A theoretical framework for trust in automation considering its relationship to technology acceptance and its influencing factors

Open Access
Conference Proceedings
Authors: Pia Sophie Charlotte DautzenbergGudrun Mechthild Irmgard Voß

Abstract: Automated cars are about to bring substantial changes in future mobility as the advantages regarding safety, comfort, and mobility independence are manifold. However, despite the many advantages, there are some factors which could affect the breakthrough of this technology. On the one hand, for automated driving to be viable, meeting technical and legal requirements is a fundamental and equally challenging prerequisite. On the other hand, user acceptance is a mandatory premise for technology adoption. But which factors are decisive for whether a technology is accepted or not? We propose that trust in automation (TiA) is a key influencing factor in terms of acceptance as the impact of trust on the acceptance of automated systems seems to be empirically proven. Nevertheless, acceptance models often either do not consider the concept of trust or seem to disagree as to whether trust has a direct and/or indirect influence on acceptance. Furthermore, influencing factors of trust are often neglected in such models. To provide a more holistic perspective on these issues, we conducted a structured literature review and analysis. Scientific papers which consider the relationship between TiA and acceptance as well as factors influencing trust in the context of automated driving were included. Based on the identified literature, we propose a theoretically derived framework on the relationship between TiA and technology acceptance as well as on influencing factors of TiA for the application context of automated driving. The framework is intended to serve as a complement to existing sound acceptance and trust models as well as a starting point for empirical verification of the theoretical assumptions in the course of further research.

Keywords: technology acceptance, trust in automation, theoretical framework, automated driving

DOI: 10.54941/ahfe1002462

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