• Journal of Korea Port Economic Association
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• v.40 no.1
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• pp.133-149
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• 2024

According to previous studies, the key factor in determining the loan interest rate for shipping companies is the default risk premium. Therefore, this study analyzes the determinants of the risk premium of shipping loans using a multiple linear regression model. With the risk premium as the dependent variable, a total of 10 independent variables are selected, including three factors: loan characteristics, borrower's creditworthiness, and economic situation. Samples are 82 shipping loans supported by Bank A from 2014 to 2022. As a result, borrower's creditworthiness(current ratio, debt ratio, firm age) and economic situation(freight index) affect the risk premium in analysis for all samples. It is found that borrower's creditworthiness has some influence on the risk premium for container ships(current ratio, cash holding ratio, debt ratio, operating income to sales) and bulk carriers(debt ratio, firm age). Market situation affects the risk premium in gas carriers. However, in the model targeting tanker ships, unlike previous studies, all factors have no effect on the risk premium.

• Food Science of Animal Resources
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• v.44 no.5
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• pp.1080-1095
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• 2024

In contemporary society, the increasing number of pet-owning households has significantly heightened interest in companion animal health, expanding the probiotics market aimed at enhancing pet well-being. Consequently, research into the gut microbiota of companion animals has gained momentum, however, ethical and societal challenges associated with experiments on intelligent and pain-sensitive animals necessitate alternative research methodologies to reduce reliance on live animal testing. To address this need, the Fermenter for Intestinal Microbiota Model (FIMM) is being investigated as an in vitro tool designed to replicate gastrointestinal conditions of living animals, offering a means to study gut microbiota while minimizing animal experimentation. The FIMM system explored interactions between intestinal microbiota and probiotics within a simulated gut environment. Two strains of commercial probiotic bacteria, Enterococcus faecium IDCC 2102 and Bifidobacterium lactis IDCC 4301, along with a newly isolated strain from domestic dogs, Lactobacillus acidophilus SLAM AK001, were introduced into the FIMM system with gut microbiota from a beagle model. Findings highlight the system's capacity to mirror and modulate the gut environment, evidenced by an increase in beneficial bacteria like Lactobacillus and Faecalibacterium and a decrease in the pathogen Clostridium. The study also verified the system's ability to facilitate accurate interactions between probiotics and commensal bacteria, demonstrated by the production of short-chain fatty acids and bacterial metabolites, including amino acids and gamma-aminobutyric acid precursors. Thus, the results advocate for FIMM as an in vitro system that authentically simulates the intestinal environment, presenting a viable alternative for examining gut microbiota and metabolites in companion animals.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.97-117
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• 2020

Product evaluation criteria is an indicator describing attributes or values of products, which enable users or manufacturers measure and understand the products. When companies analyze their products or compare them with competitors, appropriate criteria must be selected for objective evaluation. The criteria should show the features of products that consumers considered when they purchased, used and evaluated the products. However, current evaluation criteria do not reflect different consumers' opinion from product to product. Previous studies tried to used online reviews from e-commerce sites that reflect consumer opinions to extract the features and topics of products and use them as evaluation criteria. However, there is still a limit that they produce irrelevant criteria to products due to extracted or improper words are not refined. To overcome this limitation, this research suggests LDA-k-NN model which extracts possible criteria words from online reviews by using LDA and refines them with k-nearest neighbor. Proposed approach starts with preparation phase, which is constructed with 6 steps. At first, it collects review data from e-commerce websites. Most e-commerce websites classify their selling items by high-level, middle-level, and low-level categories. Review data for preparation phase are gathered from each middle-level category and collapsed later, which is to present single high-level category. Next, nouns, adjectives, adverbs, and verbs are extracted from reviews by getting part of speech information using morpheme analysis module. After preprocessing, words per each topic from review are shown with LDA and only nouns in topic words are chosen as potential words for criteria. Then, words are tagged based on possibility of criteria for each middle-level category. Next, every tagged word is vectorized by pre-trained word embedding model. Finally, k-nearest neighbor case-based approach is used to classify each word with tags. After setting up preparation phase, criteria extraction phase is conducted with low-level categories. This phase starts with crawling reviews in the corresponding low-level category. Same preprocessing as preparation phase is conducted using morpheme analysis module and LDA. Possible criteria words are extracted by getting nouns from the data and vectorized by pre-trained word embedding model. Finally, evaluation criteria are extracted by refining possible criteria words using k-nearest neighbor approach and reference proportion of each word in the words set. To evaluate the performance of the proposed model, an experiment was conducted with review on '11st', one of the biggest e-commerce companies in Korea. Review data were from 'Electronics/Digital' section, one of high-level categories in 11st. For performance evaluation of suggested model, three other models were used for comparing with the suggested model; actual criteria of 11st, a model that extracts nouns by morpheme analysis module and refines them according to word frequency, and a model that extracts nouns from LDA topics and refines them by word frequency. The performance evaluation was set to predict evaluation criteria of 10 low-level categories with the suggested model and 3 models above. Criteria words extracted from each model were combined into a single words set and it was used for survey questionnaires. In the survey, respondents chose every item they consider as appropriate criteria for each category. Each model got its score when chosen words were extracted from that model. The suggested model had higher scores than other models in 8 out of 10 low-level categories. By conducting paired t-tests on scores of each model, we confirmed that the suggested model shows better performance in 26 tests out of 30. In addition, the suggested model was the best model in terms of accuracy. This research proposes evaluation criteria extracting method that combines topic extraction using LDA and refinement with k-nearest neighbor approach. This method overcomes the limits of previous dictionary-based models and frequency-based refinement models. This study can contribute to improve review analysis for deriving business insights in e-commerce market.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.71-90
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• 2020

