Predicting motivation and intention to participate and recommend Food & Drink groups on Facebook via eWOM: a deep investigation based on the ANN analysis
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | REMark - Revista Brasileira de Marketing |
Texto Completo: | https://periodicos.uninove.br/remark/article/view/23229 |
Resumo: | Objective: Using an ANN-based analysis, this research aims to predict motivation and intention to participate and recommend Food & Drink groups on Facebook. Method: Data were collected from 345 individuals who participated in at least one Food & Drink related group. For data analysis, the non-linear method of ANN was used to predict occurrences within the same sample. Using this prediction method to test the theoretical model proposed, using scales adapted for the study, is relevant to the research. Originality/Relevance: Given the importance of the eWOM theme in social networks, being one of the prominent themes in the area, this study evolves the theme and contributes to expanding knowledge in non-linear methods. Results: Based on model 1 reviews, ‘pleasure for helping’ (44.8%) is the most important predictor of ‘eWOM motivation’. Based on the analysis of model 2, the ‘sense of belonging’ (42.7%) is the most important for the intention to recommend via eWOM. In addition, model 1 and model 2 presented fair values and observations for their validation. Theoretical/methodological contributions: A theoretical model was fitted using scales adapted for the study. With that, a survey was carried out and based on the results obtained in the sample, an approach of the ANN method was used. Social/Management Contributions: This study helps participants, administrators, moderators, and others interested in Facebook Food and Drink groups understand how they work and take advantage of the information exchanged to design strategies that meet the needs of the community. |
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Predicting motivation and intention to participate and recommend Food & Drink groups on Facebook via eWOM: a deep investigation based on the ANN analysisPrevendo a motivação e a intenção de participar e recomendar grupos de Comida e Bebida no Facebook via eWOM : uma investigação profunda com base na análise da RNAeWOM; Motivation; Intention to recommend; Groups on Facebook; Artificial Neural NetworkseWOM; Motivações; Intenção de recomendar; Grupos no Facebook; Redes Neurais ArtificiaisObjective: Using an ANN-based analysis, this research aims to predict motivation and intention to participate and recommend Food & Drink groups on Facebook. Method: Data were collected from 345 individuals who participated in at least one Food & Drink related group. For data analysis, the non-linear method of ANN was used to predict occurrences within the same sample. Using this prediction method to test the theoretical model proposed, using scales adapted for the study, is relevant to the research. Originality/Relevance: Given the importance of the eWOM theme in social networks, being one of the prominent themes in the area, this study evolves the theme and contributes to expanding knowledge in non-linear methods. Results: Based on model 1 reviews, ‘pleasure for helping’ (44.8%) is the most important predictor of ‘eWOM motivation’. Based on the analysis of model 2, the ‘sense of belonging’ (42.7%) is the most important for the intention to recommend via eWOM. In addition, model 1 and model 2 presented fair values and observations for their validation. Theoretical/methodological contributions: A theoretical model was fitted using scales adapted for the study. With that, a survey was carried out and based on the results obtained in the sample, an approach of the ANN method was used. Social/Management Contributions: This study helps participants, administrators, moderators, and others interested in Facebook Food and Drink groups understand how they work and take advantage of the information exchanged to design strategies that meet the needs of the community.Objetivo: Esta pesquisa tem como objetivo predizer a motivação e a intenção de participar e recomendar grupos de Comida e Bebida no Facebook, utilizando uma análise baseada em