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Understanding Intention to Use Conditionally Automated Vehicles in Thailand, Based on an Extended Technology Acceptance Model

doi: 10.3390/su15031868
Automated vehicles (AVs) provide several advantages in solving issues of road traffic; including enhanced safety, reduced greenhouse gas emissions, and reduced traffic congestion. As AVs are still relatively new developments in developing countries, AV adoption faces challenges from both technological and psychological issues. Therefore, our initial research focus is on identifying the factors that influence the intention to use conditionally automated vehicles (CAVs; SAE Level 3). An extended technology acceptance model (TAM), which includes Trust, Perceived Risks, and Environmental concerns, is proposed as the predictor model in this study. The 299 participants gathered through online surveys in Thailand were examined using the Structural Equation Model (SEM) technique. In this study, Trust was shown to be the strongest predictor of Intention, followed by Perceived Ease of Use, whereas Perceived Usefulness had no impact on intention to use the SAE Level 3. The results of this study will be able to guide the forming of future policies that aim at promoting the use of AVs and helping technology developers create systems to better meet the needs of users in developing nations.
- Khon Kaen University Thailand
- Khon Kaen University Thailand
- Ubon Ratchathani University Thailand
- Ubon Ratchathani University Thailand
- Chiang Mai University Thailand
Environmental effects of industries and plants, TJ807-830, trust, developing countries, psychological factors, TD194-195, Renewable energy sources, Environmental sciences, GE1-350, level 3 automation vehicles, perceived risks, environmental concerns
Environmental effects of industries and plants, TJ807-830, trust, developing countries, psychological factors, TD194-195, Renewable energy sources, Environmental sciences, GE1-350, level 3 automation vehicles, perceived risks, environmental concerns
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).7 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
