Search Results (17)

This study investigates the shopping behavior of Generation Z towards second-hand clothing in Slovakia, focusing on in-store experiences and their relationship with emotions, gender, and environmental awareness. Data were collected from 340 respondents through an online survey conducted between November 2024 and January 2025. The results indicate that feelings such as authenticity, fun, and interest in finding fashionable items are significantly associated with gender. Across all five dimensions, women perceive second-hand clothing shopping more positively than men. The biggest difference between the sexes is that women find this shopping more fun, enjoyable, and authentic. Men tend to be slightly more skeptical in their evaluation, with the lowest average score (2.65) on the question of whether shopping is “fun”. The study also reveals a strong correlation between the shopping experience and consumer attitudes. Individuals with prior experience in buying second-hand clothing exhibit greater environmental awareness, a stronger emotional connection with clothing, and a higher likelihood of participating in clothing swap events. Conversely, those without experience often harbor prejudices related to hygiene and perceive second-hand shopping as time-consuming and inconvenient. These findings highlight the importance of in-store experiences and the role of emotions in shaping consumer behavior towards second-hand clothing. The results have implications for retailers and policymakers seeking to promote sustainable consumption practices and enhance the appeal of the second-hand clothing market. Full article

In the context of new retail, the development of omni-channels is flourishing. The entry threshold for the clothing industry is low, and the popularity of online shopping has, to some extent, reduced consumers’ perception of the authenticity of clothing. As a result, returns are a serious issue in the clothing industry. This article focuses on a clothing retailer while addressing retail and return issues in the clothing industry. It develops and analyzes models for an online single-channel strategy and two omni-channel showroom strategies: “Experience in Store and Buy Online (ESBO)” with an experience store and “Buy Online and Return in Store (BORS)” with a physical store. These models are used to examine the pricing and return decisions of the retailer in the three strategic scenarios. Additionally, this study considers the impact of fashion trends on demand. It explores pricing and return strategies in two showroom models under the influence of the fashion trend decay factor. Moreover, sensitivity analyses and numerical analyses of the important parameters are performed. This research demonstrates the following: (1) In the case of high return transportation costs and online return hassle costs, clothing retailers can attract consumers to increase profits through establishing offline channels; (2) extending the sales time of fashionable clothing has a positive effect on profits, but blindly prolonging the continuation of the sales time will lead to a decrease in profits; (3) the larger the initial fashion level or the smaller the fashion level decay factor, the greater the optimal retailer profits. The impacts of the initial fashion level and fashion level decay factor on profits are more significant in omni-channel operations. This article aims to identify optimal strategies for retailers utilizing omni-channel operations and offer managerial insights for the sale of fashionable apparel. Full article

Studies have found that competitive products’ online review ratings (ORRs) have a spillover effect on the focal product’s sales. However, the spillover effect of online review sentiment (ORS) as an essential component of online review analysis has yet to be studied. In this study, we analyze online review content from JD.com using the latent Dirichlet allocation to identify the product attribute topics that consumers are most concerned about. We then construct a baseline regression model of ORS and ORRs to explore the effects of online competitive product reviews on focal product sales. Moreover, we examine how the interaction between ORS and critical factors of online reviews affect sales. Our results indicate that the ORS of competitive products has a negative effect on focal product sales, and the effect is greater than the ORS and ORRs of focal products, respectively. In addition, the ORS of competitive products inhibits the sale of focal products as evaluations of product attributes become more positive or online review usefulness increases. We also find that the effect of ORRs of competitive products is not significant, which may be because clothing, as an experiential product, requires consumers to gain more information about specific usage scenarios before making a decision. This study provides a more accurate basis for consumer decision-making and offers retailers a novel approach to developing marketing strategies. Full article

The abundance of publicly available data on the internet within the e-marketing domain is consistently expanding. A significant portion of this data revolve around consumers’ perceptions and opinions regarding the goods or services of organizations, making it valuable for market intelligence collectors in marketing, customer relationship management, and customer retention. Sentiment analysis serves as a tool for examining customer sentiment, marketing initiatives, and product appraisals. This valuable information can inform decisions related to future product and service development, marketing campaigns, and customer service enhancements. In social media, predicting ratings is commonly employed to anticipate product ratings based on user reviews. Our study provides an extensive benchmark comparison of different deep learning models, including convolutional neural networks (CNN), recurrent neural networks (RNN), and bi-directional long short-term memory (Bi-LSTM). These models are evaluated using various word embedding techniques, such as bi-directional encoder representations from transformers (BERT) and its derivatives, FastText, and Word2Vec. The evaluation encompasses two setups: 5-class versus 3-class. This paper focuses on sentiment analysis using neural network-based models for consumer sentiment prediction by evaluating and contrasting their performance indicators on a dataset of reviews of different products from customers of an online women’s clothes retailer. Full article

