• Informatization Policy
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• v.30 no.2
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• pp.22-45
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• 2023

In research on the use of AI-based voice assistant services, problems related to the user's trust and privacy protection arising from the experience of service use are constantly being raised. The purpose of this study was to investigate empirically the effects of individual trust in AI and online privacy concerns on the continued use of AI-based voice assistants, specifically the impact of their interaction. In this study, question items were constructed based on previous studies, with an online survey conducted among 405 respondents. The effect of the user's trust in AI and privacy concerns on the adoption and continuous use intention of AI-based voice assistant services was analyzed using the Heckman selection model. As the main findings of the study, first, AI-based voice assistant service usage behavior was positively influenced by factors that promote technology acceptance, such as perceived usefulness, perceived ease of use, and social influence. Second, trust in AI had no statistically significant effect on AI-based voice assistant service usage behavior but had a positive effect on continuous use intention. Third, the privacy concern level was confirmed to have the effect of suppressing continuous use intention through interaction with trust in AI. These research results suggest the need to strengthen user experience through user opinion collection and action to improve trust in technology and alleviate users' concerns about privacy as governance for realizing digital government. When introducing artificial intelligence-based policy services, it is necessary to disclose transparently the scope of application of artificial intelligence technology through a public deliberation process, and the development of a system that can track and evaluate privacy issues ex-post and an algorithm that considers privacy protection is required.

• Journal of the Korean Society of Radiology
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• v.17 no.6
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• pp.881-887
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• 2023

This study aims to analyze the development and current trends of AI-based medical imaging devices commercialized in South Korea. As of September 30, 2023, there were a total of 186 AI-based medical devices licensed, certified, and reported to the Korean Ministry of Food and Drug Safety, of which 138 were related to imaging. The study comprehensively examined the yearly approval trends, equipment types, application areas, and key functions from 2018 to 2023. The study found that the number of AI medical devices started from four products in 2018 and grew steadily until 2023, with a sharp increase after 2020. This can be attributed to the interaction between the advancement of AI technology and the increasing demand in the medical field. By equipment, AI medical devices were developed in the order of CT, X-ray, and MR, which reflects the characteristics and clinical importance of the images of each equipment. This study found that the development of AI medical devices for specific areas such as the thorax, cranial nerves, and musculoskeletal system is active, and the main functions are medical image analysis, detection and diagnosis assistance, and image transmission. These results suggest that AI's pattern recognition and data analysis capabilities are playing an important role in the medical imaging field. In addition, this study examined the number of Korean products that have received international certifications, particularly the US FDA and European CE. The results show that many products have been certified by both organizations, indicating that Korean AI medical devices are in line with international standards and are competitive in the global market. By analyzing the impact of AI technology on medical imaging and its potential for development, this study provides important implications for future research and development directions. However, challenges such as regulatory aspects, data quality and accessibility, and clinical validity are also pointed out, requiring continued research and improvement on these issues.

• Journal of Venture Innovation
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• v.4 no.3
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• pp.141-150
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• 2021

Although management decisions centered on the board of directors and directors must be made in order to effectively promote ESG management, the company's management is not obligated to make decisions considering ESG factors. A Korean corporation(company) is an established organization for commercial or other profit, and the purpose of treating a legal organization as a corporation is to easily handle the legal relationship of a group (corporate's property) and individual property of a group member, but legal person such as rights to "harm public rights" or "defend fraud". Criminal liability for illegal acts of a corporation, but the liability of a corporation (natural person) for illegal acts of a corporation is recognized within a limited range, but the criminal liability of a corporation (natural person) is limited. As the social responsibility of a corporation is great, limiting the responsibility of a corporation-related person (natural person) to civil responsibility will halve its effectiveness if considering the impact on the corporation's national economy. Objective requirements such as the completeness of control, hybridization of property, infringement of creditors' rights, and small-capitalization, and the subjective intention of abusing the company system to avoid legal application to controlling shareholders should be denied. Despite the increasing influence on corporate society, such as large-scale projects and astronomical business profits, corporate officials (natural persons) are forced to be held liable for negligence and intentional liability within a limited range. In such cases, it is necessary to introduce criminal responsibility separately from civil responsibility to legal persons (natural persons) in consideration of the maturity of capitalism in Korean society and the economic status of the world. In Korea, the requirements for recognition of corporate denial are strict, but the United States says that it is sufficient to have control or fraud. Therefore, it is not about civil responsibility, but about criminal responsibility of a legal person (natural person), so if fraud is recognized, it can strengthen the corporate social responsibility.

