Accelerating Literature Reviews with Multi-Database Information Systems for Financial Distress Research

Literature reviews are a cornerstone of doctoral research in general, and of economic and business research, in particular. However, the exponential growth of scientific publications has made comprehensive and transparent reviews increasingly difficult. Conventional approaches, largely based on manual searches across a small number of databases, tend to be slow, error-prone, and incomplete. As a result, they constrain the scope of inquiry and, consequently, the robustness of theory development and empirical validation. This paper proposes and analyses an information system architecture driven by research questions and keyword taxonomies to automate core tasks of the literature search phase across multiple academic databases. Focusing on the domain of corporate and municipal financial distress, the authors employ a two-stage research design. First, the theoretical analysis integrates the literature on systematic reviews, automation, and financial distress prediction to derive a set of functional and non-functional requirements. Second, the experimental analysis documents a prototype front-end application designed to accelerate the literature review. The prototype is conceptualised as a socio-technical artefact that enhances IT competences and scientific resilience by enabling more efficient, reproducible, and extensible reviews. The authors conclude by discussing the scientific, technical, professional, and societal implications of the prototype, including opportunities for intellectual-property protection and avenues for future research.