With a reputation for offering stylish and on-trend clothing at pocket-friendly prices, fast fashion brands resonate with the economic realities faced by many Gen Z consumers. Gen Z consumers are not just a target consumer market but also a driving force shaping the future of the fashion industry. Their preferences, values, and behaviors impact trends, reshape retail practices, and influence the overall trajectory of the fashion landscape. The evolving discourse surrounding sustainability and conscious consumerism suggests that the future may see a recalibration of the fashion landscape, with Gen Z at the forefront of demanding more responsible and transparent practices from the fashion industry. Therefore, this study aimed to identify the factors significantly influencing U.S. Gen Z consumers’ intentions to purchase fast fashion. Building on the theory of planned behavior, a research model for understanding Gen Z consumers’ intentions to buy fast fashion is proposed. Attitude, subjective norms, perceived behavioral control, environmental knowledge, need for uniqueness, materialism, and fashion leadership are investigated as predictors. Moreover, we examined how environmental knowledge, need for uniqueness, materialism, and fashion leadership affect Gen Z consumers’ attitudes toward fast fashion products. A total of 528 eligible responses were collected for analysis through a Qualtrics online survey. The proposed model’s psychometric properties were evaluated, and the hypotheses were tested using the multiple regression method. It was found that attitude, perceived consumer effectiveness, environmental knowledge, and fashion leadership significantly influenced Gen Z consumers’ intentions to shop fast fashion. Additionally, Gen Z consumers’ environmental knowledge, need for uniqueness, and fashion leadership significantly affect their attitudes toward fast fashion. The research model demonstrated strong explanatory power, explaining 68.9% of the variance in Gen Z consumers’ purchase intention toward fast fashion. Full article

The U.S. second-hand clothing industry is experiencing rapid growth, driven by increasing environmental awareness among consumers. However, there is a gap in understanding the driving forces behind this trend. This study aims to investigate the impact of external factors, including product quality, information quality, and service quality, on consumers’ internal emotions and examines how these emotional states, encompassing hedonic value, utilitarian value, environmental value, functional risk, aesthetic risk, and sanitary risk, influence their purchase intentions and word-of-mouth recommendations. Data were collected from 448 consumers who have shopped for second-hand clothing through an online survey conducted on Qualtrics. Multiple regression was applied to test the hypotheses. The findings indicate that product quality, information quality, and service quality enhance consumers’ perceived hedonic, utilitarian, and environmental values. Furthermore, service quality significantly reduces consumers’ perceived risks in terms of functionality, aesthetics, and sanitation. Additionally, consumers’ purchase intentions and word of mouth regarding second-hand clothing are positively influenced by their perceived hedonic, utilitarian, and environmental values. This research enriches the understanding of consumer behavior in the second-hand marketplace and offers insightful implications for retailers and marketers in the second-hand clothing industry. Full article

In the ever-expanding online fashion market, businesses in the clothing sales sector are presented with substantial growth opportunities. To utilize this potential, it is crucial to implement effective methods for accurately identifying clothing items. This entails a deep understanding of customer preferences, niche markets, tailored sales strategies, and an improved user experience. Artificial intelligence (AI) systems that can recognize and categorize clothing items play a crucial role in achieving these objectives, empowering businesses to boost sales and gain valuable customer insights. However, the challenge lies in accurately classifying diverse attire items in a rapidly evolving fashion landscape. Variations in styles, colors, and patterns make it difficult to consistently categorize clothing. Additionally, the quality of images provided by users varies widely, and background clutter can further complicate the task of accurate classification. Existing systems may struggle to provide the level of accuracy needed to meet customer expectations. To address these challenges, a meticulous dataset preparation process is essential. This includes careful data organization, the application of background removal techniques such as the GrabCut Algorithm, and resizing images for uniformity. The proposed solution involves a hybrid approach, combining the strengths of the ResNet152 and EfficientNetB7 architectures. This fusion of techniques aims to create a classification system capable of reliably distinguishing between various clothing items. The key innovation in this study is the development of a Two-Objective Learning model that leverages the capabilities of both ResNet152 and EfficientNetB7 architectures. This fusion approach enhances the accuracy of clothing item classification. The meticulously prepared dataset serves as the foundation for this model, ensuring that it can handle diverse clothing items effectively. The proposed methodology promises a novel approach to image identification and feature extraction, leading to impressive classification accuracy of 94%, coupled with stability and robustness. Full article

