• The Journal of the Convergence on Culture Technology
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• v.8 no.1
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• pp.175-190
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• 2022

Abusive supervision occurs in a social context in which third-party observers react and interact with the abused victims and supervisors. Despite the importance of third-party observers' behavior in abusive supervision, research on abusive supervision has mainly focused on the dyadic relationship between direct victims and supervisors. Although in recent years research on third parties' reactions to peer abusive supervision has attracted growing attention, there are still insufficient studies examining the topic especially within domestic research in Korea. As such, this study comprehensively reviews empirical studies on third parties' reactions to peer abusive supervision and aims to broaden the scope of research in the field. Firstly, the results of previous studies show that the effects of observed peer abusive supervision are mediated by cognitive and affective processes. Secondly, previous studies are found to investigate the boundary conditions where the effects of observed peer abusive supervision can be amplified or mitigated with regard to various outcomes. Overall, compared to research on direct victims, research on third-party observers of abusive supervision is found to capture a wider spectrum of responses. In order to explain the mechanisms of this phenomena, this study thoroughly examines theoretical assumptions presented in previous studies and categorizes them into five theory types. Finally, this study identifies a couple of central methodological issues, including common method bias and inadequate model specification in the literature and suggests future research directions.

• Korean Journal of Culture and Arts Education Studies
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• v.9 no.3
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• pp.45-61
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• 2014

Recently, there is a discussion about culture theory in the area of traffic safety regulation. It has the view that the subject of criticism, etc. by drivers' regulation interpretation, awareness about the danger of regulation violation and nonacceptance of regulation can be changed according to the way drivers' cultural bias was formed. According to the culture theory, fundamental views of the world in particular social relations surrounding individuals, world view or cosmology, are formed and the world view makes an effect on individual behavior and attitude. In this context, cultural cognition and cultural learning theory which are suggested in Christoph Wulf's study on historical-cultural anthropology provide new approach toward this phenomenon. According to his insistence, core mechanisms which can explain cultural cognition and cultural learning are systematized by five things; physical characteristic, mimesis, performance theory, rite and image. The purpose of this research is to investigate the changes by the way Korean people cognize traffic regulations culturally and experiences of traffic regulation violation through the analytic frame of Christoph Wulf's five core mechanisms. To achieve it, cognition of traffic culture was analyzed by analytical phenomenology for drivers who had been educated due to their violation of traffic regulations. Value, lifestyle and practicing methods which are pursued by people work in sociocultural context rather than are influenced by cognitive structure of individuals.

• Journal of Korean Society of Forest Science
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• v.108 no.3
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• pp.382-391
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• 2019

This study was conducted to verify whether smartphone hiking apps, which generate social network data including location information, are useful tools for analyzing the use characteristics of a forest recreation area. For this purpose, the study identified the functions and service characteristics of smartphone hiking apps. Also, the use characteristics of the area of Daegwallyoung were analyzed, compared with the results of the field survey, and the applicability of hiking apps was reviewed. As a result, the service types of hiking apps were analyzed in terms of three categories: "information offering," "hiking record," and "information sharing." This study focused on an app that is one of the "hiking record" types with the greatest number of users. Analysis of the data from hiking apps and a field survey in the Daegwallyoung area showed that both hiking apps and the field survey can be used to identify the movement patterns, but hiking apps based on a global positioning system (GPS) are more efficient and objective tools for understanding the use patterns in a forest recreation area, as well as for extracting user-generated photos. Second, although it is advantageous to analyze the patterns objectively through the walking-speed data generated, field surveys and observation are needed as complements for understanding the types of activities in each space. The hiking apps are based on cellphone use and are specific to "hiking" use, so user bias can limit the usefulness of the data. It is significant that this research shows the applicability of hiking apps for analyzing the use patterns of forest recreation areas through the location-based social network data of app users who record their hiking information voluntarily.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.259-278
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• 2022

