• Journal of the Korean Institute of Landscape Architecture
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• v.50 no.5
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• pp.30-39
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• 2022

This study aimed to evaluate the awareness of conservation areas and green resources and analyze their characteristics by utilizing accumulated blog data created for specific places and objects. Among all the conservation areas and resources located in the Seoul metropolitan area, places that can be evaluated were classified, and sites were evaluated by dividing them into ten categories based on the number of blog posts written. As a result of the study, the users' awareness of forests was the highest, and the awareness of conservation areas and green resources was higher in urban areas than suburban areas. The result shows that the conservation areas and green resources located around the metropolitan area serve as natural tourist destinations while being the object of conservation for users. In addition, these results are in the same vein as the research results in domestic and foreign studies on the importance of ecosystem services in urban areas. Unlike existing research methods, this study is meaningful in that it identified the level of user awareness through social media analysis and applied it to evaluating conservation areas and green resources. It can be used as basic data to prepare a management plan considering public interest and awareness or to establish a development plan to increase awareness. In addition, the cumulative amount of blog content used in the study is meaningful in that it can identify and monitor users' interest in the space. However, it was not possible to examine the contents of each blog in detail because it was evaluated based on the amount of social media content. In addition, in the case of conservation areas and green resources, it is necessary to review and supplement the evaluation contents by adding keyword analysis and content analysis for the site to be evaluated as content other than the pure viewpoint of users may be mixed with development issues.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.4
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• pp.65-76
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• 2023

In the field of technology entrepreneurship and startups, the development of Artificial Intelligence(AI) has emerged as a key topic for business model innovation. As a result, venture firms are making various efforts centered on AI to secure competitiveness(Kim & Geum, 2023). The purpose of this study is to analyze the relationship between the development of GenAI technology and the startup ecosystem by analyzing domestic news articles to identify trends in the technology startup field. Using BIG Kinds, this study examined the changes in GenAI-related news articles, major issues, and trends in Korean news articles from 1990 to August 10, 2023, focusing on the emergence of ChatGPT before and after, and visualized the relevance through network analysis and keyword visualization. The results of the study showed that the mention of GenAI gradually increased in the articles from 2017 to 2023. In particular, OpenAI's ChatGPT service based on GPT-3.5 was highlighted as a major issue, indicating the popularization of language model-based GenAI technologies such as OpenAI's DALL-E, Google's MusicLM, and VoyagerX's Vrew. This proves the usefulness of GenAI in various fields, and since the launch of ChatGPT, Korean companies have been actively developing Korean language models. Startups such as Ritten Technologies are also utilizing GenAI to expand their scope in the technology startup field. This study confirms the connection between GenAI technology and startup entrepreneurship activities, which suggests that it can support the construction of innovative business strategies, and is expected to continue to shape the development of GenAI technology and the growth of the startup ecosystem. Further research is needed to explore international trends, the utilization of various analysis methods, and the possibility of applying GenAI in the real world. These efforts are expected to contribute to the development of GenAI technology and the growth of the startup ecosystem.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.91-108
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• 2020

