• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.263-286
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• 2022

This study acquires NBA statistical information for a total of 32 years from 1990 to 2022 using web crawling, observes variables of interest through exploratory data analysis, and generates related derived variables. Unused variables were removed through a purification process on the input data, and correlation analysis, t-test, and ANOVA were performed on the remaining variables. For the variable of interest, the difference in the mean between the groups that advanced to the playoffs and did not advance to the playoffs was tested, and then to compensate for this, the average difference between the three groups (higher/middle/lower) based on ranking was reconfirmed. Of the input data, only this year's season data was used as a test set, and 5-fold cross-validation was performed by dividing the training set and the validation set for model training. The overfitting problem was solved by comparing the cross-validation result and the final analysis result using the test set to confirm that there was no difference in the performance matrix. Because the quality level of the raw data is high and the statistical assumptions are satisfied, most of the models showed good results despite the small data set. This study not only predicts NBA game results or classifies whether or not to advance to the playoffs using machine learning, but also examines whether the variables of interest are included in the major variables with high importance by understanding the importance of input attribute. Through the visualization of SHAP value, it was possible to overcome the limitation that could not be interpreted only with the result of feature importance, and to compensate for the lack of consistency in the importance calculation in the process of entering/removing variables. It was found that a number of variables related to three points and errors classified as subjects of interest in this study were included in the major variables affecting advancing to the playoffs in the NBA. Although this study is similar in that it includes topics such as match results, playoffs, and championship predictions, which have been dealt with in the existing sports data analysis field, and comparatively analyzed several machine learning models for analysis, there is a difference in that the interest features are set in advance and statistically verified, so that it is compared with the machine learning analysis result. Also, it was differentiated from existing studies by presenting explanatory visualization results using SHAP, one of the XAI models.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.4
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• pp.245-261
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• 2020

The aim of this study is to investigate the correlation between the support activities provided by the franchiser and how they affect the intention of the contract renewal and business performances made by franchisees, developing dynamic trust between these transactional partners. Various supportive activities between franchiser and franchisees were divided into financial and non-financial activities and dynamic trust into Transitional-based trust, Calculative-based trust, Relational-based trust, and Balanced-based trust. These trust types, which are variable and adjustable based on the opportunistic behaviors of business parties, were applied to define the impact of the support activities on the contract renewal intention and the performances. This study was developed around domestic franchisees. An investigator visited business owners and manager level-employees, explained the purpose of the survey prior to the response, and the answers were directly written by hands. A total of 348 copies were used for the analysis. As the results of the analysis, first, financial support activities were found to have a positive(+) effect on transitional-based trust, calculative-based trust, and balanced-based trust. On the other hand, non-financial support activities were found to have a positive(+) effect on calculative-based trust, relational-based trust, and balanced-based trust, and there was no significant relationship on transitional-based trust. Second, the dynamic trust had a statistically significant positive(+) effect on inducing the contract renewal. Lastly, in the relationship between the dynamic trust and its impact on business performances, only transitional-based trust, and relational-based trust were found to have a positive(+) effect on the financial performances. In addition, relational-based trust showed a meaningful positive(+) relationship on the non-financial performances, and non-financial performace showed a meaningful positive(+) relationship on the re-contract intention. From the results, it can be concluded that the financial and non-financial activities for a win-win partnership between franchiser and franchisees are essential in not only forming dynamic trust but also boosting business performances as well as maintaining the business relationship. Thus, it suggests that building a win-win partnership can be promoted more efficiently by specifying activities best suitable for a particular relationship. In addition, a specific set of activities could be presented for establishing the level of trust that is formed in situations that vary depending on transaction risks and interdependency arising from having the transactional relationship based on the contract as the franchise industry features. Eventually, it is expected that this study can provide a way to promote the qualitative improvement of the franchise industry by identifying factors essential to establishing a sustainable win-win system and relationships that can improve the business performance of franchisees.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.19-47
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• 2015