• Journal of Venture Innovation
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• v.6 no.1
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• pp.181-198
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• 2023

This study aims to analyze the influence of organizational support perception of workers in medical institutions on intrinsic job motivation, and to check whether there is significance in the mediating effect of work-family conflict and work-family balance factors in this process. The results of empirical analysis through the questionnaire are as follows. First, it was confirmed that organizational support recognition had a significant positive effect on work-family balance as well as intrinsic job motivation, and work-family balance had a significant positive effect on intrinsic job motivation. Second, it was confirmed that organizational support recognition had a significant negative effect on work-family conflict, but work-family conflict had no significant influence on intrinsic job motivation. Third, in order to reduce job stress for medical institution workers, it is necessary to reduce job intensity, assign appropriate workload for ability. And in order to improve manpower operation and job efficiency, Job training and staffing in the right place are needed. Fourth, in order to improve positive organizational support perception and intrinsic job motivation, It is necessary to induce long-term service by providing support and institutional devices to increase attachment to the current job and recognize organizational problems as their own problems with various incentive systems. The limitations of this study and future research directions are as follows. First, it is believed that an expanded analysis of medical institution workers nationwide by region, gender, medical institution, academic, and income will not only provide more valuable results, but also evaluate the quality of medical services. Second, it is necessary to reflect the impact of the work-life balance support system on each employee depending on the environmental uncertainty or degree of competition in the hospital to which medical institution workers belong. Third, organizational support perception will be recognized differently depending on organizational culture and organizational type, and organizational size and work characteristics, working years, and work types, so it is necessary to reflect this. Fourth, it is necessary to analyze various new personnel management techniques such as hospital's organizational structure, job design, organizational support method, motivational approach, and personnel evaluation method in line with the recent change in the government's medical institution policy and the global business environment. It is also considered important to analyze by reflecting recent and near future medical trends.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.68 no.3
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• pp.188-196
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• 2023

Wheat (Triticum aestivum L.) is an important crop in Korea, with a per capita consumption of 31.6 kg in 2019. In the southern region, wheat is grown after paddy rice, and it is harvested during the rainy season in mid-June. This timing, in combination with high humidity and untimely rainfall, activates the enzyme alpha-amylase, which breaks down starch in the wheat grains. As a result, sprouted grains have lower quality and value for flour. However, seeds that absorb water before sprouting are expected to maintain better quality. The aim of the study was to identify the critical period during wheat maturation when rainfall has the greatest impact on grain quality, to prevent price declines due to quality deterioration. Two wheat cultivars, Jokyoung and Hwanggeumal, were grown in a speed breeding room, and artificial rainfall was applied at different times after heading (30, 35, 40, 45, 50, and 55 days). The proportion of vitreous grains decreased from 40 to 55 days after heading (DAH). Both cultivars had chalky grain sections from 35 DAH, with Hwanggeumal having a higher proportion of vitreous grains. Starch degradation was observed using FE-SEM (Field Emission Scanning Electron Microscope) at 40 DAH for Jokyoung and 50 DAH for Hwanggeumal. Color measurements indicated increased L and E values from 40 DAH, with rain treatment at 55 DAH leading to a significant increase in L values for both cultivars. Ash content increased at 45 DAH, whereas SDSS decreased at 35 DAH. Overall, grain quality from 40 DAH until harvest was found to be affected to the greatest extent by direct exposure of the spikes to moisture. Red wheat showed better quality than white wheat. These findings have implications for the cultivation of high-quality wheat and can guide future research efforts in this area.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.2
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• pp.65-80
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• 2022

Despite the uncertainty and risky factors of startups, the special and critical role of accelerators in carrying out professional nurturing and investment for them is becoming increasingly significant in the startup social-system. However, academic research on investment determinants that have a profound impact on the survival of accelerators is lacking, and there are only a few empirical studies on the classification and importance of factors, and they do not enjoy the benefits of theoretical studies. This study proposes a business model innovation framework based on the business model innovation theory that reflects the nature and properties of startups that are investment targets of accelerators and derives 12 investment decision factors. The framework defines that the target, direction, and performable force of startup innovation are a business model, strategy, and dynamic capability. Besides, the framework analyzes the investment decision factors of the existing accelerators based on the business model innovation framework to verify the suitability and sufficiency of the composition. As a result of the analysis, first, most of the items were faithfully composed from a static point of view of business model innovation, but it was found that the factors related to the core activities to evaluate the activity and customer relationship were insufficient. Second, from the strategic point of view, the necessity of developing factors that can encompass the definition and content of core resources, which are internal strategic factors, was raised. Third, from the dynamic point of view, it was found that many of the investment determinants of accelerators were concentrated on the lower level of dynamic competencies. This can be judged as a result of reflecting the characteristics of a startup that needs to develop a solution with few resources and a small number of team members. In addition, the roles and interrelationships between each factor are not clear, thus it was found as a limiting point for startups to view and evaluate the direction and process in which startups dynamically innovate their business models. This study is considerably differentiated in that it provides a business model innovation framework and offers a theoretical basis for investment determinants by deriving the investment determinants of accelerators based on the framework and design the foundation for subsequent research. The business model innovation framework presented in this study has great implications in that it contributes to the achievement of startups, accelerators, and startup support organizations.

