Abstract

Level 4 autonomous vehicles (AVs) are smart vehicles that can move between two different points without any human interference. In 2018, the Saudi Arabian ban on female drivers was finally lifted, resulting in a large number of novice women drivers of different ages. The Kingdom might therefore be considered a risky place to drive, but AVs would help novices to reduce their fear of driving and reduce accidents. Previous studies focused narrowly on those who already had sufficient driving experience and held a valid driving license, but there were no studies on the adoption of smart cars by novice drivers. Based on a literature search, no studies had used a net valence model (NVM) for the adoption of AVs to understand their benefits/risks. Therefore, this study proposed an adoption model for AVs using an NVM to identify the benefit and risk factors that have an impact on beginner drivers’ adoption of autonomous vehicles. A survey method was applied using the purposive sampling technique. Data were collected from 1400 female Saudi novice drivers who had experience with driving AVs. Data analysis was performed using Smart PLS Version 3. The results showed that individuals tended to ignore potential risks and focus instead on the potential benefits of using AVs. Performance expectancy, enjoyment, and effort expectancy were found to be positively related to the perceived advantages. On the other hand, the perceived risk as a construct did not have an impact on beginner drivers’ adoption of autonomous vehicles. Therefore, the major theoretical contribution of this study was the formation of a new NVM model by incorporating three more constructs, which were social influence, personal innovativeness, and alternatives. Finally, the enhanced NVM model could assist AV developers in identifying the expected benefits and drawbacks of AV adoption.

Highlights

  • Artificial intelligence (AI) uses hundreds of digital videos and images to simulate human intelligence [1]; AI is about building smart machines that can perform human tasks

  • The key dependent variable that signified the purpose of this research was adoption intention, which was strongly influenced by perceived advantages, perceived drawbacks, alternatives, personal innovativeness, and social influence

  • For the extended version of the net valence model (NVM), as hypothesized, personal innovativeness was positively related to the intention to adopt autonomous vehicles (AVs) (ß = 0.151, p < 0.000), as were alternatives (ß = 0.084, p < 0.000) and social influence (ß = 0.325, p < 0.000), supporting H5, H8, and H7, respectively

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Summary

Introduction

Artificial intelligence (AI) uses hundreds of digital videos and images to simulate human intelligence [1]; AI is about building smart machines that can perform human tasks. One application of AI in this regard is autonomous vehicles (AVs), which can drive themselves like humans do [2]. To provide a realistic context on how AI is influencing AVs globally, autonomous driving and autonomous cars are currently among the most intensely researched and publicly watched technologies in the transportation field [2]. AVs solve several driving issues, with benefits such as safety, effectiveness, and mobility [1]. AVs are constructed to improve vehicle control by minimizing human mistakes and by providing safety and superior driving [3] by using various innovations, including autonomous cruise control, adaptive high beam, collision avoidance, automatic parking, automotive navigation, driver drowsiness detection, wrong-way driving warning, and intelligent speed adoption [2]. The Saudi Vision 2030 agenda aims to embrace every upcoming innovation in Sustainability 2021, 13, 11916.

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