• Journal of Digital Convergence
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• v.17 no.5
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• pp.137-143
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• 2019

Fake news is a form of misinformation that has the advantage of rapid spreading of information on media platforms that users interact with, such as social media. There has been a lot of social problems due to the recent increase in fake news. In this paper, we propose a method to detect such false news. Previous research on fake news detection mainly focused on text analysis. This research focuses on a network where social media news spreads, generates qualities with DeepWalk, a network embedding method, and classifies fake news using logistic regression analysis. We conducted an experiment on fake news detection using 211 news on the Internet and 1.2 million news diffusion network data. The results show that the accuracy of false network detection using network embedding is 10.6% higher than that of text analysis. In addition, fake news detection, which combines text analysis and network embedding, does not show an increase in accuracy over network embedding. The results of this study can be effectively applied to the detection of fake news that organizations spread online.

• Journal of Digital Convergence
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• v.19 no.8
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• pp.101-111
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• 2021

Irregular workers, which have recently caused various socio-economic issues and conflicts, generally have low loyalty to the organization and job satisfaction due to anxiety about employment. As a way to improve this, this study attempted to analyze the effect of organizational communication satisfaction of irregular workers on job satisfaction and organizational commitment. Among the 7th Human Capital Companies panel survey data, irregular workers survey data were collected and analyzed using the structural equation model analysis. The results were as follows: First, it was analyzed that organizational communication recognized by irregular workers had a positive(+) effect on job satisfaction and organizational commitment. Second, it was analyzed that job satisfaction had a positive(+) effect on organizational commitment. Third, it was analyzed that job satisfaction plays a mediating role in the relationship between communication satisfaction and organizational commitment. This study is significant in that it expanded the research subject to irregular workers from the existing service industry-oriented research, and that it included more diverse industries. The results of this study suggest that mission and vision sharing and communication activation system are needed to improve organizational effectiveness of irregular workers.

• Journal of Digital Convergence
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• v.20 no.4
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• pp.529-539
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• 2022

This study analyzed the index change in efficiency and productivity for the tax reorganization project of the regional headquarters of Korea Asset Management Corporation using panel data for 3 years from 2017 to 2019 using the DEA-Malmquist analysis model. According to the DEA analysis result, the average of the efficiency by the CCR model of the regional headquarters tax reorganization project of the Korea Asset Management Corporation was 0.671 in 2017, 0.772 in 2018, and 0.699 in 2019, and the average of the efficiency by the BCC model was 0.798 in 2017, 0.851 in 2018 and 0.771 in 2019. As a result of analyzing the Malmquist productivity index, the time series average productivity index MPI increased by 4.5%. These results appear to be attributable to the increase in technological efficiency, technological change, and scale-efficiency change rather than the decrease in net efficiency change. Looking at the change in MPI by year, it decreased by 14.6% in 2017-2018, but increased significantly to 27.8% in 2018-2019. Through the results of DEA analysis of specific tax projects of public corporations, each regional headquarters of Korea Asset Management Corporation will be able to contribute to reinforcing business capabilities through mutual benchmarking.

• Journal of the Korea Society for Simulation
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• v.32 no.1
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• pp.1-11
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• 2023

Smart factory environments and digital twin environments are established, and today's factories accumulate vast amounts of production data and are managed in real time as visualized results suitable for user convenience. Production simulation techniques are in the spotlight as a way to prevent delays in delivery and predict factory volatility in situations where production schedule planning becomes difficult due to the diversification of production products. With the development of the digital twin environment, new packages are developed and functions of existing packages are updated, making it difficult for users to make decisions on which packages to use to develop simulations. Therefore, in this study, the concept of Discrete Event Simulation (DES) performed based on discrete events is defined, and the characteristics of various simulation packages were compared and analyzed. To this end, studies that solved real problems using discrete event simulation software for 10 years were analyzed, and three types of software used by the majority were identified. In addition, each package was classified by simulation technique, type of industry, subject of simulation, country of use, etc., and analysis results on the characteristics and usage of DES software were provided. The results of this study provide a basis for selection to companies and users who have difficulty in selecting discrete event simulation package in the future, and it is judged that they will be used as basic data.

