WI

17th International Conference on Wirtschaftsinformatik (WI)

  • Date: 03.12.2021
  • The research groups of Professors Mädche, Satzger, Weinhardt (IISM), and Sunyaev (AIFB) will be represented with 11 papers at the "17th International Conference on Wirtschaftsinformatik (WI)", which will take place in Nuremberg next February. Congratulations to all authors!

     

    Gau, M., vom Brocke, A., and Maedche, A.: 
    DSR Buddy: A Conversational Agent Supporting Design Science Research Activities

     

    Heinz, D., Benz, C., Hunke, F., Satzger, G.: 
    An Affordance-Actualization Perspective on Smart Service Systems

     

    Jakubik J., Blumenstiel, B., Vössing, M., Hemmer, P.: 
    Instance Selection Mechanisms for Human-in-the-Loop Systems in Few Shot Learning

     

    Kölbel, T., Dann, D., Weinhardt, C.: 
    Giant or Dwarf? A Literature Review on Blockchain-enabled Marketplaces in Business Ecosystems

     

    Kölbel, T., Gawlitza, T., Weinhardt, C.: 
    Shaping Governance in Self-Sovereign Identity Ecosystems: Towards a Cooperative Business Model

     

    Lauf F., Scheider S., Bartsch J., Herrmann P., Radic M., Rebbert M., Nemat A. T., Schlueter-Langdon C., Konrad R., Sunyaev A., and Meister S.: 
    Linking Data Sovereignty and Data Economy: Arising Areas of Tension

     

    Riefle, L., Brand, A., Mietz, J., Rombach, L., Szekat, C., Benz, C.: 
    What Fits Tim Might Not Fit Tom: Exploring the Impact of User Characteristics on Users’ Experience with Conversational Interaction Modalities

     

    Schenkluhn, M., Peukert, C., Weinhardt, C.:
    Typing the Future: Designing Multimodal AR Keyboards

     

    Stein, C., Staudt, P., Greif-Winzrieth, A.:
    The Success of Others: Copy Trading and Risk

     

    Sterk, F., Peukert, C., Hunke, F., Weinhardt, C.: 
    Understanding Car Data Monetization: A Taxonomy of Data-Driven Business Models in the Connected Car Domain

     

    Zhou, Y., Henni, S., Staudt, P.: 
    Managing Intermittent Renewable Generation with Battery Storage using a Deep Reinforcement Learning Strategy