This study builds and tests a model of AI-generated educational video adoption for children's safety clothing. The model proposes perceived value and perceived quality as key antecedents, attitude, and self-efficacy as mediating variables, and a sense of safety as a direct predictor of adoption. The model highlights how perceived value and quality are linked to adoption through attitude, self-efficacy, and sense of safety. Using survey data collected from 300 kindergarten and elementary school teachers in the Republic of Korea and analyzed through partial least squares structural equation modeling (PLS-SEM), the findings show that both perceived value and perceived quality positively influence attitude toward AI-generated safety educational video and self-efficacy. In turn, attitude and self-efficacy positively influence the sense of safety which strongly drives the adoption of AI-generated safety educational videos. This study makes contributions to the body of knowledge in the researches on consumer behavior related to the AI generated safety educational video by identifying the unique roles of attitude, self-efficacy, and sense of safety as key variables in explaining the adoption of AI-generated educational video for children's safety clothing.

This study examines how safety concern influences consumer behavioral intentions toward AI-generated children's safety clothing (in terms of recommending, buying, and wearing the clothing), with an emphasis on the mediating roles of aesthetic and safety attributes. Unlike traditional approaches that rely on designers' intuition, AI systems integrate data-driven pattern recognition and generative algorithms to produce optimized designs that balance aesthetic and safety features. Using a sample of teachers - credible evaluators of children's safety needs - the study employs partial least squares structural equation modeling to uncover relationships among the variables. Results indicate that aesthetic attributes not only enhance consumers' recommendation intention but also strengthen their intention to wear the clothing, underscoring the role of appealing design in shaping favorable behavioral responses. Safety attributes, in contrast, primarily reinforce recommendation intention, highlighting their role in validating product credibility. Importantly, the study investigates the moderating effect of traditional versus non-traditional safety clothing colors, providing evidence that safety clothing colors can influence how safety clothing is perceived. By integrating perspectives on aesthetics, safety, and color symbolism, this research extends the literature on AI-generated products and offers practical implications for designers, marketers, and policymakers that can enhance and promote children's safety.

This study investigates the impact of artificial intelligence (AI) chatbots with visual search capabilities on consumer behavior within the fashion shopping sector. In particular, this research addresses a gap in the existing literature, which has primarily focused on the technical aspects or text-based functions of AI chatbots. By extending the technology acceptance model, the study examines how factors like image ubiquity and credibility influence perceived usefulness, ease of use, consumer attitudes, and intention to use AI chatbot image search services. Additionally, the moderating effect of previous chatbot usage experience has been confirmed. The findings are intended to provide theoretical insights and practical implications for fashion brands and e-commerce platforms seeking to leverage AI technology to improve consumer engagement, satisfaction, and shopping experience.

PurposeThis study investigates how brand allure capital, adapted from the erotic capital concept, influences lovemark perceptions and electronic word-of-mouth (e-WOM) behavior. It analyzes the moderating effect of purchasing power.Design/methodology/approachA survey was conducted targeting 383 Korean consumers who had previously purchased luxury fashion brands. The hypotheses were tested using a structural equation model for path and multi-group analyses for moderating analysis.FindingsThis study confirmed four constructs of brand allure capital: beauty, arousal, pleasure and desire. Brand allure capital positively influences lovemarks and e-WOM. Furthermore, the link between brand allure capital, lovemarks and e-WOM was stronger for consumers with higher purchasing power.Originality/valueThis study contributes to the literature on luxury and consumer research by investigating the recent growth of the luxury market and the development of consumer relationships through the lens of brand allure capital, drawing on affect-as-information theory and lovemark theory. The results will help luxury managers gain a deeper understanding of emerging luxury customer segments and develop brand strategies.

This study reconceptualizes the Technology Acceptance Model (TAM) from an organizational perspective to examine the factors influencing the intention to adopt artificial intelligence (AI). The proposed model incorporates three components of AI transformation - AI data-driven culture, organizational agility, and AI readiness - as independent variables, and investigates their effects on perceived usefulness and perceived ease of use. Survey data were collected from 209 employees working in purchasing, design, and sales departments of heavy manufacturing firms located in South Korea. Partial least squares structural equation modeling (PLS-SEM) was employed for analysis. The results reveal that AI data-driven culture positively affects both perceived usefulness and perceived ease of use, while organizational agility and AI readiness positively influence only perceived ease of use but not perceived usefulness. Perceived ease of use strengthens perceived usefulness, and together these factors significantly contribute to AI adoption intention. These findings underscore that in B2B environments, particularly within the heavy manufacturing industries, establishing a data-driven culture, enhancing organizational agility, and improving AI readiness are critical strategies to foster AI adoption intention.

