Publication
The role of artificial neural networks (ANNs) in supporting strategic management decisions
| datacite.subject.sdg | 04:Educação de Qualidade | |
| dc.contributor.author | Texeira Fernandes Justino, Maria do Rosário | |
| dc.contributor.author | Texeira-Quirós, Joaquín | |
| dc.contributor.author | Gonçalves, António José | |
| dc.contributor.author | Godinho Antunes, Marina | |
| dc.contributor.author | Ribeiro Mucharreira, Pedro | |
| dc.date.accessioned | 2025-05-08T15:47:11Z | |
| dc.date.available | 2025-05-08T15:47:11Z | |
| dc.date.issued | 2024-04-16 | |
| dc.description.abstract | Nowadays, the dynamism caused by constant changes to strategic decisions in markets poses an additional difficulty in an organization’s management. The strategic decisions made by managers can easily become obsolete. One of the major difficulties in managing a commercial organization is predicting, with some precision, the impact some strategic decisions have on the financial results. Business intelligence (BI) is widely used to help managers make strategic decisions. However, the methods used to achieve the conclusions are kept secret by BI company-based services. Modeling the environment may help predict the impact of an action in a real environment. A good model should provide the most accurate result of an applied action in a given environment. Artificial neural networks (ANNs) are proven to be excellent in modeling environments with very high data noise. The same strategic action can have different results when applied to different organizations. A tool that allows the evaluation of an applied strategic action in an environment will be of great importance in the field of management. Modeling the environment will save time and money for the organization, allowing the performance of the strategic plan to be improved. If one evaluates the state of the environment after a certain strategic action is applied, it can be possible to mitigate its risk of failure. As we will verify, it is possible to use ANNs to model strategic environments, allowing precision in the prediction of sales and operating results using particular strategies. | eng |
| dc.description.sponsorship | Autonomous University of Lisbon | |
| dc.identifier.citation | Justino, M. R., Texeira-Quirós, J., Gonçalves, A., Antunes, M. G, & Mucharreira, P. R. (2024). The role of Artificial Neural Networks (ANNs) to support management strategic decisions. Journal of Risk and Financial Management, 17(4),164. https://doi.org/10.3390/jrfm17040164. | |
| dc.identifier.doi | 10.3390/jrfm17040164 | |
| dc.identifier.issn | 1911-8074 | |
| dc.identifier.uri | http://hdl.handle.net/10400.21/21851 | |
| dc.language.iso | eng | |
| dc.peerreviewed | yes | |
| dc.publisher | MDPI AG | |
| dc.relation.ispartof | Journal of Risk and Financial Management | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | Management | |
| dc.subject | Strategy | |
| dc.subject | Modeling | |
| dc.subject | Risk analysis | |
| dc.subject | Artificial neural networks | |
| dc.subject | Financial results | |
| dc.title | The role of artificial neural networks (ANNs) in supporting strategic management decisions | eng |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.citation.issue | 4 | |
| oaire.citation.title | Journal of Risk and Financial Management | |
| oaire.citation.volume | 17 | |
| oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 |