목적: 본 연구는 역동적 신체 활동 상황에서 HMD 기반 가상현실 기기 내부의 온·습도 및 열지수를 실시간 측정하 고, 이러한 환경 변화가 안구건조증과 시각적 불편감에 미치는 영향을 규명하는 데 목적이 있다. 이를 통해 활동형 VR 사용 시 안전성 확보와 열·습도 관리 기반의 최적화된 VR 환경 설계의 근거를 제시하고자 한다. 방법 : 체육계열 대학생 28명을 대상으로 수행되었으며, Oculus Quest2를 이용한 역동적 VR 복싱 콘텐츠 수 행 중 HMD 내부 온도·습도 및 열파지수를 Arduino–DHT11 기반 센서로 2.5초 간격으로 실시간 측정하였다. 참 가자는 안정 상태, 운동 상태, 회복 상태 각 15분씩 총 45분간 실험에 참여하였고, 심박수는 스마트밴드를 통해 목 표 운동 강도 범위(30~50% HRR)로 유지되었다. 안구건조증 평가는 실험 전·후 비침습적 눈물막 파괴시간(NI BUT), Schirmer 검사, OSDI 설문을 통해 객관·주관 지표를 모두 수집하였다. 통계 분석은 대응표본 t-검정과 카이제곱 검정을 사용하였으며 유의수준은 p>0.050로 설정하였다. 결과 : 연구 대상자(평균 23.44세)는 VR 사용 전후 NI BUT과 Schirmer 검사에서 유의한 변화가 나타나지 않았으나(p>0.050), OSDI 점수는 실험 후 유의하게 증가하여 주관적 안구 불편감이 상승한 것으로 확인되었다 (p>0.050). HMD 내부 환경 분석에서는 운동 직후 초기 구간(15~18분)에서 온도, 습도, 열지수의 변화율이 가 장 크게 증가하여(p<0.001) 역동적 신체 활동이 VR 장치 내부 미세환경에 즉각적이고 급격한 변화를 유발함을 보여주었다. 이후 구간에서는 환경 변화 속도가 완만해지며 점차 안정화되는 경향을 보였다. 종합하면, 단기간의 고강도 VR 사용은 내부 열·습도 환경의 급격한 변동과 더불어 사용자의 주관적 시각적 피로 증가와 관련됨을 시사한다. 결론 : 고강도 신체 활동 시 VR HMD 내부의 온·습도 및 열파지수는 급격히 상승하여 열적 불편감과 시각적 피로를 더욱 유발한다. 운동 종료 후에는 빠르게 회복되는 가역적 특성을 보였음으로 이러한 결과는 활동형 VR 사 용 시 휴식·환기 전략의 필요성과 안전성 기반의 HMD 설계 및 사용자 관리 지침 마련의 중요성을 제시한다.

생성형 AI는 방대한 데이터를 기반으로 사용자의 요구에 부합하는 콘텐츠를 자동 생성함 으로써 창의성과 생산성을 동시에 향상시키고 있다. 구독경제 기반의 생성형 AI 서비스는 소비자와 기업 모두에게 혜택을 제공한다. 소비자는 높은 초기 비용 없이 지속적으로 서비스를 이용할 수 있으며, 기업은 반복적인 매출과 고객 데이터를 기반으로 서비스 품질을 고도화할 수 있다. 이와 같은 선순환 구조가 안정적으로 유지되기 위해서는 소비자의 지속적인 유료 구독이 필수적이다. 이에 본 연구는 생성형 AI 서비스에 대한 구독의도 형성 요인을 규명하기 위해 기술수용모델(TAM)과 어포던스(Affordance) 이론을 통합한 연구모형을 제시하고 실증 분석을 수행하였다. 생성형 AI 사용 경험이 있는 직장인 134명을 대상으로 온라인 설문 조사를 실시하였으며, 수집된 자료를 SPSS 27.0과 PROCESS macro를 활용하여 상관분석, 다중회귀분석, 그리고 매개효과 분석을 실시하였다. 분석 결과, 어포던스 요인 중 개인화된 도움 제공은 인지된 유용성과 인지된 용이성 모두에 유의한 영향을 미쳤고, 맥락 인지는 인지된 유용성에만 유의한 영향을 미쳤다. 반면 대화의 의인화는 두 인지 요인 모두에 유의한 영향을 보이지 않았다. 또한 인지된 용이성은 인지된 유용성에 정(+)의 영향을 미쳤으며, 두 인지 요인은 모두 이용의도에 유의한 정(+)의 영향을 미쳤다. 특히 이용의도는 구독의도에 가장 강력한 영향을 미치는 핵심 변수로 확인되었고, 인지된 유용성과 용이성은 이용의도를 매개로 구독의도에 간접적인 정(+)의 효과를 보였다. 본 연구는 생성형 AI와 구독경제의 융합이라는 새로운 소비 패러다임에 대한 이론적 기반을 제공함과 동시에, 사용자 경험 중심의 AI 서비스 설계 및 구독형 비즈니스 전략 수립에 실무적 시사점을 제시한다.

