• Journal of Industrial Convergence
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• v.21 no.11
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• pp.13-27
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• 2023

This study aims to contribute to the reduction of the failure rate and social costs resulting from business failures by analyzing factors that affect the insolvency of social enterprises, as the role of social enterprises is increasing in our economy. The data used in this study were classified as normal and insolvent companies among social enterprises (including prospective social enterprises) that were established between 2009 and 2018 and received credit guarantees from credit guarantee institutions as of the end of June 2022. Among the collected data, 439 social enterprises with available financial information were targeted; 406 (92.5%) were normal enterprises, and 33 (7.5%) were insolvent enterprises. Through a literature review, eight non-financial factors commonly used for insolvency prediction were selected. The cross-analysis results showed that four of these factors were significant. Logistic regression analysis revealed that two variables, including corporate credit rating and the personal credit rating of the representative, were significant. Financial factors such as debt ratio, sales operating profit rate, and total asset turnover were used as control variables. The empirical analysis confirmed that the two independent variables maintained their influence even after controlling for financial factors. Given that government-led support and development policies have limitations, there is a need to shift policy direction so that various companies aspiring to create social value can enter the social enterprise sector through private and regional initiatives. This would enable the social economy to create an environment where local residents can collaborate to realize social value, and the government should actively support this.

• The Korean Journal of Applied Statistics
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• v.37 no.2
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• pp.119-138
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• 2024

Text indicators are increasingly valuable in economic forecasting, but are often hindered by noise and high dimensionality. This study aims to explore post-processing techniques, specifically noise filtering and dimensionality reduction, to normalize text indicators and enhance their utility through empirical analysis. Predictive target variables for the empirical analysis include monthly leading index cyclical variations, BSI (business survey index) All industry sales performance, BSI All industry sales outlook, as well as quarterly real GDP SA (seasonally adjusted) growth rate and real GDP YoY (year-on-year) growth rate. This study explores the Hodrick and Prescott filter, which is widely used in econometrics for noise filtering, and employs sufficient dimension reduction, a nonparametric dimensionality reduction methodology, in conjunction with unstructured text data. The analysis results reveal that noise filtering of text indicators significantly improves predictive accuracy for both monthly and quarterly variables, particularly when the dataset is large. Moreover, this study demonstrated that applying dimensionality reduction further enhances predictive performance. These findings imply that post-processing techniques, such as noise filtering and dimensionality reduction, are crucial for enhancing the utility of text indicators and can contribute to improving the accuracy of economic forecasts.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.189-198
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• 2024

AI is accepted not only in the private sector but also in the defense sector as a cutting-edge technology that must be introduced for the development of national defense. In particular, artificial intelligence has been selected as a key task in defense science and technology innovation, and the importance of data is increasing. As the national defense department shifts from a closed data policy to data sharing and activation, efforts are being made to secure high-quality data necessary for the development of national defense. In particular, we are promoting a review of the business budget system to secure data so that related procedures can be improved to reflect the unique characteristics of AI and big data, and research and development can begin with sufficient large quantities and high-quality data. However, there is a need to establish standardization and quality standards for structured data and unstructured data at the national defense level, but the defense department is still proposing standardization and quality standards for structured data, so this needs to be supplemented. In this paper, we propose an unstructured data set standard format for defense unstructured data sets, which are most needed in defense artificial intelligence, and based on this, we propose a standardization method for defense unstructured data sets.

• Informatization Policy
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• v.31 no.2
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• pp.82-104
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• 2024

Smart home services are growing rapidly as the development of the Internet of Things (IoT) opens the era of the so-called "Connected Living." Although personal information leaks through smart home cameras are increasing, however, users-while concerned-tend to take passive measures to protect their personal information. This study theoretically explained and verified how to design effective software update notification messages for smart home cameras to ensure that users comply with the recommended security behavior (i.e., update installation). In a survey experiment participated in by 120 actual users, the effectiveness of both emotional appeals (i.e., security breach warning images for fear appeals) and rational appeals (i.e., loss-framed messages emphasizing the negative consequences of not installing the updates) were confirmed. The results of this study provide theoretical interpretations and practical guidelines on the message design features that are effective for threat appraisals (i.e., severity, vulnerability) of smart home camera users and their protection motivation.

