• Journal of the Korea Society of Computer and Information
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• v.29 no.3
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• pp.21-41
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• 2024

This study explores the relationship between users' attachment styles and their interactions with ChatGPT (Chat Generative Pre-trained Transformer), an advanced language model developed by OpenAI. As artificial intelligence (AI) becomes increasingly integrated into everyday life, it is essential to understand how individuals with different attachment styles engage with AI chatbots in order to build a better user experience that meets specific user needs and interacts with users in the most ideal way. Grounded in attachment theory from psychology, we are exploring the influence of attachment style on users' interaction with ChatGPT, bridging a significant gap in understanding human-AI interaction. Contrary to expectations, attachment styles did not have a significant impact on ChatGPT usage or reasons for engagement. Regardless of their attachment styles, hesitated to fully trust ChatGPT with critical information, emphasizing the need to address trust issues in AI systems. Additionally, this study uncovers complex patterns of attachment styles, demonstrating their influence on interaction patterns between users and ChatGPT. By focusing on the distinctive dynamics between users and ChatGPT, our aim is to uncover how attachment styles influence these interactions, guiding the development of AI chatbots for personalized user experiences. The introduction of the Perceived Partner Responsiveness Scale serves as a valuable tool to evaluate users' perceptions of ChatGPT's role, shedding light on the anthropomorphism of AI. This study contributes to the wider discussion on human-AI relationships, emphasizing the significance of incorporating emotional intelligence into AI systems for a user-centered future.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.1
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• pp.93-105
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• 2024

Car-sharing is a representative model of the sharing economy, and it is a service that rents or uses a car for the necessary time without owning a car. This industry is growing due to various factors such as technological advances, increasing awareness of environmental protection, and increasing demand for solving traffic congestion problems in cities. Accordingly, there is a need for a strategic approach for companies providing car-sharing services to respond quickly to market changes in order to expand market share and differentiate services. Accordingly, this study conducted a case study on open innovation activities between Gongcar and existing rental car companies, focusing on the research question "What effects do open innovation activities between car-sharing companies and existing rental car companies cause?" As a result of the study, it was confirmed that Gongcar have (1) the ability to actively respond to market fluctuations by establishing a flexible vehicle supply chain based on demand, (2) have significantly reduced growth capital expenditure (Growth Capex), and both cafe and rental car companies have (3) performed successful open innovation by improving key KPI indicators and recording financial performance. This study reveals how open innovation acts as a key business growth engine in the car-sharing industry, and its significance is found in that it empirically confirmed the successful implementation conditions of open innovation based on resource dependence theory.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.6
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• pp.32-48
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• 2023

Autonomous driving technology is shaping the future of personalized travel, encouraging personalized travel, and traffic impact could be influenced by individualized travel behavior during the transition of driving entity from human to machine. In order to evaluate traffic impact, it is necessary to estimate the total number of trips based on an understanding of individual travel characteristics. The Activity-based model(ABM), which allows for the reflection of individual travel characteristics, deals with all travel sequences of an individual. Understanding the relationship between travel and travel must be important for assessing traffic impact using ABM. However, the ABM has a limitation in the data hunger model. It is difficult to adjust in the actual demand forecasting. Therefore, we utilized a Tour-based model that can explain the relationship between travels based on household travel survey data instead. After that, vehicle registration and population data were used for correction. The result showed that, compared to the KTDB one, the traffic generation exhibited a 13% increase in total trips and approximately 9% reduction in working trips, valid within an acceptable margin of error. As a result, it can be used as a generation correction method based on Tour, which can reflect individual travel characteristics, prior to building an activity-based model to predict demand due to the introduction of autonomous vehicles in terms of road operation, which is the ultimate goal of this study.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.2
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• pp.15-21
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• 2024

The purpose of this study is to propose a framework for the introduction and evaluation of artificial intelligence (AI) services not only in general applications but also in public policies. To achieve this, the study explores AI service management and governance toolkits, providing insights into how to introduce AI services in public policies. Firstly, it offers guidelines on the direction of AI service development and what aspects to avoid. Secondly, in the development phase, it recommends using the AI governance toolkit to review content through checklists at each stage of design, development, and deployment. Thirdly, when operating AI services, it emphasizes the importance of adhering to principles related to 1) planning and design, 2) the lifecycle, 3) model construction and validation, 4) deployment and monitoring, and 5) accountability. The governance perspective of AI services is crucial for mitigating risks associated with service provision, and research in risk management aspects should be conducted. While embracing the advantages of AI, proactive measures should be taken to address limitations and risks. Efforts should be made to efficiently formulate policies using AI technology to create high value and provide meaningful societal impacts.

• Knowledge Management Research
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• v.25 no.1
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• pp.193-215
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• 2024

This study aims to identify the main factors that caused the digital divide during the COVID-19 pandemic. Utilizing data from the 2020 and 2021 Digital Divide Surveys by the National Information Agency, a research model was constructed for analysis using SmartPLS 4, applying PLS-SEM and Multigroup Analysis methods. The results of the study are as follows. First, combining 2020 and 2021, mobile internet usage during COVID-19 is positively associated with digital skills, digital usage, and usage outcomes except for networking. Second, the impact of digital usage was significantly higher during the outbreak than during the beginning of COVID-19, which may be due to the increased demand for digital usage as the outbreak continued, and the corresponding increase in internet usage. Third, we discovered that demographics are not the main factor affecting changes in mobile internet use during the COVID-19 pandemic. Instead, digital literacy affects mobile usage, which is the most important one. The results show the importance of creating programs to teach people how to use technology appropriately. We propose that digital literacy should be central to training programs for people who use digital services.

