• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.231-252
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• 2021

Artificial intelligence (AI) is a key technology that will change the future the most. It affects the industry as a whole and daily life in various ways. As data availability increases, artificial intelligence finds an optimal solution and infers/predicts through self-learning. Research and investment related to automation that discovers and solves problems on its own are ongoing continuously. Automation of artificial intelligence has benefits such as cost reduction, minimization of human intervention and the difference of human capability. However, there are side effects, such as limiting the artificial intelligence's autonomy and erroneous results due to algorithmic bias. In the labor market, it raises the fear of job replacement. Prior studies on the utilization of artificial intelligence have shown that individuals do not necessarily use the information (or advice) it provides. Algorithm error is more sensitive than human error; so, people avoid algorithms after seeing errors, which is called "algorithm aversion." Recently, artificial intelligence has begun to be understood from the perspective of the augmentation of human intelligence. We have started to be interested in Human-AI collaboration rather than AI alone without human. A study of 1500 companies in various industries found that human-AI collaboration outperformed AI alone. In the medicine area, pathologist-deep learning collaboration dropped the pathologist cancer diagnosis error rate by 85%. Leading AI companies, such as IBM and Microsoft, are starting to adopt the direction of AI as augmented intelligence. Human-AI collaboration is emphasized in the decision-making process, because artificial intelligence is superior in analysis ability based on information. Intuition is a unique human capability so that human-AI collaboration can make optimal decisions. In an environment where change is getting faster and uncertainty increases, the need for artificial intelligence in decision-making will increase. In addition, active discussions are expected on approaches that utilize artificial intelligence for rational decision-making. This study investigates the impact of artificial intelligence on decision-making focuses on human-AI collaboration and the interaction between the decision maker personal traits and advisor type. The advisors were classified into three types: human, artificial intelligence, and human-AI collaboration. We investigated perceived usefulness of advice and the utilization of advice in decision making and whether the decision-maker's personal traits are influencing factors. Three hundred and eleven adult male and female experimenters conducted a task that predicts the age of faces in photos and the results showed that the advisor type does not directly affect the utilization of advice. The decision-maker utilizes it only when they believed advice can improve prediction performance. In the case of human-AI collaboration, decision-makers higher evaluated the perceived usefulness of advice, regardless of the decision maker's personal traits and the advice was more actively utilized. If the type of advisor was artificial intelligence alone, decision-makers who scored high in conscientiousness, high in extroversion, or low in neuroticism, high evaluated the perceived usefulness of the advice so they utilized advice actively. This study has academic significance in that it focuses on human-AI collaboration that the recent growing interest in artificial intelligence roles. It has expanded the relevant research area by considering the role of artificial intelligence as an advisor of decision-making and judgment research, and in aspects of practical significance, suggested views that companies should consider in order to enhance AI capability. To improve the effectiveness of AI-based systems, companies not only must introduce high-performance systems, but also need employees who properly understand digital information presented by AI, and can add non-digital information to make decisions. Moreover, to increase utilization in AI-based systems, task-oriented competencies, such as analytical skills and information technology capabilities, are important. in addition, it is expected that greater performance will be achieved if employee's personal traits are considered.

• Management & Information Systems Review
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• v.29 no.4
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• pp.143-167
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• 2010

