2 is inspired by Vincent Fu's gologit routine (Stata Technical Bulletin Reprints 8: 160–164) and is backward...
1,805 citations
TL;DR: A review of the published studies on tourism demand modelling and forecasting since 2000 is presented in this article, where the authors identify some new research directions, which include improving the forecasting accuracy through forecast combination; integrating both qualitative and quantitative forecasting approaches, tourism cycles and seasonality analysis, events' impact assessment and risk forecasting.
977 citations