Korea's famous soccer players are steadily performing well in international leagues, which led to higher interests of Korean fans in the international leagues. Reflecting the growing social phenomenon of rising interests on international leagues by Korean fans, the study examined the overall consumer perception in the consumption of uniform by domestic soccer fans and compared the changes in perception following the transfers of the players. Among others, the paper examined the consumer perception and purchase factors of soccer fans shown in social media, focusing on periods before and after the recruitment of Heung-Min Son to English Premier League's Tottenham Football Club. To this end, the EPL uniform is the collection keyword the paper utilized and collected consumer postings from domestic website and social media via Python 3.7, and analyzed them using Ucinet 6, NodeXL 1.0.1, and SPSS 25.0 programs. The results of this study can be summarized as follows. First, the uniform of the club that consistently topped the league, has been gaining attention as a popular uniform, and the players' performance, and the players' position have been identified as key factors in the purchase and search of professional football uniforms. In the case of the club, the actual ranking and whether the league won are shown to be important factors in the purchase and search of professional soccer uniforms. The club's emblem and the sponsor logo that will be attached to the uniform are also factors of interest to consumers. In addition, in the decision making process of purchase of a uniform by professional soccer fan, uniform's form, marking, authenticity, and sponsors are found to be more important than price, design, size, and logo. The official online store has emerged as a major purchasing channel, followed by gifts for friends or requests from acquaintances when someone travels to the United Kingdom. Second, a classification of key control categories through the convergence of iteration correlation analysis and Clauset-Newman-Moore clustering algorithm shows differences in the classification of individual groups, but groups that include the EPL's club and player keywords are identified as the key topics in relation to professional football uniforms. Third, between 2002 and 2006, the central theme for professional football uniforms was World Cup and English Premier League, but from 2012 to 2015, the focus has shifted to more interest of domestic and international players in the English Premier League. The subject has changed to the uniform itself from this time on. In this context, the paper can confirm that the major issues regarding the uniforms of professional soccer players have changed since Ji-Sung Park's transfer to Manchester United, and Sung-Yong Ki, Chung-Yong Lee, and Heung-Min Son's good performances in these leagues. The paper also identified that the uniforms of the clubs to which the players have transferred to are of interest. Fourth, both male and female consumers are showing increasing interest in Son's league, the English Premier League, which Tottenham FC belongs to. In particular, the increasing interest in Son has shown a tendency to increase interest in football uniforms for female consumers. This study presents a variety of researches on sports consumption and has value as a consumer study by identifying unique consumption patterns. It is meaningful in that the accuracy of the interpretation has been enhanced by using a cluster analysis via convergence of iteration correlation analysis and Clauset-Newman-Moore clustering algorithm to identify the main topics. Based on the results of this study, the clubs will be able to maximize its profits and maintain good relationships with fans by identifying key drivers of consumer awareness and purchasing for professional soccer fans and establishing an effective marketing strategy.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.193-212
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• 2011

Mobile instant messaging service is surfacing to an important keyword in the mobile market together with popularization of Smart phones. Mobile instant messaging service in Korea has become popular to the degree of 87.9% usages from total Smartphone holders, and it is expected that using populations will be more enlarged afterwards if considering a fact that its populations of Smartphone is continuously being increased after exceeding 10 million persons (Trend Monitor, June 2011). In the instant messaging market where competitions have been deepened day by day, raising customer's royalties will be the key for company's business survivals and goals of corporate marketing strategies. It could be said that understanding on which factors affect to customer retentions and royalties is very important. Specially, as changing status is being progressed very quickly in case of innovative mobile services like the instant messaging service, research necessities on how many do consumers use the services after accepting them, how much do consumers use them variously, and whether does it connect to long-term relations have been increased, but studies on such matters are in insufficient situations actually. Therefore, this study examined on which effects were affected to use-diffusion and loyalty factors from perceived customer vales' factors having been occurred after accepting the mobile instant messaging service, namely 'functional value', 'monetary value', 'emotional value', and 'social value'. Also, the study looked into what kind of roles do the service usage and using variety play to service's continued using intents as a loyalty index, recommending intents to others, and brand switching intents. And then the study laid the main purpose in trying to provide implications for enhancing customer securities and royalties on the mobile instant messaging service through research's results. The research hypotheses are as follows; H1: Perceived values will affect influences to royalties. H2: Use-Diffusion will affect influences to loyalty. H3: Perceived value will affect influences to loyalty. H4: The use-diffusion will play intermediating roles between perceived values and loyalty. Total 276 cases among collected 284 ones were used for the statistical analysis by SPSS ver. 15 package. Reliability, Factor analysis, regression were done. As the result of research, 'monetary value' and 'emotional value' affected to 'usage' among perceived value factors, and 'emotional value' was appeared as affecting the largest influence. Besides, the usage affected to constant-using intents and recommending intents to others, and using varieties were displayed as affecting to recommending intents to others. On the other hand, 'Using' and 'Using diversity' were appeared as not affecting to 'brand switching intentions'. Meanwhile, as the result of recognizing about effects of perceived values on the loyalty, it was appeared such like 'continued using intents' affected to'functional value', 'monetary value', and 'social value' first, and also 'monetary value', 'emotional value', and 'social value' affected to 'recommending intents to others'. On the other hand, it was shown such like only 'social value' affected influences to 'brand switching intents', and thus contrary results with the factor 'constant-using intents' were displayed. So, it seems that there are many applications to service provides who are worrying about marketing strategies for making consumer retains (constant-using) and new consumer's inductions (brand-switching intents). Finally, as a result of looking into intermediating roles of the use-diffusion factor in relations between conceived values and royalties at hypothesis 4, 'using' and 'using diversity' were displayed as affecting significant influences all together. Regarding to research result's implications, for expanding and promoting continued uses of the mobile instant messaging service by service providers: First, encouraging recognitions on the perceived value connected to users' service usage are necessary. Second, setting up user's use-diffusion strategies are required so as to enhance the loyalty after understanding a fact that use-diffusion patterns affecting to the service's loyalty are different. Finally, methods of raising customer loyalties and making constant relationships have to be grouped by analyzing on what are the customer value's factors that can satisfy users in competitive alterations.