• Knowledge Management Research
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• v.25 no.1
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• pp.67-88
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• 2024

Smart tourism aims to maximize the utilization of local tourism resources, effectively manages cities and contributes to improving communication and quality of life between tourists and residents. Therefore, smart tourism emphasizes synergistic collaboration, considering both residents and tourists. This study explores smart tourism interaction and roles in enhancing regional competitiveness. By conducting thorough examination, focusing on integrating the four key elements of smart tourism city (smart experience, smart convenience, smart accessibility, and smart platform) with local residents, local businesses, regional resources, and ecosystem to foster positive synergies, Incheon smart tourism city project was employed as a single case study design. Research results indicate that the collaborative model of a smart tourism city positively impacts service satisfaction and strengthens regional tourism competitiveness. Building upon these results, this study aims to contribute to the development of smart tourism cities by proposing directions for future development and emphasizing the enhancement of regional competitiveness through the integration of smart technology and local tourism.

• Journal of the Korean Society for information Management
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• v.41 no.1
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• pp.509-536
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• 2024

The purpose of this study is to develop a metadata AP for managing the person and organization name authority data in the National Debt Redemption Movement Digital Archive, a small-scale digital archive. The design principles and core metadata elements were derived by analyzing person/organization(group or corporateBody) metadata standards, implementation practices, and guidelines of libraries and archives, and mapped to the National Debt Redemption Movement person/organization name thesaurus data and the Wikidata Linked Metadata Model, resulting in 10 elements in the identification area, 14 elements in the content area, 8 elements in the relationship area, and 4 elements in the control area. A simple structure schema was applied so that it can be applied even in small organizations, and for interoperability, the schema was proposed with reference to DublinCore and SKOS schemes, and the applicability was confirmed based on actual data. The results of this study can be utilized as a basis for institutions that recognize the importance of data management but have difficulty in applying it in practice, when they want to prepare a system for managing their own authority data.

• Journal of the Korean earth science society
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• v.45 no.3
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• pp.192-202
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• 2024

Ocean reanalysis data are extensively used in ocean circulation and climate research by integrating observational data with numerical models. This approach overcomes the spatial and temporal limitations of observational data and provides high-resolution gridded information that considers the physical interactions between ocean variables. In this study, I extended the previously produced 12-year (2011-2022) Northwest Pacific regional ocean reanalysis data to create a long-term reanalysis dataset (K-ORA22E) with a horizontal resolution of 1/24° spanning 30 years (1993-2022). These data were analyzed to diagnose long-term ocean climate change in the Korean marginal seas. Analysis of the K-ORA22E data revealed that the axis of the Kuroshio extension has shifted northward by approximately 6 km per year over the past 30 years, with a significant increase in sea surface temperature north of the Kuroshio axis. Among the waters surrounding the Korean Peninsula, the East Sea exhibited the most significant temperature increase. In the East Sea, the temperature increase was more pronounced in the middle layer than in the surface layer, with the East Korea Warm Current showing a rate two to three times higher than the global average. In the central Yellow Sea, where the Yellow Sea Bottom Cold Water appears, temperatures increased over the long-term, but decreased along the west and south coasts of the Korean Peninsula. These spatial differences in long-term temperature changes appear to be closely related to the heat transport pathways of warm water from the Kuroshio Current. High-resolution regional ocean reanalysis data, such as the K-ORA22E produced in this study, are essential foundational data for understanding long-term variability in the Korean marginal seas and analyzing the impacts of climate change.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.3
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• pp.109-121
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• 2024

This paper adopts a resource-based approach to analyze why some universities have a greater number of faculty startups, and how this impacts on performance, in terms of indictors such as the number of employees and revenue sales. More specifically, we propose 9 hypotheses which link institutional resources to faculty startups and their performance, and compare 5 different groups of university resources for cross-college variation, using data from 134 South Korean four-year universities from 2017 to 2020. We find that the institutional factors impacting on performance of faculty startups differ from other categories of startups. The results show that it is important for universities to provide a more favorable environment, incorporating more flexible personnel policies and accompanying startup support infrastructure, for faculty startups, whilest it is more effective to have more financial resources and intellectual property for other categories of startups. Our findings also indicate that university technology-holding company and technology transfer programs are crucial to increase the number of faculty startups and their performance. Our analysis results have implications for both university and government policy-makers, endeavoring to facilitate higher particaption of professors in startup formation and ultimate commercialization of associated teachnologies.

• Korean Journal of Environmental Biology
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• v.42 no.2
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• pp.193-206
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• 2024

This study aimed to quantify changes in forest cover and carbon storage of Korean Peninsular during the last two decades by integrating field measurement, satellite remote sensing, and modeling approaches. Our analysis based on 30-m Landsat data revealed that the forested area in Korean Peninsular had diminished significantly by 478,334 ha during the period of 2000-2019, with South Korea and North Korea contributing 51.3% (245,725 ha) and 48.6% (232,610 ha) of the total change, respectively. This comparable pattern of forest loss in both South Korea and North Korea was likely due to reduced forest deforestation and degradation in North Korea and active forest management activity in South Korea. Time series of above ground biomass (AGB) in the Korean Peninsula showed that South and North Korean forests increased their total AGB by 146.4Tg C (AGB at 2020=357.9Tg C) and 140.3Tg C (AGB at 2020=417.4Tg C), respectively, during the last two decades. This could be translated into net AGB increases in South and North Korean forests from 34.8 and 29.4 Mg C ha^-1 C to 58.9(+24.1) and 44.2(+14.8) Mg C ha^-1, respectively. It indicates that South Korean forests are more productive during the study period. Thus, they have sequestered more carbon. Our approaches and results can provide useful information for quantifying national scale forest cover and carbon dynamics. Our results can be utilized for supporting forest restoration planning in North Korea