An analysis on travel party composition and expenditure: a discrete-continuous model

Rashidi, Taha Hossein; Koo, Tay T.R.

doi:10.1016/j.annals.2015.10.003

Cited by 39 publications

(37 citation statements)

References 39 publications

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“…Incidentally, the analytic solution of this LV model is the logit model [ 11 ]. This shows that there is a very strong connection between the dynamic model proposed in this paper and the approach adopted by the mainstream literature on the study of touristic flows [ 24 – 26 ]. Importantly, in line with mainstream literature, this model features an outside good or option.…”

Section: Modelmentioning

confidence: 80%

A Study of Tourism Dynamics in Three Italian Regions Using a Nonautonomous Integrable Lotka–Volterra Model

Romano

2016

PLoS ONE

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This article is a first application of an integrable nonautonomous Lotka–Volterra (LV) model to the study of tourism dynamics. In particular, we analyze the interaction in terms of touristic flows among three Italian regions. Confirming an hypothesis advanced by recent theoretical works, we find that these regions not only compete against each other, but at times they also proceed in mutualism. Moreover, the kind and the intensity of the interaction changes over time, suggesting that dynamic models can play a vital role in the study of touristic flows.

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Section: Modelmentioning

confidence: 80%

A Study of Tourism Dynamics in Three Italian Regions Using a Nonautonomous Integrable Lotka–Volterra Model

Romano

2016

PLoS ONE

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“…As observed by many (Dellaert et al, 1998;Gong et al, 2018;Jeng & Fesenmaier, 2002;Rashidi & Koo, 2016;Woodside & Dubelaar, 2002;Wu et al, 2011;Yang et al, 2019;Zhang et al, 2012), tourism decision-making is an integrated process within which tourists are required to make a series of decisions involving travel participation, travel party composition, time of travel, destination, length of stay and expenditure, etc. In single tourism choice models, the modelling framework does not embed the capacity to test for interrelations between the modelled travel choice and myriad of other travel choices (Rashidi & Koo, 2016). The consequence is potentially biased results, which may generate misleading policy implications with unintended consequences (Wu et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

“…Hence, joint modelling of multiple choices has attracted the interest of an increasing number of researchers. These include the modelling of interrelation between tourism participation and expenditure (Wu et al, 2013), multi-destination trips and transport modes (Masiero & Zoltan, 2013), tourism participation, length of stay and associated expenditure (Yang et al, 2019;Zhang et al, 2012), travel party composition and expenditure (Rashidi & Koo, 2016), travel participation and trip duration (Bhat, 2005;LaMondia et al, 2008;Van Nostrand et al, 2013;Wu et al, 2011), destination choices and length of stay (Gong et al, 2018) and transport modes and length of stay (Pellegrini & Scagnolari, 2019). However, it is noted that vast majority of current modelling practice in travel and tourism research focuses on single tourism choice.…”

Section: Introductionmentioning

confidence: 99%

Exploring the temporal travel choices: a joint modelling of how long to travel and when

Rashidi

Koo

2020

Current Issues in Tourism

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Many tourism travel decisions are interrelated. From modelling viewpoint incorporating these interrelations is important for understanding the consequences of exogenous shocks, as well as the intended and unintended consequences of policy. This paper addresses two tourist choices in the temporal dimension that were previously only handled as if they were mutually exclusive. The paper adopts a discrete-continuous model framework to simulate tourists' behaviours in selecting travel seasons (choice model) and associated trip duration (accelerated failure time model) and estimates the correlation between these two tourism decisions conditional on the covariates. The empirical analysis is based on Australian domestic tourism data and reveal the factors influencing both decisions as well as those that influence only one or the other. The model results, which is tested with data from select years between 1999-2018, including years of significant exogenous shocks, show mixed evidence of stability and changes in the parameters. Building on this evidence, the paper concludes with underlying temporal choice behaviours of tourists that may be of relevance during-and post-COVID19 environment.

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“…There is however a large body of literature on the general population that have examined the key determinants of tourism expenditure. While Brida and Scuderi (2013) provide a comprehensive review of microeconometric models on what determines tourist expenditure, recent studies in this area include Bernini and Cracolici (2015), Rashidi and Koo (2016) and Wu, Zhang, and Fujiwara (2013). By and large, income, age, and education were found to be keys determinants of tourism expenditure.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Given the evidence that income support from children was significant in retiree's tourism behaviour in Taiwan (C. Chen & Chen, 2018), the amount of financial transfer from children is included. Drawing from the literature (Bernini & Cracolici, 2015;Jang & Ham, 2009;Kuo & Lu, 2013;Rashidi & Koo, 2016;Smith & Diekmann, 2017;Wu et al, 2013), other variables such as education, home ownership, gender, marital status (married/partner and otherwise), urban/rural, and retired/working are also considered in both models. The latter five variables are dummy variables taking the value of one or zero while the first two are ordered variables explained under Table 4.1.…”

Section: Data and Variablesmentioning

confidence: 99%

Examining the well-being of the elderly in China

Fan¹

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This thesis investigates the nature and determinants of well-being with a focus on older adults. The study consists of three empirical papers, examining the effect from three perspectives: social capital, household traveling expenditure and income inequality. Well-being is measured using different aspects such as happiness, life satisfaction, and mental health. Research methods used include factor analysis, estimations by generalised least square and system generalized method of moments as well as hierarchical linear modeling. Using primary survey data, the first paper provides evidence that social capital is more (less) significant than income in improving subjective (objective) well-being, which calls for recalibration in the policy balance between targeting social capital and addressing poverty. Social capital affects well-being through different channels depending on whether it provides marketable or nonmarketable returns. For instance, renqing (gifts and money an individual can give by way of reciprocity towards receiving favours from others) and community integration in the form of social networks improve objective well-being. Social capital in the form of trust on the other hand boosts subjective well-being. Age too has different effects on subjective and objective well-being. While an older person is more satisfied with life, there is however a struggle in owning material things. The lack of correlation between subjective and objective well-being measures further complicates the policy debate. A robust approach to understanding and enhancing well-being in rural, remote areas should thus be an urgent priority in regional policy analyses.

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An analysis on travel party composition and expenditure: a discrete-continuous model

Cited by 39 publications

References 39 publications

A Study of Tourism Dynamics in Three Italian Regions Using a Nonautonomous Integrable Lotka–Volterra Model

A Study of Tourism Dynamics in Three Italian Regions Using a Nonautonomous Integrable Lotka–Volterra Model

Exploring the temporal travel choices: a joint modelling of how long to travel and when

Examining the well-being of the elderly in China

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