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This study examines issues arising from AI creation of culture & arts and explores the direction of co-creation between humans and AI. In the era of AI art, the shift in media paradigm not only requires conceptual and theoretical interpretation of creation, but also raises legal and ethical challenges. First, the current status of AI creation in cultural arts. AI assists in the creation of all storytelling, including novels, and is utilized in digital art and art restoration. In addition, stage design and lighting AI technology increases the immersion of performances and creates new forms of art. AI changes the method of artistic creation, and human-AI creations express unique artistic values. Second, issues such as data bias, copyright, and legal and ethical deficiencies are increasing in the AI creative process. Bias and distortion of AI learning data can undermine the fairness and reliability of AI, lead to linguistic and socioeconomic inequality, and cause cultural diversity to be eliminated due to bias toward specific cultures and languages. The legal status of AI creations is unclear, and there are no clear standards for copyright protection, so whether or not they are legally protected is being discussed. Third, the AI creative model for culture & arts should be able to learn effectively with a small amount of data and generate creative contents without copyright infringement. As the integration of AI creation fundamentally changes the way humans interact with digital tools, the co-creation of human-AI is evolving in a direction that goes beyond human creativity. This study did not cover the detailed technical elements of AI. In follow-up studies, it is necessary to closely analyze the implementation method and creative process of the AI model for culture & arts. Although policy response measures for AI creation have been partially discussed, comparative analysis of AI copyright laws by country has not been conducted. In future studies, it is necessary to analyze legal response measures to protect AI products and creators.

This study analyzes the production process of the movie <SAND> produced using generative AI technology to examine the new possibilities and limitations of AI-based movie production, and presents a systematic methodology for prompt design. Unlike conventional movie production that follows a linear process, AI movie production shows an iterative process that breaks down the boundaries between the pre-production, production, and post-production stages. Using various AI tools such as ChatGPT, Midjourney, and Luma, we identify the importance of prompt design and develop customized strategies tailored to the unique characteristics of each tool. The results of the study suggest that although current AI technology has limitations in maintaining narrative consistency and achieving comprehensive storytelling quality, recent technological advancements are rapidly overcoming these limitations. This study is significant in that it contributes to this field by presenting an AI methodology through an AI movie production case, establishing a systematic framework for prompt design, and providing insight into the innovative potential of AI technology in future movie production.

This study explores the characteristics and implications of AI-generated films by analyzing award-winning works from major domestic and international AI film festivals. By examining how generative AI technologies are applied throughout the filmmaking process—from narrative construction to audiovisual expression and emotional performance—this research reveals the paradigm shift that AI introduces into traditional cinematic conventions. Films such as Story, Total Pixel Space, Lullaby, and The Last Alliance demonstrate how AI functions not only as a technical tool but also as a creative agent capable of shaping narrative logic, aesthetic style, and emotional resonance. Furthermore, these works raise philosophical and ethical questions regarding authorship, emotional authenticity, and legal responsibility. The findings suggest that AI cinema redefines the boundaries of artistic creation by challenging anthropocentric notions of authorship and by fostering a new model of collaborative imagination between human and machine. This study offers a humanities-based perspective on how AI films transform the structures of media production and cultural meaning in the age of artificial intelligence.

With the introduction of generative artificial intelligence(AI) tools such as OpenAI's Sora into the global film industry, including Hollywood, there has been a simultaneous emergence of innovations in film production as well as various crises. These changes are spreading throughout the entire film production process, including scriptwriting, casting, editing, and acting. This study analyzes the impact of AI on the film industry, particularly Hollywood, and explores how this technology might bring about changes in Korean cinema. AI technologies applied in the film industry offer benefits such as reducing production time and costs. However, they also pose threats to many filmmakers and actors who rely on the traditional production methods, leading to ethical and legal issues. In Hollywood blockbuster films, AI technology is used to create realistic visual effects, analyze scripts, and suggest optimal shooting angles. While these applications improve the qualitative level of films, they also reduce the human resources required in traditional film production processes. The impact on the Korean film industry is also noteworthy. Some Korean film production companies are leveraging AI to create films in a more creative and efficient manner. Efforts are being made to analyze audience data using AI and develop storylines that appeal to a larger audience. However, these technological changes are controversial among many Korean filmmakers who prefer traditional production methods. This study provides an in-depth discussion on whether the adoption of AI in the film industry can bring about positive innovation or inevitably lead to crises. It analyzes how AI technology is transforming traditional roles in the film industry and what new opportunities and challenges this change generates within the industry. Additionally. This study highlights the differences in technology adoption between Hollywood and Korean film industry and explores how each industry is embracing these technological changes.

The Fourth Industrial Revolution has spurred widespread adoption of AI-based services, driving global interest in AI semiconductors for efficient large-scale computation. Text mining research, historically using LDA, has evolved with machine learning integration, exemplified by the 2021 BERTopic technology. This study employs BERTopic to analyze AI semiconductor-related patents and research data, generating 48 topics from 2,256 patents and 40 topics from 1,112 publications. While providing valuable insights into technology trends, the study acknowledges limitations in taking a macro approach to the entire AI semiconductor industry. Future research may explore specific technologies for more nuanced insights as the industry matures.

