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Auflistung nach Autor:in "Baumann, Annika"

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  • Zeitschriftenartikel
    Investigating Innovation Diffusion in Gender-Specific Medicine: Insights from Social Network Analysis
    (Business & Information Systems Engineering: Vol. 66, No. 3, 2024) Baum, Katharina; Baumann, Annika; Batzel, Katharina
    The field of healthcare is characterized by constant innovation, with gender-specific medicine emerging as a new subfield that addresses sex and gender disparities in clinical manifestations, outcomes, treatment, and prevention of disease. Despite its importance, the adoption of gender-specific medicine remains understudied, posing potential risks to patient outcomes due to a lack of awareness of the topic. Building on the Innovation Decision Process Theory, this study examines the spread of information about gender-specific medicine in online networks. The study applies social network analysis to a Twitter dataset reflecting online discussions about the topic to gain insights into its adoption by health professionals and patients online. Results show that the network has a community structure with limited information exchange between sub-communities and that mainly medical experts dominate the discussion. The findings suggest that the adoption of gender-specific medicine might be in its early stages, focused on knowledge exchange. Understanding the diffusion of gender-specific medicine among medical professionals and patients may facilitate its adoption and ultimately improve health outcomes.
  • Zeitschriftenartikel
    Technology for Humanity
    (Business & Information Systems Engineering: Vol. 65, No. 5, 2023) Meythaler, Antonia; Baumann, Annika; Krasnova, Hanna; Hinz, Oliver; Spiekermann, Sarah
  • Zeitschriftenartikel
    The Price of Privacy
    (Business & Information Systems Engineering: Vol. 61, No. 4, 2019) Baumann, Annika; Haupt, Johannes; Gebert, Fabian; Lessmann, Stefan
    The analysis of clickstream data facilitates the understanding and prediction of customer behavior in e-commerce. Companies can leverage such data to increase revenue. For customers and website users, on the other hand, the collection of behavioral data entails privacy invasion. The objective of the paper is to shed light on the trade-off between privacy and the business value of customer information. To that end, the authors review approaches to convert clickstream data into behavioral traits, which we call clickstream features, and propose a categorization of these features according to the potential threat they pose to user privacy. The authors then examine the extent to which different categories of clickstream features facilitate predictions of online user shopping patterns and approximate the marginal utility of using more privacy adverse information in behavioral prediction models. Thus, the paper links the literature on user privacy to that on e-commerce analytics and takes a step toward an economic analysis of privacy costs and benefits. In particular, the results of empirical experimentation with large real-world e-commerce data suggest that the inclusion of short-term customer behavior based on session-related information leads to large gains in predictive accuracy and business performance, while storing and aggregating usage behavior over longer horizons has comparably less value.
  • Zeitschriftenartikel
    Values and Ethics in Information Systems
    (Business & Information Systems Engineering: Vol. 64, No. 2, 2022) Spiekermann, Sarah; Krasnova, Hanna; Hinz, Oliver; Baumann, Annika; Benlian, Alexander; Gimpel, Henner; Heimbach, Irina; Köster, Antonia; Maedche, Alexander; Niehaves, Björn; Risius, Marten; Trenz, Manuel