• Korean Journal of Ichthyology
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• v.36 no.1
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• pp.1-9
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• 2024

This study investigated the early life history of the Boleophthalmus pectinirostris living in the southern coastal Yeoja-man and compared the results with the same Gobiidae fishes. The brood stork used in the study were captured with bare hands in the tidal flats of Beolgyo-eup, Jeollanam-do, in June 2015. The amount of spawning was 411~11,688, and the eggs were short oval and the size was 1.40×0.72 mm. The time of hatched took 91 hours and 35 minutes at a water temperature of 25~27℃. Newly hatching larvae, the yolk sac had a total length of 3.02~3.31 (average 3.17±0.08, n=30) mm and did not eat rotifer. 4 days after hatching, the total length was 3.31~3.52 (3.43±0.07, n=30) mm, and as the mouth and anus opened, the fish transitioned to the preflexion larvae and fed. 14 days after hatching, the total length was 5.06~5.25 (5.16±0.06, n=30) mm, and the distal end of the vertebra was completely bent at 45° and the transitioned to the postflexion larvae. 41 days after hatching, the total length was 14.3~16.8 (15.4±0.85 mm, n=30), and the number of fins reached an integer of 5 first dorsal fins, 26~27 second dorsal fins, 24~27 anal fins, and 6 ventral fins, and the transitioned to the juveniles. As a result of the study, star-shaped melanophore were deposited from the front of the pectoral fin to the base of the caudal fin, which distinguished them in form from other postflexion larvae of Gobiidae fishes.

• Nutrition Research and Practice
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• v.18 no.1
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• pp.149-164
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• 2024

BACKGROUND/OBJECTIVES: The economic recession caused by the coronavirus disease 2019 pandemic disproportionately affected poor and vulnerable populations globally. Better uunderstanding of vulnerability to shocks in food supply and demand in the Asia Pacific region is needed. SUBJECTS/METHODS: Using secondary data from rapid assessment surveys during the pandemic response (n = 10,420 in mid-2020; n = 6,004 in mid-2021) in India, Indonesia, Myanmar, and Vietnam, this study examined the risk factors for reported income reduction or job loss in mid-2021 and the temporal trend in food security status (household food availability, and market availability and affordability of essential items) from mid-2020 to mid-2021. RESULTS: The proportion of job loss/reduced household income was highest in India (60.4%) and lowest in Indonesia (39.0%). Urban residence (odds ratio [OR] range, 2.20-4.11; countries with significant results only), female respondents (OR range, 1.40-1.69), engagement in daily waged labor (OR range, 1.54-1.68), and running a small trade/business (OR range, 1.66-2.71) were significantly associated with income reduction or job loss in three out of 4 countries (all P < 0.05). Food stock availability increased significantly in 2021 compared to 2020 in all four countries (OR range, 1.91-4.45) (all P < 0.05). Availability of all essential items at markets increased in India (OR range, 1.45-3.99) but decreased for basic foods, hygiene items, and medicine in Vietnam (OR range, 0.81-0.86) in 2021 compared to 2020 (all P < 0.05). In 2021, the affordability of all essential items significantly improved in India (OR range, 1.18-3.49) while the affordability of rent, health care, and loans deteriorated in Indonesia (OR range, 0.23-0.71) when compared to 2020 (all P < 0.05). CONCLUSIONS: Long-term social protection programs need to be carefully designed and implemented to address food insecurity among vulnerable groups, considering each country's market conditions, consumer food purchasing behaviors, and financial support capacity.

• 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 Internet Computing and Services
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• v.25 no.4
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• pp.109-119
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• 2024

This study aims to contribute to the initial research on on-device AI in an environment where generative AI-based services on mobile and other on-device platforms are increasing. To derive success strategies for generative AI-based chatbot services in a mobile environment, over 200,000 actual user experience review data collected from the Google Play Store were analyzed using the LDA topic modeling technique. Interpreting the derived topics based on the Information System Success Model (ISSM), the topics such as tutoring, limitation of response, and hallucination and outdated informaiton were linked to information quality; multimodal service, quality of response, and issues of device interoperability were linked to system quality; inter-device compatibility, utility of the service, quality of premium services, and challenges in account were linked to service quality; and finally, creative collaboration was linked to net benefits. Humanization of generative AI emerged as a new experience factor not explained by the existing model. By explaining specific positive and negative experience dimensions from the user's perspective based on theory, this study suggests directions for future related research and provides strategic insights for companies to improve and supplement their services for successful business operations.

