• Journal of Distribution Science
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• v.13 no.4
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• pp.89-98
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• 2015

Purpose - In today's globalized and modern business environment, corporate social responsibility (CSR) activities are considered to be essential for the sustainable development of enterprises. In addition, the corporate philanthropy that is related to CSR practices, as well as their being capable of reducing the anti-corporate sentiment of people have facilitated a qualitative forward leap into the quantitative growth phase. This study aims to undertake a comparative evaluation of corporate philanthropy through the Importance-Performance Analysis (IPA) method focusing on service and manufacturing industries, and to eventually determine a differentiated approach that is needed for corporate philanthropy. Research design, data, and methodology - The survey responses were collected through online research on specialized companies from consumers nationwide who were aged from 20 to 60 and who are aware of corporate philanthropy. A total of 408 sheets of questionnaire survey were used. Frequency analysis was undertaken in this study. The interviewees had demographic characteristics of gender: 206 males (50.5%) and 202 females (49.5%). They also had demographic characteristics of age: 82 people were over 20 (20.1%), 96 over 30 (23.5%), 105 over 40 (25.7%), and 125 over 50 (30.7%) years of age. The distribution of interviewees' residences is as follows: 154 persons (37.7%) in the Special City, 102 persons (25.0%) in the Metropolitan City, and 152 persons (37.3%) in the Provincial Region. The interviewees have been working for the following companies: 34 persons (8.3%) in LG Display, 80 (19.6%) in KT&G, 49 (12.0%) in Amore Pacific, 42 (10.3%) in KIA Motors, 47 (11.5%) in SBS, 52 (12.8%) in Shinhan Bank, 86 (21.1%) in Asiana Airlines, and 18 (4.4%) in Hyundai Department Store. We applied the paired t-test for the IPA analysis. PASW Statistics 18 was used for statistical analysis. Results - The results of IPA analysis indicated that the importance and performance degrees in both manufacturing and service industries were significantly different. Major empirical results showed that, in consumer, social, economic, philanthropic, and environmental dimensions, in the sub-factors of philanthropy activities in both manufacturing and service industries, the importance degree was found to be higher than performance degree. Further, the average difference between importance degree and performance degree by the sub-factors of philanthropy activities. On the other hand, the average difference of environmental dimension was found to be highest in both service and manufacturing industries. Thus, while consumers consider the philanthropy activities of the environmental dimension as most important, actual companies treat performance of philanthropy activities of the environmental dimension insufficiently or negligibly to some degree. Conclusions - The differentiated approach method that is required for corporate philanthropy may be proposed to uplift corporate accomplishments by analyzing the IPA of the attributes of the sub-factors of corporate philanthropy. This is, to an extent, insufficient in the existing studies related to the use of the IPA technique, and it shows the items that are to be conducted intensively.

• Journal of Radiation Industry
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• v.11 no.1
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• pp.1-6
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• 2017

Reported some level of bacteria in areas that are well made contact in Radiology imaging room evaluate the importance of cleanliness in the hospital management of equipment to check for the presence of pathogenic bacteria. Gwang-ju and Jeol-la city and medium-sized hospitals in the material with a cotton swab and rub evenly Radiology selection cassette, a handle, Apron of the imaging apparatus having the most contact with patients from July 2016 to August 2016 as a target in place and special studios 6, and saline solution will placed in a test tube containing. The swab sample was diluted 1,000 times, you can see the bacteria and the intestinal bacterial selective medium Trypticase Soy Agar (TSA), Muller-Hinton Agar (MHA), Eosin-Methylene Blue (EMB), ENDO(BD, NJ, USA) then incubated smear to. In the incubator (incubator, SANYO, Japan) was observed after incubation of bacteria and counting the total number of bacteria also Colonies (colony) suspected intestinal bacteria were isolated and cultured on KIA medium (BD, NJ, USA). As a result, it was found that this came Gram positive Coccus A hospital handle the F hospital, from the C Gram positive Coccus cassette and handle the F hospital. The striking yellow coloring Staphylococcus aureus 110 agar (STA 110) in the medium sample, but it is suspected staphylococcal Coccus to the final identification in the laboratory is not a single specimen of the two samples from Gram positive Coccus biochemical identification Identification Kit is an API could not, it was thought to be non-Staphylococcus aureus was cultured on blood agar suggesting that (BAP) blood of dance. Dynamic tests were conducted biochemical API kit of the two samples were identified from Gram positive Coccus bacteria Escherichia coli (E. coli) is F hospital cassette was confirmed Eenterobacter cloaca in A hospital possession. Did not aggregate O-26, O-111, O-157 and the serum test was conducted in the laboratory from the E. coli F cassette hospital.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.3
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• pp.58-72
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• 2021

