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Communications of the Association for Information Systems

Author ORCID Identifier

Arash Barfar: https://orcid.org/0000-0002-1672-8480

Max Alderman: https://orcid.org/0009-0000-7474-2793

Dana Edberg: https://orcid.org/0000-0002-9151-9507

Abstract

This interdisciplinary study integrates qualitative and AI-based methods to introduce an exploratory sequential mixed-methods design for analyzing semi-structured decisions, using judicial decisions on COVID-19 compassionate release petitions as its empirical application. The study advances three relatively underexplored domains within IS research. First, it contributes to mixed-methods research design by proposing a robust and versatile data analysis strategy that natively addresses limitations common to mixed-methods data, including high dimensionality, substantial missingness, multicollinearity, and quasi-separation, while enabling interpretable, causal-like explanatory insights that go beyond correlation. The study also empirically demonstrates how qualitative insights can be systematically converted into structured quantitative measures. Second, it advances the literature on understanding decisions by introducing a domain-agnostic methodology applicable to both decision records and post-decision interviews, supporting both global pattern discovery and case-level decision explanation. Finally, by grounding the framework in the legal domain, the study contributes to the evolution of IS research in legal informatics and highlights important implications for the design of transparent, interpretable decision-support systems. Together, these contributions position the proposed framework as a scalable and practical blueprint for rigorous, explainable mixed-methods research, advancing the role of IS as an interdisciplinary nexus for studying complex real-world phenomena across domains.

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