• 제목/요약/키워드: Data Analysis and Search

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Information Seeking and Information Avoidance among University Students: Focusing on Health and other Information

  • Kapseon KIM
    • 식품보건융합연구
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    • 제10권2호
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    • pp.27-36
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    • 2024
  • This study aims to investigate whether information awareness, search purpose, and search expectations influence information avoidance among university students. The data were collected by using a self-completion questionnaire with convenience sampling of students from one university. The collected data were analyzed by descriptive statistics, t-test, analysis of variance (ANOVA), Pearson's correlation coefficient, and multiple regression using R 4.2.3. The main results are as follows: First, both search purpose and search expectations exhibited a significant inverse correlation with all information avoidance dependent variables. Second, there was a significant difference in the mean of search expectations across majors, such that science majors had higher search expectations than humanities majors. Third, there were significant differences in the means of the information avoidance-system and information avoidance variables by major, such that both variables had lower means for the science than the humanities group. Fourth, among the independent variables, search expectation had a significant effect on information avoidance-personal: the higher the search expectation variable, the lower the information avoidance-personal variable. This study confirmed that information avoidance should not only consider the psychological, emotional, and affective aspects of information seekers, but also that information seekers' information search purpose and search expectations are predictors of information avoidance.

이미지 기반 디지털 도서관에서 이용자 검색 패턴의 효과적 이해를 위한 트랜잭션 로그 데이터 분석 (Using Transaction Logs to Better Understand User Search Session Patterns in an Image-based Digital Library)

  • Han, Hye-Jung;Joo, Soohyung;Wolfram, Dietmar
    • 한국비블리아학회지
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    • 제25권1호
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    • pp.19-37
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    • 2014
  • 본 연구는 이미지 기반 디지털 도서관의 이용자 검색 패턴을 효과적으로 분석하기 위해 이용자 검색 로그 데이터를 분석하였다. 기술 통계와 네트워크 분석 방법을 사용하여 한 달간 수집한 트랜잭션 로그 데이터를 분석하였다. 연구 결과는 이용자들이 특정 주제 내에서 검색 결과 보기와 이미지 아이템 평가를 반복적으로 수행하고 있음을 밝혀내었다. 본 연구는 이미지 자료 검색의 로그 분석을 위해 복합적 데이터 분석 방법을 이용하였다는 점에 의의가 있다.

중년기 주부의 외출복 구매시 정보탐색 및 대안평가에 관한연구 (Informations-Search and Evaluating Alternatives of Middle-aged Wives in Buying Townwears)

  • 강혜경
    • 가정과삶의질연구
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    • 제15권2호
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    • pp.1-20
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    • 1997
  • The aims of this study are as following. 1) To investgate information-search and evaluating alternatives of middle-aged wives in buying townwears as well as to examine socio-demographic variables physical varialbes and psychological variables and their influences on. 2) To compare the previous study (a study on problems recognition of mjiddle-aged wives in buying townwears) with this study and to relate this results ot previous study results. The data used in this study included 374 housewives living in Seoul and Pusan. Statistics used for data analysis were frequencies means standard deviation Pearson's correlation t-test Oneway ANOVA stepwise multiple regression analysis. major findings are as follows. 1. The degree of information-search and evaluating alternative proved more than middle point. 2. Variables that affect information-search and evaluating alternatives and three : attitude towards purchasing experience of menopause preference for expensive clothes.

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ARDL 시계열 모형을 활용한 패션 브랜드의 매출 예측 분석 -패션 브랜드와 광고모델의 웹 검색량, 정보량, 가격할인 프로모션을 중심으로- (Fashion Brand Sales Forecasting Analysis Using ARDL Time Series Model -Focusing on Brand and Advertising Endorser's Web Search Volume, Information Amount, and Brand Promotion-)

  • 서주연;김효정;박민정
    • 한국의류학회지
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    • 제46권5호
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    • pp.868-889
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    • 2022
  • Fashion companies are using a big data approach as a key strategic analysis to predict and forecast sales. This study investigated the effectiveness of the past sales, web search volume, information amount, brand promotion, and the advertising endorser on the sales forecasting model. The study conducted the autoregressive distributed lag (ARDL) time series model using the internal and external social big data of a national fashion brand. Results indicated that the brand's past sales, search volume, promotion, and amount of advertising endorser information amount significantly affected the sales forecast, whereas the brand's advertising endorser search volume and information amount did not significantly influence the sales forecast. Moreover, the brand's promotion had the highest correlation with sales forecasting. This study adds to information-searching behavior theory by measuring consumers' brand involvement. Last, this study provides digital marketers with implications for developing profitable marketing strategies on the basis of consumers' interest in the brand and advertising endorser.

