Social shopping for fashion

In spite of the significance of social shopping in the context of fashion consumption, its definitions, boundaries, and explanations have not yet been systematically established in literature. The purpose of Study 1 was to develop a reliable and valid scale of social shopping for fashion. With the scale, Study 2 aimed to develop and test a structural model of social shopping process. In Study 1, a three-step procedure for scale development was followed: item generation, scale purification, and scale validation. As a result, a five-dimensional scale, along with sixteen behavioral items, was developed representing distinctive dimension of social shopping for fashion. The result suggests that social shopping for fashion involves dynamic and complex direct/indirect interpersonal exchanges and activities. Study 1 adds significant value to the literature in three ways. First, the scale is the first attempt to synthesize dispersed concepts of social shopping. Second, by providing a reliable and valid measure of social shopping for fashion, the results advance the area of research. Third, the scale is useful for a wide range of marketing and retailing applications. In Study 2, an online survey was conducted with a random sample consisting of a total of 5,280 undergraduates aged 18 to 29 years old enrolled at a large southeastern university. A total of 858 responses were analyzed using structural equation modeling. A structural model including motivational forces and consequences of social shopping behavior was developed and tested. The results indicated that social comparison orientations were generally found to be motivators of social shopping for fashion, and social shopping contributed to shopping satisfaction. The results, however, suggest that each dimension is driven by different dimensions of social comparison orientation and generates different types of satisfaction. This study increases the understanding of social shopping by simultaneously examining a causal model depicting comprehensive motivational forces and consequences of social shopping behavior. The results contribute to building a rigor of social comparison theory and consumer satisfaction theory in the context of fashion consumption. The results also provide industry professionals with strategic cues for creation of shopping environments wherein consumers’ social needs are better served and satisfied

A Prediction Model for Environmentally Responsible Apparel Purchases: The Moderating Effects of Risk Aversion

Individuals cannot significantly diminish their environmental impact by reducing consumption, rather they can make a greater impact with purchasing environmentally responsible products. The author for this study developed a prediction model for consumers\u27 purchases of environmentally responsible apparel (ERA) based on the combination of two theories, value-belief-norm model and theory of reasoned action. This study also examined an enduring personality trait risk aversion”and its moderating role within the prediction model. An online survey was conducted using a large sample (n = 1,518) from a state university in the southern U.S. The results from structural equation modeling suggest that the prediction model significantly increases predictability for ERA purchases, especially for future purchase. The results also demonstrate the role of risk aversion as a significant moderator, which suggest that risk takers risk avoiders are different in how they shape their beliefs, attitudes and behaviors related to their ERA purchase decisions

No Longer in Vogue? The Exploration of Motivations Underlying Millennials\u27 Information Seeking Through Digital Fashion Media

Information seeking is a key component of the consumer decision making process in which consumers sift through information to enhance their knowledge and develop their attitudes towards a good or service before making a purchase decision. With increasing global digitalization, more millennial consumers are looking to digital fashion media (fashion blogs and relevant social media) for information seeking as opposed to traditional media (fashion magazines). This study explores the millennial consumers\u27 psychological motivations to engage in information seeking through digital fashion media grounded by Functional Theory. In order to understand these motivations, six of focus group interviews were conducted with twenty four female consumers between the ages of 18 and 33. Following thematic analysis, six key motivations for information seeking through digital fashion media emerged: search autonomy, instant gratification, visual inspiration, authenticity, virtual storage and gratuitous information. Theoretical and practical implications are discussed

FedSplitX: Federated Split Learning for Computationally-Constrained Heterogeneous Clients

Foundation models (FMs) have demonstrated remarkable performance in machine learning but demand extensive training data and computational resources. Federated learning (FL) addresses the challenges posed by FMs, especially related to data privacy and computational burdens. However, FL on FMs faces challenges in situations with heterogeneous clients possessing varying computing capabilities, as clients with limited capabilities may struggle to train the computationally intensive FMs. To address these challenges, we propose FedSplitX, a novel FL framework that tackles system heterogeneity. FedSplitX splits a large model into client-side and server-side components at multiple partition points to accommodate diverse client capabilities. This approach enables clients to collaborate while leveraging the server's computational power, leading to improved model performance compared to baselines that limit model size to meet the requirement of the poorest client. Furthermore, FedSplitX incorporates auxiliary networks at each partition point to reduce communication costs and delays while enhancing model performance. Our experiments demonstrate that FedSplitX effectively utilizes server capabilities to train large models, outperforming baseline approaches

Profiling of RNAs from Human Islet-Derived Exosomes in a Model of Type 1 Diabetes

Type 1 diabetes (T1D) is characterized by the immune-mediated destruction of insulin-producing islet β cells. Biomarkers capable of identifying T1D risk and dissecting disease-related heterogeneity represent an unmet clinical need. Toward the goal of informing T1D biomarker strategies, we profiled coding and noncoding RNAs in human islet-derived exosomes and identified RNAs that were differentially expressed under proinflammatory cytokine stress conditions. Human pancreatic islets were obtained from cadaveric donors and treated with/without IL-1β and IFN-γ. Total RNA and small RNA sequencing were performed from islet-derived exosomes to identify mRNAs, long noncoding RNAs, and small noncoding RNAs. RNAs with a fold change ≥1.3 and a p-value <0.05 were considered as differentially expressed. mRNAs and miRNAs represented the most abundant long and small RNA species, respectively. Each of the RNA species showed altered expression patterns with cytokine treatment, and differentially expressed RNAs were predicted to be involved in insulin secretion, calcium signaling, necrosis, and apoptosis. Taken together, our data identify RNAs that are dysregulated under cytokine stress in human islet-derived exosomes, providing a comprehensive catalog of protein coding and noncoding RNAs that may serve as potential circulating biomarkers in T1D

Microspinning: Local Surface Mixing via Rotation of Magnetic Microparticles for Efficient Small-Volume Bioassays

The need for high-throughput screening has led to the miniaturization of the reaction volume of the chamber in bioassays. As the reactor gets smaller, surface tension dominates the gravitational or inertial force, and mixing efficiency decreases in small-scale reactions. Because passive mixing by simple diffusion in tens of microliter-scale volumes takes a long time, active mixing is needed. Here, we report an efficient micromixing method using magnetically rotating microparticles with patterned magnetization induced by magnetic nanoparticle chains. Because the microparticles have magnetization patterning due to fabrication with magnetic nanoparticle chains, the microparticles can rotate along the external rotating magnetic field, causing micromixing. We validated the reaction efficiency by comparing this micromixing method with other mixing methods such as simple diffusion and the use of a rocking shaker at various working volumes. This method has the potential to be widely utilized in suspension assay technology as an efficient mixing strategy