With the spread of Artificial Intelligence (AI), various AI-based services are expanding in the financial sector such as service recommendation, automated customer response, fraud detection system(FDS), credit scoring services, etc. At the same time, problems related to reliability and unexpected social controversy are also occurring due to the nature of data-based machine learning. The need Based on this background, this study aimed to contribute to improving trust in AI-based financial services by proposing a checklist to secure fairness in AI-based credit scoring services which directly affects consumers' financial life. Among the key elements of trustworthy AI like transparency, safety, accountability, and fairness, fairness was selected as the subject of the study so that everyone could enjoy the benefits of automated algorithms from the perspective of inclusive finance without social discrimination. We divided the entire fairness related operation process into three areas like data, algorithms, and user areas through literature research. For each area, we constructed four detailed considerations for evaluation resulting in 12 checklists. The relative importance and priority of the categories were evaluated through the analytic hierarchy process (AHP). We use three different groups: financial field workers, artificial intelligence field workers, and general users which represent entire financial stakeholders. According to the importance of each stakeholder, three groups were classified and analyzed, and from a practical perspective, specific checks such as feasibility verification for using learning data and non-financial information and monitoring new inflow data were identified. Moreover, financial consumers in general were found to be highly considerate of the accuracy of result analysis and bias checks. We expect this result could contribute to the design and operation of fair AI-based financial services.

• Journal of Venture Innovation
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• v.3 no.2
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• pp.95-109
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• 2020

The source of value-added creation in modern times has been transformed from material to man's value-added generating power, and ownership of the means of production has been converted from a particular landlord, capitalist to a person with value-added capacity, and a system of capital participation is needed beyond the profit-sharing system or performance incentive system in which workers of an enterprise participate in simple profits if they significantly increase the added value of the company. It is also necessary to introduce our private stock system as a means of addressing the problem of capital bias and for the stable development of capitalism. The purpose of Employee Stock Ownership Plans is to improve the economic and social status of workers and promote labor-management cooperation by allowing workers to acquire and hold shares of the stock company in which the employee ownership association is established through the employee ownership association, but the reality is that our stock ownership system has failed to achieve its purpose due to insufficient protection against the employee. In terms of welfare, the acquisition of our company shares should include active government support for the welfare of workers' ownership on a social welfare level rather than on the logic of the capital market, and in terms of investment, it would not be appropriate to apply the regulation for investor protection to see workers' acquisition of our company shares as 'investment' in the view of workers' willingness to own shares on the stock market. Therefore, as a way to support and deregulate employee's stock acquisition, 1. Expanding direct support, such as tax support, 2. As employee's stock ownership association is being discussed as a division's nature, it is less effective in terms of various management, not investment, and 3. Those who own stocks with 1% of the company's shares and 300 million won in face value will be classified as major shareholders. As a way to reduce the risk of management of our company owners and cooperative funds, As a measure to reduce the risk of management of our company owners and cooperative funds, only our employee shareholders' association shall manage the fund in a long-term deposit, and even though our employee's stock is managed by the association or company after the end of the deposit period, the management of each employee shall be allowed and In terms of improving the utilization of our company's stock and fund, 1. Employee's stockholders are prohibited from lending during the deposit period, but it is necessary to improve profitability by allowing them to borrow under strict restrictions, 2. It is necessary to make the use of the employee's welfare funds available for the preservation of losses, and to stipulate the redemption obligations of unlisted companies in order to improve the redemption system of our company.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.141-166
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• 2019

