• Journal of Korean Society of Disaster and Security
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• v.13 no.1
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• pp.59-75
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• 2020

This study is a case study that applied 'UNDRR's Urban Disaster Resilience Scorecard', an evaluation tool necessary for Incheon Metropolitan City to be certified as an international safe city. I would like to present an example that the results derived from this scorecard contributed to the Incheon Metropolitan City Disaster Reduction Plan. Of course, the Disaster Resilience Scorecard can't provide a way to improve the resilience of every disaster facing the city. However, it is to find the weakness of the resilience that the city faces, and to propose a solution to reduce the city's disaster risk. This is to help practitioners to recognize the disaster risks that Incheon Metropolitan City faces. In addition, the solution recommended by UNDRR was suggested to provide resilience in areas vulnerable to disasters. It was confirmed that this process can contribute to improving the disaster resilience of Incheon Metropolitan City. UNDRR has been spreading 'Climate Change, Disaster-resistant City Creation Campaign', aka MCR (Making Cities Resilient) Campaign, to cities all over the world since 2010 to reduce global cities' disasters. By applying the disaster relief guidelines adopted by UNDRR, governments, local governments, and neighboring cities are encouraged to collaborate. As a result of this study, Incheon Metropolitan city's UN Urban Resilience Scorecard was evaluated as a strong resilience field by obtaining scores of 4 or more (4.3~5.0) in 5 of 10 essentials; 1. Prepare organization for disaster resilience and prepare for implementation, 4. Strong resilience Urban development and design pursuit, 5. Preservation of natural cushions to enhance the protection provided by natural ecosystems, 9. Ensure effective disaster preparedness and response, 10. Rapid restoration and better reconstruction. On the other hand, in the other five fields, scores of less than 4 (3.20~3.85) were obtained and evaluated as weak resilience field; 2. Analyze, understand and utilize current and future risk scenarios, 3. Strengthen financial capacity for resilience, 6. Strengthen institutional capacity for resilience, 7. Understanding and strengthening social competence for resilience, 8. Strengthen resilience of infrastructure. In addition, through this study, the risk factors faced by Incheon Metropolitan City could be identified by priority, resilience improvement measures to minimize disaster risks, urban safety-based urban development plans, available disaster reduction resources, and integrated disasters. Measures were prepared.

• Information Systems Review
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• v.25 no.4
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• pp.131-159
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• 2023

Recently, amidst the backdrop of the COVID-19 pandemic shifting towards an endemic phase, there has been a rise in discussions and debates about the future of the metaverse. Simultaneously, major metaverse platforms like Roblox have been launching services integrated with generative AI, and Apple's mixed reality hardware, Vision Pro, has been announced, creating new expectations for the metaverse. In this situation where the outlook for the metaverse is divided, it is crucial to diagnose the metaverse from an ecosystem perspective, examine its key ecological features, driving forces for development, and future possibilities for advancement. This study utilized Wang's (2021) Information Ecology Theory (IET) framework, which is representative of ecosystem research in the field of Information Systems (IS), to derive the Metaverse Industrial Ecosystem (MIE). The analysis revealed that the MIE consists of four main domains: Tech Landscape, Category Ecosystem, Metaverse Platform, and Product/Service Ecosystem. It was found that the MIE exhibits characteristics such as digital connectivity, the integration of real and virtual worlds, value creation capabilities, and value sharing (Web 3.0). Furthermore, the interactions among the domains within the MIE and the four characteristics of the ecosystem were identified as driving forces for the development of the MIE at an ecosystem level. Additionally, the development of the MIE at an ecosystem level was categorized into three distinct stages: Narrow Ecosystem, Expanded Ecosystem, and Everywhere Ecosystem. It is anticipated that future advancements in related technologies and industries, such as robotics, AI, and 6G, will promote the transition from the current Expanded Ecosystem level of the MIE to an Everywhere Ecosystem level, where the connection between the real and virtual worlds is pervasive. This study provides several implications. Firstly, it offers a foundational theory and analytical framework for ecosystem research, addressing a gap in previous metaverse studies. It also presents various research topics within the metaverse domain. Additionally, it establishes an academic foundation that integrates concept definition research and impact studies, which are key areas in metaverse research. Lastly, referring to the developmental stages and conditions proposed in this study, businesses and governments can explore future metaverse markets and related technologies. They can also consider diverse metaverse business strategies. These implications are expected to guide the exploration of the emerging metaverse market and facilitate the evaluation of various metaverse business strategies.

