As social media becomes increasingly integrated into daily life, it has reshaped how people communicate and consume advertising. Instagram, a visually-oriented platform, uses advanced targeting and shopping features to deliver personalized advertising, particularly in the fashion retail sector. Grounded in the cognitive-affective-behavioral model and human information processing theory, this study investigates how Instagram’s personalized fashion advertising influences consumer perception and behavior, focusing on recommendation system quality (accuracy, novelty, diversity) and content quality (vividness, diagnosticity). A survey of 403 Korean adults aged 20–69 was conducted to assess causal relationships among these variables. The findings reveal that accuracy and diversity in recommendation systems, along with diagnosticity of content quality, positively influence user satisfaction, which, in turn, influences their click-through and purchase intentions. However, novelty and vividness exhibited no significant effects. Academically, the study contributes to a deeper understanding of the mechanisms underlying personalized advertising on visuallyoriented platforms like Instagram. Practically, it underscores the importance of creating high-quality, personalized content that aligns with user preferences and provides clear product information. Brands can enhance user engagement by designing visually appealing advertisements and optimizing linked web pages to foster emotional bonds with consumers. These strategies can cultivate long-term customer relationships and enhance brand loyalty while maximizing advertising effectiveness on Instagram.

본 연구는 생성형 AI의 일종인 GPTs를 활용하여 중국어 회화 교육의 혁신적인 방안을 제시하였다. 특히 실시간 피드백과 개별화된 학습 환경을 통해 학습자의 자 기주도적 학습을 효과적으로 지원하는 방안을 탐구하였다. GPTs의 음성 분석 및 피 드백 기능은 발음, 성조, 억양과 같은 음성학적 요소의 교정에서 뛰어난 효과를 보였 으며, 학습자들은 실시간 피드백을 통해 자신의 오류를 즉각적으로 인지하고 수정할 수 있었다. 교재와의 연계성을 강화하고 실제적인 의사소통 상황을 반영한 GPTs 기 반 학습 시스템은 교실 수업과 자기주도학습의 효과적인 통합을 이끌어냈으며, 이를 통해 교수자의 역할이 지식 전달자에서 학습 촉진자로 변화하면서 중국어 회화 교육 의 새로운 가능성을 제시하였다.

본 연구는 가치기반수용모델을 바탕으로 AI 기반 맞춤형 화장품 추천 서비스의 지각된 가치와 이용의도에 미치는 영향 요인을 규명하고자 하였다. 이를 위해 설문지 241부를 수집하여 SPSS 27.0으로 빈도분석, 요인분석, 신뢰도 분석, 상관관계분석, 회귀분석을 실시하였다. 첫째, 유용성과 즐거움은 지각된 가치에 정(+)적 영향을 미치는 것으로 나타났다. 둘째, 복잡성은 지각된 가치에 부(-)적 영향을 미치는 것 으로 나타났으나, 위험성은 유의한 영향을 미치지 않는 것으로 나타났다. 셋째, 지각된 가치는 이용의도에 정(+)적 영향을 미치는 것으로 나타났다. 그러므로 지각적 가치와 이용의도를 증진시키기 위해서는 유용한 정보롸 흥미를 유발할 수 있는 재미 요소를 제공하고, 복잡한 과정을 간단하게 축소할 필요가 있다.

