• Journal of the Korean Society for information Management
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• v.41 no.1
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• pp.367-387
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• 2024

This study aims to identify the recent trends and intellectual structure of international research in the field of catalog, which is undergoing a major change due to the enactment of new standards and rules and the anticipated future. For this purpose, we collected 680 articles published in the 14 years since 2010 and analyzed 1,942 author keywords extracted from them after preprocessing. The main findings of the analysis are as follows First, overseas cataloging research has seen notable growth since 2017. Second, the most frequent research topics were: cataloging, metadata, RDA, university libraries, authority control, linked data, FRBR, catalog, LCSH, libraries, andonline cataloging. Third, the research themes were divided into two clusters, one related to the traditional aspects of library cataloging and the other related to the more recently discussed topics of authority control, cooperative cataloging, RDA, and linked data, which were further subdivided into 14 subclusters. Fourth, we looked at the growth index and standard performance index of the 14 keyword clusters and found that all but one cluster showed growth in terms of discipline growth. This study is significant in that it can be used as a basis for predicting the future development of inventories for Korean academia and the field and for related education.

• Journal of the Korean Society for information Management
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• v.35 no.3
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• pp.11-39
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• 2018

The study examines the research trends of doctoral dissertations in Library Science and Information Science published in the U.S. for the last 5 years. Data collected from PQDT Global includes 1,016 doctoral dissertations containing "Library Science" or "Information Science" as subject headings, and keywords extracted from those dissertations were used for a network analysis, which helps identifying the intellectual structure of the dissertations. Also, the analysis using 103 subject heading keywords resulted in various centrality measures, including triangle betweenness centrality and nearest neighbor centrality, as well as 26 clusters of associated subject headings. The most frequently studied subjects include computer-related subjects, education-related subjects, and communication-related subjects, and a cluster with information science as the most central subject contains most of the computer-related keywords, while a cluster with library science as the most central subject contains many of the education-related keywords. Other related subjects include various user groups for user studies, and subjects related to information systems such as management, economics, geography, and biomedical engineering.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.7
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• pp.292-302
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• 2020

The purpose of this study was to investigate the sensory characteristics of 11 noodles from four countries by descriptive analysis and consumer acceptance test. Noodle types were udon, pasta, kalguksu, and plain noodles. Fourteen sensory descriptors were developed to evaluate the sensory characteristics of the noodles. The sensory characteristics of the noodles were sweetness, saltiness, springiness, hardness, chewiness, milk flavor, yeasty odor, milk powder flavor, loose particles, and so on. PCA conducted to visually summarize the sensory characteristics of noodles by consumer preference test revealed that 77.1% of the variance was positively defined by sweet, springiness, cohesiveness, chewiness, and moisture and was negatively defined by hardness, color, and floury taste. Yk_udng_KR, thin_KR showed high scores for attributes of springiness and cohesiveness. Overall consumer acceptance was surveyed. Udng_KR scored the highest for attributes such as gusu. Udng_KR showed the highest springiness and high moisture, taste acceptance, and overall acceptance. Note: Please confirm this word.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.3
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• pp.141-156
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• 2023

Due to the unprecedented worldwide pandemic of the new Covid-19 infection, business trends of companies have changed significantly. Therefore, it is strongly required to monitor the rapid changes of innovation trends to design and plan future businesses. Since the pandemic, many studies have attempted to analyze business changes, but they are limited to specific industries and are insufficient in terms of data objectivity. In response, this study aims to analyze business trends after Covid-19 using Crunchbase, a global startup data. The data is collected and preprocessed every two years from 2018 to 2021 to compare the business trends. To capture the major trends, a network analysis is conducted for the industry groups and industry information based on the co-occurrence. To analyze the minor trends, LDA-based topic modelling and word2vec-based clustering is used. As a result, e-commerce, education, delivery, game and entertainment industries are promising based on their technological advances, showing extension and diversification of industry boundaries as well as digitalization and servitization of business contents. This study is expected to help venture capitalists and entrepreneurs to understand the rapid changes under the impact of Covid-19 and to make right decisions for the future.

