• Journal of the Korean Institute of Landscape Architecture
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• v.45 no.1
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• pp.52-62
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• 2017

Overseas landscape practices have already benefited from the awareness of BIM while landscape-related organizations are encouraging its use and the number of landscape projects using BIM is increasing. However, since BIM has not yet been introduced in the domestic field, this study investigated and analyzed overseas landscape projects and discussed the positive effects and implementation of BIM. For this purpose, landscape projects were selected to show three effects of BIM: improvement of design work efficiency, building of a platform for cooperation, and performance of topography design. These three projects were analyzed across four aspects of implementation methods: landscape information, 3D modeling, interoperability, and visualization uses of BIM. First, in terms of landscape information, a variety of building information was constructed in the form of 3D libraries or 2D CAD format from detailed landscape elements to infrastructure. Second, for 3D modeling, a landscape space including simple terrain and trees was modeled with Revit while elaborate and complex terrain was modeled with Maya, a professional 3D modeling tool. One integrated model was produced by periodically exchanging, reviewing, and finally combining each model from interdisciplinary fields. Third, interoperability of data from different fields was achieved through the unification of file formats, conversion of differing formats, or compliance with information standards. Lastly, visualized 3D models helped coordination among project partners, approval of design, and promotion through public media. Reviewing of the case studies shows that BIM functions as a process to improve work efficiency and interdisciplinary collaboration, rather than simply as a design tool. It has also verified that landscape architects could play an important role in integrated projects using BIM. Just as the introduction of BIM into the architecture, engineering and construction industries saw great benefits and opportunities, BIM should also be introduced to landscape architecture.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.89-115
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• 2019

This study was conducted to present the stylistic differences between Arthur Conan Doyle and Agatha Christie, famous as writers of classical mystery novels, through data analysis, and further to present the analytical methodology of the study of style based on text mining. The reason why we chose mystery novels for our research is because the unique devices that exist in classical mystery novels have strong stylistic characteristics, and furthermore, by choosing Arthur Conan Doyle and Agatha Christie, who are also famous to the general reader, as subjects of analysis, so that people who are unfamiliar with the research can be familiar with them. The primary objective of this study is to identify how the differences exist within the text and to interpret the effects of these differences on the reader. Accordingly, in addition to events and characters, which are key elements of mystery novels, the writer's grammatical style of writing was defined in style and attempted to analyze it. Two series and four books were selected by each writer, and the text was divided into sentences to secure data. After measuring and granting the emotional score according to each sentence, the emotions of the page progress were visualized as a graph, and the trend of the event progress in the novel was identified under eight themes by applying Topic modeling according to the page. By organizing co-occurrence matrices and performing network analysis, we were able to visually see changes in relationships between people as events progressed. In addition, the entire sentence was divided into a grammatical system based on a total of six types of writing style to identify differences between writers and between works. This enabled us to identify not only the general grammatical writing style of the author, but also the inherent stylistic characteristics in their unconsciousness, and to interpret the effects of these characteristics on the reader. This series of research processes can help to understand the context of the entire text based on a defined understanding of the style, and furthermore, by integrating previously individually conducted stylistic studies. This prior understanding can also contribute to discovering and clarifying the existence of text in unstructured data, including online text. This could help enable more accurate recognition of emotions and delivery of commands on an interactive artificial intelligence platform that currently converts voice into natural language. In the face of increasing attempts to analyze online texts, including New Media, in many ways and discover social phenomena and managerial values, it is expected to contribute to more meaningful online text analysis and semantic interpretation through the links to these studies. However, the fact that the analysis data used in this study are two or four books by author can be considered as a limitation in that the data analysis was not attempted in sufficient quantities. The application of the writing characteristics applied to the Korean text even though it was an English text also could be limitation. The more diverse stylistic characteristics were limited to six, and the less likely interpretation was also considered as a limitation. In addition, it is also regrettable that the research was conducted by analyzing classical mystery novels rather than text that is commonly used today, and that various classical mystery novel writers were not compared. Subsequent research will attempt to increase the diversity of interpretations by taking into account a wider variety of grammatical systems and stylistic structures and will also be applied to the current frequently used online text analysis to assess the potential for interpretation. It is expected that this will enable the interpretation and definition of the specific structure of the style and that various usability can be considered.