• Journal of Intelligence and Information Systems
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• v.27 no.2
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• pp.33-54
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• 2021

With the fourth industrial revolution and the arrival of the New Normal era due to Corona, the importance of Non-contact technologies such as artificial intelligence and big data research has been increasing. Convergent research is being conducted in earnest to keep up with these research trends, but not many studies have been conducted in the area of nuclear research using artificial intelligence and big data-related technologies such as natural language processing and text mining analysis. This study was conducted to confirm the applicability of data science analysis techniques to the field of nuclear research. Furthermore, the study of identifying trends in nuclear spent fuel recognition is critical in terms of being able to determine directions to nuclear industry policies and respond in advance to changes in industrial policies. For those reasons, this study conducted a media trend analysis of pyroprocessing, a spent nuclear fuel treatment technology. We objectively analyze changes in media perception of spent nuclear fuel dry treatment techniques by applying text mining analysis techniques. Text data specializing in Naver's web news articles, including the keywords "Pyroprocessing" and "Sodium Cooled Reactor," were collected through Python code to identify changes in perception over time. The analysis period was set from 2007 to 2020, when the first article was published, and detailed and multi-layered analysis of text data was carried out through analysis methods such as word cloud writing based on frequency analysis, TF-IDF and degree centrality calculation. Analysis of the frequency of the keyword showed that there was a change in media perception of spent nuclear fuel dry treatment technology in the mid-2010s, which was influenced by the Gyeongju earthquake in 2016 and the implementation of the new government's energy conversion policy in 2017. Therefore, trend analysis was conducted based on the corresponding time period, and word frequency analysis, TF-IDF, degree centrality values, and semantic network graphs were derived. Studies show that before the 2010s, media perception of spent nuclear fuel dry treatment technology was diplomatic and positive. However, over time, the frequency of keywords such as "safety", "reexamination", "disposal", and "disassembly" has increased, indicating that the sustainability of spent nuclear fuel dry treatment technology is being seriously considered. It was confirmed that social awareness also changed as spent nuclear fuel dry treatment technology, which was recognized as a political and diplomatic technology, became ambiguous due to changes in domestic policy. This means that domestic policy changes such as nuclear power policy have a greater impact on media perceptions than issues of "spent nuclear fuel processing technology" itself. This seems to be because nuclear policy is a socially more discussed and public-friendly topic than spent nuclear fuel. Therefore, in order to improve social awareness of spent nuclear fuel processing technology, it would be necessary to provide sufficient information about this, and linking it to nuclear policy issues would also be a good idea. In addition, the study highlighted the importance of social science research in nuclear power. It is necessary to apply the social sciences sector widely to the nuclear engineering sector, and considering national policy changes, we could confirm that the nuclear industry would be sustainable. However, this study has limitations that it has applied big data analysis methods only to detailed research areas such as "Pyroprocessing," a spent nuclear fuel dry processing technology. Furthermore, there was no clear basis for the cause of the change in social perception, and only news articles were analyzed to determine social perception. Considering future comments, it is expected that more reliable results will be produced and efficiently used in the field of nuclear policy research if a media trend analysis study on nuclear power is conducted. Recently, the development of uncontact-related technologies such as artificial intelligence and big data research is accelerating in the wake of the recent arrival of the New Normal era caused by corona. Convergence research is being conducted in earnest in various research fields to follow these research trends, but not many studies have been conducted in the nuclear field with artificial intelligence and big data-related technologies such as natural language processing and text mining analysis. The academic significance of this study is that it was possible to confirm the applicability of data science analysis technology in the field of nuclear research. Furthermore, due to the impact of current government energy policies such as nuclear power plant reductions, re-evaluation of spent fuel treatment technology research is undertaken, and key keyword analysis in the field can contribute to future research orientation. It is important to consider the views of others outside, not just the safety technology and engineering integrity of nuclear power, and further reconsider whether it is appropriate to discuss nuclear engineering technology internally. In addition, if multidisciplinary research on nuclear power is carried out, reasonable alternatives can be prepared to maintain the nuclear industry.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.45-69
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• 2016

