• Journal of the Korean Society of Food Science and Nutrition
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• v.39 no.12
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• pp.1880-1892
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• 2010

The purposes of this study were to: a) provide evidence concerning the effects of emotional intelligence on job outcomes, b) examine the impacts of emotional intelligence on employee-related variables such as 'job satisfaction', 'organizational commitment', 'organizational performance', and 'turnover intention' c) identify the conceptual framework underlying emotional intelligence. A survey was conducted to collect data from foodservice managers (N=231). Statistical analyses were completed using SPSS Win (16.0) for descriptive analysis, reliability analysis, factor analysis, t-test, correlation analysis, cluster analysis and AMOS (16.0) for confirmatory factor analysis and structural equation modeling. The concept of emotional intelligence (EI) has been on the radar screens of many leaders and managers over the last several decades. The emotional intelligence is generally accepted to be a combination of emotional and interpersonal competencies that influence behavior, thinking and interaction with others. The main results of this study were as follows. The four EI (Emotional Intelligence) dimensions correlated significantly with age. The means of job satisfaction score were above the midpoint (3.04 point) scale. The organizational commitment score was above the midpoint (3.41 point) scale and was higher at 'loyalty' factor than 'commitment' factor. The means of organizational performance score were above the midpoint (3.34) scale. The correlations among the four EI (emotional intelligence) factors were significant with job satisfaction; organizational commitment, organizational performance and turnover intention. The test of hypothesis using structural equation modeling found that emotional intelligence produced positive effects on job attitude and job performance. Emotional intelligence enhanced organizational commitment, and in turn, managers' attitude produced positive effects on organizational performance; emotional intelligence also had a direct impact on organizational performance. This study has identified the effect of emotional intelligence on organizational performance and attitudes toward one's job.

• Journal of Global Scholars of Marketing Science
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• v.20 no.1
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• pp.15-26
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• 2010

