Consumer sentiments in automotive purchases before and after COVID-19: a text-mining study

Ashok Bhattarai, Jiaxi Luo, Shih Yung Chou and Charles Ramser

International Journal of Business Environment, 2025, vol. 16, issue 1, 62-76

Abstract: The COVID-19 pandemic has led to shortages in the automotive industry due to a limited supply of semiconductor chips, which has created a nonlinear dynamic and chaotic business environment in the industry. This leads to the following important yet unanswered questions: 1) Is there a divergence in consumer emphases placed on the car buying process prior to and after COVID-19?; 2) How do consumer sentiment patterns affect their ratings of car dealerships prior to and after COVID-19? To answer these questions, we utilise a text-mining approach and perform an ordered probit regression analysis. Results illustrate the following. First, the sentiment keyword 'fast' had a positive impact on consumer online ratings after COVID-19, whereas 'clean' had a positive impact on consumer online ratings before COVID-19. Third, the sentiment keyword 'wait' had a negative impact on consumer online ratings after COVID-19. Fourth, the sentiment keyword 'willing' had a negative impact on consumer online ratings both before and after COVID-19. Finally, the sentiment keyword 'mess' had a negative impact on consumer online ratings both before and after COVID-19.

Keywords: automotive purchase; COVID-19; consumer sentiments; text mining. (search for similar items in EconPapers)
Date: 2025
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