• Journal of agricultural medicine and community health
• /
• v.25 no.1
• /
• pp.65-83
• /
• 2000

To know the relationship of general characteristics with activities of daily living(ADL) and instrumental activities of daily living(IADL), we carried out the study on the elderies living in Chollabuk-Do area during 6 months, from June to December in 1999. Study subjects were 281, women and men were 195(69.6%) and 85(30.4%) respectively. Mean ages of women and men were 71.9 and 70.8 respectively. 81.1% elderies has disease and 18.9% were disease free. Disease prevalences of movement joint disease, others, circulatory disease, digestive disease, dental disease, respiratory disease were 50.1%, 25.0%, 10.5%, 9.4%, 8.5%, and 6.3% respectively. The percentages to the use of medical institution in recent were 40.0% for hospital, 16.8% for oriental hospital, 14.5% for public health center, 10.9% for drug store, 10.0% for others, and 7.8% for dental service. The percentages to the improvement of symptom after the use of medical institution were 62.3% for normal, 19.4% for improvement, and 18.2% for non-improvement. The percentages to the health situation were 37.1% for bad, 35.7% for good, and 27.1% for normal. Activities of daily living were 67.1% for 6 scores, 27.9% for 5 scores, 2.1% for 4 scores and ADL of women was lower than the men's. Instrumental activities of daily living were 50.4% for 5 scores, 19.3% for 3 scores, 12.1% for 4 scores and IADL of women was lower than the men's. Frequencies of disability in ADL were 28.9% for incontinence, 6.1% for bathing, 2.9% for meal, 2.5% for walking around house, 1.8% for toilet use, 1.4% for dressing and disability frequencies of women in 6 items of ADL were higher than the men's. The percentages of high, intermediate, low ADL in activities of daily living were 67.1%, 32.5%, 0.4% respectively and decrease of high ADL, increase of intermediate ADL were found with the increasing of age. Frequencies of disability in IADL were 42.9% for payment in and out, 31.8% for payment of written claim, 21.1% for shopping, 16.4% for preparation of meal, and 11.8% for use of bus. All items of women in IADL was higher than the men's but preparation of meal. The percentages of high, intermediate, low IADL in instrumental activities of daily living were 50.4%, 42.5%, 7.1% and decrease of high IADL, increase of intermediate IADL were found with the increasing of age. Mean of ADL with the general characteristics was 5.56 and 2 variables of level of education, health situation were statistically significant. Mean of IADL with the general characteristics was 3.76 and 8 variables of age, sex, level of education, occupation, presence of spouse, duty of living cost, health situation, category of ADL were statistically significant. With the result of stepwise regression, ADL was statistically related with religion, health situation and ADL was statistically related with level of education, living together with family, duty of living cost, health situation.

• Journal of Preventive Medicine and Public Health
• /
• v.24 no.2 s.34
• /
• pp.232-242
• /
• 1991

To determine the incidence rate of child battering and related factors, a questionnaire survey was Conducted on 1,255 children in 4th and 5th grades of two elementary schools (one in the upper economic class area with 519 students and the other in the lower economic class area with 504 students) in Taegu and two schools in rural areas of Kyungpook province (120 and 112 students, respectively) from 1st May to 10th May 1990. Total number of children who were battered during one-month period (1-30 April 1990) prior to the survey was 918 (73.1%). Among the battered children 87 (6.9%) were severely battered (twice or more in a month by kicking or more severe method) and 831 children (66.2%) were moderately battered (all other battering than severe battering). The percentage of battered children and degree of battering were not significantly different between two schools in Taegu and between urban and rural areas. Common reasons for battering were disobediance (61.9%), making troubles (34.9%), and poor school performance (33.3%). However, 16.1% of severely battered children responded that the perpetrators battered them to wreak their anger and 5.7% of them did not know the reason why they were battered. A majority of the battered children (65%) regretted their fault after being battered but 20.7% of the severely battered children wanted to run away and 9.2% of them had an urge to commit suicide. While most of the physical injuries due to battering were minor as bruise (52.7%) but some of them were severe, e.g., bone fracture (2.5%), skin laceration (1.5%), and loss of consciousness. (0.2%). The common psycho-behavioral complaints of the severely battered children were unwillingness to study (31%), unwillingness to live (17.2%), and reluctance to go home (13.8%). The incidence rate of severe battering was significantly higher (p=0.018) among the children living in a quarter attached to a store (14.0%) than the children living in an apartment (6.6%) and individual house (6.2%). The incidence rate of severe battering was higher among children living in a rental house (8.4%) than children living in their own house 6.3%) (p=0.005). The children of father only working (5.1%) and mother only working (4.5%) had a lower incidence rate of severe battering than the children of both parents working (9.1%) and both parents unemployed (20.7%) (p=0.006). More children were battered when there was a sick family member (80.8%) compared with the children without a sick family member (71.4%) (p=0.001). The incidence rates of severe and moderate battering increased as the frequency of quarreling between mother and father increased (P=0.000). The percentage of unbattered children was higher among children whose father's occupation was professional (39.4%) than that of the total study subjects (26.9%) (p<0.001).