Due to the globalization across various industries and cultural trade among many countries, oriental concepts have been attracting world’s attentions. In fashion industry, one's traditional culture is often developed as fashion theme for designers' creation and became strong strategies to stand out among competitors. Because of the increase of preferences for oriental images, opportunities abound to introduce traditional fashion goods and expand culture based business to global fashion markets. However, global fashion brands that include Korean traditional culture are yet to be developed. In order to develop a global fashion brand with Korean taste, it is very important for native citizen to accept their own culture in domestic apparel market prior to expansion into foreign market. Loungewear is evaluated to be appropriate for adopting Korean traditional details into clothing since this wardrobe category embraces various purposes which will easily lead to natural adaptation and wide spread use. Also, this market is seeing an increased demand for multipurpose wardrobes and fashionable underwear (Park et al. 2009). Despite rapid growth in the loungewear market, specific studies of loungewear is rare; and among research on developing modernized-traditional clothing, fashion items and brands do not always include the loungewear category. Therefore, this study investigated the Korean loungewear market and studied consumer evaluation toward loungewear with Korean traditional fashion design elements. Relationship among antecedents of purchase intention for Korean traditional fashion design elements were analyzed and compared between lifestyle groups for consumer targeting purposes. Product quality, retail service quality, perceived value, and preference on loungewear with Korean traditional design elements were chosen as antecedents of purchase intention and a structural equation model was designed to examine their relationship as well as their influence on purchase intention. Product quality and retail service quality among marketing mixes were employed as factors affecting preference and perceived value of loungewear with Korean traditional fashion design elements. Also effects of preference and perceived value on purchase intention were examined through the same model. A total of 357 self-administered questionnaires were completed by female consumers via web survey system. A questionnaire was developed to measure samples' lifestyle, product and retail service quality as purchasing criteria, perceived value, preference and purchase intention of loungewear with Korean traditional fashion design elements. Also, loungewear purchasing and usage behavior were asked as well in order to examine Korean loungewear market status. Data was analyzed through descriptive analysis, factor analysis, cluster analysis, ANOVA and structural equation model was tested via AMOS 7.0. As for the result of Korean loungewear market status investigation, loungewear was purchased by most of the consumers in our sample. Loungewear is currently recognized as clothes that are worn at home and consumers are showing comparably low involvement toward loungewear. Most of consumers in this study purchase loungewear only two to three times a year and they spend less than US$10. A total of 12 items and four factors of loungewear consumer lifestyle were found: traditional value oriented lifestyle, brand-affected lifestyle, pursuit of leisure lifestyle, and health oriented lifestyle. Drawing on lifestyle factors, loungewear consumers were classified into two groups; Well-being and Conservative. Relationships among constructs of purchasing behavior related to loungewear with Korean traditional fashion design elements were estimated. Preference and perceived value of loungewear were affected by both product quality and retail service quality. This study proved that high qualities in product and retail service develop positive preference toward loungewear. Perceived value and preference of loungewear positively influenced purchase intention. The results indicated that high preference and perceived value of loungewear with Korean traditional fashion design elements strengthen purchase intention and proved importance of developing preference and elevate perceived value in order to make sales. In a model comparison between two lifestyle groups: Well-being and Conservative lifestyle groups, results showed that product quality and retail service quality had positive influences on both preference and perceived value in case of Well-being group. However, for Conservative group, only retail service quality had a positive effect on preference and its influence to purchase intention. Since Well-being group showed more significant influence on purchase intention, loungewear brands with Korean traditional fashion design elements may want to focus on characteristics of Well-being group. However, Conservative group's relationship between preference and purchase intention of loungewear with Korean traditional fashion design elements was stronger, so that loungewear brands with Korean traditional fashion design elements should focus on creating conservative consumers' positive preference toward loungewear. The results offered information on Korean loungewear consumers' lifestyle and provided useful information for fashion brands that are planning to enter Korean loungewear market, particularly targeting female consumers similar to the sample of the present study. This study offers strategic and marketing insight for loungewear brands and also for fashion brands that are planning to create highly value-added fashion brands with Korean traditional fashion design elements. Considering different types of lifestyle groups that are associated with loungewear or traditional fashion goods, brand managers and marketers can use the results of this paper as a reference to positioning, targeting and marketing strategy buildings.

• Pediatric Gastroenterology, Hepatology & Nutrition
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• v.7 no.1
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• pp.31-39
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• 2004

Purpose: Although acute nonspecific mesenteric lymphadenitis (ANML) is probably common cause of abdominal pain in children, which can be severe enough to be an abdominal emergency, the clinical features of mesenteric lymphadenitis are not clear. Also, a differential diagnosis with acute appendicitis (APPE) is indispensable to avoid serious complications. The clinical features of ANML were determined, and the risk factors for differential diagnosis with APPE were analyzed. Methods: Between November 2000 and May 2001, data from 26 patients (aged 1 to 11 years) with ANML and 21 patients (aged 2 to 13 years) with APPE were reviewed. ANML was defined as a cluster of five or more lymph nodes measuring 10 mm or greater in their longitudinal diameter in the right lower quadrant (RLQ) without an identifiable specific inflammatory process on the ultrasonographic examination. There were risk factors on patient's history, physical examination, and laboratory examination; the location of abdominal pain, abdominal rigidity, rebound tenderness, fever, nocturnal pain, the vomiting intensity, the diarrhea intensity, the symptom duration, and the peripheral blood leukocytes count. Results: Of the 26 ANML patients and 21 APPE patients, abdominal pain was noted on periumbilical (76.9% vs 14.2%), on RLQ (11.5% vs 71.4%), with abdomen rigidity (7.6% vs 80.9%), with rebound tenderness (0.0% vs 76.1%)(p<0.05), in the lower abdomen (11.5% vs 14.2%), and at night (80.8% vs 100.0%) (p>0.05). The clinical symptoms were vomiting (38.4% vs 90.4%), the vomiting intensity ($1.5{\pm}0.7$ [1~3]/day vs $4.5{\pm}2.9$ [1~10]/day), diarrhea (65.3% vs 28.5%) (p<0.05), and fever (61.5% vs 76.2%)(p>0.05). The period to the subsidence of abdominal pain in the ANMA patients was $2.5{\pm}0.5$ (2~3) days. The laboratory data showed a significant difference in the peripheral blood leukocytes count ($8,403{\pm}1,737[5,900{\sim}12,300]/mm^3\;vs\;15,471{\pm}3,749[5,400{\sim}20,800]/mm^3$)(p<0.05). Discriminant analysis between ANML and APPE showed that the independent discriminant factors were a vomiting intensity and the peripheral blood leukocytes count and the discriminant power was 95.7%. Conclusion: The clinical characteristics of ANML were abrupt onset of periumbilical pain without rigidity or rebound tenderness, a mild vomiting intensity, normal peripheral leukocytes count, and relatively short clinical course. If the abdominal pain persist for more than 3 days, and/or the vomiting intensity is more than 3 times/day, and/or the peripheral leukocytes count is over $13,500/mm^3$, abdominal ultrasonography is recommended to rule out APPE.