Recently, the global insurance industry is rapidly developing digital transformation through the use of artificial intelligence technologies such as machine learning, natural language processing, and deep learning. As a result, more and more foreign insurers have achieved the success of artificial intelligence technology-based InsurTech and platform business, and Ping An Insurance Group Ltd., China's largest private company, is leading China's global fourth industrial revolution with remarkable achievements in InsurTech and Digital Platform as a result of its constant innovation, using 'finance and technology' and 'finance and ecosystem' as keywords for companies. In response, this study analyzed the InsurTech and platform business activities of Ping An Insurance Group Ltd. through the ser-M analysis model to provide strategic implications for revitalizing AI technology-based businesses of domestic insurers. The ser-M analysis model has been studied so that the vision and leadership of the CEO, the historical environment of the enterprise, the utilization of various resources, and the unique mechanism relationships can be interpreted in an integrated manner as a frame that can be interpreted in terms of the subject, environment, resource and mechanism. As a result of the case analysis, Ping An Insurance Group Ltd. has achieved cost reduction and customer service development by digitally innovating its entire business area such as sales, underwriting, claims, and loan service by utilizing core artificial intelligence technologies such as facial, voice, and facial expression recognition. In addition, "online data in China" and "the vast offline data and insights accumulated by the company" were combined with new technologies such as artificial intelligence and big data analysis to build a digital platform that integrates financial services and digital service businesses. Ping An Insurance Group Ltd. challenged constant innovation, and as of 2019, sales reached $155 billion, ranking seventh among all companies in the Global 2000 rankings selected by Forbes Magazine. Analyzing the background of the success of Ping An Insurance Group Ltd. from the perspective of ser-M, founder Mammingz quickly captured the development of digital technology, market competition and changes in population structure in the era of the fourth industrial revolution, and established a new vision and displayed an agile leadership of digital technology-focused. Based on the strong leadership led by the founder in response to environmental changes, the company has successfully led InsurTech and Platform Business through innovation of internal resources such as investment in artificial intelligence technology, securing excellent professionals, and strengthening big data capabilities, combining external absorption capabilities, and strategic alliances among various industries. Through this success story analysis of Ping An Insurance Group Ltd., the following implications can be given to domestic insurance companies that are preparing for digital transformation. First, CEOs of domestic companies also need to recognize the paradigm shift in industry due to the change in digital technology and quickly arm themselves with digital technology-oriented leadership to spearhead the digital transformation of enterprises. Second, the Korean government should urgently overhaul related laws and systems to further promote the use of data between different industries and provide drastic support such as deregulation, tax benefits and platform provision to help the domestic insurance industry secure global competitiveness. Third, Korean companies also need to make bolder investments in the development of artificial intelligence technology so that systematic securing of internal and external data, training of technical personnel, and patent applications can be expanded, and digital platforms should be quickly established so that diverse customer experiences can be integrated through learned artificial intelligence technology. Finally, since there may be limitations to generalization through a single case of an overseas insurance company, I hope that in the future, more extensive research will be conducted on various management strategies related to artificial intelligence technology by analyzing cases of multiple industries or multiple companies or conducting empirical research.