In July 2021, UNCTAD classified Korea as a developed country. After the Korean War in the 1950s, economic development was promoted despite difficult conditions, resulting in epoch-making national growth. However, in order to respond to the rapidly changing global economy, it is necessary to continuously study the domestic industrial ecosystem and prepare strategies for continuous change and growth. This study analyzed the industrial ecosystem of the automobile industry where it is possible to obtain transaction data between companies by applying complexity spatial network analysis. For data, 295 corporate data(node data) and 607 transaction data (link data) were used. As a result of checking the spatial distribution by geocoding the address of the company, the automobile industry-related companies were concentrated in the Seoul metropolitan area and the Southeastern(Dongnam) region. The node importance was measured through degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality, and the network structure was confirmed by identifying density, distance, community detection, and assortativity and disassortivity. As a result, among the automakers, Hyundai Motor, Kia Motors, and GM Korea were included in the top 15 in 4 indicators of node centrality. In terms of company location, companies located in the Seoul metropolitan area were included in the top 15. In terms of company size, most of the large companies with more than 1,000 employees were included in the top 15 for degree centrality and betweenness centrality. Regarding closeness centrality and eigenvector centrality, most of the companies with 500 or less employees were included in the top 15, except for automakers. In the structure of the network, the density was 0.01390522 and the average distance was 3.422481. As a result of community detection using the fast greedy algorithm, 11 communities were finally derived.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.6
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• pp.74-79
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• 2019

The military vehicle produces electric power through an engine-integrated serial hybrid generator that is connected to the engine and does not have a separate generator installation space. However, depending on the mechanical characteristics of the connection between the generator and the engine, iron oxide for internal rusting and lubrication grew scattered. The iron oxide is adhered to the starter to deteriorate the starting performance, and there is a problem that the noise of the leg due to wear of the gear is increased. To solve this problem, the connection spline material and the surface treatment of the engine were improved and the shape was changed to a grease sealing type to prevent the generation of iron oxide inside. As the shape of the generator connector composing the shafting system was changed, the integrity of the structure was confirmed through the torsional endurance test. In addition, through the actual vehicle load test, it was verified that no corrosion occurred during the target life span without internal corrosion. It was confirmed that the anti-scattering structure of the grease effectively suppresses the generation of iron oxide, thereby reducing the noise generated from the generator. In this paper, we propose a fundamental solution to the degradation of the starter and the noise generation by preventing the back corrosion caused by the serial hybrid generator installed between the engine and the transmission.

• The Journal of Bigdata
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• v.5 no.2
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• pp.77-84
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• 2020

In this study, we conducted a study on the win-loss predicton analysis of korean professional baseball by artificial intelligence models. Based on the model, we predicted the winner as well as each team's final rank in the league. Additionally, we developed a website for viewers' understanding. In each game's first, third, and fifth inning, we analyze to select the best model that performs the highest accuracy and minimizes errors. Based on the result, we generate the rankings. We used the predicted data started from May 5, the season's opening day, to August 30, 2020 to generate the rankings. In the games which Kia Tigers did not play, however, we used actual games' results in the data. KNN and AdaBoost selected the most optimized machine learning model. As a result, we observe a decreasing trend of the predicted results' ranking error as the season progresses. The deep learning model recorded 89% of the model accuracy. It provides the same result of decreasing ranking error trends of the predicted results that we observe in the machine learning model. We estimate that this study's result applies to future KBO predictions as well as other fields. We expect broadcasting enhancements by posting the predicted winning percentage per inning which is generated by AI algorism. We expect this will bring new interest to the KBO fans. Furthermore, the prediction generated at each inning would provide insights to teams so that they can analyze data and come up with successful strategies.

