Consumer Happiness in the Purchase of Electric Vehicles: a Fuzzy Logic Model

Fernando; Aboud  Barsekh Onji; Leticia Refugio Chavarría López; Paola Judith Maldonado Colín

doi:10.32870/myn.vi54.7776

Autores/as

Fernando Instituto Politécnico Nacional https://orcid.org/0000-0002-0216-1647
Aboud Barsekh Onji Qatar Embassy https://orcid.org/0009-0004-5440-8092
Leticia Refugio Chavarría López Consejo Superior del Cooperativismo de la República Mexicana https://orcid.org/0000-0001-9586-2241
Paola Judith Maldonado Colín Instituto Politécnico Nacional https://orcid.org/0009-0005-4595-9578

DOI:

https://doi.org/10.32870/myn.vi54.7776

Palabras clave:

Fuzzy Logic, Consumer happiness, Fuzzy logic models, Decision-making

Resumen

This study analyzes customer happiness in acquiring an electric vehicle, considering pleasure as an ambiguous language term that conventional models have inadequately incorporated. This research was conducted using a fuzzy Delphi method survey targeting a specific consumer group and two fuzzy inference systems: a multi-input single-output FIS model and an FIS Tree employing a hierarchical fuzzy inference structure, which leverages the survey's training data to optimize the models using different machine learning algorithms. The FIS tree model demonstrated superior efficacy in predicting the consumer satisfaction index, achieving an average forecast error of 0.65%. This approach could assist automobile agency marketers in creating accurate predictions to evaluate the purchasing decision-making process.

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