In this era of knowledge economy, a variety of virtual communities are proliferating for the purpose of knowledge creation and utilization. Since the voluntary contributions of members are the essential source of knowledge, member turnover can have significant implications on the survival and success of virtual communities. However, there is a dearth of research on the effect of membership turnover and even the method of measurement for membership turnover is left unclear in virtual communities. In a traditional context, membership turnover is calculated as the ratio of the number of departing members to the average number of members for a given time period. In virtual communities, while the influx of newcomers can be clearly measured, the magnitude of departure is elusive since explicit withdrawals are seldom executed. In addition, there doesn't exist a common way to determine the average number of community members who return and contribute intermittently at will. This study initially examines the limitations in applying the concept of traditional turnover to virtual communities, and proposes five membership fluidity measures based on a preliminary analysis of editing behaviors of 2,978 featured articles in English Wikipedia. Subsequently, this work investigates the relationships between three selected membership fluidity measures and group collaboration performance, reflecting a moderating effect dependent on work characteristic. We obtained the following results: First, membership turnover relates to collaboration efficiency in a right-shortened U-shaped manner, with a moderating effect from work characteristic; given the same turnover rate, the promotion likelihood for a more professional task is lower than that for a less professional task, and the likelihood difference diminishes as the turnover rate increases. Second, contribution period relates to collaboration efficiency in a left-shortened U-shaped manner, with a moderating effect from work characteristic; the marginal performance change per unit change of contribution period is greater for a less professional task. Third, the number of new participants per month relates to collaboration efficiency in a left-shortened reversed U-shaped manner, for which the moderating effect from work characteristic appears to be insignificant.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.1 no.1
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• pp.127-156
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• 2006

This paper introduced successful case studies of the EIP (Entrepreneurship Intensive Programs) programs that are planed by 5MBA (Small and Medium Business Administration) and executed by Hoseo University and Jinju National University in 2004. Therefore, the illustrations and examples used in this paper are based on the EIP programs of those schools. Currently there are five graduate schools for Entrepreneurship educations that are originated from the EIP program models and those graduateschools are actively and successfully working. The purpose of this paper is to find out the diffusing strategies of the Entrepreneurship spirits and Entrepreneurship Education programs after careful analysis and review of the EIP programs. The main factors of the Success of the EIP are as follows. First, there were excellent modules of the education process. Second, there were firm and clear goals of the education. Three, there were differentiated contents of the entrepreneurship education programs. Four. each and every education performance was monitored. Five, during the programs, real start-up cases were actually handled and students had many opportunities to present their cases. However, there were some shortcomings to improve and change of the entrepreneurship education. First of all, it was very difficult to make proper education formation that is fit in the purpose of the program. Second, motivating students to find out their own business opportunities so as to turn them into real business was not satisfactory or easy. Third, there were some limitations in distributing and executing the EIP budgets. Therefore, to improve the efficiency of the Entrepreneurship education, following expanding strategies should be complemented. First, continuous redesigning of the entrepreneurship education programs is very important. Second, the specialization of the contents of the entrepreneurship education programs is essential. Third, there should be some discretionary room for the management of the entrepreneurship programs. Fourth, it is also important activating the entrepreneurship networks among schools of the entrepreneurship education. Finally, it is necessary to give some incentives and motivations based on the proper performance evaluation system.

• Journal of Korean Academy of Nursing
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• v.22 no.2
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• pp.207-226
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• 1992

