The Voice of the Crowd—An Innovation Mining Study on Autonomous Driving
Michael Bartl, Juan Rosenzweig · Palgrave Macmillan UK eBooks · 2016
There is little doubt that the Internet has changed the way consumers communicate. An increasing number of users actively gather together online and communicate in web forums, blogs, and various kinds of user-generated content (UGC) platforms. They exchange personal experiences and opinions about products and their usage and talk about opportunities for solving product-related problems. Some of them even develop product modifications and innovations, which they post online and share with other community members. This turns online communities into powerful sources of innovation (Füller et al. 2006; Bartl et al. 2012; Bilgram et al. 2008). Within this context organizations are experimenting with a variety of new and modified innovation research approaches promoting the role of consumers as valuable cocreators of products and services (von Hippel 2005; Chesbrough 2003; Prahalad and Ramaswamy 2000; Cui and Wu 2015; Gemser and Perks 2015). One example is the concept of crowdsourcing with the underlying idea of taking tasks traditionally performed by companies and outsourcing them to an undefined, generally large group of people in the form of an open call (Howe 2006). Other advancements are made in developing further qualitative research approaches with Netnography as a prominent example (Kozinets 2002; Brem and Bilgram 2015; Wiles et al. 2013; Zhang et al. 2013). Evolved from ethnographic research, the core idea of Netnography is to gain unbiased, unobtrusive consumer insights by “listening in” the user conversation. The advantage of the researcher’s in-depth qualitative analysis of consumer quotes is the strength of Netnography and, at the same time, its limitation. In order to manage the exponentially growing data volumes of UGC, new quantitative approaches relying on automation in text analysis of software-based information retrieval are on the rise. The aim of this chapter is to introduce innovation mining as a new powerful quantitative research technique and systematic procedure to identify, select, and analyze large volumes of user conversations on the Internet and make them usable for innovation challenges. Sections 4.2 and 4.3 describe the field of autonomous driving as a disruptive field of innovation which is chosen to showcase the innovation mining method. Section 4.4 describes the five methodological steps of innovation mining. Section 4.5 summarizes the study results followed by a concluding outlook in Sect. 4.6. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.