• Sleep Medicine and Psychophysiology
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• v.17 no.2
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• pp.100-108
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• 2010

Objectives: "Internet-addiction" came into common use not only in clinical setting but also in everyday life. But, pathophysiology and diagnostic criteria of the internet addiction remain unknown. Because adolescents are in developing period, they might be vulnerable to the internet addiction, depression and sleep-related problem. The objectives of this study were to investigate the characteristics of internet addiction and its association with sleep pattern and depression in Korean adolescence. Methods: Subjects were 799 middle and high school students in Seoul, Korea. We administered a self-reported questionnaire including socio-demographic data, Korean versions of Young's Internet Addiction Scale (YIAS), Pittsburgh Sleep Quality Index (PS-QI), the Center for Epidemiologic Studies for Depression Scale (CES-D) and questions about internet using patterns. Data of 696 subjects were included in analysis. Chi-square tests were used to analyze proportional differences, and ANOVA with post-hoc tests were used to analyze differences among groups. Partial correlation analyses were performed to analyze the correlation of internet addiction with other variables (two-tailed, p<0.05). Results: Of the 696 participants (grade 2 of middle school; M2 135 vs. grade 1 of high school; H1 238 vs. grade 2 of high school; H2 323), 2.0% (n=14) were internet-addicted (IA), 27.7% (n=193) were over-using (OU) and 70.3% (n=489) were not-addicted (NA). The mean scores of YIAS, PSQI and CES-D scores were 35.24${\pm}$12.78, 5.53${\pm}$3.04 and 16.72${\pm}$8.69, respectively. In higher grade students, average total sleep time was shorter (M2 426.20${\pm}$67.68 min. vs. H1 380.47${\pm}$62.57 min. vs. H2 354.67${\pm}$73.37 min., F=51.909, p<0.001), and PSQI (4.69${\pm}$3.14 vs. 5.42${\pm}$3.15 vs. 5.97${\pm}$2.83, F=8.871, p<0.001) CES-D (13.53${\pm}$8.37 vs. 16.96${\pm}$8.24 vs. 17.87${\pm}$8.84, F=12.373, p<0.001) scores were higher than those of lower grade students. Comparing variables among IA, OU and NA groups, computer using time not for study (96.36${\pm}$63.31 min. vs. 134.92${\pm}$86.79 min. vs. 213.57${\pm}$136.87 min., F=34.287, p<0.001) and portable device using time not for study (84.22${\pm}$79.11 min. vs. 96.97${\pm}$91.89 min. vs. 152.31${\pm}$93.64 min., F= 5.400, p=0.005) were different among groups. PSQI (5.26${\pm}$2.97 vs. 6.08${\pm}$2.97 vs. 7.50${\pm}$4.41, F=8.218, p<0.001) and CES-D scores (15.40${\pm}$8.08 vs. 19.05${\pm}$8.42 vs. 30.43${\pm}$13.69, F=32.692, p<0.001) were also different among groups. YIAS score were correlated with computer using time not for study (r=0.356, p<0.001) and portable device using time not for study (r= 0.136, p<0.001). PSQI score (r=0.237, p<0.001) and CES-D score (r=0.332, p<0.001). YIAS score and PSQI score (r=0.131, p= 0.001), YIAS and CES-D score (r=0.265, p<0.001), PSQI score and CES-D score (r=0.357, p<0.001) were correlated each other. Conclusion: These results suggested that adolescents' internet-addiction was correlated with not only computer and portable device using time not for study but also depression and sleep-related problems. We should pay attention to depression and sleep-related problems, when evaluating internet-addiction in adolescents.