The ever rising amount of business communications results in a growing amount of qualitative data relevant to many decision situations. This increase in information volume and velocity threatens to overburden decision makers. We provide a structured approach towards this problem using topic-models to reduce information overload by filtering content and by providing context-relevant information to decision makers. Building upon theoretical considerations related to phases of the decision process established by Herbert A. Simon, we implement the proposed approach on the example of a large document collection of stock analyst reports and analyst conference calls using Latent Dirichlet Allocation (a topic model). Thereby, we extract investment-relevant topics from the model and discuss the opportunities for decision support resulting from the chosen approach.
Eickhoff, Matthias and Muntermann, Jan, "HOW TO CONQUER INFORMATION OVERLOAD? SUPPORTING FINANCIAL DECISIONS BY IDENTIFYING RELEVANT CONFERENCE CALL TOPICS" (2016). PACIS 2016 Proceedings. 317.