Search Results (51)

This paper introduces an integrated inventory model that employs customized neural networks to tackle the challenge of non-stationary demand for newsvendor-type products, such as vegetables and fashion items. In this newsvendor context, demand is intertemporal-dependent and influenced by external factors such as prices, promotions, and holidays. Contrary to traditional machine-learning-based inventory models that assume stationary and independent demand, our method accounts for the temporal dynamics and the impact of external factors on demand. Our customized neural network model integrates demand estimation with inventory optimization, circumventing the potential suboptimality of sequential estimation and optimization methods. We conduct a case study on optimizing the vegetable ordering decisions for a supermarket. The findings indicate the proposed model’s effectiveness in enhancing ordering decisions, thereby reducing inventory costs by up to 21.14%. By customizing an integrated neural network, this paper presents a precise and cost-effective inventory management solution to address real-world complexities of demand, like seasonality and external factor dependency. The proposed approach is particularly beneficial for retailers in industries dealing with perishable items and market volatility, enabling them to mitigate waste (e.g., unsold vegetables) and stockouts (e.g., seasonal fashion items). This directly confronts challenges related to sustainability and profitability. Furthermore, the integrated framework provides a methodological inspiration for adapting neural networks to other time-sensitive supply chain problems. Full article

In recent years, the fashion industry has undergone a significant transformation driven by digital innovations, particularly with the emergence of the digital retail theater (DRT). A DRT integrates augmented reality (AR), virtual reality (VR), and 3D modeling to create immersive shopping experiences that bridge the physical and digital worlds. This study specifically focuses on apparel DRTs and investigates the key factors influencing U.S. consumers’ intention to adopt this technology. Drawing on the unified theory of acceptance and use of technology (UTAUT) and perceived risk theory, we developed and tested an integrative research model. Primary data were collected through a structured online survey administered via Amazon Mechanical Turk (MTurk). A total of 400 valid responses were obtained from U.S. consumers. Data were analyzed using multiple regression analysis to examine the hypothesized relationships. The results indicate that effort expectancy (ease of use), facilitating conditions (technical infrastructure), physical risk (concerns about potential harm), and time/convenience loss risk significantly influence consumers’ intention to adopt apparel DRTs. Surprisingly, performance expectancy and social influence were not significant predictors of DRT adoption. These findings provide valuable insights for apparel retailers, emphasizing the importance of user-friendly designs, robust technical infrastructure, and minimizing perceived risks to foster DRT adoption. 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

This study considers ways to increase the consumption of sustainable fashion given the significant environmental and social damages associated with the industry. A series of experiments were conducted examining the impacts of choice architecture (nudges) under field conditions in collaboration with one of Israel’s largest shopping centers. This study sought to identify which interventions at the retail level successfully motivate sustainable fashion behavioral change regarding purchases and willingness to pay more, along with agreement with several statements regarding the climate crisis and sustainable fashion. Among the types of nudges examined in this field study were providing information, increasing accessibility to sustainable alternatives and appealing to social identity in relation to demographics and green self-image. This study found that offering alternatives to consumers constituted the most effective way to “nudge” consumers toward more sustainable purchasing behavior. Nonetheless, this does not negate the contribution of providing information and strengthening social norms regarding sustainable fashion. Additionally, in all groups, most participants reported that they did not know how to distinguish between sustainable and non-sustainable fashion, nor did they believe that the clothes they purchased were actually sustainable. The findings emphasize the need for policies that will increase the accessibility of sustainable fashion. Full article

The fashion industry is recognized among the most impactful industries in relation to environmental and social damage due to unsustainable models. While countless research studies focus on the supply chain and product innovation, the presented inquiry studies the fashion retail sector with the goal of systematizing existing sustainable practices based on service offerings. We present the results of an extensive case-study analysis conducted on 370 initiatives of different natures gathered among international companies and local actors in the European context that offer services oriented toward sustainability. As a result, we propose an ontology that encompasses three areas (services for environmental sustainability, culture of sustainability, and implementation of social sustainability) and comprehensively describes existing sustainability strategies for helping retailers preserve the environment, while promoting socially inclusive practices and disseminating the culture of sustainability. The innovation of retailing from a similar perspective aims at rendering businesses more resilient to survive market and social transformations, especially in consideration of the boost to sustainable and circular transformation advocated by recent European legislation. Full article

Digital innovations have significantly transformed the marketing landscape, with visual technology solutions having become mainstream in the fashion industry approximately a decade ago. Digital technology offers a range of benefits to online fashion retailers, enhancing their online shopping platforms with augmented reality features that allow customers to “try on” products digitally before making a purchase. This research aims to explore the key factors influencing the use of augmented reality applications and e-commerce sites for purchasing apparel. A qualitative study was conducted to examine the visual experience and usage of augmented reality applications among young customers. The findings highlight the most relevant factors in the online fashion purchasing process, the visual experience, and the potential future use of augmented reality applications in fashion product purchasing. These insights are crucial for developing effective marketing strategies and communication messages. Full article