• Science of Emotion and Sensibility
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• v.9 no.1
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• pp.49-61
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• 2006

The present mobile phone industry has significant effect on the domestic economy and has taken root as the core item that has the responsibility to lead the Korean economy for a considerable period of time. As the mobile phone market becomes gigantic, the mobile phone is being used by people in broader age bracket, and functions or designs preferred by people of various age are getting more diverse. Like that, as the mobile phone has greater effect on and meaning in our daily lives, consumers of mobile phone have growing expectation of the mobile phone Now, the core function of voice communication via the mobile phone is not a great concern to consumers. But the function, such as more convenient and friendly information input and output, processing and storage, and the design, which is more sophisticated and optimized for the user environment, are being demanded, not just the simple voice communication. And as the modern design is getting more similar to the objects of traditional high art consumed by consumers every day, the aesthetic aspect of design can play an important role, as the factor that differentiates the product, in creating new value which forms the spiritual and emotional value of human beings to improve the quality of living, and in addition, the willingness of consumers to buy is determined by the design that they prefer the most. Like that, a new design of mobile phone based on a new dimension and preferred by the consumers the most is urgently required to be developed by shedding light on the factors related to the preference of consumers on the basis of the analysis on the aesthetic aspect, which can be said to be the most critical factor in the design process. Therefore, this study aims to identity the common preference and different factors of aesthetic aspects through the analysis on the aesthetic aspects of the mobile phone preferred by users among countries, and figure out the formative artistic factors of aesthetic aspects that are considered to be important, in order to propose the guideline on the aesthetic aspect of mobile phone that can be applied to the design of mobile phone practically.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.131-142
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• 2011

In recent years, Social Network Service, which is defined as a web-based service that allows an individual to construct a public or a semi-public profile within a bounded system, articulates a list of other users with whom they share connections, and traverses their list of connections. For example, Facebook and Twitter are the representative sites of Social Network Service, and these sites are the big issue in the world. A lot of people use Social Network Services to connect and maintain social relationship. Recently the users of Social Network Services have increased dramatically. Accordingly, many organizations become interested in Social Network Services as means of marketing, media, communication with their customers, and so on, because social network services can offer a variety of benefits to organizations such as companies and associations. In other words, organizations can use Social Network Services to respond rapidly to various user's behaviors because Social Network Services can make it possible to communicate between the users more easily and faster. And marketing cost of the Social Network Service is lower than that of existing tools such as broadcasts, news papers, and direct mails. In addition, Social network Services are growing in market place. So, the organizations such as companies and associations can acquire potential customers for the future. However, organizations uniformly communicate with users through Social Network Service without consideration of the characteristics of the networks although networks have different effects on information deliveries. For example, members' cohesion in an offline communication is higher than that in an online communication because the members of the offline communication are very close. that is, the network of the offline communication has a strong tie. Accordingly, information delivery is fast in the network of the offline communication. In this study, we compose two networks which have different characteristic of communication in Twitter. First network is constructed with data based on an offline communication such as friend, family, senior and junior in school. Second network is constructed with randomly selected data from users who want to associate with friends in online. Each network size is 250 people who divide with three groups. The first group is an ego which means a person in the center of the network. The second group is the ego's followers. The last group is composed of the ego's follower's followers. We compare the networks through social network analysis and follower's reaction analysis. We investigate density and centrality to analyze the characteristic of each network. And we analyze the follower's reactions such as replies and retweets to find differences of information delivery in each network. Our experiment results indicate that density and centrality of the offline communicationbased network are higher than those of the online-based network. Also the number of replies are larger than that of retweets in the offline communication-based network. On the other hand, the number of retweets are larger than that of replies in the online based network. We identified that the effect of information delivery in the offline communication-based network was different from those in the online communication-based network through experiments. So, you configure the appropriate network types considering the characteristics of the network if you want to use social network as an effective marketing tool.

• Management & Information Systems Review
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• v.37 no.1
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• pp.103-122
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• 2018

This paper expands the results of previous studies indicating that manager's ability positively affects business performance to analyze whether the degree to which the role of manager's ability improves business performance appears differently according to the characteristics of enterprises. As for the characteristics of enterprises, whether enterprises correspond to enterprises with high levels of funding constraints or late movers in the market is considered. Enterprises with high levels of funding constraints greatly require managers' roles not only for efficient use of funds but also for smooth financing. Late movers require more judgments of professional managers to overcome insufficient resources held and low profitability. In the case of enterprises with corporate characteristics with high dependency on the manager, the business performance is expected to greatly vary with the ability of the manager. The empirical analysis was conducted with listed companies from 2010 to 2014, manager's ability was measured by first measuring the efficiency of the entire enterprise through data envelopment analysis (DEA) using the methodology of Demerjian et al.(2012) and removing enterprise characteristics factors thereafter. Business performance was measured by the return on industrial fixed assets. The results of the empirical analysis indicated that the degree to which manager's ability improves business performance was higher in managerial competence enhances managerial performance in enterprises with high levels of funding constraints and late movers. Business performance is considered to have been improved further in cases where manager's ability is high because investments were made more efficiently through smooth funding. In addition, in the case of late movers in relatively poor environments, business performance was improved further because high manager's ability induced efficient decision making. In this paper, we extend the precedent study that the manager's ability improves the management performance, and confirm that the manager's ability to improve the managerial performance can be different according to the situation of the company. In addition, it is meaningful to analyze empirically whether a company's managerial ability is more important. This paper expanded the results of previous studies indicating that manager's ability improves performance to identify that the degree to which manager's ability improves business performance may appear differently according to situations in which enterprises are placed. In addition, this paper is meaningful in that it empirically analyzed what enterprises require manager's ability more importantly.