This study examines the implementation of CEFR Basic User-level descriptors within a systematic instructional design framework to support learner-centered instruction and formative assessment in Korean elementary English education. Focusing on Grades 3 and 4, the study aligned CEFR’s four communicative functions—reception, production, interaction, and mediation—with the 2015 Revised National English Curriculum. A CEFR-based self-assessment tool was developed and administered before and after instruction to measure learners’ perceived communicative development. Findings revealed functional gains across multiple communicative domains, with variation in growth patterns depending on learner group and task type. K-means clustering identified three learner profiles—low-growth, reception-focused, and high-growth—which informed differentiated instructional strategies tailored to learner needs. The results demonstrate that CEFR-informed self-assessment, when combined with systematic instructional design, enhances curriculum coherence and supports data-driven pedagogy. This study provides empirical evidence on the practical integration of CEFR descriptors in primary classrooms, highlighting their potential to inform learner diagnosis and guide targeted instruction in localized educational contexts.

This study investigates the structural mechanisms underlying user acceptance of generative AI services by integrating cognitive and affective dimensions of user experience. Based on the Technology Acceptance Model, Expectation–Confirmation Theory, and flow theory, a research model was developed and tested through an online survey of 387 Korean users with more than three months of experience. Structural equation modeling confirmed that cognitive and affective responses significantly influence satisfaction and trust, which in turn predict loyalty, with trust showing the strongest direct effect. Satisfaction and trust also mediated these relationships, while flow strengthened the satisfaction–loyalty path and resistance to technology was not significant. These findings highlight the importance of incorporating emotional and experiential factors alongside functional aspects. Practical implications suggest that fostering trust, engagement, and perceived value is essential for sustaining loyalty in generative AI services.

This study explores the transformative impact of artificial intelligence (AI) on the structure of the music industry and the evolving patterns of digital music consumption. The research aims to investigate how AI-driven music platforms foster new forms of user engagement, leading to a paradigm shift from passive listening to active participation and co-creation. Traditionally, digital music consumption has centered on streaming and algorithmic recommendations. However, with the advent of AI technologies, users are increasingly engaging in personalized experiences, interactive creation, and collaborative musical expression. Drawing upon theoretical frameworks of digital consumption and user engagement, this study reviews the evolution of music consumption behaviors and examines key AI music platforms such as Spotify’s AI DJ, Boomy, and Mubert. Through case analysis, the research identifies how personalized recommendations, generative AI music composition, and interactive interfaces enhance user motivation, emotional involvement, and behavioral participation. The findings reveal that AI music platforms not only provide tailored listening experiences but also empower users to take part in music creation, transforming their role from passive consumers to active co-creators. This shift signifies a broader transformation in the digital music ecosystem, where consumption becomes a participatory and expressive act. Ultimately, the study highlights how the integration of AI and user engagement is redefining the meaning of music consumption, generating new cultural and economic value within platform-based ecosystems. The results provide theoretical and practical implications for future music industry strategies and platform business models.

The Public Procurement Service was established to ensure efficient supply of necessary goods for public institutions and their quality stability. The Public Procurement Service operates the Excellent Product Designation System for quality improvement. Due to the convenience of sole-source contracts and pricing advantages, suppliers prefer to obtain excellent product certification. However, despite these advantages, the designation does not guarantee customer satisfaction, as consumers pay higher prices without assured quality improvements. The goal of this study is to propose a new evaluation system better reflecting customer satisfaction and repurchase intention. Metal window products were selected as the study subject. Candidate factors were derived through literature review, and surveys were conducted to identify significant items for the new evaluation system. Item weights were then calculated using AHP analysis. The proposed system was validated through case analysis comparing two excellent products with two general products.