The fashion apparel industry is facing an increasingly growing demand, compounded by the short sales lifecycle and strong seasonality of clothing, posing significant challenges to inventory management in the retail sector. Despite some retailers like Uniqlo and Zara implementing inventory management and dynamic pricing strategies, challenges persist due to the dynamic nature of fashion trends and the stochastic factors affecting inventory. To address these issues, we construct a mathematical model based on the mathematical expression of the deterministic fashion level function, where the geometric Brownian motion, widely applied in finance, is initially utilized in the stochastic fashion level function. Drawing on research findings from deteriorating inventory management and stochastic optimization, we investigate the fluctuation of inventory levels, optimal dynamic pricing, optimal production rates, and profits—four crucial indicators—via Pontryagin’s maximum principle. Analytical solutions are derived, and the numerical simulation is provided to verify and compare the proposed model with deterministic fashion level function models. The model emphasizes the importance of considering stochastic factors in decision-making processes and provides insights to enhance profitability, inventory management, and sustainable consumption in the fashion product industry. 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

Using a single RGB camera to obtain accurate body dimensions, rather than measuring these manually or via more complex multicamera systems or more expensive 3D scanners, has a high application potential for the apparel industry. We present a system that estimates upper human body measurements using a hybrid set of techniques from both classic computer vision and recent machine learning. The main steps involve (1) using a camera to obtain two views (frontal and side); (2) isolating in the image pair a set of main body parts; (3) improving the image quality; (4) extracting body contours and features from the images of body parts; (5) indicating markers on these images; (6) performing a calibration step; and (7) producing refined final 3D measurements. We favour a unique geometric shape, that of an ellipse, to approximate human body main horizontal cross-sections. We focus on the more challenging parts of the body, i.e., the upper body from the head to the hips, which, we show, can be well represented by varying an ellipse’s eccentricity for each individual. Then, evaluating each fitted ellipse’s perimeter allows us to obtain better results than the current state-of-the-art methods for use in the fashion and online retail industry. In our study, we selected a set of two equations, out of many other possible choices, to best estimate upper human body section circumferences. We experimented with the system on a diverse sample of 78 female participants. The results for the upper human body measurements in comparison to the traditional manual method of tape measurements, when used as a reference, show ±1 cm average differences, which are sufficient for many applications, including online retail. Full article

Sustainability has increasingly become one of the single biggest issues requiring immediate global attention by both the retail industry and the consumer. As the end users of garments, consumers play a key role in reversing the current devastating environmental trends. For consumers to do this, there is a need to understand those motivating factors that would encourage them to increase their involvement in sustainability-related practices. This study, therefore, addresses the motivations, values, beliefs, and norms of the consumer’s sustainable consumption behavior (SCB). The value-belief-norm (VBN) theory was used as a framework to explain beliefs and behaviors which serve as predictors for attitudes and behavioral intentions. Data were collected through interviews with 14 female participants in six countries selected because of their environmentally-conscious behaviors. Findings indicate poor fit is a motivating factor to extend the life of clothing by providing participants with a sense of control and empowerment. Participants believed fashion was an unlimited reusable resource. Future research might explore connections among creativity, mental wellness, and empowerment. Both fashion industry and academic practitioners would benefit, as a need exists to update consumer behavior models, and increase understanding, based on the wants and needs of the sustainability-conscious consumer. Full article

Many industries, including healthcare, banking, the auto industry, education, and retail, have already undergone significant changes because of artificial intelligence (AI). Business-to-Customer (B2C) e-commerce has considerably increased the use of AI in recent years. The purpose of this research is to examine the significance and impact of AI in the realm of fashion e-commerce. To that end, a systematic review of the literature is carried out, in which data from the Web Of Science and Scopus databases were used to analyze 219 publications on the subject. The articles were first categorized using AI techniques. In the realm of fashion e-commerce, they were divided into two categories. These categorizations allowed for the identification of research gaps in the use of AI. These gaps offer potential and possibilities for further research. Full article

Providing the right products, at the right place and time, according to their customer’s preferences, is a problem-seeking solution, especially for companies operating in the retail industry. This study presents an integrated product RS that combines various data mining techniques with this motivation. The proposed approach consists of the following steps: (1) customer segmentation; (2) adding the location dimension and determining the association rules; (3) the creation of product recommendations. We used the RFM technique for customer segmentation and the k-means clustering algorithm to create customer segments with customer-based RFM values. Then, the Apriori algorithm, one of the association rule mining algorithms, is used to create accurate rules. In this way, cluster-based association rules are created. Finally, product recommendations are presented with a rule-based heuristic algorithm. This is the first system that considers customers’ demographic data in the fashion retail industry in the literature. Furthermore, the customer location information is used as a parameter for the first time for the clustering phase of a fashion retail product RS. The proposed systematic approach is aimed at producing hyper-personalized product recommendations for customers. The proposed system is implemented on real-world e-commerce data and compared with the current RSs used according to well-known metrics and the average sales information. The results show that the proposed system provides better values. Full article