• Trans-
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• v.1
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• pp.21-44
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• 2016

Advertisement is composed with symbol and sign with messages trying to express. Especially, ad with dancer introduces goods or meaning of contents with the motion of dance. In this, contents of dance or motion of dancer contains symbol and sign, understanding how ad and dance are expressed meanings with which symbol and the symbolic meaning of dance or dancer on ad. To that end, this study is for analyzing expressed symbol with dance corresponds with the aim of ad and finding the way or attitude of how normal people accept dance by reevaluating symbolic meaning of dance itself. In this study, advertisement producer and director's related data is secured for understanding direction and intention of producer, and previous study related with the study purpose, image, and effect are analyzed for understanding image of dance as a physical sign on TV advertisement. With data from www.TVCF.co.kr. TV advertisement analysis is conducted only with four ads in 2008(Nam Kwang Eng. & Const Co., Lotte Dept. Store(premium sale/gift card), Hyundai Motor Company Santa Fe -Pilobolus) and one ad in 2011(PNS The zone Sash Italy Arena di Verona when dance was used for advertisement with the highest frequency per year. Also, based on considered important factors from repeatedly watching each advertisement, scenes where movement or motion of dancer and screen word is greatly changed are analyzed as a priority. Image analysis of dance is conducted with structure studies based on physical image(line, costume, expression) and dan image(type motion, qualitative feature, mood of dance). As a result, the symbolic dance image appeared in TV advertisement can be discussed as follows. First, symbol and sign of dance on advertisement corresponds with material objects of advertisement. For instance, on the TV advertisement where Lee Youngwoo appeared, his motion as a signifer means challenge for the future of Nam Kwang Eng. & Const Co., with fast turn, jump, assemble turning jump, and sliding. Second, physical image of dancer depending on intention of sender corresponds in general, but there are somewhat differences in image of dance. This makes people to unconsciously recognize symbolic image of dance on TV ad while they watch it at the same time. Especially, when it comes to advertisement, it exposes frequently with broadcasting of organized programs from a broadcaster, living long-time memory. It can be differ based on idea and character of each of receiver. Advertisement is a medium making people naturally adopt cultural art for ordinary people in their lives. Broadcasting public art from TV advertisement widely exposes pure art to people, which was only avaliable for minority, sublimating it as an art of public culture.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.183-203
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• 2018

News articles are the most suitable medium for examining the events occurring at home and abroad. Especially, as the development of information and communication technology has brought various kinds of online news media, the news about the events occurring in society has increased greatly. So automatically summarizing key events from massive amounts of news data will help users to look at many of the events at a glance. In addition, if we build and provide an event network based on the relevance of events, it will be able to greatly help the reader in understanding the current events. In this study, we propose a method for extracting event networks from large news text data. To this end, we first collected Korean political and social articles from March 2016 to March 2017, and integrated the synonyms by leaving only meaningful words through preprocessing using NPMI and Word2Vec. Latent Dirichlet allocation (LDA) topic modeling was used to calculate the subject distribution by date and to find the peak of the subject distribution and to detect the event. A total of 32 topics were extracted from the topic modeling, and the point of occurrence of the event was deduced by looking at the point at which each subject distribution surged. As a result, a total of 85 events were detected, but the final 16 events were filtered and presented using the Gaussian smoothing technique. We also calculated the relevance score between events detected to construct the event network. Using the cosine coefficient between the co-occurred events, we calculated the relevance between the events and connected the events to construct the event network. Finally, we set up the event network by setting each event to each vertex and the relevance score between events to the vertices connecting the vertices. The event network constructed in our methods helped us to sort out major events in the political and social fields in Korea that occurred in the last one year in chronological order and at the same time identify which events are related to certain events. Our approach differs from existing event detection methods in that LDA topic modeling makes it possible to easily analyze large amounts of data and to identify the relevance of events that were difficult to detect in existing event detection. We applied various text mining techniques and Word2vec technique in the text preprocessing to improve the accuracy of the extraction of proper nouns and synthetic nouns, which have been difficult in analyzing existing Korean texts, can be found. In this study, the detection and network configuration techniques of the event have the following advantages in practical application. First, LDA topic modeling, which is unsupervised learning, can easily analyze subject and topic words and distribution from huge amount of data. Also, by using the date information of the collected news articles, it is possible to express the distribution by topic in a time series. Second, we can find out the connection of events in the form of present and summarized form by calculating relevance score and constructing event network by using simultaneous occurrence of topics that are difficult to grasp in existing event detection. It can be seen from the fact that the inter-event relevance-based event network proposed in this study was actually constructed in order of occurrence time. It is also possible to identify what happened as a starting point for a series of events through the event network. The limitation of this study is that the characteristics of LDA topic modeling have different results according to the initial parameters and the number of subjects, and the subject and event name of the analysis result should be given by the subjective judgment of the researcher. Also, since each topic is assumed to be exclusive and independent, it does not take into account the relevance between themes. Subsequent studies need to calculate the relevance between events that are not covered in this study or those that belong to the same subject.

