Hiden Topics in Robotic Process Automation -- an Approach based on AI

Abstract

Robotic Process Automation (RPA) has rapidly evolved into a widely recognized and influential software technology. Its growing relevance has sparked diverse research efforts across various disciplines. This study aims to map the scientific landscape of RPA by identifying key thematic areas, tracking their development over time, and assessing their academic impact. To achieve this, we apply an unsupervised machine learning technique Latent Dirichlet Allocation (LDA) to analyze the abstracts of over 2,000 scholarly articles. Our analysis reveals 100 distinct research topics, with 15 of the most prominent themes featured in a science map designed to support future exploration and understanding of RPA's expanding research frontier.