Fast Fashion Retailers Research Articles

Demand forecasting in clearance season for fast fashion retail: a residual learning approach

PurposeThis study focuses on forecasting demand for markdown pricing in situations where historical data are limited and proposes using a hybrid knowledge-based residual (KRL) network to enhance the accuracy of demand predictions in fast fashion retailing.Design/methodology/approachWe use a hybrid KRL network structure to increase forecasting accuracy in fast fashion data by combining artificial neural network (ANN) and theoretical demand models (TDMs) such as linear, exponential and multinomial logit. We used a linear demand model (LDM) as a theoretical demand model, which is one of the popular TDMs in the literature, and combined it with neural networks utilizing the residual network structure. We tested it on real fast fashion data to estimate the next week’s demand at the clearance seasons of 5 years.FindingsThe results underscore KRL’s capability to derive mutual benefits from both neural networks and LDM, especially in the specific context of limited fast fashion data. Furthermore, KRL outperforms LDM and ANN models when used individually in forecasting accuracy.Originality/valueThe research paper proposes a scientific, quantitative method for forecasting the sales for markdown settings, combining data driven and TDMs using residual learning thereby resolving the issue of insufficient sales data in the fashion industry.

Digital Platform, Spatial-Digital Fix, and the Reconfiguration of Apparel Production Networks

This article proposes an analytical framework of spatial-digital fix to explain how emerging online platform retailers utilize digital technologies to fix their crises in backward and forward value chains—both digitally and spatially—and their subsequent positive and negative impacts on various actors in production and distribution networks. By applying the analytical framework on Shein (an emerging online fast fashion retailer originally based in China), we demonstrate how it utilizes spatial-digital infrastructures to fix various crises/shocks across the entire value chain, from the fashion design and procurement to supply chain management through its in-house algorithmic systems, fostering an ultra-fast rate of capital accumulation. Beyond the inherently crisis-prone nature and the corresponding dark sides highlighted in the literature, we focus on the dynamic processes of capital accumulation and the corresponding fixes that lead to darker (negative) and brighter (positive) sides in various actors, from the constant pressure on cost minimization to synergistic intersections between emerging digital fast fashion retailers, subcontractors, and suppliers in the production networks. Moreover, these spatial-digital fixes are temporary and give rise to new shocks due to their inherently crisis-prone nature, from intellectual property lawsuits to labor and environmental issues. This article complements the conventional case of a race to the bottom among subcontractors by highlighting a continuum between the brighter and darker sides of Shein’s fixes and their disruptive effects on the power relationships and value distributions between incumbent fast fashion retailers, emerging online retailers, and other actors in apparel production networks.

TEXTILE MERCHANDISING IN THE FAST FASHION ERA: ADAPTING TO RAPID CHANGE IN CONSUMER DEMANDS

Rapid changes in consumer preferences, technology breakthroughs, and mounting sustainability concerns have all had an impact on the fast fashion sector. This study investigates how textile merchandising is changing in this fast-paced world, with a particular emphasis on how companies are adjusting to meet customer expectations while maintaining speed and cost-effectiveness. By examining how these characteristics affect merchandising tactics, the study hopes to shed light on how fast fashion retailers can effectively negotiate the challenges of contemporary retail. The main goal of this study is to find out how fast fashion companies can handle sustainability issues and quickly adapt to changing consumer preferences by optimizing their merchandising strategies. The goal of the study is to comprehend how consumer behavior, sustainability, and technology interact to shape textile marketing techniques. To find new trends and best practices, this study uses a qualitative methodology to analyze secondary data from document analysis and scholarly literature. According to key results, technology is essential for enabling more individualized and flexible merchandising strategies, which enables firms to react swiftly to changing consumer expectations. Companies must manage this issue because the fast fashion model's emphasis on speed and inexpensive production frequently clashes with environmental objectives. Additionally, inventory management and coordination across many sales platforms are made more difficult by the omnichannel retail environment. The dependence on secondary data, which might not adequately reflect the subtleties of current customer behavior or the internal tactics of certain brands, is one of its limitations. Nonetheless, the results have important ramifications for theory and practice. The study theoretically advances our knowledge of how technology, sustainability, and consumer behavior interact in fast fashion merchandising. Practically speaking, it offers fast fashion companies’ useful information to improve their tactics, especially in the areas of inventory control, sustainability, and technological integration. Businesses can improve their competitiveness and long-term viability in a market that is changing quickly by implementing more adaptable, customer-centric strategies.