RNA. Método: Os dados foram coletados de 345 indivíduos, com participação em pelo menos um grupo relacionado ao de Comida e Bebida. Para a análise dos dados, o método não linear da RNA foi utilizado para predizer ocorrências dentro de uma mesma amostra. A relevância da pesquisa está na utilização desse método de predição para testar o modelo teórico proposto, utilizando escalas adaptadas para o estudo. Originalidade/Relevância: Dada a importância do tema eWOM nas redes sociais, sendo um dos temas de destaque na área, este estudo colabora com o aprofundamento do tema e contribui para a ampliação do conhecimento em métodos não lineares. Resultados: Como resultado, com base nas revisões do modelo 1, ‘prazer em ajudar’ (44,8%) é o preditor mais importante de ‘motivações para eWOM’. Enquanto, com base na análise do modelo 2, o ‘senso de pertencimento’ (42,7%) é o mais importante para a intenção de recomendar via eWOM. Além disso, o modelo 1 e o modelo 2 apresentaram valores justos e observações para sua validação. Contribuições teórico-metodológicas: Ajustou-se um modelo teórico por meio de escalas adaptadas para o estudo. Com isso, foi realizado um levantamento e, com base nos resultados obtidos na amostra, utilizou-se uma abordagem do método da RNA. Contribuições sociais/de gestão: Este estudo ajuda participantes, administradores, moderadores e outros interessados em grupos de Comida e Bebida do Facebook a entender como eles funcionam e a aproveitar as informações trocadas para projetar estratégias que atendam às necessidades da comunidade.Universidade Nove de Julho - Uninove2023-12-29info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionPeer-reviewed ArticleAvaliado por Paresapplication/pdfhttps://periodicos.uninove.br/remark/article/view/2322910.5585/remark.v22i5.23229ReMark - Revista Brasileira de Marketing; v. 22 n. 5 (2023): (out.dez.); 1888-19542177-5184reponame:REMark - Revista Brasileira de Marketinginstname:Universidade Nove de Julho (UNINOVE)instacron:RBMporhttps://periodicos.uninove.br/remark/article/view/23229/10641Copyright (c) 2023 ReMark - Revista Brasileira de Marketinghttps://creativecommons.org/licenses/by-nc-sa/4.0info:eu-repo/semantics/openAccessSouza, Laís Mitsue SimokomakiPinochet, Luis Hernan ContrerasPardim, Vanessa Itacaramby2024-01-02T14:24:35Zoai:ojs.periodicos.uninove.br:article/23229Revistahttps://periodicos.uninove.br/remarkPRIhttps://periodicos.uninove.br/remark/oaiclaudiaraac@uol.com.br || admin@revistabrasileiramarketing.org || admin@revistabrasileiramarketing.org2177-51842177-5184opendoar:2024-01-02T14:24:35REMark - Revista Brasileira de Marketing - Universidade Nove de Julho (UNINOVE)false |
dc.title.none.fl_str_mv |
Predicting motivation and intention to participate and recommend Food & Drink groups on Facebook via eWOM: a deep investigation based on the ANN analysis Prevendo a motivação e a intenção de participar e recomendar grupos de Comida e Bebida no Facebook via eWOM : uma investigação profunda com base na análise da RNA |
title |
Predicting motivation and intention to participate and recommend Food & Drink groups on Facebook via eWOM: a deep investigation based on the ANN analysis |
spellingShingle |
Predicting motivation and intention to participate and recommend Food & Drink groups on Facebook via eWOM: a deep investigation based on the ANN analysis Souza, Laís Mitsue Simokomaki eWOM; Motivation; Intention to recommend; Groups on Facebook; Artificial Neural Networks eWOM; Motivações; Intenção de recomendar; Grupos no Facebook; Redes Neurais Artificiais |
title_short |
Predicting motivation and intention to participate and recommend Food & Drink groups on Facebook via eWOM: a deep investigation based on the ANN analysis |
title_full |
Predicting motivation and intention to participate and recommend Food & Drink groups on Facebook via eWOM: a deep investigation based on the ANN analysis |
title_fullStr |
Predicting motivation and intention to participate and recommend Food & Drink groups on Facebook via eWOM: a deep investigation based on the ANN analysis |
title_full_unstemmed |
Predicting motivation and intention to participate and recommend Food & Drink groups on Facebook via eWOM: a deep investigation based on the ANN analysis |