• Journal of agricultural medicine and community health
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• v.25 no.1
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• pp.29-49
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• 2000

Objectives: To estimate the prevalence rate of hypertension, the changes of health behavior, and compliance for the drug treatment after diagnosed as hypertension. Methods: 7,030 persons who live in Cheonan City of Chungnam Province were selected by the cluster sampling method, and 5,372 persons were surveyed by questionnaire and health examination. This data is analyzed by Chi-square test on each variable. Results: 49.8%- of men and 38.8%- of women had been diagnosed as hypertension, and the prevalence rate of hypertension was significantly increased with aging in both gender. The prevalence rate tended to decrease in highly educated women group. Unemployed persons or obese persons showed relatively higher prevalence rate. The prevalence rate of hypertension increased in groups with higher total cholesterol levels over 240 mg/dl, and groups with glucose level over 200 mg/dl. 53.1%- of male patients and 66.6%- of female patients showed compliance for antihypertensive treatment. Compliance for treatment was higher in aged group or lower educated group in both gender. Among men, proportion of compliant subjects was higher in unemployed group(49.3%-), and lower in labor or primary industry than the others but among women, there was not any significant difference. And men with compliance for treatment had higher monthly income than the others, but women did not show any. Conclusion : This population had a high prevalence rate of hypertension which may lead to cardiovascular disease. Therefore health education programs and distribution of information must be emphasized in order to increase compliance to treatment and encourage the change of health behavior to promote health.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.57-73
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• 2021

