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

Author

Listed:
  • Ashok Bhattarai
  • Jiaxi Luo
  • Shih Yung Chou
  • Charles Ramser

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.

Suggested Citation

  • Ashok Bhattarai & Jiaxi Luo & Shih Yung Chou & Charles Ramser, 2025. "Consumer sentiments in automotive purchases before and after COVID-19: a text-mining study," International Journal of Business Environment, Inderscience Enterprises Ltd, vol. 16(1), pages 62-76.
    • Handle: RePEc:ids:ijbenv:v:16:y:2025:i:1:p:62-76
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