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.

목적 : 본 연구에서는 지역사회의 경도인지장애 및 경도치매환자에게 개별 맞춤형 회상치료를 적용하여 인 지기능, 우울, 삶의 질 및 작업수행에 미치는 영향을 확인하고자 하였다. 연구방법 : 재가노인지원센터에서 총 24명의 경도인지장애노인(12명)과 경도치매환자(12명)를 각 6명씩 무작위로 배정하여 실험군에는 개별맞춤형 회상치료를, 대조군에는 집단회상치료를 12주간 60분씩 주 1 회 적용하였다. 인지기능의 효과를 검증하기 위해 노인용 서울언어학습검사(Seoul Verbal Learning Test-Eldery, SVLT-E)와 숫자 외우기 검사(Digit Span Test), 단축형 한국판 보스톤 이름대기 검사 (Short-form Korean version Boston Naming Test; S-K-BNT)를 이용하였다. 우울은 단축형 노인 우울척도(Short version of Geriatric Depression Sclae; SGDS), 삶의 질은 치매노인 삶의 질 척도 (Geriatric Quality of Life-Dementia; GQOL-D), 작업수행은 캐나다작업수행측정(Canadian Occupational Performance Measure; COPM)으로 중재 전과 후에 평가하였다. 결과분석은 기술통계와 비모수통계를 이용하여 분석하였다. 결과 : SVLT-E의 경우, 즉시회상과 지연회상에서는 실험군과 대조군 모두 점수가 유의하게 향상되었으나 지연재인의 경우에는 실험군에서만 유의한 차이가 나타났다. DST는 두 집단 모두 유의미한 변화가 나타나 지 않았으며, S-K-BNT와 COPM은 실험군에서만 유의한 점수의 변화가 나타났다. SGDS와 GQOL-D 에서는 두 집단 모두 중재 전과 후에 유의한 점수의 변화가 확인되었다. 결론 : 맞춤형 회상치료 프로그램이 치매환자의 인지기능, 우울, 삶의 질과 작업수행에 긍정적인 효과가 있음을 확인할 수 있었다. 따라서 추후, 재가치매환자를 위한 지역사회 작업치료의 적용 시 대상자에 특성에 맞는 개별적 접근이 다양하게 활용되기를 기대한다.

초연결사회(hyper connected society)의 IoTs의 발달로 U-헬스케어 시대가 전개되며, 웰니스 라이프, 인간수명 연장 등의 사회 전반에 걸쳐 많은 변화가 야기되고 있다. 한국은 2017년 고령사회에 진입하며 초연결사회의 IoTs의 이기를 적용한 실버산업이 빠르게 성장할 것으로 전망된다. 특히 기존의 실버세대와 달리 높은 활동력과 경제력을 지닌 뉴실버세 대의 웰니스 라이프에 기반한 건강증진에 관련된 높은 관심은 시니어시프트(senior shift)현상을 야기시켰다. 이에 본 연구에서는 뉴실버세대의 특성을 규명하고, 그에 맞는 U-Hospital 서비스의 일환으로 개인 맞춤형 운동처방 실행을 위한 운동 흥미 유도 목적의 웨어러블 시리어스 게임 기획 방향을 심층면접을 통해서 도출하였다. 그 결과, ‘U-실버세대’ 를 위한 웨어러블 시리어스 게임의 사용 시나리오는 전문의료진(게임진행 지도사)과 검진의뢰자(U-실버세대) 그리고 인터페이스(컴퓨터), 3자 간의 트라이앵글 시스템이 적용된 건강검진 및 재활 모니터링을 위한 수단으로 활용되어야 할 사회적 필요성을 도출하였다. 또한 이는 실버세대의 신체기능 저하에 대한 우려를 개인 맞춤형 운동처방 수행으로 개선하기 위한 목적이며, 검진 종료 후에는 온/오프라인 커뮤니티 활동을 융합하여 실버세대의 친목도모의 니즈와 일상성을 탈피해야 하며, 게임 레벨 상승에 따른 성취감 부여를 통해 게임의 지속 가능성을 증대시켜야 할 필요성 등이 도출되었다. 이를 토대로 검진의뢰자가 선호하는 식도락 여행을 주제로 친숙한 인터페이스를 사용하고, 단순한 게임 규칙을 적용하여 디지털 디바이스 사용에 미숙한 U-실버세대의 사용성을 높인 웨어러블 시리어스 게임을 기획하였다.

Personalised nutrition can contribute significantly to the prevention of non-communicable dietary related diseases by providing dietary suggestions based on individual’s nutritional needs. Adoption of the concept of personalised nutrition by individuals is crucial for the success of personalised nutrition services. However, consumers’ adoption intention of personalised nutrition services is not only the result of cognitive deliberations of benefits and risks, but several studies in other contexts show that affective and contextual factors also play an important role in explaining consumers’ adoption intention. This study therefore examines whether affective factors (i.e., measured by means of ambivalent feelings) and contextual factors (i.e., eating context) increase the understanding of consumers' intentions to use personalized nutrition services. An online survey study was conducted among a total of 996 participants in the Netherlands. The results of a number of estimated fully latent structural regression models show that the intention to use personalized nutrition is not only positively driven by a weighing of benefits and risks (i.e., privacy calculus), which is also established in previous studies, but also negatively by ambivalent feelings. In turn, the results show that ambivalence towards personalized nutrition is predicted by privacy risk and the extent to which someone perceives the eating context as a barrier for personalized nutrition. Taken together, the current study implies that to stimulate the adoption of personalized nutrition services not only benefits and risks of personalized nutrition should be addressed, but also consumers’ ambivalent feelings regarding the concept and contextual factors that may prohibit adoption.