• Journal of Intelligence and Information Systems
• /
• v.19 no.3
• /
• pp.113-125
• /
• 2013

Every company wants to know customer's requirement and makes an effort to meet them. Cause that, communication between customer and company became core competition of business and that important is increasing continuously. There are several strategies to find customer's needs, but VOC (Voice of customer) is one of most powerful communication tools and VOC gathering by several channels as telephone, post, e-mail, website and so on is so meaningful. So, almost company is gathering VOC and operating VOC system. VOC is important not only to business organization but also public organization such as government, education institute, and medical center that should drive up public service quality and customer satisfaction. Accordingly, they make a VOC gathering and analyzing System and then use for making a new product and service, and upgrade. In recent years, innovations in internet and ICT have made diverse channels such as SNS, mobile, website and call-center to collect VOC data. Although a lot of VOC data is collected through diverse channel, the proper utilization is still difficult. It is because the VOC data is made of very emotional contents by voice or text of informal style and the volume of the VOC data are so big. These unstructured big data make a difficult to store and analyze for use by human. So that, the organization need to automatic collecting, storing, classifying and analyzing system for unstructured big VOC data. This study propose an intelligent VOC analyzing system based on opinion mining to classify the unstructured VOC data automatically and determine the polarity as well as the type of VOC. And then, the basis of the VOC opinion analyzing system, called domain-oriented sentiment dictionary is created and corresponding stages are presented in detail. The experiment is conducted with 4,300 VOC data collected from a medical website to measure the effectiveness of the proposed system and utilized them to develop the sensitive data dictionary by determining the special sentiment vocabulary and their polarity value in a medical domain. Through the experiment, it comes out that positive terms such as "칭찬, 친절함, 감사, 무사히, 잘해, 감동, 미소" have high positive opinion value, and negative terms such as "퉁명, 뭡니까, 말하더군요, 무시하는" have strong negative opinion. These terms are in general use and the experiment result seems to be a high probability of opinion polarity. Furthermore, the accuracy of proposed VOC classification model has been compared and the highest classification accuracy of 77.8% is conformed at threshold with -0.50 of opinion classification of VOC. Through the proposed intelligent VOC analyzing system, the real time opinion classification and response priority of VOC can be predicted. Ultimately the positive effectiveness is expected to catch the customer complains at early stage and deal with it quickly with the lower number of staff to operate the VOC system. It can be made available human resource and time of customer service part. Above all, this study is new try to automatic analyzing the unstructured VOC data using opinion mining, and shows that the system could be used as variable to classify the positive or negative polarity of VOC opinion. It is expected to suggest practical framework of the VOC analysis to diverse use and the model can be used as real VOC analyzing system if it is implemented as system. Despite experiment results and expectation, this study has several limits. First of all, the sample data is only collected from a hospital web-site. It means that the sentimental dictionary made by sample data can be lean too much towards on that hospital and web-site. Therefore, next research has to take several channels such as call-center and SNS, and other domain like government, financial company, and education institute.