지각된 위험과 의복관여도가 정보탐색 활동에 미치는 영향 -대학생을 중심으로- (The Influences of Risk Perceptions and Clothing Involvements on Information Search Behavior)

  • 임경복
    • 한국의류학회지
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    • 제25권2호
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    • pp.206-216
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    • 2001
  • This study was intended to identify the information search behavior according to the risk perception and clothing involvement. The data were collected via a questionnaire from 369 students of Semyung University in Checheon and data were analyzed by frequency, factor analysis, Cronbachs alpha and multiple regression. The results of this study were as follows; 1. Multiple regression revealed the fact that each type of involvement dimensions were influenced by the risk perception dimensions. Among four involvement dimensions, importance of clothing was the best influenced factor according to the risk perceptions. 2. Also information search behavior was influenced by risk perception and fashion involvement. Among four information search behaviors, industry providing search was the best influenced factor by the risk perception and clothing involvement.

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Developing a Method to Define Mountain Search Priority Areas Based on Behavioral Characteristics of Missing Persons

  • Yoo, Ho Jin;Lee, Jiyeong
    • 한국측량학회지
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    • 제37권5호
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    • pp.293-302
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    • 2019
  • In mountain accident events, it is important for the search team commander to determine the search area in order to secure the Golden Time. Within this period, assistance and treatment to the concerned individual will most likely prevent further injuries and harm. This paper proposes a method to determine the search priority area based on missing persons behavior and missing persons incidents statistics. GIS (Geographic Information System) and MCDM (Multi Criteria Decision Making) are integrated by applying WLC (Weighted Linear Combination) techniques. Missing persons were classified into five types, and their behavioral characteristics were analyzed to extract seven geographic analysis factors. Next, index values were set up for each missing person and element according to the behavioral characteristics, and the raster data generated by multiplying the weight of each element are superimposed to define models to select search priority areas, where each weight is calculated from the AHP (Analytical Hierarchy Process) through a pairwise comparison method obtained from search operation experts. Finally, the model generated in this study was applied to a missing person case through a virtual missing scenario, the priority area was selected, and the behavioral characteristics and topographical characteristics of the missing persons were compared with the selected area. The resulting analysis results were verified by mountain rescue experts as 'appropriate' in terms of the behavior analysis, analysis factor extraction, experimental process, and results for the missing persons.

웹 로그 데이터를 이용한 온라인 소비자의 가격민감도 영향 요인에 관한 연구 (Determinants of Online Price Sensitivity Using Web Log Data)

  • 전종근;박철
    • Journal of Information Technology Applications and Management
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    • 제13권1호
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    • pp.1-16
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    • 2006
  • This paper empirically analyzed consumer price search behavior using Web log data of a Korean web site for price comparison. Consumer click-stream data of the site was used to test the effects of price level, product category, third party certification, reputation of retailers on click behavior. According to the descriptive statistics, 67.4% of shopbot users clicked the offer which was the lowest price returned in a search. We found that third party certification and reputation of retailers were significant determinants of clicking the lowest priced offer from legit analysis. We also applied Tobit regression analysis to estimate the price premium of the two determinants, but only reputation of retailers was found to have price premium of 4.9%.