Maintenance and prevention of failure through anomaly detection of ICT infrastructure is becoming important. System monitoring data is multidimensional time series data. When we deal with multidimensional time series data, we have difficulty in considering both characteristics of multidimensional data and characteristics of time series data. When dealing with multidimensional data, correlation between variables should be considered. Existing methods such as probability and linear base, distance base, etc. are degraded due to limitations called the curse of dimensions. In addition, time series data is preprocessed by applying sliding window technique and time series decomposition for self-correlation analysis. These techniques are the cause of increasing the dimension of data, so it is necessary to supplement them. The anomaly detection field is an old research field, and statistical methods and regression analysis were used in the early days. Currently, there are active studies to apply machine learning and artificial neural network technology to this field. Statistically based methods are difficult to apply when data is non-homogeneous, and do not detect local outliers well. The regression analysis method compares the predictive value and the actual value after learning the regression formula based on the parametric statistics and it detects abnormality. Anomaly detection using regression analysis has the disadvantage that the performance is lowered when the model is not solid and the noise or outliers of the data are included. There is a restriction that learning data with noise or outliers should be used. The autoencoder using artificial neural networks is learned to output as similar as possible to input data. It has many advantages compared to existing probability and linear model, cluster analysis, and map learning. It can be applied to data that does not satisfy probability distribution or linear assumption. In addition, it is possible to learn non-mapping without label data for teaching. However, there is a limitation of local outlier identification of multidimensional data in anomaly detection, and there is a problem that the dimension of data is greatly increased due to the characteristics of time series data. In this study, we propose a CMAE (Conditional Multimodal Autoencoder) that enhances the performance of anomaly detection by considering local outliers and time series characteristics. First, we applied Multimodal Autoencoder (MAE) to improve the limitations of local outlier identification of multidimensional data. Multimodals are commonly used to learn different types of inputs, such as voice and image. The different modal shares the bottleneck effect of Autoencoder and it learns correlation. In addition, CAE (Conditional Autoencoder) was used to learn the characteristics of time series data effectively without increasing the dimension of data. In general, conditional input mainly uses category variables, but in this study, time was used as a condition to learn periodicity. The CMAE model proposed in this paper was verified by comparing with the Unimodal Autoencoder (UAE) and Multi-modal Autoencoder (MAE). The restoration performance of Autoencoder for 41 variables was confirmed in the proposed model and the comparison model. The restoration performance is different by variables, and the restoration is normally well operated because the loss value is small for Memory, Disk, and Network modals in all three Autoencoder models. The process modal did not show a significant difference in all three models, and the CPU modal showed excellent performance in CMAE. ROC curve was prepared for the evaluation of anomaly detection performance in the proposed model and the comparison model, and AUC, accuracy, precision, recall, and F1-score were compared. In all indicators, the performance was shown in the order of CMAE, MAE, and AE. Especially, the reproduction rate was 0.9828 for CMAE, which can be confirmed to detect almost most of the abnormalities. The accuracy of the model was also improved and 87.12%, and the F1-score was 0.8883, which is considered to be suitable for anomaly detection. In practical aspect, the proposed model has an additional advantage in addition to performance improvement. The use of techniques such as time series decomposition and sliding windows has the disadvantage of managing unnecessary procedures; and their dimensional increase can cause a decrease in the computational speed in inference.The proposed model has characteristics that are easy to apply to practical tasks such as inference speed and model management.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.45-68
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• 2022

The central and local governments of the Republic of Korea provided information necessary for disaster response through wireless emergency alerts (WEAs) in order to overcome the pandemic situation in which COVID-19 rapidly spreads. Among all channels for delivering disaster information, wireless emergency alert is the most efficient, and since it adopts the CBS(Cell Broadcast Service) method that broadcasts directly to the mobile phone, it has the advantage of being able to easily access disaster information through the mobile phone without the effort of searching. In this study, the characteristics of wireless emergency alerts sent to Seoul during the past year and one month (January 2020 to January 2021) were derived through various text mining methodologies, and various types of information contained in wireless emergency alerts were analyzed. In addition, it was confirmed through the population mobility by age in the districts of Seoul that what kind of influence it had on the movement behavior of people. After going through the process of classifying key words and information included in each character, text analysis was performed so that individual sent characters can be used as an analysis unit by applying a document cluster analysis technique based on the included words. The number of WEAs sent to the Seoul has grown dramatically since the spread of Covid-19. In January 2020, only 10 WEAs were sent to the Seoul, but the number of the WEAs increased 5 times in March, and 7.7 times over the previous months. Since the basic, regional local government were authorized to send wireless emergency alerts independently, the sending behavior of related to wireless emergency alerts are different for each local government. Although most of the basic local governments increased the transmission of WEAs as the number of confirmed cases of Covid-19 increases, the trend of the increase in WEAs according to the increase in the number of confirmed cases of Covid-19 was different by region. By using structured econometric model, the effect of disaster information included in wireless emergency alerts on population mobility was measured by dividing it into baseline effect and accumulating effect. Six types of disaster information, including date, order, online URL, symptom, location, normative guidance, were identified in WEAs and analyzed through econometric modelling. It was confirmed that the types of information that significantly change population mobility by age are different. Population mobility of people in their 60s and 70s decreased when wireless emergency alerts included information related to date and order. As date and order information is appeared in WEAs when they intend to give information about Covid-19 confirmed cases, these results show that the population mobility of higher ages decreased as they reacted to the messages reporting of confirmed cases of Covid-19. Online information (URL) decreased the population mobility of in their 20s, and information related to symptoms reduced the population mobility of people in their 30s. On the other hand, it was confirmed that normative words that including the meaning of encouraging compliance with quarantine policies did not cause significant changes in the population mobility of all ages. This means that only meaningful information which is useful for disaster response should be included in the wireless emergency alerts. Repeated sending of wireless emergency alerts reduces the magnitude of the impact of disaster information on population mobility. It proves indirectly that under the prolonged pandemic, people started to feel tired of getting repetitive WEAs with similar content and started to react less. In order to effectively use WEAs for quarantine and overcoming disaster situations, it is necessary to reduce the fatigue of the people who receive WEA by sending them only in necessary situations, and to raise awareness of WEAs.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.1-23
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• 2013