• Journal of Wetlands Research
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• v.26 no.1
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• pp.41-50
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• 2024

Plant Microbial Fuel Cells (P-MFCs) are biomass-based energy technologies that generate electricity from plant and root microbial communities and are suitable for natural fundamental solutions considering sustainable environments. In order to develop P-MFC technology suitable for domestic waterfront space, it is necessary to analyze international research trends first. Therefore, in this study, 700 P-MFC-related research papers were investigated in Web of Science, and the core keywords were derived using VOSviewer, a word analysis program, and the research trends were analyzed. First, P-MFC-related research has been on the rise since 1998, especially since the mid to late 2010s. The number of papers submitted by each country was "China," "U.S." and "India." Since the 2010s, interest in P-MFCs has increased, and the number of publications in the Philippines, Ukraine, and Mexico, which have abundant waterfront space and wetland environments, is increasing. Secondly, from the perspective of research trends in different periods, 1998-2015 mainly carried out microbial fuel cell performance verification research in different environments. The 2016-2020 period focuses on the specific conditions of microbial fuel cell use, the structure of P-MFC and how it develops. From 2021 to 2023, specific research on constraints and efficiency improvement in the development of P-MFC was carried out. The P-MFC-related international research trends identified through this study can be used as useful data for developing technologies suitable for domestic waterfront space in the future. In addition to this study, further research is needed on research trends and levels in subsectors, and in order to develop and revitalize P-MFC technologies in Korea, research on field applicability should be expanded and policies and systems improved.

• The Korean Journal of Franchise Management
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• v.13 no.3
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• pp.1-15
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• 2022

Purpose: In the restaurant sector, it has been known that consumers' positive perception of brands influences their positive WOM intention, and information sources play an important role in increasing credibility by enhancing consumer awareness and developing differentiated brands. This study examines the effects of information sources (e.g., advertisement, WOM, SNS) on trust (cognitive and affective) and, WOM and eWOM intention in the restaurant context. In the model, cognitive and affective trust play mediating roles in the relationships between information sources (e.g., advertisement, WOM, SNS) WOM and eWOM intention. Research design, data, and methodology: Research models and hypotheses were developed according to the research direction. The survey questionnaire items were developed and used appropriately according to the contents of this paper based on prior studies. All constructs were measured with multiple items developed and validated in prior studies. A total of 502 responses were collected from an online survey. The research model was evaluated using SmartPLS 4.0. Frequency analysis was performed to understand the demographic characteristics of the survey respondents. The reliability, convergent validity, and discriminant validity were assessed using measurement model analysis. The proposed model was verified using the structural equation model. Results: Advertisement, WOM, and SNS information sources all had a positive effect on affective trust, whereas only WOM had a significant effect on cognitive trust. In addition, affective trust had a positive effect on cognitive trust and eWOM intention but did not affect WOM intention. Finally, cognitive trust was found to have a positive effect on both WOM intention and eWOM intention. Conclusions: This study redefines the concept of where restaurant service companies should focus when providing consumers with information about their products and services. As a result, the conceptual framework of positive word of mouth intention to increase new customer visits to the restaurant brand has been expanded. In addition, this study not only presents an information source management strategy for restaurant brands, but also presents practical implications for resource allocation guidelines for customer management in the restaurant sector.

