Effect of autonomous vehicle-related eWOM on (fe)males’ attitude and perceived risk as passengers and pedestrians

Snehasish Banerjee*, Alton Y.K. Chua

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Purpose: This study investigates how autonomous vehicle (AV)-related electronic word-of-mouth (eWOM) of different polarities affects attitude and perceived risk from the perspectives of both passengers and pedestrians and whether any gender differences exist. It also seeks to identify AV-adoption user archetypes. Design/methodology/approach: An online experiment was conducted, manipulating eWOM polarity (positive, negative or mixed) as a between-participants factor. Findings: While eWOM polarity did not affect attitude, perceived risk was the highest in the mixed eWOM condition. Males and females differed from each other in terms of attitude toward AVs from a passenger perspective, attitude toward AVs from a pedestrian perspective and perceived risk for passengers in AVs. Four AV-adoption user archetypes were identified: AV watchfuls, AV optimists, AV nonchalants and AV skeptics. Originality/value: The paper contributes to the AV adoption literature by adding the effects of eWOM. It not only sheds light on how AV-related eWOM polarity affects attitude and perceived risk but also teases out nuances from the perspectives of passengers and pedestrians as a function of gender.

Original languageEnglish
Pages (from-to)841-859
Number of pages19
JournalInternet Research
Volume35
Issue number2
DOIs
Publication statusPublished - Mar 18 2025
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2024, Emerald Publishing Limited.

ASJC Scopus Subject Areas

  • Communication
  • Sociology and Political Science
  • Economics and Econometrics

Keywords

  • AI attitude
  • Artificial intelligence
  • Autonomous vehicles
  • Electronic word-of-mouth
  • eWOM polarity
  • eWOM valence
  • Perceived risk
  • Public perceptions
  • Self-driving cars
  • Social media
  • User-generated content

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