• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.117-129
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• 2015

As the Internet becomes ubiquitous, a large volume of information is posted on the Internet with exponential growth every day. Accordingly, it is not unusual that investors in stock markets gather and compile firm-specific or market-wide information through online searches. Importantly, it becomes easier for investors to acquire value-relevant information for their investment decision with the help of powerful search tools on the Internet. Our study examines whether or not the Internet helps investors assess a firm's value better by using firm-level data over long periods spanning from January 2004 to December 2013. To this end, we construct weekly-based search volume for information technology (IT) services firms on the Internet. We limit our focus to IT firms since they are often equipped with intangible assets and relatively less recognized to the public which makes them hard-to measure. To obtain the information on those firms, investors are more likely to consult the Internet and use the information to appreciate the firms more accurately and eventually improve their investment decisions. Prior studies have shown that changes in search volumes can reflect the various aspects of the complex human behaviors and forecast near-term values of economic indicators, including automobile sales, unemployment claims, and etc. Moreover, search volume of firm names or stock ticker symbols has been used as a direct proxy of individual investors' attention in financial markets since, different from indirect measures such as turnover and extreme returns, they can reveal and quantify the interest of investors in an objective way. Following this line of research, this study aims to gauge whether the information retrieved from the Internet is value relevant in assessing a firm. We also use search volume for analysis but, distinguished from prior studies, explore its impact on return comovements with market returns. Given that a firm's returns tend to comove with market returns excessively when investors are less informed about the firm, we empirically test the value of information by examining the association between Internet searches and the extent to which a firm's returns comove. Our results show that Internet searches are negatively associated with return comovements as expected. When sample is split by the size of firms, the impact of Internet searches on return comovements is shown to be greater for large firms than small ones. Interestingly, we find a greater impact of Internet searches on return comovements for years from 2009 to 2013 than earlier years possibly due to more aggressive and informative exploit of Internet searches in obtaining financial information. We also complement our analyses by examining the association between return volatility and Internet search volumes. If Internet searches capture investors' attention associated with a change in firm-specific fundamentals such as new product releases, stock splits and so on, a firm's return volatility is likely to increase while search results can provide value-relevant information to investors. Our results suggest that in general, an increase in the volume of Internet searches is not positively associated with return volatility. However, we find a positive association between Internet searches and return volatility when the sample is limited to larger firms. A stronger result from larger firms implies that investors still pay less attention to the information obtained from Internet searches for small firms while the information is value relevant in assessing stock values. However, we do find any systematic differences in the magnitude of Internet searches impact on return volatility by time periods. Taken together, our results shed new light on the value of information searched from the Internet in assessing stock values. Given the informational role of the Internet in stock markets, we believe the results would guide investors to exploit Internet search tools to be better informed, as a result improving their investment decisions.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.39-57
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• 2012

Recently, due to the introduction of high-tech equipment in interactive exhibits, many people's attention has been concentrated on Interactive exhibits that can double the exhibition effect through the interaction with the audience. In addition, it is also possible to measure a variety of audience reaction in the interactive exhibition. Among various audience reactions, this research uses the change of the facial features that can be collected in an interactive exhibition space. This research develops an artificial neural network-based prediction model to predict the response of the audience by measuring the change of the facial features when the audience is given stimulation from the non-excited state. To present the emotion state of the audience, this research uses a Valence-Arousal model. So, this research suggests an overall framework composed of the following six steps. The first step is a step of collecting data for modeling. The data was collected from people participated in the 2012 Seoul DMC Culture Open, and the collected data was used for the experiments. The second step extracts 64 facial features from the collected data and compensates the facial feature values. The third step generates independent and dependent variables of an artificial neural network model. The fourth step extracts the independent variable that affects the dependent variable using the statistical technique. The fifth step builds an artificial neural network model and performs a learning process using train set and test set. Finally the last sixth step is to validate the prediction performance of artificial neural network model using the validation data set. The proposed model is compared with statistical predictive model to see whether it had better performance or not. As a result, although the data set in this experiment had much noise, the proposed model showed better results when the model was compared with multiple regression analysis model. If the prediction model of audience reaction was used in the real exhibition, it will be able to provide countermeasures and services appropriate to the audience's reaction viewing the exhibits. Specifically, if the arousal of audience about Exhibits is low, Action to increase arousal of the audience will be taken. For instance, we recommend the audience another preferred contents or using a light or sound to focus on these exhibits. In other words, when planning future exhibitions, planning the exhibition to satisfy various audience preferences would be possible. And it is expected to foster a personalized environment to concentrate on the exhibits. But, the proposed model in this research still shows the low prediction accuracy. The cause is in some parts as follows : First, the data covers diverse visitors of real exhibitions, so it was difficult to control the optimized experimental environment. So, the collected data has much noise, and it would results a lower accuracy. In further research, the data collection will be conducted in a more optimized experimental environment. The further research to increase the accuracy of the predictions of the model will be conducted. Second, using changes of facial expression only is thought to be not enough to extract audience emotions. If facial expression is combined with other responses, such as the sound, audience behavior, it would result a better result.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.177-192
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• 2012