Recently, sentiment analysis using open Internet data is actively performed for various purposes. As online Internet communication channels become popular, companies try to capture public sentiment of them from online open information sources. This research is conducted for the purpose of analyzing pulbic sentiment of Korean Top-10 companies using a multi-categorical sentiment lexicon. Whereas existing researches related to public sentiment measurement based on big data approach classify sentiment into dimensions, this research classifies public sentiment into multiple categories. Dimensional sentiment structure has been commonly applied in sentiment analysis of various applications, because it is academically proven, and has a clear advantage of capturing degree of sentiment and interrelation of each dimension. However, the dimensional structure is not effective when measuring public sentiment because human sentiment is too complex to be divided into few dimensions. In addition, special training is needed for ordinary people to express their feeling into dimensional structure. People do not divide their sentiment into dimensions, nor do they need psychological training when they feel. People would not express their feeling in the way of dimensional structure like positive/negative or active/passive; rather they express theirs in the way of categorical sentiment like sadness, rage, happiness and so on. That is, categorial approach of sentiment analysis is more natural than dimensional approach. Accordingly, this research suggests multi-categorical sentiment structure as an alternative way to measure social sentiment from the point of the public. Multi-categorical sentiment structure classifies sentiments following the way that ordinary people do although there are possibility to contain some subjectiveness. In this research, nine categories: 'Sadness', 'Anger', 'Happiness', 'Disgust', 'Surprise', 'Fear', 'Interest', 'Boredom' and 'Pain' are used as multi-categorical sentiment structure. To capture public sentiment of Korean Top-10 companies, Internet news data of the companies are collected over the past 25 months from a representative Korean portal site. Based on the sentiment words extracted from previous researches, we have created a sentiment lexicon, and analyzed the frequency of the words coming up within the news data. The frequency of each sentiment category was calculated as a ratio out of the total sentiment words to make ranks of distributions. Sentiment comparison among top-4 companies, which are 'Samsung', 'Hyundai', 'SK', and 'LG', were separately visualized. As a next step, the research tested hypothesis to prove the usefulness of the multi-categorical sentiment lexicon. It tested how effective categorial sentiment can be used as relative comparison index in cross sectional and time series analysis. To test the effectiveness of the sentiment lexicon as cross sectional comparison index, pair-wise t-test and Duncan test were conducted. Two pairs of companies, 'Samsung' and 'Hanjin', 'SK' and 'Hanjin' were chosen to compare whether each categorical sentiment is significantly different in pair-wise t-test. Since category 'Sadness' has the largest vocabularies, it is chosen to figure out whether the subgroups of the companies are significantly different in Duncan test. It is proved that five sentiment categories of Samsung and Hanjin and four sentiment categories of SK and Hanjin are different significantly. In category 'Sadness', it has been figured out that there were six subgroups that are significantly different. To test the effectiveness of the sentiment lexicon as time series comparison index, 'nut rage' incident of Hanjin is selected as an example case. Term frequency of sentiment words of the month when the incident happened and term frequency of the one month before the event are compared. Sentiment categories was redivided into positive/negative sentiment, and it is tried to figure out whether the event actually has some negative impact on public sentiment of the company. The difference in each category was visualized, moreover the variation of word list of sentiment 'Rage' was shown to be more concrete. As a result, there was huge before-and-after difference of sentiment that ordinary people feel to the company. Both hypotheses have turned out to be statistically significant, and therefore sentiment analysis in business area using multi-categorical sentiment lexicons has persuasive power. This research implies that categorical sentiment analysis can be used as an alternative method to supplement dimensional sentiment analysis when figuring out public sentiment in business environment.

