Factors That Influence Consumers’ Behaviors in Fashion Market

Xiang, Xitong

doi:10.4236/ojbm.2021.96154

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…Therefore, only through artistic treatment and the innovative design of clothing materials and shapes, can fashion designers meet the personalized needs of different consumers. For fashion designers, the use of scientific and technological materials can help them design fashion products that lead the trend to improve market competitiveness and attract consumers' attention [ 34 ]. (2) The determinants of fashion design.…”

Section: Methodsmentioning

confidence: 99%

Clothing Design Style Recommendation Using Decision Tree Algorithm Combined with Deep Learning

Yang¹

2022

Computational Intelligence and Neuroscience

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Most clothing recommendation methods have problems such as high resource consumption and inconsistent subjectively labeled clothing labels. Based on this, a multilabel classification algorithm based on deep learning (DL) theory is introduced, based on which the clothing style recognition model is constructed. Next, the concept of the decision tree algorithm is given, and the clothing recommendation model is built based on this algorithm. Moreover, the clothing style recognition model based on a multilabel classification algorithm and the clothing recommendation system based on a decision tree algorithm are tested by building simulation experiments and combining neural network technology. Finally, the application of the decision tree algorithm and DL theory in clothing recommendation design is studied through the literature collection method. The research focus is to realize the recognition of clothing through decision tree algorithm and DL method to achieve the intelligent recommendation of clothing style. The results show that: (1) the neural network technology in DL theory can realize efficient recognition and classification of clothing style by automatically extracting image features and combining with a multilabel classification algorithm. (2) The decision tree algorithm can make an initial recommendation according to users’ style preferences, then make implicit recommendations through user retrieval, browsing, and other operations, and make dynamic clothing style recommendations to users. (3) When the neural network based on a multilabel classification algorithm is trained, the precision, recall rate, and F1 values are 0.73, 0.43, and 0.55, respectively. (4) After using the clothing recommendation system based on the decision tree algorithm, the subjects’ average satisfaction is 86.25%, indicating that this system can give users a better clothing recommendation experience. This exploration aims to provide a crucial reference for further improving the quality of clothing recommendation services. It has important theoretical significance and practical value for the development of artificial intelligence in the field of fashion design, and is expected to provide a reference for the development of bionics.

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Section: Methodsmentioning

confidence: 99%

Clothing Design Style Recommendation Using Decision Tree Algorithm Combined with Deep Learning

Yang¹

2022

Computational Intelligence and Neuroscience

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“…Based on the article 'Factors That Influence Consumers' Behaviors in Fashion Market', individuals with a consistently high income have already fulfilled their essential needs, allowing them to prioritize the acquisition of luxurious items to fulfill their self-esteem desires. These goods serve as symbols of social standing and facilitate the garnering of admiration and esteem from others [8]. A typical example of this can be the economic fluctuation during the COVID-19.…”

Section: Economic Influence On the Fashion Industrymentioning

confidence: 99%

The Mutual Relationship Between the Citys Economy and Fashion Industry

Zhang

2024

CHR

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A citys economy and fashion industry have significant impacts on its development. This paper mainly focuses on exploring how a citys economy and fashion industry influence mutually. The fashion industry provides citizens with job opportunities and attracts famous companies and celebrities. It also serves as a platform for creativity and innovation, promoting the citys economic growth and international influence. Since the fashion industry needed to be supported by the advanced economy, and a citys economy also needed the fashion industry to promote it through investment and trade, they had influence mutually. Through investigations, a citys economic growth pace is similar to the development of its fashion. The research findings emphasize the importance of fostering a strong relationship between a city's economy and its fashion industry in order to stimulate economic growth and enhance the city's international influence. This study highlights the innovative and creative nature of the fashion industry. The findings of this study can provide valuable insights for policymakers and industry professionals, enabling them to make informed decisions in supporting the growth of both the city's economy and the fashion industry. Furthermore, this research contributes to the existing literature by deepening the understanding of the interconnection between economic development and the fashion industry. Overall, this study emphasizes the significance of a symbiotic relationship between a city's economy and its fashion industry and the potential benefits that can be gained from fostering this relationship.

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Fashion Resale Behaviours and Technology Disruption

Mohr

Fuxman

Mahmoud

2022

Advances in Marketing, Customer Relationship Management, and E-Services

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This chapter provides an in-depth discussion of the disruptive nature of reselling technologies. The collaborative consumption movement, a popular emerging trend, encourages consumers to live in a more collective, sharing economy. This is where we can discuss the disruptive nature of the reselling technologies, particularly as they impact the fashion industry, prompting an explosion of vintage/second-hand retailing. Secondary market behaviors such as reselling, recycling, gifting, swapping, and reusing are becoming the most significantly growing consumer segments. The notion of a rotating wardrobe has been increasingly frequently accepted. This is especially prominent with younger consumers like Generation Z, who would consider spending more money on sustainably produced and delivered products while showing a strong preference for switching to brands with sustainable initiatives. Mobile apps and personalization have made buying used products as easy as buying new ones.

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Factors That Influence Consumers’ Behaviors in Fashion Market

Cited by 3 publications

References 11 publications

Clothing Design Style Recommendation Using Decision Tree Algorithm Combined with Deep Learning

Clothing Design Style Recommendation Using Decision Tree Algorithm Combined with Deep Learning

The Mutual Relationship Between the Citys Economy and Fashion Industry

Fashion Resale Behaviours and Technology Disruption

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