The scale of the representation of the Islamic head covering has increased exponentially over the last decade because of a range of factors, including growth in the modest fashion business sector and increased visibility of Muslim women in hijab in the public space. Social media has played a big role in changing perceptions of the Islamic head covering, via promotion and advertising. Meanwhile, the mainstream fashion industry has included options targeting the modest Muslim female market further, adding to changes in the representation and perception of the hijab. This research will examine the impact of social media and mainstream retail on Australian Muslim women’s perceptions and expressions of hijab. Using interviews and online surveys it explores the links between the fashion industry, social media, and changes in how Muslim women view the hijab. The majority of Australian Muslim women spoken to followed various hijabi bloggers or influencers although only a small proportion adopted recommendations from these hijabi bloggers or influencers (such as purchasing products, or incorporating suggestions on modest clothing or modest style trends). They believed migration, liberalism, social media marketing, and the inclusion of Muslim women in mainstream fashion has contributed to a form of commodification and commercialisation of the hijab. Furthermore, using hijab models as promotional tools to market the products, as well as the use of social media bloggers and influencers to represent them was perceived as tokenistic and disingenuous. Full article

This study’s purposes were to examine how selected demographic variables affect frequency of use of precautionary measures when shopping for clothing in retail stores; and how uncertainty avoidance/ambiguity intolerance and fashion innovativeness affect (a) precautionary measures used when shopping in retail stores during a pandemic and (b) compensatory consumption. Participants (122 US men; 209 US women aged 20 to 64) completed an online questionnaire containing demographic items plus measures of uncertainty avoidance/ambiguity intolerance, compensatory consumption, precautionary measures, and fashion innovativeness. Data analysis included reliability, factor analysis, M/ANOVA and SNK. Older adults, adults with higher education, and married adults more frequently used precautionary measures when shopping in retail stores. Men and women reported similar frequency of use. Fashion innovators and consumers with less tolerance for uncertainty/ambiguity more frequently used precautionary measures. Fashion innovators and consumers higher in uncertainty avoidance/ambiguity intolerance engaged in more compensatory consumption. Generalization of the results is limited because the data are context-specific: country (US), time period (during a pandemic), and sample. Guidelines for the general public regarding precautionary measures came from within organizations, between organizations and experts but the general public was not consulted (public open innovation) perhaps hindering compliance with precautionary measures. Full article

As sports garments are stretchable, different sizing tables are used than for retail clothing. However, customers measuring themselves leads to errors and unsatisfaction, since these customized branded garments cannot be returned. Using fitting sets avoids this, but this is not always feasible, especially in an online retail environment. Therefore, this research aims to use descriptive measures—parameters that do not require manual measuring because they are readily known by heart by almost any customer—to predict users’ body measurements, which can, thus, be used by customers to determine the size of their sports garment from a sizing chart. To validate if these input measures are sufficient to predict the correct size, three prediction methods are used and compared with baseline manual measurements. The methods are: (i) clothing size predictions from shape models with descriptive measures as inputs, (ii) clothing size predictions from a regression analysis, and (iii) clothing size predictions from a shape model based on extensive 3D scanned measurements as input. The conclusion is that a regression algorithm with, as input variables, the straightforward demographics of age, gender, stature, and weight is more accurate than the algorithm with the same inputs but with a shape model behind it. Moreover, chest and hip circumferences have an intraclass correlation coefficient rating above 0.9 and are, thus, suited for online retail of stretchable garments, such as cycling clothes. As validated by end-users, the regression predictions are shown to agree with preferred garment sizes of the participants, within the natural variation of personal preferences. Full article

These days, many sustainability-minded consumers face a major problem when trying to identify environmentally sustainable products. Indeed, there are a variety of confusing sustainability certifications and few labels capturing the overall environmental impact of products, as the existing procedures for assessing the environmental impact of products throughout their life cycle are time consuming, costly, and require a lot of data and input from domain experts. This paper explores the use of supervised machine learning tools to extrapolate the results of existing life cycle assessment studies (LCAs) and to develop a model—applied to the clothing product category—that could easily and quickly assess the products’ environmental sustainability throughout their life cycle. More precisely, we assemble a dataset of clothing products with their life cycle characteristics and corresponding known total environmental impact and test, on a 5-fold cross-validation basis, nine state-of-the-art supervised machine learning algorithms. Among them, the random forest algorithm has the best performance with an average accuracy of 91% over the five folds. The resulting model provides rapid environmental feedback on a variety of clothing products with the limited data available to online retailers. It could be used to quickly provide interested consumers with product-level sustainability information, or even to develop a unique and all-inclusive environmental label. Full article

