• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.507-513
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• 2022

This study developed a model for predicting the number of VOD uses of IPTV, an online market in the film industry. A machine learning-based prediction model was established using the VOD usage data collected by the Korean Film Council from 2017 to 2021. Through literature research and cluster analysis, the difference between the offline market and the online market is revealed, and a new category of VOD usage is proposed. The purpose is to help IPTV companies establish marketing strategies as well as support decision-making by developing a machine learning-based VOD usage prediction model.

• Journal of Korea Technology Innovation Society
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• v.22 no.1
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• pp.119-157
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• 2019

It is important for the people in the 3D printing industry to determine which factors influence the decision-making that determine the adoption of 3D printers and the role of the factors. Through this, we intend to find ways to contribute to the development of 3D printing industry in Korea by increasing utilization of 3D printer used in domestic companies and increasing investment in related industries. 3D printers are making rapid progress according to the development of technology, the public interest, and the activation of investment. Foreign countries have made remarkable progress in equipment, materials, software, and industrial applications, but they are lower than expected in Korea. It is necessary to introduce a smooth 3D printer in order to revitalize the 3D printer industry and enlarge the base, but it is insufficient for actual introduction and field application. The independent variables that represent economic, technological, and environmental characteristics were selected through a literature survey, and a model for accepting integrated technology for convergence of societies in the 3D printing industry was proposed. This study confirms that economic factors such as output unit price, government support, and environmental factors such as 3D contents should be developed organically for the introduction of 3D printing technology and equipment. This require systematic and effective support from the government, and it is necessary to improve the economic support, related laws, and systems that can be directly experienced by the user as a user. As the domestic 3D printing industry develops with economic, technological and time investment, 3D printing industry should be the key engine of the 4th industrial revolution.

• Journal of Technology Innovation
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• v.20 no.3
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• pp.93-128
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• 2012

Defense technology planning identifies medium-and long-term core technologies to accomplish future defense goals and suggests strategies for future R&D. In order to promote the export-oriented defense industry, planning paradigms should be shifted from technology-oriented planning that focuses on weapon systems to market- oriented R&D planning. This study aims to strategically support 'preliminary technology planning' the Defense Agency for Technology and Quality is pushing ahead with. Through market-orientation analysis models of weapon systems based on defense R&D planning, data research on previous market-oriented research, and the analyses and examples of global defence markets, it evaluates market attractiveness to UAVs and drew methods for exploring markets and enhancing competitiveness of military equipment. The market-oriented analysis model of weapon systems is considered to be a helpful reference as a relevant factor for decision making on establishing and verifying requirement planning. In particular, if a market-oriented defense R&D planning process is established institutionally, it will enable us to make export strategies tailored to different equipment from the planning phase and to support marketing strategically.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.9-17
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• 2023

Research on virtual assets has been mainly interested in policy preparation or legislation for the introduction of virtual assets, or virtual asset operation technology, but this study presents investment factors that most asset investors consider important when making investment decisions. By doing so, we came up with research results that are practically helpful to virtual asset investors. According to the research results, (1) virtual asset investors consider business models such as marketability and competitive advantage of virtual assets as the most important factors, and (2) are highly interested in factors that can be objectively judged when investing in virtual assets. The results of this study suggest that a virtual asset trading market environment that can provide objective investment information and discover various judgment factors that enable virtual asset investors to objectively judge virtual assets should be prepared, and that virtual asset businesses using core technologies will continue to grow. This suggests that a variety of policy support is needed to enable this.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.6
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• pp.703-710
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• 2018

Although various efforts have been made every year to reduce electric fire accidents such as accident analysis and inspection for electric fire accidents, there is no effective countermeasure due to lack of effective decision support system and existing cumulative data utilization method. The purpose of this study is to develop an algorithm for predicting electric fire based on data such as electric safety inspection data, electric fire accident information, building information, and weather information. Through the pre-processing of collected data for each institution such as Korea Electrical Safety Corporation, Meteorological Administration, Ministry of Land, Infrastructure, and Transport, Fire Defense Headquarters, convergence, analysis, modeling, and verification process, we derive the factors influencing electric fire and develop prediction models. The results showed insulation resistance value, humidity, wind speed, building deterioration(aging), floor space ratio, building coverage ratio and building use. The accuracy of prediction model using random forest algorithm was 74.7%.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.11
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• pp.348-354
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• 2016