Can you be unique by wearing fast fashion? Exploring South African contemporary female consumers’ creative behaviour towards fast fashion uniqueness

Contemporary female consumers manage their appearance through current fashion trends and styles. The desire to be unique drives consumers to develop appearances that are perceived to differentiate them from others. In a world where fast fashion delivers multiple copies of one fashion item, it is difficult to imagine if fast fashion could deliver a unique appearance. Little research has considered the possibility of achieving fast fashion uniqueness. The behaviour of South African female consumers and their desire for fast fashion uniqueness as proposed in the theory of the need for uniqueness has also not been researched. The purpose of this study was to determine the meaning of fast fashion uniqueness and the behaviour related to the dimensions of uniqueness. An exploratory descriptive qualitative study was used to determine the fast fashion unique experiences of female fashion shoppers in South Africa. Thematic analysis of electronic individual interviews revealed the meaning of uniqueness manifested through mechanisms of self-expression and design creativity. Contribution to uniqueness theory is expressed through a socially acceptable appearance typified by creative choice counter-conformity behaviour. Unpopular choice counter-conformity behaviour was expressed through precautionary and guarding behaviour. Avoidance of similarity behaviour resulted in similarity acceptance behaviour characterized by helplessness and acceptance of fast fashion duplication, due to the inability to avoid similarity experienced during fast-fashion retail purchases. Coping strategies and avoidance behaviour tactics were applied to avoid fashion similarities. Fashion creativity serves as the mechanism through which contemporary fast fashion consumers achieve fast fashion appearance uniqueness. Fast fashion retailers in South Africa may need to improvise fashion offerings and give consumers alternative appearances to accommodate the creative uniqueness that female consumers are compelled to apply to achieve fast fashion uniqueness.

Joint transshipment, markdown, and clearance decisions at a fast-fashion retailer

This paper considers a joint transshipment, markdown, and clearance sales decisions of multiple products for a fast-fashion retailer that owns and operates a large network of retail stores. This problem is motivated by the logistics operations of the largest apparel retailer in Turkey, namely LC Waikiki. Particularly, the emphasis is on the following questions: How should the products be re-distributed among retail stores after a few weeks of demand observation? What should be the price level for each product? And, finally, which products should be marked for clearance sales? To answer these questions, a mathematical model is proposed considering the business rules and the practices of the retailer encounters. We develop a Bender-Decomposition-based heuristic to find upper bounds and a simulated annealing-based metaheuristic to find incumbent solutions, both of which have proven to be quite effective. A Covering-Cut-Bundle approach is used to accelerate the convergence of the Bender-Decomposition algorithm. We have also conducted a set of experiments to uncover the impact of business rules on the retailer's operations and the effectiveness of joint markdown and transshipment decisions.

VSEM-SAMMI: An Explainable Multimodal Learning Approach to Predict User-Generated Image Helpfulness and Product Sales

Using user-generated content (UGC) is of utmost importance for e-commerce platforms to extract valuable commercial information. In this paper, we propose an explainable multimodal learning approach named the visual–semantic embedding model with a self-attention mechanism for multimodal interaction (VSEM-SAMMI) to predict user-generated image (UGI) helpfulness and product sales. Focusing on SHEIN (i.e. a fast-fashion retailer), we collect the images posted by consumers, along with product and portrait characteristics. Moreover, we use VSEM-SAMMI, which adopts a self-attention mechanism to enforce attention weights between image and text, to extract features from UGI then use machine learning algorithms to predict UGI helpfulness and product sales. We explain features using a caption generation model and test the predictive power of embeddings and portrait characteristics. The results indicate that when predicting commercial information, embeddings are more informative than product and portrait characteristics. Combining VSEM-SAMMI with light gradient boosting (LightGBM) yields a mean squared error (MSE) of 0.208 for UGI helpfulness prediction and 0.184 for product sales prediction. Our study offers valuable insights for e-commerce platforms, enhances feature extraction from UGI through image–text joint embeddings for UGI helpfulness and product sales prediction, and pioneers a caption generation model for interpreting image embeddings in the e-commerce domain.