• The Korean Journal of Franchise Management
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• v.3 no.2
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• pp.20-38
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• 2012

After being introduced into franchises industry, franchise has made a phenomenal growth in a short time and a substantial contribution to job creation and economic revitalization. Nevertheless, franchise business operators failed a business or low profit because of a lack of information and indiscriminate foundation. Therefore the first object of this study is characteristics of franchise's factors on disclosure agreement in franchise associate website. second is examinations about casual relationship between factor and franchise performance with using Excel and SPSS 18.0 versions. The findings of present study were as follows. First, franchises manage small business mostly(financial data, scale so on) and franchise's type focused the food service industry. Specially, a business district select unprotected contract. Second, in franchise's factors, we could find statistically significant effect on annual average sales and annual average net profit. However growth rate of franchise don't have statistically significant effect. Third, we could find statistically significant difference on analysis both franchises' factors and financial data. In conclusion, we must consider of franchise industry environment and success effect on performance in starting one's business. Furthermore franchises plan ways for their sustained growth and protection of rights and interests. Finally business operator draw up their information and upgrade continuously for franchises industry growth. Discussion and theoretical and managerial implications of the results were described along with future franchise research suggestions.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.139-161
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• 2019

The utilization of the e-commerce market has become a common life style in today. It has become important part to know where and how to make reasonable purchases of good quality products for customers. This change in purchase psychology tends to make it difficult for customers to make purchasing decisions in vast amounts of information. In this case, the recommendation system has the effect of reducing the cost of information retrieval and improving the satisfaction by analyzing the purchasing behavior of the customer. Amazon and Netflix are considered to be the well-known examples of sales marketing using the recommendation system. In the case of Amazon, 60% of the recommendation is made by purchasing goods, and 35% of the sales increase was achieved. Netflix, on the other hand, found that 75% of movie recommendations were made using services. This personalization technique is considered to be one of the key strategies for one-to-one marketing that can be useful in online markets where salespeople do not exist. Recommendation techniques that are mainly used in recommendation systems today include collaborative filtering and content-based filtering. Furthermore, hybrid techniques and association rules that use these techniques in combination are also being used in various fields. Of these, collaborative filtering recommendation techniques are the most popular today. Collaborative filtering is a method of recommending products preferred by neighbors who have similar preferences or purchasing behavior, based on the assumption that users who have exhibited similar tendencies in purchasing or evaluating products in the past will have a similar tendency to other products. However, most of the existed systems are recommended only within the same category of products such as books and movies. This is because the recommendation system estimates the purchase satisfaction about new item which have never been bought yet using customer's purchase rating points of a similar commodity based on the transaction data. In addition, there is a problem about the reliability of purchase ratings used in the recommendation system. Reliability of customer purchase ratings is causing serious problems. In particular, 'Compensatory Review' refers to the intentional manipulation of a customer purchase rating by a company intervention. In fact, Amazon has been hard-pressed for these "compassionate reviews" since 2016 and has worked hard to reduce false information and increase credibility. The survey showed that the average rating for products with 'Compensated Review' was higher than those without 'Compensation Review'. And it turns out that 'Compensatory Review' is about 12 times less likely to give the lowest rating, and about 4 times less likely to leave a critical opinion. As such, customer purchase ratings are full of various noises. This problem is directly related to the performance of recommendation systems aimed at maximizing profits by attracting highly satisfied customers in most e-commerce transactions. In this study, we propose the possibility of using new indicators that can objectively substitute existing customer 's purchase ratings by using RFM multi-dimensional analysis technique to solve a series of problems. RFM multi-dimensional analysis technique is the most widely used analytical method in customer relationship management marketing(CRM), and is a data analysis method for selecting customers who are likely to purchase goods. As a result of verifying the actual purchase history data using the relevant index, the accuracy was as high as about 55%. This is a result of recommending a total of 4,386 different types of products that have never been bought before, thus the verification result means relatively high accuracy and utilization value. And this study suggests the possibility of general recommendation system that can be applied to various offline product data. If additional data is acquired in the future, the accuracy of the proposed recommendation system can be improved.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.49-65
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• 2014