Recently, channels like social media and SNS create enormous amount of data. In all kinds of data, portions of unstructured data which represented as text data has increased geometrically. But there are some difficulties to check all text data, so it is important to access those data rapidly and grasp key points of text. Due to needs of efficient understanding, many studies about text summarization for handling and using tremendous amounts of text data have been proposed. Especially, a lot of summarization methods using machine learning and artificial intelligence algorithms have been proposed lately to generate summary objectively and effectively which called "automatic summarization". However almost text summarization methods proposed up to date construct summary focused on frequency of contents in original documents. Those summaries have a limitation for contain small-weight subjects that mentioned less in original text. If summaries include contents with only major subject, bias occurs and it causes loss of information so that it is hard to ascertain every subject documents have. To avoid those bias, it is possible to summarize in point of balance between topics document have so all subject in document can be ascertained, but still unbalance of distribution between those subjects remains. To retain balance of subjects in summary, it is necessary to consider proportion of every subject documents originally have and also allocate the portion of subjects equally so that even sentences of minor subjects can be included in summary sufficiently. In this study, we propose "subject-balanced" text summarization method that procure balance between all subjects and minimize omission of low-frequency subjects. For subject-balanced summary, we use two concept of summary evaluation metrics "completeness" and "succinctness". Completeness is the feature that summary should include contents of original documents fully and succinctness means summary has minimum duplication with contents in itself. Proposed method has 3-phases for summarization. First phase is constructing subject term dictionaries. Topic modeling is used for calculating topic-term weight which indicates degrees that each terms are related to each topic. From derived weight, it is possible to figure out highly related terms for every topic and subjects of documents can be found from various topic composed similar meaning terms. And then, few terms are selected which represent subject well. In this method, it is called "seed terms". However, those terms are too small to explain each subject enough, so sufficient similar terms with seed terms are needed for well-constructed subject dictionary. Word2Vec is used for word expansion, finds similar terms with seed terms. Word vectors are created after Word2Vec modeling, and from those vectors, similarity between all terms can be derived by using cosine-similarity. Higher cosine similarity between two terms calculated, higher relationship between two terms defined. So terms that have high similarity values with seed terms for each subjects are selected and filtering those expanded terms subject dictionary is finally constructed. Next phase is allocating subjects to every sentences which original documents have. To grasp contents of all sentences first, frequency analysis is conducted with specific terms that subject dictionaries compose. TF-IDF weight of each subjects are calculated after frequency analysis, and it is possible to figure out how much sentences are explaining about each subjects. However, TF-IDF weight has limitation that the weight can be increased infinitely, so by normalizing TF-IDF weights for every subject sentences have, all values are changed to 0 to 1 values. Then allocating subject for every sentences with maximum TF-IDF weight between all subjects, sentence group are constructed for each subjects finally. Last phase is summary generation parts. Sen2Vec is used to figure out similarity between subject-sentences, and similarity matrix can be formed. By repetitive sentences selecting, it is possible to generate summary that include contents of original documents fully and minimize duplication in summary itself. For evaluation of proposed method, 50,000 reviews of TripAdvisor are used for constructing subject dictionaries and 23,087 reviews are used for generating summary. Also comparison between proposed method summary and frequency-based summary is performed and as a result, it is verified that summary from proposed method can retain balance of all subject more which documents originally have.

• Journal of Korean Society of Transportation
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• v.23 no.3 s.81
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• pp.95-108
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• 2005

