Representing the influence of multiple social interactions on monthly tourism participation behavior

Wu, Ling-Ling; Zhang, Junyi; Chikaraishi, Makoto

doi:10.1016/j.tourman.2012.09.010

Cited by 25 publications

(14 citation statements)

References 30 publications

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“…To estimate the car ownership model and car use model, multilevel Bayesian procedure based on the Markov Chain Monte Carlo (MCMC) method was conducted, which could overcome the deficiency resulting from the maximum likelihood estimation method [ 42 , 54 ]. The estimation method is based on Bayes’ Theorem as follows.…”

Section: Methodsmentioning

confidence: 99%

“…

is the prior distribution. Based on the posterior distribution of parameters, MCMC method can generate a chain to make point and interval estimations through successive sampling [ 54 , 55 ]. Different from p value estimation based on the mean and the variance, MCMC method provides a more direct way through the posterior distributions of parameters.…”

Section: Methodsmentioning

confidence: 99%

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Exploring the Influence of Built Environment on Car Ownership and Use with a Spatial Multilevel Model: A Case Study of Changchun, China

Wang

Shao

Yin

et al. 2018

IJERPH

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Although the impacts of built environment on car ownership and use have been extensively studied, limited evidence has been offered for the role of spatial effects in influencing the interaction between built environment and travel behavior. Ignoring the spatial effects may lead to misunderstanding the role of the built environment and providing inconsistent transportation policies. In response to this, we try to employ a two-step modeling approach to investigate the impacts of built environment on car ownership and use by combining multilevel Bayesian model and conditional autocorrelation (CAR) model to control for spatial autocorrelation. In the two-step model, the predicting car ownership status in the first-step model is used as a mediating variable in the second-step car use model. Taking Changchun as a case study, this paper identifies the presence of spatial effects in influencing the effects of built environment on car ownership and use. Meanwhile, the direct and cascading effects of built environment on car ownership and use are revealed. The results show that the spatial autocorrelation exists in influencing the interaction between built environment and car dependency. The results suggest that it is necessary for urban planners to pay attention to the spatial effects and make targeted policy according to local land use characteristics.

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Section: Methodsmentioning

confidence: 99%

“…

Section: Methodsmentioning

confidence: 99%

Exploring the Influence of Built Environment on Car Ownership and Use with a Spatial Multilevel Model: A Case Study of Changchun, China

Wang

Shao

Yin

et al. 2018

IJERPH

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“…using a hold-out sample -and would perhaps have helped to more strongly relate the classes of vacationers to observable characteristics, such as socio-demographic variables. Secondly, travel motives and the attitudes of vacationers (Castro et al, 2007;Lee, 2009) or intra-household dynamics and social interactions (Wu et al, 2013) may play a role in explaining the vacationers' response behaviour but these were not considered in this study. Lastly, this study is based on the responses stated in the context of a highly hypothetical scenario.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Short-term responses of Dutch vacationers to a sharp increase in transport costs

Cranenburgh

2016

Current Issues in Tourism

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“…6 Li and Lee (2009) counter the rational expectations assumption by using data that measured behavioral perceptions and Manski (2004) Compliance motivations have been limited in travel models of discrete choice and the specific tactics relevant to travel behavior will be described. Social norms are considered by Wu et al (2013) and Goetzke and Weinberger (2012) by using exogenous influence sources. Social norms are ''[rules that state] expectations about the appropriate and correct behavior in a situation'' (Pratkanis, 2007, p. 38).…”

Section: Compliancementioning

confidence: 99%

“…The importance of this has been briefly mentioned in the transportation literature (e.g. Dugundji and Walker, 2005;Kuwano et al, 2011;Wu et al, 2013). The general pattern is to choose socioeconomic indicators and place individuals into groups based on these categories.…”

Section: Link Generation Processmentioning

confidence: 99%

Generalized behavioral framework for choice models of social influence: Behavioral and data concerns in travel behavior

Maness

Cirillo

Dugundji

2015

Journal of Transport Geography

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a b s t r a c tOver the past two decades, transportation has begun a shift from an individual focus to a social focus. Accordingly, discrete choice models have begun to integrate social context into its framework. Social influence, the process of having one's behavior be affected by others, has been one approach to this integration. This paper provides a review and discussion of the incorporation of social influence into discrete choice models with specific application in travel behavior analysis. The discussion begins with a generalized framework to describe choice models of social influence. This framework focuses on the behavioral microfoundations of social influence and choice by separating the social influence mechanism from the source of its influence and by explicitly acknowledging the role of the social network in the model structure. This contrasts with prior work that focused on the measurement of contextual, endogenous, and correlated effects. Then, the state of the art in travel behavior research is reviewed using a taxonomy based on the generalized framework with research performed in sociology, social psychology, and social network analysis. The discussion then shifts to the importance of understanding the motivations for social influence, and the formation and structure of social networks are explored. Additionally, the challenges of collecting data for social influence studies are mentioned and the paper concludes with a look at the challenges in the field and areas for future research.

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Representing the influence of multiple social interactions on monthly tourism participation behavior

Cited by 25 publications

References 30 publications

Exploring the Influence of Built Environment on Car Ownership and Use with a Spatial Multilevel Model: A Case Study of Changchun, China

Exploring the Influence of Built Environment on Car Ownership and Use with a Spatial Multilevel Model: A Case Study of Changchun, China

Short-term responses of Dutch vacationers to a sharp increase in transport costs

Generalized behavioral framework for choice models of social influence: Behavioral and data concerns in travel behavior

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