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빅데이터 분석을 활용한 스마트팩토리 연구 동향 분석 (Analysis of Smart Factory Research Trends Based on Big Data Analysis)

  • 이은지;조철호
    • 품질경영학회지
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    • 제49권4호
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    • pp.551-567
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    • 2021
  • Purpose: The purpose of this paper is to present implications by analyzing research trends on smart factories by text analysis and visual analysis(Comprehensive/ Fields / Years-based) which are big data analyses, by collecting data based on previous studies on smart factories. Methods: For the collection of analysis data, deep learning was used in the integrated search on the Academic Research Information Service (www.riss.kr) to search for "SMART FACTORY" and "Smart Factory" as search terms, and the titles and Korean abstracts were scrapped out of the extracted paper and they are organize into EXCEL. For the final step, 739 papers derived were analyzed using the Rx64 4.0.2 program and Rstudio using text mining, one of the big data analysis techniques, and Word Cloud for visualization. Results: The results of this study are as follows; Smart factory research slowed down from 2005 to 2014, but until 2019, research increased rapidly. According to the analysis by fields, smart factories were studied in the order of engineering, social science, and complex science. There were many 'engineering' fields in the early stages of smart factories, and research was expanded to 'social science'. In particular, since 2015, it has been studied in various disciplines such as 'complex studies'. Overall, in keyword analysis, the keywords such as 'technology', 'data', and 'analysis' are most likely to appear, and it was analyzed that there were some differences by fields and years. Conclusion: Government support and expert support for smart factories should be activated, and researches on technology-based strategies are needed. In the future, it is necessary to take various approaches to smart factories. If researches are conducted in consideration of the environment or energy, it is judged that bigger implications can be presented.

강원도 영동권 지역 대학생들의 외식동기에 의한 정보탐색방법 (The Information Search Method According to Eating-out Motivation of College Students in Eastern Area of Kangwon Province)

  • 윤태환
    • 한국식품조리과학회지
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    • 제22권2호
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    • pp.213-221
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    • 2006
  • Although motivation and information search have both been studied continuously and separately as important marketing strategies, the relation between cause and effect has received little research attention. Therefore the objective of this study was to research the causal relationships between motivation and information search method. Frequency analysis and reliability analysis, factor analysis, and SEM(Structure Equation Model) were adopted to analyze the data. Motivation was divided into 5 factors which significantly influenced information search method. Factor 1, 'Reception and congratulation', influenced information search positively through 'newspaper, magazine', and 'word of mouth' but negatively through 'TV-advertising' and 'Flyer, Press copy'. Factor 2, 'Change of dietary life', influenced positively 'TV-advertising'. Factor 3, 'Economic saving', influenced positively 'newspaper, magazine', and 'the e-mail's advertising' Factor 4, 'Preference motivation', influenced negatively 'word of mouth' Factor 5, 'Advertisement and companion's need', influenced positively 'newspaper, magazine', and 'the e-mail's advertising' but negatively 'TV-advertising' As a result, customers appeared to choose various information search methods according to their eating-out motivation. 'The e-mail's advertising', and 'word of mouth' are popular among customers' information search methods. Therefore, food-service corporations need to try eliminating negative images of various advertisements and activate positive word of mouth marketing, promotion through internet.

Analysis on Types of Golf Tourism After COVID-19 by using Big Data

  • Hyun Seok Kim;Munyeong Yun;Gi-Hwan Ryu
    • International Journal of Advanced Culture Technology
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    • 제12권1호
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    • pp.270-275
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    • 2024
  • Introduction. In this study, purpose is to analize the types of golf tourism, inbound or outbound, by using big data and see how movement of industry is being changed and what changes have been made during and after Covid-19 in golf industry. Method Using Textom, a big data analysis tool, "golf tourism" and "Covid-19" were selected as keywords, and search frequency information of Naver and Daum was collected for a year from 1 st January, 2023 to 31st December, 2023, and data preprocessing was conducted based on this. For the suitability of the study and more accurate data, data not related to "golf tourism" was removed through the refining process, and similar keywords were grouped into the same keyword to perform analysis. As a result of the word refining process, top 36 keywords with the highest relevance and search frequency were selected and applied to this study. The top 36 keywords derived through word purification were subjected to TF-IDF analysis, visualization analysis using Ucinet6 and NetDraw programs, network analysis between keywords, and cluster analysis between each keyword through Concor analysis. Results By using big data analysis, it was found out option of oversea golf tourism is affecting on inbound golf travel. "Golf", "Tourism", "Vietnam", "Thailand" showed high frequencies, which proves that oversea golf tour is now the re-coming trends.