This is a study of match-making systems that considers the mutual satisfaction of matching partners. Recently, recommendation systems have been applied to people recommendation, such as recommending new friends, employees, or dating partners. One of the prominent domain areas is match-making systems that recommend suitable dating partners to customers. A match-making system, however, is different from a product recommender system. First, a match-making system needs to satisfy the recommended partners as well as the customer, whereas a product recommender system only needs to satisfy the customer. Second, match-making systems need to include as many participants in a matching pool as possible for their recommendation results, even with unpopular customers. In other words, recommendations should not be focused only on a limited number of popular people; unpopular people should also be listed on someone else's matching results. In product recommender systems, it is acceptable to recommend the same popular items to many customers, since these items can easily be additionally supplied. However, in match-making systems, there are only a few popular people, and they may become overburdened with too many recommendations. Also, a successful match could cause a customer to drop out of the matching pool. Thus, match-making systems should provide recommendation services equally to all customers without favoring popular customers. The suggested match-making system, called Mutually Beneficial Matching (MBM), considers the reciprocal satisfaction of both the customer and the matched partner and also considers the number of customers who are excluded in the matching. A brief outline of the MBM method is as follows: First, it collects a customer's profile information, his/her preferable dating partner's profile information and the weights that he/she considers important when selecting dating partners. Then, it calculates the preference score of a customer to certain potential dating partners on the basis of the difference between them. The preference score of a certain partner to a customer is also calculated in this way. After that, the mutual preference score is produced by the two preference values calculated in the previous step using the proposed formula in this study. The proposed formula reflects the symmetry of preferences as well as their quantities. Finally, the MBM method recommends the top N partners having high mutual preference scores to a customer. The prototype of the suggested MBM system is implemented by JAVA and applied to an artificial dataset that is based on real survey results from major match-making companies in Korea. The results of the MBM method are compared with those of the other two conventional methods: Preference-Based Matching (PBM), which only considers a customer's preferences, and Arithmetic Mean-Based Matching (AMM), which considers the preferences of both the customer and the partner (although it does not reflect their symmetry in the matching results). We perform the comparisons in terms of criteria such as average preference of the matching partners, average symmetry, and the number of people who are excluded from the matching results by changing the number of recommendations to 5, 10, 15, 20, and 25. The results show that in many cases, the suggested MBM method produces average preferences and symmetries that are significantly higher than those of the PBM and AMM methods. Moreover, in every case, MBM produces a smaller pool of excluded people than those of the PBM method.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.147-161
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• 2010

