Research Trends in Smart Workflow Automation: A Bibliometric Analysis of Emerging Themes

Abstract

Smart workflow automation (SWA) — encompassing robotic process automation (RPA), AI-driven process mining, low-code/no-code (LCNC) platforms, and cognitive or hyperautomation systems — has emerged as one of the fastest-growing research domains at the intersection of computer science, management information systems, and industrial engineering. This paper presents a comprehensive bibliometric analysis of SWA research published between 2012 and 2023, drawing on 2,847 peer-reviewed documents retrieved from Scopus, Web of Science, IEEE Xplore, and the ACM Digital Library. Using co-citation analysis, keyword co-occurrence mapping, and citation trend modeling performed in VOSviewer and Bibliometrix, five major thematic clusters are identified: RPA and bot-based automation, AI/ML-driven process mining, low-code/no-code platforms, workflow orchestration and business process management (BPM), and cognitive/intelligent automation. The analysis reveals that while traditional RPA and BPM literature exhibits mature, steady citation growth, AI-driven process mining and low-code/no-code platforms demonstrate the highest annual growth rates, at 22.7% and 34.1% respectively, indicating a marked shift in research attention toward intelligent, accessible automation tooling. Scatter graph analysis of citation accumulation against publication year, and of keyword frequency against growth rate, provides quantitative confirmation of this thematic evolution. The findings offer researchers, practitioners, and funding bodies an evidence-based map of the SWA research landscape and identify under-explored intersections — particularly explainable AI in automation governance and human-in-the-loop hyperautomation — as priority directions for future investigation.