• Journal of The Korean Association For Science Education
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• v.41 no.1
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• pp.33-46
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• 2021

Inquiry occupies an important place in science education, and research related to inquiry is widely conducted. However, due to the inclusiveness of the concept of "exploration," each researcher perceives its meaning differently, and approaches may vary. In addition, criticisms have been raised that the results of classes using inquiry in science education do not guarantee meaningful changes to students. Therefore, this study attempts to identify the trend of SSCI-level research papers dealing with inquiry in science education over the past three years to confirm the current status and effectiveness of the inquiry. Researches used in the analysis are International Journal of Science Education, Journal of Research in Science Teaching, Research in Science Education, and Science Education, and limited to those that directly suggest "inquiry (enquiry)" as a keyword. Based on extracted 75 papers, the classification process was conducted, and an analysis frame was derived inductively by reflecting the subject and characteristics. Specific cases for each category were presented by dividing into three aspects: perception and perspective on inquiry, support and strategy for inquiry, and teacher professional development for inquiry. The results of examining the implications for scientific inquiry are as follows: First, rather than defining inquiry as an implicit proposition or presenting it as a step-by-step procedure, it was induced to grasp the meaning of inquiry more comprehensively and holistically. Second, as to whether the inquiry-based instruction is effective in all aspects of the cognitive, functional, and affective domains of science, the limitations are clearly presented, and the context-dependent and subject-specific properties and limitations of inquiry are emphasized. Third, uncertainty in science inquiry-based instruction can help learners to begin their inquiry and develop interest, but in the process of recognizing data and restructuring knowledge, explicit and specific guidance and scaffolding should be provided at an appropriate timing.

• 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.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.93-110
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• 2015

The recommender system is a system which recommends products to the customers who are likely to be interested in. Based on automated information filtering technology, various recommender systems have been developed. Collaborative filtering (CF), one of the most successful recommendation algorithms, has been applied in a number of different domains such as recommending Web pages, books, movies, music and products. But, it has been known that CF has a critical shortcoming. CF finds neighbors whose preferences are like those of the target customer and recommends products those customers have most liked. Thus, CF works properly only when there's a sufficient number of ratings on common product from customers. When there's a shortage of customer ratings, CF makes the formation of a neighborhood inaccurate, thereby resulting in poor recommendations. To improve the performance of CF based recommender systems, most of the related studies have been focused on the development of novel algorithms under the assumption of using a single profile, which is created from user's rating information for items, purchase transactions, or Web access logs. With the advent of big data, companies got to collect more data and to use a variety of information with big size. So, many companies recognize it very importantly to utilize big data because it makes companies to improve their competitiveness and to create new value. In particular, on the rise is the issue of utilizing personal big data in the recommender system. It is why personal big data facilitate more accurate identification of the preferences or behaviors of users. The proposed recommendation methodology is as follows: First, multimodal user profiles are created from personal big data in order to grasp the preferences and behavior of users from various viewpoints. We derive five user profiles based on the personal information such as rating, site preference, demographic, Internet usage, and topic in text. Next, the similarity between users is calculated based on the profiles and then neighbors of users are found from the results. One of three ensemble approaches is applied to calculate the similarity. Each ensemble approach uses the similarity of combined profile, the average similarity of each profile, and the weighted average similarity of each profile, respectively. Finally, the products that people among the neighborhood prefer most to are recommended to the target users. For the experiments, we used the demographic data and a very large volume of Web log transaction for 5,000 panel users of a company that is specialized to analyzing ranks of Web sites. R and SAS E-miner was used to implement the proposed recommender system and to conduct the topic analysis using the keyword search, respectively. To evaluate the recommendation performance, we used 60% of data for training and 40% of data for test. The 5-fold cross validation was also conducted to enhance the reliability of our experiments. A widely used combination metric called F1 metric that gives equal weight to both recall and precision was employed for our evaluation. As the results of evaluation, the proposed methodology achieved the significant improvement over the single profile based CF algorithm. In particular, the ensemble approach using weighted average similarity shows the highest performance. That is, the rate of improvement in F1 is 16.9 percent for the ensemble approach using weighted average similarity and 8.1 percent for the ensemble approach using average similarity of each profile. From these results, we conclude that the multimodal profile ensemble approach is a viable solution to the problems encountered when there's a shortage of customer ratings. This study has significance in suggesting what kind of information could we use to create profile in the environment of big data and how could we combine and utilize them effectively. However, our methodology should be further studied to consider for its real-world application. We need to compare the differences in recommendation accuracy by applying the proposed method to different recommendation algorithms and then to identify which combination of them would show the best performance.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.131-146
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• 2011