• Information Systems Review
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• v.26 no.1
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• pp.19-56
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• 2024

This study is empirical research to enhance understanding of AI (artificial intelligence) training data project in South Korea. It primarily focuses on the various concerns regarding data quality from policy-executing institutions, data construction companies, and organizations utilizing AI training data to develop the most reliable algorithm for society. For academic contribution, this study suggests a theoretical foundation and research model for understanding AI training data quality and its antecedents, as well as the unique data and ethical aspects of AI. For this purpose, this study proposes a research model with important antecedents related to AI training data quality, such as data attribute factors, data building environmental factors, and data type-related factors. The study collects 393 sample data from actual practitioners and personnel from companies building artificial intelligence training data and companies developing artificial intelligence services. Data analysis was conducted through Fuzzy Set Qualitative Comparative Analysis (fsQCA) and Artificial Neural Network analysis (ANN), presenting academic and practical implications related to the quality of AI training data.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.187-204
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• 2016

Document classification based on emotional polarity has become a welcomed emerging task owing to the great explosion of data on the Web. In the big data age, there are too many information sources to refer to when making decisions. For example, when considering travel to a city, a person may search reviews from a search engine such as Google or social networking services (SNSs) such as blogs, Twitter, and Facebook. The emotional polarity of positive and negative reviews helps a user decide on whether or not to make a trip. Sentiment analysis of customer reviews has become an important research topic as datamining technology is widely accepted for text mining of the Web. Sentiment analysis has been used to classify documents through machine learning techniques, such as the decision tree, neural networks, and support vector machines (SVMs). is used to determine the attitude, position, and sensibility of people who write articles about various topics that are published on the Web. Regardless of the polarity of customer reviews, emotional reviews are very helpful materials for analyzing the opinions of customers through their reviews. Sentiment analysis helps with understanding what customers really want instantly through the help of automated text mining techniques. Sensitivity analysis utilizes text mining techniques on text on the Web to extract subjective information in the text for text analysis. Sensitivity analysis is utilized to determine the attitudes or positions of the person who wrote the article and presented their opinion about a particular topic. In this study, we developed a model that selects a hot topic from user posts at China's online stock forum by using the k-means algorithm and self-organizing map (SOM). In addition, we developed a detecting model to predict a hot topic by using machine learning techniques such as logit, the decision tree, and SVM. We employed sensitivity analysis to develop our model for the selection and detection of hot topics from China's online stock forum. The sensitivity analysis calculates a sentimental value from a document based on contrast and classification according to the polarity sentimental dictionary (positive or negative). The online stock forum was an attractive site because of its information about stock investment. Users post numerous texts about stock movement by analyzing the market according to government policy announcements, market reports, reports from research institutes on the economy, and even rumors. We divided the online forum's topics into 21 categories to utilize sentiment analysis. One hundred forty-four topics were selected among 21 categories at online forums about stock. The posts were crawled to build a positive and negative text database. We ultimately obtained 21,141 posts on 88 topics by preprocessing the text from March 2013 to February 2015. The interest index was defined to select the hot topics, and the k-means algorithm and SOM presented equivalent results with this data. We developed a decision tree model to detect hot topics with three algorithms: CHAID, CART, and C4.5. The results of CHAID were subpar compared to the others. We also employed SVM to detect the hot topics from negative data. The SVM models were trained with the radial basis function (RBF) kernel function by a grid search to detect the hot topics. The detection of hot topics by using sentiment analysis provides the latest trends and hot topics in the stock forum for investors so that they no longer need to search the vast amounts of information on the Web. Our proposed model is also helpful to rapidly determine customers' signals or attitudes towards government policy and firms' products and services.