• Korean Journal of Clinical Laboratory Science
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• v.48 no.3
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• pp.217-224
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• 2016

Acinetobacter baumannii (A. baumannii) is prevalent in hospital environments and is an important opportunistic pathogen of nosocomial infection. It is known that this pathogen cause herd infection in hospitals, and the mortality rate is remarkably higher for patients infected with this pathogen and already have other underlying diseases. Herein, we investigated the antibiotic resistance rate and the type of resistance genes in 85 isolates of multi-drug resistant A. baumannii from the samples commissioned to laboratory medicine in two university hospitals-in hospital A and hospital B-located in Cheonan and Chungcheong provinces, respectively, in Korea. As a result, $bla_{OXA-23-like}$ and $bla_{OXA-51-like}$ were detected in 82 stains (96.5%). These 82 strains of $bla_{OXA-23-like}$ producing A. baumannii were confirmed with the ISAba1 gene found at the top of the $bla_{OXA-23-like}$ genes by PCR, inducing the resistance against carbapenemase. The armA, AME gene that induces the resistance against aminoglycoside was detected in 34 strains out of 38 strains from Hospital A (89.5%), and in 40 strains out of 47 strains from Hospital B (85.1%), while AMEs were found in 33 strains out of 38 strains from Hospital A (70.2%) and in 44 strains out of 47 strains in Hospital B (93.6%). Therefore, it was found that most multi-drug resistant A. baumannii from the Cheonan area expressed both acethyltransferase and adenyltransferase. This study investigated the multi-drug resistant A. baumannii isolated from Cheonan and Chungcheong provinces in Korea, and it is thought that the results of the study can be utilized as the basic information to cure multi-drug resistant A. baumannii infections and to prevent the spread of drug resistance.

• Environmental and Resource Economics Review
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• v.32 no.4
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• pp.217-238
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• 2023

Currently, automobile tax in Korea is imposed by multiplying the vehicle's engine displacement by a certain tax rate. However, the need for revision is being raised as it is pointed out that the current system does not reflect the immediate task of reducing greenhouse gas emissions. Accordingly, this study focuses on the positive relationship between engine displacement and CO₂ emissions, and seeks to calculate an appropriate automobile tax rate considering average CO₂ emissions. To this end, first, we estimated the average annual CO₂ emissions (kg/vehicle) for each engine displacement using the average CO₂ emissions for each vehicle displacement as of 2020. Next, multiple scenarios were analyzed considering the standard tax rate at $75 per ton of CO₂ emissions proposed by the IMF (2019). In particular, we compared the case of imposing a uniform carbon tax of $75 and the case of imposing a progressive tax based on CO₂ emissions by displacement. According to the results, it was confirmed that the uniform tax rate proposed by the IMF is difficult to apply to Korea as it is due to the impact of a decrease in tax revenue, and a tax scheme needs to be designed appropriately considering maintenance of tax revenue according to the current automobile tax, greenhouse gas reduction effect, and automobile tax reform trends in developed countries. For example, in the case of the K3 (1,598cc) of Kia Motors, a representative compact car sold in Korea, if we compare the tax burdens for each tax scenario, the tax burden will be about 220,000 KRW under the current system, about 79,000 KRW under the uniform tax rate, about 83,000 KRW under the progressive tax rate, and about 240,000 KRW under the progressive tax rate similar to the UK tax system, respectively. In this way, this study identified the current statuses of automobile registration and tax in Korea, and automobile tax reform trends in major developed countries, and analyzed the impact of automobile tax reform considering engine displacement and CO₂ emissions, focusing on the tax burden of the people.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.25-38
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• 2019

