Determinants of Green Smartphone Application Adoption for Sustainable Food Consumption Among University Students
Autor(a) principal: | |
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Data de Publicação: | 2024 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://u3isjournal.isvouga.pt/index.php/ijmcnm/article/view/794 |
Resumo: | This study aims to investigate the determinants of green smartphone application adoption among users. The study employs content richness model and modified Unified Theory of Acceptance and Use of Technology (UTAUT) as well as extrinsic constructs such as customisation and environmental concerns. A quantitative approach using a survey is utilised by collecting 700 responses. The data is analysed using Structural Equation Modelling (SEM) and three machine learning techniques including Artificial Neural Networks (ANN), Classification Regression Tree (CRT) and Chi-Squared Automatic Interaction Detection (CHAID). The results indicate that UTAUT, customisation and environmental concerns positively impact the adoption of green applications. Further analysis revealed fitness of analytical methods and the importance of variables for the overall sample and the subsamples derived. The study provides theoretical and practical contributions to academics, marketers and software developers in understanding consumer behaviour in the field. The result assist developers and marketers to decipher consumer behaviour towards green applications for sustainable consumption. The research contributes to theory and practice by employing an integrative model to investigate the role of technology in sustainable consumption. Moreover, the findings revealed the fitness of three machine learning methods to analyse the data collected for green consumption and the importance of variables in the model. The data is collected by employing convenience sampling. Hence, the results cannot be generalised accurately. Furthermore, data collection is conducted using a cross-sectional approach. Future researchers can add to the findings using a probability sampling and/or longitudinal data collection to generalise the results and reveal the changes in consumer behaviour. DOI: https://doi.org/10.54663/2182-9306.2023.v11.n21.179-212 |
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Determinants of Green Smartphone Application Adoption for Sustainable Food Consumption Among University StudentsTechnology adoption; Green smartphone applications; Sustainability; Food consumption behaviour; UTAUT; SEM; Machine LearningThis study aims to investigate the determinants of green smartphone application adoption among users. The study employs content richness model and modified Unified Theory of Acceptance and Use of Technology (UTAUT) as well as extrinsic constructs such as customisation and environmental concerns. A quantitative approach using a survey is utilised by collecting 700 responses. The data is analysed using Structural Equation Modelling (SEM) and three machine learning techniques including Artificial Neural Networks (ANN), Classification Regression Tree (CRT) and Chi-Squared Automatic Interaction Detection (CHAID). The results indicate that UTAUT, customisation and environmental concerns positively impact the adoption of green applications. Further analysis revealed fitness of analytical methods and the importance of variables for the overall sample and the subsamples derived. The study provides theoretical and practical contributions to academics, marketers and software developers in understanding consumer behaviour in the field. The result assist developers and marketers to decipher consumer behaviour towards green applications for sustainable consumption. The research contributes to theory and practice by employing an integrative model to investigate the role of technology in sustainable consumption. Moreover, the findings revealed the fitness of three machine learning methods to analyse the data collected for green consumption and the importance of variables in the model. The data is collected by employing convenience sampling. Hence, the results cannot be generalised accurately. Furthermore, data collection is conducted using a cross-sectional approach. Future researchers can add to the findings using a probability sampling and/or longitudinal data collection to generalise the results and reveal the changes in consumer behaviour. DOI: https://doi.org/10.54663/2182-9306.2023.v11.n21.179-212ISVOUGA - Instituto Superior de Entre Douro e Vouga2024-01-19info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://u3isjournal.isvouga.pt/index.php/ijmcnm/article/view/794International Journal of Marketing, Communication and New Media; Vol 11, No 21 (2023)2182-9306reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAPenghttp://u3isjournal.isvouga.pt/index.php/ijmcnm/article/view/794http://u3isjournal.isvouga.pt/index.php/ijmcnm/article/view/794/376Copyright (c) 2024 Arian Matin, Tornike Khoshtaria, Nugzar Todua, Ola Bareja-Wawryszuk, Tomasz Pajewski, Nia Toduainfo:eu-repo/semantics/openAccessMatin, ArianKhoshtaria, TornikeTodua, NugzarBareja-Wawryszuk, OlaPajewski, TomaszTodua, Nia2024-01-26T10:45:40Zoai:u3isjournal.isvouga.pt:article/794Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T01:57:38.822173Repositório 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 |
