• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.45-69
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• 2016

Recently, sentiment analysis using open Internet data is actively performed for various purposes. As online Internet communication channels become popular, companies try to capture public sentiment of them from online open information sources. This research is conducted for the purpose of analyzing pulbic sentiment of Korean Top-10 companies using a multi-categorical sentiment lexicon. Whereas existing researches related to public sentiment measurement based on big data approach classify sentiment into dimensions, this research classifies public sentiment into multiple categories. Dimensional sentiment structure has been commonly applied in sentiment analysis of various applications, because it is academically proven, and has a clear advantage of capturing degree of sentiment and interrelation of each dimension. However, the dimensional structure is not effective when measuring public sentiment because human sentiment is too complex to be divided into few dimensions. In addition, special training is needed for ordinary people to express their feeling into dimensional structure. People do not divide their sentiment into dimensions, nor do they need psychological training when they feel. People would not express their feeling in the way of dimensional structure like positive/negative or active/passive; rather they express theirs in the way of categorical sentiment like sadness, rage, happiness and so on. That is, categorial approach of sentiment analysis is more natural than dimensional approach. Accordingly, this research suggests multi-categorical sentiment structure as an alternative way to measure social sentiment from the point of the public. Multi-categorical sentiment structure classifies sentiments following the way that ordinary people do although there are possibility to contain some subjectiveness. In this research, nine categories: 'Sadness', 'Anger', 'Happiness', 'Disgust', 'Surprise', 'Fear', 'Interest', 'Boredom' and 'Pain' are used as multi-categorical sentiment structure. To capture public sentiment of Korean Top-10 companies, Internet news data of the companies are collected over the past 25 months from a representative Korean portal site. Based on the sentiment words extracted from previous researches, we have created a sentiment lexicon, and analyzed the frequency of the words coming up within the news data. The frequency of each sentiment category was calculated as a ratio out of the total sentiment words to make ranks of distributions. Sentiment comparison among top-4 companies, which are 'Samsung', 'Hyundai', 'SK', and 'LG', were separately visualized. As a next step, the research tested hypothesis to prove the usefulness of the multi-categorical sentiment lexicon. It tested how effective categorial sentiment can be used as relative comparison index in cross sectional and time series analysis. To test the effectiveness of the sentiment lexicon as cross sectional comparison index, pair-wise t-test and Duncan test were conducted. Two pairs of companies, 'Samsung' and 'Hanjin', 'SK' and 'Hanjin' were chosen to compare whether each categorical sentiment is significantly different in pair-wise t-test. Since category 'Sadness' has the largest vocabularies, it is chosen to figure out whether the subgroups of the companies are significantly different in Duncan test. It is proved that five sentiment categories of Samsung and Hanjin and four sentiment categories of SK and Hanjin are different significantly. In category 'Sadness', it has been figured out that there were six subgroups that are significantly different. To test the effectiveness of the sentiment lexicon as time series comparison index, 'nut rage' incident of Hanjin is selected as an example case. Term frequency of sentiment words of the month when the incident happened and term frequency of the one month before the event are compared. Sentiment categories was redivided into positive/negative sentiment, and it is tried to figure out whether the event actually has some negative impact on public sentiment of the company. The difference in each category was visualized, moreover the variation of word list of sentiment 'Rage' was shown to be more concrete. As a result, there was huge before-and-after difference of sentiment that ordinary people feel to the company. Both hypotheses have turned out to be statistically significant, and therefore sentiment analysis in business area using multi-categorical sentiment lexicons has persuasive power. This research implies that categorical sentiment analysis can be used as an alternative method to supplement dimensional sentiment analysis when figuring out public sentiment in business environment.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.123-138
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• 2017