Selecting high-quality information that meets the interests and needs of users among the overflowing contents is becoming more important as the generation continues. In the flood of information, efforts to reflect the intention of the user in the search result better are being tried, rather than recognizing the information request as a simple string. Also, large IT companies such as Google and Microsoft focus on developing knowledge-based technologies including search engines which provide users with satisfaction and convenience. Especially, the finance is one of the fields expected to have the usefulness and potential of text data analysis because it's constantly generating new information, and the earlier the information is, the more valuable it is. Automatic knowledge extraction can be effective in areas where information flow is vast, such as financial sector, and new information continues to emerge. However, there are several practical difficulties faced by automatic knowledge extraction. First, there are difficulties in making corpus from different fields with same algorithm, and it is difficult to extract good quality triple. Second, it becomes more difficult to produce labeled text data by people if the extent and scope of knowledge increases and patterns are constantly updated. Third, performance evaluation is difficult due to the characteristics of unsupervised learning. Finally, problem definition for automatic knowledge extraction is not easy because of ambiguous conceptual characteristics of knowledge. So, in order to overcome limits described above and improve the semantic performance of stock-related information searching, this study attempts to extract the knowledge entity by using neural tensor network and evaluate the performance of them. Different from other references, the purpose of this study is to extract knowledge entity which is related to individual stock items. Various but relatively simple data processing methods are applied in the presented model to solve the problems of previous researches and to enhance the effectiveness of the model. From these processes, this study has the following three significances. First, A practical and simple automatic knowledge extraction method that can be applied. Second, the possibility of performance evaluation is presented through simple problem definition. Finally, the expressiveness of the knowledge increased by generating input data on a sentence basis without complex morphological analysis. The results of the empirical analysis and objective performance evaluation method are also presented. The empirical study to confirm the usefulness of the presented model, experts' reports about individual 30 stocks which are top 30 items based on frequency of publication from May 30, 2017 to May 21, 2018 are used. the total number of reports are 5,600, and 3,074 reports, which accounts about 55% of the total, is designated as a training set, and other 45% of reports are designated as a testing set. Before constructing the model, all reports of a training set are classified by stocks, and their entities are extracted using named entity recognition tool which is the KKMA. for each stocks, top 100 entities based on appearance frequency are selected, and become vectorized using one-hot encoding. After that, by using neural tensor network, the same number of score functions as stocks are trained. Thus, if a new entity from a testing set appears, we can try to calculate the score by putting it into every single score function, and the stock of the function with the highest score is predicted as the related item with the entity. To evaluate presented models, we confirm prediction power and determining whether the score functions are well constructed by calculating hit ratio for all reports of testing set. As a result of the empirical study, the presented model shows 69.3% hit accuracy for testing set which consists of 2,526 reports. this hit ratio is meaningfully high despite of some constraints for conducting research. Looking at the prediction performance of the model for each stocks, only 3 stocks, which are LG ELECTRONICS, KiaMtr, and Mando, show extremely low performance than average. this result maybe due to the interference effect with other similar items and generation of new knowledge. In this paper, we propose a methodology to find out key entities or their combinations which are necessary to search related information in accordance with the user's investment intention. Graph data is generated by using only the named entity recognition tool and applied to the neural tensor network without learning corpus or word vectors for the field. From the empirical test, we confirm the effectiveness of the presented model as described above. However, there also exist some limits and things to complement. Representatively, the phenomenon that the model performance is especially bad for only some stocks shows the need for further researches. Finally, through the empirical study, we confirmed that the learning method presented in this study can be used for the purpose of matching the new text information semantically with the related stocks.

