• Journal of the Korean Society for Library and Information Science
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• v.58 no.2
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• pp.363-384
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• 2024

This study was conducted to examine the user's experiences with the public library in Daejeon using big data analysis, focusing on the text mining technique. To know this, first, the overall evaluation and perception of users about the public library in Daejeon were explored by collecting data on social media. Second, through analysis using online news articles, the pending issues that are being discussed socially were identified. As a result of the analysis, the proportion of users with children was first high. Next, it was found that topics through LDA analysis appeared in four categories: 'cultural event/program', 'data use', 'physical environment and facilities', and 'library service'. Finally, it was confirmed that keywords for the additional construction of libraries and complex cultural spaces and the establishment of a library cooperation system appeared at the core in the news article data. Based on this, it was proposed to build a library in consideration of regional balance and to create a social parenting community network through business agreements with childcare and childcare institutions. This will contribute to identifying the policy and social trends of public libraries in Daejeon and implementing data-based public library operations that reflect local community demands.

• Journal of the Korean Society for Library and Information Science
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• v.58 no.2
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• pp.151-175
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• 2024

This study analyzes the current status of research data management at NST-affiliated government-funded research institutes for the purpose of promoting the sharing and use of research data, and based on this, suggests methods for establishing a research data sharing and management system. The survey on the status of research data management was conducted twice in 2022 and 2023 for a total of 20 research institutes. In addition, difficulties and areas that need to be improved in the management and sharing of research data were identified, and based on this, methods for establishing a research data sharing and management system were proposed by dividing them into policy aspects, system aspects, and linkage system construction aspects. In order to establish a research data sharing system, it would be desirable to prepare a policy basis and present contents such as the definition of research data, scope of application, contents of management, utilization method, and leading institutes. In addition, for systematic and unified research data management, it would be recommended that each institute will establish and manage a repository and management system. By linking this with DataON, the national research data platform, and providing one-stop services, the accessibility and usability of data will be improved.

• Journal of the Korean Society for Library and Information Science
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• v.58 no.2
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• pp.251-268
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• 2024

Recently, with the implementation of the "e-Government Performance Management Guidelines," there is a growing demand for setting performance indicators for information systems. For systems that provide information services to the public, such as CODIL, it is not easy to set performance indicators. This study presented a research model that applies Monte Carlo simulation to set expected performance targets that can be achieved through CODIL based on objective evidence. Among the survey contents conducted from 2015 to 2023, the statistical characteristics of user satisfaction regarding the frequency of use of construction technology information provided by CODIL were designated as input variables. Future expected targets and confidence intervals from 2024 to 2026 were designated as outcome variables. The expected target value was measured by generating 5 simulation alternatives and 1,000 random numbers for each alternative. Next, the measured expected goals were interpreted and compared with the results of time series regression analysis measured in previous studies. Although, as in previous studies, the expected target value could not be predicted based on time series regression analysis that considers the correlation between years. However, compared to previous studies, this study can be considered a more accurate analysis result because it predicted the expected target value based on 5,000 input variables.

• Journal of Christian Education in Korea
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• v.77
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• pp.171-201
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• 2024

Purpose of Research : Since ChatGPT's 2022 release, the educational sector faces mixed reactions to generative AI, sparking innovation but raising concerns about student cognition and communication. While Christian colleges employ AI reflecting their values, secular institutions stress ethical usage. This study explores ethical AI use in these settings, aiming to integrate findings into educational practices. Research content and method : Analyzing AI use and ethics guidelines from 50 domestic and international universities, differences between Christian and secular institutions were explored. Data was categorized, conceptualized via open coding, and components were identified through axial coding. The importance of components for Christian colleges' AI guides was assessed based on the initial data and previous research, leading to the development of tailored AI utilization components for Christian universities. Conclusion : Studies revealed secular institutions have six AI guide components, while Christian colleges found seven in both utilization and ethics guides, focusing on truthfulness, responsibility, and diversity. Emphasizing the need for ethical AI use in Christian colleges, the findings advocate developing AI ethics guidelines to aid marginalized groups and establish a new educational paradigm through further research.

• Journal of Korea Port Economic Association
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• v.39 no.4
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• pp.191-203
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• 2023

This paper analyzed the overall status of the domestic freight transportation business, established SWOT analysis and strategy through existing literature research and designed an AHP model to derive priorities for each strategy. The SWOT analysis analyzed the management model of the consignment borrowers belonging to a transportation company that did not handle the supplies with the lowest satisfaction with the consignment system. The AHP model was designed by establishing a SWOT strategy through SWOT analysis. As a result of the analysis of the upper class, priorities were derived in the order of WO strategy, SO strategy, ST strategy, and WT strategy. As a result of comprehensive priorities for the development strategy of the domestic freight transportation business, WO strategy's "Improvement of cooperative relations between transportation companies and consignment owners through fair consignment contracts" was first, SO strategy's "Public promotion of the necessity of consignment systems based on high economic feasibility and reliability" was second, and ST strategy's "Proposal of policies to strengthen financial performance through the introduction of freight transport platforms" was fourth, followed by WT strategy's "Improvement of satisfaction with transport services through the introduction of freight transport platforms" and SO strategy's "Expansion of safe freight systems" in sixth, respectively.