Decoding the Fast Fashion Phenomenon in Vietnam: A Multidimensional Analysis of Impulse Buying Dynamics and Price Sensitivity in Consumer Purchase Behavior

This study investigates the factors influencing impulse buying behavior in fast fashion consumption, focusing on the roles of fashion involvement, perceived value, store environment, and price sensitivity. Employing a mixed-method approach, we combined structural equation modeling (SEM) and fuzzy-set Qualitative Comparative Analysis (fsQCA) to analyze data collected from 412 fast fashion consumers. The SEM results reveal that fashion involvement, perceived value, and store environment positively influence impulse buying behavior, with fashion involvement exhibiting the strongest effect. Price sensitivity moderates the relationships between fashion involvement and impulse buying, as well as between perceived value and impulse buying. The fsQCA findings complement these results by identifying multiple configurations of factors leading to high impulse buying behavior. Our research contributes to the existing literature by providing a comprehensive model of impulse buying in the fast fashion context, integrating multiple factors and their interactions. The use of both SEM and fsQCA methodologies offers a more nuanced understanding of the complex relationships among the variables. Furthermore, our findings on the moderating role of price sensitivity add to the growing body of literature on individual differences in consumer behavior. These insights have practical implications for fast fashion retailers in developing effective marketing strategies and enhancing the shopping experience to encourage impulse purchases.

The Influence of Customer Perceived Risk on Online Purchasing Intention: with reference to Fast fashion Retail industry in Sri Lanka

Consumers' lifestyles and behavioral habits have altered substantially since the advent of digital marketing platforms, which has a big influence on consumption. More significantly, despite the fact that internet buying has many benefits, some consumers are a little more skeptical about it. According to the literature, there are several restrictions that affect consumers' purchasing intentions for online consumption of retail fashions and a nation like Sri Lanka is not particularly strong for online transactions or online shopping. This study is primarily based on online consumers’ behavioral shopping intentions. The independent variable, customer perceived risk is comprised with Financial Risk, Product Risk, Information Security Risk, Time Risk, Psychological Risk and delivery Risk. This study uses the positivistic research paradigm and deductive approach. Accordingly, purposive sampling technique was used by delivering the 400 questionnaires to internet users in Colombo district. Accordingly, data were analyzed using SPSS software after removing outliers. To evaluate the data, the study employed descriptive statistics, correlation analysis, and regression analysis. According to the findings, Time risk was found to be not significant for purchasing online. Delivery Risk and Information security risk were the top-ranked determining variables among the major elements. Consequently, by reducing their perceived risk, this research assists in educating all dealers on how to draw clients to their online platform.

EMPLOYEE PERFORMANCE: THE MODERATING ROLE OF LEADER-MEMBER EXCHANGE (A STUDY ON THE FAST-FASHION RETAIL INDUSTRY)

This study investigated the knowledge gap in the retail sector by exploring the relationship between job satisfaction, counterproductive work behavior, and organizational citizenship behavior on employee per­form­ance moderated by leader-member exchange. The quantitative study used a survey method to collect data with purposive sampling and technique. The survey was conducted on 150 employees from 17 Jakarta, Bogor, Depok, Tangerang, and Bekasi (JABODETABEK) outlets. The data was analyzed using the structural equation modeling partial least square technique processed with smart-pls 3.3.3 tools. The study's findings were as follows: job satisfaction negatively and significantly affected counterproductive work behavior, counter­productive work behavior affected organizational citizenship behavior, and organizational citizenship behavior positively and significantly affected employee performance. In the meantime, leader-member exchange mo­derated job satisfaction's effect on counterproductive work behavior and employee performance but did not moderate counterproductive work behavior's effect on organizational citizenship behavior. The research had novelty from the aspect of a specific sample characteristic: the employees of the fast-fashion retail. It used a conceptual framework that combined theories of job satisfaction, counterproductive work behavior, organizational citizenship behavior, and leader-member exchange to explain employee performance, which had never been done in previous studies in the fast-fashion retail context.

