• Journal of Families and Better Life
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• v.10 no.1 s.19
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• pp.27-42
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• 1992

This study focused on consumer information search activity and consumer's post-purchase satisfaction. For these purpose, a survey was conducted suing questionaires on 430 homemakers that lived in seoul. Statistics used for data were Frequency Distribution. Percentile, Mean, One-way AAANOVA., Scheffe-test, T-test, Pearson's correlation. Multiple Regression Analysis and Multiple Classification Analysis. The major findings were ; 1) The level of each amount information search was lower than average. And the level of consumer's post-purchase satisfaction was a little higher than average. 2) On amount of "noncommercial-personal" information search, the influencing variables were desire to seek information, education, brand royalty in turn. These three variables explained 7% of dependent variable's variance. 3) On amount of "noncommercial-media" information search, the influencing variables were desire to seek information, amount of internal information, education, occupational status in turn. These variables explained 14% of dependent variable's variance. 4) On amount of "commercial-personal" information search, the influencing variable was desire to seek information, and this variable explained 3.1% of dependent variable'a variance. 5) On amount of "commercial-media" information search, the influencing variables were desire to seek information, education, amount of internal information in turn. These three variables explained 12.1% dependent variable's variance. 6) Resulting from multiple classification analysis, influencing variables on consumer's post-purchase satisfaction were amount of noncommercial-media information search and printed media search, and brand royalty. These three variables explained 9% of dependent variable's variance. Furthermore, througout all the subareas of consumer's satisfaction, the amount of noncommercial-media information search was the most influencing variable.

• Journal of Korean Society of Archives and Records Management
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• v.11 no.1
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• pp.93-112
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• 2011

This study investigated whether search terms analysis of archival Web sites could be utilized effectively for archival information services. The focus was on designing a methodology which brings the search terms analysis and development of archival information services closer, especially for the contents services. The data were collected from the Presidential Archives because it can be characterized as a public archives as well as subject archives. It also tends to draw interests from a broad range of general public. The analysis was conducted with respect to three dimensions: (1) general search terms; (2) names of individual president; (3) subject categories of search terms. The results of search terms analysis have a number of practical implications for developing archival information services including contents services, decision on the menu of the Web sites, exhibition, and education.

• International Journal of Advanced Culture Technology
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• v.11 no.4
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• pp.378-384
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• 2023

In this study, we explore social awareness, interest, and acceptance of generative AI, including chatGPT, which has revolutionized web search, 30 years after web search was released. For this purpose, we performed a machine learning-based topic modeling analysis based on Korean news big data collected from November 30, 2022, when chatGPT was released, to August 31, 2023. As a result of our research, we have identified seven topics related to chatGPT and generative AI; (1)growth of the high-performance hardware market, (2)service contents using generative AI, (3)technology development competition, (4)human resource development, (5)instructions for use, (6)revitalizing the domestic ecosystem, (7)expectations and concerns. We also explored monthly frequency changes in topics to explore social interest related to chatGPT and Generative AI. Based on our exploration results, we discussed the high social interest and issues regarding generative AI. We expect that the results of this study can be used as a precursor to research that analyzes and predicts the diffusion of innovation in generative AI.

• Journal of Convergence for Information Technology
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• v.10 no.1
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• pp.139-148
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• 2020

As the internet and computer technology are developed, there is a need for service of traditional culture that can effectively search and create culture, history, and tradition-related materials in online contents. In this paper, we developed an RDF-based ontology retrieval service platform and verified usability and validity. This platform is divided into triple search, keyword search, network graph search, story search and management, curation management module. Based on this, the search results can be visualized based on the relationship between data, network graph search and story search can be used to easily understand the relationship between the keywords. An platform evaluation was conducted for verification, and it was evaluated that an intelligent search that can easily identify the relationship between information and shorten the analysis and search time than the existing search function.

• Journal of Digital Convergence
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• v.15 no.1
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• pp.527-536
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• 2017

The aim of this study was to develop an understanding of the mediating effect of job search efficacy on perceived career development support and job support intensity of university students of physical education major. Data were collected by a total of 223 junior and senior students who are undergraduate majoring in physical education in Seoul, Chungnam, and Gyeongsang Provinvces. For the study, the reliability and validity test of the questionnaire and correlation analysis were conducted by using SPSS 20.0 program, and Structural Equation Model(SEM) using AMOS 20.0 program was conducted to analyze the data. The results were as follows. First, career development support perceived by university students of physical education majors had a statistically significant effect on job search efficacy, whereas it had no significant effect on job search intensity. Second, job search efficacy of the students had a statistically significant effect on job search intensity. Lastly, it was found that job search efficacy totally mediated the relationship between perceived career development support and job search intensity.

• The Journal of Bigdata
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• v.5 no.1
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• pp.77-88
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• 2020

Recently, many researches habe been done to organize and analyze various complex relationships in real world, represented in the form of graphs. In particular, the computer field literature data system, such as DBLP, is a representative graph data in which can be composed of papers, their authors, and citation among papers. Becasue graph data is very complex in storage structure and expression, it is very difficult task to search, analysis, and visualize a large size of bibliographic big data. In this paper, we develop a graphic user interface tool, called EEUM, which visualizes bibliographic big data in the form of graphs. EEUM provides the features to browse bibliographic big data according to the connected graph structure by visually displaying graph data, and implements search, management and analysis of the bibliographc big data. It also shows that EEUM can be conveniently used to search, explore, and analyze by applying EEUM to the bibliographic graph big data provided by DBLP. Through EEUM, you can easily find influential authors or papers in every research fields, and conveniently use it as a search and analysis tool for complex bibliographc big data, such as giving you a glimpse of all the relationships between several authors and papers.