• The Journal of Bigdata
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• v.9 no.1
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• pp.169-186
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• 2024

This study aims to analyze the acceptance factors for expanding the adoption of AI by SMEs and draw practical and policy implications. To this, we conducted an empirical analysis of AI acceptance factors among 315 SMEs in various industries such as manufacturing, service, and information and communication sectors located in Korea. Based on the UTAUT, we examined the influence of decision-making reliability, perceived awareness, policy support, education and training, perceived cost, perceived risk, and system complexity, and found that decision-making reliability positively affects performance expectancy and social influence, perceived awareness positively affects performance expectancy and effort expectancy, policy support positively affects social influence and facilitating conditions, and education and training positively affects effort expectancy and facilitating conditions. Perceived cost had a negative effect on social influence and facilitating conditions, and perceived risk had a negative effect on performance expectancy and social influence. System complexity had a negative effect on effort expectancy but no effect on facilitating conditions. These results are expected to be widely utilized as basic research for the diffusion of AI in industry and provide practical and policy implications for promoting the adoption of AI in SMEs.

• The Journal of Bigdata
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• v.9 no.1
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• pp.31-47
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• 2024

With the advancement of artificial intelligence, the travel and hospitality industry is also adopting AI and machine learning technologies for various purposes. In the tourism industry, demand forecasting is recognized as a very important factor, as it directly impacts service efficiency and revenue maximization. Demand forecasting requires the consideration of time-varying data flows, which is why statistical techniques and machine learning models are used. In recent years, variations and integration of existing models have been studied to account for the diversity of demand forecasting data and the complexity of the natural world, which have been reported to improve forecasting performance concerning uncertainty and variability. This study also proposes a new model that integrates various machine-learning approaches to improve the accuracy of hotel sales demand forecasting. Specifically, this study proposes a new time series forecasting model based on XGBoost that selectively utilizes a local model by clustering with DTW K-means and a global model using the entire data to improve forecasting performance. The hotel demand forecasting model that selectively utilizes global and regional models proposed in this study is expected to impact the growth of the hotel and travel industry positively and can be applied to forecasting in other business fields in the future.

• Health Policy and Management
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• v.34 no.3
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• pp.282-292
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• 2024

Background: This study aimed to identify the relationship between regional health outcomes and the living population, which may reflect the characteristics of population migration in Seoul. Methods: This study used raw data on cause of death statistics from Statistics Korea's Micro Data Integration Service. To identify the independent variable, the living population, we used living population data provided by Korean Telecom for 25 districts of Seoul. The control variables were based on the four domains of SDoH (social determinants of health; economic stability, healthcare access and quality, neighborhood and built environment, and social and community context). Panel generalized estimating equations (GEE) analysis was used to determine the relationship between living population and regional health outcomes. Results: The panel GEE analysis showed that all mortality-related health outcomes (avoidable, preventable, and treatable mortality) had a statistically significant negative relationship with the living population. This indicated that an increase in living population had a positive effect on mortality-related health outcomes. Conclusion: The identification of a notable relationship between regional health outcomes and population density underscores the utility of incorporating living population metrics as key indicators in the development of policies aimed at mitigating health disparities. Moreover, this finding advocates for strategic expansions of local infrastructure, with a particular emphasis on areas characterized by low living populations.

• Knowledge Management Research
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• v.25 no.3
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• pp.279-299
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• 2024

The socio-cultural and economic activities of users in virtual worlds are increasing, and virtual consumption of purchasing virtual products is expanding. The future growth potential of this virtual consumption market is very high and has the potential to change the existing traditional consumption ecosystem. This study was interested in the phenomenon of virtual consumption in the most rapidly growing gaming virtual world, and based on the consumption values theory and the concept of self-improvement, major factors promoting virtual consumption in the gaming virtual world were derived. Then, the influence of factors on intention to continue virtual consumption was verified. In particular, this study compared and analyzed whether the influence of factors varies depending on demographic groups (age group and gender) and types of virtual products mainly consumed. This study collected data from users who actually experienced virtual consumption in the game virtual world and empirically analyzed the influence of factors promoting virtual consumption and differences by group. Accordingly, it provides implications for knowledge management in terms of establishing a service development strategy in response to the virtual consumption phenomenon in virtual worlds, which will expand further in the future, and revitalizing the convergence economic ecosystem between the virtual and reality economy.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.71-89
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• 2016

Sentiment analysis is used for identifying emotions or sentiments embedded in the user generated data such as customer reviews from blogs, social network services, and so on. Various research fields such as computer science and business management can take advantage of this feature to analyze customer-generated opinions. In previous studies, the star rating of a review is regarded as the same as sentiment embedded in the text. However, it does not always correspond to the sentiment polarity. Due to this supposition, previous studies have some limitations in their accuracy. To solve this issue, the present study uses a supervised sentiment classification model to measure a more accurate sentiment polarity. This study aims to propose an advanced sentiment classifier and to discover the correlation between movie reviews and box-office success. The advanced sentiment classifier is based on two supervised machine learning techniques, the Support Vector Machines (SVM) and Feedforward Neural Network (FNN). The sentiment scores of the movie reviews are measured by the sentiment classifier and are analyzed by statistical correlations between movie reviews and box-office success. Movie reviews are collected along with a star-rate. The dataset used in this study consists of 1,258,538 reviews from 175 films gathered from Naver Movie website (movie.naver.com). The results show that the proposed sentiment classifier outperforms Naive Bayes (NB) classifier as its accuracy is about 6% higher than NB. Furthermore, the results indicate that there are positive correlations between the star-rate and the number of audiences, which can be regarded as the box-office success of a movie. The study also shows that there is the mild, positive correlation between the sentiment scores estimated by the classifier and the number of audiences. To verify the applicability of the sentiment scores, an independent sample t-test was conducted. For this, the movies were divided into two groups using the average of sentiment scores. The two groups are significantly different in terms of the star-rated scores.