As broadcasting and communication are converged recently, communication is jointed to TV. TV viewing has brought about many changes. The IPTV (Internet Protocol Television) provides information service, movie contents, broadcast, etc. through internet with live programs + VOD (Video on demand) jointed. Using communication network, it becomes an issue of new business. In addition, new technical issues have been created by imaging technology for the service, networking technology without video cuts, security technologies to protect copyright, etc. Through this IPTV network, users can watch their desired programs when they want. However, IPTV has difficulties in search approach, menu approach, or finding programs. Menu approach spends a lot of time in approaching programs desired. Search approach can't be found when title, genre, name of actors, etc. are not known. In addition, inserting letters through remote control have problems. However, the bigger problem is that many times users are not usually ware of the services they use. Thus, to resolve difficulties when selecting VOD service in IPTV, a personalized service is recommended, which enhance users' satisfaction and use your time, efficiently. This paper provides appropriate programs which are fit to individuals not to save time in order to solve IPTV's shortcomings through filtering and recommendation-related system. The proposed recommendation system collects TV program information, the user's preferred program genres and detailed genre, channel, watching program, and information on viewing time based on individual records of watching IPTV. To look for these kinds of similarities, similarities can be compared by using ontology for TV programs. The reason to use these is because the distance of program can be measured by the similarity comparison. TV program ontology we are using is one extracted from TV-Anytime metadata which represents semantic nature. Also, ontology expresses the contents and features in figures. Through world net, vocabulary similarity is determined. All the words described on the programs are expanded into upper and lower classes for word similarity decision. The average of described key words was measured. The criterion of distance calculated ties similar programs through K-medoids dividing method. K-medoids dividing method is a dividing way to divide classified groups into ones with similar characteristics. This K-medoids method sets K-unit representative objects. Here, distance from representative object sets temporary distance and colonize it. Through algorithm, when the initial n-unit objects are tried to be divided into K-units. The optimal object must be found through repeated trials after selecting representative object temporarily. Through this course, similar programs must be colonized. Selecting programs through group analysis, weight should be given to the recommendation. The way to provide weight with recommendation is as the follows. When each group recommends programs, similar programs near representative objects will be recommended to users. The formula to calculate the distance is same as measure similar distance. It will be a basic figure which determines the rankings of recommended programs. Weight is used to calculate the number of watching lists. As the more programs are, the higher weight will be loaded. This is defined as cluster weight. Through this, sub-TV programs which are representative of the groups must be selected. The final TV programs ranks must be determined. However, the group-representative TV programs include errors. Therefore, weights must be added to TV program viewing preference. They must determine the finalranks.Based on this, our customers prefer proposed to recommend contents. So, based on the proposed method this paper suggested, experiment was carried out in controlled environment. Through experiment, the superiority of the proposed method is shown, compared to existing ways.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.99-120
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• 2010

Due to the wide spread of customers' frequent access of non face-to-face services, there have been many attempts to improve customer satisfaction using huge amounts of data accumulated throughnon face-to-face channels. Usually, a call center is regarded to be one of the most representative non-faced channels. Therefore, it is important that a call center has enough agents to offer high level customer satisfaction. However, managing too many agents would increase the operational costs of a call center by increasing labor costs. Therefore, predicting and calculating the appropriate size of human resources of a call center is one of the most critical success factors of call center management. For this reason, most call centers are currently establishing a department of WFM(Work Force Management) to estimate the appropriate number of agents and to direct much effort to predict the volume of inbound calls. In real world applications, inbound call prediction is usually performed based on the intuition and experience of a domain expert. In other words, a domain expert usually predicts the volume of calls by calculating the average call of some periods and adjusting the average according tohis/her subjective estimation. However, this kind of approach has radical limitations in that the result of prediction might be strongly affected by the expert's personal experience and competence. It is often the case that a domain expert may predict inbound calls quite differently from anotherif the two experts have mutually different opinions on selecting influential variables and priorities among the variables. Moreover, it is almost impossible to logically clarify the process of expert's subjective prediction. Currently, to overcome the limitations of subjective call prediction, most call centers are adopting a WFMS(Workforce Management System) package in which expert's best practices are systemized. With WFMS, a user can predict the volume of calls by calculating the average call of each day of the week, excluding some eventful days. However, WFMS costs too much capital during the early stage of system establishment. Moreover, it is hard to reflect new information ontothe system when some factors affecting the amount of calls have been changed. In this paper, we attempt to devise a new model for predicting inbound calls that is not only based on theoretical background but also easily applicable to real world applications. Our model was mainly developed by the interactive decision tree technique, one of the most popular techniques in data mining. Therefore, we expect that our model can predict inbound calls automatically based on historical data, and it can utilize expert's domain knowledge during the process of tree construction. To analyze the accuracy of our model, we performed intensive experiments on a real case of one of the largest car insurance companies in Korea. In the case study, the prediction accuracy of the devised two models and traditional WFMS are analyzed with respect to the various error rates allowable. The experiments reveal that our data mining-based two models outperform WFMS in terms of predicting the amount of accident calls and fault calls in most experimental situations examined.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.