Word of Mouth (WOM) is a behavior used by consumers to transfer or communicate their product or service experience to other consumers. Due to the popularity of social media such as Facebook, Twitter, blogs, and online communities, electronic WOM (e-WOM) has become important to the success of products or services. As a result, most enterprises pay close attention to e-WOM for their products or services. This is especially important for movies, as these are experiential products. This paper aims to identify the network factors of an online movie community that impact box office revenue using social network analysis. In addition to traditional WOM factors (volume and valence of WOM), network centrality measures of the online community are included as influential factors in box office revenue. Based on previous research results, we develop five hypotheses on the relationships between potential influential factors (WOM volume, WOM valence, degree centrality, betweenness centrality, closeness centrality) and box office revenue. The first hypothesis is that the accumulated volume of WOM in online product communities is positively related to the total revenue of movies. The second hypothesis is that the accumulated valence of WOM in online product communities is positively related to the total revenue of movies. The third hypothesis is that the average of degree centralities of reviewers in online product communities is positively related to the total revenue of movies. The fourth hypothesis is that the average of betweenness centralities of reviewers in online product communities is positively related to the total revenue of movies. The fifth hypothesis is that the average of betweenness centralities of reviewers in online product communities is positively related to the total revenue of movies. To verify our research model, we collect movie review data from the Internet Movie Database (IMDb), which is a representative online movie community, and movie revenue data from the Box-Office-Mojo website. The movies in this analysis include weekly top-10 movies from September 1, 2012, to September 1, 2013, with in total. We collect movie metadata such as screening periods and user ratings; and community data in IMDb including reviewer identification, review content, review times, responder identification, reply content, reply times, and reply relationships. For the same period, the revenue data from Box-Office-Mojo is collected on a weekly basis. Movie community networks are constructed based on reply relationships between reviewers. Using a social network analysis tool, NodeXL, we calculate the averages of three centralities including degree, betweenness, and closeness centrality for each movie. Correlation analysis of focal variables and the dependent variable (final revenue) shows that three centrality measures are highly correlated, prompting us to perform multiple regressions separately with each centrality measure. Consistent with previous research results, our regression analysis results show that the volume and valence of WOM are positively related to the final box office revenue of movies. Moreover, the averages of betweenness centralities from initial community networks impact the final movie revenues. However, both of the averages of degree centralities and closeness centralities do not influence final movie performance. Based on the regression results, three hypotheses, 1, 2, and 4, are accepted, and two hypotheses, 3 and 5, are rejected. This study tries to link the network structure of e-WOM on online product communities with the product's performance. Based on the analysis of a real online movie community, the results show that online community network structures can work as a predictor of movie performance. The results show that the betweenness centralities of the reviewer community are critical for the prediction of movie performance. However, degree centralities and closeness centralities do not influence movie performance. As future research topics, similar analyses are required for other product categories such as electronic goods and online content to generalize the study results.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.4
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• pp.231-244
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• 2020

With the rapid progress of the 4th industrial revolution, technical human capitals are considered to be the core competing factors of the enterprise. Technical manpower training of the 4th industrial revolution through technical education has become an essential task of venture start-ups. The opening of technical training courses and the education support system of companies are increasing, but the shortage of technical manpower is getting worse. This study was conducted to analyze the factors affecting participation intention of the 4th industrial revolution technology education. The research model was established based on the model of goal-directed behavior. For the analysis, 250 valid questionnaire data were used to test with a structural equation model. The results of the study are as follows. First, attitude had a positive effect on the intention to participate in education. Second, subjective norms had a positive effect on the intention to participate in education. Third, the perceived behavioral control has not been tested for a significant influence on educational participation intention. Fourth, positive and negative anticipated emotions had a significant effect on educational intention. The impact of significant variables were found in the order of positive anticipated emotions, attitudes, negative anticipated emotions, subjective norms. On the other hand, as a result of testing the mediating effect of desires, it was found that desires plays a mediating role between attitude, subjective norm, perceived behavioral control, positive anticipated emotions, negative anticipated emotions, and participation intention. In particular, the causal relationship between perceived behavioral control and intention to participate in education was not significant, but perceived behavioral control had a significant effect(full mediation) on participation intention through desires. Based on the results of this study, the following implication were suggested. First, the model of goal-directed behavior(MGB) was applied to the technical education field. Second, the direct relationship between antecedent variables and behavioral intentions was simultaneously tested. Third, unlike the existing education-related research, the factors affecting participation in education were analyzed. Fourth, the importance of desires for education were suggested.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.1-17
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• 2019