This study was designed to develop a conceptual framework for the curriculum and develop the details of the learning content for the education of Community Health Practitioners (CHPs). Since education programs for CHPs started 10 years ago, concepts related to CHP services have changed because of changes in society. The objectives of the study were as follows : 1) to analyse the usefulness of the present education program for CHPs, 2) to analyse the Job performance and self -confidence of the CHPs, 3) to identify the health needs of the clients served by the CHPs and the community problems related to health. 4) to develop a conceptual framework for the curriculum, for the education of CHPs, 5) to develops details for the learning content of the education program for CHPs. Phase I of the study was conducted by questionnaires to 150 CHPs who have worked in remote rural areas for more than 2 years. Among them, 147 responded. Data was collected from August 16, to August 25, 1990. In order to identify the health needs of the community people, research within the last five years was reviewed and analyzed. The data on 1, 842 communities gathered by the WHO Nursing Collaborations Center of the College of Nursing, Yonsei University was utilized to identify community problems related to health and the self - confidence in job performance of the CHPs. Psase II of the study consisted of a workshop with 13 professionals including Community Health Practitioners to evaluate the existing education program and a conceptual framework of the curriculum for the job education of CHPs. The results of the study are Summariged below : 1. The only 26 among 45 content items of the education program related to job skills was used by 80% of the responding CHPs. The knowledge of $\ulcorner$Networking community organization$\lrcorner$ was used by only 53.7% of the respondents. Educational content about $\ulcorner$Mental disease$\lrcorner$ was used by less than 50% of CHPs because of a knowledge deficit. 2. The CHPs reported that their activities concentrated on clinical services during the last six months. The survey showed that they seemed to neglect the activities for health promotion and disease prevention. Thus, $\ulcorner$Education for community loaders$\lrcorner$(15.9%), $\ulcorner$Activity for eavironmental health$\lrcorner$(16.3%) and $\ulcorner$Social work for needey people$\lrcorner$(23.3%) were done by less than 30% of CHPs. 3. More than 90% of CHPs reported being self - confident for the activities of $\ulcorner$Health education and counselling$\lrcorner$, $\ulcorner$Medicine prescription$\lrcorner$ and $\ulcorner$Immunization$\lrcorner$. But 50% of CHPs reported that they were not have self - confident in $\ulcorner$Management of water and environmental health$\lrcorner$ and only 25.6% of CHPs could insert an IUD independently. 4. It was identified that respiratory diseases and the gastrointestinal diseases were most common problems for the community people, followed by musculoskeletal and skin problems. 5. The community problems were classified into eight categories : physical environmental problems, environmental hygiene, health problems, health behavior, social problem, lack of resources, financial problem and the problems of the cultural and value system. 6. The conceptual framework consisted of the target population and their health status, nursing process working site and primary health care services such as health promotion, disease prevention, treatment and rehabilitation. 7. The contents of curriculum of education program for CHPs were formulated from the results of this study.

• Asia pacific journal of information systems
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• v.18 no.1
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• pp.145-162
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• 2008

One of the big concerns in e-society is privacy issue. In special, in developing robust ubiquitous smart space and corresponding services, user profile and preference are collected by the service providers. Privacy issue would be more critical in context-aware services simply because most of the context data themselves are private information: user's current location, current schedule, friends nearby and even her/his health data. To realize the potential of ubiquitous smart space, the systems embedded in the space should corporate personal privacy preferences. When the users invoke a set of services, they are asked to allow the service providers or smart space to make use of personal information which is related to privacy concerns. For this reason, the users unhappily provide the personal information or even deny to get served. On the other side, service provider needs personal information as rich as possible with minimal personal information to discern royal and trustworthy customers and those who are not. It would be desirable to enlarge the allowable personal information complying with the service provider's request, whereas minimizing service provider's requiring personal information which is not allowed to be submitted and user's submitting information which is of no value to the service provider. In special, if any personal information required by the service provider is not allowed, service will not be provided to the user. P3P (Platform for Privacy Preferences) has been regarded as one of the promising alternatives to preserve the personal information in the course of electronic transactions. However, P3P mainly focuses on preserving the buyers' personal information. From time to time, the service provider's business data should be protected from the unintended usage from the buyers. Moreover, even though the user's privacy preference could depend on the context happened to the user, legacy P3P does not handle the contextual change of privacy preferences. Hence, the purpose of this paper is to propose a mutual P3P-based negotiation mechanism. To do so, service provider's privacy concern is considered as well as the users'. User's privacy policy on the service provider's information also should be informed to the service providers before the service begins. Second, privacy policy is contextually designed according to the user's current context because the nomadic user's privacy concern structure may be altered contextually. Hence, the methodology includes mutual privacy policy and personalization. Overall framework of the mechanism and new code of ethics is described in section 2. Pervasive platform for mutual P3P considers user type and context field, which involves current activity, location, social context, objects nearby and physical environments. Our mutual P3P includes the privacy preference not only for the buyers but also the sellers, that is, service providers. Negotiation methodology for mutual P3P is proposed in section 3. Based on the fact that privacy concern occurs when there are needs for information access and at the same time those for information hiding. Our mechanism was implemented based on an actual shopping mall to increase the feasibility of the idea proposed in this paper. A shopping service is assumed as a context-aware service, and data groups for the service are enumerated. The privacy policy for each data group is represented as APPEL format. To examine the performance of the example service, in section 4, simulation approach is adopted in this paper. For the simulation, five data elements are considered: $\cdot$ UserID $\cdot$ User preference $\cdot$ Phone number $\cdot$ Home address $\cdot$ Product information $\cdot$ Service profile. For the negotiation, reputation is selected as a strategic value. Then the following cases are compared: $\cdot$ Legacy P3P is considered $\cdot$ Mutual P3P is considered without strategic value $\cdot$ Mutual P3P is considered with strategic value. The simulation results show that mutual P3P outperforms legacy P3P. Moreover, we could conclude that when mutual P3P is considered with strategic value, performance was better than that of mutual P3P is considered without strategic value in terms of service safety.

