Research on the Potential of the Consumption of Slow Fashion Products in Brazil

Solino, Lívia Juliana Silva; Dantas, Ítalo José de Medeiros; Teixeira, Breno Moore de Lima; Alves, Ionara Tereza Pereira; Medeiros, Ana Luísa Pereira De

doi:10.1007/978-3-031-16773-7_50

Advances in Fashion and Design Research 2022

DOI: 10.1007/978-3-031-16773-7_50

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Research on the Potential of the Consumption of Slow Fashion Products in Brazil

Lívia Juliana Silva Solino

Ítalo José de Medeiros Dantas²,

Breno Moore de Lima Teixeira³

et al.

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2023

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Cited by 2 publications

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Machine Learning Algorithms for Slow Fashion Consumer Prediction: Theoretical and Managerial Implications

Dantas,

Curth

2023

R. Intelig. Compet.

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Purpose: To compare, propose, and discuss the implications of five machine learning algorithms for predicting Slow fashion consumer profiles. Methodology/approach: We use the Python programming language to build the models with scikit-learn libraries. We tested the potential of five algorithms to correct classifier Slow fashion consumers: I) extremely randomized trees, II) random forest, III) support vector machine, IV) gradient boosting Tree, and V) naïve bayes. Originality/Relevance: This paper's originality lies in its combination of sustainability concerns, consumer behavior analysis, and machine learning techniques. It addresses a critical issue in the fashion industry and offers practical implications that can be beneficial for companies seeking to align their practices with Slow fashion principles. This interdisciplinary approach makes it a relevant contribution to both academia and industry. Key findings: The performance metrics revealed satisfactory values for all algorithms. Nevertheless, the support vector machine presents a better precision (96%) on the dataset for Slow fashion consumer profiling, while random forest performs the worst (87%). Theoretical/methodological contributions: We understood that the model can be helpful for companies that wish to adopt more targeted and practical approaches in the context of Slow fashion, allowing them to make more informed and strategic decisions. Therefore, these insights can guide future research in optimizing machine learning applications for consumer behavior analysis and provide valuable guidance for fashion marketers seeking to enhance their targeting and engagement strategies.

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Machine Learning Algorithms for Slow Fashion Consumer Prediction: Theoretical and Managerial Implications

Dantas,

Curth

2023

R. Intelig. Compet.

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Machine Learning Algorithms for Slow Fashion Consumer Prediction: Theoretical and Managerial Implications

Dantas,

Curth

2023

R. Intelig. Compet.

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scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

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Research on the Potential of the Consumption of Slow Fashion Products in Brazil

Cited by 2 publications

References 12 publications

Machine Learning Algorithms for Slow Fashion Consumer Prediction: Theoretical and Managerial Implications

Machine Learning Algorithms for Slow Fashion Consumer Prediction: Theoretical and Managerial Implications

Machine Learning Algorithms for Slow Fashion Consumer Prediction: Theoretical and Managerial Implications

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