• Journal of Digital Policy
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• v.3 no.2
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• pp.1-14
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• 2024

The purpose of this study was to analyze the effect of SNS marketing characteristics on hair shop image formation and visit intention in the hair beauty industry. SNS marketing is a strategy to carry out marketing activities through interaction with customers, information provision, information trust, and playfulness using modern social media platforms. It was intended to analyze how these characteristics of SNS marketing affect the formation of hair shop images and visit intention to customers in the hair beauty industry. For the study, a total of 307 customers with experience using hair-related SNS were surveyed. The questionnaire included items related to SNS marketing characteristics, hair shop images, and visit intention, and the collected data was statistically analyzed using SPSS 26.0. The results of the research problem were derived by applying analysis methods such as frequency analysis, factor analysis, reliability analysis, correlation analysis, simple regression analysis, multiple regression analysis, and mediated regression analysis. As a result of the study, it was found that information provision, information reliability, playfulness, and interaction, which are characteristics of SNS marketing, have a positive effect on the formation of hair shop images. In addition, it was confirmed that the hair shop image had a positive effect on the intention to visit. In addition, it was found that the hair shop image plays a mediating role between the SNS marketing characteristics and the intention to visit. This provides important insights that can improve image formation and customer visit intention in the hair beauty industry through SNS marketing.

• Journal of Cadastre & Land InformatiX
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• v.54 no.1
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• pp.89-102
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• 2024

The recent advancements in Smart City and Digital Twin technologies have highlighted the critical role of integrating GIS and BIM in urban planning and construction projects. This integration ensures the consistency and accuracy of information, facilitating smooth information exchange. However, achieving interoperability requires standardization and effective project integration management strategies. This study proposes interoperability solutions for the integration of GIS and BIM for managing various projects. The research involves an in-depth analysis of the IFC schema and data structures based on the latest IFC4 version and proposes methods to ensure the consistency of reference point coordinates and coordinate systems. The study was conducted by setting the EPSG:5186 coordinate system, used by the National Geographic Information Institute's digital topographic map, and applying virtual shift origin coordinates. Through BIMvision, the results of the shape and error check coordinates' movement in the BIM model were reviewed, confirming that the error check coordinates moved consistently with the reference point coordinates. Additionally, it was verified that even when the coordinate system was changed to EPSG:5179 used by Naver Map and road name addresses, or EPSG:5181 used by Kakao Map, the BIM model's shape and coordinates remained consistently unchanged. Notably, by inputting the EPSG code information into the IFC file, the potential for coordinate system interoperability between projects was confirmed. Therefore, this study presents an integrated and systematic management approach for information sharing, automation processes, enhanced collaboration, and sustainable development of GIS and BIM. This is expected to improve compatibility across various software platforms, enhancing information consistency and efficiency across multiple projects.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.71-90
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• 2020

