An application of OWA operators in fuzzy business diagnosis
- Autores
- Vigier, Hernán Pedro; Scherger, Valeria; Terceño, Antonio
- Año de publicación
- 2016
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión enviada
- Descripción
- The paper aims to develop an adjustment index based on OWA operators to enrich the results of diagnostic fuzzy models of business failure. A proposal to verify the diseases prediction accuracy of the models is also added. This allows a reduction of the map of causes or diseases detected in strategic defined areas. At the same time, these key areas can be disaggregated when an alert indicator is identified, and shows which of the causes need special attention. This application of OWA can encourage the development of suitable computer systems for monitoring companies’ problems, warn of failures and facilitate decision-making. In addition, taking Vigier and Terceno’s ˜ 2008 model as a benchmark, causes aggregation operators are introduced to evaluate alternative groupings, and the adjustment measure using approximate solutions is proposed to test the model’s prediction. The empirical estimation and the verification of the improvement proposals in a set of small and medium- sized enterprises (SMEs) in the construction industry are also presented. The functionality and the prediction capacity are thus measured and detected by monitoring key areas that warn about insolvency situations in the firm
- Materia
-
Economía y Negocios
Economic-financial diagnosis
Forecast
Symptoms
Causes
Fuzzy relations
Business failure
OWA operators - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by-nc-nd/4.0/
- Repositorio
- Institución
- Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
- OAI Identificador
- oai:digital.cic.gba.gob.ar:11746/5059
Ver los metadatos del registro completo
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An application of OWA operators in fuzzy business diagnosisVigier, Hernán PedroScherger, ValeriaTerceño, AntonioEconomía y NegociosEconomic-financial diagnosisForecastSymptomsCausesFuzzy relationsBusiness failureOWA operatorsThe paper aims to develop an adjustment index based on OWA operators to enrich the results of diagnostic fuzzy models of business failure. A proposal to verify the diseases prediction accuracy of the models is also added. This allows a reduction of the map of causes or diseases detected in strategic defined areas. At the same time, these key areas can be disaggregated when an alert indicator is identified, and shows which of the causes need special attention. This application of OWA can encourage the development of suitable computer systems for monitoring companies’ problems, warn of failures and facilitate decision-making. In addition, taking Vigier and Terceno’s ˜ 2008 model as a benchmark, causes aggregation operators are introduced to evaluate alternative groupings, and the adjustment measure using approximate solutions is proposed to test the model’s prediction. The empirical estimation and the verification of the improvement proposals in a set of small and medium- sized enterprises (SMEs) in the construction industry are also presented. The functionality and the prediction capacity are thus measured and detected by monitoring key areas that warn about insolvency situations in the firm2016-06-20info:eu-repo/semantics/articleinfo:eu-repo/semantics/submittedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttps://digital.cic.gba.gob.ar/handle/11746/5059enginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.asoc.2016.06.026info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-nd/4.0/reponame:CIC Digital (CICBA)instname:Comisión de Investigaciones Científicas de la Provincia de Buenos Airesinstacron:CICBA2025-09-29T13:39:55Zoai:digital.cic.gba.gob.ar:11746/5059Institucionalhttp://digital.cic.gba.gob.arOrganismo científico-tecnológicoNo correspondehttp://digital.cic.gba.gob.ar/oai/snrdmarisa.degiusti@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:94412025-09-29 13:39:55.582CIC Digital (CICBA) - Comisión de Investigaciones Científicas de la Provincia de Buenos Airesfalse |
dc.title.none.fl_str_mv |
An application of OWA operators in fuzzy business diagnosis |
title |
An application of OWA operators in fuzzy business diagnosis |
spellingShingle |
An application of OWA operators in fuzzy business diagnosis Vigier, Hernán Pedro Economía y Negocios Economic-financial diagnosis Forecast Symptoms Causes Fuzzy relations Business failure OWA operators |
title_short |
An application of OWA operators in fuzzy business diagnosis |
title_full |