This study was conducted to investigate policy priority with respect to development of a smart farming system. Professors, researchers and policy makers in related fields were surveyed to develop a long-term plan and improved direction for the smart farming system, and the AHP (Analytic Hierarchy Process) was used for analysis. Overall, 61 experts in the frontiers of the field were surveyed and 42 questionnaires were analyzed. The results showed that the most important factors influencing development plans are increasing the income of farmers, minimizing management costs and standardizing and localizing smart farming systems. However, some plans differ with respect to economic and technology factors; therefore, those things should be analyzed in more detail by experts.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.103-128
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• 2021

Recently, investors' interest and the influence of stock-related information dissemination are being considered as significant factors that explain stock returns and volume. Besides, companies that develop, distribute, or utilize innovative new technologies such as artificial intelligence have a problem that it is difficult to accurately predict a company's future stock returns and volatility due to macro-environment and market uncertainty. Market uncertainty is recognized as an obstacle to the activation and spread of artificial intelligence technology, so research is needed to mitigate this. Hence, the purpose of this study is to propose a machine learning model that predicts the volatility of a company's stock price by using the internet search volume of artificial intelligence-related technology keywords as a measure of the interest of investors. To this end, for predicting the stock market, we using the VAR(Vector Auto Regression) and deep neural network LSTM (Long Short-Term Memory). And the stock price prediction performance using keyword search volume is compared according to the technology's social acceptance stage. In addition, we also conduct the analysis of sub-technology of artificial intelligence technology to examine the change in the search volume of detailed technology keywords according to the technology acceptance stage and the effect of interest in specific technology on the stock market forecast. To this end, in this study, the words artificial intelligence, deep learning, machine learning were selected as keywords. Next, we investigated how many keywords each week appeared in online documents for five years from January 1, 2015, to December 31, 2019. The stock price and transaction volume data of KOSDAQ listed companies were also collected and used for analysis. As a result, we found that the keyword search volume for artificial intelligence technology increased as the social acceptance of artificial intelligence technology increased. In particular, starting from AlphaGo Shock, the keyword search volume for artificial intelligence itself and detailed technologies such as machine learning and deep learning appeared to increase. Also, the keyword search volume for artificial intelligence technology increases as the social acceptance stage progresses. It showed high accuracy, and it was confirmed that the acceptance stages showing the best prediction performance were different for each keyword. As a result of stock price prediction based on keyword search volume for each social acceptance stage of artificial intelligence technologies classified in this study, the awareness stage's prediction accuracy was found to be the highest. The prediction accuracy was different according to the keywords used in the stock price prediction model for each social acceptance stage. Therefore, when constructing a stock price prediction model using technology keywords, it is necessary to consider social acceptance of the technology and sub-technology classification. The results of this study provide the following implications. First, to predict the return on investment for companies based on innovative technology, it is most important to capture the recognition stage in which public interest rapidly increases in social acceptance of the technology. Second, the change in keyword search volume and the accuracy of the prediction model varies according to the social acceptance of technology should be considered in developing a Decision Support System for investment such as the big data-based Robo-advisor recently introduced by the financial sector.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.13 no.6
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• pp.177-189
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• 2018

Business environment is always full of challenges. Despite various strategic efforts, there are so many failure cases of misfit. With the weaker resource base and institutional foundation, startup firms find it more difficult to find the right spot in the stiff competition. In the middle of evolutionary process, the startup firms need proper strategies meeting the differential challenges along the multiple stages of growth. Following the idea of product life cycle, this study applies the four stages of growth-startup, initial growth, accelerated growth, matured, and decliing. The next step for the startup manager is meeting each stage of growth with proper strategic efforts, including strategy, structure, decision-making pattern, and control method. When the knowledge factor is introduced, there is a potential for higher performance. Based on the 'Detailed Survey on Startup Ventures in 2017,' this study explores the impact of the government subsidy program on the firm competitiveness and performance-along the four stages of growth. In each stage, the strategy factors showed differential impact.