• Journal of Intelligence and Information Systems
• /
• v.26 no.3
• /
• pp.91-108
• /
• 2020

Korea's famous soccer players are steadily performing well in international leagues, which led to higher interests of Korean fans in the international leagues. Reflecting the growing social phenomenon of rising interests on international leagues by Korean fans, the study examined the overall consumer perception in the consumption of uniform by domestic soccer fans and compared the changes in perception following the transfers of the players. Among others, the paper examined the consumer perception and purchase factors of soccer fans shown in social media, focusing on periods before and after the recruitment of Heung-Min Son to English Premier League's Tottenham Football Club. To this end, the EPL uniform is the collection keyword the paper utilized and collected consumer postings from domestic website and social media via Python 3.7, and analyzed them using Ucinet 6, NodeXL 1.0.1, and SPSS 25.0 programs. The results of this study can be summarized as follows. First, the uniform of the club that consistently topped the league, has been gaining attention as a popular uniform, and the players' performance, and the players' position have been identified as key factors in the purchase and search of professional football uniforms. In the case of the club, the actual ranking and whether the league won are shown to be important factors in the purchase and search of professional soccer uniforms. The club's emblem and the sponsor logo that will be attached to the uniform are also factors of interest to consumers. In addition, in the decision making process of purchase of a uniform by professional soccer fan, uniform's form, marking, authenticity, and sponsors are found to be more important than price, design, size, and logo. The official online store has emerged as a major purchasing channel, followed by gifts for friends or requests from acquaintances when someone travels to the United Kingdom. Second, a classification of key control categories through the convergence of iteration correlation analysis and Clauset-Newman-Moore clustering algorithm shows differences in the classification of individual groups, but groups that include the EPL's club and player keywords are identified as the key topics in relation to professional football uniforms. Third, between 2002 and 2006, the central theme for professional football uniforms was World Cup and English Premier League, but from 2012 to 2015, the focus has shifted to more interest of domestic and international players in the English Premier League. The subject has changed to the uniform itself from this time on. In this context, the paper can confirm that the major issues regarding the uniforms of professional soccer players have changed since Ji-Sung Park's transfer to Manchester United, and Sung-Yong Ki, Chung-Yong Lee, and Heung-Min Son's good performances in these leagues. The paper also identified that the uniforms of the clubs to which the players have transferred to are of interest. Fourth, both male and female consumers are showing increasing interest in Son's league, the English Premier League, which Tottenham FC belongs to. In particular, the increasing interest in Son has shown a tendency to increase interest in football uniforms for female consumers. This study presents a variety of researches on sports consumption and has value as a consumer study by identifying unique consumption patterns. It is meaningful in that the accuracy of the interpretation has been enhanced by using a cluster analysis via convergence of iteration correlation analysis and Clauset-Newman-Moore clustering algorithm to identify the main topics. Based on the results of this study, the clubs will be able to maximize its profits and maintain good relationships with fans by identifying key drivers of consumer awareness and purchasing for professional soccer fans and establishing an effective marketing strategy.

• Journal of Intelligence and Information Systems
• /
• v.19 no.2
• /
• pp.177-189
• /
• 2013

In Smart Education, the roles of digital textbook is very important as face-to-face media to learners. The standardization of digital textbook will promote the industrialization of digital textbook for contents providers and distributers as well as learner and instructors. In this study, the following three objectives-oriented digital textbooks are looking for ways to standardize. (1) digital textbooks should undertake the role of the media for blended learning which supports on-off classes, should be operating on common EPUB viewer without special dedicated viewer, should utilize the existing framework of the e-learning learning contents and learning management. The reason to consider the EPUB as the standard for digital textbooks is that digital textbooks don't need to specify antoher standard for the form of books, and can take advantage od industrial base with EPUB standards-rich content and distribution structure (2) digital textbooks should provide a low-cost open market service that are currently available as the standard open software (3) To provide appropriate learning feedback information to students, digital textbooks should provide a foundation which accumulates and manages all the learning activity information according to standard infrastructure for educational Big Data processing. In this study, the digital textbook in a smart education environment was referred to open digital textbook. The components of open digital textbooks service framework are (1) digital textbook terminals such as smart pad, smart TVs, smart phones, PC, etc., (2) digital textbooks platform to show and perform digital contents on digital textbook terminals, (3) learning contents repository, which exist on the cloud, maintains accredited learning, (4) App Store providing and distributing secondary learning contents and learning tools by learning contents developing companies, and (5) LMS as a learning support/management tool which on-site class teacher use for creating classroom instruction materials. In addition, locating all of the hardware and software implement a smart education service within the cloud must have take advantage of the cloud computing for efficient management and reducing expense. The open digital textbooks of smart education is consdered as providing e-book style interface of LMS to learners. In open digital textbooks, the representation of text, image, audio, video, equations, etc. is basic function. But painting, writing, problem solving, etc are beyond the capabilities of a simple e-book. The Communication of teacher-to-student, learner-to-learnert, tems-to-team is required by using the open digital textbook. To represent student demographics, portfolio information, and class information, the standard used in e-learning is desirable. To process learner tracking information about the activities of the learner for LMS(Learning Management System), open digital textbook must have the recording function and the commnincating function with LMS. DRM is a function for protecting various copyright. Currently DRMs of e-boook are controlled by the corresponding book viewer. If open digital textbook admitt DRM that is used in a variety of different DRM standards of various e-book viewer, the implementation of redundant features can be avoided. Security/privacy functions are required to protect information about the study or instruction from a third party UDL (Universal Design for Learning) is learning support function for those with disabilities have difficulty in learning courses. The open digital textbook, which is based on E-book standard EPUB 3.0, must (1) record the learning activity log information, and (2) communicate with the server to support the learning activity. While the recording function and the communication function, which is not determined on current standards, is implemented as a JavaScript and is utilized in the current EPUB 3.0 viewer, ths strategy of proposing such recording and communication functions as the next generation of e-book standard, or special standard (EPUB 3.0 for education) is needed. Future research in this study will implement open source program with the proposed open digital textbook standard and present a new educational services including Big Data analysis.