To discover significant social issues such as unemployment, economy crisis, social welfare etc. that are urgent issues to be solved in a modern society, in the existing approach, researchers usually collect opinions from professional experts and scholars through either online or offline surveys. However, such a method does not seem to be effective from time to time. As usual, due to the problem of expense, a large number of survey replies are seldom gathered. In some cases, it is also hard to find out professional persons dealing with specific social issues. Thus, the sample set is often small and may have some bias. Furthermore, regarding a social issue, several experts may make totally different conclusions because each expert has his subjective point of view and different background. In this case, it is considerably hard to figure out what current social issues are and which social issues are really important. To surmount the shortcomings of the current approach, in this paper, we develop a prototype system that semi-automatically detects social issue keywords representing social issues and problems from about 1.3 million news articles issued by about 10 major domestic presses in Korea from June 2009 until July 2012. Our proposed system consists of (1) collecting and extracting texts from the collected news articles, (2) identifying only news articles related to social issues, (3) analyzing the lexical items of Korean sentences, (4) finding a set of topics regarding social keywords over time based on probabilistic topic modeling, (5) matching relevant paragraphs to a given topic, and (6) visualizing social keywords for easy understanding. In particular, we propose a novel matching algorithm relying on generative models. The goal of our proposed matching algorithm is to best match paragraphs to each topic. Technically, using a topic model such as Latent Dirichlet Allocation (LDA), we can obtain a set of topics, each of which has relevant terms and their probability values. In our problem, given a set of text documents (e.g., news articles), LDA shows a set of topic clusters, and then each topic cluster is labeled by human annotators, where each topic label stands for a social keyword. For example, suppose there is a topic (e.g., Topic1 = {(unemployment, 0.4), (layoff, 0.3), (business, 0.3)}) and then a human annotator labels "Unemployment Problem" on Topic1. In this example, it is non-trivial to understand what happened to the unemployment problem in our society. In other words, taking a look at only social keywords, we have no idea of the detailed events occurring in our society. To tackle this matter, we develop the matching algorithm that computes the probability value of a paragraph given a topic, relying on (i) topic terms and (ii) their probability values. For instance, given a set of text documents, we segment each text document to paragraphs. In the meantime, using LDA, we can extract a set of topics from the text documents. Based on our matching process, each paragraph is assigned to a topic, indicating that the paragraph best matches the topic. Finally, each topic has several best matched paragraphs. Furthermore, assuming there are a topic (e.g., Unemployment Problem) and the best matched paragraph (e.g., Up to 300 workers lost their jobs in XXX company at Seoul). In this case, we can grasp the detailed information of the social keyword such as "300 workers", "unemployment", "XXX company", and "Seoul". In addition, our system visualizes social keywords over time. Therefore, through our matching process and keyword visualization, most researchers will be able to detect social issues easily and quickly. Through this prototype system, we have detected various social issues appearing in our society and also showed effectiveness of our proposed methods according to our experimental results. Note that you can also use our proof-of-concept system in http://dslab.snu.ac.kr/demo.html.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.1-33
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• 2019