• The Journal of Bigdata
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• v.8 no.1
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• pp.111-129
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• 2023

With the diffusion of digital innovation, the adoption of innovative medical technologies based on artificial intelligence is increasing in the medical field. This is driving the launch and adoption of AI-based SaMD(Software as a Medical Device), but there is a lack of research on the factors that influence the adoption of SaMD by medical institutions. The purpose of this study is to identify key factors that influence medical institutions' decisions to adopt AI-based SaMDs, and to analyze the weights and priorities of these factors. For this purpose, we conducted Delphi surveys based on the results of literature studies on technology acceptance models in healthcare industry, medical AI and SaMD, and developed a research model by combining HOTE(Human, Organization, Technology and Environment) framework and HABIO(Holistic Approach {Business, Information, Organizational}) framework. Based on the research model with 5 main criteria and 22 sub-criteria, we conducted an AHP(Analytical Hierarchy Process) analysis among the experts from domestic medical institutions and SaMD providers to empirically analyze SaMD adoption factors. The results of this study showed that the priority of the main criteria for determining the adoption of AI-based SaMD was in the order of technical factors, economic factors, human factors, organizational factors, and environmental factors. The priority of sub-criteria was in the order of reliability, cost reduction, medical staff's acceptance, safety, top management's support, security, and licensing & regulatory levels. Specifically, technical factors such as reliability, safety, and security were found to be the most important factors for SaMD adoption. In addition, the comparisons and analyses of the weights and priorities of each group showed that the weights and priorities of SaMD adoption factors varied by type of institution, type of medical institution, and type of job in the medical institution.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.67-86
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• 2020

Recommender Systems have been huge influence users and business more and more. Recently the importance of E-commerce has been reached rapid growth greatly in world-wide COVID-19 pandemic. Recommender system is the center of E-commerce lively. Top ranked E-commerce managers mentioned that recommender systems have a major influence on customer's purchase such as about 50% of Netflix, Amazon sales from their recommender systems. Most algorithms have been focused on improving accuracy of recommender system regardless of novelty, diversity, serendipity etc. Recommender systems with only high accuracy cannot satisfy business long-term profit because of generating sales polarization. In addition, customers do not experience enjoyment of shopping from only focusing accuracy recommender system because customer's preference is changed constantly. Therefore, recommender systems with various values need to be developed for user's high satisfaction. Reranking is the most useful methodology to realize diversity of recommender system. In this paper, diversity of recommender system is represented through constructing high similarity with users who have different preference using each user's purchased item's category algorithm. It is distinguished from past research approach which is changing the algorithm of recommender system without user's diversity preference level. We tried to discover user's diversity preference level and observed the results how the effect was different according to user's diversity preference level. In addition, graph-based recommender system was used to show diversity through user's network, not collaborative filtering. In this paper, Amazon Grocery and Gourmet Food data was used because the low-involvement product, such as habitual product, foods, low-priced goods etc., had high probability to show customer's diversity. First, a bipartite graph with users and items simultaneously is constructed to make graph-based recommender system. However, each users and items unipartite graph also need to be established to show diversity of recommender system. The weight of each unipartite graph has played crucial role changing Jaccard Distance of item's category. We can observe two important results from the user's unipartite network. First, the user's diversity preference level is observed from the network and second, dissimilar users can be discovered in the user's network. Through the research process, diversity of recommender system is presented highly with small accuracy loss and optimalization for higher accuracy is possible controlling diversity ratio. This paper has three important theoretical points. First, this research expands recommender system research for user's satisfaction with various values. Second, the graph-based recommender system is developed newly. Third, the evaluation indicator of diversity is made for diversity. In addition, recommender systems are useful for corporate profit practically and this paper has contribution on business closely. Above all, business long-term profit can be improved using recommender system with diversity and the recommender system can provide right service according to user's diversity level. Lastly, the corporate selling low-involvement products have great effect based on the results.