• Journal of Intelligence and Information Systems
• /
• v.25 no.4
• /
• pp.141-154
• /
• 2019

Rapid growth of internet technology and social media is progressing. Data mining technology has evolved to enable unstructured document representations in a variety of applications. Sentiment analysis is an important technology that can distinguish poor or high-quality content through text data of products, and it has proliferated during text mining. Sentiment analysis mainly analyzes people's opinions in text data by assigning predefined data categories as positive and negative. This has been studied in various directions in terms of accuracy from simple rule-based to dictionary-based approaches using predefined labels. In fact, sentiment analysis is one of the most active researches in natural language processing and is widely studied in text mining. When real online reviews aren't available for others, it's not only easy to openly collect information, but it also affects your business. In marketing, real-world information from customers is gathered on websites, not surveys. Depending on whether the website's posts are positive or negative, the customer response is reflected in the sales and tries to identify the information. However, many reviews on a website are not always good, and difficult to identify. The earlier studies in this research area used the reviews data of the Amazon.com shopping mal, but the research data used in the recent studies uses the data for stock market trends, blogs, news articles, weather forecasts, IMDB, and facebook etc. However, the lack of accuracy is recognized because sentiment calculations are changed according to the subject, paragraph, sentiment lexicon direction, and sentence strength. This study aims to classify the polarity analysis of sentiment analysis into positive and negative categories and increase the prediction accuracy of the polarity analysis using the pretrained IMDB review data set. First, the text classification algorithm related to sentiment analysis adopts the popular machine learning algorithms such as NB (naive bayes), SVM (support vector machines), XGboost, RF (random forests), and Gradient Boost as comparative models. Second, deep learning has demonstrated discriminative features that can extract complex features of data. Representative algorithms are CNN (convolution neural networks), RNN (recurrent neural networks), LSTM (long-short term memory). CNN can be used similarly to BoW when processing a sentence in vector format, but does not consider sequential data attributes. RNN can handle well in order because it takes into account the time information of the data, but there is a long-term dependency on memory. To solve the problem of long-term dependence, LSTM is used. For the comparison, CNN and LSTM were chosen as simple deep learning models. In addition to classical machine learning algorithms, CNN, LSTM, and the integrated models were analyzed. Although there are many parameters for the algorithms, we examined the relationship between numerical value and precision to find the optimal combination. And, we tried to figure out how the models work well for sentiment analysis and how these models work. This study proposes integrated CNN and LSTM algorithms to extract the positive and negative features of text analysis. The reasons for mixing these two algorithms are as follows. CNN can extract features for the classification automatically by applying convolution layer and massively parallel processing. LSTM is not capable of highly parallel processing. Like faucets, the LSTM has input, output, and forget gates that can be moved and controlled at a desired time. These gates have the advantage of placing memory blocks on hidden nodes. The memory block of the LSTM may not store all the data, but it can solve the CNN's long-term dependency problem. Furthermore, when LSTM is used in CNN's pooling layer, it has an end-to-end structure, so that spatial and temporal features can be designed simultaneously. In combination with CNN-LSTM, 90.33% accuracy was measured. This is slower than CNN, but faster than LSTM. The presented model was more accurate than other models. In addition, each word embedding layer can be improved when training the kernel step by step. CNN-LSTM can improve the weakness of each model, and there is an advantage of improving the learning by layer using the end-to-end structure of LSTM. Based on these reasons, this study tries to enhance the classification accuracy of movie reviews using the integrated CNN-LSTM model.

