Conceptual model to explain, predict, and improve user acceptance of driverless podlike vehicles

S Nordhoff, B Van Arem… - Transportation research …, 2016 - journals.sagepub.com
Transportation research record, 2016journals.sagepub.com
This paper presents a synthesis of existing empirical acceptance studies on automated
driving and scientific literature on technology acceptance. The objective of the study was to
study user acceptance of SAE Level 4 vehicles or driverless podlike vehicles without a
steering wheel and pedals that operated within the constraints of dedicated infrastructure.
The review indicates that previous acceptance studies on automated driving are skewed
toward car users and thus create a need for targeted acceptance studies, including users of …
This paper presents a synthesis of existing empirical acceptance studies on automated driving and scientific literature on technology acceptance. The objective of the study was to study user acceptance of SAE Level 4 vehicles or driverless podlike vehicles without a steering wheel and pedals that operated within the constraints of dedicated infrastructure. The review indicates that previous acceptance studies on automated driving are skewed toward car users and thus create a need for targeted acceptance studies, including users of public transport. For obvious reasons, previous studies targeted respondents who had not experienced driverless vehicles. As driverless vehicles are currently being demonstrated in pilot projects, their acceptance by users inside and outside such vehicles can now be investigated. Addressing the multidimensional nature of acceptance, a conceptual model that integrates a holistic and comprehensive set of variables to explain, predict, and improve user acceptance of driverless vehicles was developed. The model linked two dominant models from the technology acceptance management literature, the unified theory of acceptance and use of technology and the pleasure–arousal–dominance framework, with a number of external variables that were divided into system-specific, user, and contextual characteristics.
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