• Journal of Convergence for Information Technology
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• v.9 no.4
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• pp.152-159
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• 2019

The purpose of this study is to investigate the effect of transaction characteristics on transaction cost and choice intention by applying transaction cost theory to experiential product. Experience-specificity, transaction uncertainty, and personal uncertainty are proposed to reflect the characteristics of experiential products, and the effects of these variables on transaction costs and transaction costs are assumed to have an influence on the choice intention. The results of this study are summarized as follows. First, experience-specificity(site, physical equipment, knowledge skill, temporal), transactional uncertainty(product-, process-), personal uncertainty (preference-, and situation-) have a significant positive effect on transaction cost. Second, transaction costs (search, comparison, examination, negotiation, payment, delivery) have a significant negative effect on the choice intention of the experiential product. The results of this study show that the increase of transaction costs can reduce the choice of experiential products and the strategic consideration of experience specificity, transaction uncertainty and individual uncertainty are required to reduce transaction costs. In addition, experiential products lacked access from a transactional and cost-based point of view, and this study contributes theoretically by compensating for the lack.

• Knowledge Management Research
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• v.21 no.4
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• pp.59-88
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• 2020

This study is to examine the influence of Asian countries on the economic field, and to explain the characteristics and purposes of China's Belt and Road Initiative using data analysis. The purpose of this study is to identify and analyze the influence and characteristics of China's One-to-One Road Initiative on the economic sector by examining trade and investment in Asian countries adjacent to China. In particular, the One-to-One Road initiative is proceeding in a way that connects China and neighboring countries. It is to understand the dependence of the Asian countries in China on the Chinese economy. In addition, it is intended to derive implications by grasping and evaluating what the level is based on data. This study also attempts to grasp the influence and ripple effects of the one-on-one strategy on the Chinese economy and the North Korean economy, where dependence is deepening. Recently, the strategy for Asian countries through a one-to-one initiative in China has been restructured in the framework of the construction of the "21st century Maritime Silk Road" and emphasizes the cooperation mechanism led by the country. In progress of the one Belt and One Road, Chinese ICT companies are remarkable. This study looked at the influence of China's digital one Belt and One Road on Asian countries.

• Knowledge Management Research
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• v.23 no.1
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• pp.89-109
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• 2022

Due to COVID-19 and soaring participation of individual investors, large-scale transactions exceeding system capacity limits have been reported frequently in the capital market. The capital market IT systems, which the impact of system failure is very critical, have encountered unexpectedly tremendous transactions in 2020, resulting in a sharp increase in system failures. Despite the fact that many companies maintained large-scale system capacity planning policies, recent transaction influx suggests that a new approach to capacity planning is required. Therefore, this study developed capital market IT system capacity planning models using machine learning techniques and analyzed those performances. In addition, the performance of the best proposed model was improved by using sentiment index that can promptly reflect the behavior of investors. The model uses empirical data including the COVID-19 period, and has high performance and stability that can be used in practice. In practical significance, this study maximizes the cost-efficiency of a company, but also presents optimal parameters in consideration of the practical constraints involved in changing the system. Additionally, by proving that the sentiment index can be used as a major variable in system capacity planning, it shows that the sentiment index can be actively used for various other forecasting demands.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.129-164
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• 2023

In a situation where the use and introduction of Large Language Models (LLMs) is expanding due to recent developments in generative AI technology, it is difficult to find actual application cases or implementation methods for the use of internal company data in existing studies. Accordingly, this study presents a method of implementing generative AI services using the LLM application architecture using the most widely used LangChain framework. To this end, we reviewed various ways to overcome the problem of lack of information, focusing on the use of LLM, and presented specific solutions. To this end, we analyze methods of fine-tuning or direct use of document information and look in detail at the main steps of information storage and retrieval methods using the retrieval augmented generation (RAG) model to solve these problems. In particular, similar context recommendation and Question-Answering (QA) systems were utilized as a method to store and search information in a vector store using the RAG model. In addition, the specific operation method, major implementation steps and cases, including implementation source and user interface were presented to enhance understanding of generative AI technology. This has meaning and value in enabling LLM to be actively utilized in implementing services within companies.