• Journal of Korean Medical classics
• /
• v.20 no.3
• /
• pp.121-141
• /
• 2007

The outline and characteristics of the important doctors of the 'Zhe Zhong Pai'(折衷派) are as follows. Part 1. In the late Edo(江戶) period The 'Zhe Zhong Pai', which tried to take the theory and clinical treatment of the 'Hou Shi Pai (後世派)' and the 'Gu Fang Pai (古方派)' and get their strong points to make treatments perfect, appeared. Their point was 'The main part is the art of the ancients, The latter prescriptions are to be used'(以古法爲主, 後世方爲用) and the "Shang Han Lun(傷寒論)" was revered for its treatments but in actual use it was not kept at that. As mentioned above The 'Zhe Zhong Pai ' viewed treatments as the base, which was the view of most doctors in the Edo period, However, the reason the 'Zhe Zhong Pai' is not valued as much as the 'Gu Fang Pai' by medical history books in Japan is because the 'Zhe Zhong Pai' does not have the substantiation or uniqueness of the 'Gu Fang Pai', and also because the view of 'gather as well as store up' was the same as the 'Kao Zheng Pai', Moreover, the 'compromise'(折衷) point of view was from taking in both Chinese and western medical knowledge systems(漢蘭折衷), Generally the pioneer of the 'Zhe Zhong Pai' is seen as Mochizuki Rokumon(望月鹿門) and after that was Fukui Futei(福井楓亭), Wadato Kaku(和田東郭), Yamada Seichin(山田正珍) and Taki Motohiro(多紀元簡), Part 2. The lives of Wada Tokaku(和田東郭), Nakagame Kinkei(中神琴溪), Nei Teng Xi Zhe(內藤希哲), the important doctors of the 'Zhe Zhong Pai', are as follows First. Wada Tokaku(和田東郭, 1743-1803) was born when the 'Hou Shi Pai' was already declining and the 'Gu Fang Pai' was flourishing and learned medicine from a 'Hou Shi Pai' doctor, Hu Tian Xu Shan(戶田旭山) and a 'Gu Fang Pai' doctor, Yoshimasu Todo(吉益東洞). He was not hindered by 'the old ways(古方), and did not lean towards 'the new ways(後世方)' and formed a way of compromise that 'looked at hardness and softness as the same'(剛柔相摩) by setting 'the cure of the disease' as the base, and said that to cure diseases 'the old way' must be used, but 'the new way' was necessary to supplement its shortcomings. His works include "Dao Shui Suo Yan", "Jiao Chiang Fang Yi Je" and "Yi Xue Sho(醫學說)" Second. Nakagame Kinkei(中神琴溪, 1744-1833) was famous for leaving Yoshirnasu Todo(吉益東洞) and changing to the 'Zhe Zhong Pai', and in his early years used qing fen(輕粉) to cure geisha(妓女) of syphilis. His argument was "the "Shang Han Lun" must be revered but needs to be adapted", "Zhong jing can be made into a follower but I cannot become his follower", "the later medical texts such as "Ru Men Shi Qin(儒門事親)" should only be used for its prescriptions and not its theories". His works include "Shang Han Lun Yue Yan(傷寒論約言) Third. Nei Teng Xi Zhe(內藤希哲, 1701-1735) learned medicine from Qing Shui Xian Sheng(淸水先生) and went out to Edo. In his book "Yi Jing Jie Huo Lun(醫經解惑論)" he tells of how he went from 'learning'(學) to 'skepticism'(惑) and how skepticism made him learn in 'the six skepticisms'(六惑). In the latter years Xi Zhe(希哲) combines the "Shen Nong Ben Cao jing(神農本草經)", the main text for herbal medicine, "Ming Tang jing(明堂經)" of accupuncture, basic theory texts "Huang Dui Nei jing(黃帝內徑)" and "Nan jing(難經)" with the "Shang Han Za Bing Lun", a book that the 'Gu Fang Pai' saw as opposing to the rest, and became 'an expert of five scriptures'(五經一貫). Part 3. Asada Showhaku(淺田宗伯, 1815-1894) started medicine at Zhong Cun Zhong(中村中倧) and learned 'the old way'(古方) from Yoshirnasu Todo and got experience through Chuan Yue(川越) and Fu jing(福井) and received teachings in texts, history and Wang Yangmin's principles(陽明學) from famous teachers. Showhaku(宗伯) meets a medical official of the makufu(幕府), Ben Kang Zong Yuan(本康宗圓), and recieves help from the 3 great doctors of the Edo period, Taki Motokato(多紀元堅), Xiao Dao Xue GU(小島學古) and Xi Duo Cun Kao Chuang and further develops his arts. At 47 he diagnoses the general Jia Mao(家茂) with 'heart failure from beriberi'(脚氣衝心) and becomes a Zheng Shi(徵I), at 51 he cures a minister from France and received a present from Napoleon, at 65 he becomes the court physician and saves Ming Gong(明宮) jia Ren Qn Wang(嘉仁親王, later the 大正犬皇) from bodily convulsions and becomes 'the vassal of merit who saved the national polity(國體)' At the 7th year of the Meiji(明治) he becomes the 2nd owner of Wen Zhi She(溫知社) and takes part in the 'kampo continuation movement'. In his latter years he saw 14000 patients a year, so we can estimate the quality and quantity of his clinical skills Showhaku(宗伯) wrote over 80 books including the "Ju Chuang Shu Ying(橘窓書影)", "WU Wu Yao Shi Fang Han(勿誤藥室方函)", "Shang Han Biang Shu(傷寒辨術)", "jing Qi Shen Lun(精氣神論)", "Hunag Guo Ming Yi Chuan(皇國名醫傳)" and the "Xian Jhe Yi Hua(先哲醫話)". Especially in the "Ju Chuang Shu Ying(橘窓書影)" he says "the old theories are the main, and the new prescriptions are to be used"(以古法爲主, 後世方爲用), stating the 'Zhe Zhong Pai' way of thinking. In the first volume of "Shung Han Biang Shu(傷寒辨術) and "Za Bing Lun Shi(雜病論識)", 'Zong Ping'(總評), He discerns the parts that are not Zhang Zhong Jing's writings and emphasizes his theories and practical uses.