Diverse research topics relating to Social Network Services (SNS) such as, social affective factors in relationships among internet users, social capital value of SNS, comparing attributes why users are intending to participate in SNS, user's lifestyle and their preferences, and the exploratory seeking potential of SNS as a social capital need to be focused on. However, these researches that have been undertaken only consider facts at a particular period of the changing computing environment. In accordance with this indispensability, the integrated view on what technical, social and business characteristics and attributes need to be acknowledged. The purpose of this study is to analyze the evolving attributes and characteristics of SNS from Web 1.0 to Mobile web 2.0 through the Web 2.0 and Mobile 1.0 period. Based on the relevant literature, the attributes that drive the changing technological, social and business aspects of SNS have been developed and analyzed. This exploratory study analyzed major attributes and relationships between SNS and users by changing the paradigms which represented each period. It classified and chronicled each period by representing paradigms and deducted the attributes by considering three aspects such as technological, social and business administration. The major findings of this study are, firstly, the web based computing environment has been changed into the platform attribute for users in the aspect of technology. Users can only read, listen and view information through the web site in the early stages, but now it is possible that users can create, modify and distribute all kinds of information. Secondly, the few knowledge producers of web services have been changed into a collective intelligence by groups of people in the aspect of society. Information authority has been distributed and there is no limit to its spread. Many businesses recognized the potential of the SNS and they are considering how to utilize these advantages toward channel of promotion and marketing. Thirdly, the conventional marketing channel has been changed into oral transmission by using SNS. The market of innovative mobile technology such as smart phones, which provide convenience and access-ability toward customers, has been enlarged. New opportunities to build friendly relationship between business and customers as a new marketing chance have been created. Finally, the role of the consumer has been changed into the leading role of a prosumer. Users can create, modify and distribute information, and are performing the dual role of customer and producer.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.155-169
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• 2014

With the rapid advances in technologies, organizations are more likely to depend on information systems in their decision-making processes. Business Intelligence (BI) systems, in particular, have become a mainstay in dealing with complex problems in an organization, partly because a variety of advanced computational methods from statistics, machine learning, and artificial intelligence can be applied to solve business problems such as demand forecasting. In addition to the ability to analyze past and present trends, these predictive analytics capabilities provide huge value to an organization's ability to respond to change in markets, business risks, and customer trends. While the performance effects of BI system use in organization settings have been studied, it has been little discussed on the use of predictive analytics technologies embedded in BI systems for forecasting tasks. Thus, this study aims to find important factors that can help to take advantage of the benefits of advanced technologies of a BI system. More generally, a BI system can be viewed as an advisor, defined as the one that formulates judgments or recommends alternatives and communicates these to the person in the role of the judge, and the information generated by the BI system as advice that a decision maker (judge) can follow. Thus, we refer to the findings from the advice-giving and advice-taking literature, focusing on the role of explanations of the system in users' advice taking. It has been shown that advice discounting could occur when an advisor's reasoning or evidence justifying the advisor's decision is not available. However, the majority of current BI systems merely provide a number, which may influence decision makers in accepting the advice and inferring the quality of advice. We in this study explore the following key factors that can influence users' advice taking within the setting of a BI system: explanations on how the box-office grosses are predicted, types of advisor, i.e., system (data mining technique) or human-based business advice mechanisms such as prediction markets (aggregated human advice) and human advisors (individual human expert advice), users' evaluations of the provided advice, and individual differences in decision-makers. Each subject performs the following four tasks, by going through a series of display screens on the computer. First, given the information of the given movie such as director and genre, the subjects are asked to predict the opening weekend box office of the movie. Second, in light of the information generated by an advisor, the subjects are asked to adjust their original predictions, if they desire to do so. Third, they are asked to evaluate the value of the given information (e.g., perceived usefulness, trust, satisfaction). Lastly, a short survey is conducted to identify individual differences that may affect advice-taking. The results from the experiment show that subjects are more likely to follow system-generated advice than human advice when the advice is provided with an explanation. When the subjects as system users think the information provided by the system is useful, they are also more likely to take the advice. In addition, individual differences affect advice-taking. The subjects with more expertise on advisors or that tend to agree with others adjust their predictions, following the advice. On the other hand, the subjects with more knowledge on movies are less affected by the advice and their final decisions are close to their original predictions. The advances in predictive analytics of a BI system demonstrate a great potential to support increasingly complex business decisions. This study shows how the designs of a BI system can play a role in influencing users' acceptance of the system-generated advice, and the findings provide valuable insights on how to leverage the advanced predictive analytics of the BI system in an organization's forecasting practices.

