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PurposeThis article develops a methodological framework to support qualitative analyses of legal texts. Scholars across the social sciences and humanities use qualitative methods to study legal phenomena but often overlook formal legal texts as productive sites for analysis. Moreover, when qualitative researchers do analyze legal texts, they rarely discuss the methodological underpinnings that support their approach. A thorough consideration of the methodological underpinnings of qualitative approaches to legal analysis is therefore warranted.Design/methodology/approachBy bringing critical legal theory into conversation with qualitative methodology, this article outlines a set of key principles to inform qualitative approaches to reading the law.FindingsTo construct this methodological framework, this article first distinguishes between qualitative approaches to textual analysis and the doctrinal approaches undertaken in legal practice and formal legal scholarship. It then considers how this qualitative approach might be applied to one particular genre of legal text: namely, judicial opinions, otherwise known as reasons for judgment. In doing so, it argues that robust qualitative analyses of legal texts must consider the unique characteristics of those texts, such as their distinct form, voice, rhetorical structure, and performative capabilities.Originality/valueThe methodological framework outlined here should encourage qualitative researchers to approach legal texts more readily and challenge the hegemony of doctrinal approaches to legal interpretation in social science research.

AbstractConsumers rely on intermediaries ("influencers") such as social media recommendations to provide information about products. The advice may be mixed with endorsement in a way that is unobservable to the follower, creating a trade‐off for influencers between the best advice and the most revenue. This article models the dynamic relationship between an influencer and a follower. The relationship evolves between periods of less and more revenue. The model can provide insight into policies such as the Federal Trade Commission's mandatory disclosure rules. An opt‐in policy may be superior: it deregulates influencers who are reaping the rewards of past good advice.