• Journal of Distribution Research
• /
• v.15 no.3
• /
• pp.1-34
• /
• 2010

Introduction The purpose of this research is to develop overall model which involves the effect of ongoing support services by franchisor on franchisee's relationship quality(trust, satisfaction, and commitment) and business performance(financial and non-financial performance), and to investigate the relationships among trust, satisfaction, commitment, financial and non-financial performance. This study also suggests franchise business or franchise system should be based on long-term orientation between franchisor and franchisee rather than short-term orientation, or transactional relationship, and proposes the most effective way of providing on-going support services by franchisor with franchisee thru symbiotic relationship among franchisor and franchisee Research Model and Hypothesis The research model as Figure 1 shows the variables on-going support services which affect the relationship quality between franchisor and franchisee such as trust, satisfaction, and commitment, and also analyze the effects of relationship quality on business performance including financial and non-financial performance We established 12 hypotheses to test as follows; Relationship between on-going support services and trust H1: On-going support services factors (product category & price, logistics service, promotion, information providing & problem solving capability, supervisor's support, and education & training support) have positive effect on franchisee's trust. Relationship between on-going support services and satisfaction H2: On-going support services factors (product category & price, logistics service, promotion, information providing & problem solving capability, supervisor's support, and education & training support) have positive effect on franchisee's satisfaction. Relationship between on-going support services and commitment H3: On-going support services factors (product category & price, logistics service, promotion, information providing & problem solving capability, supervisor's support, and education & training support) have positive effect on franchisee's commitment. Relationship among relationship quality: trust, satisfaction, and commitment H4: Franchisee's trust has positive effect on franchisee's satisfaction. H5: Franchisee's trust has positive effect on franchisee's commitment. H6: Franchisee's satisfaction has positive effect on franchisee's commitment. Relationship between relationship quality and business performance H7: Franchisee's trust has positive effect on franchisee's financial performance. H8: Franchisee's trust has positive effect on franchisee's non-financial performance. H9: Franchisee's satisfaction has positive effect on franchisee's financial performance. H10: Franchisee's satisfaction has positive effect on franchisee's non-financial performance. H11: Franchisee's commitment has positive effect on franchisee's financial performance. H12: Franchisee's commitment has positive effect on franchisee's non-financial performance. Method The on-going support services were defined as an organized system of continuous supporting services by franchisor for the purpose of satisfying the expectation of franchisee based on long-term orientation and classified into six constructs such as product category & price, logistics service, promotion, providing information & problem solving capability, supervisor's support, and education & training support. The six constructs were measured agreement using a 7-point Likert-type scale (1 = strongly disagree to 7 = strongly agree)as follows. The product category & price was measured by four items: menu variety, price of food material provided by franchisor, and support for developing new menu. The logistics service was measured by six items: distribution system of franchisor, return policy for provided food materials, timeliness, inventory control level of franchisor, accuracy of order, and flexibility of emergency order. The promotion was measured by five items: differentiated promotion activities, brand image of franchisor, promotion effect such as customer increase, long-term plan of promotion, and micro-marketing concept in promotion. The providing information & problem solving capability was measured by information providing of new products, information of competitors, information of cost reduction, and efforts for solving problems in franchisee's operations. The supervisor's support was measured by supervisor operations, frequency of visiting franchisee, support by data analysis, processing the suggestions by franchisee, diagnosis and solutions for the franchisee's operations, and support for increasing sales in franchisee. Finally, the of education & training support was measured by recipe training by specialist, service training for store people, systemized training program, and tax & human resources support services. Analysis and results The data were analyzed using Amos. Figure 2 and Table 1 present the result of the structural equation model. Implications