• Korean journal of communication and information
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• v.45
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• pp.151-186
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• 2009

This article focuses on analysing and interpreting cultural imperialism issues that have long been debated. One of issues among them is related to the characteristics of international cultural fluid. What had been debated is that cultural imperialism has been ended or not and the debates were connected to the so called 'reverse cultural imperialism'. The other issue is about this fluid is the continuation of globalization or a part of localization and this is related to the phenomena of cultural homogenization or hybrid. General trend was that simplistic cultural imperialism which had been occurred during the 1970s and 1980s was no longer effective but global dominance by transnational global media is still strong and worldwide. Therefore my research interest is centered on how they look international cultural exchanges with regard to cultural imperialism. And is the framework of cultural imperialism thesis still effective tool in researching and analysing international cultural flows? How do we look globalization relating to international exchanges? Is globalization an another aspect of imperialism? In conclusion, international cultural fluid is not so simple. Like natural phenomena or human progress, a phenomenon is not consisted of a single factor or relation. Cultural fluid is a complex one mixed with various phenomena and relations. This is related to internal and external contradictions, internal and external dynamics of a society and nation, and social and cultural life of human beings. Recent research results show that globalization and localization are closely related to many country's programming schedule, and particular cultural interpretation of specific programmes and culture are adopted to the country's culture and patterns. Cultural fluid has both-sides. One the hand it has useful and positive sides and on the other hand it has harmful and negative aspects. Imperialistic factor, globalization, cultural homogenization or hybrid are all operated and functioned together in cultual fluid. It is difficult to say that cultural imperialism thesis has been ended because American and global media's dominance is still effective. What needed at this time for us is a complex and dynamic analysis of international cultural fluid instead of simplistic cultural imperialism.

• The Korean Journal of Financial Management
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• v.25 no.4
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• pp.53-78
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• 2008

Foreign investors who invest in the Korean stock markets are exposed to two kinds of foreign exchange rate risk, the economic exposure and the translation exposure. The former is the foreign exchange rate exposure in return generating process of the assets invested and the latter is the foreign exchange rate exposure in the translation of domestic return into foreign investors' currency. Domestic investors, however, are exposed only to foreign exchange rate exposure in the asset invested. This different situation on foreign exchange rate exposure between foreign investors and domestic investors can induce different response to exchange rate change by investor groups. Previous studies on foreign exchange rate exposure of Korean firms reported that quite a few Korean firms are exposed to foreign exchange risks and suggested to manage the foreign exchange risks. Also, many studies on the market segmentation showed that a market can be practically segmented according to the characteristics of investor groups. These studies support the hypothesis that the Korean stock market can be practically segmented by the foreign investors' attitude to the foreign exchange rate exposure. This study examines the response of both foreign investors and domestic investors to the foreign exchange rate exposures in Korean stock markets. Test results show that foreign investors increase their sell transactions when the foreign exchange rate exposure of the previous day is negative. This result can be possible when foreign investors attempt to actively manage the decrease in value of their assets due to rising of exchange rate. Analysis on the sell order data is also supportive to this interpretation. Foreign investors also increase their buy transactions when the foreign exchange rate exposure of the previous day is negative. This result can be possible when foreign investors use actively the relation between the increase in asset value and the translation gain due to declining of exchange rate. Analyses on buy order data, however, do not show the same result as the analyses on transaction data. This difference may come from the difference of information contained in transaction data and order data. In summary, the result of the paper supports the hypothesis that foreign investors response differently to foreign exchange rate exposure compared with domestic, Korean investors. Two groups do not show different response when exchange rate exposure is positive, i.e., as foreign exchange rate is increase (decrease), the asset value is increase (decrease). However, foreign investors' response is different from that of domestic investors when exchange rate exposure is negative, i.e., as foreign exchange rate is increase (decrease), the asset value is decrease (increase). These results mean that foreign investors and domestic investors are placed in different situations related to foreign exchange rate exposure, and these differences are reflected in the Korean stock markets. And domestic investors need to consider foreign investors' different attitude to the foreign exchange rate exposure when they analysis foreign investors' trading behavior.