The purpose of this study is to develop the ITS evaluation model using the Probabilistic Risk Analysis (PRA) methodology and to demonstrate the goodness-of-fit of the large ITS projects through the comparative analysis between DEA and PRA model. The results of this study are summarized below. First, the evaluation mode] using PRA with Monte-Carlo Simulation(MCS) and Latin-Hypercube Sampling(LHS) is developed and applied to one of ITS projects initiated by local government. The risk factors are categorized with cost, benefit and social-economic factors. Then, PDF(Probability Density Function) parameters of these factors are estimated. The log-normal distribution, beta distribution and triangular distribution are well fitted with the market and delivered price. The triangular and uniform distributions are valid in benefit data from the simulation analysis based on the several deployment scenarios. Second, the decision making rules for the risk analysis of projects for cost and economic feasibility study are suggested. The developed PRA model is applied for the Daejeon metropolitan ITS model deployment project to validate the model. The results of cost analysis shows that Deterministic Project Cost(DPC), Deterministic Total Project Cost(DTPC) is the biased percentile values of CDF produced by PRA model and this project need Contingency Budget(CB) because these values are turned out to be less than Target Value(TV;85% value), Also, this project has high risk of DTPC and DPC because the coefficient of variation(C.V) of DTPC and DPC are 4 and 15 which are less than that of DTPC(19-28) and DPC(22-107) in construction and transportation projects. The results of economic analysis shows that total system and subsystem of this project is in type II, which means the project is economically feasible with high risk. Third, the goodness-of-fit of PRA model is verified by comparing the differences of the results between PRA and DEA model. The difference of evaluation indices is up to 68% in maximum. Because of this, the deployment priority of ITS subsystems are changed in each mode1. In results. ITS evaluation model using PRA considering the project risk with the probability distribution is superior to DEA. It makes proper decision making and the risk factors estimated by PRA model can be controlled by risk management program suggested in this paper. Further research not only to build the database of deployment data but also to develop the methodologies estimating the ITS effects with PRA model is needed to broaden the usage of PRA model for the evaluation of ITS projects.

• Asia pacific journal of information systems
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• v.24 no.4
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• pp.559-575
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• 2014

Individual investors are increasingly flocking to message boards to seek, clarify, and exchange information. Businesses like Seekingalpha.com and business magazines like Fortune are evaluating, synthesizing, and reporting the comments made on message boards or blogs. In March of 2012, Yahoo! Finance Message Boards recorded 45 million unique visitors per month followed by AOL Money and Finance (19.8 million), and Google Finance (1.6 million) [McIntyre, 2012]. Previous studies in the finance literature suggest that online communities often provide more accurate information than analyst forecasts [Bagnoli et al., 1999; Clarkson et al., 2006]. Some studies empirically show that the volume of posts in online communities have a positive relationship with market activities (e.g., trading volumes) [Antweiler and Frank, 2004; Bagnoli et al., 1999; Das and Chen, 2007; Tumarkin and Whitelaw, 2001]. The findings indicate that information in online communities does impact investors' investment decisions and trading behaviors. However, research explicating the correlation between information on online communities and stock market activities (e.g., trading volume) is still evolving. Thus, it is important to ask whether a volume of posts on online communities influences trading volumes and whether trading volumes also influence these communities. Online stock message boards offer two different types of information, which can be explained using an economic and a psychological perspective. From a purely economic perspective, one would expect that stock message boards would have a beneficial effect, since they provide timely information at a much lower cost [Bagnoli et al., 1999; Clarkson et al., 2006; Birchler and Butler, 2007]. This indicates that information in stock message boards may provide valuable information investors can use to predict stock market activities and thus may use to make better investment decisions. On the other hand, psychological studies have shown that stock message boards may not necessarily make investors more informed. The related literature argues that confirmation bias causes investors to seek other investors with the same opinions on these stock message boards [Chen and Gu, 2009; Park et al., 2013]. For example, investors may want to share their painful investment experiences with others on stock message boards and are relieved to find they are not alone. In this case, the information on these stock message boards mainly reflects past experience or past information and not valuable and predictable information for market activities. This study thus investigates the two roles of stock message boards-providing valuable information to make future investment decisions or sharing past experiences that reflect mainly investors' painful or boastful stories. If stock message boards do provide valuable information for stock investment decisions, then investors will use this information and thereby influence stock market activities (e.g., trading volume). On the contrary, if investors made investment decisions and visit stock message boards later, they will mainly share their past experiences with others. In this case, past activities in the stock market will influence the stock message boards. These arguments indicate that there is a correlation between information posted on stock message boards and stock market activities. The previous literature has examined the impact of stock sentiments or the number of posts on stock market activities (e.g., trading volume, volatility, stock prices). However, the studies related to stock sentiments found it difficult to obtain significant results. It is not easy to identify useful information among the millions of posts, many of which can be just noise. As a result, the overall sentiments of stock message boards often carry little information for future stock movements [Das and Chen, 2001; Antweiler and Frank, 2004]. This study notes that as a dependent variable, trading volume is more reliable for capturing the effect of stock message board activities. The finance literature argues that trading volume is an indicator of stock price movements [Das et al., 2005; Das and Chen, 2007]. In this regard, this study investigates the correlation between a number of posts (information on stock message boards) and trading volume (stock market activity). We collected about 100,000 messages of 40 companies at KOSPI (Korea Composite Stock Price Index) from Paxnet, the most popular Korean online stock message board. The messages we collected were divided into in-trading and after-trading hours to examine the correlation between the numbers of posts and trading volumes in detail. Also we collected the volume of the stock of the 40 companies. The vector regression analysis and the granger causality test, 3SLS analysis were performed on our panel data sets. We found that the number of posts on online stock message boards is positively related to prior stock trade volume. Also, we found that the impact of the number of posts on stock trading volumes is not statistically significant. Also, we empirically showed the correlation between stock trading volumes and the number of posts on stock message boards. The results of this study contribute to the IS and finance literature in that we identified online stock message board's two roles. Also, this study suggests that stock trading managers should carefully monitor information on stock message boards to understand stock market activities in advance.

