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Washington Post fashion critic Rachel Tashjian shares her cheat sheet for common words and phrases that come up when shopping for used clothing.

PurposeThe demand for second-hand clothing has risen rapidly in the past few years. Yet, the understanding of the motivations of consumers buying second-hand clothing is very limited. The purpose of this study is to propose and empirically test an integrated model of the theory of planned behaviour and the theory of consumption values to explain consumers' intention to buy second-hand clothing.Design/methodology/approachData (n = 290) are collected from consumers in Malaysia and analysed by partial least squares structural equation modelling (PLS-SEM).FindingsResults of this study show that attitudes towards second-hand clothing, injunctive norms, descriptive norms, moral norms, and perceived behavioural control have a significant positive influence on consumers' intention to buy second-hand clothing. Furthermore, emotional value and environmental value are found to have a significant positive influence on attitudes. However, no support is found for the positive influence of social value and epistemic value on attitudes.Originality/valueThe study confirms that the integrated model is useful in explaining consumers' intention to buy second-hand clothing. Furthermore, this study also provides some valuable suggestions to practitioners.

This work develops a general model of a two-echelon supply chain in which a dominant retailer interacts with a manufacturer via a consignment contract with revenue sharing. The manufacturer’s cost function is known only to him, whereas the retailer has only an estimation of this function, which is based on common knowledge. We formulate the interaction between the parties as a Stackelberg game in which the less informed party (the retailer) moves first. We investigate a strategic information-sharing policy of the manufacturer under general formulations of (i) the supply chain’s revenue and cost functions, and (ii) the manufacturer’s decision functions. Two models are considered: (i) a point-estimation model—the retailer relies on a single-valued estimation of the manufacturer’s cost function, based on her “best belief”; and (ii) an interval-estimation model—the retailer faces uncertainty with regard to the cost function and thus estimates its parameter values within intervals. We find a condition that distinguishes between a case in which it is optimal for both parties for the manufacturer to share his exact cost function and a case in which such information-sharing is not optimal for the manufacturer but is optimal for the retailer. In the interval-estimation model, equilibrium is obtained using a normative (probabilistic) approach as well as behavioral-decision criteria (max–max, max–min and regret minimization). Under a normative approach both hidden and known superiority of the manufacturer are considered. Finally, we use our model to analyze a supply chain of a mobile application.

Because of uncertainty in customer demand and lack of understanding in customer reactions to price changes, it is a challenge for many companies, such as manufacturers and retailers, to match supply and demand. Most of the models in the operations literature, however, have focused on the case in which the underlying customer demand information is known a priori, which is not true in many applications. In “Coordinating Pricing and Inventory Replenishment with Nonparametric Demand Learning,” B. Chen, X. Chao, and H. Ahn develop a data-driven algorithm for pricing and inventory decisions that learns the demand and customer information from sales data on the fly, and they show that the profit generated from the algorithm converges to the clairvoyant optimal profit at the quickest possible rate. We consider a firm (e.g., retailer) selling a single nonperishable product over a finite-period planning horizon. Demand in each period is stochastic and price sensitive, and unsatisfied demands are backlogged. At the beginning of each period, the firm determines its selling price and inventory replenishment quantity with the objective of maximizing total profit, but it knows neither the average demand (as a function of price) nor the distribution of demand uncertainty a priori; hence, it has to make pricing and ordering decisions based on observed demand data. We propose a nonparametric, data-driven algorithm that learns about the demand on the fly and, concurrently, applies learned information to make replenishment and pricing decisions. The algorithm integrates learning and action in a sense that the firm actively experiments on pricing and inventory levels to collect demand information with minimum profit loss. Besides convergence of optimal policies, we show that the regret of the algorithm, defined as the average profit loss compared with that of the optimal solution had the firm known the underlying demand information, vanishes at the fastest possible rate as the planning horizon increases.

Fashion retail has a large and ever-increasing popularity and relevance, allowing customers to buy anytime finding the best offers and providing satisfactory experiences in the shops. Consequently, Customer Relationship Management solutions have been enhanced by means of several technologies to better understand the behaviour and requirements of customers, engaging and influencing them to improve their shopping experience, as well as increasing the retailers’ profitability. Current solutions on marketing provide a too general approach, pushing and suggesting on most cases, the popular or most purchased items, losing the focus on the customer centricity and personality. In this paper, a recommendation system for fashion retail shops is proposed, based on a multi clustering approach of items and users’ profiles in online and on physical stores. The proposed solution relies on mining techniques, allowing to predict the purchase behaviour of newly acquired customers, thus solving the cold start problems which is typical of the systems at the state of the art. The presented work has been developed in the context of Feedback project partially founded by Regione Toscana, and it has been conducted on real retail company Tessilform, Patrizia Pepe mark. The recommendation system has been validated in store, as well as online.

