Abstract
Customer overall satisfaction regarding a public transport system is dependent on the satisfaction of the users with the attributes that make up the service, as well as the contribution that each of these attributes makes to explain the overall satisfaction. A common way of analysing the contribution of service attributes to explain overall satisfaction is through the use of ordered logit or probit models. This article presents an ordered logit model that considers the weighting of independent variables through the explicit importance calculated on the basis of a best-worst case 1 choice task. For the calculation of importance, a multinomial logit model has been estimated which considers the heterogeneity of the sample through systematic variations in user tastes. In this way, it is possible to establish a level of importance of each specific attribute for each type of user. The results show that the importance varies considerably depending on different socio-economic and mobility-base variables. On the other hand, the inclusion of the weighted variables in the ordered logit model improves its fit. Therefore, the results make possible to develop policies focused on improving satisfaction on specific user targets.
Highlights
The satisfaction with a public transport system is conditioned by the satisfaction about the different attributes that compose it
This article has developed a method to include weighted variables in ordered logit models that considers the importance that users give to the different attributes that make up a public transport service
The results have shown that the importance of the attributes is conditioned by the socioeconomic and travel characteristics of the users, improving in turn the predictive capacity of the models
Summary
The satisfaction with a public transport system is conditioned by the satisfaction about the different attributes that compose it. The study developed in this paper intends to take a further step in the research carried out by Echaniz et al [42] by weighting the variables of an ordered model by using the importance levels obtained through a Best-Worst type questions. It is not possible to establish the drivers that affect the variation on importance levels To study this fact in more detail, this paper develops a BW-based model that considers the systematic variations of the sample, defining those variables that most influence the variation of the perception of importance. The random variation found in Echaniz et al [48] is explained through systematic, observable variables These modelling results are combined with the ordered logit model, to improve the model’s fit and to check whether the importance of the attributes affects the modelling of overall user satisfaction. The paper ends with the conclusions of the main findings, with some directions for future study
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