• Asia Marketing Journal
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• v.14 no.1
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• pp.83-98
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• 2012

In a market where new and used cars are competing with each other, we would run the risk of obtaining biased estimates of cross elasticity between them if we focus on only new cars or on only used cars. Unfortunately, most of previous studies on the automobile industry have focused on only new car models without taking into account the effect of used cars' pricing policy on new cars' market shares and vice versa, resulting in inadequate prediction of reactive pricing in response to competitors' rebate or price discount. However, there are some exceptions. Purohit (1992) and Sullivan (1990) looked into both new and used car markets at the same time to examine the effect of new car model launching on the used car prices. But their studies have some limitations in that they employed the average used car prices reported in NADA Used Car Guide instead of actual transaction prices. Some of the conflicting results may be due to this problem in the data. Park (1998) recognized this problem and used the actual prices in his study. His work is notable in that he investigated the qualitative effect of new car model launching on the pricing policy of the used car in terms of reinforcement of brand equity. The current work also used the actual price like Park (1998) but the quantitative aspect of competitive price promotion between new and used cars of the same model was explored. In this study, I develop a model that assumes that the cross elasticity between new and used cars of the same model is higher than those amongst new cars and used cars of the different model. Specifically, I apply the nested logit model that assumes the car model choice at the first stage and the choice between new and used cars at the second stage. This proposed model is compared to the IIA (Independence of Irrelevant Alternatives) model that assumes that there is no decision hierarchy but that new and used cars of the different model are all substitutable at the first stage. The data for this study are drawn from Power Information Network (PIN), an affiliate of J.D. Power and Associates. PIN collects sales transaction data from a sample of dealerships in the major metropolitan areas in the U.S. These are retail transactions, i.e., sales or leases to final consumers, excluding fleet sales and including both new car and used car sales. Each observation in the PIN database contains the transaction date, the manufacturer, model year, make, model, trim and other car information, the transaction price, consumer rebates, the interest rate, term, amount financed (when the vehicle is financed or leased), etc. I used data for the compact cars sold during the period January 2009- June 2009. The new and used cars of the top nine selling models are included in the study: Mazda 3, Honda Civic, Chevrolet Cobalt, Toyota Corolla, Hyundai Elantra, Ford Focus, Volkswagen Jetta, Nissan Sentra, and Kia Spectra. These models in the study accounted for 87% of category unit sales. Empirical application of the nested logit model showed that the proposed model outperformed the IIA (Independence of Irrelevant Alternatives) model in both calibration and holdout samples. The other comparison model that assumes choice between new and used cars at the first stage and car model choice at the second stage turned out to be mis-specfied since the dissimilarity parameter (i.e., inclusive or categroy value parameter) was estimated to be greater than 1. Post hoc analysis based on estimated parameters was conducted employing the modified Lanczo's iterative method. This method is intuitively appealing. For example, suppose a new car offers a certain amount of rebate and gains market share at first. In response to this rebate, a used car of the same model keeps decreasing price until it regains the lost market share to maintain the status quo. The new car settle down to a lowered market share due to the used car's reaction. The method enables us to find the amount of price discount to main the status quo and equilibrium market shares of the new and used cars. In the first simulation, I used Jetta as a focal brand to see how its new and used cars set prices, rebates or APR interactively assuming that reactive cars respond to price promotion to maintain the status quo. The simulation results showed that the IIA model underestimates cross elasticities, resulting in suggesting less aggressive used car price discount in response to new cars' rebate than the proposed nested logit model. In the second simulation, I used Elantra to reconfirm the result for Jetta and came to the same conclusion. In the third simulation, I had Corolla offer $1,000 rebate to see what could be the best response for Elantra's new and used cars. Interestingly, Elantra's used car could maintain the status quo by offering lower price discount ($160) than the new car ($205). In the future research, we might want to explore the plausibility of the alternative nested logit model. For example, the NUB model that assumes choice between new and used cars at the first stage and brand choice at the second stage could be a possibility even though it was rejected in the current study because of mis-specification (A dissimilarity parameter turned out to be higher than 1). The NUB model may have been rejected due to true mis-specification or data structure transmitted from a typical car dealership. In a typical car dealership, both new and used cars of the same model are displayed. Because of this fact, the BNU model that assumes brand choice at the first stage and choice between new and used cars at the second stage may have been favored in the current study since customers first choose a dealership (brand) then choose between new and used cars given this market environment. However, suppose there are dealerships that carry both new and used cars of various models, then the NUB model might fit the data as well as the BNU model. Which model is a better description of the data is an empirical question. In addition, it would be interesting to test a probabilistic mixture model of the BNU and NUB on a new data set.