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01
Digitalization has altered many assumptions underpinning research on innovation management. At the early innings of exploring how digital innovation management stands out, there is a need for further studies in this area. Previous research on how firms use artificial intelligence has distinguished between automation and augmentation of human activities. In this paper, we explore how firms implement artificial intelligence within research and development. Utilizing an international news database spanning 956 articles from 122 newspapers published in 2020, we find that artificial intelligence is primarily adopted to augment human activities (55%) within research and development, rather than to automate matters (11%). We observe differences across sectors where automation is more common in government, information and communication technology (ICT), and technology and software. Our systematic coding shows that artificial intelligence is primarily adopted for exploration research and development (64%), rather than exploitation (5%). Based on these findings, we conclude that research and development from artificial intelligence primarily focuses on novel markets and areas of operations, rather than enhancing existing product markets and activities. Moreover, it augments human labor rather than replaces it; hence, job losses related to artificial intelligence do not seem to be taking place within research and development.
02
In the book chapter "Making Use of Digital Methods to Study Influencer Marketing," Prince Chacko Johnson and Christian Sandström explore the evolving landscape of influencer marketing, a burgeoning field due to the rise of social media and digital platforms. They discuss how traditional research methods are being augmented by digital tools that allow for the collection and analysis of large datasets on influencer activities and their effects on audience behavior. The chapter highlights specific digital methodologies like Social Media Analytics and web scraping, showcasing how these approaches can offer deeper insights into the practices of influencers and the dynamics of digital influence. Johnson and Sandström emphasize the potential of these methods to provide a more nuanced understanding of digital influence strategies and their effectiveness, marking a significant shift towards more empirical and data-driven studies in this area.