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
CIC Digital (CICBA)
Institución
Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
OAI Identificador
oai:digital.cic.gba.gob.ar:11746/5059

id CICBA_d5054c5d6e936d208f1f65471d2cd6dc
oai_identifier_str oai:digital.cic.gba.gob.ar:11746/5059
network_acronym_str CICBA
repository_id_str 9441
network_name_str CIC Digital (CICBA)
spelling 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
_version_ 1844618586161676288
score 13.070432