Framing research on corruption and public administration in management studies: research trends and future directions

Purpose – Over the years, the pervasive existence of corruption within the public administrations of many countries worldwide has interested large numbers of scholars. Often involving accountability issues within public institutions, the topic has been explored by research adopting multiple perspectives. This study aims to explore the current state of development in management research on corruption and public administration. It highlights the complexities that define their relationship and offers valuable insights into emerging trends in this field. Design/methodology/approach – This study adopts a topic modelling approach to explore the topics and research trends deriving from corruption in public administration studies published by management scholars. Specifically, the study adopts a machine learning algorithm, namely “latent Dirichlet allocation,” to explore the underlying topics behind portions of scientific text. Findings – The analysis has revealed eight different and connected research trends: the role of transparency as an anti-corruption tool, the impact of political corruption on government quality, the psychological effects of corruption on communities, the reduction of public information credibility due to corruption, the use of anticorruption practices as performance management tools, the influence of corruption on social and economic development, the effect of public corruption on external companies’ behaviour and the theoretical perspectives on corruption in public administration. Originality/value – This study adopts a machine learning approach to map the topics explored by management scholars in prior research studies while providing a perspective on the following research trends on corruption in the public context.