Slow Profile: Estudo das Orientações ao Consumo de Slow Fashion

Sobreira, Érica Maria Calíope; Silva, Clayton Robson Moreira da; Romero, Cláudia Buhamra Abreu

doi:10.18568/internext.v15i3.589

Cited by 5 publications

(8 citation statements)

References 34 publications

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“…Moreover, the results have revealed the profile of slow fashion consumers: most are single women with a prominent level of education and income and aged 31 on average. These results corroborate Sobreira et al . (2020b) in which groups of young adults (25–34 years old) were found to be the most oriented towards the consumption of slow fashion in the state of Ceará, Brazil.…”

Section: Final Remarkssupporting

confidence: 93%

“…Therefore, consumption-oriented slow fashion consumers practice this type of consumption, for example, by extending the life cycle of their clothes, buying clothing at thrift stores, promoting the reuse of second-hand items, properly disposing of their items or through donation, fabric reuse and repair (Clark, 2008). On this account, according to Sobreira et al (2020b), slow fashion also occurs by recycling and reusing existing clothes, and it can be combined with local production and handicrafts, which are outstanding characteristics of the apparel LPA addressed here. In addition, slow fashion consumers also tend to prioritize responsible local brands that produce high-quality and durable clothing at a slower speed, promoting ethical work relationships and production processes that generate less waste and pollution (Freudenreich and Schaltegger, 2020;Clark, 2008).…”

Section: Theoretical Framework 21 Slow Fashion Consumptionmentioning

confidence: 99%

Section: Theoretical Frameworkmentioning

confidence: 99%

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Theory of planned behavior, ethics and intention of conscious consumption in Slow Fashion Consumption

Lira

Costa

2022

JFMM

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PurposeThis study seeks to investigate the influence of the theory of planned behavior (TPB), of the conscious consumption intention and of the consumer ethical considerations, on Slow Fashion Consumption in the region known as Agreste Pernambucano, in Brazil, which is known for being an apparel manufacturing area.Design/methodology/approachTo achieve this purpose, descriptive quantitative research using non-probabilistic sampling was conducted. Data were collected through an online survey and distributed through the snowball technique. The sample consists of 486 respondents and relies on structural equation modeling for data analysis.FindingsThe results highlight that the Slow Fashion Consumption, in the scope of Local Productive Arrangement (LPA) of clothing manufacturing in the Agreste region, is influenced by the intention of conscious consumption, the ethical considerations in consumer behavior and the perceived behavioral control. Moreover, the results highlight the role of the influence of subjective norms both in the attitudes of consumers and the intention of conscious consumption.Originality/valueThe primary contribution of this study is to demonstrate that perceived behavioral control is positively associated with Slow Fashion Consumption, which, in turn, shows that respondents believe they have control over their sustainable actions.

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Section: Final Remarkssupporting

confidence: 93%

Section: Theoretical Framework 21 Slow Fashion Consumptionmentioning

confidence: 99%

Section: Theoretical Frameworkmentioning

confidence: 99%

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Theory of planned behavior, ethics and intention of conscious consumption in Slow Fashion Consumption

Lira

Costa

2022

JFMM

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“…The COSF scale was previously validated in Brazil, focusing on the Brazilian state of Ceará through Sobreira, Silva, and Romero (2020). Therefore, in this research, the aim is to investigate the consumption orientation profiles of Brazilians in general from a new perspective.…”

Section: Exclu3 -I Enjoy Having Clothes That Others Do Not Table 01: ...mentioning

confidence: 99%

“…Concerning Brazil, this scale was first applied and validated by Sobreira, Silva, and Romero (2020), but the authors investigated only Ceará, a Brazilian state, not expanding the work focusing on the entire country. The authors (Sobreira, Silva, and Romero, 2020) investigated a total of 461 volunteers; with this, the researchers discovered the existence of three consumption profiles: "high orientation to slow fashion," "functionality-oriented," and "averse to exclusivity. "…”

Section: Introductionmentioning

confidence: 99%

Sustainable Consumption Behavior - Slow Fashion in Brazil

Dantas,

Solino,

Freire

et al. 2024

MIX Sustent

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The interest in social justice and sustainability issues in the fashion industry has grown exponentially in recent years, given the concern for the worldwide preservation of the natural goods that remain on the earth and the valorization of professionals in the production chain. Slow fashion emerges as a relevant movement in this context. It seeks to slow down the mass industrial production of the fashion area that is currently known by overriding the valuing of quality and people who work throughout the production chain to the detriment of quantity and exploitation of natural resources. This paper aims to verify whether the five dimensions of the Consumer Orientation to Slow Fashion (COSF) scale fit satisfactorily to Brazil and then draw profiles of slow fashion product consumption in this exact scenario. Thus, we planned and applied a virtual questionnaire using the COSF scale with 414 volunteers from Brazil. We treated the collected data by exploratory and confirmatory factor analysis, k-means, structural equation modeling, and analysis of variance. As a result, we observed that the COSF scale did not thoroughly adjust to the Brazilian context. Thus, we identified three orientation profiles for slow fashion consumption: averse to exclusivity, highly, and low orientation.

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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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Slow Profile: Estudo das Orientações ao Consumo de Slow Fashion

Cited by 5 publications

References 34 publications

Theory of planned behavior, ethics and intention of conscious consumption in Slow Fashion Consumption

Theory of planned behavior, ethics and intention of conscious consumption in Slow Fashion Consumption

Sustainable Consumption Behavior - Slow Fashion in Brazil

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

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