Sample-based longitudinal discrete choice experiments: preferences for electric vehicles over time

Abstract

Discrete choice experiments have emerged as the state-of-the-art method for measuring preferences, but they are mostly used in cross-sectional studies. In seeking to make them applicable for longitudinal studies, our study addresses two common challenges: working with different respondents and handling altering attributes. We propose a sample-based longitudinal discrete choice experiment in combination with a covariate-extended hierarchical Bayes logit estimator that allows one to test the statistical significance of changes. We showcase this method’s use in studies about preferences for electric vehicles over six years and empirically observe that preferences develop in an unpredictable, non-monotonous way. We also find that inspecting only the absolute differences in preferences between samples may result in misleading inferences. Moreover, surveying a new sample produced similar results as asking the same sample of respondents over time. Finally, we experimentally test how adding or removing an attribute affects preferences for the other attributes.

Influence of Social Relationships on Decisions in Device-to-Device Communication

“…This experimental design is more realistic than traditional conjoint analyses due to the similarity of these choices to real-world decisions, so that CBCs explain actual adoption behavior well. CBCs with no-choice options are therefore a popular and frequently used survey method [44], [45]. One drawback is that when respondents select the nochoice option, we do not reveal any information about the trade-off between the attractiveness of the attribute levels in the product alternatives.…”

Identifying patient preferences for diabetes care: A protocol for implementing a discrete choice experiment in Samoa

“…Few longitudinal studies using discrete choice experiments have been undertaken [90], therefore analyses will be largely exploratory. Because time between assessments (12 months) is short, the same DCE instrument will be used at each timepoint [90,91]. Following best practices, we will analyze models at each timepoint using multinomial logit modeling [91].…”

“…By including demographic, socioeconomic, and care-related questionnaire data into our models, we will be able to explore how external factors, and especially changes in external factors such as income status, care use/disuse, and diabetes management and distress, are associated with changes in preferences over time. In the unlikely case that we experience greater loss to followup and attrition than expected, we will use repeated cross-sectional DCE approaches (such as a covariate extended model) to measure if differences in preferences across samples are significant [90]. Analyses will be completed in Stata v. 17 (College Station, TX) using the Choice Models (CM) suite of commands [92].…”

How to Green the European Auto ABS Market? A Literature Survey

“…Surveys have shown that the factors of electric vehicle adoption include moral and social motives (Kastner et al, 2021;Bobeth and Kastner, 2020), charging infrastructure concerns (Haustein and Jensen, 2018), and real life usage experience (Jensen et al, 2014), among others. However, preferences can change over time as shown by Keller et al (2021). Covering the period 2013 to 2019, the authors find that preferences for EVs develop in an unpredictable and non-monotonous way.…”

“…The extent to which EVs will become a common means of transportation is still uncertain due to multiple reasons. For instance, Jensen et al (2014) observe that the purchase intention for EVs decreases after the individual real life trial period of using the vehicle, while Keller et al (2021) find that prospective buyers are concerned about batteries catching fire and the general environmental friendliness of EVs (e.g., due to electricity being generated in coal-fired power plants).…”