• Journal of Venture Innovation
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• v.3 no.1
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• pp.117-142
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• 2020

The purpose of this study is to explore the factors affecting the 6th industry experience by Schmitt experience model. The newly introduced variables are the cognitive experience, emotional experience, and social experience that are reconstructed based on Schmitt's experience theory and gender, family as a moderrating variable and trust as a mediation variable. In addition to experience intention. The hypothesis was set as follows. the experience factors that are the cognitive factor, the emotional factor, and the social factor will have a positive(+) influence on the intention to experience. Mooring factors will have a negative(-) effect on intention to experience. For statistical analysis, SPSS 24 and AMOS 23 statistical packages were used to test the research hypothesis. The research was based on 320 questionnaire data and tested by 314 valid responses were analyzed. As a result of the research, First, cognitive, emotional, and social factors had positive(+) effects on experience intention. Among the factors that directly affect the experience intention, the magnitude of influence appeared in the order of cognitive factors > social factors > emotional factors > mooring factors. Second, mooring factors have negative(-) effects on experience intention. Third, Trust has been partially influenced by factors of attraction, cognitive, emotional, and social. Fourth, there are significant statistical differences between men and women in cognitive and mooring factors in the path differences. Fifth, Social factors and mooring factors differed significantly in the composition of the household. Social factors with significant differences in path analysis have also been statistically demonstrated. The results of this study are academically verified that the cognitive, emotional, and social factors have an important influence on the experience intention in the 6th industry experience and the Schmitt's experience model proposed in this study is useful framework of analysis. In practical terms, it could provide implications for what factors should be strategically and marketingly focused to activate the 6th industry experience.

• Journal of the Korean Society of Food Science and Nutrition
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• v.41 no.11
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• pp.1639-1644
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• 2012

This study analyzed the Importance-Performance Analysis of selective attributes of Home Meal Replacement (HMR). This study was conducted using a primary field survey on department stores and wholesale markets in Seoul and Gyeonggi province. A total of 201 out of 234 questionnaires were analyzed. First, the highest intake frequency was 1~3 times a month (100 respondents: 48%), the most common purchasing place was wholesale market (148 respondents: 73.6%), and the most cited reason for preference was convenience (115 respondents: 57.2%). According to the IPA results, selective attributes with low satisfaction and high importance in the second quadrant were 'quality', 'health', 'hygiene', 'origin of food', and 'safety'. These results suggest that the microbiological and sensory qualities of HMR production should be improved to meet consumer's expectations.

• Journal of forensic and investigative science
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• v.1 no.2
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• pp.5-19
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• 2006

Data mining is an information extraction activity to discover hidden facts contained in databases. Using a combination of machine learning, statistical analysis, modeling techniques and database technology, data mining finds patterns and subtle relationships in data and infers rules that allow the prediction of future results. Typical applications include market segmentation, customer profiling, fraud detection, evaluation of retail promotions, and credit risk analysis. Law enforcement agencies deal with mass data to investigate the crime and its amount is increasing due to the development of processing the data by using computer. Now new challenge to discover knowledge in that data is confronted to us. It can be applied in criminal investigation to find offenders by analysis of complex and relational data structures and free texts using their criminal records or statement texts. This study was aimed to evaluate possibile application of data mining and its limitation in practical criminal investigation. Clustering of the criminal cases will be possible in habitual crimes such as fraud and burglary when using data mining to identify the crime pattern. Neural network modelling, one of tools in data mining, can be applied to differentiating suspect's photograph or handwriting with that of convict or criminal profiling. A case study of in practical insurance fraud showed that data mining was useful in organized crimes such as gang, terrorism and money laundering. But the products of data mining in criminal investigation should be cautious for evaluating because data mining just offer a clue instead of conclusion. The legal regulation is needed to control the abuse of law enforcement agencies and to protect personal privacy or human rights.