• Knowledge Management Research
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• v.23 no.4
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• pp.315-338
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• 2022

In 1986, Korea established legal systems to support small and medium-sized start-ups, which becomes the main pillars of national development. The legal systems have stimulated start-up ecosystems to have more than 1 million new start-up companies founded every year during the past 30 years. To analyze the trend of Korea's start-up ecosystem, in this study, we collected 1.18 million news articles from 1991 to 2020. Then, we extracted news articles that have the keywords "start-up", "venture", and "start-up". We employed network analysis and topic modeling to analyze collected news articles. Our analysis can contribute to analyzing the government policy direction shown in the history of start-up support policy. Specifically, our analysis identifies the dynamic characteristics of government influenced by external environmental factors (e.g., society, economy, and culture). The results of our analysis suggest that the start-up ecosystems in Korea have changed and developed mainly by the government policies for corporation governance, industrial development planning, deregulation, and economic prosperity plan. Our frequency keyword analysis contributes to understanding entrepreneurial productivity attributed to activities among the networked components in industrial ecosystems. Our analyses and results provide practitioners and researchers with practical and academic implications that can help to establish dedicated support policies through forecast tasks of the economic environment surrounding the start-ups. Korean entrepreneurial productivity has been empowered by growing numbers of large companies in the mobile phone industry. The spectrum of large companies incorporates content startups, platform providers, online shopping malls, and youth-oriented start-ups. In addition, economic situational factors contribute to the growth of Korean entrepreneurial productivity the economic, which are related to the global expansions of the mobile industry, and government efforts to foster start-ups. Our research is methodologically implicative. We employ natural language processes for 30 years of media articles, which enables more rigorous analysis compared to the existing studies which only observe changes in government and policy based on a qualitative manner.

• Asia Marketing Journal
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• v.13 no.4
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• pp.105-137
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• 2012

After relationship marketing emerged as a new approach in the marketing field in the 1980s, it has been widely studied in the United States, Europe and Asia. Rapid environmental changes and global competition has made it inevitable for companies to consider their relationships with the environment more closely. Under these circumstances, relationship marketing has held a position as a pivotal paradigm in the field of strategy as well as in marketing. In addition, relationship marketing has overcome the limitations of a traditional marketing research while providing richer implications in company's marketing activities. The paradigm shift to relationship marketing has brought fundamental changes in a marketing point of view. First, in philosophical aspects, unlike past research which focused solely on customer satisfaction, organizational relationship parameters which focuses on trust and commitment has become key elements of successful relationship marketing while shifts in thoughts naturally take place from adaptive marketing to strategic marketing. Second, in structural aspects, the relational mechanism of governance such as network structure with a variety of relational partners has emerged as a new marketing organization from the previous simple structure focusing on the micro-economic, marketbased trading between seller and customer. Third, in behavioral aspects, it proposed the strategic course of the action of gaining an advantage over the competition on the individual firm level by focusing on building long-term relationships and considering partnership with the components in the entire marketing system, rather than with one-time transaction-centric action between a seller and a customer. Fourth, in the aspects of marketing performance, marketing performance was sought through the long-term and cooperative relationship with various stakeholders, including customers in the marketing system, focusing on the overall competitive advantage based on relationship rather than individual performance of individual companies' marketing activities, such as market share and customer satisfaction. However, studies of relationship marketing were mostly centered in interorganizational relationships focusing on the relational structure and properties of commercial sector in the marketing system. Paradoxically, the circumstance of the consumer's side that must be considered is evolving again in relationship marketing. In structural aspects, a community, as the new relationship governance structure in the digital environment, and in behavioral aspects, the changing role of consumer participation demanding big changes in the digital environment engaged in the marketing system. The possibility of building a relationship marketing community for common value creation is presented in terms of organization of consumers with the focus on changing marketing environment and marketing system according to the new realities of the 21st century- the popularity of digital environments and the diffusion of customer participation. Therefore, future research of relationship marketing must seek for a truly integrated model including all of the existing structure and properties of the research oriented relationship from both the commercial and consumer sector.