The results of this research are as follows: Firstly, the factors of product category, information providing and problem solving capacity influence only franchisee's satisfaction and commitment. Secondly, logistic services and supervising factors influence only trust and satisfaction. Thirdly, continuing education and training factors influence only franchisee's trust and commitment. Fourthly, sales promotion factor influences all the relationship quality representing trust, satisfaction, and commitment. Fifthly, regarding relationship among relationship quality, trust positively influences satisfaction, however, does not directly influence commitment, but satisfaction positively affects commitment. Therefore, satisfaction plays a mediating role between trust and commitment. Sixthly, trust positively influence only financial performance, and satisfaction and commitment influence positively both financial and non-financial performance.

• Journal of Intelligence and Information Systems
• /
• v.19 no.1
• /
• pp.95-110
• /
• 2013

Recently, the amount of unstructured data being generated through a variety of social media has been increasing rapidly, resulting in the increasing need to collect, store, search for, analyze, and visualize this data. This kind of data cannot be handled appropriately by using the traditional methodologies usually used for analyzing structured data because of its vast volume and unstructured nature. In this situation, many attempts are being made to analyze unstructured data such as text files and log files through various commercial or noncommercial analytical tools. Among the various contemporary issues dealt with in the literature of unstructured text data analysis, the concepts and techniques of opinion mining have been attracting much attention from pioneer researchers and business practitioners. Opinion mining or sentiment analysis refers to a series of processes that analyze participants' opinions, sentiments, evaluations, attitudes, and emotions about selected products, services, organizations, social issues, and so on. In other words, many attempts based on various opinion mining techniques are being made to resolve complicated issues that could not have otherwise been solved by existing traditional approaches. One of the most representative attempts using the opinion mining technique may be the recent research that proposed an intelligent model for predicting the direction of the stock index. This model works mainly on the basis of opinions extracted from an overwhelming number of economic news repots. News content published on various media is obviously a traditional example of unstructured text data. Every day, a large volume of new content is created, digitalized, and subsequently distributed to us via online or offline channels. Many studies have revealed that we make better decisions on political, economic, and social issues by analyzing news and other related information. In this sense, we expect to predict the fluctuation of stock markets partly by analyzing the relationship between economic news reports and the pattern of stock prices. So far, in the literature on opinion mining, most studies including ours have utilized a sentiment dictionary to elicit sentiment polarity or sentiment value from a large number of documents. A sentiment dictionary consists of pairs of selected words and their sentiment values. Sentiment classifiers refer to the dictionary to formulate the sentiment polarity of words, sentences in a document, and the whole document. However, most traditional approaches have common limitations in that they do not consider the flexibility of sentiment polarity, that is, the sentiment polarity or sentiment value of a word is fixed and cannot be changed in a traditional sentiment dictionary. In the real world, however, the sentiment polarity of a word can vary depending on the time, situation, and purpose of the analysis. It can also be contradictory in nature. The flexibility of sentiment polarity motivated us to conduct this study. In this paper, we have stated that sentiment polarity should be assigned, not merely on the basis of the inherent meaning of a word but on the basis of its ad hoc meaning within a particular context. To implement our idea, we presented an intelligent investment decision-support model based on opinion mining that performs the scrapping and parsing of massive volumes of economic news on the web, tags sentiment words, classifies sentiment polarity of the news, and finally predicts the direction of the next day's stock index. In addition, we applied a domain-specific sentiment dictionary instead of a general purpose one to classify each piece of news as either positive or negative. For the purpose of performance evaluation, we performed intensive experiments and investigated the prediction accuracy of our model. For the experiments to predict the direction of the stock index, we gathered and analyzed 1,072 articles about stock markets published by "M" and "E" media between July 2011 and September 2011.

