The many ways to Transparency

Abstract

This article explores a sample of the literature on transparency in the 1984-2020 period through a systematic review. The sample consists of 242 works (articles, books, and book chapters) collected from different academic databases. Latent dirichlet allocation (LDA) probabilistic topic modelling – an unsupervised machine learning approach – is employed in order to classify and construct a typology of topics within the literature. This approach is complemented by a structured overview of the varieties of transparency framework and is aimed at addressing three research questions: a) What analytical approaches are identified in the literature? b) How is transparency conceptualised through such analytical approaches? And, c) where has transparency’s focus been placed in relation to an event-process framework? The findings show unequal methodological approaches, topics, and issues investigated. These insights and the novel approach utilised outline key challenges and opportunities for future transparency research.