• Korea journal of population studies
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• v.11 no.2
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• pp.33-53
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• 1988

This study aimed at developing a desirable family planning policy and strategy by examining the current status of family planning practice in rural Korea and by indentifying the crucial factors which affect fertility control behavior. For this purpose, an analytical study was conducted, using the survey data collected in July 1985, on an interview basis, on 1, 440 married women living in the Soyi, Wonnam and Maingdong townships of Eumseong County(in North Chungcheong Province). This study population has the typical characteristics of rural areas, and the results of the analysis can be summarized as follows: 1. In regard to the demographic characteristics of the study population : their average age at marriage was 23.7, they had an average of 2.6 children( 1.3 boys, 1.3 girls) :10% experienced the death of their child (ren) :14% had spontaneous abortion(s) :4% weathered stillbirth(s) :35% went through induced abortion (s) : and 5.5% were currently pregnant. The average of their ideal numbers of children was 2.2, while 44% felt that they must have a son. 2. Looking at the contact rate with medical & health institutions, over the past 1 year, the visit rate to health subcenters was 43.7%, while 26.9% visited the (county) health center :59.6% had been to private clinics : and 41.5% went to the Soonchunhyang - Eumsung hospital : thus showing a relatively high rate of accessibility. 3. The utilization rate of family planning services was 76.5%, with tubectomy being the most prominent method at 52.3%, while the informants were health workers in 54.2% of the acceptors. Of the 8.4% who discontinued the use of contraceptive methods, only 26% did so due to want for pregnancy, natural infertility (meno - pause), or other reasons, while the remaining 74% stopped usage on account of side effects, failure in the methods themselves, and inconvenience of use, thus pointing to a situation where the proper choice of family planning methods have not yet been made. It can be noted that there is a strong motivation for early birth stopping as 35.3% practice family planning even with only one child, of which 38.3% have had sterilization operations. According to results of a multiple regression analysis, among the variables affecting contraception usage the most significant variable was the number of sons. 4. 34.8% experienced induced abortions. It was shown as a result of multiple regression analysis that the number of children and attitudes toward induced abortions extensively affected their frequency of abortions conducted. 5. In the regard to the relation between family planning and induced abortions, 33.7% of the women used both, while 52.0% of them used only the former(family planning), with only 1.4 % utilizing solely the latter(abortion), and 12.9% totally abstaining from fertility regulation : again, the discriminant analysis indicated that the choice of family planning and/or induced abortion was determined by the number of children and attitudes toward induced abortion. In view of the above mentioned results, the following are some comments and suggestions concerning problems related to the current family planning policies, in Korea : 1. It is difficult to expect a further quantitative expansion in family planning program operations, as there has been an excessive supply of target-oriented sterilization operations on women. From a maternal and child health care point of view, it will be desirable to have a diversification of service points in the future where family planning methods may be properly chosen, so that choices of methods which suit the mothers' characteristics and tastes may be made by the individuals themselves by strengthening their quality of family planning information services. 2. Along with the strengthening of the qualitative improvement of family planning services policies must be implemented to effectively promote the moral (ethical) deterrents to induced abortions and to preference for sons. From a maternal care standpoint, the social permissive norm toward induced abortion must be modified, and the bias towards son must be analyzed as the women with more daughters have a lower rate of family planning acceptance. Such changes in attitudes, however, can not be hoped to be accomplished with ad hoc policies, but will only be possible when an enhancement of the women's status(within the society) is brought about in a long - term perspective.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.157-173
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• 2021

