• Journal of Intelligence and Information Systems
• /
• v.29 no.1
• /
• pp.289-305
• /
• 2023

The rapid increase in the use of mobile devices is changing consumers' online shopping behavior. However, the difference in the effect on the conversion rate according to the time when consumers switch from a small screen to a large screen has not been sufficiently studied. In addition, the differences in the effect of device conversion on purchase performance according to the characteristics of each country's infrastructure have not been sufficiently studied. Against this background, this study aims to analyze whether the timing of switching from mobile devices to PC devices and the country's mobile Internet penetration rate are moderating the positive effect of device switching on purchase performance. For empirical analysis, Google Merchandise Store data was collected and 101,466 data from 130 countries were analyzed with a multilevel model. As a result of the analysis, consumers' device switching (i.e., mobile to PC) had a positive effect when it occurred in the middle of the consumer journey. However, it was analyzed that when device switching occurred at the later stage of the consumer journey, it had a negative effect on purchase performance. In addition, it was analyzed that the higher the mobile Internet penetration rate, the weaker the positive effect of consumer device conversion on purchase performance.

• Journal of the Korean Applied Science and Technology
• /
• v.40 no.3
• /
• pp.496-506
• /
• 2023

The purpose of this study was to identify the relationship between social-emotional isolation, depression, and smartphone addiction of college students who experienced social distancing caused by COVID-19 and to identify the factors influencing smartphone addiction of college students. Total of 220 students from four universities participated in this study, and data collection was conducted by organizing a questionnaire in a Google form. Data were analyzed using the SPSS/WIN 28.0. There were significantly correlation among smartphone addiction of college students, social-emotional isolation (r=.44, p<.001) and depression (r=.51, p<.001). The factors affecting smartphone addiction of college students were gender (β=.176, p=.001), weekend smartphone usage time 7-9 hours (β=.387, p=.001), 10-12 hours (β=.313, p=.006), 12 hours or more (β=.299, p=<.001), depression (β=.302, p<.001), and social-emotional isolation (β=.210, p=.004). The regression model was statistically significant (F=15.81, p<.001). The explanatory power of the model was 43% (adj R²=.43, p<.001). Therefore, in order to prevent smartphone addiction of college students, it is necessary to develop and utilize a mental health promotion program that can reduce social-emotional isolation and depression.

• Journal of Industrial Convergence
• /
• v.21 no.4
• /
• pp.157-164
• /
• 2023

The purpose of this study was to examine the mediating effect of self-esteem in the effect of nursing caregivers' job enthusiasm on job satisfaction. For research analysis, 160 nursing caregivers' engaged in 2 institutions located in Gyeonggi-do, 3 institutions located in Seoul, 5 institutions located in Chungcheongnam-do, and 9 institutions located in Chungcheongbuk-do were surveyed from April 1 to September 2022. Data were collected non-face-to-face using Google links until the 30th. From the collected data of 140 people, 118 copies were used for the final analysis, excluding 22 incomplete responses. First, the effect of positive(+) effect on self-esteem of nursing care workers was confirmed. Second, it was confirmed that caregivers' job enthusiasm had a positive(+) effect on job satisfaction. Third, in the effect of caregiver's job enthusiasm on job satisfaction, it was confirmed as a partial mediating effect of self-esteem. In addition, the Sobel Test was conducted to confirm the significance of the mediating effect, and the significance of the mediating effect was also confirmed. This study has significance in that it suggests social welfare practices and policy interventions necessary to increase job enthusiasm and self-esteem for job satisfaction of nursing care workers, who are the main agents of caring for the elderly.