Digitalization, as well as sustainability, are gaining increased relevance and have attracted significant attention in research and practice. However, the research already published about this topic examining digitalization in the retail sector does not consider the acceptance of related innovations, nor their impact on sustainability. Therefore, this article critically analyzes the acceptance of customers towards digital technologies in fashion stores as well as their impact on sustainability in the textile industry. The comprehensive analysis of the literature and the current state of research provide the basis of this paper. Theoretical models, such as the Technology-Acceptance-Model (TAM) and the Unified Theory of Acceptance and Use of Technology 2 (UTAUT 2) enable the evaluation of expectations and acceptance, as well as the assessment of possible inhibitory factors for the subsequent descriptive and statistical examination of the acceptance of digital technologies in fashion stores. The research on this subject was examined in a quantitative way. The key findings show that customers do accept digital technologies in fashion stores. The final part of this contribution describes the innovative Digitalization 4 Sustainability Framework which shows that digital technologies at the point of sale (PoS) in fashion stores could have a positive impact on sustainability. Overall, this paper shows that it is particularly important for fashion stores to concentrate on their individual strengths and customer needs as well as to indicate a more sustainable way by using digital technologies, in order to achieve added value for the customers and to set themselves apart from the competition while designing a more sustainable future. Moreover, fashion stores should make it a point of their honor to harness the power of digitalization for sake of sustainability and economic value creation. Full article

Social sustainability is a topic that is gaining increased attention and yet has not been overly discussed, in particular with reference to the fashion industry. There is a shift in consumer demands, where brands are urged to stand for values, affect change in the industry, and have a clear purpose and positive impact over society. At the same time, brands are struggling to provide offers beyond products, or product-related experiences, at the risk of dissatisfying consumers expectations. Part of such dissatisfaction is clearly represented by the lack of footfall in retail stores and the fast-pace abandonment of the high-street by brands that cannot afford empty stores. This paper suggests an opportunity to rethink the retail store functionality as a space for brands to provide consumers with educational initiatives related to important societal issues, hence build their socially responsible profile. A netnographic exploratory analysis of Patagonia platforms was conducted in order to pinpoint potential positive reaction to a purpose-driven brand and its educational initiatives. The brand was chosen due to its value-committed strategy and constant educational effort towards consumers, both offline (product-related) and online (societal-related). This paper suggests that brands such as Patagonia, purpose and value driven in positively impacting society, should bring their activism and educational efforts on the high-street and in the retail spaces. By doing so, brands would concurrently provide consumers with experiences beyond product consumption, could revitalise our high-street, and could reinstate a sense of community belonging while raise their socially sustainable profile. This paper contributes to the existing literature of consumer education in retailing by expanding into the specific domain of fashion, a domain in which many social issues could be successfully addressed through a socially-driven consumer education at the moment still overlooked by researchers and brands. Full article

The textile and apparel (fashion) industry has been influenced by developments in societal socio-cultural and economic structures. Due to a change in people’s preferences from economic functionality to supra-functionality beyond economic value, the fashion industry is at the forefront of digitalization. The growing digitalization in the fashion industry corresponds to digital fashion, which can satisfy the rapid shift in consumers’ preferences. This paper explores the evolving con-cept of innovations in digital fashion in the textile and apparel industry. Specifically, it centers on the evaluation of Amazon’s digital fashion initiatives, which have made the platform the United States’ top fashion retailer. An analysis of the business model of Amazon’s digital fashion business showed that with the advancements in artificial intelligence (AI) powered by advanced Amazon Web Services (AWS), Amazon has introduced novel digital solutions for the fashion industry, such as advanced digital fashions (ADFs), on-demand manufacturing, neo-luxury, and, ultimately, cloud-based digital fashion platforms, that is, a supra-omnichannel, where all stakeholders are integrated, and their activities are visible in real time. This can be attributed to the learning orchestration externality strategy. This study concludes that with the advancement of digital innovations, Amazon has fused a self-propagating function that advances digital solutions. This study shows that Amazon is the largest R&D company. Its R&D process is based on users’ knowledge gained by their participation through AWS-driven ICT tools. This promotes a culture of experimentation in the development of user-driven innovations. Such innovations have further advanced the functionality of AWS in data analysis and business solutions. This dynamism promotes the development of soft innovation resources and revenue streams. These endeavors are demonstrated in a model, and their reliability is validated through an empirical analysis focused on the emergence of ADF solutions. Therefore, based on an analysis of the development trajectories of Amazon’s digital fashion technologies, such as ADFs, on-demand manufacturing, and neo-luxury, insightful suggestions and a framework for solutions beyond e-commerce are provided. Full article