With the development of information technology, the amount of available information increases daily. However, having access to so much information makes it difficult for users to easily find the information they seek. Users want a visualized system that reduces information retrieval and learning time, saving them from personally reading and judging all available information. As a result, recommendation systems are an increasingly important technologies that are essential to the business. Collaborative filtering is used in various fields with excellent performance because recommendations are made based on similar user interests and preferences. However, limitations do exist. Sparsity occurs when user-item preference information is insufficient, and is the main limitation of collaborative filtering. The evaluation value of the user item matrix may be distorted by the data depending on the popularity of the product, or there may be new users who have not yet evaluated the value. The lack of historical data to identify consumer preferences is referred to as data sparsity, and various methods have been studied to address these problems. However, most attempts to solve the sparsity problem are not optimal because they can only be applied when additional data such as users' personal information, social networks, or characteristics of items are included. Another problem is that real-world score data are mostly biased to high scores, resulting in severe imbalances. One cause of this imbalance distribution is the purchasing bias, in which only users with high product ratings purchase products, so those with low ratings are less likely to purchase products and thus do not leave negative product reviews. Due to these characteristics, unlike most users' actual preferences, reviews by users who purchase products are more likely to be positive. Therefore, the actual rating data is over-learned in many classes with high incidence due to its biased characteristics, distorting the market. Applying collaborative filtering to these imbalanced data leads to poor recommendation performance due to excessive learning of biased classes. Traditional oversampling techniques to address this problem are likely to cause overfitting because they repeat the same data, which acts as noise in learning, reducing recommendation performance. In addition, pre-processing methods for most existing data imbalance problems are designed and used for binary classes. Binary class imbalance techniques are difficult to apply to multi-class problems because they cannot model multi-class problems, such as objects at cross-class boundaries or objects overlapping multiple classes. To solve this problem, research has been conducted to convert and apply multi-class problems to binary class problems. However, simplification of multi-class problems can cause potential classification errors when combined with the results of classifiers learned from other sub-problems, resulting in loss of important information about relationships beyond the selected items. Therefore, it is necessary to develop more effective methods to address multi-class imbalance problems. We propose a collaborative filtering model using CGAN to generate realistic virtual data to populate the empty user-item matrix. Conditional vector y identify distributions for minority classes and generate data reflecting their characteristics. Collaborative filtering then maximizes the performance of the recommendation system via hyperparameter tuning. This process should improve the accuracy of the model by addressing the sparsity problem of collaborative filtering implementations while mitigating data imbalances arising from real data. Our model has superior recommendation performance over existing oversampling techniques and existing real-world data with data sparsity. SMOTE, Borderline SMOTE, SVM-SMOTE, ADASYN, and GAN were used as comparative models and we demonstrate the highest prediction accuracy on the RMSE and MAE evaluation scales. Through this study, oversampling based on deep learning will be able to further refine the performance of recommendation systems using actual data and be used to build business recommendation systems.