Recently, the global insurance industry is rapidly developing digital transformation through the use of artificial intelligence technologies such as machine learning, natural language processing, and deep learning. As a result, more and more foreign insurers have achieved the success of artificial intelligence technology-based InsurTech and platform business, and Ping An Insurance Group Ltd., China's largest private company, is leading China's global fourth industrial revolution with remarkable achievements in InsurTech and Digital Platform as a result of its constant innovation, using 'finance and technology' and 'finance and ecosystem' as keywords for companies. In response, this study analyzed the InsurTech and platform business activities of Ping An Insurance Group Ltd. through the ser-M analysis model to provide strategic implications for revitalizing AI technology-based businesses of domestic insurers. The ser-M analysis model has been studied so that the vision and leadership of the CEO, the historical environment of the enterprise, the utilization of various resources, and the unique mechanism relationships can be interpreted in an integrated manner as a frame that can be interpreted in terms of the subject, environment, resource and mechanism. As a result of the case analysis, Ping An Insurance Group Ltd. has achieved cost reduction and customer service development by digitally innovating its entire business area such as sales, underwriting, claims, and loan service by utilizing core artificial intelligence technologies such as facial, voice, and facial expression recognition. In addition, "online data in China" and "the vast offline data and insights accumulated by the company" were combined with new technologies such as artificial intelligence and big data analysis to build a digital platform that integrates financial services and digital service businesses. Ping An Insurance Group Ltd. challenged constant innovation, and as of 2019, sales reached $155 billion, ranking seventh among all companies in the Global 2000 rankings selected by Forbes Magazine. Analyzing the background of the success of Ping An Insurance Group Ltd. from the perspective of ser-M, founder Mammingz quickly captured the development of digital technology, market competition and changes in population structure in the era of the fourth industrial revolution, and established a new vision and displayed an agile leadership of digital technology-focused. Based on the strong leadership led by the founder in response to environmental changes, the company has successfully led InsurTech and Platform Business through innovation of internal resources such as investment in artificial intelligence technology, securing excellent professionals, and strengthening big data capabilities, combining external absorption capabilities, and strategic alliances among various industries. Through this success story analysis of Ping An Insurance Group Ltd., the following implications can be given to domestic insurance companies that are preparing for digital transformation. First, CEOs of domestic companies also need to recognize the paradigm shift in industry due to the change in digital technology and quickly arm themselves with digital technology-oriented leadership to spearhead the digital transformation of enterprises. Second, the Korean government should urgently overhaul related laws and systems to further promote the use of data between different industries and provide drastic support such as deregulation, tax benefits and platform provision to help the domestic insurance industry secure global competitiveness. Third, Korean companies also need to make bolder investments in the development of artificial intelligence technology so that systematic securing of internal and external data, training of technical personnel, and patent applications can be expanded, and digital platforms should be quickly established so that diverse customer experiences can be integrated through learned artificial intelligence technology. Finally, since there may be limitations to generalization through a single case of an overseas insurance company, I hope that in the future, more extensive research will be conducted on various management strategies related to artificial intelligence technology by analyzing cases of multiple industries or multiple companies or conducting empirical research.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.21-44
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• 2018

In recent years, the rapid development of internet technology and the popularization of smart devices have resulted in massive amounts of text data. Those text data were produced and distributed through various media platforms such as World Wide Web, Internet news feeds, microblog, and social media. However, this enormous amount of easily obtained information is lack of organization. Therefore, this problem has raised the interest of many researchers in order to manage this huge amount of information. Further, this problem also required professionals that are capable of classifying relevant information and hence text classification is introduced. Text classification is a challenging task in modern data analysis, which it needs to assign a text document into one or more predefined categories or classes. In text classification field, there are different kinds of techniques available such as K-Nearest Neighbor, Naïve Bayes Algorithm, Support Vector Machine, Decision Tree, and Artificial Neural Network. However, while dealing with huge amount of text data, model performance and accuracy becomes a challenge. According to the type of words used in the corpus and type of features created for classification, the performance of a text classification model can be varied. Most of the attempts are been made based on proposing a new algorithm or modifying an existing algorithm. This kind of research can be said already reached their certain limitations for further improvements. In this study, aside from proposing a new algorithm or modifying the algorithm, we focus on searching a way to modify the use of data. It is widely known that classifier performance is influenced by the quality of training data upon which this classifier is built. The real world datasets in most of the time contain noise, or in other words noisy data, these can actually affect the decision made by the classifiers built from these data. In this study, we consider that the data from different domains, which is heterogeneous data might have the characteristics of noise which can be utilized in the classification process. In order to build the classifier, machine learning algorithm is performed based on the assumption that the characteristics of training data and target data are the same or very similar to each other. However, in the case of unstructured data such as text, the features are determined according to the vocabularies included in the document. If the viewpoints of the learning data and target data are different, the features may be appearing different between these two data. In this study, we attempt to improve the classification accuracy by strengthening the robustness of the document classifier through artificially injecting the noise into the process of constructing the document classifier. With data coming from various kind of sources, these data are likely formatted differently. These cause difficulties for traditional machine learning algorithms because they are not developed to recognize different type of data representation at one time and to put them together in same generalization. Therefore, in order to utilize heterogeneous data in the learning process of document classifier, we apply semi-supervised learning in our study. However, unlabeled data might have the possibility to degrade the performance of the document classifier. Therefore, we further proposed a method called Rule Selection-Based Ensemble Semi-Supervised Learning Algorithm (RSESLA) to select only the documents that contributing to the accuracy improvement of the classifier. RSESLA creates multiple views by manipulating the features using different types of classification models and different types of heterogeneous data. The most confident classification rules will be selected and applied for the final decision making. In this paper, three different types of real-world data sources were used, which are news, twitter and blogs.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.59-69
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• 2012

