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

Abstract

Over the past decade, the rapid development of artificial intelligence has propelled the transition of autonomous vehicles from laboratories to real-world applications. However, autonomous vehicles are a long way from fully integrating into most people's lives. Previous studies indicate that the word-of-mouth effect is often used by consumers to determine the quality of innovative technologies. Word-of-mouth recommendation can not only increase the income of enterprises by attracting new customers, but also greatly reduce the promotion and publicity expenses of enterprises. Through the word-of-mouth effect, the intention to recommend can contribute to the growth of the autonomous driving market. Therefore, current research explores the mechanisms among the perceived risk of privacy safety, perceived defect, perceived behavioral control, intention to use, and intention to recommend through path analysis. Our findings, based on 433 online questionnaires, indicate that the perceived risk of privacy safety, perceived defects, and perceived behavioral control influence the intention to recommend. Notably, perceived risk of privacy safety and perceived defect directly affects the intention to recommend and also correlates with perceived behavioral control. These findings provide some empirical evidence for the recommendation of autonomous vehicles and the expansion of consumer groups.

Keywords

• Autonomous vehicles
• Intention to recommend
• Path analysis
• Perceived defect
• Privacy safety