MODELING USER’S PREFERENCES TOWARDS ELECTRIC VEHICLES. A DISCREET CHOICE MODEL APPROACH

Authors

  • Tamás Ujházi University of Szeged, Faculty of Economics and Business Administration
  • Bence Vereckei-Poór

DOI:

https://doi.org/10.46541/978-86-7233-416-6_73

Keywords:

Electric car, Sustainability, Consumer behavior, Preferences, Choice Based Conjoint, MaxDiff

Abstract

Sustainability, and the demand for sustainable products came to the four in the last years. One of the most polluting acts of humanity is transportation. Even tough changing our location is an unavoidable part of the everyday life, we can choose alternatives which are less burdensome on the environment. Electric vehicles (EVs) present an environmentally friendlier solution in the future of mobility. The existence of these cars can already be seen on the streets of our cities, as they are available to the masses. At the same time, the spread of such vehicles is not so rapid, as there are compromises need to be made while using them. Since there is little information about electric cars, the range anxiety and high price tags also prevent EVs from spreading in large numbers. At the same time, besides being less polluting, there are multiple advantages of driving e-cars. However, the benefits of EVs will only come true if people will accept and use them. At present there are several car manufacturers who produce EVs, although people seem to still prefer internal combustion engine vehicles. In our research we use Choice Based Conjoint and Maxdiff analysis to understand people’s preferences towards EVs. In our results, we determine the best combination of attribute levels, that present the most preferable EV and show which are the most preferred EV brands. Our online research was published in the beginning of February for three weeks period, and we have reached 206 people.

Published

2024-02-29

How to Cite

Ujházi, T., & Vereckei-Poór, B. (2024). MODELING USER’S PREFERENCES TOWARDS ELECTRIC VEHICLES. A DISCREET CHOICE MODEL APPROACH. International Scientific Conference Strategic Management and Decision Support Systems in Strategic Management, 578-588. https://doi.org/10.46541/978-86-7233-416-6_73