• Journal of Korean Society of Rural Planning
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• v.20 no.4
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• pp.253-262
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• 2014

This, an association research for design-improvement and model-development of exhibition shops at rural tourism communities, is to secure objective data by analyzing customers' awareness-tendency of and demand for agricultural-specialty exhibition shops. Survey-questions for finding out consumers' awareness-tendency and demand were determined through brainstorming of a professional council, 30 rural communities of which visit-rate by consumers is considerably high were selected for the recruit of 200 consumers. For investigation and analysis, survey and in-depth interview were carried out at the scene with the application of frequency analysis and summarization of their opinions, which revealed that they have a strong will to visit the rural tourism communities for the purchase of agricultural specialties along with the experience of learning-program and on-the-scene direct dealing and that their viewpoint on the direct dealing at the scene was very positive. Also it was confirmed hat their satisfaction with the purchase of agricultural specialties by on-the-scene direct dealing, their pleasure at the purchase, their satisfaction with services and their intention for re-purchase of them were very high while their satisfaction with the exhibition shops was very low. With on-the-scene survey, the consumers' opinions could be listened to in depth. Almost all of them said their satisfaction with the trip to those rural tourism communities was considerably high since they could go to those communities themselves to relieve the stress from their modern life, to experience healing and to see the goods on the scene. Their satisfaction also was attributed to the fact that they have enough trust in purchase along with feeling the warm-heartedness of rural residents. As to their awareness of exhibition shops, they showed a positive response to the on-the-scene direct dealing at rural communities while they, thinking that the space in those exhibition shops was not sufficiently wide, demanded for more systematic counters in more accessible and affordable exhibition shops so that they might be more satisfied with the exhibition shops. Their demand for the necessity of exhibition shops selling agricultural specialties was found to be over 80%, which indicates that the necessity is very high. As to the suitability of function, they have the opinion that the business at those shops had better be focused on sales since they have the understanding of information when they take a trip to the rural communities, while there was another opinion: since agricultural products are seasonal items they should be exhibited and sold at the same time. More than 90% of the respondents had a positive viewpoint on direct dealing of agricultural specialties on the scene, which showed that their response to it was very high. They preferred the permanent shops equipped with roll-around table-booths. In addition, it was revealed that they want systematic exhibition shops in rural communities because they frequent those communities for on-the-scene direct purchase. The preferred type and opinion resulting from estimation of consumers' demands have been reflected for development of practical designs. The structure of variable principles has been designed so that the types of display-case and table-booth might be created. The result of this study is a positive data as a design model which can be utilized at rural communities and will be commercialized for the verification of its validity.

• Korean Security Journal
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• no.43
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• pp.67-97
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• 2015

