To Drive or not to Drive - A Critical Review regarding the Acceptance of Autonomous Vehicles

With the advent of autonomous vehicles (AVs), research has put much effort in investigating the factors relevant for the acceptance of this new technology. In order to identify, critically assess, and combine extant findings, we performed a structured literature review regarding the acceptance of self-driving vehicles. Results of this review spanning 58 articles include (1) a comprehensive AV acceptance framework outlining significant factors across three areas: individual characteristics, vehicle characteristics and policy/society. We also (2) analyze the operationalization of relevant constructs and items in the identified studies as they strongly diverge in extant literature. This new level of detail helps researchers and practitioners to pervade and compare the AV acceptance research in-depth. Additionally, we contribute to the AV research stream as we (3) identify possible future research avenues, which we examine regarding content, method, and focus

Mobility in the Advent of Autonomous Driving – Toward an Understanding of User Acceptance and Quality Perception Factors

Recent advancements in intelligent technologies and sensor-based data collections pave the way for autonomous driving and facilitate a radical transformation of today’s mobility. Based on auspicious market projections, traditional automotive manufacturers and technology companies invest heavily in the development of autonomous vehicles (AVs). In addition to the profits that the industry expects from self-driving vehicles, this new type of mobility should also solve societal issues like reducing traffic accidents and fatalities by eliminating human driving errors. More efficient autonomous driving is expected to bring improvements in terms of fewer congestions and less fuel consumption, thereby reducing greenhouse emissions. Besides, AVs pledge to entail advantages for their users. Specifically, they increase mobility for the disabled and the older generation. In contrast, younger passengers associate autonomous driving with improved productivity and an enhanced hedonic experience as non-driving activities, such as working or watching a movie, are made possible. Contrary to the above expectations, people also raise concerns regarding self-driving vehicles. They are worried about whether the sensors and systems can correctly interpret complex environmental conditions. Above all, there are doubts whether the technology, even being intelligent, can react appropriately in critical traffic situations made up of humans who sometimes behave unpredictably. In case of unavoidable traffic accidents, ethical questions come into play regarding how the vehicle makes decisions that could result in a person being injured or killed. Finally, the new and sophisticated technology could have vulnerabilities that can be exploited by cybercriminals or allow unauthorized third parties to obtain passenger data. Motivated by the anticipated improvements that AVs entail and the breadth of factors that might influence their adoption, a large body of research investigating relevant adoption factors has accumulated. In order to collect, organize, and combine extant findings, research paper A conducts a structured literature review on the acceptance of autonomous vehicles. Based on 58 articles, it develops an AV acceptance framework consisting of individual user characteristics, vehicle characteristics, and political/societal elements. The framework indicates for each factor whether available research results identify the effect as either positively or negatively significant. Thereby, the paper also sheds light on diverging construct operationalizations, aiming to support researchers in comparing available findings. Eventually, paper A proposes future research avenues across various themes and methods, which build a foundation for further research pursued in this dissertation’s subsequent papers. However, solely balancing significant against non-significant results can come to wrong conclusions since the sample size alone can lead to varying significance levels. Because of this, paper B builds on the literature review and conducts a meta-analysis to include further quantitative analyses. It calculates the mean effect sizes for each AV acceptance factor based on published research results. By doing so, the paper identifies attitude, perceived usefulness, efficiency, trust in AVs, safety, and subjective norms to correlate most strongly with the behavioral intention to use an automated car. A subsequent moderator-analysis shows that almost all acceptance factors are influenced by the study’s methodology and location, the AV’s level of automation, and the examined ownership model, i.e., private cars, car sharing, or public transport. In doing so, paper B observes that most of the available research is on privately owned AVs and hence lacks to assess public as well as shared automated mobility. To fill this gap, paper C investigates characteristics relevant for automated mobility as a service (AMaaS). Based on 23 exploratory interviews with the general public, the paper derives a set of AMaaS requirements. Mobility experts sort these requirements based on commonalities so that a cluster analysis can conceptualize the expected AMaaS characteristics from a practitioner’s view. The paper identifies traffic safety, information privacy, cybersecurity, regulations, flexibility, accessibility, efficiency, and convenience to be relevant service characteristics. It discusses each required characteristic and thereby delineates the constructs’ scopes so that subsequent research can build appropriate measurement instruments. Besides, paper C discovers strongly diverging priorities regarding the respective service characteristics when comparing the potential users’ conversation shares with the experts’ relevance ratings. Paper D builds on the qualitative results of paper C as it develops and validates a hierarchical quality scale for AMaaS. The paper proposes a theoretical model and operationalizes the previously identified service characteristics. Throughout multiple empirical studies with 1,431 participants, the proposed quality scale is refined iteratively until satisfactory psychometric properties are achieved. Nomological validity ensures the scale’s predictability. Paper D progresses research from focussing on the mere acceptance of autonomous driving to the user’s quality perception, which significantly influences user satisfaction and the success of AMaaS. This, in turn, is necessary to realize the promised benefits of autonomous driving in a sustainable manner