• Journal of The Korean Society of Grassland and Forage Science
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• v.32 no.4
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• pp.429-436
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• 2012

This study was conducted to examine the effect of feeding levels of concentrate feed on growth performance, meat quality and economic evaluation of Korean black goats. The 40 male Korean native goat were divided into four treatment groups T1 (1.5%), T2 (2.0%), T3 (2.5% feeding of concentrate feed body weight per day) and T4 (ab libitum) with ab libitum rice straw. Rice straw intake decrease as against feeding levels, however average daily gains were 22, 50, 69 and 94 g/day in T1, T2, T3 and T4 groups respectively levels (p<0.05). Dressing percentage were 41.77, 42.78, 46.12 and 49.78% in each group (p<0.05), also fat percentage were a significant increase according to feeding levels. In economic efficacy, T4 was higher than other treatment groups. In conclusion, both rice straw and concentrate feed ab libitum are good for feeding and management system on Korean black goats.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.237-262
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• 2022

There has been a growing interest in IPOs (Initial Public Offerings) due to the profitable returns that IPO stocks can offer to investors. However, IPOs can be speculative investments that may involve substantial risk as well because shares tend to be volatile, and the supply of IPO shares is often highly limited. Therefore, it is crucially important that IPO investors are well informed of the issuing firms and the market before deciding whether to invest or not. Unlike institutional investors, individual investors are at a disadvantage since there are few opportunities for individuals to obtain information on the IPOs. In this regard, the purpose of this study is to provide individual investors with the information they may consider when making an IPO investment decision. This study presents a model that uses machine learning and text analysis to predict whether an IPO stock price would move up or down after the first 5 trading days. Our sample includes 691 Korean IPOs from June 2009 to December 2020. The input variables for the prediction are three tone variables created from IPO prospectuses and quantitative variables that are either firm-specific, issue-specific, or market-specific. The three prospectus tone variables indicate the percentage of positive, neutral, and negative sentences in a prospectus, respectively. We considered only the sentences in the Risk Factors section of a prospectus for the tone analysis in this study. All sentences were classified into 'positive', 'neutral', and 'negative' via text analysis using TF-IDF (Term Frequency - Inverse Document Frequency). Measuring the tone of each sentence was conducted by machine learning instead of a lexicon-based approach due to the lack of sentiment dictionaries suitable for Korean text analysis in the context of finance. For this reason, the training set was created by randomly selecting 10% of the sentences from each prospectus, and the sentence classification task on the training set was performed after reading each sentence in person. Then, based on the training set, a Support Vector Machine model was utilized to predict the tone of sentences in the test set. Finally, the machine learning model calculated the percentages of positive, neutral, and negative sentences in each prospectus. To predict the price movement of an IPO stock, four different machine learning techniques were applied: Logistic Regression, Random Forest, Support Vector Machine, and Artificial Neural Network. According to the results, models that use quantitative variables using technical analysis and prospectus tone variables together show higher accuracy than models that use only quantitative variables. More specifically, the prediction accuracy was improved by 1.45% points in the Random Forest model, 4.34% points in the Artificial Neural Network model, and 5.07% points in the Support Vector Machine model. After testing the performance of these machine learning techniques, the Artificial Neural Network model using both quantitative variables and prospectus tone variables was the model with the highest prediction accuracy rate, which was 61.59%. The results indicate that the tone of a prospectus is a significant factor in predicting the price movement of an IPO stock. In addition, the McNemar test was used to verify the statistically significant difference between the models. The model using only quantitative variables and the model using both the quantitative variables and the prospectus tone variables were compared, and it was confirmed that the predictive performance improved significantly at a 1% significance level.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.307-325
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• 2023