• Information Systems Review
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• v.25 no.3
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• pp.139-162
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• 2023

With the emergence of new digital technologies into a supply chain, it is essential for companies to incorporate these technologies in managing their supply chains. However, various challenges have been identified in digital supply chain management, especially when it comes to its assessment. There are no universally agreed measurements for the performance of digital supply chain management within the research community so far. This paper explores an option of using user experience as one of possible measurements. Therefore, three different focus-group discussions were held and later analyzed with a qualitative content analysis. The subscription-based video on demand service, Netflix was used as an example in those discussions. Due to the fact that Netflix provides a digital product as a streamline service, user experience is critical for the company. Especially, user experience with a recommender system and related privacy issues have become significant for a company to retain existing customers and attract new customers in many fields. Since the recommender system and related privacy issues are parts of a digital supply chain, user experience can be one of appropriate measurements for digital supply chain management. This study opens a new perspective for research on performance measurements of digital supply chain management.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.187-204
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• 2016

Document classification based on emotional polarity has become a welcomed emerging task owing to the great explosion of data on the Web. In the big data age, there are too many information sources to refer to when making decisions. For example, when considering travel to a city, a person may search reviews from a search engine such as Google or social networking services (SNSs) such as blogs, Twitter, and Facebook. The emotional polarity of positive and negative reviews helps a user decide on whether or not to make a trip. Sentiment analysis of customer reviews has become an important research topic as datamining technology is widely accepted for text mining of the Web. Sentiment analysis has been used to classify documents through machine learning techniques, such as the decision tree, neural networks, and support vector machines (SVMs). is used to determine the attitude, position, and sensibility of people who write articles about various topics that are published on the Web. Regardless of the polarity of customer reviews, emotional reviews are very helpful materials for analyzing the opinions of customers through their reviews. Sentiment analysis helps with understanding what customers really want instantly through the help of automated text mining techniques. Sensitivity analysis utilizes text mining techniques on text on the Web to extract subjective information in the text for text analysis. Sensitivity analysis is utilized to determine the attitudes or positions of the person who wrote the article and presented their opinion about a particular topic. In this study, we developed a model that selects a hot topic from user posts at China's online stock forum by using the k-means algorithm and self-organizing map (SOM). In addition, we developed a detecting model to predict a hot topic by using machine learning techniques such as logit, the decision tree, and SVM. We employed sensitivity analysis to develop our model for the selection and detection of hot topics from China's online stock forum. The sensitivity analysis calculates a sentimental value from a document based on contrast and classification according to the polarity sentimental dictionary (positive or negative). The online stock forum was an attractive site because of its information about stock investment. Users post numerous texts about stock movement by analyzing the market according to government policy announcements, market reports, reports from research institutes on the economy, and even rumors. We divided the online forum's topics into 21 categories to utilize sentiment analysis. One hundred forty-four topics were selected among 21 categories at online forums about stock. The posts were crawled to build a positive and negative text database. We ultimately obtained 21,141 posts on 88 topics by preprocessing the text from March 2013 to February 2015. The interest index was defined to select the hot topics, and the k-means algorithm and SOM presented equivalent results with this data. We developed a decision tree model to detect hot topics with three algorithms: CHAID, CART, and C4.5. The results of CHAID were subpar compared to the others. We also employed SVM to detect the hot topics from negative data. The SVM models were trained with the radial basis function (RBF) kernel function by a grid search to detect the hot topics. The detection of hot topics by using sentiment analysis provides the latest trends and hot topics in the stock forum for investors so that they no longer need to search the vast amounts of information on the Web. Our proposed model is also helpful to rapidly determine customers' signals or attitudes towards government policy and firms' products and services.

• Korea Science and Art Forum
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• v.21
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• pp.31-43
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• 2015