As exhibition industry, which is a part of 17 new growth engines of the government, is related to other industries such as tourism, transportation and financial industries. So it has a significant ripple effect on other industries. Exhibition is a knowledge-intensive, eco-friendly and high value-added Industry. Over 13,000 exhibitions are held every year around the world which contributes to getting foreign currency. Exhibition industry is closely related with culture and tourism and could be utilized as local and national development strategies and improve national brand image as well. Many countries try various efforts to invigorate exhibition industry by arranging related laws and support system. In Korea, more than 200 exhibitions are being held every year, but only 2~3 exhibitions are hosted with over 400 exhibitors and except these exhibitions most exhibitions have few foreign exhibitors. The main reason of weakness of domestic trade show is that there are no agencies managing exhibitionrelated statistics and there is no specific and reliable evaluation. This might cause impossibility of providing buyer or seller with reliable data, poor growth of exhibitions in terms of quality and thus service quality of trade shows cannot be improved. Hosting a lot of visitors (Public/Buyer/Exhibitor) is very crucial to the development of domestic exhibition industry. In order to attract many visitors, service quality of exhibition and visitor's satisfaction should be enhanced. For this purpose, a variety of real-time customized services through digital media and the services for creating new customers and retaining existing customers should be provided. In addition, by providing visitors with personalized information services they could manage their time and space efficiently avoiding the complexity of exhibition space. Exhibition industry can have competitiveness and industrial foundation through building up exhibition-related statistics, creating new information and enhancing research ability. Therefore, this paper deals with customized service with visitor's smart-phone at the exhibition space and designing mobile framework which enables exhibition devices to interact with other devices. Mobile server framework is composed of three different systems; multi-server interaction, server, client, display device. By making knowledge pool of exhibition environment, the accumulated data for each visitor can be provided as personalized service. In addition, based on the reaction of visitors each of all information is utilized as customized information and so the cyclic chain structure is designed. Multiple interaction server is designed to have functions of event handling, interaction process between exhibition device and visitor's smart-phone and data management. Client is an application processed by visitor's smart-phone and could be driven on a variety of platforms. Client functions as interface representing customized service for individual visitors and event input and output for simultaneous participation. Exhibition device consists of display system to show visitors contents and information, interaction input-output system to receive event from visitors and input toward action and finally the control system to connect above two systems. The proposed mobile framework in this paper provides individual visitors with customized and active services using their information profile and advanced Knowledge. In addition, user participation service is suggested as well by using interaction connection system between server, client, and exhibition devices. Suggested mobile framework is a technology which could be applied to culture industry such as performance, show and exhibition. Thus, this builds up the foundation to improve visitor's participation in exhibition and bring about development of exhibition industry by raising visitor's interest.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2004.05b
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• pp.217-220
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• 2004

According as utilization for web increases rapidly, it is demanded that model about support interaction between web-based applications systematically and solutions can integrate new distributed platforms and existing environment effectively, accordingly, Web Services appeared by solution in reply. These days, a lot of software and hardware companies try to adoption of Web Services to their market, attenpt to construct their applications associationing components from various Web Services providers. However, to execute Web Services completely. it must have interoperability and need the standardization work that avoid thing which is subject to platform, application as well as service and programming language from other companies. WS-I (Web Services Interoperability organization) have established Basic Profile 1.0 based on XML, UDDI, WSDL and SOAP for web services interoperability and developed usage scenario Profile to apply Web Services in practice. In this paper, to verify suitability Web Services interoperability between heterogeneous two applications, have design and implements the Book Information Web Services that based on the Web Services Client of J2SE platform and the Web Services Server of .NET platform, so that analysis and verify the service by adaptation of WS-I Basic Profile.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.4
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• pp.153-165
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• 2021