EMPLOYEE PERFORMANCE: THE MODERATING ROLE OF LEADER-MEMBER EXCHANGE (A STUDY ON THE FAST-FASHION RETAIL INDUSTRY)

This study investigated the knowledge gap in the retail sector by exploring the relationship between job satisfaction, counterproductive work behavior, and organizational citizenship behavior on employee per­form­ance moderated by leader-member exchange. The quantitative study used a survey method to collect data with purposive sampling and technique. The survey was conducted on 150 employees from 17 Jakarta, Bogor, Depok, Tangerang, and Bekasi (JABODETABEK) outlets. The data was analyzed using the structural equation modeling partial least square technique processed with smart-pls 3.3.3 tools. The study's findings were as follows: job satisfaction negatively and significantly affected counterproductive work behavior, counter­productive work behavior affected organizational citizenship behavior, and organizational citizenship behavior positively and significantly affected employee performance. In the meantime, leader-member exchange mo­derated job satisfaction's effect on counterproductive work behavior and employee performance but did not moderate counterproductive work behavior's effect on organizational citizenship behavior. The research had novelty from the aspect of a specific sample characteristic: the employees of the fast-fashion retail. It used a conceptual framework that combined theories of job satisfaction, counterproductive work behavior, organizational citizenship behavior, and leader-member exchange to explain employee performance, which had never been done in previous studies in the fast-fashion retail context.

DEI representation on Instagram: An analysis of two fast fashion retailers

As fashion retailers have started to emphasize their responsibility in society, the significance of diversity, equity and inclusion (DEI) in the fashion industry has increased. This study explores the DEI commitments made by two fast fashion retailers (i.e. H&M and Zara) to examine whether they uphold and portray their DEI commitments through their Instagram postings by focusing on the following four DEI subcategories: people of colour, size inclusivity, LGBTQIA+ and physical disabilities. The study first analysed each retailer’s DEI statement to determine what claims each retailer makes regarding DEI. The data collection process comprised an examination of Instagram posts during the first week of every month from February 2021 to January 2022 utilizing the National Retail Federation (NRF) 4-5-4 calendar. Researchers collected qualitative/quantitative data and used content/comparative analysis to analyse the data. The findings indicated that representation might not be as equitable as their claims state. Based on the findings of this research, the study provides practical implications for enhancing DEI representation in retailers’ Instagram posts and marketing to facilitate more effective communication. Further, this study contributes to the existing literature on DEI commitments in the fashion industry by highlighting the practices of fast fashion retailers in their Instagram posts and marketing.

How to discover consumer attention to design topics of fast fashion: a topic modeling approach

PurposeIt is widely known that fast-fashion retailers are struggling to keep up with consumer attention for quick responses within the fashion industry. With the advance of Internet and e-commerce, consumers prefer to purchase online. Online platform information has become an essential source for exploring consumer attention. However, there is often a mismatch between the information provided by retailers and the feedback received from consumers, leading to an imbalance between the supply side and demand side of online information. The purpose of this study is therefore to provide a unified approach to discover consumer attention from the design topic aspect by revealing the information imbalance between supply side and demand side.Design/methodology/approachTo address the issue of online information imbalance and discover consumer attention, this study proposed an approach that focuses on the design topic perspective. The design topic is a collection of design elements that represent a clothing-design feature more comprehensively and accurately compared to a single design element. The proposed approach begins with generating design topics through topic modeling based on online information provided by retailers on e-commerce platforms. Two indicators, influence degree and attention degree, are then used to quantify the intensity of supply information and consumer attention related to design topics. Finally, design topic strategy diagrams are constructed to reveal information imbalance and discover consumer attention.FindingsThe experimental case demonstrates the existence of information imbalance, indicating that the intensity of supply information and consumer attention from the perspective of design topics is not uniform, although both follow the Pareto principle. The results of consumer attention distribution with heavy power-law tails are consistent with current research findings. This further demonstrates that the proposed approach is capable of discovering consumer attention in the design topic strategy diagrams.Practical implicationsThe issue of information imbalance between retailers and consumers poses a challenge in keeping up with customer attention. The proposed approach offers a practical solution by visually identifying the symptoms of information imbalance and discovering consumer attention through design topic strategy diagrams. This approach provides fast-fashion retailers with a valuable reference to seize market opportunities, improve product design and adjust marketing or management strategies.Originality/valueThis study proposes a novel approach to disclose the issue of information imbalance between supply side and demand side and therefore to discover consumer attention from the perspective of design topics. In addition, guidelines for applying the proposed approach for fast-fashion marketing and management are presented.