• Korean Journal of Psychosomatic Medicine
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• v.8 no.2
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• pp.212-227
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• 2000

This study was designed to testify the reliability and validation on the Korean version of the Social Adaptation Self-rating Scale(SASS) which was developed from Bose et al. for the evaluation of social motivation and behavior of depressed patients in 1997. Interests for the social world, those of social functioning, of patients were involved in the addition of new measure of disturbance. And those were distinct from abnormalities of thought, mood and symptoms of patients with major depression. As the previous reports there were several evidences that treatments may be less likely to be effective if the system they act on is dysfunctional. Thus, a better social situation favoured better outcome. As a matter of fact, however, those reports were developed in the course of the evaluation of interpersonal therapy(IPT) and cognitive therapy. Accordingly the conversed question -whether pharmacological therapy with antidepressants can impact on social functioning in addition to addressing the core features of illness- has been addressed. To date, anyhow, it is accepted that enhancement of social functioning may be a therapeutic principle in its own right and illness rarely divorced from social context. In terms of those concepts the introduction of an assessment of social functioning into pharmacotherapeutic studies of depression has been welcomed and might be a potent instrument for evaluating the relative pharmacoeconomic benefits of different treatments. Despite of many scales which were applied for the evaluation of symptoms in the patients with depression, however, the scale for the evaluation of social functiong has not been introduced in Korea yet. Thus, this study was designed to introduce the concepts of social functioning in the patients with depression and to testify the reliability and validation on Korean version of SASS. This Korean version of SASS was submitted to a reliability and validation procedure based on the data from healthy general population survey in 291 individuals and 40 patients with major depression. Cronbach a was 0.790 in total subjects group and the correlation of test-retest was statistically significant(y=0.653, p<0.0l). Thus, the Korean version of SASS might be shown to be valid and reliable. The results of multivariate analyses allowed the identification of 3 principle factors(factor 1 = intersts in social activities, factor 2 = active interpersonal relationship, factor 3 = selfesteem) in normal group, however, it could be counted as only one factor in the depression group because nearly total items of SASS were involved in factor 1. In the view of these results, the Korean version of SASS may be useful additional tool for the evaluation of social functioning in depression.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.59-77
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• 2012

This study focuses on predicting which firms will increase capital by issuing new stocks in the near future. Many stakeholders, including banks, credit rating agencies and investors, performs a variety of analyses for firms' growth, profitability, stability, activity, productivity, etc., and regularly report the firms' financial analysis indices. In the paper, we develop predictive models for rights issues using these financial analysis indices and data mining techniques. This study approaches to building the predictive models from the perspective of two different analyses. The first is the analysis period. We divide the analysis period into before and after the IMF financial crisis, and examine whether there is the difference between the two periods. The second is the prediction time. In order to predict when firms increase capital by issuing new stocks, the prediction time is categorized as one year, two years and three years later. Therefore Total six prediction models are developed and analyzed. In this paper, we employ the decision tree technique to build the prediction models for rights issues. The decision tree is the most widely used prediction method which builds decision trees to label or categorize cases into a set of known classes. In contrast to neural networks, logistic regression and SVM, decision tree techniques are well suited for high-dimensional applications and have strong explanation capabilities. There are well-known decision tree induction algorithms such as CHAID, CART, QUEST, C5.0, etc. Among them, we use C5.0 algorithm which is the most recently developed algorithm and yields performance better than other algorithms. We obtained data for the rights issue and financial analysis from TS2000 of Korea Listed Companies Association. A record of financial analysis data is consisted of 89 variables which include 9 growth indices, 30 profitability indices, 23 stability indices, 6 activity indices and 8 productivity indices. For the model building and test, we used 10,925 financial analysis data of total 658 listed firms. PASW Modeler 13 was used to build C5.0 decision trees for the six prediction models. Total 84 variables among financial analysis data are selected as the input variables of each model, and the rights issue status (issued or not issued) is defined as the output variable. To develop prediction models using C5.0 node (Node Options: Output type = Rule set, Use boosting = false, Cross-validate = false, Mode = Simple, Favor = Generality), we used 60% of data for model building and 40% of data for model test. The results of experimental analysis show that the prediction accuracies of data after the IMF financial crisis (59.04% to 60.43%) are about 10 percent higher than ones before IMF financial crisis (68.78% to 71.41%). These results indicate that since the IMF financial crisis, the reliability of financial analysis indices has increased and the firm intention of rights issue has been more obvious. The experiment results also show that the stability-related indices have a major impact on conducting rights issue in the case of short-term prediction. On the other hand, the long-term prediction of conducting rights issue is affected by financial analysis indices on profitability, stability, activity and productivity. All the prediction models include the industry code as one of significant variables. This means that companies in different types of industries show their different types of patterns for rights issue. We conclude that it is desirable for stakeholders to take into account stability-related indices and more various financial analysis indices for short-term prediction and long-term prediction, respectively. The current study has several limitations. First, we need to compare the differences in accuracy by using different data mining techniques such as neural networks, logistic regression and SVM. Second, we are required to develop and to evaluate new prediction models including variables which research in the theory of capital structure has mentioned about the relevance to rights issue.