• Environmental and Resource Economics Review
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• v.28 no.1
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• pp.121-145
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• 2019

This paper investigates the relationship between the oil price and the major petroleum products prices at the trading hubs such as Singapore, North West Europe and the US New York Harbor. We focus on the lead-lag relationship between the weekly petroleum prices from 2009 to 2016 based on the vector error correction model. We find that the oil price leads the prices of petroleum products in the long term, while there is bidirectional causality in the short term. On the other hand, prices of petroleum products in regions with high import dependency, such as Europe gas oil and jet fuel price, are exogenous in the long term. We also present evidence that prices of petroleum products in region with a large global-market share lead prices in other regions. However, if the region is in an over-production situation and low industry concentration, it may lose its price leadership due to intense competition. The result in this study can provide a useful information to petroleum refining companies in forecasting fluctuations of product price, and hence in planning their regional arbitrage trading activities.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.5
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• pp.303-314
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• 2018

In the past few years, government agencies and corporations have succumbed to stealthy, tailored cyberattacks designed to exploit vulnerabilities, disrupt operations and steal valuable information. Security Information and Event Management (SIEM) is useful tool for cyberattacks. SIEM solutions are available in the market but they are too expensive and difficult to use. Then we implemented basic SIEM functions to research and development for future security solutions. We focus on collection, aggregation and analysis of real-time logs from host. This tool allows parsing and search of log data for forensics. Beyond just log management it uses intrusion detection and prioritize of security events inform and support alerting to user. We select Elastic Stack to process and visualization of these security informations. Elastic Stack is a very useful tool for finding information from large data, identifying correlations and creating rich visualizations for monitoring. We suggested using vulnerability check results on our SIEM. We have attacked to the host and got real time user activity for monitoring, alerting and security auditing based this security information management.

• Land and Housing Review
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• v.14 no.4
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• pp.63-76
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• 2023

Investors should understand and actively consider factors like location, future value, policies, pricing, market trends, and their income, as these elements can shift with changing local, social, economic, and policy environments. This study seeks to clarify the impact of investment factors on the performance and reinvestment intentions of Sejong City investors by surveying those who have invested in real estate. This study employs a structural equation model with confirmatory factor analysis, focusing on four aspects: value, economic and policy, psychological, and financial. We find that the investment value factor has the largest impact on investment performance, indicating that investors prioritize the investment value of real estate in Sejong City. In addition, factors increasing asset value and expected satisfaction were significant, indicating that real estate investment in Sejong City yields high returns and investor satisfaction. with a positive outlook for future reinvestment.

• Environmental and Resource Economics Review
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• v.13 no.2
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• pp.271-300
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• 2004

We investigate the two recent simulations of the proto-type domestic carbon emission trading system in Korea and draw some policy implications. The first simulation includes the 5 electric power companies based on baseline and credit. But the second one is with the 7 energy-intensive companies based on cap and trade. The voluntary approaches in this paper revealed the instability of market equilibrium, i.e., price volatility or distortion, excess supply or demand. These phenomena stems from excess incentives to the players, asymmetric information, players' irresponsible strategic behaviors, and non acquaintance of trading system. This paper suggests the basic design for domestic carbon trading system in future and a stepwise introduction strategy for it including the incentive auction scheme, the total quantity of incentive needed, and how to finance it. Meantime, the further simulations on the various sectors based on voluntary participation must be essential for learning experiences and better policy design.

• Journal of Distribution Science
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• v.9 no.4
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• pp.15-27
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• 2011

Although a huge number of academic researchers have paid considerable attention to exploring both the degree to which store image influences retailer brand customers and how to develop store personality, they have overlooked the contemporary retail context in which retailers have developed many different types of retailer brands, that is, price-oriented or quality-oriented retailer brands. Rather than focusing on the latter, much literature has looked at the former. Accordingly, even though there are many articles related to store image, a few authors have shown their interest in identifying the extent to which store personality affects customers purchasing retailer brands at lower prices. As a result, their efforts have been to illustrate the relationship between store image and consumer behaviours buying retailer brands. In that multiple retailers over the world such as E-Mart, Lotte-Mart, Tesco Korea and Tesco UK have actively introduced not only the quality-focused retailer brands that quality is better than, or equal to national brands, and prices are slightly higher than, or equal to them, but also price-focused retailer brands, academicians should make an effort to investigate how store image affects customers purchasing a quality-oriented retailer brand, comparing with previous research results. That is why the authors illustrate the extent to which store personality components influence retailer brand customers, including particularly quality-oriented retailer brand customers through an empirical research. By adopting a questionnaire method as a research technique to illuminate the relationship between store image components and retailer brand customers, research validity increases and further, data gathered through a field survey are analysed through a few statistic analysis methods, in order to minimise statistical deviations. Compared with the prior research concentrated on price-focused retailer brands, the authors have significantly shed light on customer behaviours purchasing retailer brand products with higher quality. When it comes to store personality components, the research suggests the following five items: merchandise attributes, services, physical facilities, promotions, and institutional image, considering the subcomponents mentioned by the previous research. Proposing the conceptual research model which those elements are differently hypothesised, according to retailer brand types: PR (Price-oriented Retailer brand) and QR (Quality-oriented Retailer brand), the research is proceeded. Through empirical research, the authors found that amongst the five items, only promotion influenced retailer brand customers in the Korean retailing marketplace, unlike other countries explored by many researchers, such as UK. Although much literature emphasises that those elements are closely related to retailer brand buying proneness, it is completely not fit to the Korean market. Also, research findings provide new insights into the degree of store image effects on retailer brand customers for academiciansand practitioners. Whether the retailer brand development program that a retailer has carried simultaneously both price-focused and quality-focused retailer brand types is practically profitable should be explored in the future.

• Journal of Service Research and Studies
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• v.3 no.2
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• pp.75-89
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• 2013

In the services industry and firms, the successful new service development is very important issue today, But the innovation process for service firms is comprehensively little treated until now. This study was performed to suggest the new service development process model for the firms level in the perspective of the radical service innovation. So, in this paper the new process development model can be made by reviewing the concepts about the radical service innovation and by analyzing the some existing new service development process models. In the suggested service development process model, the three key process such as technology forecast, market analysis, and strategy development were included for front phase activity as the new service development process. Also the four key process for searching phase, and the other three key process for implementation phase were included. And for the application for the service firms' service innovation, the innovation's outcome estimation reference model is included. I hope to be executed the various case research and the improvement and optimization for this suggested process model in the future.

• Environmental and Resource Economics Review
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• v.29 no.2
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• pp.219-246
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• 2020

From the Phase II (2018~2020) of K-ETS, the offset credit from 'CDM projects that domestic companies and others have carried out in foreign countries' can be used in the K-ETS. As a result, stakeholders in the K-ETS market are actively developing overseas CDM projects, such as the 'high-efficiency cook stove project'. which can secure a large amount of credits while marginal cost is relatively low. This paper develops the investment decision-making model of offset project for the 'high-efficiency cook stove project' using the real option approach. Under the uncertainty of the emission allowance price, the optimal investment threshold (p^⁎) is derived and sensitivity analysis is conducted. As a result, in the standard scenario (PoA-S), the optimal investment threshold is 29,054won/ton, which is lower than the stock price (p^⁎_spot). However, allocation entities are not only economics in the CDM project, but also CDM risk factors such as non-renewable biomass ratio, cook stove replacement ratio, equity ratio with host country, investment period and submission limitation of emission allowance. In addition, offset project developers will be able to derive the optimal investment threshold for each business stage and use it for economic feasibility checks.

• 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.

• 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.

• 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.