• Journal of The Korean Association For Science Education
• /
• v.43 no.2
• /
• pp.99-110
• /
• 2023

In this study, in order to strengthen the AI utilization competency of pre-service secondary science teachers, a project activity in which pre-service teachers directly create an 'AI-based molecular structure customized learning support tool' by using Google's teachable machine was developed and applied. To this end, the program developed for 26 third-grade pre-service teachers enrolled in the Department of Chemistry Education at H University in Chungcheongbuk-do was applied for 14 sessions during extracurricular activities. Then, the perceptions of 'understanding how AI works', 'efficacy of using AI in science classes', and 'plans to utilize AI in science classes' were investigated. As a result of the study, it was found that the program developed in this study was effective in helping pre-service teachers understand the operating principle of AI technology for machine learning at a basic level and learning how to use it. In addition, the program developed in this study was found to be effective in increasing the efficacy of pre-service teachers for the use of AI in science classes. And it was also found that pre-service teachers recognized the aspect of using AI technology as a new teaching·learning strategy and tool that can help students understand science concepts. Accordingly, it was found that the program developed in this study had a positive impact on pre-service teachers' AI utilization competency reinforcement and perception improvement at the basic level. Implications of this were discussed.

• Journal of the Korea Society of Computer and Information
• /
• v.14 no.10
• /
• pp.43-53
• /
• 2009

Knowledge retrieval service that uses collective intelligence which has special quality of open structure and can share the accumulative data is gaining popularity. However, acquiring the right needs for users from massive public knowledge is getting harder. Recently, search results from Google which is known for it's exquisite algorism, shows results for collective intelligence such as Wikipedia, Yahoo Q/A at the highest rank. Objective of this paper is to show that most answers come from human and to find the most influential people in on-line knowledge retrieval service. Hereupon, this paper suggest the influence value calculation algorism by analyzing user relation as centrality which social network is based on user activeness and reliance in Naver 지식iN. The influence value calculated by the suggested algorism will be an important index in distinguishing reliable and the right user for the question by ranking users with troubleshooting solutions in the knowledge retrieval service. This will contribute in search satisfaction by acquiring the right information and knowledge for the users which is the most important objective for knowledge retrieval service.

• Journal of Industrial Convergence
• /
• v.21 no.3
• /
• pp.139-148
• /
• 2023

This study was conducted through Google online survey from November 24 to November 26, 2021 targeting 276 students from the department of dental hygiene at a university in Gangwon-do. The purpose of this study was to investigate the infection awareness of uniforms, recognition of washing rules, and the intention to prevent infectious diseases through individualism and collectivist self-esteem. Statistical methods were analyzed using SPSS Statistics 24.0 and AMOS 21.0 as follows. For analysis, frequency analysis, exploratory factor analysis, confirmatory factor analysis, reliability analysis, structural equation, and ANOVA analysis were performed. As a result, it was confirmed that the models of uniform infection awareness, uniform washing rule recognition(p<.001), self-esteem, individualism, and collectivist intention to prevent infectious diseases were suitable(p<.001). Collectivism was found to affect the perception of uniform infection, the recognition of uniform washing rules, and the intention to prevent infectious diseases, confirming that self-esteem and collectivism had an effect on the change of perception for infection prevention. In the future, it will be possible to use the uniform washing method considering collectivism in infection control education of the dental hygiene.

• Journal of Digital Convergence
• /
• v.20 no.3
• /
• pp.333-341
• /
• 2022

Despite rapidly increasing demand for fishing, there is a lack of studies and information related to fishing, and there is a limit to obtaining the data on the global distribution of fish resources. Since the existing method of investigating fish resource distribution is designed to collect the fish resource information by visiting the investigation area using a throwing net, it is almost impossible to collect nation-wide data, such as streams, rivers, and seas. In addition, the existing method of measuring the length of fish used a tape measure, but in this study, a FishingTAG's smart measure was developed. When recording a picture using a FishingTAG's smart measure, the length of the fish and the environmental data when the fish was caught are automatically collected, and there is no need to carry a tape measure, so the user's convenience can be increased. With the development of a global fishing application using a FishingTAG's smart measure, first, it is possible to collect fish resource samples in a wide area around the world continuously on a real time basis. Second, it is possible to reduce the enormous cost for collecting fish resource data and to monitor the distribution and expansion of the alien fish species disturbing the ecosystem. Third, by visualizing global fish resource information through the Google Maps, users can obtain the information on fish resources according to their location. Since it provides the fish resource data collected on a real time basis, it is expected to of great help to various studies and the establishment of policies.