현대 사회에서 음악은 일상생활에 깊숙이 자리 잡아, 개인의 음악적 취향과 감정 상태에 맞는 콘텐츠를 손쉽게 찾고 소비하는 것이 중요해지고 있다. 콘텐츠 소비 증가와 더불어 제작 속도 및 효율 또한 중요한 요소로 부상하고 있다. 그러나 기존 음악 콘텐츠 제작 방식은 주로 기존 음악을 플레이리스트로 만들고 간단한 애니메이션이나 이미지를 영상으로 추가하는 방식이다. 이러한 한계를 극복하고자, 인공지능(AI) 기술을 활용하여 사용자 맞춤형 음악을 생성하고 콘 텐츠를 제공하는 어플리케이션을 개발하였다. AI 모델을 통해 사용자의 감정 상태를 분석하고, 이를 기반으로 음악적 요소를 최적화하여 개인화된 음악 콘텐츠를 생성하는 것에 목표를 두었 다. Mel-frequency cepstral coefficients(MFCC)와 템포 분석을 통해 음악 데이터의 특징을 추출하고, 이를 기반으로 사용자 감정에 부합하는 프롬프트를 생성하였다. 생성된 프롬프트는 MusicGen 모델에 입력되어, 사용자의 감정 상태와 음악적 취향을 반영한 새로운 음악을 생성 하는 데 활용하였다. 또한, ComfyUI를 활용하여 텍스트-이미지-비디오 변환 파이프라인을 구 축함으로써, 생성된 프롬프트를 기반으로 다양한 멀티미디어 콘텐츠 제작을 가능하게 하였다. 기존 음악 콘텐츠 제작 방식의 시간 및 비용 문제를 해결하고, 사용자에게 보다 정교하고 개 인화된 음악 경험을 제공하는 데 기여할 수 있을 것으로 기대된다. 향후 다양한 분야에서의 응용 가능성을 제시한다.

This study aims to develop a deep learning model to monitor rice serving amounts in institutional foodservice, enhancing personalized nutrition management. The goal is to identify the best convolutional neural network (CNN) for detecting rice quantities on serving trays, addressing balanced dietary intake challenges. Both a vanilla CNN and 12 pre-trained CNNs were tested, using features extracted from images of varying rice quantities on white trays. Configurations included optimizers, image generation, dropout, feature extraction, and fine-tuning, with top-1 validation accuracy as the evaluation metric. The vanilla CNN achieved 60% top-1 validation accuracy, while pre-trained CNNs significantly improved performance, reaching up to 90% accuracy. MobileNetV2, suitable for mobile devices, achieved a minimum 76% accuracy. These results suggest the model can effectively monitor rice servings, with potential for improvement through ongoing data collection and training. This development represents a significant advancement in personalized nutrition management, with high validation accuracy indicating its potential utility in dietary management. Continuous improvement based on expanding datasets promises enhanced precision and reliability, contributing to better health outcomes.

This paper elucidates the novel direction of food research in the era of the 4th Industrial Revolution characterized by personalized approaches. Since conventional approaches for identifying novel food materials for health benefits are expensive and time-consuming, there is a need to shift towards AI-based approaches which offer more efficient and costeffective methods, thus accelerating progress in the field of food science. However, relevant research papers in this field present several challenges such as regional and ethnic differences and lack of standardized data. To tackle this problem, our study proposes to address the issues by acquiring and normalizing food and biological big data. In addition, the paper demonstrates the association between heath status and biological big data such as metabolome, epigenome, and microbiome for personalized healthcare. Through the integration of food-health-bio data with AI technologies, we propose solutions for personalized healthcare that are both effective and validated.

As the use of artificial intelligence (AI) grows, so do the questions regarding this new technology and its potential uses. Among the various possibilities and employment that could be offered by AI is personalized news technology. Nowadays, it is already possible to produce journalistic content through AI (Carlson, 2014; Graefe & Haim, 2018). Digital storytelling has become a reality through automated journalism powered by AI (Caswell & Dörr, 2018; Galily, 2018; Linden, 2017; Thorne, 2020). “Artificial intelligence applies advanced analysis and logic-based techniques, including machine learning, to interpret events, support and automate decisions, and take actions” (Gartner Group, 2019). In personalized news technology, algorithms are responsible for selecting content and sorting it according to the personalization criteria (Powers, 2017). So far, AI has been studied in different fields with distinct research focuses (Loureiro et al., 2021). Studies of news-personalization technologies have mainly focused on research engines and filtering mechanisms (Darvishy et al., 2020; Haim et al., 2017; Manoharan & Senthilkumar, 2020). Few studies examine news aggregators (Haim et al., 2018; Kwak et al., 2021) and the effects of news personalization on audiences (Merten, 2021; Swart, 2021; Thurman et al., 2019), thus demanding further research. AI is an imminent reality for the future, reshaping the news media (Brennen et al., 2022; Linden, 2017; Thorne, 2020). Hence, it is still necessary to investigate the impacts that this technology potentially offers to users. Therefore, the current study seeks to respond to this need to deepen research into the area of news personalization through AI, by analyzing the response of audiences toward current and future technological tendencies. The main aim of this research is to investigate the levels of trust that users have in AI-generated personalized video news.