• Journal of the Korean Applied Science and Technology
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• v.36 no.2
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• pp.498-506
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• 2019

This study is based on a coating method that provides utilization value as a micronised powder for cosmetic raw materials using natural minerals buried in Bonghwa, Gyeongsangbuk-do in Korea. The mineral powder name is called Buseok, and chemical name is pumice powder. The results of a study on the efficacy of cosmetics are reported by the development of particulate powder to assess the performance of this powder. First of all, in order to coat the surface of this powder with oil, aluminum hydroxide was coated on the particulate surface and then coated with alkylsilan. In addition, it was coated with vegetable oil to prevent condensation of the powder and increase the dispersion in the oil phase. First; the particle size of pumice powder was from 10 to 50mm having porous holes on the surface of the particles. Second; The components of this powder contained $SiO_2$, $Al_2O_3$, $Fe_2O_3$, MgO, CaO, $K_2O_2$, $Na_2O$, $TiO_2$, $TiO_2$, MnO, $Cr_2O_3$, $V_2O_5$. Third: The particles of this powder have a planetary structure and are reddish-brown with porosity through SEM and TEM analysis. Fourth; the far-infrared radiation rate of this parabolic powder was $0.924{\mu}m$, and the radiative energy was $3.72{\times}102W/m^2$ and ${\mu}m$. In addition, the anion emission is 128 ION/cc, which shows that the coating remains unchanged. Based on these results, it is expected to be widely applied to basic cosmetics such as BB cream, cushion foundation, powderfect, and other color-coordinated cosmetics, sunblock cream, wash-off massage pack as an application of cosmetics. (Small and Medium Business Administration: S2601385)

• Information Systems Review
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• v.16 no.3
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• pp.161-177
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• 2014

Corporate credit rating assessment consists of complicated processes in which various factors describing a company are taken into consideration. Such assessment is known to be very expensive since domain experts should be employed to assess the ratings. As a result, the data-driven corporate credit rating prediction using statistical and artificial intelligence (AI) techniques has received considerable attention from researchers and practitioners. In particular, statistical methods such as multiple discriminant analysis (MDA) and multinomial logistic regression analysis (MLOGIT), and AI methods including case-based reasoning (CBR), artificial neural network (ANN), and multiclass support vector machine (MSVM) have been applied to corporate credit rating.2) Among them, MSVM has recently become popular because of its robustness and high prediction accuracy. In this study, we propose a novel optimized MSVM model, and appy it to corporate credit rating prediction in order to enhance the accuracy. Our model, named 'GAMSVM (Genetic Algorithm-optimized Multiclass Support Vector Machine),' is designed to simultaneously optimize the kernel parameters and the feature subset selection. Prior studies like Lorena and de Carvalho (2008), and Chatterjee (2013) show that proper kernel parameters may improve the performance of MSVMs. Also, the results from the studies such as Shieh and Yang (2008) and Chatterjee (2013) imply that appropriate feature selection may lead to higher prediction accuracy. Based on these prior studies, we propose to apply GAMSVM to corporate credit rating prediction. As a tool for optimizing the kernel parameters and the feature subset selection, we suggest genetic algorithm (GA). GA is known as an efficient and effective search method that attempts to simulate the biological evolution phenomenon. By applying genetic operations such as selection, crossover, and mutation, it is designed to gradually improve the search results. Especially, mutation operator prevents GA from falling into the local optima, thus we can find the globally optimal or near-optimal solution using it. GA has popularly been applied to search optimal parameters or feature subset selections of AI techniques including MSVM. With these reasons, we also adopt GA as an optimization tool. To empirically validate the usefulness of GAMSVM, we applied it to a real-world case of credit rating in Korea. Our application is in bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. The experimental dataset was collected from a large credit rating company in South Korea. It contained 39 financial ratios of 1,295 companies in the manufacturing industry, and their credit ratings. Using various statistical methods including the one-way ANOVA and the stepwise MDA, we selected 14 financial ratios as the candidate independent variables. The dependent variable, i.e. credit rating, was labeled as four classes: 1(A1); 2(A2); 3(A3); 4(B and C). 80 percent of total data for each class was used for training, and remaining 20 percent was used for validation. And, to overcome small sample size, we applied five-fold cross validation to our dataset. In order to examine the competitiveness of the proposed model, we also experimented several comparative models including MDA, MLOGIT, CBR, ANN and MSVM. In case of MSVM, we adopted One-Against-One (OAO) and DAGSVM (Directed Acyclic Graph SVM) approaches because they are known to be the most accurate approaches among various MSVM approaches. GAMSVM was implemented using LIBSVM-an open-source software, and Evolver 5.5-a commercial software enables GA. Other comparative models were experimented using various statistical and AI packages such as SPSS for Windows, Neuroshell, and Microsoft Excel VBA (Visual Basic for Applications). Experimental results showed that the proposed model-GAMSVM-outperformed all the competitive models. In addition, the model was found to use less independent variables, but to show higher accuracy. In our experiments, five variables such as X7 (total debt), X9 (sales per employee), X13 (years after founded), X15 (accumulated earning to total asset), and X39 (the index related to the cash flows from operating activity) were found to be the most important factors in predicting the corporate credit ratings. However, the values of the finally selected kernel parameters were found to be almost same among the data subsets. To examine whether the predictive performance of GAMSVM was significantly greater than those of other models, we used the McNemar test. As a result, we found that GAMSVM was better than MDA, MLOGIT, CBR, and ANN at the 1% significance level, and better than OAO and DAGSVM at the 5% significance level.