This study proposed and empirically validated an integrated conceptual model combining protection motivation theory (PMT) and the theory of planned behavior (TPB) to explain the policy acceptance of special evacuation stair installation and the evacuation intentions of users in deep subway stations. An online survey was conducted among metropolitan subway riders (18 items total, 15 core items), and data were analyzed using SPSS Statistics 26.0 for exploratory factor analysis (EFA), multiple regression, K-means cluster analysis, and chi-square tests. EFA confirmed a four-factor structure—awareness, perceived feasibility and trust, behavioral intention, and policy acceptance—with Cronbach α ≥ 0.78 for all factors. Regression results indicated that attitude and perceived behavioral control significantly predicted behavioral intention (p < 0.001) that in turn demonstrated strong explanatory power for policy acceptance (p < 0.01). Cluster analysis identified three user typologies (“high awareness–high acceptance,” “moderate awareness–moderate acceptance,” and “low–awareness–low acceptance”), and chi-square tests revealed significant group differences in prior training and in-depth guidance participation (p < 0.05). The findings suggested that the integrated PMT–TPB model effectively captured the determinants of evacuation stair acceptance and intention, providing a foundation for tailored communication and training strategies.

This study was conducted to quantitatively verify whether payment motives and usage patterns differ according to the motivations of users of mobile character-collecting games. For this purpose, three user types were derived based on game motivation profiles tailored to mobile character-collecting games: Cluster 1 (Communicators), Cluster 2 (Character Collectors), and Cluster 3 (Competitors). It was confirmed that each cluster was mutually exclusive and exhibited different payment motives and usage patterns. These findings emphasize the value of a motivation-based segmentation strategy in understanding the behavior of mobile game users. Furthermore, based on the results of this study, game developers and publishers can more sophisticatedly tailor game content, monetization strategies, and marketing campaigns based on users' motivational profiles, rather than relying on simple demographic variables. Future research needs to expand the diversity of the sample by including a wider age range and considering a balanced gender distribution. Additionally, to gain a more in-depth understanding of the temporal changes in user motivation, future studies should explore potential causal relationships through a longitudinal research design.

Since the small screen must be watched at the production and manufacturing site, when using the monitor without a separate paper for work and production instructions, it is necessary to look at the work instruction screen installed in a separate space to prevent work efficiency from deteriorating. It plays a role through a monitoring system using DPS or Barcode and RF-ID recognition as a safety device for installing heterogeneous parts in manufacturing and missing parts, but due to the high cost of introducing the system and difficulty in maintaining management, Visual POP is put into the production line. This study was produced by paying attention to the following five points in order to reduce the weight of these industrial Visual POPs and have global specifications and uses. These include instrument design and design, processing production, UI and control, application, thermal stress analysis and thermal analysis. In this study, it is considered to User Interface and control of Visual POP for two models.

This study aims to identify latent classes among shared e-scooter users based on their characteristics and analyze the differences in personal and usage characteristics across these classes. Specifically, the study has the following key objectives: (1) to select variables related to the personal and usage characteristics of shared e-scooter users; (2) to collect data on the personal and usage characteristics of shared e-scooter users; (3) to derive the latent classes of shared e-scooter users; and (4) to test the differences in personal and usage characteristics across the identified latent classes. Variables related to the personal and usage characteristics of shared e-scooter users were selected based on a literature review. Through a survey, data on the personal and usage characteristics of shared e-scooter users were collected. A latent class analysis (LCA) was performed to derive the latent classes of shared e-scooter users. Finally, a chi-square analysis was conducted to test the differences in personal and usage characteristics across the latent classes of shared e-scooter users. The results of this study are as follows. The personal characteristics of shared e-scooter users were identified as age and sex, whereas the usage characteristics were identified as usage frequency, time periods of e-scooter usage, return/rental zones, return/rental places, and types of roads used. Data on sex, age, usage frequency, periods of e-scooter usage, and return/rental locations were collected from 278 shared e-scooter users. Based on information criterion, statistical validation, and the entropy index, four latent classes of shared e-scooter users were identified: “male users with a commuting purpose in business zones,” “male users with a homeward commuting purpose in residential zones,” “female users with a leisure purpose in park/green zones,” and “users in their 20s with a commuting purpose in residential zones.” The results of a chisquare analysis revealed statistically significant differences (p < 0.05) in the personal and usage characteristics across the latent classes. Shared e-scooter user types were classified through Latent Class Analysis (LCA), and differences in personal and usage characteristics were identified across the classes. The preferred usage environments and conditions for each class of shared e-scooter users are determined. Variables related to the return/rental zone and periods of e-scooter usage showed the most significant differences among the classes. These findings can contribute to the development of customized user policies and the improvement of services based on the characteristics of shared e-scooter users.