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

Ítalo José de Medeiros Dantas; Marcelo Curth

doi:10.24883/eagleSustainable.v13i.439

Authors

Ítalo José de Medeiros Dantas Universidade Feevale (Feevale), Rio Grande do Sul https://orcid.org/0000-0003-0710-6142
Marcelo Curth Universidade Feevale (Feevale), Rio Grande do Sul https://orcid.org/0000-0002-9361-8373

DOI:

https://doi.org/10.24883/eagleSustainable.v13i.439

Keywords:

Sustainability, Consumer Classification, Machine Learning, Environmental Awareness, Strategic Decision-making

Abstract

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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Author Biographies

Ítalo José de Medeiros Dantas, Universidade Feevale (Feevale), Rio Grande do Sul

PhD student in Information Science at the University of Otago (New Zealand) and in Cultural Processes and Manifestations at Feevale University (Brazil); Master in Design from the Federal University of Campina Grande (2021); Specialist in Communication, Semiotics and Visual Languages from Braz Cubas University (2021); and, graduated in Fashion Design from the Federal Institute of Education, Science and Technology of Rio Grande do Norte (2019). Currently, he is also studying for a Bachelor's degree in Statistics at Centro Universitário IBMR. He was a substitute professor in the area of management and quality control processes in the clothing industry at the Federal Institute of Education, Science and Technology of Rio Grande do Norte - Campus Caicó between the years 2022 and 2023. His dissertation was selected for exhibition at the 35th Design Award from the Museu da Casa Brasileira, in the unpublished written works category. Multidisciplinary researcher with academic and professional interests in different areas, with an emphasis on Design, Fashion and Statistics, working mainly on the following topics: visual communication; morphology of artifacts; empirical studies; market research, focusing on consumption; statistical inference; and, exploratory data analysis.

Marcelo Curth, Universidade Feevale (Feevale), Rio Grande do Sul

He has a PhD in Administration from the University of Vale do Rio dos Sinos (UNISINOS), a Master's degree in Administration and Business from the Catholic University of Rio Grande do Sul (PUC-RS), Postgraduate in Administration and Marketing from Universidade Gama Filho, Postgraduate in Education from the Faculty (SENAC-RS) and a postgraduate degree in Mentoring Teacher Education (University of Tampere - Finland) and a degree in Sports Sciences from the Lutheran University of Brazil (ULBRA). He is a PPG professor in Cultural Processes and Manifestations at Feevale University, working as a researcher on the topic of Marketing: Identity and Culture. Professor at undergraduate and postgraduate levels of subjects on Strategic Marketing, Relationship Marketing, Consumer Behavior, Management and Entrepreneurship in Health and Sports. Course coordinator at postgraduate level (Lato Sensu) in Sports Management and Training and Exercise Prescription. Coordinator of innovation projects in the Health area with funding agencies. Manager of sports and extension programs and projects. Thematic coordinator of the Sports Marketing and Business Modeling and Entrepreneurship GTTs of the Brazilian Sports Management Association (ABRAGESP). Acting as Partner owner of consultancy, advisory and training companies, providing consultancy on marketing strategies in micro and small companies.

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