• Journal of the Korean Geographical Society
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• v.47 no.3
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• pp.375-389
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• 2012

Many policy makers believe that geographically clustered business activities, in order to stimulate regional innovation, are the keys to development. Underlying the assumption between cluster and development, mere co-located forms engaged in innovative process helped from knowledge transmission locally. To address the constraints of creative economy, creative milieu require networking to support innovation activities. This paper examines the development of the mini cluster reconsidered in creative milieu that shifted environment development through network activities. It shows that the powerful node get more powerful, weak node get weaker in institutional network. Moreover, the significant of cluster in creative milieu is that brings out synergy to spill over knowledge through inter-relationship based on face-to-face meeting. Creative milieu benefits from and contributes to the competitiveness of the city, they are also embedded in its network activities.

• Journal of Convergence for Information Technology
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• v.10 no.9
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• pp.251-261
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• 2020

The purpose of this research is to examine the relationship between motivation, reliability, satisfaction, and sustainability of YouTube content and suggest the implications needed for content consumption or production of beauty workers in the future as K-beauty's interest is increasing due to the global Hallyu craze, and beauty industry workers are also watching YouTube hair care content and exchanging the latest trends. The analysis methods included frequency analysis, factor analysis, reliability analysis, correlation analysis, and multi-recovery analysis. As a result, the correlation between the motivation for content use, reliability, satisfaction, and persistence was shown, the motivation for content use was statistically significant (+) and the impact of content reliability on YouTube content satisfaction and sustainability (a significant statistical+).

• Korean Journal of Poultry Science
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• v.16 no.1
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• pp.29-52
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• 1989

Selection of breeds, adequate environment and sanitary condition must be considered to improve production ability. Minimizing of production cost through reasonable management and farm system can introduce maximizing net income. Followings are the summary of poultry industry in Japan： (1) To decreasing consumers Price and to gurantee the quality of eggs, poultry farms changed their farm system as complex which contain feed factory, GP center and processing factory as well as raising laying hens. (2) Broiler farms moved from suburbs to several remote from cites owing to development of transferation , and rising price of land. (3) The egg Price in Japan was depended on season. From fall to spring, the Price was usually rised due to increased consumption, and from May to August the price always dropped. (4) The average production per one you was around 17kg/hen and average feed consumption was around 2.45. Rearing rate was around 98.5% and motality of hens was around 7%. And one person reared 15,000-20,000 hens in over 100 thousand chicken reared farms even though average ability per person showed 3,266 hens.