• The Journal of Information Systems
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• v.15 no.2
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• pp.247-270
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• 2006

This study identifies communication patterns of groups using Instant Messenger for their group decision-making, and examines how these patterns are associated with creative solutions to problems. Our research suggests that certain communication behavior of groups, when appropriately organized, can be of help in enhancing creative production of outcomes. A qualitative study was conducted on communication patterns based on an analysis of text-based electronic conversation protocols. Specifically this research tried to overcome existing studies on electronic groups by focusing on interactive process of communication among participants. The major study conclusions are: (1) Satisfation of group decision-making may depend on the process or sequence of discussion among group members with Instant Messenger. That is, proper interactive responses and appropriate control of the discussion process are essential to obtain a high level of performance. (2) It is important to ]mike discuss rules based on meta-cognitive and interactive protocols in the early stage. Explicit rules relating to internal group processes as well as communication medium use are even more important to groups with Instant Messenger than face-to-face groups.

• Journal of Information Technology Applications and Management
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• v.14 no.1
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• pp.21-36
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• 2007

We face with numerous situation of decision making. In this situation, we would make decision through individual's own information, or others' decision making with ignoring private information, Also we would make decision through compromise of private information and others' information. like this, we call situation to imitate information of previous decision maker, with disregarding private own information,'information cascades' Also, anchoring effects are results of insufficient adjustment from an arbitrary value. In this paper, we examined how information cascades effects and anchoring effects would be generated in the people who use IT technique as instant poll of website. And this paper presents alternatives to decrease information cascades effects and anchoring effects. This exercise provides facts anchoring effects occur when voters can see poll result. And this paper shows that more degree of output difference is deepened, and more anchoring effects occur. Also this paper shows that when website gives positive payoff, more anchoring effects occur.

• Toxicological Research
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• v.32 no.4
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• pp.275-280
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• 2016

Prenatal nicotine exposure over an entire pregnancy has been associated with an increased prevalence of hyperactivity, anxiety-like behavior and depression-like behavior in mature rats. However, the effects of maternal nicotine exposure in late gestation and lactation on the psychology and behavior of adolescent rat offspring are unclear. Thus, we investigated the effect of nicotine exposure during late gestation and lactation on anxiety-like and impulsive decision-making behavior in adolescent offspring of rat. Female rats were orally exposed to nicotine which is within range of plasma level of human chronic smokers during the period of third last period of gestation and lactation. When the offspring were weaned, we observed alterations in the anxiety-like behavior and decision-making ability of adolescent rat offspring using light/dark box test and T-maze delay-based cost-benefit decision-making task. The maternal consumption of nicotine reduced both the time spent in the light compartment and the number of transitions compared to nicotine-free rats. Moreover, such nicotine exposed adolescent offspring rats showed impulsive decision making which chose the instant reward in a decision-making situation. We found that nicotine exposure during late gestation and lactation induces an increase in anxiety-like and impulsive decision-making behavior at this developmental stage. These findings suggest that maternal nicotine-exposed offspring are at an increased risk of developing anxious and impulsive behavior.

• Journal of the Korean Data and Information Science Society
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• v.16 no.1
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• pp.51-58
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• 2005

The purpose of the Delphi technique is to elicit information and judgments from participants to facilitate problem-solving, planning, and decision-making. It does so without physically assembling the contributors. Instead of it, information is exchanged via mail, FAX, or email. This technique is designed to take advantage of participants' creativity as well as the facilitating effects of group involvement and interaction. It is structured to capitalize on the merits of group problem-solving and minimize the liabilities of group problem-solving. In this paper, we design and implement delphi system using instant messenger.

• Proceedings of the Korea Association of Information Systems Conference
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• 2003.11a
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• pp.41-54
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• 2003

This study examines group process patterns when Instant Messaging is used for decision-making, and examines how these patterns are associated with creative solutions to problems. Our research suggests that certain communication behavior of a group, when appropriately organized, can enhance creative production of outcomes. A qualitative analysis is conducted on communication patterns based on text-based conversation protocols. Specifically, this research tries to extend existing studies on group-work by focusing on the interactive communication process among participants. Study results include that the production of creative outcome depends on the temporal sequence of discussion pattern among group members. (1) Appropriate control of the discussion process is essential to obtain a high level of performance. (2) It is also important to set up discussion rules and rules for the use of communication medium in the early stages of the discussion . (3) Active participants use various protocol types while less-active members rely mainly on 'cognitive' protocols.