• Journal of Internet Computing and Services
• /
• v.14 no.6
• /
• pp.71-84
• /
• 2013

Log data, which record the multitude of information created when operating computer systems, are utilized in many processes, from carrying out computer system inspection and process optimization to providing customized user optimization. In this paper, we propose a MongoDB-based unstructured log processing system in a cloud environment for processing the massive amount of log data of banks. Most of the log data generated during banking operations come from handling a client's business. Therefore, in order to gather, store, categorize, and analyze the log data generated while processing the client's business, a separate log data processing system needs to be established. However, the realization of flexible storage expansion functions for processing a massive amount of unstructured log data and executing a considerable number of functions to categorize and analyze the stored unstructured log data is difficult in existing computer environments. Thus, in this study, we use cloud computing technology to realize a cloud-based log data processing system for processing unstructured log data that are difficult to process using the existing computing infrastructure's analysis tools and management system. The proposed system uses the IaaS (Infrastructure as a Service) cloud environment to provide a flexible expansion of computing resources and includes the ability to flexibly expand resources such as storage space and memory under conditions such as extended storage or rapid increase in log data. Moreover, to overcome the processing limits of the existing analysis tool when a real-time analysis of the aggregated unstructured log data is required, the proposed system includes a Hadoop-based analysis module for quick and reliable parallel-distributed processing of the massive amount of log data. Furthermore, because the HDFS (Hadoop Distributed File System) stores data by generating copies of the block units of the aggregated log data, the proposed system offers automatic restore functions for the system to continually operate after it recovers from a malfunction. Finally, by establishing a distributed database using the NoSQL-based Mongo DB, the proposed system provides methods of effectively processing unstructured log data. Relational databases such as the MySQL databases have complex schemas that are inappropriate for processing unstructured log data. Further, strict schemas like those of relational databases cannot expand nodes in the case wherein the stored data are distributed to various nodes when the amount of data rapidly increases. NoSQL does not provide the complex computations that relational databases may provide but can easily expand the database through node dispersion when the amount of data increases rapidly; it is a non-relational database with an appropriate structure for processing unstructured data. The data models of the NoSQL are usually classified as Key-Value, column-oriented, and document-oriented types. Of these, the representative document-oriented data model, MongoDB, which has a free schema structure, is used in the proposed system. MongoDB is introduced to the proposed system because it makes it easy to process unstructured log data through a flexible schema structure, facilitates flexible node expansion when the amount of data is rapidly increasing, and provides an Auto-Sharding function that automatically expands storage. The proposed system is composed of a log collector module, a log graph generator module, a MongoDB module, a Hadoop-based analysis module, and a MySQL module. When the log data generated over the entire client business process of each bank are sent to the cloud server, the log collector module collects and classifies data according to the type of log data and distributes it to the MongoDB module and the MySQL module. The log graph generator module generates the results of the log analysis of the MongoDB module, Hadoop-based analysis module, and the MySQL module per analysis time and type of the aggregated log data, and provides them to the user through a web interface. Log data that require a real-time log data analysis are stored in the MySQL module and provided real-time by the log graph generator module. The aggregated log data per unit time are stored in the MongoDB module and plotted in a graph according to the user's various analysis conditions. The aggregated log data in the MongoDB module are parallel-distributed and processed by the Hadoop-based analysis module. A comparative evaluation is carried out against a log data processing system that uses only MySQL for inserting log data and estimating query performance; this evaluation proves the proposed system's superiority. Moreover, an optimal chunk size is confirmed through the log data insert performance evaluation of MongoDB for various chunk sizes.