With the disappearance of boundaries between online and offline, the O2O(online to offline) platform service is rapidly growing. Unlike general products, freshness is an important decision-making factor for agri-food, and there are many limiting factors for growth as an open market among O2O platforms due to the characteristics of difficult refunds and exchanges compared to other items and new transaction methods. In order to overcome these obstacles, consumer innovation must be considered. The purpose of this study was to investigate the influence of O2O(online to offline) platform characteristics perception on agri-food repurchase intentions. And an empirical survey of the hypothesis is made that innovation will show a moderating effect between agri-food O2O platform characteristics and repurchase intention. And an empirical survey of the hypothesis is made that innovation will show a moderating effect between agri-food O2O platform characteristics and repurchase intention. For this purpose, Using a convenience sampling technique, an online survey was conducted through Google survey from April 1 to April 15, 2021. A total of final analysis data were collected from a total of 270 purchase experienced of agri-food O2O(online to offline) platform. The SPSS program was used for analysis, and multiple regression analysis was used for hypothesis verification. The results showed that Economic, Interaction, and Playfulness had a significant positive effect on agri-food repurchase intend. Also, Interactivity × innovation, playfulness × innovation were found to have a significant positive (+) effect on repurchase intention. The results of this study show that innovation reduces the burden on consumers for new systems and mobile transactions. The results of this study suggest that convenient interface design is important for activating O2O transactions of agri-food. In addition, education and support are needed to strengthen the IT competency of farmers. The results of this study will be able to contribute to the establishment of infrastructure for agri-food open market shopping malls. In future studies, the influence of the O2O platform type on the purchase intention should be studied continuously.

• Journal of Internet of Things and Convergence
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• v.8 no.3
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• pp.1-10
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• 2022

The purpose of this study was to investigate how the characteristics of mobile-based financial platforms have an impact on the intention to use loan service users. In addition, it was attempted to investigate whether usefulness and ease of use had a mediating effect in the relationship between each characteristic of the mobile financial platform on the intention to use the loan service. Data collection was conducted from March 1 to April 30, 2022, and 200 people participated in the study. Analysis methods were frequency analysis, exploratory factor analysis, reliability analysis, correlation analysis, hierarchical multiple regression analysis, and three-step mediation regression analysis. The research results are as follows. First, the influence of user factors, technical factors, and environmental factors of a financial platform on the intention to use a mobile loan service was found to be ubiquity in user factors, reliability in technical factors, and facilitation conditions in environmental factors. Second, in the relationship between convenience and intention to use user factors, usefulness had a completely mediating effect. Third, in the relationship between reliability of technical factors and intention to use, usefulness showed a partial mediating effect. Fourth, in the relationship between the social impact of environmental factors and facilitation conditions and intention to use, the usefulness showed a partial mediating effect. Fifth, ease of use showed a completely mediating effect in the relationship between convenience and intention of use of user factors. Sixth, in the relationship between reliability of technical factors and intention to use, ease of use showed a partial mediating effect. Seventh, in the relationship between the social impact of environmental factors and intention to use, ease of use showed a partial mediating effect, and in the relationship between facilitation conditions and intention to use, ease of use showed a fully mediating effect. Through this study, we tried to present basic data on the determinants of the user's acceptable intention to use the mobile loan service.

• Journal of the Korean Society for Library and Information Science
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• v.56 no.3
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• pp.47-72
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• 2022

