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.

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.

This study attempts to analyze the characteristics of parent's perception types of online game and suggest personalized intervention strategies for improving parents' perception of online games. The data was collected through the online survey from 345 parents. First, the parent's perception types of online game were classified as 4 types through online game perception scale. Second, the characteristics of parent's perception types of online game were analyzed by integrating the results of this research and previous study. As a result, each type of perceptions has differences in the criteria of positive-negative perception of online games, the attitude and reaction to children’s gaming, game familarity, Internet literacy of parents and etc. Third, based on the characteristics of perception types of online game, we suggest intervention strategies for improving parents' perception of online games. This study holds its significance in suggesting the personalized intervention strategies based on the characteristics of perception types of online game.