In Korea, a single emergency number, such as 911 of the USA and 999 of the UK, does not exist. This issue became highly controversial, when the Sewol Ferry Sinking disaster occurred last year. So, the Korean government has planned to adopt a single emergency number, integrating 112 of the Police, 119 of the Fire and Ambulance, 122 of the Korean Coast Guard, and many other emergency numbers. However, the integration plan recently proposed by the Ministry of Public Safety Security seems to be, what is called, a "partial integration model" which repeals the 122 number, but still maintains 112, 119, and 110 respectively. In this context, the study looks into USA's (diverse) 911 operating system, and subsequently tries to draw general features or characteristics. Further, the research attempts to derive policy implication from the general features. If the proposed partial integration model reflects the policy implications, the model can virtually operate like the 911 system -i.e. a single emergency number system - creating inter-operability between responding agencies such as police, fire, and ambulance, even though it is not a perfect integration model. The features drawn are (1) integration of emergency call-taking, (2) functional separation of call-taking and dispatching, (3) integration of physical facilities for call-taking and dispatching, and (4) professional call-takers and dispatchers. Moreover, the policy implications derived from the characteristics are (1) a user-friendly system - fast but accurate responses, (2) integrated responses to accidents, (3) professional call-taking and dispatching & objective and comprehensive risk assessment, and finally (4) active organizational learning in emergency call centers. Considering the policy implications, the following suggestions need to be applied to the current proposed plan: 1. Emergency services' systems should be tightly linked and connected in a systemic way so that they can communicate and exchange intelligence with one another. 2. Public safety answering points (call centers) of each emergency service should share their education and training modules, manuals, etc. Common training and manuals are also needed for inter-operability. 3. Personal management to enable-long term service in public safety answering points (call centers) should be established as one of the ways to promote professionalism.

• Korea journal of population studies
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• v.10 no.1
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• pp.70-86
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• 1987

Going beyond the previous formulations of development theories, the present paper explores the effects other than political economy on quality of life in a rapidly developing country. The major analysis takes up the historical trend and nature of the developmental transformation that is partially a consequences of state structures and partially autonomous form it in South Korea. Also, it diagnoses developmental pathways for the future track by constructing a baseline model for state transition on the basis of power game between the state and civil society in the country. The results of the historical analysis show that civil society has been transformed in the course of confrontations and interactions between the state and nationalist social movement. The distinction between developmental(or bureaucratic authoritarian) and democratic state is presented to show that these are two qualitatively different aspects of state of state power, requiring separate analytical treatment. Furthermore, the state-centric approach which emphasizes the active role of the state at the sacrifice of societal fabric-constraining social conditions for quality of life - appears to be modified. On the contrary, the impact of civil society is transmitted both directly and indirectly via labor and ecological movement for quality of life, which is critical to the formation of the welfare state in the country. The prospect for sustainable development in Korea lies in providng and expanding quality of life in terms of the financial feasibility of the state through the public-private cooperation, and abstaining from drastic and radical commitment to welfare services as is the case with the European declines in welfare state, Further studies are needed to examine the interrelationships in different historical and cultural settings of developing counties to estimate a theory of quality of life and social justice.

• Research in Plant Disease
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• v.11 no.1
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• pp.28-34
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• 2005

This study was conducted to investigate the control possibility of garlic white rot causing severe yield losses of Allium species and cultivars using cultural practices such as optimal sowing date and burial depth, and lime application. Inoculum density in infested field soil was investigated at different soil depth, and that on the diseased plant debris was done. Inoculum density and recovery ratio of white rot pathogen of garlic was highly different between two species of Sclerotium cepivorum forming comparatively small sclerotia and Sclerotium sp. forming comparatively large ones. It was confirmed that S. cepivorum formed more sclerotia on bulbs of garlic than S. sp., and sclerotial recovery of S. cepivorum was higher than that of S. sp. Inoculum density of white rot pathogen in the infested field at garlic seeding period ranged from one to thirteen sclerotia per 30 g soil. Inoculum density of white rot pathogen decreased remarkably with increasing soil depth and above 95% of sclerotia were distributed within 5 cm of soil depth. Disease severity of white rot was higher on slightly planted garlics than deeply-planted ones. Garlic seed bulbs infected by white rot pathogens were confirmed to be one of main inoculum sources of white rot in the field and the disease incidences caused by garlic seed transmission showed big differences among garlic varieties. When nine garlic varieties harvested from infested plots were sown in the field, highly susceptible varieties, ‘Wando’, ‘Daeseo’, ‘Namdo’ and ‘Kodang’ showed high disease incidences, whereas other five varieties were not infected at all. It was confirmed that white rot occurred higher on early-sown garlics, before middle October, than on late-sown ones, after late October. Meanwhile, increasing application rate of lime ranged from 100 to 300 g reduced disease severity of white rot.