• Journal of the Korean Home Economics Association
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• v.42 no.5
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• pp.107-126
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• 2004

As the information society matures, an analysis on possible outputs of internet usage is needed. Thus, this study was peformed in order to understand the utility cognition and negative outputs of internet users. The method used in this study was a web-based questionnaire that was administered to the internet users. Five hundred married men and women were analyzed with a factor and a multiple regression analysis. Results were as follows: First, many kinds of internet usages - information searching, internet shopping, electronic mail, instant messaging, and decision-making dependent on internet information - were differentiated with age. Second, the altitude about the internet was an important explanatory variable in the types of internet usage. Third, negative outputs of internet usage were great in terms of information resource management and unbalanced scheduling in daily time spent. Fourth, utility cognition was affected by qualitative elements on internet usage more than the quantitative ones.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.11
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• pp.2167-2175
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• 2017

In recent years, an IoT is dramatically developing according to the enhancement of AI, the increase of connected devices, and the high-performance cloud systems. Huge data produced by many devices and sensors is expanding the scope of services, such as an intelligent diagnostics, a recommendation service, as well as a smart monitoring service. The studies of edge computing are limited as a role of small server system with high quality HW resources. However, there are specialized requirements in a smart factory domain needed edge computing. The edges are needed to pre-process containing tiny filtering, pre-formatting, as well as merging of group contexts and manage the regional rules. So, in this paper, we extract the features and requirements in a scope of efficiency and robustness. Our edge offers to decrease a network resource consumption and update rules and learning models. Moreover, we propose architecture of edge computing based on learning factors sharing with a cloud system in a smart factory.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.155-175
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• 2017

As social data become into the spotlight, mainstream web search engines provide data indicate how many people searched specific keyword: Web Search Traffic data. Web search traffic information is collection of each crowd that search for specific keyword. In a various area, web search traffic can be used as one of useful variables that represent the attention of common users on specific interests. A lot of studies uses web search traffic data to nowcast or forecast social phenomenon such as epidemic prediction, consumer pattern analysis, product life cycle, financial invest modeling and so on. Also web search traffic data have begun to be applied to predict tourist inbound. Proper demand prediction is needed because tourism is high value-added industry as increasing employment and foreign exchange. Among those tourists, especially Chinese tourists: Youke is continuously growing nowadays, Youke has been largest tourist inbound of Korea tourism for many years and tourism profits per one Youke as well. It is important that research into proper demand prediction approaches of Youke in both public and private sector. Accurate tourism demands prediction is important to efficient decision making in a limited resource. This study suggests improved model that reflects latest issue of society by presented the attention from group of individual. Trip abroad is generally high-involvement activity so that potential tourists likely deep into searching for information about their own trip. Web search traffic data presents tourists' attention in the process of preparation their journey instantaneous and dynamic way. So that this study attempted select key words that potential Chinese tourists likely searched out internet. Baidu-Chinese biggest web search engine that share over 80%- provides users with accessing to web search traffic data. Qualitative interview with potential tourists helps us to understand the information search behavior before a trip and identify the keywords for this study. Selected key words of web search traffic are categorized by how much directly related to "Korean Tourism" in a three levels. Classifying categories helps to find out which keyword can explain Youke inbound demands from close one to far one as distance of category. Web search traffic data of each key words gathered by web crawler developed to crawling web search data onto Baidu Index. Using automatically gathered variable data, linear model is designed by multiple regression analysis for suitable for operational application of decision and policy making because of easiness to explanation about variables' effective relationship. After regression linear models have composed, comparing with model composed traditional variables and model additional input web search traffic data variables to traditional model has conducted by significance and R squared. after comparing performance of models, final model is composed. Final regression model has improved explanation and advantage of real-time immediacy and convenience than traditional model. Furthermore, this study demonstrates system intuitively visualized to general use -Youke Mining solution has several functions of tourist decision making including embed final regression model. Youke Mining solution has algorithm based on data science and well-designed simple interface. In the end this research suggests three significant meanings on theoretical, practical and political aspects. Theoretically, Youke Mining system and the model in this research are the first step on the Youke inbound prediction using interactive and instant variable: web search traffic information represents tourists' attention while prepare their trip. Baidu web search traffic data has more than 80% of web search engine market. Practically, Baidu data could represent attention of the potential tourists who prepare their own tour as real-time. Finally, in political way, designed Chinese tourist demands prediction model based on web search traffic can be used to tourism decision making for efficient managing of resource and optimizing opportunity for successful policy.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.89-105
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• 2014