Due to the prolonged COVID-19 pandemic, the frequency of people who are tired of living indoors visiting nearby mountains and national parks to relieve depression and lethargy has exploded. There is a place where thousands of people who came out of nature stop walking and breathe and rest, that is the mineral spring. Even in mountains or national parks, there are about 600 mineral springs that can be found occasionally in neighboring parks or trails in the metropolitan area. However, due to irregular and manual water quality tests, people drink mineral water without knowing the test results in real time. Therefore, in this study, we intend to develop a model that can predict the quality of the spring water in real time by exploring the factors affecting the quality of the spring water and collecting data scattered in various places. After limiting the regions to Seoul and Gyeonggi-do due to the limitations of data collection, we obtained data on water quality tests from 2015 to 2020 for about 300 mineral springs in 18 cities where data management is well performed. A total of 10 factors were finally selected after two rounds of review among various factors that are considered to affect the suitability of the mineral spring water quality. Using AutoML, an automated machine learning technology that has recently been attracting attention, we derived the top 5 models based on prediction performance among about 20 machine learning methods. Among them, the catboost model has the highest performance with a prediction classification accuracy of 75.26%. In addition, as a result of examining the absolute influence of the variables used in the analysis through the SHAP method on the prediction, the most important factor was whether or not a water quality test was judged nonconforming in the previous water quality test. It was confirmed that the temperature on the day of the inspection and the altitude of the mineral spring had an influence on whether the water quality was unsuitable.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.127-148
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• 2020

The data center is a physical environment facility for accommodating computer systems and related components, and is an essential foundation technology for next-generation core industries such as big data, smart factories, wearables, and smart homes. In particular, with the growth of cloud computing, the proportional expansion of the data center infrastructure is inevitable. Monitoring the health of these data center facilities is a way to maintain and manage the system and prevent failure. If a failure occurs in some elements of the facility, it may affect not only the relevant equipment but also other connected equipment, and may cause enormous damage. In particular, IT facilities are irregular due to interdependence and it is difficult to know the cause. In the previous study predicting failure in data center, failure was predicted by looking at a single server as a single state without assuming that the devices were mixed. Therefore, in this study, data center failures were classified into failures occurring inside the server (Outage A) and failures occurring outside the server (Outage B), and focused on analyzing complex failures occurring within the server. Server external failures include power, cooling, user errors, etc. Since such failures can be prevented in the early stages of data center facility construction, various solutions are being developed. On the other hand, the cause of the failure occurring in the server is difficult to determine, and adequate prevention has not yet been achieved. In particular, this is the reason why server failures do not occur singularly, cause other server failures, or receive something that causes failures from other servers. In other words, while the existing studies assumed that it was a single server that did not affect the servers and analyzed the failure, in this study, the failure occurred on the assumption that it had an effect between servers. In order to define the complex failure situation in the data center, failure history data for each equipment existing in the data center was used. There are four major failures considered in this study: Network Node Down, Server Down, Windows Activation Services Down, and Database Management System Service Down. The failures that occur for each device are sorted in chronological order, and when a failure occurs in a specific equipment, if a failure occurs in a specific equipment within 5 minutes from the time of occurrence, it is defined that the failure occurs simultaneously. After configuring the sequence for the devices that have failed at the same time, 5 devices that frequently occur simultaneously within the configured sequence were selected, and the case where the selected devices failed at the same time was confirmed through visualization. Since the server resource information collected for failure analysis is in units of time series and has flow, we used Long Short-term Memory (LSTM), a deep learning algorithm that can predict the next state through the previous state. In addition, unlike a single server, the Hierarchical Attention Network deep learning model structure was used in consideration of the fact that the level of multiple failures for each server is different. This algorithm is a method of increasing the prediction accuracy by giving weight to the server as the impact on the failure increases. The study began with defining the type of failure and selecting the analysis target. In the first experiment, the same collected data was assumed as a single server state and a multiple server state, and compared and analyzed. The second experiment improved the prediction accuracy in the case of a complex server by optimizing each server threshold. In the first experiment, which assumed each of a single server and multiple servers, in the case of a single server, it was predicted that three of the five servers did not have a failure even though the actual failure occurred. However, assuming multiple servers, all five servers were predicted to have failed. As a result of the experiment, the hypothesis that there is an effect between servers is proven. As a result of this study, it was confirmed that the prediction performance was superior when the multiple servers were assumed than when the single server was assumed. In particular, applying the Hierarchical Attention Network algorithm, assuming that the effects of each server will be different, played a role in improving the analysis effect. In addition, by applying a different threshold for each server, the prediction accuracy could be improved. This study showed that failures that are difficult to determine the cause can be predicted through historical data, and a model that can predict failures occurring in servers in data centers is presented. It is expected that the occurrence of disability can be prevented in advance using the results of this study.