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• W3162215081 abstract "External search for knowledge and foresight have become strategically important activities for firms in an increasingly uncertain and complex business environment. Novel methods to monitor development are therefore essential for both firms and scholars. This article illustrates how firms can apply one such novel method called Social Media Analytics, a multiplatform approach incorporating multiple external sources drawn from Web 2.0, that enable external search for knowledge but simultaneously avoid information overload. To illustrate the potential of the method, this article draws upon a dataset spanning 36 months from August 2016 to August 2019 and 100 283 publicly posted user-generated contents concerning Tesla to analyze their autopilot and the controversies surrounding autonomous driving. The results show that indications of the regulatory scrutiny Tesla's driverless technology faced in 2019 could be seen in the data across several platforms at an early point and that these signals became stronger over time, especially on blogs and Facebook which exhibited strong indications of future regulatory scrutiny in contrast to Twitter and Instagram. Our results underscore the potential of the Social Media Analytics for external search for knowledge and open foresight that enable firms to tune in to weak signals and scan the periphery." @default.
• W3162215081 created "2021-05-24" @default.
• W3162215081 creator A5025137147 @default.
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• W3162215081 date "2022-04-01" @default.
• W3162215081 modified "2023-09-26" @default.
• W3162215081 title "Social Media Analytics as an Enabler for External Search and Open Foresight—The Case of Tesla's Autopilot and Regulatory Scrutiny of Autonomous Driving" @default.
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• W3162215081 doi "https://doi.org/10.1109/tem.2021.3072677" @default.
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