Omnichannel retailing and sustainability are two important challenges for the fast fashion industry. However, the sustainable behavior of fast fashion consumers in an omnichannel environment has not received much attention from researchers. This paper aims to examine the factors that determine consumers’ willingness to participate in fast fashion brands’ used clothes recycling plans in an omnichannel retail environment. In particular, we examine the impact of individual consumer characteristics (environmental attitudes, consumer satisfaction), organizational arrangements constitutive for omnichannel retailing (channel integration), and their interplay (brand identification, impulsive consumption). A conceptual model was developed based on findings from previous research and tested on data that were collected online from Chinese fast fashion consumers. Findings suggest that consumers’ intentions for clothes recycling are mainly determined by individual factors, such as environmental attitudes and consumer satisfaction. Organizational arrangements (perceived channel integration) showed smaller effects. This study contributes to the literature on omnichannel (clothing) retail, as well as on sustainability in the clothing industry, by elucidating individual and organizational determinants of consumers’ recycling intentions for used clothes in an omnichannel environment. It helps retailers to organize used clothes recycling plans in an omnichannel environment and to motivate consumers to participate in them. Full article

Online retailers are challenged to present their products in an appropriate way to attract customers’ attention. To test the impact of product presentation features on customers’ visual attention, webcam eye tracking might be an alternative to infrared eye tracking, especially in situations where face-to-face contact is difficult. The aim of this study was to examine whether webcam eye tracking is suitable for investigating the influence of certain exogenous factors on customers’ visual attention when visiting online clothing shops. For this purpose, screenshots of two websites of two well-known online clothing retailers were used as stimuli. Linear regression analyses were conducted to determine the influence of the spatial position and the presence of a human model on the percentage of participants visiting a product depiction. The results show that products presented by human models and located in the upper middle area of a website were visited by more participants. From this, we were able to derive recommendations for optimising product presentation in online clothing shops. Our results fit well with those of other studies on visual attention conducted with infrared eye tracking, suggesting that webcam eye tracking could be an alternative to infrared eye tracking, at least for similar research questions. Full article

In this work, we propose a novel recommender system model based on a technology commonly used in natural language processing called word vector embedding. In this technology, a word is represented by a vector that is embedded in an n-dimensional space. The distance between two vectors expresses the level of similarity/dissimilarity of their underlying words. Since item similarities and user similarities are the basis of designing a successful collaborative filtering, vector embedding seems to be a good candidate. As opposed to words, we propose a vector embedding approach for learning vectors for items and users. There have been very few recent applications of vector embeddings in recommender systems, but they have limitations in the type of formulations that are applicable. We propose a novel vector embedding that is versatile, in the sense that it is applicable for the prediction of ratings and for the recommendation of top items that are likely to appeal to users. It could also possibly take into account content-based features and demographic information. The approach is a simple relaxation algorithm that optimizes an objective function, defined based on target users’, items’ or joint user–item’s similarities in their respective vector spaces. The proposed approach is evaluated using real life datasets such as “MovieLens”, “ModCloth”, “Amazon: Magazine_Subscriptions” and “Online Retail”. The obtained results are compared with some of the leading benchmark methods, and they show a competitive performance. Full article

Electronic shopping is getting more and more popular, and it is not only clothes and electronics that people buy online, but groceries and household products too. Based on real-life data from a major cosmetics and household products retailer in the Czech Republic, this study set to assess the life cycle environmental impact of parcel delivery. Two archetype parcels containing common household and hygiene products were designed and packed in two distinct ways, and the environmental impact was quantified using the Life Cycle Assessment method. It showed that it is environmentally beneficial to use plastic cushions to insulate the goods instead of paper. However, the most important process contributing to the environmental burden was found to be electricity consumption in the logistics center. Hence, the importance of energy efficiency and efficient space utilization was demonstrated on alternative scenarios. Since the cardboard box the goods are packed in turned out to be another important contributor, an alternative scenario was designed where a reusable plastic crate was used instead. Even though the scenario was based on several simplistic assumptions, it showed a clear potential to be environmentally beneficial. In the study, contribution of other processes was scrutinized, as well as sensitivity to variation of parameters, e.g. transportation distances. The main scientific contribution of this work is to show the importance of logistics and distribution of products to end customers in the rapidly developing field of electronic retail of household products. Full article