Understanding The Acceptability Of Shared Autonomous Vehicle (Sav) In Ride-Hailing Services
The automotive industry is witnessing a major shift with the introduction of autonomous vehicles (AV) that offer improved road safety and urban mobility. However, the high cost of AV poses a challenge. Shared autonomous vehicles (SAV), particularly Robo-taxi services with ride-sharing capabilities,...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2023
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| Subjects: | |
| Online Access: | http://eprints.usm.my/60175/1/LIEW%20YING%20WEI%20-%20TESIS%20cut.pdf http://eprints.usm.my/60175/ |
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| Summary: | The automotive industry is witnessing a major shift with the introduction of autonomous vehicles (AV) that offer improved road safety and urban mobility. However, the high cost of AV poses a challenge. Shared autonomous vehicles (SAV), particularly Robo-taxi services with ride-sharing capabilities, present a potential solution. While previous studies have explored factors influencing AV acceptance, little research focuses on ride-hailing users' cognitive perspectives on Artificially Intelligent (AI) based SAV in daily commutes. This study introduces the Robo-taxi Acceptance Model (RAM), integrating the Cognitive Appraisal Theory, the Artificially Intelligent Device Use Acceptance (AIDUA) model, and the Value-based Adoption Model (VAM). A survey was conducted in August 2022 with 344 respondents. The collected data was analysed using SmartPLS 4.0.8 and SPSS 27. The results indicated that perceived enjoyment, perceived policy support, relative advantage, trust, and social influence had positive effects, while perceived fee had a negative impact on perceived value. Relative advantage and self-efficacy positively influenced perceived usefulness, and trust negatively affected perceived risk. Perceived value significantly predicted emotion, and emotion influenced acceptance, even after accounting for the potential confounding effects of the control variables. These findings have practical implications for service providers and authorities in preparing for the adoption of AI-based Robo-taxi, providing insights into cognitive, affective, and psychological factors in service encounters. |
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