• The Journal of Internal Korean Medicine
• /
• v.44 no.3
• /
• pp.313-353
• /
• 2023

Objectives: Heart failure is a chronic disease with increasing prevalence rates despite advancements in medical technology. Korean medicine utilizes herbal prescriptions to treat heart failure, but little is known about the specific herbal medicines comprising the network of herbal prescriptions for heart failure. This study proposes a novel methodology that can efficiently develop prescriptions and facilitate experimental research on heart failure by utilizing existing databases. Methods: Herbal medicine prescriptions for heart failure were identified through a PubMed search and compiled into a Google Sheet database. NetMiner 4 was used for network analysis, and the individual networks were classified according to the herbal medicine classification system to identify trends. K-HERB NETWORK was utilized to derive related prescriptions. Results: Network analysis of heart failure prescriptions and herbal medicines using NetMiner 4 produced 16 individual networks. Uhwangcheongsim-won (牛黃淸心元), Gamiondam-tang (加味溫膽湯), Bangpungtongseong-san (防風通聖散), and Bunsimgi-eum (分心氣飮) were identified as prescriptions with high similarity in the entire network. A total of 16 individual networks utilized K-HERB NETWORK to present prescriptions that were most similar to existing prescriptions. The results provide 1) an indication of existing prescriptions with potential for use to treat heart failure and 2) a basis for developing new prescriptions for heart failure treatment. Conclusion: The proposed methodology presents an efficient approach to developing new heart failure prescriptions and facilitating experimental research. This study highlights the potential of network pharmacology methodology and its possible applications in other diseases. Further studies on network pharmacology methodology are recommended.

• The Journal of the Convergence on Culture Technology
• /
• v.9 no.3
• /
• pp.167-181
• /
• 2023

This study utilized TEXTOM, a social network analysis program to analyze changes in current tourism behavior after travel restrictions were eased after the outbreak of COVID-19. Data on the keywords 'domestic travel' and 'overseas travel' were collected from blogs, cafes, and news provided by Naver, Google, and Daum. The collection period was set from April to December 2022 when social distancing was lifted, and 2019 and 2020 were each set as one year and compared and analyzed with 2022. A total of 80 key words were extracted through text mining and centrality analysis was performed using NetDraw. Finally, through the CONCOR, the correlated keywords were clustered into 4. As a result of the study, tourism behavior in 2022 shows tourism recovery before the outbreak of COVID-19, segmentation of travel based on each person's preferred theme, prioritization of each country's corona mitigation policy, and then selecting a tourist destination. It is expected to provide basic data for the development of tourism marketing strategies and tourism products for the newly emerging tourism ecosystem after COVID-19.

• Journal of Korea Water Resources Association
• /
• v.56 no.8
• /
• pp.471-484
• /
• 2023

Deep learning models based on generative adversarial neural networks are specialized in generating new information based on learned information. The deep generative models (DGMR) model developed by Google DeepMind is an generative adversarial neural network model that generates predictive radar images by learning complex patterns and relationships in large-scale radar image data. In this study, the DGMR model was trained using radar rainfall observation data from the Ministry of Environment, and rainfall prediction was performed using an generative adversarial neural network for a heavy rainfall case in August 2021, and the accuracy was compared with existing prediction techniques. The DGMR generally resembled the observed rainfall in terms of rainfall distribution in the first 60 minutes, but tended to predict a continuous development of rainfall in cases where strong rainfall occurred over the entire area. Statistical evaluation also showed that the DGMR method is an effective rainfall prediction method compared to other methods, with a critical success index of 0.57 to 0.79 and a mean absolute error of 0.57 to 1.36 mm in 1 hour advance prediction. However, the lack of diversity in the generated results sometimes reduces the prediction accuracy, so it is necessary to improve the diversity and to supplement it with rainfall data predicted by a physics-based numerical forecast model to improve the accuracy of the forecast for more than 2 hours in advance.