title_sort |
Predicting motivation and intention to participate and recommend Food & Drink groups on Facebook via eWOM: a deep investigation based on the ANN analysis |
author |
Souza, Laís Mitsue Simokomaki |
author_facet |
Souza, Laís Mitsue Simokomaki Pinochet, Luis Hernan Contreras Pardim, Vanessa Itacaramby |
author_role |
author |
author2 |
Pinochet, Luis Hernan Contreras Pardim, Vanessa Itacaramby |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Souza, Laís Mitsue Simokomaki Pinochet, Luis Hernan Contreras Pardim, Vanessa Itacaramby |
dc.subject.por.fl_str_mv |
eWOM; Motivation; Intention to recommend; Groups on Facebook; Artificial Neural Networks eWOM; Motivações; Intenção de recomendar; Grupos no Facebook; Redes Neurais Artificiais |
topic |
eWOM; Motivation; Intention to recommend; Groups on Facebook; Artificial Neural Networks eWOM; Motivações; Intenção de recomendar; Grupos no Facebook; Redes Neurais Artificiais |
description |
Objective: Using an ANN-based analysis, this research aims to predict motivation and intention to participate and recommend Food & Drink groups on Facebook. Method: Data were collected from 345 individuals who participated in at least one Food & Drink related group. For data analysis, the non-linear method of ANN was used to predict occurrences within the same sample. Using this prediction method to test the theoretical model proposed, using scales adapted for the study, is relevant to the research. Originality/Relevance: Given the importance of the eWOM theme in social networks, being one of the prominent themes in the area, this study evolves the theme and contributes to expanding knowledge in non-linear methods. Results: Based on model 1 reviews, ‘pleasure for helping’ (44.8%) is the most important predictor of ‘eWOM motivation’. Based on the analysis of model 2, the ‘sense of belonging’ (42.7%) is the most important for the intention to recommend via eWOM. In addition, model 1 and model 2 presented fair values and observations for their validation. Theoretical/methodological contributions: A theoretical model was fitted using scales adapted for the study. With that, a survey was carried out and based on the results obtained in the sample, an approach of the ANN method was used. Social/Management Contributions: This study helps participants, administrators, moderators, and others interested in Facebook Food and Drink groups understand how they work and take advantage of the information exchanged to design strategies that meet the needs of the community. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-12-29 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article Avaliado por Pares |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://periodicos.uninove.br/remark/article/view/23229 10.5585/remark.v22i5.23229 |
url |
https://periodicos.uninove.br/remark/article/view/23229 |
identifier_str_mv |
10.5585/remark.v22i5.23229 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
https://periodicos.uninove.br/remark/article/view/23229/10641 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2023 ReMark - Revista Brasileira de Marketing https://creativecommons.org/licenses/by-nc-sa/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2023 ReMark - Revista Brasileira de Marketing https://creativecommons.org/licenses/by-nc-sa/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Nove de Julho - Uninove |
publisher.none.fl_str_mv |
Universidade Nove de Julho - Uninove |
dc.source.none.fl_str_mv |
ReMark - Revista Brasileira de Marketing; v. 22 n. 5 (2023): (out.dez.); 1888-1954 2177-5184 reponame:REMark - Revista Brasileira de Marketing instname:Universidade Nove de Julho (UNINOVE) instacron:RBM |
instname_str |
Universidade Nove de Julho (UNINOVE) |
instacron_str |
RBM |
institution |
RBM |
reponame_str |
REMark - Revista Brasileira de Marketing |
collection |
REMark - Revista Brasileira de Marketing |
repository.name.fl_str_mv |
REMark - Revista Brasileira de Marketing - Universidade Nove de Julho (UNINOVE) |
repository.mail.fl_str_mv |
claudiaraac@uol.com.br || admin@revistabrasileiramarketing.org || admin@revistabrasileiramarketing.org |
_version_ |
1799138639722053632 |