Since the stock market is driven by the expectation of traders, studies have been conducted to predict stock price movements through analysis of various sources of text data. In order to predict stock price movements, research has been conducted not only on the relationship between text data and fluctuations in stock prices, but also on the trading stocks based on news articles and social media responses. Studies that predict the movements of stock prices have also applied classification algorithms with constructing term-document matrix in the same way as other text mining approaches. Because the document contains a lot of words, it is better to select words that contribute more for building a term-document matrix. Based on the frequency of words, words that show too little frequency or importance are removed. It also selects words according to their contribution by measuring the degree to which a word contributes to correctly classifying a document. The basic idea of constructing a term-document matrix was to collect all the documents to be analyzed and to select and use the words that have an influence on the classification. In this study, we analyze the documents for each individual item and select the words that are irrelevant for all categories as neutral words. We extract the words around the selected neutral word and use it to generate the term-document matrix. The neutral word itself starts with the idea that the stock movement is less related to the existence of the neutral words, and that the surrounding words of the neutral word are more likely to affect the stock price movements. And apply it to the algorithm that classifies the stock price fluctuations with the generated term-document matrix. In this study, we firstly removed stop words and selected neutral words for each stock. And we used a method to exclude words that are included in news articles for other stocks among the selected words. Through the online news portal, we collected four months of news articles on the top 10 market cap stocks. We split the news articles into 3 month news data as training data and apply the remaining one month news articles to the model to predict the stock price movements of the next day. We used SVM, Boosting and Random Forest for building models and predicting the movements of stock prices. The stock market opened for four months (2016/02/01 ~ 2016/05/31) for a total of 80 days, using the initial 60 days as a training set and the remaining 20 days as a test set. The proposed word - based algorithm in this study showed better classification performance than the word selection method based on sparsity. This study predicted stock price volatility by collecting and analyzing news articles of the top 10 stocks in market cap. We used the term - document matrix based classification model to estimate the stock price fluctuations and compared the performance of the existing sparse - based word extraction method and the suggested method of removing words from the term - document matrix. The suggested method differs from the word extraction method in that it uses not only the news articles for the corresponding stock but also other news items to determine the words to extract. In other words, it removed not only the words that appeared in all the increase and decrease but also the words that appeared common in the news for other stocks. When the prediction accuracy was compared, the suggested method showed higher accuracy. The limitation of this study is that the stock price prediction was set up to classify the rise and fall, and the experiment was conducted only for the top ten stocks. The 10 stocks used in the experiment do not represent the entire stock market. In addition, it is difficult to show the investment performance because stock price fluctuation and profit rate may be different. Therefore, it is necessary to study the research using more stocks and the yield prediction through trading simulation.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.95-108
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• 2017

Recently, AlphaGo which is Bakuk (Go) artificial intelligence program by Google DeepMind, had a huge victory against Lee Sedol. Many people thought that machines would not be able to win a man in Go games because the number of paths to make a one move is more than the number of atoms in the universe unlike chess, but the result was the opposite to what people predicted. After the match, artificial intelligence technology was focused as a core technology of the fourth industrial revolution and attracted attentions from various application domains. Especially, deep learning technique have been attracted as a core artificial intelligence technology used in the AlphaGo algorithm. The deep learning technique is already being applied to many problems. Especially, it shows good performance in image recognition field. In addition, it shows good performance in high dimensional data area such as voice, image and natural language, which was difficult to get good performance using existing machine learning techniques. However, in contrast, it is difficult to find deep leaning researches on traditional business data and structured data analysis. In this study, we tried to find out whether the deep learning techniques have been studied so far can be used not only for the recognition of high dimensional data but also for the binary classification problem of traditional business data analysis such as customer churn analysis, marketing response prediction, and default prediction. And we compare the performance of the deep learning techniques with that of traditional artificial neural network models. The experimental data in the paper is the telemarketing response data of a bank in Portugal. It has input variables such as age, occupation, loan status, and the number of previous telemarketing and has a binary target variable that records whether the customer intends to open an account or not. In this study, to evaluate the possibility of utilization of deep learning algorithms and techniques in binary classification problem, we compared the performance of various models using CNN, LSTM algorithm and dropout, which are widely used algorithms and techniques in deep learning, with that of MLP models which is a traditional artificial neural network model. However, since all the network design alternatives can not be tested due to the nature of the artificial neural network, the experiment was conducted based on restricted settings on the number of hidden layers, the number of neurons in the hidden layer, the number of output data (filters), and the application conditions of the dropout technique. The F1 Score was used to evaluate the performance of models to show how well the models work to classify the interesting class instead of the overall accuracy. The detail methods for applying each deep learning technique in the experiment is as follows. The CNN algorithm is a method that reads adjacent values from a specific value and recognizes the features, but it does not matter how close the distance of each business data field is because each field is usually independent. In this experiment, we set the filter size of the CNN algorithm as the number of fields to learn the whole characteristics of the data at once, and added a hidden layer to make decision based on the additional features. For the model having two LSTM layers, the input direction of the second layer is put in reversed position with first layer in order to reduce the influence from the position of each field. In the case of the dropout technique, we set the neurons to disappear with a probability of 0.5 for each hidden layer. The experimental results show that the predicted model with the highest F1 score was the CNN model using the dropout technique, and the next best model was the MLP model with two hidden layers using the dropout technique. In this study, we were able to get some findings as the experiment had proceeded. First, models using dropout techniques have a slightly more conservative prediction than those without dropout techniques, and it generally shows better performance in classification. Second, CNN models show better classification performance than MLP models. This is interesting because it has shown good performance in binary classification problems which it rarely have been applied to, as well as in the fields where it's effectiveness has been proven. Third, the LSTM algorithm seems to be unsuitable for binary classification problems because the training time is too long compared to the performance improvement. From these results, we can confirm that some of the deep learning algorithms can be applied to solve business binary classification problems.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.93-112
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• 2015