Data are dramatically increased and big data technology is spotlighted innovative technology among the latest information technologies. Organizations are interested in adoption of big data system to analyze various data format and to identify new business opportunity. The purpose of this study is to build a unified model for a system adoption through analysis of impact that affects behavioral intention and usage behavior of using big data. This study in addition to Technology-Organization-Environment (TOE), that is used the introduction of organizational studies, and Diffusion of Innovation (DOI) have implemented an extended unified model including the unified theory of acceptance and use of technology (UTAUT) that is usually used in personal level adoption study. The hypothesis was set up after implementing research model, and then got 411 effective survey data to target the member of organizations. As a result, all models (UTAUT, TOE, DOI) are affect to behavioral intention and usage behavior. It is verified that the suggested unified model was appropriate.

The purpose of this study is to explore the factors that influence the purchase of AI speakers in China. We integrate the Unified Theory of Acceptance and Use of Technology (UTAUT) and Task-technology fit (TTF) model into one model and put forward assumptions. According to the characteristics of AI speakers, we selected 6 independent variables, such as Performance Expectation, Effort Expectation, Social Influence, Facilitating Conditions, Task and Technology-characteristics. The final impact on purchase behavior is evaluated through Task-technology fit and purchase intention. After counting 478 samples, through SPSS22.0 and AMOS analysis, hypotheses have been proved by strong experimental data, except facilitating conditions. These results also imply that improving the technical level of AI speakers and enhancing consumers’ purchasing intention are the central line of marketing. Based on this, we put forward several suggestions to marketers, including strengthening the research and development of AI speaker technology, and building a circle of friends of AI speakers.

With the increase in online shopping and delivery food consumption since the pandemic, solving environmental problems caused by single-use packaging has become an important issue. ‘Upcycling’ is a combination of ‘Upgrade’ and ‘Recycle’, and it is the rebirth of obsolete or discarded objects by adding new value to them, and there are currently various upcycled products on the market. In order to activate upcycling, consumers’ awareness of the environment and their values for consumption are very important. This study aims to investigate the influence of students’ environmental concern, environmental experience, and consumption value on their purchase intention of upcycled products. Based on the results of previous studies on environmental concern, environmental experience, and consumption value, hypotheses were set, and a survey was conducted among university students nationwide to test the hypotheses. The results of this study are as follows First, environmental concern has a significant positive effect on purchase intention of upcycled products. It can be seen that the more environmental concerns such as global warming and waste disposal problems increase, the more positive attitudes toward upcycled products increase. Second, the research hypothesis that environmental knowledge will have a positive effect on the purchase intention of upcycled products is rejected. It was found that environmental knowledge is acquired through environmental education and many SNS, but it does not have a direct effect on the purchase intention of upcycled products. Third, it was found that the consumption value of college students has a positive effect on the purchase intention of upcycled products by increasing their positive perception of upcycled products. Fourth, college students’ purchase intention of upcycled products has a positive effect on their behavioral intention to purchase upcycled products. The results of the study provide implications for relevant organizations such as universities and companies to effectively design upcycling-related education. It is also expected to have a positive impact on the use of upcycled products by providing basic information on the characteristics of consumers who purchase upcycled products.

E-Health services are seen as promising to healthcare promotion, but low usage by patients limits their effectiveness. The Technology Acceptance Model (TAM) has shown to be explanatory in the adoption of e-Health services. As the use of e-Health for self-management grows, it is important to identify factors influencing perceived ease-of-use (PEOU) and usefulness (PU) to encourage acceptance. However, the selection of external variables in this context lacks a clear pattern. We applied the Heuristic-Systematic Model (HSM) with the aim of further explaining the external variables in TAM especially in the area of e-health, and selected three external variables: information quality, health information literacy, and social influence. Hence, our study combines the Heuristic-Systematic Model (HSM) and TAM to investigate the mechanism and external factors that promote individuals to act for their health benefits. A total of 198 responses were collected among people having completed an online diabetes risk test on the website of the Finnish Diabetes Association. This data was then analyzed using partial least squares structural equation modeling (PLS-SEM). Our study finds that heuristic cues like health information literacy and social influences impact PEOU, while systematic cues, especially information quality, positively influence PU. Also, higher PU is associated with increased intention to use e-Health services and engage in health-promoting actions, highlighting the importance of the systematic path in the e-Health context. Our theoretical contributions are twofold. First, we add to TAM research in the area of e-health by providing an explanation why heuristic cues link to PEOU while systematic cues link to PU. Second, our research is among very few applying HSM to e-health and finds that overall, the systematic path is more influential than the heuristic path. We also provide practical advice for healthcare providers to improve the impact of their e-health initiatives.

AI technology is evaluated as a core technology leading the digital transformation of industry. At the same time, social demands for responsible AI and AI governance that emphasize ethical use of data and transparency of algorithms are also continuously expanding. Accordingly, this study defined the concept of AI governance through a review of prior research, and conducted a case study from a historical perspective to examine the interrelationship between AI governance and non-financial value of a company, such as social reputation. In order to diagnose the AI governance of a company, the AI policy of the company, that is, the terms of use and data processing policy of a generative AI platform company, was used as surrogates, and text mining was used to analyze the AI policy of each platform over time to examine the relationship between the company's reputation and AI governance. As a result, while governance research has traditionally focused on building a system for an organization that manages and supervises managers as agents from the perspective of shareholders, AI governance can be defined as a AI policy management system of a company that manages the reputation of a company by reflecting the demands of various stakeholders in AI policies for responsible AI development and distribution. The establishment of governance that accommodates stakeholders’ demands for the development and distribution of responsible AI can be a goal that companies must achieve in terms of non-financial value, as it secures transparency in innovative creations such as AI, builds trust with stakeholders, and enhances their reputation as a ‘responsible AI provider.’