After emergence of Internet, social media with highly interactive Web 2.0 applications has provided very user friendly means for consumers and companies to communicate with each other. Users have routinely published contents involving their opinions and interests in social media such as blogs, forums, chatting rooms, and discussion boards, and the contents are released real-time in the Internet. For that reason, many researchers and marketers regard social media contents as the source of information for business analytics to develop business insights, and many studies have reported results on mining business intelligence from Social media content. In particular, opinion mining and sentiment analysis, as a technique to extract, classify, understand, and assess the opinions implicit in text contents, are frequently applied into social media content analysis because it emphasizes determining sentiment polarity and extracting authors' opinions. A number of frameworks, methods, techniques and tools have been presented by these researchers. However, we have found some weaknesses from their methods which are often technically complicated and are not sufficiently user-friendly for helping business decisions and planning. In this study, we attempted to formulate a more comprehensive and practical approach to conduct opinion mining with visual deliverables. First, we described the entire cycle of practical opinion mining using Social media content from the initial data gathering stage to the final presentation session. Our proposed approach to opinion mining consists of four phases: collecting, qualifying, analyzing, and visualizing. In the first phase, analysts have to choose target social media. Each target media requires different ways for analysts to gain access. There are open-API, searching tools, DB2DB interface, purchasing contents, and so son. Second phase is pre-processing to generate useful materials for meaningful analysis. If we do not remove garbage data, results of social media analysis will not provide meaningful and useful business insights. To clean social media data, natural language processing techniques should be applied. The next step is the opinion mining phase where the cleansed social media content set is to be analyzed. The qualified data set includes not only user-generated contents but also content identification information such as creation date, author name, user id, content id, hit counts, review or reply, favorite, etc. Depending on the purpose of the analysis, researchers or data analysts can select a suitable mining tool. Topic extraction and buzz analysis are usually related to market trends analysis, while sentiment analysis is utilized to conduct reputation analysis. There are also various applications, such as stock prediction, product recommendation, sales forecasting, and so on. The last phase is visualization and presentation of analysis results. The major focus and purpose of this phase are to explain results of analysis and help users to comprehend its meaning. Therefore, to the extent possible, deliverables from this phase should be made simple, clear and easy to understand, rather than complex and flashy. To illustrate our approach, we conducted a case study on a leading Korean instant noodle company. We targeted the leading company, NS Food, with 66.5% of market share; the firm has kept No. 1 position in the Korean "Ramen" business for several decades. We collected a total of 11,869 pieces of contents including blogs, forum contents and news articles. After collecting social media content data, we generated instant noodle business specific language resources for data manipulation and analysis using natural language processing. In addition, we tried to classify contents in more detail categories such as marketing features, environment, reputation, etc. In those phase, we used free ware software programs such as TM, KoNLP, ggplot2 and plyr packages in R project. As the result, we presented several useful visualization outputs like domain specific lexicons, volume and sentiment graphs, topic word cloud, heat maps, valence tree map, and other visualized images to provide vivid, full-colored examples using open library software packages of the R project. Business actors can quickly detect areas by a swift glance that are weak, strong, positive, negative, quiet or loud. Heat map is able to explain movement of sentiment or volume in categories and time matrix which shows density of color on time periods. Valence tree map, one of the most comprehensive and holistic visualization models, should be very helpful for analysts and decision makers to quickly understand the "big picture" business situation with a hierarchical structure since tree-map can present buzz volume and sentiment with a visualized result in a certain period. This case study offers real-world business insights from market sensing which would demonstrate to practical-minded business users how they can use these types of results for timely decision making in response to on-going changes in the market. We believe our approach can provide practical and reliable guide to opinion mining with visualized results that are immediately useful, not just in food industry but in other industries as well.