This study aims to understand the trends of subjects related to real estate policies and public's emotional opinion on the policies. Two keywords related to real estate policies such as "real estate policy" and "real estate measure" were used to collect tweets created from February 25, 2008 to August 31, 2021. A total of 91,740 tweets were collected and we applied sentiment analysis and dynamic topic modeling to the final preprocessed and categorized data of 18,925 tweets. Sentiment analysis and dynamic topic model analysis were conducted for a total of 18,925 posts after preprocessing data and categorizing them into supply, real estate tax, interest rate, and population variance. Keywords of each category are as follows: the supply categories (rental housing, greenbelt, newlyweds, homeless, supply, reconstruction, sale), real estate tax categories (comprehensive real estate tax, acquisition tax, holding tax, multiple homeowners, speculation), interest rate categories (interest rate), and population variance categories (Sejong, new city). The results of the sentiment analysis showed that one person posted on average one or two positive tweets whereas in the case of negative and neutral tweets, one person posted two or three. In addition, we found that part of people have both positive as well as negative and neutral opinions towards real estate policies. As the results of dynamic topic modeling analysis, negative reactions to real estate speculative forces and unearned income were identified as major negative topics and as for positive topics, expectation on increasing supply of housing and benefits for homeless people who purchase houses were identified. Unlike previous studies, which focused on changes and evaluations of specific real estate policies, this study has academic significance in that it collected posts from Twitter, one of the social media platforms, used emotional analysis, dynamic topic modeling analysis, and identified potential topics and trends of real estate policy over time. The results of the study can help create new policies that take public opinion on real estate policies into consideration.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.69-94
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• 2017

Recently, increase of demand for big data analysis has been driving the vigorous development of related technologies and tools. In addition, development of IT and increased penetration rate of smart devices are producing a large amount of data. According to this phenomenon, data analysis technology is rapidly becoming popular. Also, attempts to acquire insights through data analysis have been continuously increasing. It means that the big data analysis will be more important in various industries for the foreseeable future. Big data analysis is generally performed by a small number of experts and delivered to each demander of analysis. However, increase of interest about big data analysis arouses activation of computer programming education and development of many programs for data analysis. Accordingly, the entry barriers of big data analysis are gradually lowering and data analysis technology being spread out. As the result, big data analysis is expected to be performed by demanders of analysis themselves. Along with this, interest about various unstructured data is continually increasing. Especially, a lot of attention is focused on using text data. Emergence of new platforms and techniques using the web bring about mass production of text data and active attempt to analyze text data. Furthermore, result of text analysis has been utilized in various fields. Text mining is a concept that embraces various theories and techniques for text analysis. Many text mining techniques are utilized in this field for various research purposes, topic modeling is one of the most widely used and studied. Topic modeling is a technique that extracts the major issues from a lot of documents, identifies the documents that correspond to each issue and provides identified documents as a cluster. It is evaluated as a very useful technique in that reflect the semantic elements of the document. Traditional topic modeling is based on the distribution of key terms across the entire document. Thus, it is essential to analyze the entire document at once to identify topic of each document. This condition causes a long time in analysis process when topic modeling is applied to a lot of documents. In addition, it has a scalability problem that is an exponential increase in the processing time with the increase of analysis objects. This problem is particularly noticeable when the documents are distributed across multiple systems or regions. To overcome these problems, divide and conquer approach can be applied to topic modeling. It means dividing a large number of documents into sub-units and deriving topics through repetition of topic modeling to each unit. This method can be used for topic modeling on a large number of documents with limited system resources, and can improve processing speed of topic modeling. It also can significantly reduce analysis time and cost through ability to analyze documents in each location or place without combining analysis object documents. However, despite many advantages, this method has two major problems. First, the relationship between local topics derived from each unit and global topics derived from entire document is unclear. It means that in each document, local topics can be identified, but global topics cannot be identified. Second, a method for measuring the accuracy of the proposed methodology should be established. That is to say, assuming that global topic is ideal answer, the difference in a local topic on a global topic needs to be measured. By those difficulties, the study in this method is not performed sufficiently, compare with other studies dealing with topic modeling. In this paper, we propose a topic modeling approach to solve the above two problems. First of all, we divide the entire document cluster(Global set) into sub-clusters(Local set), and generate the reduced entire document cluster(RGS, Reduced global set) that consist of delegated documents extracted from each local set. We try to solve the first problem by mapping RGS topics and local topics. Along with this, we verify the accuracy of the proposed methodology by detecting documents, whether to be discerned as the same topic at result of global and local set. Using 24,000 news articles, we conduct experiments to evaluate practical applicability of the proposed methodology. In addition, through additional experiment, we confirmed that the proposed methodology can provide similar results to the entire topic modeling. We also proposed a reasonable method for comparing the result of both methods.