TY - JOUR A1 - Kakatkar, Chinmay A1 - Bilgram, Volker A1 - Füller, Johann T1 - Innovation analytics: Leveraging artificial intelligence in the innovation process JF - Business Horizons N2 - Artificial intelligence (AI) is about imbuing machines with a kind of intelligence that is mainly attributed to humans. Extant literature—coupled with our experiences as practitioners—suggests that while AI may not be ready to completely take over highly creative tasks within the innovation process, it shows promise as a significant support to innovation managers. In this article, we broadly refer to the derivation of computer-enabled, data-driven insights, models, and visualizations within the innovation process as innovation analytics. AI can play a key role in the innovation process by driving multiple aspects of innovation analytics. We present four different case studies of AI in action based on our previous work in the field. We highlight benefits and limitations of using AI in innovation and conclude with strategic implications and additional resources for innovation managers. KW - AI-based innovation management KW - AI-augmented innovation KW - Machine Learning KW - Natural language processing KW - Front-end innovation KW - Innovation process Y1 - 2020 U6 - https://doi.org/10.1016/j.bushor.2019.10.006 SN - 0007-6813 VL - 63 IS - 2 SP - 171 EP - 181 PB - Elsevier BV ER - TY - JOUR A1 - Bilgram, Volker A1 - Füller, Johann T1 - Mit Emotion AI zu erfolgreichen Innovationen JF - Handelsblatt Journal KW - Emotion AI, Innovation Y1 - 2020 PB - Euroforum ER - TY - CHAP A1 - Marchuk, Anna A1 - Biel, Stefan A1 - Bilgram, Volker A1 - Worning, Signe A1 - Jensen, Løgstrup T1 - The Best of Both Worlds : Methodological Insights on Combining Human and AI Labor in Netnography T2 - Netnography Unlimited : Understanding Technoculture Using Qualitative Social Media Research N2 - 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. Y1 - 2020 SN - 9781003001430 U6 - https://doi.org/10.4324/9781003001430 PB - Routledge CY - New York, NY ER -