The impact of the COVID-19 pandemic on airlines’ passenger satisfaction

Detalhes bibliográficos
Autor(a) principal: Pereira, F.
Data de Publicação: 2023
Outros Autores: Costa, J. M., Ramos, R. F., Raimundo, A.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10071/28918
Resumo: This study aims to understand airline passengers' satisfaction trends by analyzing the most influential factors on satisfaction before and during the COVID-19 pandemic. The sample consists of a dataset with 9745 passenger reviews published on airlinequality.com. The reviews were analyzed with a sentiment analysis tool calibrated for the aviation industry for accuracy. Machine learning algorithms were then implemented to predict review sentiment based on airline company, travelers' type and class, and country of origin. Findings show passengers were unhappy before the pandemic, aggravated after the COVID-19 outbreak. The staff's behavior is the main factor influencing passengers' satisfaction. Predictive modeling showed that it is possible to predict negative review sentiments with satisfactory performance rather than positive reviews. The main takeaway is that passengers, after the pandemic, are most worried about refunds and aircraft cabin cleanliness. From a managerial standpoint, airline companies can benefit from the created knowledge to adjust their strategies in agreement and meet their customers' expectations.
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spelling The impact of the COVID-19 pandemic on airlines’ passenger satisfactionCustomer satisfactionSentiment analysisAirlinesCOVID-19This study aims to understand airline passengers' satisfaction trends by analyzing the most influential factors on satisfaction before and during the COVID-19 pandemic. The sample consists of a dataset with 9745 passenger reviews published on airlinequality.com. The reviews were analyzed with a sentiment analysis tool calibrated for the aviation industry for accuracy. Machine learning algorithms were then implemented to predict review sentiment based on airline company, travelers' type and class, and country of origin. Findings show passengers were unhappy before the pandemic, aggravated after the COVID-19 outbreak. The staff's behavior is the main factor influencing passengers' satisfaction. Predictive modeling showed that it is possible to predict negative review sentiments with satisfactory performance rather than positive reviews. The main takeaway is that passengers, after the pandemic, are most worried about refunds and aircraft cabin cleanliness. From a managerial standpoint, airline companies can benefit from the created knowledge to adjust their strategies in agreement and meet their customers' expectations.Elsevier2023-07-05T10:55:34Z2023-01-01T00:00:00Z20232023-07-05T11:54:36Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10071/28918eng0969-699710.1016/j.jairtraman.2023.102441Pereira, F.Costa, J. M.Ramos, R. F.Raimundo, A.info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-09T17:51:58Zoai:repositorio.iscte-iul.pt:10071/28918Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:25:50.301989Repositório 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 The impact of the COVID-19 pandemic on airlines’ passenger satisfaction
title The impact of the COVID-19 pandemic on airlines’ passenger satisfaction
spellingShingle The impact of the COVID-19 pandemic on airlines’ passenger satisfaction
Pereira, F.
Customer satisfaction
Sentiment analysis
Airlines
COVID-19
title_short The impact of the COVID-19 pandemic on airlines’ passenger satisfaction
title_full The impact of the COVID-19 pandemic on airlines’ passenger satisfaction
title_fullStr The impact of the COVID-19 pandemic on airlines’ passenger satisfaction
title_full_unstemmed The impact of the COVID-19 pandemic on airlines’ passenger satisfaction
title_sort The impact of the COVID-19 pandemic on airlines’ passenger satisfaction
author Pereira, F.
author_facet Pereira, F.
Costa, J. M.
Ramos, R. F.
Raimundo, A.
author_role author
author2 Costa, J. M.
Ramos, R. F.
Raimundo, A.
author2_role author
author
author
dc.contributor.author.fl_str_mv Pereira, F.
Costa, J. M.
Ramos, R. F.
Raimundo, A.
dc.subject.por.fl_str_mv Customer satisfaction
Sentiment analysis
Airlines
COVID-19
topic Customer satisfaction
Sentiment analysis
Airlines
COVID-19
description This study aims to understand airline passengers' satisfaction trends by analyzing the most influential factors on satisfaction before and during the COVID-19 pandemic. The sample consists of a dataset with 9745 passenger reviews published on airlinequality.com. The reviews were analyzed with a sentiment analysis tool calibrated for the aviation industry for accuracy. Machine learning algorithms were then implemented to predict review sentiment based on airline company, travelers' type and class, and country of origin. Findings show passengers were unhappy before the pandemic, aggravated after the COVID-19 outbreak. The staff's behavior is the main factor influencing passengers' satisfaction. Predictive modeling showed that it is possible to predict negative review sentiments with satisfactory performance rather than positive reviews. The main takeaway is that passengers, after the pandemic, are most worried about refunds and aircraft cabin cleanliness. From a managerial standpoint, airline companies can benefit from the created knowledge to adjust their strategies in agreement and meet their customers' expectations.
publishDate 2023
dc.date.none.fl_str_mv 2023-07-05T10:55:34Z
2023-01-01T00:00:00Z
2023
2023-07-05T11:54:36Z
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 http://hdl.handle.net/10071/28918
url http://hdl.handle.net/10071/28918
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 0969-6997
10.1016/j.jairtraman.2023.102441
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
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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
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