ABSTRACT
Rapidly growing volumes of data create opportunities. For netnographic researchers, they also create challenges. As the amount of data increases, collecting, understanding, and meaningfully combining data can become more difficult. Traditionally, the netnography research process was predominantly in the hands of humans with limited software support. We assert that advances in the field of artificial intelligence and deep learning in particular allow intelligent machines to take over more and more of the steps in netnography. The challenge is to understand what parts of netnography can be performed by machines, what is better accomplished by humans, and how both can outperform any prior approach by working together. In this chapter, we analyzed two HYVE innovation projects conducted with the company Beiersdorf. Both projects had exactly the same briefing. However, one project followed a human-driven insight process, and the other approach relied heavily on the latest technologies in the domain of machine learning. By comparing these two paths, our study reveals a new vision for a Golden Age of cooperation between human and machine netnographers that might exist in the near future of netnographic research.
