• 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.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.2
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• pp.79-93
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• 2021

The question of why do you want to start a "start?" Is the most basic step in trying to do something. In other words, previous studies have shown that the degree of confidence in an individual's decision affects the setting of a specific purpose. Based on this, this study aims to provide basic data for deriving the direction of entrepreneurship education in college students by analyzing the effects of goal-oriented behavioral model on college students' self-determination and intention to start a business through the 5 factor model. To achieve the purpose of the study, a self-report questionnaire was conducted from October 01 to November 11, 2019 for university students attending located in Gyeonggi-do, Seoul. A total of 150 questionnaires were distributed, and 125 parts were used for the final analysis, except 25 parts with insincere responses or errors. Data were analyzed using SPSS Win 24, and reliability, validity analysis, frequency analysis, One-way ANOVA and regression analysis were performed, and three-step regression analysis and Sobel verification were performed for mediating effects. The summary of the study is as follows. First, the influence of university students' goal-oriented behavioral model on self-determination showed that attitudes, subjective norms, and perceived behavioral controls had statistically significant positive effects, and positive and negative expectations were statistically significant. Did not affect. Therefore, the higher the attitude, subjective norms, and perceived behavioral control, the higher the university students' self-determination. Second, the influence of college students' goal-oriented behavioral model on the intention to start a business was as follows.). As a result, the higher the perceived behavioral control and positive expectation, the higher the intention to start up. Third, regression model 1 showed that the behavioral control and positive expectation sentiment among the goal-oriented behavioral model had a significant positive influence on the college students' intention to start a business. Affected. Regression model II added the parameters of the 5 factor model, which increased 2.5% of explanatory power than the first regression model. Perceived behavioral control and positive expectations had a statistically significant positive effect, negative expectations had a statistically significant negative effect, and among the 5 factor model, openness had a statistically significant positive (+) Affected. From these results, it can be seen that the Big Five personality factors have a mediating effect on the relationship between goal-oriented behavior model and intention to start up. This study confirmed that the goal-oriented behavioral model of college students is an important variable in implementing self-determination and intention to start a business. In addition, by using his Big 5 personality factors as positive feedback, he has proved to play an important role by identifying the mediation role that can be set, planned and utilized to plan and achieve his life. The result of this study is that college students are interested in the intention of individual start-ups, so they are not freed from difficult employment difficulties. It is intended to provide basic data useful in the age of creation of government.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.2
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• pp.177-188
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• 2021

On December 2015, Financial Supervisory Service(FSS) announced the four key thematic audit review areas, one of them is an appropriation of unbilled revenue. Accounting of unbilled revenue is intertwined with a percentage of completion, that is concerned about discretionary decision by manager. Therefore, if manager motivated by income-increasing manipulation is exaggerating percentage of completion, unbilled revenue is excessively recognized. This problem is caused the serious accounting issues(e.g., shock at a loss for 2013 fiscal year by some construction firms, malpractice of accounting in order-made production industry). Distrust of accounting was grown because the shipbuilding and construction industries successively went poor management and bad accounting of them is revealed. Those accounting issues were the trigger for problem recognition of unbilled revenue, they were background for the designation of appropriation unbilled revenue as thematic audit review areas by FSS. Therefore, this study verified effectiveness of thematic audit review by empirically analyzing whether designation of thematic audit review makes the firm increases conservative behavior. Conservative accounting is estimated by using Basu(1997) model. We analyzed the effect of the thematic audit review on conservative accounting of unbilled revenue by comparing with reflecting unbilled revenue or not. The sample for test consists of firm-years the manufacturing and construction industries from 2012 to 2017. The test results of this study suggested that the conservative accounting of unbilled revenue after designation of the thematic audit review was significantly increased. We also tested again by classifying whether or not it is construction industry. We found that construction industry is more conservative than the other industry only for the designated year of the thematic audit review, otherwise there was not any evidence for significantly increasing conservatism. This study contributes to the literature by empirically analysing relationship of the unbilled revenue to the thematic audit review from the perspective of the conservatism and verifying effectiveness of the thematic audit review.

• The Korean Society of Law and Medicine
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• v.21 no.2
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• pp.163-207
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• 2020

Korea has been positioned as the leading country in the industry of clinical trials as the clinical trail of Korea has developed for the recent 10 years. Clinical trial has plays a significant role in the development of medicine and the increase of curability. However, it has inevitable risk as the purpose of the clinical trial is to prove the safety and effectiveness of new drugs. Therefore, the clinical trial should be controlled properly to protect the health of the subjects of clinical trial and to ensure that they exercise a right of self-determination. In this context, the fiduciary duties of doctors who conduct clinical trials is especially important. The Pharmaceutical Affairs Act and the relevant regulations define several duties of doctors who conduct clinical trials. In particular, the duty to protection of subjects and the duty to provide information constitute the main fiduciary duties to the subjects. Those are essentially similar to the fiduciary duties of doctors in usual treatment from the perspective of the values promoted by the law and the content of the law. Nonetheless, clinical trials put more emphasis on the duties to provide explanation than in usual treatment. Further research and study are required to establish the concrete standard for the duty of care. However, if the blind pursuit of higher standards for the duty of care or to pass the burden of proof to doctors may result in disrupting the development of clinical trials, limiting the accessibility of patients to new treatment and even violating the principle of sharing damage equally and properly. In addition to these duties, the laws of clinical trials define several duties of doctors. Any decision on whether the violation of the law constitutes the violation of the fiduciary duty and justifies the demand for compensation of damages should be based on whether relevant law aims to protect the safety and benefit of subjects, even if in an incidental way, the degree to which such violation breaches the values promoted by the law and the concrete of violation of benefit of law, the detailed acts of such violation. The legal interests of the subjects can be protected effectively by guaranteeing compliance with those duties and establishing judicial and administrative controls to ensure that the benefit of subjects are protected properly in individual cases.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.1
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• pp.172-178
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• 2021