Depending on the change in consumer's consumption pattern, existing retail shop has evolved in hypermarket or convenience store offering grocery and daily products mostly. Therefore, it is important to maintain the inventory levels and proper product configuration for effectively utilize the limited space in the retail store and increasing sales. Accordingly, this study proposed proper product configuration and inventory level strategy based on RFM(Recency, Frequency, Monetary) model and SOM(self-organizing map) for manage the retail shop effectively. RFM model is analytic model to analyze customer behaviors based on the past customer's buying activities. And it can differentiates important customers from large data by three variables. R represents recency, which refers to the last purchase of commodities. The latest consuming customer has bigger R. F represents frequency, which refers to the number of transactions in a particular period and M represents monetary, which refers to consumption money amount in a particular period. Thus, RFM method has been known to be a very effective model for customer segmentation. In this study, using a normalized value of the RFM variables, SOM cluster analysis was performed. SOM is regarded as one of the most distinguished artificial neural network models in the unsupervised learning tool space. It is a popular tool for clustering and visualization of high dimensional data in such a way that similar items are grouped spatially close to one another. In particular, it has been successfully applied in various technical fields for finding patterns. In our research, the procedure tries to find sales patterns by analyzing product sales records with Recency, Frequency and Monetary values. And to suggest a business strategy, we conduct the decision tree based on SOM results. To validate the proposed procedure in this study, we adopted the M-mart data collected between 2014.01.01~2014.12.31. Each product get the value of R, F, M, and they are clustered by 9 using SOM. And we also performed three tests using the weekday data, weekend data, whole data in order to analyze the sales pattern change. In order to propose the strategy of each cluster, we examine the criteria of product clustering. The clusters through the SOM can be explained by the characteristics of these clusters of decision trees. As a result, we can suggest the inventory management strategy of each 9 clusters through the suggested procedures of the study. The highest of all three value(R, F, M) cluster's products need to have high level of the inventory as well as to be disposed in a place where it can be increasing customer's path. In contrast, the lowest of all three value(R, F, M) cluster's products need to have low level of inventory as well as to be disposed in a place where visibility is low. The highest R value cluster's products is usually new releases products, and need to be placed on the front of the store. And, manager should decrease inventory levels gradually in the highest F value cluster's products purchased in the past. Because, we assume that cluster has lower R value and the M value than the average value of good. And it can be deduced that product are sold poorly in recent days and total sales also will be lower than the frequency. The procedure presented in this study is expected to contribute to raising the profitability of the retail store. The paper is organized as follows. The second chapter briefly reviews the literature related to this study. The third chapter suggests procedures for research proposals, and the fourth chapter applied suggested procedure using the actual product sales data. Finally, the fifth chapter described the conclusion of the study and further research.