Because stock price forecasting is an important issue both academically and practically, research in stock price prediction has been actively conducted. The stock price forecasting research is classified into using structured data and using unstructured data. With structured data such as historical stock price and financial statements, past studies usually used technical analysis approach and fundamental analysis. In the big data era, the amount of information has rapidly increased, and the artificial intelligence methodology that can find meaning by quantifying string information, which is an unstructured data that takes up a large amount of information, has developed rapidly. With these developments, many attempts with unstructured data are being made to predict stock prices through online news by applying text mining to stock price forecasts. The stock price prediction methodology adopted in many papers is to forecast stock prices with the news of the target companies to be forecasted. However, according to previous research, not only news of a target company affects its stock price, but news of companies that are related to the company can also affect the stock price. However, finding a highly relevant company is not easy because of the market-wide impact and random signs. Thus, existing studies have found highly relevant companies based primarily on pre-determined international industry classification standards. However, according to recent research, global industry classification standard has different homogeneity within the sectors, and it leads to a limitation that forecasting stock prices by taking them all together without considering only relevant companies can adversely affect predictive performance. To overcome the limitation, we first used random matrix theory with text mining for stock prediction. Wherever the dimension of data is large, the classical limit theorems are no longer suitable, because the statistical efficiency will be reduced. Therefore, a simple correlation analysis in the financial market does not mean the true correlation. To solve the issue, we adopt random matrix theory, which is mainly used in econophysics, to remove market-wide effects and random signals and find a true correlation between companies. With the true correlation, we perform cluster analysis to find relevant companies. Also, based on the clustering analysis, we used multiple kernel learning algorithm, which is an ensemble of support vector machine to incorporate the effects of the target firm and its relevant firms simultaneously. Each kernel was assigned to predict stock prices with features of financial news of the target firm and its relevant firms. The results of this study are as follows. The results of this paper are as follows. (1) Following the existing research flow, we confirmed that it is an effective way to forecast stock prices using news from relevant companies. (2) When looking for a relevant company, looking for it in the wrong way can lower AI prediction performance. (3) The proposed approach with random matrix theory shows better performance than previous studies if cluster analysis is performed based on the true correlation by removing market-wide effects and random signals. The contribution of this study is as follows. First, this study shows that random matrix theory, which is used mainly in economic physics, can be combined with artificial intelligence to produce good methodologies. This suggests that it is important not only to develop AI algorithms but also to adopt physics theory. This extends the existing research that presented the methodology by integrating artificial intelligence with complex system theory through transfer entropy. Second, this study stressed that finding the right companies in the stock market is an important issue. This suggests that it is not only important to study artificial intelligence algorithms, but how to theoretically adjust the input values. Third, we confirmed that firms classified as Global Industrial Classification Standard (GICS) might have low relevance and suggested it is necessary to theoretically define the relevance rather than simply finding it in the GICS.

• The Journal of the Korea Contents Association
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• v.15 no.4
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• pp.391-406
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• 2015

As the individual participation in online news sites proliferates, the importance of online news comments has been increasing. Social comment services which help people leave comments on news articles using their own SNS (social networking site) accounts have gained popularity recently. Using data gathered from an online news site, this study, therefore, (1) identifies factors differentiating social comments from general comments, (2) examines how social comments are significantly different from general comments in terms of each factor, (3) and further validates how the social comments' characteristics vary among different type of SNS. Then, we investigated this study by applying t-test, ANOVA, and Duncan test of SPSS Statistics. Our results provide insights on the significant differences in all the factors between general and social comments. We also found that there is a significant difference between Facebook and Twitter groups among three types of SNS. The findings of this study would help assess the actual benefit of social comment services as they may provide us with several valuable leads to solve the malicious comments issue. Moreover, they would suggest the need to apply this service to other areas, such as online environments in private and public sectors.