Inventory Control and Learning for One-Warehouse Multistore System with Censored Demand

Efficient Learning Algorithms for Dynamic Inventory Allocation in Multiwarehouse Multistore Systems with Censored Demand Motivated by collaboration with a prominent fast-fashion retailer in Europe, the researchers focus their attention on the one-warehouse multistore (OWMS) inventory control problem, specifically addressing scenarios in which the demand distribution is unknown a priori. The OWMS problem revolves around a central warehouse that receives initial replenishments and subsequently distributes inventory to multiple stores within a finite time horizon. The objective lies in minimizing the total expected cost. To overcome the hurdles posed by the unknown demand distribution, the researchers propose a primal-dual algorithm that continuously learns from demand observations and dynamically adjusts inventory control decisions in real time. Thorough theoretical analysis and empirical evaluations highlight the promising performance of this approach, offering valuable insights for efficient inventory allocation within the ever-evolving retail industry.

The Emergence of a New Business Approach in the Fashion and Apparel Industry: The Ethical Retailer

This study sought to examine new marketing ethics (ME) practices that can foster strong moral grounding in the fashion and apparel retail firms to delineate a new approach within this industry. We built on the distributive justice and stakeholder orientation literature to conduct a multi-case study with 15 self-proclaimed ethical fashion and apparel retailers to identify whether and how they differ from traditional and fast fashion retailers. Several data collection techniques were used to gather the evidence (i.e., direct observation, physical and online interviews) combined with a netnographic approach (i.e., online observation of websites and social media content). Our findings show that these firms are guided by ethical-centered values, which are reflected in their product components, purpose, communication practices, and sourcing practices, which point to the emergence of a new business approach. We contribute to the macromarketing literature by identifying a new perspective on the role of morality in ME based on distributive justice principles and stakeholder orientation. We also propose a more refined definition of ethical fashion and apparel retailers.

Factors affecting omnichannel buying online and return in store: evidence from fast-fashion retail

PurposeThe authors analysed the operations of two synchronised channels by focusing on “buy online and return in store” (BORS) strategies in fast-fashion retail by investigating internal and external factors affecting this omnichannel strategy.Design/methodology/approachThe authors apply a combination of techniques to identify the BORS factors. Firstly, a strengths, weaknesses, opportunities and threats (SWOT) analysis was used to define the operational factors of BORS adoption. The authors then apply analytic hierarchy process (AHP) to evaluate the factors under four SWOT categories for kids, male and female consumer groups. The factors of BORS were then ranked using the fuzzy VlseKriterijumska Optimizacija I Kompromisno Resenje (fuzzy VIKOR) approach.FindingsCombining the SWOT, AHP and fuzzy VIKOR techniques, the authors identified 21 factors in this study. The opportunity that BORS provides for trying in the fitting room for a better convenient shopping experience was ranked as the most important factor, followed by the opportunity to create a loyal customer profile with an easy and well-organised return process. Furthermore, the results reveal that the child consumer group is the most critical of the stated operations factors, followed by male and female consumers.Practical implicationsThe authors described the operational factors and supported the decision-making system of BORS for each consumer group with a priority ranking to realise effective managerial management for fast-fashion retailers and practitioners.Originality/valueThe study contributes to the growing literature on the BORS omnichannel strategy, specifically for fast-fashion retail based on consumer needs.

Shopping Hard or Hardly Shopping: Revealing Consumer Segments Using Clickstream Data

The recent rise of big data analytics is transforming the apparel retailing industry. E-retailers, for example, effectively use large volumes of data generated as a result of their day-to-day business operations data to aid operations and supply chain management. Although logs of how consumers navigate through an e-commerce website are readily available in a form of clickstream data, clickstream analysis is rarely used to derive insights that can support marketing decisions, leaving it an under-researched area of study. Adding to this research stream by exploring the case of a U.K.-based fast-fashion retailer, this article reveals unique consumer segments and links them to the revenue they are capable of generating. Applying the partitioning around medoids algorithm to three random samples of 10 000 unique consumer visits to the e-commerce site of a fast-fashion retailer, six consumer segments are identified. This article shows that although the “mobile window shoppers” segment consists of the largest consumer segment, it attracts the lowest revenue. In contrast, “visitors with a purpose,” although one of the smallest segments, generates the highest revenue. The findings of this article contribute to marketing research and inform practice, which can use these insights to target customer segments in a more tailored fashion.