The small and medium sized enterprises (hereinafter SMEs) are already at a competitive disadvantaged when compared to large companies with more abundant resources. Manufacturing SMEs not only need a lot of information needed for new product development for sustainable growth and survival, but also seek networking to overcome the limitations of resources, but they are faced with limitations due to their size limitations. In a new era in which connectivity increases the complexity and uncertainty of the business environment, SMEs are increasingly urged to find information and solve networking problems. In order to solve these problems, the government funded research institutes plays an important role and duty to solve the information asymmetry problem of SMEs. The purpose of this study is to identify the differentiating characteristics of SMEs that utilize the public information support infrastructure provided by SMEs to enhance the innovation capacity of SMEs, and how they contribute to corporate performance. We argue that we need an infrastructure for providing information support to SMEs as part of this effort to strengthen of the role of government funded institutions; in this study, we specifically identify the target of such a policy and furthermore empirically demonstrate the effects of such policy-based efforts. Our goal is to help establish the strategies for building the information supporting infrastructure. To achieve this purpose, we first classified the characteristics of SMEs that have been found to utilize the information supporting infrastructure provided by government funded institutions. This allows us to verify whether selection bias appears in the analyzed group, which helps us clarify the interpretative limits of our study results. Next, we performed mediator and moderator effect analysis for multiple variables to analyze the process through which the use of information supporting infrastructure led to an improvement in external networking capabilities and resulted in enhancing product competitiveness. This analysis helps identify the key factors we should focus on when offering indirect support to SMEs through the information supporting infrastructure, which in turn helps us more efficiently manage research related to SME supporting policies implemented by government funded institutions. The results of this study showed the following. First, SMEs that used the information supporting infrastructure were found to have a significant difference in size in comparison to domestic R&D SMEs, but on the other hand, there was no significant difference in the cluster analysis that considered various variables. Based on these findings, we confirmed that SMEs that use the information supporting infrastructure are superior in size, and had a relatively higher distribution of companies that transact to a greater degree with large companies, when compared to the SMEs composing the general group of SMEs. Also, we found that companies that already receive support from the information infrastructure have a high concentration of companies that need collaboration with government funded institution. Secondly, among the SMEs that use the information supporting infrastructure, we found that increasing external networking capabilities contributed to enhancing product competitiveness, and while this was no the effect of direct assistance, we also found that indirect contributions were made by increasing the open marketing capabilities: in other words, this was the result of an indirect-only mediator effect. Also, the number of times the company received additional support in this process through mentoring related to information utilization was found to have a mediated moderator effect on improving external networking capabilities and in turn strengthening product competitiveness. The results of this study provide several insights that will help establish policies. KISTI's information support infrastructure may lead to the conclusion that marketing is already well underway, but it intentionally supports groups that enable to achieve good performance. As a result, the government should provide clear priorities whether to support the companies in the underdevelopment or to aid better performance. Through our research, we have identified how public information infrastructure contributes to product competitiveness. Here, we can draw some policy implications. First, the public information support infrastructure should have the capability to enhance the ability to interact with or to find the expert that provides required information. Second, if the utilization of public information support (online) infrastructure is effective, it is not necessary to continuously provide informational mentoring, which is a parallel offline support. Rather, offline support such as mentoring should be used as an appropriate device for abnormal symptom monitoring. Third, it is required that SMEs should improve their ability to utilize, because the effect of enhancing networking capacity through public information support infrastructure and enhancing product competitiveness through such infrastructure appears in most types of companies rather than in specific SMEs.