It is becoming more essential than ever for office professionals to become competent in information collection/gathering and problem solving in today's global business society. In particular, office professionals do not only assist simple chores but are also forced to make decisions as quickly and efficiently as possible in problematic situations that can end in either profit or loss to their company. Since office professionals rely heavily on their tacit knowledge to solve problems that arise in everyday business situations, it is truly helpful and efficient to refer to similar business cases from the past and share or reuse such previous business knowledge for better performance results. Case-based reasoning(CBR) is a problem-solving method which utilizes previous similar cases to solve problems. Through CBR, the closest case to the current business situation can be searched and retrieved from the case or knowledge base and can be referred to for a new solution. This reduces the time and resources needed and increase success probability. The main purpose of this study is to design a system called COKRS(Case-based reasoning Office Knowledge Recommender System) and develop a prototype for it. COKRS manages cases and their meta data, accepts key words from the user and searches the casebase for the most similar past case to the input keyword, and communicates with users to collect information about the quality of the case provided and continuously apply the information to update values on the similarity table. Core concepts like system architecture, definition of a case, meta database, similarity table have been introduced, and also an algorithm to retrieve all similar cases from past work history has also been proposed. In this research, a case is best defined as a work experience in office administration. However, defining a case in office administration was not an easy task in reality. We surveyed 10 office professionals in order to get an idea of how to define a case in office administration and found out that in most cases any type of office work is to be recorded digitally and/or non-digitally. Therefore, we have defined a record or document case as for COKRS. Similarity table was composed of items of the result of job analysis for office professionals conducted in a previous research. Values between items of the similarity table were initially set to those from researchers' experiences and literature review. The results of this study could also be utilized in other areas of business for knowledge sharing wherever it is necessary and beneficial to share and learn from past experiences. We expect this research to be a reference for researchers and developers who are in this area or interested in office knowledge recommendation system based on CBR. Focus group interview(FGI) was conducted with ten administrative assistants carefully selected from various areas of business. They were given a chance to try out COKRS in an actual work setting and make some suggestions for future improvement. FGI has identified the user-interface for saving and searching cases for keywords as the most positive aspect of COKRS, and has identified the most urgently needed improvement as transforming tacit knowledge and knowhow into recorded documents more efficiently. Also, the focus group has mentioned that it is essential to secure enough support, encouragement, and reward from the company and promote positive attitude and atmosphere for knowledge sharing for everybody's benefit in the company.

• Trans-
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• v.3
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• pp.55-86
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• 2017

