Modelling user satisfaction in public transport systems considering missing information
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Collecting data to obtain insights into customer satisfaction with public transport services is very time-consuming and costly. Many factors such as service frequency, reliability and comfort during the trip have been found important drivers of customer satisfaction. Consequently, customer satisfaction surveys are quite lengthy, resulting in many interviews not being completed within the aboard time of the passengers/respondents. This paper questions as to whether it is possible to reduce the amount of information collected without a compromise on insights. To address this research question, we conduct a comparative analysis of different Ordered Probit models: one with a full list of attributes versus one with partial set of attributes. For the latter, missing information was imputed using three different methods that are based on modes, single imputations using predictive models and multiple imputation. Estimation results show that the partial model using the multiple imputation method behaves in a similar way to the model that is based on the full survey. This finding opens an opportunity to reduce interview time which is critical for most customer satisfaction surveys.