Robotic process automation: A systematic literature review
Bibliographic record
Abstract
In this dissertation work, an extensive analysis of the research scenario in Robotic Process Automation (RPA) applied in the business scope is conducted. The study aims to detect primary themes, emerging research trends, influential authors, and prominent journals in the field of RPA. For this purpose, the VOSviewer software was used to perform a bibliometric analysis of a set of 118 articles extracted from selected databases. This analysis encompassed multiple data evaluations, including co-authorship analysis, citation analysis, bibliographic coupling analysis, and co-occurrence analysis. As a result, it was found that countries such as the United States, the United Kingdom, France, Canada, and Australia have made significant contributions in the field of RPA. Furthermore, the analysis of the of the main most cited articles highlighted the transformative role of RPA in process optimization, improving efficiency, and promoting digital transformation. The fusion of RPA with customer-oriented strategies, digital technologies such as Big Data Analytics (BDA), and emerging leadership practices were identified as key elements in delivering personalized services, enhancing customer experience, and fostering organizational success. With this work, a deeper understanding of the RPA research landscape is achieved, providing insights for future research and policy formulation, to support researchers and professionals working in this rapidly evolving field.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.027 | 0.024 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".