Purpose This study aims to investigate the impact of the fashion destination experience through fashion week on city brand equity, e-word of mouth and revisit intention. Design/methodology/approach A survey was conducted targeting 301 tourists who had previously visited Paris during fashion week. AMOS was used to carry out structural equation modeling analyses. Findings Fashion destination experience through the fashion week event positively affects city brand equity, e-WOM and intention to revisit the city. Perceived quality, one of the city brand equity’s attributes, had no significant influence on the intention to revisit in the context of fashion destination experience. Originality/value This study focuses on the role of fashion in the context of city brand equity and visitor behavior. The findings will help tourism managers understand the effect of fashion destination experiences during fashion week and develop city and tourism plans.

The fashion industry is rapidly transforming with the integration of artificial intelligence (AI). Although research on Fashion AI began in the early 2000s, scholarly and industry interest has surged in recent years, yet a comprehensive overview remains lacking. This study reviews 164 peer-reviewed articles published between 2000 and 2023 to provide a systematic understanding of Fashion AI. First, the study analyzes the major theories and variables underlying Fashion AI research. Second, a two-dimensional framework maps AI adoption across the fashion production lifecycle (pre-production, production, and post-production) and classifies AI into three types: Mechanical AI, Thinking AI, and Feeling AI. The findings reveal that AI's role varies by both fashion production lifecycle and AI type. The study concludes that Fashion AI contributes not only to efficiency but also to consumer-brand relationships, offering consolidated theoretical perspectives and process-specific insights to advance both academic inquiry and managerial practice.

Fast fashion's rapid buy-and-discard cycle is a major driver of environmental harm. One promising remedy is to nudge consumers toward a "buy less, buy premium" (BLBP) preference-that is, purchasing a small number of high-end items instead of many mid-range alternatives. Across four experiments, we show that making consumers feel the self-transcendent emotion of awe strengthens BLBP preference. Study 1 establishes the main effect, then Studies 2 and 3 unpack the mechanism: awe broadens consumers' future-oriented time perspective, heightening sustainability concerns, which in turn promote BLBP preference. Study 3 also demonstrates managerial relevance by using awe-evoking advertising imagery to shift preferences. Finally, Study 4 identifies a boundary condition consistent with the proposed mechanism: when mid-range options are framed as sustainable, awe no longer boosts BLBP preference. Together, these findings position awe as a distinctive emotional lever that can reduce waste by redirecting consumption toward premium, longer-lasting goods.

Generative artificial intelligence (AI) is reshaping fashion through personalized styling and interactive shopping. Yet the balance between its functional utility and emotional engagement remains underexplored. Drawing on expectation confirmation theory, this study examines how emotional and functional attributes affect satisfaction and continuance intention. Survey data from 297 consumers with experience using fashion AI assistants were analyzed via structural equation modeling. Findings show that personalization, emotional engagement, and information accuracy enhance expectation confirmation, while emotional engagement emerged as the strongest predictor of satisfaction. Satisfaction strongly predicts continuance intention, with gender partially moderating effects. The study extends expectation confirmation theory by incorporating emotional dynamics and provides implications for designing generative AI systems that foster satisfaction and long-term consumer engagement.

The metaverse is a nascent advertising landscape, characterized as a virtual environment that can provide consumers with immersive visual information. However, studies on metaverse advertising are limited. This study hypothesized that the interplay between avatar presence (a fundamental component of the metaverse) and product type (avatar-wearable vs. non-avatar-wearable) will augment advertising effectiveness by allowing consumers to simulate a product's real-world application more vividly. Two studies were conducted. Study 1 employed a co-occurrence analysis to investigate avatar- and product-centered advertising experiences by examining posts and photographs from Gucci World on Zepeto. Study 2 was a laboratory experiment on Korean university students to test whether the presentation of avatar-wearable products alongside avatars in the metaverse enhances brand attitude and whether this effect is mediated by imagery fluency. The findings provide insights into applying imagery fluency in metaverse advertising, demonstrating its potential for high effectiveness in this rapidly developing virtual landscape.

Luxury fashion brands are among the first movers in metaverse-based non-fungible tokens (NFTs). As luxury fashion brands aim to appeal to younger audiences, NFTs present an enticing marketing approach. Despite the growing interest in NFTs among fashion brands and consumers, no research has examined the core characteristics of NFTs and their impact on advertising outcomes in the luxury context. This research adopts a mixed-methods approach to provide foundational insights on luxury fashion NFTs. Qualitative research (i.e., case study, in-depth interviews) identifies five key NFT characteristics. Quantitative research (a survey of 300 consumers) shows how these characteristics influence brand attitudes and other downstream advertising metrics. This research contributes to advertising scholarship by (a) devising a conceptual framework for NFTs in the luxury fashion context, (b) delineating characteristics most relevant to three different game types, providing advertisers with specific direction, and (c) examining the meanings of authenticity for NFTs in the context.