ENHANCED NET VALENCE MODEL FOR ADOPTION OF AUTONOMOUS VEHICLES AMONG FEMALE NOVICE DRIVERS IN SAUDI ARABIA

Abstract

Autonomous Vehicles-Level4 (AVs) are vehicles which can drive themselves from point A to point B, without the need of any interaction from the driver because of the ability to sense the surroundings and to detect the objects and environment around. In 2018, Saudi females can finally drive by themselves after the ban was lifted. Since then, the kingdom is facing a big problem of female novice drivers from different ages which might make the kingdom such a risky place to drive. Therefore, AVs-Level4 would help novice drivers to overcome their fear of driving, decrease accidents and increase drive safety. Based on the literature, previous studies in pre-adoption of AVs focused narrowly on those who already have enough driving experience and already holding a valid driving license. Research on the pre-adoption of AVs-Level4 by novice drivers was still not well explored. Moreover, none of the previous studies had used Net Valence Model (NVM) for AVs pre-adoption to understand the benefits/risks surrounding the pre-adoption. Realizing this gap, this study aimed to propose an enhanced pre-adoption model for AVs by using NVM to identify the benefits/risks factors that influence the pre-adoption of AVs by novice drivers. This study extended NVM by adding three factors which are social influence, personal innovativeness, and alternatives attractiveness. The theoretical contribution of this study offered a theoretical model for measuring the intention to adopt AVs-Level4 by novice drivers. A survey method was applied using the purposive sampling technique. Data were collected from 1400 participants Saudi women novice drivers who had prior experience in driving AVs-Level4 at least once. Data analysis was performed using SmartPLS Version 3. The results show that individuals tend to ignore the potential risks and focus more on potential benefits. Performance expectancy, enjoyment, and effort expectancy were found to be positively related to perceived benefits. On the other side, financial and time risks were found to be positively related to perceived risks. Perceived risks as a construct did not directly influence the intention to adopt AVs-Level4 which means none of the five types of risks was directly influencing the pre-adoption. Among the additional factors for NVM, which were social influence, personal innovativeness, and alternatives attractiveness, this study found all three factors significantly influenced the pre-adoption. In addition, according to the results of Importance-Performance Matrix Analysis, social influence was found as the second most important factor toward the pre-adoption of AVs. Personal innovativeness and alternatives are the third and the fifth most important factor respectively toward the pre-adoption of AVs. Finally, the enhanced NVM model would help AVs-Level4 developers to identify the most critical factor influencing novice drivers’ behavioral intention to adopt AVs-Level4 from the novice drivers’ perspectives.