Replication Data for: "What Drives Public Opinion on Disruptive Technologies? A Self-Driving Bus Trial be-tween Familiarity and Fatal Accidents"
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Date
2020-01Type
- Data Collection
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Abstract
Disruptive technologies that fundamentally affect markets and society, such as self-driv-ing vehicles, are associated with various positive effects, including environmental benefits due to increased efficiency. These expected benefits may only materialise, however, if such technologies receive sufficient public acceptance. Public acceptance of technology is a necessary precursor of a technology transition. In other contexts, acceptance increased when individuals experienced suc-cessful implementation of a trial. Thus, higher acceptance can be expected following implementa-tion, as preliminary concerns prove unfounded and individuals gain familiarity with the new tech-nology. Fatal accidents with high media coverage, however, tend to negatively affect acceptance of technologies like self-driving vehicles. This paper examines citizens’ acceptance of self-driving ve-hicles. Mainly, I focus on comparing and contrasting acceptance pre- and post-implementation of a self-driving bus service in Switzerland and before and after the first fatal accident involving a self-driving vehicle in 2018. To gauge acceptance, I conducted a three-wave panel survey using a ran-dom sample of residents from three Swiss municipalities between 2018 and 2019. By applying quasi-experimental methods, I can draw causal inference regarding the experience effect with a disruptive technology on acceptance. The results indicate that a fatal accident exerts a stronger effect on acceptance than the experience of an implemented self-driving bus trial. Individuals ap-pear to have some latent scepticism towards disruptive technologies, which causes acceptance to decrease in the event of an accident. Acceptance levels are, however, stable and at high levels, which also explains the comparably low familiarity effects. In addition, this effect of the fatal acci-dent only lasts short term. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000392008Publisher
ETH ZurichSoftware
RDate collected
2018-02/2019-10Organisational unit
03446 - Bernauer, Thomas / Bernauer, Thomas
02890 - Inst. of Science, Technology, and Policy / Inst. of Science, Technology, and Policy
Related publications and datasets
Is cited by: https://doi.org/10.3929/ethz-b-000403448
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ETH Bibliography
yes
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