• Journal of Nutrition and Health
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• v.48 no.2
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• pp.180-191
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• 2015

Purpose: There is a lack of data on the energy cost of children's everyday activities, adult values are often used as surrogates. In addition, the influence of body weight on the energy cost of activity when expressed as metabolic equivalents (METs) has not been vigorously explored. Methods: In this study 20 elementary school students 9~12 years of age completed 18 various physical activities while energy expenditure was measured continuously using a portable telemetry gas exchange system ($K_4b^2$, Cosmed, Rome, Italy). Results: The average age was 10.4 years and the average height and weight was 145.1 cm and 43.6 kg, respectively. Oxygen consumption ($VO_2$), energy expenditure and METs at the time of resting of the subjects were 5.41 mL/kg/min, 1.44 kcal/kg/h, and 1.5 METs, respectively. METs values by 18 physical activities were as follows: Homework and reading books (1.6 METs), playing game with a mobile phone or video while sitting (1.6 METs), watching TV while sitting on a comfortable chair (1.7 METs), playing video game or mobile phone game while standing (1.9 METs), sweeping a room with a broom (2.7 METs) and playing a board game (2.8 METs) belong to light intensity physical activities. By contrary, speedy walking and running were 6.6 and 6.7 METs, respectively, which belong to high intensity physical activities over 6.0 METs. When the effect of body weight on physical activity energy expenditure was determined, $R^2$ values increased with 0.116 (playing a game at sitting), 0.176 (climbing up and down stairs), 0.246 (slow walking), and 0.455 (running), which showed that higher activity intensity increased explanation power of body weight on METs value. Conclusion: This study is important for direct evaluation of energy expenditure by physical activities of children, and it could be used directly for revising and complementing the existing activity classification table to fit for children.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.3 no.1
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• pp.26-27
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• 1978

It can be expected that intelligence and knowledge will be the core of the post-industrial society in a near future. Accordingly, the age of intelligence shall be accelerated extensively to find ourselves in an age of 'Communication' service enterprise. The communication actions will increase its efficiency and multiply its utility, indebted to its scientic principles and legal idea. The two basic elements of communication action, that is, communication station and communication men are considered to perform their function when they are properly supported and managed by the government administration. Since the communication action itself is composed of various factors, the elements such as communication stations and officials must be cultivated and managed by specialist or experts with continuous and extensive study practices concerned. With the above mind, this study reviewed our public service officials law with a view to improve it by providing some suggestions for communication experts and researchers to find suitable positions in the framework of government administration. In this study, I would like to suggest 'Occupational Group of Communication' that is consisted of a series of comm, management positions and research positions in parallel to the existing series of comm, technical position. The communication specialist or expert is required to be qualified with necessary scientific knowledge and techniques of communication, as well as prerequisites as government service officials. Communication experts must succeed in the first hand to obtain government licence concerned in with the government law and regulation, and international custom before they can be appointed to the official positions. This system of licence-prior-to-appointment is principally applied in the communication management position. And communication research positions are for those who shall engage themselves to the work of study and research in the field of both management and technical nature. It is hopefully expected that efficient and extensive management of communication activities, as well as scientific and continuous study over than communication enterprise will be upgraded at national dimensions.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.125-148
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• 2018

Recently, as the demand for big data analysis increases, cases of analyzing unstructured data and using the results are also increasing. Among the various types of unstructured data, text is used as a means of communicating information in almost all fields. In addition, many analysts are interested in the amount of data is very large and relatively easy to collect compared to other unstructured and structured data. Among the various text analysis applications, document classification which classifies documents into predetermined categories, topic modeling which extracts major topics from a large number of documents, sentimental analysis or opinion mining that identifies emotions or opinions contained in texts, and Text Summarization which summarize the main contents from one document or several documents have been actively studied. Especially, the text summarization technique is actively applied in the business through the news summary service, the privacy policy summary service, ect. In addition, much research has been done in academia in accordance with the extraction approach which provides the main elements of the document selectively and the abstraction approach which extracts the elements of the document and composes new sentences by combining them. However, the technique of evaluating the quality of automatically summarized documents has not made much progress compared to the technique of automatic text summarization. Most of existing studies dealing with the quality evaluation of summarization were carried out manual summarization of document, using them as reference documents, and measuring the similarity between the automatic summary and reference document. Specifically, automatic summarization is performed through various techniques from full text, and comparison with reference document, which is an ideal summary document, is performed for measuring the quality of automatic summarization. Reference documents are provided in two major ways, the most common way is manual summarization, in which a person creates an ideal summary by hand. Since this method requires human intervention in the process of preparing the summary, it takes a lot of time and cost to write the summary, and there is a limitation that the evaluation result may be different depending on the subject of the summarizer. Therefore, in order to overcome these limitations, attempts have been made to measure the quality of summary documents without human intervention. On the other hand, as a representative attempt to overcome these limitations, a method has been recently devised to reduce the size of the full text and to measure the similarity of the reduced full text and the automatic summary. In this method, the more frequent term in the full text appears in the summary, the better the quality of the summary. However, since summarization essentially means minimizing a lot of content while minimizing content omissions, it is unreasonable to say that a "good summary" based on only frequency always means a "good summary" in its essential meaning. In order to overcome the limitations of this previous study of summarization evaluation, this study proposes an automatic quality evaluation for text summarization method based on the essential meaning of summarization. Specifically, the concept of succinctness is defined as an element indicating how few duplicated contents among the sentences of the summary, and completeness is defined as an element that indicating how few of the contents are not included in the summary. In this paper, we propose a method for automatic quality evaluation of text summarization based on the concepts of succinctness and completeness. In order to evaluate the practical applicability of the proposed methodology, 29,671 sentences were extracted from TripAdvisor 's hotel reviews, summarized the reviews by each hotel and presented the results of the experiments conducted on evaluation of the quality of summaries in accordance to the proposed methodology. It also provides a way to integrate the completeness and succinctness in the trade-off relationship into the F-Score, and propose a method to perform the optimal summarization by changing the threshold of the sentence similarity.

