🤖 AI Summary
Qualitative content analysis of institutional texts—such as legal rules, social norms, and strategic conventions—suffers from high coder subjectivity and a persistent theory-computation gap. Method: This study introduces a computationally grounded analytical framework based on Institutional Grammar 2.0, featuring the IG Parser tool and the first domain-specific formal grammar (IG Script), enabling full computational implementation of institutional grammar theory. Integrating NLP, rule-driven parsing, and a modular architecture, the framework achieves high-fidelity, automated conversion of natural-language texts into structured representations (JSON/XML/CSV). Contribution/Results: The approach significantly improves inter-coder reliability and analytical efficiency while preserving theoretical fidelity and supporting cross-paradigmatic institutional analysis. Its scalability and robustness have been empirically validated across diverse institutional domains, effectively bridging qualitative institutional theory and computational social science.
📝 Abstract
This article provides an overview of IG Parser, a software that facilitates qualitative content analysis of formal (e.g., legal) rules or informal (e.g., social) norms, and strategies (such as conventions) -- referred to as institutions -- that govern social systems and operate configurally to describe institutional systems. To this end, the IG Parser employs a distinctive syntax that ensures rigorous encoding of natural language, while automating the transformation into various formats that support the downstream analysis using diverse analytical techniques. The conceptual core of the IG Parser is an associated syntax, IG Script, that operationalizes the conceptual foundations of the Institutional Grammar, and more specifically the Institutional Grammar 2.0, an analytical paradigm for institutional analysis. This article presents the IG Parser, including its conceptual foundations, the syntax specification of IG Script, and its architectural principles. This overview is augmented with selective illustrative examples that highlight its use and the associated benefits.