Factors influencing recommendation intentions for autonomous vehicles: A path analysis in a pilot study

Area of Science:

• Artificial Intelligence
• Human-Computer Interaction
• Transportation Technology

Background:

• Autonomous vehicles (AVs) are transitioning from labs to real-world use, driven by AI advancements.
• Consumer adoption of innovative technologies like AVs heavily relies on word-of-mouth (WOM) effects.
• WOM recommendations can significantly boost enterprise revenue and reduce marketing costs for new technologies.

Purpose of the Study:

• To investigate the factors influencing consumers' intention to recommend autonomous vehicles.
• To analyze the relationships between perceived privacy safety risk, perceived defect, perceived behavioral control, and WOM intention.
• To provide empirical evidence supporting the expansion of consumer groups for AVs.

Main Methods:

• Path analysis was employed to examine the proposed mechanisms.
• Data was collected through 433 online questionnaires from potential AV users.
• Statistical analysis was performed to identify significant influencing factors.

Main Results:

• Perceived risk of privacy safety, perceived defect, and perceived behavioral control were found to significantly influence the intention to recommend AVs.
• Perceived risk of privacy safety and perceived defect directly impact recommendation intention.
• Perceived risk of privacy safety and perceived defect also correlate with perceived behavioral control.

Conclusions:

• Consumer perceptions of privacy safety risks and product defects are key barriers or enablers for recommending autonomous vehicles.
• Perceived behavioral control plays a role in shaping recommendation intentions, alongside direct risk and defect perceptions.
• Findings offer empirical support for strategies aimed at increasing autonomous vehicle adoption through positive WOM.