• The Korean Journal of Air & Space Law and Policy
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• v.35 no.1
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• pp.63-101
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• 2020

'Flight safety zone' means a zone that the Minister of National Defense designates under Articles 4 and 6 of the Protection of Military Bases and Installations Act (hereinafter 'PMBIA') for the safety of flight during takeoff and landing of military aircrafts. The purpose of flight safety zone is to contribute to the national security by providing necessary measures for the protection of military bases and installations and smooth conduct of military operations. In this case, when the state set and used the flight safety zone, the landowner claimed restitution of unjust enrichment against the country. This article is an analysis based on the existing legal theory regarding the legitimacy of plaintiff's claim, and the summary of the discussion is as follows. A person who without any legal ground derives a benefit from the property or services of another and thereby causes loss to the latter shall be bound to return such benefit (Article 741 of the Civil Act). Since the subject matter is an infringing profit, the defendant must prove that he has a legitimate right to retain the profit. The State reserves the right to use over the land designated as a flight safety zone in accordance with legitimate procedures established by the PMBIA for the safe takeoff and landing of military aircrafts. Therefore, it cannot be said that the State gained an unjust enrichment equivalent to the rent over the land without legal cause. Expropriation, use or restriction of private property from public necessity and compensation therefor shall be governed by Act: provided, that in such a case, just compensation shall be paid (Article 23 (1) of the Constitution of The Republic of KOREA). Since there is not any provision in the PMBIA for loss compensation for the case where a flight safety zone is set over land as in this case, next question would be whether or not it is unconstitutional. Even if it is designated as a flight safety zone and the use and profits of the land are limited, the justification of the purpose of the flight safety zone system, the appropriateness of the means, the minimization of infringement, and the balance of legal interests are still recognized; thus just not having any loss compensation clause does not make the act unconstitutional. In conclusion, plaintiff's claim for loss compensation based on the 'Act on Acquisition of and Compensation for land, etc. for Public Works Projects', which has no provision for loss compensation due to public limits, is unjust.

• Journal of architectural history
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• v.14 no.1 s.41
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• pp.89-105
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• 2005

