Abstract
If service providers can identify reasons users are in favor of or against a service, they have insightful information that can help them understand user behavior and what they need to do to change such behavior. This article argues that the novel text-mining technique referred to as information-seeking argument mining (IS-AM) can identify these reasons. The empirical study applies IS-AM to news articles and reviews about electric scooter-sharing systems (i.e., a service enabling the short-term rentals of electric motorized scooters). Its results point to IS-AM as a promising technique to improve service; the data enable the authors to identify 40 reasons to use or not use electric scooter-sharing systems, as well as their importance to users. Furthermore, the results show that news articles are better data sources than reviews because they are longer and contain more arguments and, thus, reasons.
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