• Information Systems Review
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• v.20 no.2
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• pp.39-62
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• 2018

Thanks to the growth of computing power and the recent development of data analytics, researchers have started to work on the data produced by users through the Internet or social media. This study is in line with these recent research trends and attempts to adopt data analytical techniques. We focus on the impact of "internal marketing" factors on firm performance, which is typically studied through survey methodologies. We looked into the job review platform Jobplanet (www.jobplanet.co.kr), which is a website where employees and former employees anonymously review companies and their management. With web crawling processes, we collected over 40K data points and performed morphological analysis to classify employees' reviews for internal marketing data. We then implemented econometric analysis to see the relationship between internal marketing and market capitalization. Contrary to the findings of extant survey studies, internal marketing is positively related to a firm's market capitalization only within a limited area. In most of the areas, the relationships are negative. Particularly, female-friendly environment and human resource development (HRD) are the areas exhibiting positive relations with market capitalization in the manufacturing industry. In the service industry, most of the areas, such as employ welfare and work-life balance, are negatively related with market capitalization. When firm size is small (or the history is short), female-friendly environment positively affect firm performance. On the contrary, when firm size is big (or the history is long), most of the internal marketing factors are either negative or insignificant. We explain the theoretical contributions and managerial implications with these results.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.109-122
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• 2014

People are nowadays creating a tremendous amount of data on Social Network Service (SNS). In particular, the incorporation of SNS into mobile devices has resulted in massive amounts of data generation, thereby greatly influencing society. This is an unmatched phenomenon in history, and now we live in the Age of Big Data. SNS Data is defined as a condition of Big Data where the amount of data (volume), data input and output speeds (velocity), and the variety of data types (variety) are satisfied. If someone intends to discover the trend of an issue in SNS Big Data, this information can be used as a new important source for the creation of new values because this information covers the whole of society. In this study, a Twitter Issue Tracking System (TITS) is designed and established to meet the needs of analyzing SNS Big Data. TITS extracts issues from Twitter texts and visualizes them on the web. The proposed system provides the following four functions: (1) Provide the topic keyword set that corresponds to daily ranking; (2) Visualize the daily time series graph of a topic for the duration of a month; (3) Provide the importance of a topic through a treemap based on the score system and frequency; (4) Visualize the daily time-series graph of keywords by searching the keyword; The present study analyzes the Big Data generated by SNS in real time. SNS Big Data analysis requires various natural language processing techniques, including the removal of stop words, and noun extraction for processing various unrefined forms of unstructured data. In addition, such analysis requires the latest big data technology to process rapidly a large amount of real-time data, such as the Hadoop distributed system or NoSQL, which is an alternative to relational database. We built TITS based on Hadoop to optimize the processing of big data because Hadoop is designed to scale up from single node computing to thousands of machines. Furthermore, we use MongoDB, which is classified as a NoSQL database. In addition, MongoDB is an open source platform, document-oriented database that provides high performance, high availability, and automatic scaling. Unlike existing relational database, there are no schema or tables with MongoDB, and its most important goal is that of data accessibility and data processing performance. In the Age of Big Data, the visualization of Big Data is more attractive to the Big Data community because it helps analysts to examine such data easily and clearly. Therefore, TITS uses the d3.js library as a visualization tool. This library is designed for the purpose of creating Data Driven Documents that bind document object model (DOM) and any data; the interaction between data is easy and useful for managing real-time data stream with smooth animation. In addition, TITS uses a bootstrap made of pre-configured plug-in style sheets and JavaScript libraries to build a web system. The TITS Graphical User Interface (GUI) is designed using these libraries, and it is capable of detecting issues on Twitter in an easy and intuitive manner. The proposed work demonstrates the superiority of our issue detection techniques by matching detected issues with corresponding online news articles. The contributions of the present study are threefold. First, we suggest an alternative approach to real-time big data analysis, which has become an extremely important issue. Second, we apply a topic modeling technique that is used in various research areas, including Library and Information Science (LIS). Based on this, we can confirm the utility of storytelling and time series analysis. Third, we develop a web-based system, and make the system available for the real-time discovery of topics. The present study conducted experiments with nearly 150 million tweets in Korea during March 2013.