Determinants of Green Smartphone Application Adoption for Sustainable Food Consumption Among University Students |
title |
Determinants of Green Smartphone Application Adoption for Sustainable Food Consumption Among University Students |
spellingShingle |
Determinants of Green Smartphone Application Adoption for Sustainable Food Consumption Among University Students Matin, Arian Technology adoption; Green smartphone applications; Sustainability; Food consumption behaviour; UTAUT; SEM; Machine Learning |
title_short |
Determinants of Green Smartphone Application Adoption for Sustainable Food Consumption Among University Students |
title_full |
Determinants of Green Smartphone Application Adoption for Sustainable Food Consumption Among University Students |
title_fullStr |
Determinants of Green Smartphone Application Adoption for Sustainable Food Consumption Among University Students |
title_full_unstemmed |
Determinants of Green Smartphone Application Adoption for Sustainable Food Consumption Among University Students |
title_sort |
Determinants of Green Smartphone Application Adoption for Sustainable Food Consumption Among University Students |
author |
Matin, Arian |
author_facet |
Matin, Arian Khoshtaria, Tornike Todua, Nugzar Bareja-Wawryszuk, Ola Pajewski, Tomasz Todua, Nia |
author_role |
author |
author2 |
Khoshtaria, Tornike Todua, Nugzar Bareja-Wawryszuk, Ola Pajewski, Tomasz Todua, Nia |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
Matin, Arian Khoshtaria, Tornike Todua, Nugzar Bareja-Wawryszuk, Ola Pajewski, Tomasz Todua, Nia |
dc.subject.por.fl_str_mv |
Technology adoption; Green smartphone applications; Sustainability; Food consumption behaviour; UTAUT; SEM; Machine Learning |
topic |
Technology adoption; Green smartphone applications; Sustainability; Food consumption behaviour; UTAUT; SEM; Machine Learning |
description |
This study aims to investigate the determinants of green smartphone application adoption among users. The study employs content richness model and modified Unified Theory of Acceptance and Use of Technology (UTAUT) as well as extrinsic constructs such as customisation and environmental concerns. A quantitative approach using a survey is utilised by collecting 700 responses. The data is analysed using Structural Equation Modelling (SEM) and three machine learning techniques including Artificial Neural Networks (ANN), Classification Regression Tree (CRT) and Chi-Squared Automatic Interaction Detection (CHAID). The results indicate that UTAUT, customisation and environmental concerns positively impact the adoption of green applications. Further analysis revealed fitness of analytical methods and the importance of variables for the overall sample and the subsamples derived. The study provides theoretical and practical contributions to academics, marketers and software developers in understanding consumer behaviour in the field. The result assist developers and marketers to decipher consumer behaviour towards green applications for sustainable consumption. The research contributes to theory and practice by employing an integrative model to investigate the role of technology in sustainable consumption. Moreover, the findings revealed the fitness of three machine learning methods to analyse the data collected for green consumption and the importance of variables in the model. The data is collected by employing convenience sampling. Hence, the results cannot be generalised accurately. Furthermore, data collection is conducted using a cross-sectional approach. Future researchers can add to the findings using a probability sampling and/or longitudinal data collection to generalise the results and reveal the changes in consumer behaviour. DOI: https://doi.org/10.54663/2182-9306.2023.v11.n21.179-212 |
publishDate |
2024 |
dc.date.none.fl_str_mv |
2024-01-19 |
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://u3isjournal.isvouga.pt/index.php/ijmcnm/article/view/794 |
url |
http://u3isjournal.isvouga.pt/index.php/ijmcnm/article/view/794 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
http://u3isjournal.isvouga.pt/index.php/ijmcnm/article/view/794 http://u3isjournal.isvouga.pt/index.php/ijmcnm/article/view/794/376 |
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 |
ISVOUGA - Instituto Superior de Entre Douro e Vouga |
publisher.none.fl_str_mv |
ISVOUGA - Instituto Superior de Entre Douro e Vouga |
dc.source.none.fl_str_mv |
International Journal of Marketing, Communication and New Media; Vol 11, No 21 (2023) 2182-9306 reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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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 |
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