Predictive potential of the bankruptcy global models in the tourism industry
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
DOI: | 10.18089/tms.2021.170402 |
Texto Completo: | https://doi.org/10.18089/tms.2021.170402 |
Resumo: | The globalization process and the recent economic crises have increased the development of models to identify the factors related to business bankruptcy. As a consequence, previous research has provided bankruptcy prediction models both of a global nature for certain industries or regions of the world, and models focused on certain economic activities. The tourism industry is not immune to this concern and in the previous literature there are certain bankruptcy prediction models, generally focused on hotels or restaurants. However, there are no experiences of global models for tourism companies, although in the bankruptcy prediction literature there is numerous evidence of the advantages offered by global models compared to those focused on single business activity. This study develops a global bankruptcy prediction model capable of predicting with high precision any of the activities carried out in the tourism industry. To this end, a sample of 406 Spanish companies that have developed their activity in three sectors of the tourism industry (hotels, restaurants, and travel agencies) in the period 2017-2019 has been used. This sample includes bankrupt and non-bankrupt corporations and has allowed the comparison between a global model and various focused models applying artificial neural network techniques. The results have confirmed the superiority of the global model and provide different sample selection and cost minimization solutions for bankruptcy prediction modeling in the tourism industry. |
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Predictive potential of the bankruptcy global models in the tourism industryBankruptcypredictiontourist firmsartificial neural networksmulti-layer perceptronThe globalization process and the recent economic crises have increased the development of models to identify the factors related to business bankruptcy. As a consequence, previous research has provided bankruptcy prediction models both of a global nature for certain industries or regions of the world, and models focused on certain economic activities. The tourism industry is not immune to this concern and in the previous literature there are certain bankruptcy prediction models, generally focused on hotels or restaurants. However, there are no experiences of global models for tourism companies, although in the bankruptcy prediction literature there is numerous evidence of the advantages offered by global models compared to those focused on single business activity. This study develops a global bankruptcy prediction model capable of predicting with high precision any of the activities carried out in the tourism industry. To this end, a sample of 406 Spanish companies that have developed their activity in three sectors of the tourism industry (hotels, restaurants, and travel agencies) in the period 2017-2019 has been used. This sample includes bankrupt and non-bankrupt corporations and has allowed the comparison between a global model and various focused models applying artificial neural network techniques. The results have confirmed the superiority of the global model and provide different sample selection and cost minimization solutions for bankruptcy prediction modeling in the tourism industry.University of Algarve, Campus da Penha, 8005-139 Faro, Portugal2021-10-31info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://doi.org/10.18089/tms.2021.170402https://doi.org/10.18089/tms.2021.170402Revista Encontros Científicos - Tourism & Management Studies; v. 17 n. 4 (2021); 23-31Tourism & Management Studies; Vol. 17 N.º 4 (2021); 23-31Tourism & Management Studies; Vol. 17 No. 4 (2021); 23-31Revista Encontros Científicos - Tourism & Management Studies; Vol. 17 Núm. 4 (2021); 23-312182-8466reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAPenghttps://tmstudies.net/index.php/ectms/article/view/1512https://tmstudies.net/index.php/ectms/article/view/1512/pdf_377Copyright (c) 2021 Tourism & Management Studiesinfo:eu-repo/semantics/openAccessDel Castillo García, AgustínFernández Miguélez, Sergio Manuel2024-07-31T04:21:49Zoai:ojs.pkp.sfu.ca:article/1512Portal AgregadorONGhttps://www.rcaap.pt/oai/openairemluisa.alvim@gmail.comopendoar:71602024-07-31T04:21:49Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Predictive potential of the bankruptcy global models in the tourism industry |
title |
Predictive potential of the bankruptcy global models in the tourism industry |
spellingShingle |
Predictive potential of the bankruptcy global models in the tourism industry Predictive potential of the bankruptcy global models in the tourism industry Del Castillo García, Agustín Bankruptcy prediction tourist firms artificial neural networks multi-layer perceptron Del Castillo García, Agustín Bankruptcy prediction tourist firms artificial neural networks multi-layer perceptron |
title_short |
Predictive potential of the bankruptcy global models in the tourism industry |
title_full |
Predictive potential of the bankruptcy global models in the tourism industry |
title_fullStr |
Predictive potential of the bankruptcy global models in the tourism industry Predictive potential of the bankruptcy global models in the tourism industry |
title_full_unstemmed |
Predictive potential of the bankruptcy global models in the tourism industry Predictive potential of the bankruptcy global models in the tourism industry |
title_sort |
Predictive potential of the bankruptcy global models in the tourism industry |
author |
Del Castillo García, Agustín |
author_facet |
Del Castillo García, Agustín Del Castillo García, Agustín Fernández Miguélez, Sergio Manuel Fernández Miguélez, Sergio Manuel |
author_role |
author |
author2 |
Fernández Miguélez, Sergio Manuel |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Del Castillo García, Agustín Fernández Miguélez, Sergio Manuel |
dc.subject.por.fl_str_mv |
Bankruptcy prediction tourist firms artificial neural networks multi-layer perceptron |
topic |
Bankruptcy prediction tourist firms artificial neural networks multi-layer perceptron |
description |
The globalization process and the recent economic crises have increased the development of models to identify the factors related to business bankruptcy. As a consequence, previous research has provided bankruptcy prediction models both of a global nature for certain industries or regions of the world, and models focused on certain economic activities. The tourism industry is not immune to this concern and in the previous literature there are certain bankruptcy prediction models, generally focused on hotels or restaurants. However, there are no experiences of global models for tourism companies, although in the bankruptcy prediction literature there is numerous evidence of the advantages offered by global models compared to those focused on single business activity. This study develops a global bankruptcy prediction model capable of predicting with high precision any of the activities carried out in the tourism industry. To this end, a sample of 406 Spanish companies that have developed their activity in three sectors of the tourism industry (hotels, restaurants, and travel agencies) in the period 2017-2019 has been used. This sample includes bankrupt and non-bankrupt corporations and has allowed the comparison between a global model and various focused models applying artificial neural network techniques. The results have confirmed the superiority of the global model and provide different sample selection and cost minimization solutions for bankruptcy prediction modeling in the tourism industry. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-10-31 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://doi.org/10.18089/tms.2021.170402 https://doi.org/10.18089/tms.2021.170402 |
url |
https://doi.org/10.18089/tms.2021.170402 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://tmstudies.net/index.php/ectms/article/view/1512 https://tmstudies.net/index.php/ectms/article/view/1512/pdf_377 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2021 Tourism & Management Studies info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2021 Tourism & Management Studies |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
University of Algarve, Campus da Penha, 8005-139 Faro, Portugal |
publisher.none.fl_str_mv |
University of Algarve, Campus da Penha, 8005-139 Faro, Portugal |
dc.source.none.fl_str_mv |
Revista Encontros Científicos - Tourism & Management Studies; v. 17 n. 4 (2021); 23-31 Tourism & Management Studies; Vol. 17 N.º 4 (2021); 23-31 Tourism & Management Studies; Vol. 17 No. 4 (2021); 23-31 Revista Encontros Científicos - Tourism & Management Studies; Vol. 17 Núm. 4 (2021); 23-31 2182-8466 reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
mluisa.alvim@gmail.com |
_version_ |
1822181859394584576 |
dc.identifier.doi.none.fl_str_mv |
10.18089/tms.2021.170402 |