The central aim of this thesis is to see if the structure of Palsangjeon(捌相殿) in Pubjoo Temple(法住寺), a five sto wooden pagoda in Chosen(朝鮮) Dynasty, was handed down from the ancient and middle ages. This study was performed through an analysis of Gilt-Bronze Pagoda built in Koryo(高麗) period. In other words, it is aimed at analyzing which lineage the structure of Palsangjeonbelongs to as a wooden pagoda. In analyzing the structure of Palsangjeon, I attempted to find out its source from the remains of Koryo period prior to the Chosen Dynasty. Examples are the Gilt-Bronze Pagoda, built during the Koryo period. I have also examined its relationship with other existing wooden pagodas and remains. The analysis of Palsangjeon, a five story wooden pagoda in Chosen Dynasty, focuses on the following: First, I explored the possibilities of whether the structure of Palsangjeon was newly invented in Chosen Dynasty, or if it had been derived from the wooden pagodas in the Koryo period. Secondly, I tried to find out if the stable vertical planes, with a great successive diminution ratio, were derived from the middle age, i.e. Koryo period. The results of the study of Palsangjeon through Gilt-Bronze Pagoda analysis are as follows: 1. The structure of Gilt-Bronze Pagoda, a wooden pagoda from the Koryo period, is roughly classified into the accumulation type, using pipe pillars, and the one story type using whole pillars. In the accumulation type, stories are connected in either a flat format or an intervening format. The Gilt-Bronze Pagoda is mainly composed of pipe pillars, with some whole pillars. However, the central pillar was omitted in the building structure. Generally, the upper and lower stories are connected by pipe pillars in a crutch format. All the pillars, whether they are pipe pillars or whole pillars, used Naiten(內轉) technology. The Eave supporter has the Haang type(下昻) and the Muhaang type(無下昻). In most cases, high balustrades are furnished, but few tables of high balustrades have been found. The slanting roof formats have been handed down from Paekche(百濟), Silla(新羅), or Koryo(高麗). However, the structure of the octagon is assumed to be derived from Koguryo(高句麗). The structure of the Gilt-Bronze Pagoda from the Koryo period is mainly composed of accumulated flat squares, with some spire types. intervening format, the structure of Palsangjeon used whole pillars in a half story format in which upper level side pillars are installed on the lower level tie beam. From the Bronze Pagoda from the Koryo period, we can assume that the half story format of wooden pagodas that has stable vertical planes with a great successive diminution ratio was created during the mid-Koryo period at the latest and had been idly developed by the time of the Chosen Dynasty. 3. The whole pillars in Palsangjeon are also found in Gilt-Bronze Pagodas from the Koryo period. Hence, all of the pillars in Palsangjeon seem to have been handed down from the ancient construction technology. They were also used in the construction of wooden pagodas from the Koryo period. Therefore, it is assumed that Palsangjeon was constructed using the construction technology of the Chosen Dynasty that had been developed from the wooden pagoda construction technology of the Koryo period. The stable vertical planes with a great successive diminution ratio in Palsangjeon are derived from ancient Korean wooden pagodas, which have developed into indigenous Korean wooden pagodas with fairly stable vertical planes and a great design, in the half story format of Koryo and Chosen Dynasty. Therefore, it is assumed that the structure of Palsangjeon has a systematic relationship with traditional Korean wooden pagodas and is one of the indigenous Korean wooden pagoda structures. 4. In China, the intervening format has been mainly used between stories in multi-story architecture since the ancient days. At the same time, the flat format as also used in ancient and middle ages. However, the flat format was replaced by whole pillars during the Ming(明) and Manchu(淸) Dynasties, in favor of simple and compact construction. The half-story format, in which upper level side pillars are installed on tie beams, has been found in some cases, but it doesn't seem to have been the primary construction technology. Few traces of the half-story format have been found in multi-story architecture in Japan, and it has not been used as a general construction format. By contrast, the half-story format, which seems to have been derived from the Koryo period, was used as a general construction format in multi-story architecture of the Chosen Dynasty. The construction technology of multi-story architecture is related to that of multi-story wooden pagodas, but they have different production technologies. It seems that the structure of Palsangjeon did not just adopt the construction technology of multi-story architecture in the Chosen Dynasty, but it was developed from wooden pagodas in the Koryo period, including the Gilt-Bronze Pagoda. 5. Since the ancient days, most Chinese and Japanese wooden pagodas have adopted an accumulation type of structure using pipe pillars, with accumulated pointed towers. On the other hand, though most Korean wooden pagodas have also adopted an accumulation type of structure from the ancientdays, one story type using whole pillars was created in the Koryo and Chosen Dynasties. The wooden pagoda structure of Palsangjeon, with stable vertical planes in a half story format, is a unique Korean construction technology, different from the construction technologies of Chinese and Japanese wooden pagodas. This thesis clearly determined the structural characteristics of Palsangjeon. However, various remains have yet to be analyzed in depth, to establish an accurate construction technology system. In the beginning of this thesis, I had difficulty in precisely interpreting the internal structure of the Gilt-Bronze Pagoda from its appearance. However, in the process of study, the more serious problem was that there are few remains or ruins of multi-story architecture in ancient and the middle ages of Korea. Therefore, it is urgent to discover various remains in the future. This thesis succeeded in determining the structural characteristics of Palsangjeon. However, it fell short of clarifying the structural lineage of the stable vertical planes, although they show indigenous Korean architectural taste, representing the unique national emotion, and the construction format of multi-story wooden pagodas in Korea. I hope this is clarified in the future research.

