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Drawing from Goffman’s original observations on stigma and the consequences of interactions between the stigmatized and supportive or stigmatizing audiences, we conduct a 20-year review of the diverse literature on stigma to revisit the collective nature of stigmatization processes. We find that studies on stigma’s origins, responses, processes, and outcomes have diverged from Goffman’s relational view of stigma as they have overlooked important relational mechanisms explaining the processes of (de)stigmatization. We draw from those conclusions to justify the need to study stigma as a collective phenomenon. We develop a relational perspective on stigma based on understanding how attributes are stigmatized (or not) by audiences in their interactions. We argue that to advance stigma research, it is necessary to build on Goffman’s theory to include the stigmatizers (i.e., the normal) and supporters (i.e., the wise); how they create, sustain, or remove stigma; and how they relate to the stigmatized (i.e., the targets). Accordingly, we provide a research agenda on stigma as a collective phenomenon that theorizes a relational perspective, proposes a typology of how audiences relate to stigmatization, and identifies patterns of relations between audiences. We thus offer a missing piece to existing accounts of stigma by focusing on the key role of audiences (i.e., stigmatizers or supporters of the stigmatized) rather than on the targets of stigma (i.e., the own).

A growing interest in the study of discourses has spread in management research, but so far, it has mostly relied on in-depth qualitative analyses of textual material. With the increasing availability of large textual data, several challenges arise. This paper offers a mixed-methods approach to integrate critical discourse analysis with structural topic modeling to turn these challenges into valuable opportunities. We argue that combining both approaches overcomes their limitations and provides great potential for exploring phenomena that matter in our mediatized society. Based on an explanatory sequential mixed-methods design, we develop a stepwise model that provides practical and theoretical guidance to conduct a critical analysis of large textual data. Our illustrative example focuses on the discursive legitimation struggles around the tobacco industry. We demonstrate how an integrated mixed-methods approach allows capturing the breadth and depth of discourses used by different actors in the tobacco debates.

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