Header bidding refers to an innovative, smart service for online advertising. It facilitates concurrent, automatic real-time bidding for ad spaces on online publishers' websites and thereby supports online publishers' in their efforts to tackle Google's dominance in the online advertising market. Interdisciplinary service science research on the opportunities and challenges of smart header bidding services is still scarce. To this end, we analyze a comprehensive dataset, covering header bidding solutions and connected supply-side platforms, for the top 20 German website publishers. We carve out requirements for establishing header bidding as a successful smart service in the digital arena and offer some guidance on how to design and take advantage of header bidding services for monetizing publishers' online content. We further suggest that publishers must monitor rising header bidding substitutes, such as Google's exchange bidding, to secure sustainable value creation from header bidding as technology-based service transformation in the digital world.

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In an era of accelerating digitization and advanced big data analytics, harnessing quality data and insights will enable innovative research methods and management approaches. Among others, Artificial Intelligence Imagery Analysis has recently emerged as a new method for analyzing the content of large amounts of pictorial data. In this paper, we provide background information and outline the application of Artificial Intelligence Imagery Analysis for analyzing the content of large amounts of pictorial data. We suggest that Artificial Intelligence Imagery Analysis constitutes a profound improvement over previous methods that have mostly relied on manual work by humans. In this paper, we discuss the applications of Artificial Intelligence Imagery Analysis for research and practice and provide an example of its use for research. In the case study, we employed Artificial Intelligence Imagery Analysis for decomposing and assessing thumbnail images in the context of marketing and media research and show how properly assessed and designed thumbnail images promote the consumption of online videos. We conclude the paper with a discussion on the potential of Artificial Intelligence Imagery Analysis for research and practice across disciplines.

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