Endothermy is a typical convergent phenomenon which has evolved independently at least eight times in vertebrates, and is of significant advantage to organisms in extending their niches. However, how vertebrates other than mammals or birds, especially teleosts, achieve endothermy has not previously been fully understood. In this study, we sequenced the genomes of two billfishes (swordfish and sailfish), members of a representative lineage of endothermic teleosts. Convergent amino acid replacements were observed in proteins related to heat production and the visual system in two endothermic teleost lineages, billfishes and tunas. The billfish-specific genetic innovations were found to be associated with heat exchange, thermoregulation, and the specialized morphology, including elongated bill, enlarged dorsal fin in sailfish and loss of the pelvic fin in swordfish.

Geographic patterns in human genetic diversity carry footprints of population history and provide insights for genetic medicine and its application across human populations. Summarizing and visually representing these patterns of diversity has been a persistent goal for human geneticists, and has revealed that genetic differentiation is frequently correlated with geographic distance. However, most analytical methods to represent population structure do not incorporate geography directly, and it must be considered post hoc alongside a visual summary of the genetic structure. Here, we estimate “effective migration” surfaces to visualize how human genetic diversity is geographically structured. The results reveal local patterns of differentiation in detail and emphasize that while genetic similarity generally decays with geographic distance, the relationship is often subtly distorted. Overall, the visualizations provide a new perspective on genetics and geography in humans and insight to the geographic distribution of human genetic variation.

The resilience of established business strategies has been tested in the wake of recent global supply chain upheavals triggered by events like the COVID-19 pandemic, Russia-Ukraine combat, Hamas-Israel war, and other geopolitical conflicts. Organizations are compelled to integrate sustainable practices into their supply chains to navigate the complexities of the post-COVID-19 era and mitigate the far-reaching consequences of such disruptions. However, exploring supply chain imperatives from sustainability dimensions still remains underexplored, presenting a significant research gap, particularly in the fashion retail sector. In response, this study aims to pioneer an innovative approach by amalgamating Pareto analysis, Bayes theorem, and the Best-Worst Method to evaluate sustainability imperatives comprehensively. Focusing on emerging economies like Bangladesh and its fashion retail industry, this methodology synthesizes insights from literature reviews, expert feedback, and Pareto analysis to curate a definitive set of influential imperatives. Finally, the Bayesian Best-Worst Method is applied to examine them. The results reveal the availability of government support schemes to promote sustainability, developing strategic supply chain interventions to ameliorate the impact of disruptive events, and digitalizing the supply chain as the most monumental imperatives under economic, social, and environmental perspectives, respectively. The study’s innovative methodology and its implications for sustainable supply chain management offer valuable insights for both academic research and practical application, presenting a strategic blueprint for the fashion retail industry to navigate and thrive in the post-COVID-19 era. This work can not only advance the theoretical understanding of supply chain sustainability but also provide actionable guidance for industry leaders in developing robust, resilient, and sustainable supply chain strategies.

Abstract Understanding the genetic architecture of complex traits is a major objective in biology. The standard approach for doing so is genome-wide association studies (GWAS), which aim to identify genetic polymorphisms responsible for variation in traits of interest. In human genetics, consistency across studies is commonly used as an indicator of reliability. However, if traits are involved in adaptation to the local environment, we do not necessarily expect reproducibility. On the contrary, results may depend on where you sample, and sampling across a wide range of environments may decrease the power of GWAS because of increased genetic heterogeneity. In this study, we examine how sampling affects GWAS in the model plant species Arabidopsis thaliana. We show that traits like flowering time are indeed influenced by distinct genetic effects in local populations. Furthermore, using gene expression as a molecular phenotype, we show that some genes are globally affected by shared variants, whereas others are affected by variants specific to subpopulations. Remarkably, the former are essentially all cis-regulated, whereas the latter are predominately affected by trans-acting variants. Our result illustrate that conclusions about genetic architecture can be extremely sensitive to sampling and population structure.

Purpose This study aims to comprehensively examine generative artificial intelligence (GenAI) applications in the fashion retail value chain and propose a practical framework to guide effective GenAI innovation for fashion retailers. Design/methodology/approach An extensive literature review was conducted, including use cases of GenAI within the fashion industry. Given GenAI’s evolving nature, a forward-looking perspective is applied to explore its potential and challenges within fashion retail. The technology-organization-environment (TOE) theory was used as the foundation for the proposed framework. Findings This study presented a systematic, future-oriented view of GenAI’s opportunities and challenges across the fashion retail value chain. GenAI enhances productivity across the fashion retail value chain by streamlining design, enhancing operations, improving marketing and customer experiences and optimizing decision-making. However, its implementation presents key challenges in four areas: data issues, technical constraints, output reliability and social concerns. A TOE-based practical framework and actionable insights were proposed to help fashion retailers navigate GenAI’s complexities, harness its potential and address related challenges. Originality/value Our study provides a holistic view and actionable guidance for fashion retailers on effectively and responsibly implementing GenAI across the retail value chain. These insights can also benefit GenAI practitioners in other industries.