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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
Farinha, D., Pereira, R. & Almeida, R. (2024). A framework to support Robotic Process Automation. Journal of Information Technology. 39 (1), 149-166
Exportar Referência (IEEE) 
D. Farinha et al.,  "A framework to support Robotic Process Automation", in Journal of Information Technology, vol. 39, no. 1, pp. 149-166, 2024
Exportar BibTeX 
@article{farinha2024_1721791748855,
	author = "Farinha, D. and Pereira, R. and Almeida, R.",
	title = "A framework to support Robotic Process Automation",
	journal = "Journal of Information Technology",
	year = "2024",
	volume = "39",
	number = "1",
	doi = "10.1177/02683962231165066",
	pages = "149-166",
	url = "https://journals.sagepub.com/doi/10.1177/02683962231165066"
}
Exportar RIS 
TY  - JOUR
TI  - A framework to support Robotic Process Automation
T2  - Journal of Information Technology
VL  - 39
IS  - 1
AU  - Farinha, D.
AU  - Pereira, R.
AU  - Almeida, R.
PY  - 2024
SP  - 149-166
SN  - 0268-3962
DO  - 10.1177/02683962231165066
UR  - https://journals.sagepub.com/doi/10.1177/02683962231165066
AB  - With the increasing demand for digitalization, organizations look to emerging technologies such as Robotic Process Automation (RPA) to increase their business performance. This makes it essential to identify and select the most suitable processes to maximize the benefits for organizations. However, despite the increasing interest of academics and professionals in RPA, the literature lacks a study on the main criteria organizations should consider to decide which processes to automate. Therefore, this research lists the main criteria for process automation based on scientific and professional know-how. A systematic literature review was performed, followed by a Delphi study with RPA professionals, to tune the former insights collected from the scientific literature. Our findings point to 32 criteria that organizations and decision-makers should consider before choosing which processes to automate. Feasibility, process description, and input and output data are the most voted. The criteria are evaluated with 18 processes in six organizations with positive results. While professionals may find valuable information in this document to help them decide which processes must be automated first, academics are now aware of which areas deserve further investigation. 
ER  -