Information and Communications Technology (ICT) is one of the technologies which represent the core value of the creative economy. It has served as a vehicle connecting the existing industry and corporate infrastructure, developing existing products and services and creating new products and services. In addition to the ICT, new devices including big data, mobile gadgets and wearable products are gaining a great attention sending an expectation for a new market-pioneering. Further, Internet of Things (IoT) is helping solidify the ICT-based social development connecting human-to-human, human-to-things and things-to-things. This means that the manufacturing-based hardware development needs to be achieved simultaneously with software development through convergence. The essential element the convergence between hardware and software is OS, for which world's leading companies such as Google and Apple have launched an intense development recognizing the importance of software. Against this backdrop, the status-quo of the software market has been examined for the study of the present report (Korea Evaluation Institute of Industrial Technology: Professional Design Technology Development Project). As a result, the software platform-based Google's android and Apple's iOS are dominant in the global market and late comers are trying to enter the market through various pathways by releasing web-based OS and similar OS to provide a new paradigm to the market. The present study is aimed at finding the way to utilize a smart content by which anyone can be a developer based on OS responding to such as social change, newly defining a smart content to be universally utilized and analyzing the market to deal with a rapid market change. The study method, scope and details are as follows: Literature investigation, Analysis on the app market according to a smart classification system, Trend analysis on the current content market, Identification of five common trends through comparison among the universal definition of smart content, the status-quo of application represented in the app market and content market situation. In conclusion, the smart content market is independent but is expected to develop in the form of a single organic body being connected each other. Therefore, the further classification system and development focus should be made in a way to see the area from multiple perspectives including a social point of view in terms of the existing technology, culture, business and consumers.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.3
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• pp.53-68
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• 2024

Generative AI is receiving a lot of attention around the world, and ways to effectively utilize it in the business environment are being explored. In particular, since the public release of the ChatGPT service, which applies the GPT-3.5 model, a large language model developed by OpenAI, it has attracted more attention and has had a significant impact on the entire industry. This study focuses on the emergence of Generative AI, especially ChatGPT, which applies OpenAI's GPT-3.5 model, to investigate its impact on the startup industry and compare the changes that occurred before and after its emergence. This study aims to shed light on the actual application and impact of generative AI in the business environment by examining in detail how generative AI is being used in the startup industry and analyzing the impact of ChatGPT's emergence on the industry. To this end, we collected company information of generative AI-related startups that appeared before and after the ChatGPT announcement and analyzed changes in industry, business content, and investment information. Through keyword analysis, topic modeling, and network analysis, we identified trends in the startup industry and how the introduction of generative AI has revolutionized the startup industry. As a result of the study, we found that the number of startups related to Generative AI has increased since the emergence of ChatGPT, and in particular, the total and average amount of funding for Generative AI-related startups has increased significantly. We also found that various industries are attempting to apply Generative AI technology, and the development of services and products such as enterprise applications and SaaS using Generative AI has been actively promoted, influencing the emergence of new business models. The findings of this study confirm the impact of Generative AI on the startup industry and contribute to our understanding of how the emergence of this innovative new technology can change the business ecosystem.

• Journal of Aerospace System Engineering
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• v.14 no.5
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• pp.83-90
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• 2020

Disasters are causing significant damage to the lives and property of our society and are recognized as social problems that need to be solved nationally and globally. The 4th industrial revolution technologies affecting society as a whole such as the Internet of Things(IoT), Artificial Intelligence(AI), Drones(Unmanned Aerial Vehicles), and Big Data are continuously absorbed into the disaster and safety industries as scientific and technological tools for solving social problems. Very soon, twenty-nine domestic UAV-related organizations/companies will complete the construction of a multicopter type small UAV integrated system ('17~'20) that can be operated at disaster and security sites. The current work considers and proposes the mid-to-long term R&D direction of disaster UAV as a strategic asset of the national disaster response system. First, the trends of disaster and safety industry and policy are analyzed. Subsequently, the development status and future plans of small UAV, securing shortage technology, and strengthening competitiveness are analyzed. Finally, step-by-step R&D direction of disaster UAV in terms of development strategy, specialized mission, platform, communication, and control and operation is proposed.

• Journal of Software Engineering Society
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• v.28 no.2
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• pp.1-6
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• 2019

There are various stakeholders in the blockchain ecosystem. Since the emergence of Ethereum, many transactions have been made using smart contracts, and a wider range of stakeholders are participating, including not only developers, but also investors, banks, companies, and general users. However, various stakeholders have a problem in that it is difficult and complicated to check the state of smart contracts. If it becomes difficult to check the state, the reliability of the smart contract will be lowered and the utilization will be lowered. Also, if the state check is difficult and complicated for the developer, it will be difficult to provide high quality due to the difficulty of testing and debugging the smart contract developed by the developer. In this research, we propose a design and implementation method of the Ethereum Smart Contract State Monitoring System that enables various stakeholders and developers to easily and continuously check the state of smart contracts and analyze them using historical data.