In the ship repair market, interest in maintenance and repair is steadily increasing due to the reinforcement of prevention of environmental pollution caused by ships and the reinforcement of safety standards for ship structures. By reflecting this effect, the number of requests for repairs by foreign shipping companies increases to repair shipbuilders in the Southwest Sea. However, because most of the repair shipbuilders in the southwestern area are small and medium-sized companies, it is difficult to lead to the integrated synergy effect of the repair shipbuilding companies. Moreover, the infrastructure is not integrated; hence, using the infrastructure jointly is a challenge, which acts as an obstacle to the activation of the repair shipbuilding industry. Floating docks are indispensable to operating the repair shipbuilding business; in addition, most of them are operated through renovation/repair after importing aging caisson docks from overseas. However, their service life is more than 30 years; additionally, there is no structure inspection standard. Therefore, it is vulnerable to the safety field. In this study, the finite element analysis program of ANSYS was used to evaluate the structural safety of the modified caisson dock and obtain additional structural reinforcement schemes to solve the derived problems. For the floating docks, there are classification regulations; however, concerning structural strength, the regulations are insufficient, and the applicability is inferior. These insufficient evaluation areas were supplemented through a detailed structural FE-analysis. The reinforcement plan was decided by reinforcing the pontoon deck and reinforcement of the side tank, considering the characteristics of the repair shipyard condition. The final plan was selected to reinforce the side wing tank through the structural analysis of the decision; in addition, the actual structure was fabricated to reflect the reinforcement plan. Our results can be used as reference data for improving the structural strength of similar facilities; we believe that the optimal solution can be found quickly if this method is used during renovation/repair.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.47-64
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• 2021

The global spread of COVID-19 around the world has not only affected many parts of our daily life but also has a huge impact on many areas, including the economy and society. As the number of confirmed cases and deaths increases, medical staff and the public are said to be experiencing psychological problems such as anxiety, depression, and stress. The collective tragedy that accompanies the epidemic raises fear and anxiety, which is known to cause enormous disruptions to the behavior and psychological well-being of many. Long-term negative emotions can reduce people's immunity and destroy their physical balance, so it is essential to understand the psychological state of COVID-19. This study suggests a method of monitoring medial news reflecting current days which requires striving not only for physical but also for psychological quarantine in the prolonged COVID-19 situation. Moreover, it is presented how an easier method of analyzing social media networks applies to those cases. The aim of this study is to assist health policymakers in fast and complex decision-making processes. News plays a major role in setting the policy agenda. Among various major media, news headlines are considered important in the field of communication science as a summary of the core content that the media wants to convey to the audiences who read it. News data used in this study was easily collected using "Bigkinds" that is created by integrating big data technology. With the collected news data, keywords were classified through text mining, and the relationship between words was visualized through semantic network analysis between keywords. Using the KrKwic program, a Korean semantic network analysis tool, text mining was performed and the frequency of words was calculated to easily identify keywords. The frequency of words appearing in keywords of articles related to COVID-19 emotions was checked and visualized in word cloud 'China', 'anxiety', 'situation', 'mind', 'social', and 'health' appeared high in relation to the emotions of COVID-19. In addition, UCINET, a specialized social network analysis program, was used to analyze connection centrality and cluster analysis, and a method of visualizing a graph using Net Draw was performed. As a result of analyzing the connection centrality between each data, it was found that the most central keywords in the keyword-centric network were 'psychology', 'COVID-19', 'blue', and 'anxiety'. The network of frequency of co-occurrence among the keywords appearing in the headlines of the news was visualized as a graph. The thickness of the line on the graph is proportional to the frequency of co-occurrence, and if the frequency of two words appearing at the same time is high, it is indicated by a thick line. It can be seen that the 'COVID-blue' pair is displayed in the boldest, and the 'COVID-emotion' and 'COVID-anxiety' pairs are displayed with a relatively thick line. 'Blue' related to COVID-19 is a word that means depression, and it was confirmed that COVID-19 and depression are keywords that should be of interest now. The research methodology used in this study has the convenience of being able to quickly measure social phenomena and changes while reducing costs. In this study, by analyzing news headlines, we were able to identify people's feelings and perceptions on issues related to COVID-19 depression, and identify the main agendas to be analyzed by deriving important keywords. By presenting and visualizing the subject and important keywords related to the COVID-19 emotion at a time, medical policy managers will be able to be provided a variety of perspectives when identifying and researching the regarding phenomenon. It is expected that it can help to use it as basic data for support, treatment and service development for psychological quarantine issues related to COVID-19.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.1
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• pp.147-159
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• 2021