• Journal of Distribution Research
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• v.17 no.1
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• pp.37-63
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• 2012

In franchise business, exclusive sales territory (sometimes EST in table) protection is a very important issue from an economic, social and political point of view. It affects the growth and survival of both franchisor and franchisee and often raises issues of social and political conflicts. When franchisee is not familiar with related laws and regulations, franchisor has high chance to utilize it. Exclusive sales territory protection by the manufacturer and distributors (wholesalers or retailers) means sales area restriction by which only certain distributors have right to sell products or services. The distributor, who has been granted exclusive sales territories, can protect its own territory, whereas he may be prohibited from entering in other regions. Even though exclusive sales territory is a quite critical problem in franchise business, there is not much rigorous research about the reason, results, evaluation, and future direction based on empirical data. This paper tries to address this problem not only from logical and nomological validity, but from empirical validation. While we purse an empirical analysis, we take into account the difficulties of real data collection and statistical analysis techniques. We use a set of disclosure document data collected by Korea Fair Trade Commission, instead of conventional survey method which is usually criticized for its measurement error. Existing theories about exclusive sales territory can be summarized into two groups as shown in the table below. The first one is about the effectiveness of exclusive sales territory from both franchisor and franchisee point of view. In fact, output of exclusive sales territory can be positive for franchisors but negative for franchisees. Also, it can be positive in terms of sales but negative in terms of profit. Therefore, variables and viewpoints should be set properly. The other one is about the motive or reason why exclusive sales territory is protected. The reasons can be classified into four groups - industry characteristics, franchise systems characteristics, capability to maintain exclusive sales territory, and strategic decision. Within four groups of reasons, there are more specific variables and theories as below. Based on these theories, we develop nine hypotheses which are briefly shown in the last table below with the results. In order to validate the hypothesis, data is collected from government (FTC) homepage which is open source. The sample consists of 1,896 franchisors and it contains about three year operation data, from 2006 to 2008. Within the samples, 627 have exclusive sales territory protection policy and the one with exclusive sales territory policy is not evenly distributed over 19 representative industries. Additional data are also collected from another government agency homepage, like Statistics Korea. Also, we combine data from various secondary sources to create meaningful variables as shown in the table below. All variables are dichotomized by mean or median split if they are not inherently dichotomized by its definition, since each hypothesis is composed by multiple variables and there is no solid statistical technique to incorporate all these conditions to test the hypotheses. This paper uses a simple chi-square test because hypotheses and theories are built upon quite specific conditions such as industry type, economic condition, company history and various strategic purposes. It is almost impossible to find all those samples to satisfy them and it can't be manipulated in experimental settings. However, more advanced statistical techniques are very good on clean data without exogenous variables, but not good with real complex data. The chi-square test is applied in a way that samples are grouped into four with two criteria, whether they use exclusive sales territory protection or not, and whether they satisfy conditions of each hypothesis. So the proportion of sample franchisors which satisfy conditions and protect exclusive sales territory, does significantly exceed the proportion of samples that satisfy condition and do not protect. In fact, chi-square test is equivalent with the Poisson regression which allows more flexible application. As results, only three hypotheses are accepted. When attitude toward the risk is high so loyalty fee is determined according to sales performance, EST protection makes poor results as expected. And when franchisor protects EST in order to recruit franchisee easily, EST protection makes better results. Also, when EST protection is to improve the efficiency of franchise system as a whole, it shows better performances. High efficiency is achieved as EST prohibits the free riding of franchisee who exploits other's marketing efforts, and it encourages proper investments and distributes franchisee into multiple regions evenly. Other hypotheses are not supported in the results of significance testing. Exclusive sales territory should be protected from proper motives and administered for mutual benefits. Legal restrictions driven by the government agency like FTC could be misused and cause mis-understandings. So there need more careful monitoring on real practices and more rigorous studies by both academicians and practitioners.