This study begins with the necessity of the concept of reincarnation of film media and the inclusion of specific tendencies of contemporary films as post - cinema comes. Variable movements around recent films Challenging and experimental films show aesthetics that are difficult to approach with the analysis of classical mise en scene and montage. In this way, I review the dispositif proposed by Martin in films that are puzzling to criticize with the classical conceptual framework. This is because the concept of dispositive is a conceptual pile that extends more than a mise en scene and a montage. Dispositif films tend to be non-reproducible and non-narrative, but not all non-narrativef tendencies are dispositif films. Only the dispositif film is included in the flow. Dispositif movement has increased dramatically in the modern environment on which digital technology is based, but it is not a tendency to be found in any particular age. The movement has been detected in classical films, and the dispositif tendency has continued to exist in avant-garde films in the 1920s and some modernist films. First, for clear conceptualization of cinematic dispositif, this study examines the sources of dispositif debates that are being introduced into film theory today. In this process, the theory of Jean Louis Baudry, Michel Foucault, Agamben, Flusser, and Deleuze will help. The concept of dispositif was discussed by several scholars, including Baudry and Foucault, and today the notion of dispositif is defined across all these definitions. However, these various discussions are distinctly different from the cinematic dispositif or dispositif films that Martin advocates. Martin's proposed concept reminds us of the fundamentals of cinematic aesthetics that have distinguished between the mise-en-scene and the montage. And it will be able to reconsider those concepts and make it possible to view a thing a new light or create new films. The basic implications of dispositif are apparatus as devices, disposition and arrangement, the combination of heterogeneity. Thus, if you define a dispositif film in a word, it is a new 'constraint' consisting of rearrangement and arrangement of the heterogeneous elements that make up the conditions of the classical film. In order for something to become a new design, changes must be made in the arrangement and arrangement of the elements, forces, and forces that make up it. Naturally, the elements encompass both internal and external factors. These dispositif films have a variety of possibilities, such as reflection on the archival possibilities and the role of supervision, the reestablishment of active and creative audience, the reason for the film medium, and the ideological reflection. films can also 'network' quickly and easily with other media faster than any medium and create a new 'devised' aesthetic style. And the dispositif film that makes use of this will be a key keyword in reading the films that present the new trend of modern film. Because dispositif are so comprehensive and have a broad implication, there are certainly areas that are difficult to sophisticate. However this will have a positive effect on the future activation of dispositif studies end for end. Dispositif is difficult to elaborate the concept clearly, so it can be accessed from a wide range of dimensions and has theoretically infinite extensibility. At the beginning and end of the 21st century film, the concept of cinematic dispositif will become a decisive factor to dismantle old film aesthetics.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.1-13
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• 2015

As opinion mining in big data applications has been highlighted, a lot of research on unstructured data has made. Lots of social media on the Internet generate unstructured or semi-structured data every second and they are often made by natural or human languages we use in daily life. Many words in human languages have multiple meanings or senses. In this result, it is very difficult for computers to extract useful information from these datasets. Traditional web search engines are usually based on keyword search, resulting in incorrect search results which are far from users' intentions. Even though a lot of progress in enhancing the performance of search engines has made over the last years in order to provide users with appropriate results, there is still so much to improve it. Word sense disambiguation can play a very important role in dealing with natural language processing and is considered as one of the most difficult problems in this area. Major approaches to word sense disambiguation can be classified as knowledge-base, supervised corpus-based, and unsupervised corpus-based approaches. This paper presents a method which automatically generates a corpus for word sense disambiguation by taking advantage of examples in existing dictionaries and avoids expensive sense tagging processes. It experiments the effectiveness of the method based on Naïve Bayes Model, which is one of supervised learning algorithms, by using Korean standard unabridged dictionary and Sejong Corpus. Korean standard unabridged dictionary has approximately 57,000 sentences. Sejong Corpus has about 790,000 sentences tagged with part-of-speech and senses all together. For the experiment of this study, Korean standard unabridged dictionary and Sejong Corpus were experimented as a combination and separate entities using cross validation. Only nouns, target subjects in word sense disambiguation, were selected. 93,522 word senses among 265,655 nouns and 56,914 sentences from related proverbs and examples were additionally combined in the corpus. Sejong Corpus was easily merged with Korean standard unabridged dictionary because Sejong Corpus was tagged based on sense indices defined by Korean standard unabridged dictionary. Sense vectors were formed after the merged corpus was created. Terms used in creating sense vectors were added in the named entity dictionary of Korean morphological analyzer. By using the extended named entity dictionary, term vectors were extracted from the input sentences and then term vectors for the sentences were created. Given the extracted term vector and the sense vector model made during the pre-processing stage, the sense-tagged terms were determined by the vector space model based word sense disambiguation. In addition, this study shows the effectiveness of merged corpus from examples in Korean standard unabridged dictionary and Sejong Corpus. The experiment shows the better results in precision and recall are found with the merged corpus. This study suggests it can practically enhance the performance of internet search engines and help us to understand more accurate meaning of a sentence in natural language processing pertinent to search engines, opinion mining, and text mining. Naïve Bayes classifier used in this study represents a supervised learning algorithm and uses Bayes theorem. Naïve Bayes classifier has an assumption that all senses are independent. Even though the assumption of Naïve Bayes classifier is not realistic and ignores the correlation between attributes, Naïve Bayes classifier is widely used because of its simplicity and in practice it is known to be very effective in many applications such as text classification and medical diagnosis. However, further research need to be carried out to consider all possible combinations and/or partial combinations of all senses in a sentence. Also, the effectiveness of word sense disambiguation may be improved if rhetorical structures or morphological dependencies between words are analyzed through syntactic analysis.