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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) 
Laureano, R. M. S., Trindade, G. & Laureano, L. M. S. (2021). Predictive models for managing financial incentives oriented to companies: Application to Portugal 2020. In Rocha, A., Gonçalves, R., Penalvo, F. G., & Martins, J. (Ed.), 2021 16th Iberian Conference on Information Systems and Technologies (CISTI). Chaves: IEEE.
Exportar Referência (IEEE) 
R. M. Laureano et al.,  "Predictive models for managing financial incentives oriented to companies: Application to Portugal 2020", in 2021 16th Iberian Conf. on Information Systems and Technologies (CISTI), Rocha, A., Gonçalves, R., Penalvo, F. G., & Martins, J., Ed., Chaves, IEEE, 2021
Exportar BibTeX 
@inproceedings{laureano2021_1732198986673,
	author = "Laureano, R. M. S. and Trindade, G. and Laureano, L. M. S.",
	title = "Predictive models for managing financial incentives oriented to companies: Application to Portugal 2020",
	booktitle = "2021 16th Iberian Conference on Information Systems and Technologies (CISTI)",
	year = "2021",
	editor = "Rocha, A., Gonçalves, R., Penalvo, F. G., & Martins, J.",
	volume = "",
	number = "",
	series = "",
	doi = "10.23919/CISTI52073.2021.9476655",
	publisher = "IEEE",
	address = "Chaves",
	organization = "AISTI",
	url = "https://ieeexplore.ieee.org/xpl/conhome/9476245/proceeding"
}
Exportar RIS 
TY  - CPAPER
TI  - Predictive models for managing financial incentives oriented to companies: Application to Portugal 2020
T2  - 2021 16th Iberian Conference on Information Systems and Technologies (CISTI)
AU  - Laureano, R. M. S.
AU  - Trindade, G.
AU  - Laureano, L. M. S.
PY  - 2021
SN  - 2166-0727
DO  - 10.23919/CISTI52073.2021.9476655
CY  - Chaves
UR  - https://ieeexplore.ieee.org/xpl/conhome/9476245/proceeding
AB  - Since Portugal joined the European Union (EU) that it has been receiving incentives/funds to reduce disparities with other EU countries. Despite this goal, disparities between European regions still exist and the impact of such funds is questionable. What if it is possible to predict the success of such incentives when the funds are awarded to the beneficiaries? Using data from the database of The Agency for Competitiveness and Innovation (IAPMEI), for the programs National Strategic Reference Framework (QREN), and Portugal 2020, two predictive models are developed to estimate the number of applications to be received and the schedule of expected payments to beneficiaries for a four-month period. The results allow for a better prediction, on one hand, of the resources to be allocated to the evaluation process of the applications, and on the other hand, of the financial execution plan for the upcoming period, in order to prepare the financial execution.
ER  -