• Journal of Korean Port Research
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• v.12 no.2
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• pp.373-384
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• 1998

In search and rescue mission the leeway formula based on the field experiments are utilized for the estimation of wind effect on distressed targets. This paper summarized the leeway formula from the available references. In the summary the environmental data collection method and experimental conditions are described along with the formula. Also the formula currently used in CASP of the U.S. Coast Gurard and CANSARP of the Canadian Coast Guard are discussed.

• Fashion & Textile Research Journal
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• v.19 no.1
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• pp.19-29
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• 2017

This study identified the purchase decision-making process of outdoor wear consumers and examined the hypothesis that consumer satisfaction mediates the relationship between purchase decision-making process and repurchase intention. Data were acquired from a survey and analyzed with descriptive, factor analysis, reliability analysis and multiple regression analysis. Respondents were 454 adults who have purchased outdoor wear. The results are as follows. First, the purchase decision-making processes of outdoor wear consumers consisted of a series of steps: need recognition & passive information search, active information search, evaluation of alternatives with practical attributes, evaluation of alternatives with unpractical attributes, purchase decision, and post-purchase evaluation. Second, four purchase decision-making processes (except for need recognition & passive information search and active information search) had significantly positive effects on consumer satisfaction. Third, the need recognition & passive information search, the evaluation of alternatives with unpractical attributes and post-purchase evaluation had significantly positive effects on repurchase intention. Lastly, the partial mediation of consumer satisfaction in the relationship between two purchase decision-making processes (evaluation of alternatives with unpractical attributes and post-purchase evaluation) and repurchase intention were indicated. This academic study will help to understand the purchase decision-making processes of outdoor wear and allow companies to obtain information (from the industrial aspect) about which process to invest in and how to manage the process.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.93-111
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• 2013

As Internet and information technology (IT) continues to develop and evolve, the issue of big data has emerged at the foreground of scholarly and industrial attention. Big data is generally defined as data that exceed the range that can be collected, stored, managed and analyzed by existing conventional information systems and it also refers to the new technologies designed to effectively extract values from such data. With the widespread dissemination of IT systems, continual efforts have been made in various fields of industry such as R&D, manufacturing, and finance to collect and analyze immense quantities of data in order to extract meaningful information and to use this information to solve various problems. Since IT has converged with various industries in many aspects, digital data are now being generated at a remarkably accelerating rate while developments in state-of-the-art technology have led to continual enhancements in system performance. The types of big data that are currently receiving the most attention include information available within companies, such as information on consumer characteristics, information on purchase records, logistics information and log information indicating the usage of products and services by consumers, as well as information accumulated outside companies, such as information on the web search traffic of online users, social network information, and patent information. Among these various types of big data, web searches performed by online users constitute one of the most effective and important sources of information for marketing purposes because consumers search for information on the internet in order to make efficient and rational choices. Recently, Google has provided public access to its information on the web search traffic of online users through a service named Google Trends. Research that uses this web search traffic information to analyze the information search behavior of online users is now receiving much attention in academia and in fields of industry. Studies using web search traffic information can be broadly classified into two fields. The first field consists of empirical demonstrations that show how web search information can be used to forecast social phenomena, the purchasing power of consumers, the outcomes of political elections, etc. The other field focuses on using web search traffic information to observe consumer behavior, identifying the attributes of a product that consumers regard as important or tracking changes on consumers' expectations, for example, but relatively less research has been completed in this field. In particular, to the extent of our knowledge, hardly any studies related to brands have yet attempted to use web search traffic information to analyze the factors that influence consumers' purchasing activities. This study aims to demonstrate that consumers' web search traffic information can be used to derive the relations among brands and the relations between an individual brand and product attributes. When consumers input their search words on the web, they may use a single keyword for the search, but they also often input multiple keywords to seek related information (this is referred to as simultaneous searching). A consumer performs a simultaneous search either to simultaneously compare two product brands to obtain information on their similarities and differences, or to acquire more in-depth information about a specific attribute in a specific brand. Web search traffic information shows that the quantity of simultaneous searches using certain keywords increases when the relation is closer in the consumer's mind and it will be possible to derive the relations between each of the keywords by collecting this relational data and subjecting it to network analysis. Accordingly, this study proposes a method of analyzing how brands are positioned by consumers and what relationships exist between product attributes and an individual brand, using simultaneous search traffic information. It also presents case studies demonstrating the actual application of this method, with a focus on tablets, belonging to innovative product groups.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.9
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• pp.387-398
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• 2023

The COVID-19 outbreak has significantly impacted human lifestyles and patterns. It was recommended to avoid face-to-face contact and over-crowded indoor places as much as possible as COVID-19 spreads through air, as well as through droplets or aerosols. Therefore, if a person who has contacted a COVID-19 patient or was at the place where the COVID-19 patient occurred is concerned that he/she may have been infected with COVID-19, it can be fully expected that he/she will search for COVID-19 symptoms on Google. In this study, an exploratory data analysis using deep learning models(DNN & LSTM) was conducted to see if we could predict the number of confirmed COVID-19 cases by summoning Google Trends, which played a major role in surveillance and management of influenza, again and combining it with data on the number of confirmed COVID-19 cases. In particular, search term frequency data used in this study are available publicly and do not invade privacy. When the deep neural network model was applied, Seoul (9.6 million) with the largest population in South Korea and Busan (3.4 million) with the second largest population recorded lower error rates when forecasting including search term frequency data. These analysis results demonstrate that search term frequency data plays an important role in cities with a population above a certain size. We also hope that these predictions can be used as evidentiary materials to decide policies, such as the deregulation or implementation of stronger preventive measures.