After the 2020 Corona 19 pandemic, consumers' online consumption is increasing rapidly, and non-store online retail channels are showing high growth. In particular, social media is gaining its status as a social media market where direct transactions take place in the means of promoting companies' brands and products. In this study, changes in consumer behavior after the Corona 19 pandemic are different in choosing online shopping media such as existing online shopping malls and SNS markets that can be classified into open social media and closed social media when purchasing agri-food online. We tried to find out what type of product is preferred in the selection of agri-food products. For this study, demographic characteristics of consumers, perceived risk of consumers, and dietary lifestyle were set as independent variables to investigate the effect on online shopping media type and product selection. The summary of the empirical analysis results is as follows. When consumers purchase agri-food online, there are significant differences in demographic characteristics, consumer perception risks, and detailed factors of dietary lifestyle in selecting shopping channels such as online shopping malls, open social media, and closed social media. Appeared to be. The consumers who choose the open SNS market are higher in men than in women, with lower household income, and higher in consumers seeking health and taste. Consumers who choose the closed SNS market were analyzed as consumers who live in rural areas and have a high degree of risk perception for delivery. Consumers who choose existing online shopping malls have high educational background, high personal income, and high consumers seeking taste and economy. Through this study, we tried to provide practical assistance by providing a basis for judgment to farmers who have difficulty in selecting an online shopping medium suitable for their product characteristics. As a shopping channel for agri-food, social media is not a simple promotional channel, but a direct transaction. It can be differentiated from existing studies in that it is approached as a market that arises.

• 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.

• Journal of the Korean Institute of Landscape Architecture
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• v.49 no.4
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• pp.41-54
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• 2021

This study targets the urban park corresponding to the core areas (Hubs) of Green Infrastructure and estimates their value utilizing the Contingent Valuation Method (CVM) and determines the planning factors which affect them. The research aims to provide basic data for supporting the value improvement in the planning stage for urban parks representing green infrastructure. The primary purpose of this research is to derive variables that affect economic value and planning factors to improve the use-value of urban parks, one of the Hubs of the green infrastructure. In this study, Sejong Lake Park, located in Sejong City, is the target site. This study collected the responses of 105 people by conducting a survey on the intention to pay for the use-value and the planning factors that affect it, targeting visitors to Sejong Lake Park. The study conducts Contingent Valuation Method (CVM) on this survey responses. The results are as follows: first, as a result of analyzing the variables which affect willingness to pay for use-value, residence and age influence the willingness to pay significantly among socioeconomic characteristics. Next, the survey responses of Double-bounded dichotomous choices (DB-DC) CVM are converted into variables through statistic techniques. Furthermore, the variables are used for a Logit model to draw coefficients. The average willingness to pay per person for the use-value of Sejong Lake Park using the derived coefficients was approximately found to be 8,597 won. Therefore, as of 2019, Sejong Lake Park, with a total of 430,000 visitors, is estimated to have an annual economic value of 3.7 billion won. Third, the average Likert scale of the planning factor affecting the decision to pay for the economic value of Sejong Lake Park was the highest along the waterfront landscape, and the convenience facilities and waterfront landscape showed the highest willingness to pay, 10,000 won. In the range between 2,500 won and 5,000 won, the waterfront area ranks highest. Therefore, it can be said that visitors to Sejong Lake Park take account of the economic value of using the waterfront landscape the most. This study is meaningful as a thesis on use-value and the planning factors that affected value evaluation results of urban parks, and the analysis of the correlation between the planning factors of urban parks as hubs located in urban areas.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.374-390
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• 2021

Growth and yield of field vegetable crops would be affected by climate conditions, which cause a relatively large fluctuation in crop production and consumer price over years. The yield prediction system for these crops would support decision-making on policies to manage supply and demands. The objectives of this study were to compile literatures related to onion and garlic and to perform data-mining analysis, which would shed lights on the development of crop models for these major field vegetable crops in Korea. The literatures on crop growth and yield were collected from the databases operated by Research Information Sharing Service, National Science & Technology Information Service and SCOPUS. The keywords were chosen to retrieve research outcomes related to crop growth and yield of onion and garlic. These literatures were analyzed using text mining approaches including word cloud and semantic networks. It was found that the number of publications was considerably less for the field vegetable crops compared with rice. Still, specific patterns between previous research outcomes were identified using the text mining methods. For example, climate change and remote sensing were major topics of interest for growth and yield of onion and garlic. The impact of temperature and irrigation on crop growth was also assessed in the previous studies. It was also found that yield of onion and garlic would be affected by both environment and crop management conditions including sowing time, variety, seed treatment method, irrigation interval, fertilization amount and fertilizer composition. For meteorological conditions, temperature, precipitation, solar radiation and humidity were found to be the major factors in the literatures. These indicate that crop models need to take into account both environmental and crop management practices for reliable prediction of crop yield.