• Journal of Climate Change Research
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• v.2 no.2
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• pp.93-106
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• 2011

Republic of Korea officially announced its mid term reduction target which reduce about 30% of BAU GHG emission by 2020 in the 15th meeting of UNFCCC(COP 15) held in Copenhagen, Denmark 2009. To achieve this goal, it is necessary to understand the serious of climate change and take part in GHG reduction not only industry but also the nation. However, such positive participation in green life which may cause inconvenient of the life of the people. It should be accomplished with providing reliable information. This study suggests the scientific potentialities of GHG emission by guideline on low carbon life and green life to form and change a lifestyle suitable for coping with climate change. And also, this study quantitate the GHG reduction which may reduce demand for air conditioning by cool biz and warm biz. In Korea, this campaign has become known as 'CoolMaebsi' by Ministry of Environmental of Korea. 'CoolMaebsi' is a compound word of 'Cool' which means feel refreshed, and 'Maebsi' is a Korean word which means attire. Though this campaign is effective and significant to reduce the GHG emission yet there were no study on quantitative analysis. Therefore this study calculated reduced energy consumption and potential GHG emission by measuring variation of skin temperature. As the result, wearing warm biz and cool biz have an effect of reducing not only the energy consumption but also GHG emission. To achieve the low carbon society, it is necessary to improve the energy saving system and introduce the policy which guide to change a life style.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.101-123
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• 2013

Recently due to the advancements of science and information technology, the socio-economic business areas are changing from the industrial economy to a knowledge economy. Furthermore, companies need to do creation of new value through continuous innovation, development of core competencies and technologies, and technological convergence. Therefore, the identification of major trends in technology research and the interdisciplinary knowledge-based prediction of integrated technologies and promising techniques are required for firms to gain and sustain competitive advantage and future growth engines. The aim of this paper is to understand the recent research trend in management of technology (MOT) and to foresee promising technologies with deep knowledge for both technology and business. Furthermore, this study intends to give a clear way to find new technical value for constant innovation and to capture core technology and technology convergence. Bibliometrics is a metrical analysis to understand literature's characteristics. Traditional bibliometrics has its limitation not to understand relationship between trend in technology management and technology itself, since it focuses on quantitative indices such as quotation frequency. To overcome this issue, the network focused bibliometrics has been used instead of traditional one. The network focused bibliometrics mainly uses "Co-citation" and "Co-word" analysis. In this study, a keywords network analysis, one of social network analysis, is performed to analyze recent research trend in MOT. For the analysis, we collected keywords from research papers published in international journals related MOT between 2002 and 2011, constructed a keyword network, and then conducted the keywords network analysis. Over the past 40 years, the studies in social network have attempted to understand the social interactions through the network structure represented by connection patterns. In other words, social network analysis has been used to explain the structures and behaviors of various social formations such as teams, organizations, and industries. In general, the social network analysis uses data as a form of matrix. In our context, the matrix depicts the relations between rows as papers and columns as keywords, where the relations are represented as binary. Even though there are no direct relations between papers who have been published, the relations between papers can be derived artificially as in the paper-keyword matrix, in which each cell has 1 for including or 0 for not including. For example, a keywords network can be configured in a way to connect the papers which have included one or more same keywords. After constructing a keywords network, we analyzed frequency of keywords, structural characteristics of keywords network, preferential attachment and growth of new keywords, component, and centrality. The results of this study are as follows. First, a paper has 4.574 keywords on the average. 90% of keywords were used three or less times for past 10 years and about 75% of keywords appeared only one time. Second, the keyword network in MOT is a small world network and a scale free network in which a small number of keywords have a tendency to become a monopoly. Third, the gap between the rich (with more edges) and the poor (with fewer edges) in the network is getting bigger as time goes on. Fourth, most of newly entering keywords become poor nodes within about 2~3 years. Finally, keywords with high degree centrality, betweenness centrality, and closeness centrality are "Innovation," "R&D," "Patent," "Forecast," "Technology transfer," "Technology," and "SME". The results of analysis will help researchers identify major trends in MOT research and then seek a new research topic. We hope that the result of the analysis will help researchers of MOT identify major trends in technology research, and utilize as useful reference information when they seek consilience with other fields of study and select a new research topic.