Technology, for example, Personalized Technology Services (PTS), has groomed consumers to expect an integrated and personalized shopping experience regardless of the channels, such as websites, mobile apps, physical stores, etc. PTS refers to technologies that offer personalization functions to meet customer needs at the time of their shopping for a seamless experience. The purpose of this study is to investigate the role of retailer mobile apps’ PTS in consumers’ omnichannel shopping experiences by: (1) identifying PTS values specific to retail mobile apps for in-store shopping and (2) testing the PTS values – channel integration – consumer responses links based on Information Integration Theory (IIT). We first proposed that PTS via mobile apps holds various positive values. Second, we postulated four hypotheses: H1. PTS values enhance the integration of PTS values, H2. Integration of PTS values positively affects customer engagement, H3. Customer engagement positively affects customer satisfaction and H4. Customer engagement mediates the relationship between integration and customer satisfaction. Two web-based survey studies were employed with US consumers who had an experience with mobile app-mediated PTS offered by retailers. For study 1, a total of 239 US consumers participated in the survey. Study 1 identified five value dimensions of the app-mediated PTS: hedonic value, utilitarian value, self-efficacy, co-creation, and synchronicity. For study 2, a total of 373 US participants completed the survey. Study 2 confirmed the proposed structural model that PTS values positively affected channel integration which, in turn, positively influenced customer engagement and shopping satisfaction. Additionally, customer engagement partially mediated the effect of integration on shopping satisfaction. This study expanded the literature on omnichannel retailing by exploring consumer in-store shopping experience using retail mobile apps from PTS and channel integration perspectives. Practically, the study findings provided insights for marketers into how to design the retailers’ mobile apps to enhance the integrated shopping experience of consumers.

Personalized pricing provides great potential for revenue, but is also accompanied by negative consumer reactions. Therefore, it is of great importance to investigate potential mechanisms and variables that could mitigate these negative effects. In this context, the following paper examines the role of perceived fairness, cognitive dissonance, and product categories.

The hospitality industry is widely using customer data to develop successful personalized marketing communication. However, in the event of information leakage, personalized advertising may escalate customers’ privacy distress. Building on Conservation of Resources theory, this study proposes three dimensions for privacy threats that impact the relationship between personalized hospitality advertising and consumer responses. Findings from six experiments across high and low involvement hospitality products demonstrate diverging effects of personalized advertising depending on the type of privacy threat communicated. Results further indicate that customers’ psychological comfort mediates the relationship between high-personalized advertising and the customer response to the advertising when privacy threat is high. Additionally, when the perceived severity and distance of the announced privacy threat are high and low respectively, rational appeals generate higher levels of psychological comfort, while the same happens for emotional appeals when the perceived scope of the threat is high. The study concludes with value-adding theoretical and managerial implications for the hospitality industry.

Treatment and management of chronic low back pain (CLBP) should be tailored to the patient’s individual context. However, there are limited resources available in which to find and manage the causes and mechanisms for each patient. In this study, we designed and developed a personalized context awareness system that uses machine learning techniques to understand the relationship between a patient’s lower back pain and the surrounding environment. A pilot study was conducted to verify the context awareness model. The performance of the lower back pain prediction model was successful enough to be practically usable. It was possible to use the information from the model to understand how the variables influence the occurrence of lower back pain.