인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
개인구독
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지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
피인용 0
초록·키워드
Objective: To investigate the effects of factors on the adoption of autonomous vehicles (AVs) using a new extended technology acceptance model. Background: Since self-driving cars are being implemented and developed to use for driving purposes, it is important to find the factors that can hinder the acceptance of these technologies. Factors in traditional technology acceptance theories were used to determine the actual use of AVs. However, factors in behavior theories are not directly observable (latent variables), t herefore the effect of technological factors (manifest variables) on these factors were also studied. In addition, the differences in the acceptance of AVs regarding various human factors (e.g., education, income, driving experience) were evaluated. Method: Research hypotheses of the model were proposed to predict acceptance of AVs. Data were collected by an online survey among 81 residents of South Korea to confirm the hypotheses proposed. Structure Equation Modeling (SEM) was used to validate the relationship between factors and effects on the adoption of AVs. Results: The measurement model showed good reliability and validity after model and hypotheses testing results. A total of 18 of the 25 hypotheses were supported. Moreover, the differences between users who have different driving experience were observed, while no significant effect of e ducation and income were found. Conclusion: It is confirmed that behavioral intention is the main determinant of the actual use of AVs, while it is mostly affected by attitude, subjective norms, and perceived enjoyment. Technological factors, in particular, compatibility, relative advantage, reliability, and complexity have significant effects on factors in behavior theories. Users with less experience with driving are highly dependent on other’s opinions and suggestions to use AVs. It can be helpful for developers to focus on safety risks than privacy, since young adults are not likely to concern about sharing their data with AVs. Application: Key findings of this study will be used as a basis for developing a new extended technology acceptance model for self-driving cars.
본문·목차
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