Creating Awareness among Consumers on Purchase of Sustainable Textile Products in Pakistan

Aim of the Study: The aim of the study to assess the impact of knowledge on consumer willingness to purchase (WTP) sustainable clothing. For this purpose, firstly the study design to explore the association between consumer environmental concern (CEC), awareness (CA), involvement (CI), and willingness to purchase (WTP) sustainable clothing and secondly determine the effect of consumer awareness on willingness to purchase ecofriendly clothing. Methodology: The research design based on true experiment with pre and post questionnaire followed by power point presentation as an intervention tool for 30 minutes. A sample of N=365 consumers with the age range of 16-35 were selected through simple random sampling technique. Results: Results indicated that positive correlation exist among all variables. As well as result of paired sample t test also revealed that intervention tool had increased the awareness regarding sustainable clothing among consumers which enhance their involvement in WTP sustainable clothing. Conclusion: The important contribution of this research study to ecofriendly textiles and sustainable clothing market was that it included three factors of consumer purchase decision which impact on WTP sustainable clothing. These are significant determinants for garment manufacturers which may effect on consumer buying decision regarding sustainable apparels. This study is important to fast fashion retailers, marketers, environmental campaigners, charitable organizations and public policy makers.

Product Design Enhancement for Fashion Retailing

As the fashion industry increasingly embraces artificial intelligence (AI), we investigate how a fast-fashion retailer should choose between using a manual design strategy or an AI-assisted design strategy to enhance existing products. A manual design is a traditional and basic approach that involves human designers only, whereas an AI-assisted design is a more innovative approach that involves both human designers and AI technologies. In this paper, the overall product enhancement is measured by two key attributes: product quality and product trendiness. Product quality can be measured by the product’s longevity as reflected by the quality of the materials and types of fabric and stitching used, where the product’s improvement level can be determined by the retailer in a continuous range. Consequently, the retailer may choose different levels of product quality under different design strategies. The two design approaches also lead to different natures of product trendiness, which is reflected by features such as styles, new materials, and colors, to name just a few. Specifically, we assume that the traditional manual design can predict well how trendy or popular the new product is. Hence, the trendiness attribute under the manual design is deterministic. However, given the uncertain nature of the AI-assisted design technology and the needed coordination between human designers and the adopted technologies, the trendiness of the new product designed under the AI-assisted approach is assumed uncertain. Two sets of designing costs are considered in product enhancement: the fixed design cost that is irrespective of the production volume and the variable marginal cost. Our analysis of the base model highlights the importance of decomposing different costs in determining the optimal design strategy. Specifically, the manual design is preferred when the fixed cost carries more weight, whereas the AI-assisted design is preferred when the marginal cost is a more important factor. Moreover, a higher level of innovation uncertainty under the AI-assisted design gives this strategy an advantage over the manual design. In our extended models, we demonstrate that (1) these results are robust even if the retailer does not have the flexibility to offer the existing product when the AI-assisted design is unpopular, and (2) the relative position of human designers in the two design approaches has an impact on the effects of these costs. Supplemental Material: The online appendix is available at https://doi.org/10.1287/serv.2023.0315 .

Believing in Analytics: Managers’ Adherence to Price Recommendations from a DSS

Problem definition: We study the adherence to the recommendations of a decision support system (DSS) for clearance markdowns at Zara, the Spanish fast fashion retailer. Our focus is on behavioral drivers of the decision to deviate from the recommendation, and the magnitude of the deviation when it occurs. Academic/practical relevance: A major obstacle in the implementation of prescriptive analytics is users’ lack of trust in the tool, which leads to status quo bias. Understanding the behavioral aspects of managers’ usage of these tools, as well as the specific biases that affect managers in revenue management contexts, is paramount for a successful rollout. Methodology: We use data collected by Zara during seven clearance sales campaigns to analyze the drivers of managers’ adherence to the DSS. Results: Adherence to the DSS’s recommendations was higher, and deviations were smaller, when the products were predicted to run out before the end of the campaign, consistent with the fact that inventory and sales were more salient to managers than revenue. When there was a higher number of prices to set, managers of Zara’s own stores were more likely to deviate from the DSS’s recommendations, whereas franchise managers did the opposite and showed a weak tendency to adhere more often instead. Two interventions aimed at shifting salience from inventory and sales to revenue helped increase adherence and overall revenue. Managerial implications: Our findings provide insights on how to increase voluntary adherence that can be used in any context in which a company wants an analytical tool to be adopted organically by its users. We also shed light on two common biases that can affect managers in a revenue management context, namely salience of inventory and sales, and cognitive workload. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2022.1166 .

Product Variety and Customer Behaviour in Online Fast Fashion Retailing

Product Variety and Customer Behaviour in Online Fast Fashion Retailing