An application of OWA operators in fuzzy business diagnosis |
title_fullStr |
An application of OWA operators in fuzzy business diagnosis |
title_full_unstemmed |
An application of OWA operators in fuzzy business diagnosis |
title_sort |
An application of OWA operators in fuzzy business diagnosis |
dc.creator.none.fl_str_mv |
Vigier, Hernán Pedro Scherger, Valeria Terceño, Antonio |
author |
Vigier, Hernán Pedro |
author_facet |
Vigier, Hernán Pedro Scherger, Valeria Terceño, Antonio |
author_role |
author |
author2 |
Scherger, Valeria Terceño, Antonio |
author2_role |
author author |
dc.subject.none.fl_str_mv |
Economía y Negocios Economic-financial diagnosis Forecast Symptoms Causes Fuzzy relations Business failure OWA operators |
topic |
Economía y Negocios Economic-financial diagnosis Forecast Symptoms Causes Fuzzy relations Business failure OWA operators |
dc.description.none.fl_txt_mv |
The paper aims to develop an adjustment index based on OWA operators to enrich the results of diagnostic fuzzy models of business failure. A proposal to verify the diseases prediction accuracy of the models is also added. This allows a reduction of the map of causes or diseases detected in strategic defined areas. At the same time, these key areas can be disaggregated when an alert indicator is identified, and shows which of the causes need special attention. This application of OWA can encourage the development of suitable computer systems for monitoring companies’ problems, warn of failures and facilitate decision-making. In addition, taking Vigier and Terceno’s ˜ 2008 model as a benchmark, causes aggregation operators are introduced to evaluate alternative groupings, and the adjustment measure using approximate solutions is proposed to test the model’s prediction. The empirical estimation and the verification of the improvement proposals in a set of small and medium- sized enterprises (SMEs) in the construction industry are also presented. The functionality and the prediction capacity are thus measured and detected by monitoring key areas that warn about insolvency situations in the firm |
description |
The paper aims to develop an adjustment index based on OWA operators to enrich the results of diagnostic fuzzy models of business failure. A proposal to verify the diseases prediction accuracy of the models is also added. This allows a reduction of the map of causes or diseases detected in strategic defined areas. At the same time, these key areas can be disaggregated when an alert indicator is identified, and shows which of the causes need special attention. This application of OWA can encourage the development of suitable computer systems for monitoring companies’ problems, warn of failures and facilitate decision-making. In addition, taking Vigier and Terceno’s ˜ 2008 model as a benchmark, causes aggregation operators are introduced to evaluate alternative groupings, and the adjustment measure using approximate solutions is proposed to test the model’s prediction. The empirical estimation and the verification of the improvement proposals in a set of small and medium- sized enterprises (SMEs) in the construction industry are also presented. The functionality and the prediction capacity are thus measured and detected by monitoring key areas that warn about insolvency situations in the firm |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-06-20 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/submittedVersion http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
format |
article |
status_str |
submittedVersion |
dc.identifier.none.fl_str_mv |
https://digital.cic.gba.gob.ar/handle/11746/5059 |
url |
https://digital.cic.gba.gob.ar/handle/11746/5059 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.asoc.2016.06.026 |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-nd/4.0/ |
eu_rights_str_mv |
openAccess |
rights_invalid_str_mv |
http://creativecommons.org/licenses/by-nc-nd/4.0/ |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:CIC Digital (CICBA) instname:Comisión de Investigaciones Científicas de la Provincia de Buenos Aires instacron:CICBA |
reponame_str |
CIC Digital (CICBA) |
collection |
CIC Digital (CICBA) |
instname_str |
Comisión de Investigaciones Científicas de la Provincia de Buenos Aires |
instacron_str |
CICBA |
institution |
CICBA |
repository.name.fl_str_mv |
CIC Digital (CICBA) - Comisión de Investigaciones Científicas de la Provincia de Buenos Aires |
repository.mail.fl_str_mv |
marisa.degiusti@sedici.unlp.edu.ar |
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1844618586161676288 |
score |
13.070432 |