Description
In this paper, we address the question if there is a disruption of individual mobility by self-driving cars ahead. In order to answer this question, we take the user perspective and conduct a longitudinal study of social media data about self-driving cars from Twitter. The study analyzes 601,778 tweets from March 2015 to July 2016. We use supervised machine learning classification to extract relevant information from this huge amount of unstructured text. Based on the classification, we analyze how risk and benefit perceptions of self-driving cars develop over time, and how they are influenced by certain events. Based on the perceived risks and benefits, we draw conclusions for the acceptance of self-driving cars. Our study shows that a disruptive innovation of self-driving cars is not likely as risk and benefit perception issues indicate a lack of acceptance. We provide suggestions for improving the acceptance of self-driving cars.
Disruption of Individual Mobility Ahead? A Longitudinal Study of Risk and Benefit Perceptions of Self-Driving Cars on Twitter
In this paper, we address the question if there is a disruption of individual mobility by self-driving cars ahead. In order to answer this question, we take the user perspective and conduct a longitudinal study of social media data about self-driving cars from Twitter. The study analyzes 601,778 tweets from March 2015 to July 2016. We use supervised machine learning classification to extract relevant information from this huge amount of unstructured text. Based on the classification, we analyze how risk and benefit perceptions of self-driving cars develop over time, and how they are influenced by certain events. Based on the perceived risks and benefits, we draw conclusions for the acceptance of self-driving cars. Our study shows that a disruptive innovation of self-driving cars is not likely as risk and benefit perception issues indicate a lack of acceptance. We provide suggestions for improving the acceptance of self-driving cars.