The Value of Unstructured Data – Essays on Information Disclosure

Data guides decision-making in information consuming processes, shaping the life reality of individuals and whole societies alike. Although advances in communication technologies and the abundance of information have transformed decision-making, efficiency remains constrained by bounded rationality, information asymmetry, and the complexity of unstructured data. This dissertation examines how unstructured data affects decision-making in financial markets and scientific knowledge generation, and explores how these challenges can be mitigated through methodological innovation, institutional design, legislative frameworks, and the integration of qualitative data sources. The cumulative dissertation consists of five independent studies related to information consuming processes. The first work examines how credit rating agencies influence merger and acquisition decisions, highlighting their role as information mediators. The second study contributes methodologically by refining event-study techniques for corporate bond markets. The third paper analyses the incorporation of environmental, social, and governance information in credit ratings, demonstrating its growing materiality for market participants. The fourth work evaluates how analyst team diversity enhances the timeliness and accuracy of ratings, underlining the human factor in information processing. Finally, the fifth study extends the inquiry beyond financial markets by assessing the informational value of full-texts in Scientometrics, showing that abstracts serve as robust information proxies. Moreover, the study contributes methodologically by proposing a novel, linguistic information content measure. Together, these works advance the understanding of how unstructured data contributes to better-informed decision-making. They highlight both institutional and methodological avenues for overcoming information processing challenges, offering implications for policymakers, practitioners, and researchers in finance, data science, and knowledge management.