Search results

This paper introduces a behavioral framework to model residential relocation decision in island areas, at which the decision in question is influenced by the characteristics of island regions, policy variables related to accessibility measures, and housing prices at the proposed island area, as well as personal, household (HH), job, and latent characteristics of the decision makers.

The model framework corresponds to an integrated choice and latent variable (ICLV) setting where the discrete choice model includes latent variables that capture attitudes and perceptions of the decision makers. The latent variable model is composed of a group of structural equations describing the latent variables as a function of observable exogenous variables and a group of measurement equations, linking the latent variables to observable indicators.

An empirical study has been developed for the Greek Aegean island area. Data were collected from 900 HHs in Greece contacted via telephone. The HHs were presented hypothetical scenarios involving policy variables, where 2010 was the reference year. ICLV binary logit (BL) and mixed binary logit (MBL) relocation choice models were estimated sequentially. Findings suggest that MBL models are superior to BL models, while both the policy and the latent variables significantly affect the relocation decision and improve considerably the models' goodness of fit. Sample enumeration method is finally used to aggregate the results over the Greek population.

Endogeneity or nonorthogonality in discrete choice models occurs when the systematic part of the utility is correlated with the error term. Under this misspecification, the model's estimators are inconsistent. When endogeneity occurs at the level of each observation, the principal technique used to treat for it is the control-function method, where a function that accounts for the endogenous part of the error term is constructed and is then included as an additional variable in the choice model. Alternatively, the latent-variable method can also address endogeneity. In this case, the omitted quality attribute is considered as a latent variable and modeled as a function of observed variables and/or measured through indicators. The link between the controlfunction and the latent-variable methods in the correction for endogeneity has not been established in previous work. This paper analyzes the similarities and differences between a set of variations of both methods, establishes the formal link between them in the correction for endogeneity, and illustrates their properties using a Monte Carlo experiment. The paper concludes with suggestions for future lines of research in this area.

The purpose of this paper is to develop a better understanding of the factors that influence a shipper's decision to choose air cargo as a mode of shipment.

A disaggregate multinomial discrete choice model is developed using freight shipment survey data to identify critical factors influencing air cargo mode choice. Disaggregate revealed preference data is obtained from surveying 347 manufacturers, freight forwarders, and other third-party service providers.

The empirical model developed in this research shows that the rate of shipment, time of transit, cost-per-pound shipped, quantity shipped, perishability and delay rate of the mode are significant factors that influence mode choice.

The discrete choice model developed can be improved by taking into account logistics costs not considered in this research. Perhaps more in-depth surveys of the shippers and freight forwarders are needed. Additionally, improving the mode choice model by including stated preference data and subsequently incorporating service quality latent variables would be beneficial.

Identifying the sensitivity of the shippers to various factors influencing mode selection enables transportation planners make better demand forecast for each mode of transportation.

This paper extends previous mode choice studies by analyzing mode selection between air cargo and other modes. Better forecasting is achieved by replacing the logit model with probit, heteroscedastic extreme value and mixed logit models.

Advertisers frequently use social media for interactive and customer-oriented relationship marketing (RM) purposes. Moreover, sports clubs and players have been using their social media accounts to post content of their sponsors and other advertising companies. Such posts create visibility and have value for these advertising companies, something which has not been empirically quantified in the existing literature. Hence, this paper's purpose is to identify the factors or attributes that influence the value of such advertisement posts.

A discrete choice approach is used to empirically estimate the utility that sponsorship managers derive from a post advertising their company or product on football clubs' and players' social media.

The results indicate that more followers, better on-field performance and a lower price significantly increase the advertising company's utility. Moreover, the used social media channel has a significant influence too, since Facebook and Instagram are preferred over Twitter, due to the latter's limited degrees of freedom for advertisers.

Considering additional factors such as the image fit between sponsor and sponsee and presence on the Chinese social media market offers an interesting avenue for future research.

The empirical estimates allow commercial managers of clubs and players to derive companies' relative willingness to pay (WTP) for changes in characteristics of advertisements on their social media from the calculated utilities. This information can be used in the pricing decision when social media posts are sold or included in sponsorship packages.

This is the first study applying discrete choice modelling to link social media marketing (SMM) and sports marketing.

This research aims to analyse the housing demand in northern France with respect to socio-demographic variables and the distance between the residence and the workplace.

Econometrics with discrete choice models are used to study the three main dimensional choices of housing demand: tenure, type and location. A contribution is to use a heteroscedastic logit model where the variance of the error term is allowed to differ over alternatives and to capture in particular the heterogeneity of tastes. As a matter of fact, household characteristics are very likely to influence the magnitude of the scale parameter in the choice of housing alternatives and then influencing the results if it is not taken into account. Applications for housing demand are nearly non-existent. This paper fills this gap.

Econometric estimation confirms that residential choices are influenced by age, income and size of the household, as well as by the rent-to-income ratio. An increase in any of these variables decreases the probability of choices of all the alternatives other than the most often chosen alternative (which is for this application house ownership in the suburb). Moreover, the distance to work systematically influences the housing choice for single-parent families and two-earner households. Additionally, preferences are found to significantly differ between local housing markets, specifically between Lille (a large agglomeration and capital city of the North area) and Dunkerque (an industrialised area). The geographical areas are defined based on INSEE employment zones (“zones d’emplois”).

This research has been performed for the north of France and may not hold for other areas even though the methodology can be replicated and the mechanisms at play are quite similar elsewhere.

An important conclusion for sustainable development is the importance to improve city centre amenities relative to those of the suburb or to increase the services associated with high-density dwelling because clearly the most desired alternative remains a house in the suburb. The housing market in the Dunkerque area has some special features characterised by a strong industrial landscape (with port and heavy-duty industrial activities). In this context, amenities provided by the city centre offset the strong attraction of a house in the suburb.

This research shows that households with similar characteristics tend to prefer the same type of real estate property. Therefore, to avoid social segregation, it is important that housing supply respond to different household preferences and needs in the different segments of the housing market. Moreover, the housing supply should take into account the specificities of the geographical areas (both in terms of population who may have a different profile) and in terms of amenities.

This research is one of the very few conducted ones on discrete housing choices in France (with the notable exception of De Palma et al., 2007 for the choice of location). Three simultaneous choices are considered: tenure (including social housing which is almost always ignored), type of building and location. The authors have shown that it is important to take into account the heterogeneity of the preferences in the econometric model with a heteroscedastic logit model.

This article addresses simultaneously two important features in random utility maximisation (RUM) choice modelling: choice set generation and unobserved taste heterogeneity. It is proposed to develop and to compare definitions and properties of econometric specifications that are based on mixed logit (MXL) and latent class logit (LCL) RUM models in the additional presence of prior compensatory screening decision rules. The latter allow for continuous latent bounds that determine choice alternatives to be or not to be considered for decision making. It is also proposed to evaluate and to test each against the other ones in an application to home-to-work mode choice in the Paris region of France using 2002 data.

Choice under risk has a large stochastic (unpredictable) component. This chapter examines five stochastic models for binary discrete choice under risk and how they combine with “structural” theories of choice under risk. Stochastic models are substantive theoretical hypotheses that are frequently testable in and of themselves, and also identifying restrictions for hypothesis tests, estimation and prediction. Econometric comparisons suggest that for the purpose of prediction (as opposed to explanation), choices of stochastic models may be far more consequential than choices of structures such as expected utility or rank-dependent utility.