• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.5
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• pp.17-32
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• 2023

The entrepreneurial activities of college students play a significant role in modern economic and social development, particularly as a solution to the changing economic landscape and youth unemployment issues. Introducing innovative ideas and technologies into the market through entrepreneurship can contribute to sustainable economic growth and social value. Additionally, the entrepreneurial intentions of college students are shaped by various factors, making it crucial to deeply understand and appropriately support these elements. To this end, this study systematically explores the importance and impact of role models through a multiple serial mediation analysis. Through a survey of 300 college students, the study analyzed how two psychological variables, growth mindset and entrepreneurial self-efficacy, mediate the influence of role models on entrepreneurial intentions. The presence and success stories of role models were found to enhance the growth mindset of college students, which in turn boosts their entrepreneurial self-efficacy and ultimately strengthens their entrepreneurial intentions. The analysis revealed that exposure to role models significantly influences the formation of a growth mindset among college students. This mindset fosters a positive attitude towards viewing challenges and failures in entrepreneurship as learning opportunities. Such a mindset further enhances entrepreneurial self-efficacy, thereby strengthening the intention to engage in entrepreneurial activities. This research offers insights by integrating various theories, such as mindset theory and social learning theory, to deeply understand the complex process of forming entrepreneurial intentions. Practically, this study provides important guidelines for the design and implementation of college entrepreneurship education. Utilizing role models can significantly enhance students' entrepreneurial intentions, and educational programs can strengthen students' growth mindset and entrepreneurial self-efficacy by sharing entrepreneurial experiences and knowledge through role models. In conclusion, this study provides a systematic and empirical analysis of the various factors and their complex interactions that impact the entrepreneurial intentions of college students. It confirms that psychological factors like growth mindset and entrepreneurial self-efficacy play a significant role in shaping entrepreneurial intentions, beyond mere information or technical education. This research emphasizes that these psychological factors should be comprehensively considered when developing and implementing policies and programs related to college entrepreneurship education.

• Journal of the Korean Society of Food Science and Nutrition
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• v.34 no.6
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• pp.840-849
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• 2005

In order to investigate the relationship between intake conditions of health food and health-related factors by residence and sex in Tong-Young area, a survey was carried out from 1,303 adults. Health foods were classified 3 groups including vitamin and mineral supplements, toner foods and manufactured health food supplements. Health-related factors were stress, fatigue, smoking and drinking. The $29.5\%$ of the subjects had taken some health food for health. Especially the male took more toner foods habitually than the female did. In take of vitamin and mineral supplements by residence, there was a significant difference $(p\leq0.01)$ as follows. The subjects in island $(20.0\%)$ who took vitamin/mineral supplements were about two times as compared with the subjects in Dong $(10.8\%)$, or Eub-Myeon $(10.0\%)$. The subjects taking supplementary food replied over fair $(82.8\%)$, the subjects taking toner food replied over fair (90.3$\%$) scored higher than who replied bad or very bad in self-perceived health status. Therefore, the better the subjects felt self-perceived health status, the more they took health foods for health themselves. In self-perceived stress status, the subjects who replied a little $(50.0\%,\;45.3\%)$ or little $(19.9\%,\;26.4\%)$, took vitamin and mineral supplements or manufactured health foods a lot. In toner food there was a significant correlation $(p\leq0.05)$ as follows. The less the subjects felt stress, the more they took dietry supplement. No smoker $(12.9\%)$intake rate of vitamin and mineral supplements was higher than smoker $(8.8\%)$. Smokers $(6.5\%)$ intake rate of toner food was higher than no smoker $(4.0\%)$. It was not significant the relationship between intake condition of health food and drinking. The main motivation for taking health food were by self-decision and invitation of friends or neighbors.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.27-65
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• 2020

Many companies on information and communication technology make public their own developed AI technology, for example, Google's TensorFlow, Facebook's PyTorch, Microsoft's CNTK. By releasing deep learning open source software to the public, the relationship with the developer community and the artificial intelligence (AI) ecosystem can be strengthened, and users can perform experiment, implementation and improvement of it. Accordingly, the field of machine learning is growing rapidly, and developers are using and reproducing various learning algorithms in each field. Although various analysis of open source software has been made, there is a lack of studies to help develop or use deep learning open source software in the industry. This study thus attempts to derive a strategy for adopting the framework through case studies of a deep learning open source framework. Based on the technology-organization-environment (TOE) framework and literature review related to the adoption of open source software, we employed the case study framework that includes technological factors as perceived relative advantage, perceived compatibility, perceived complexity, and perceived trialability, organizational factors as management support and knowledge & expertise, and environmental factors as availability of technology skills and services, and platform long term viability. We conducted a case study analysis of three companies' adoption cases (two cases of success and one case of failure) and revealed that seven out of eight TOE factors and several factors regarding company, team and resource are significant for the adoption of deep learning open source framework. By organizing the case study analysis results, we provided five important success factors for adopting deep learning framework: the knowledge and expertise of developers in the team, hardware (GPU) environment, data enterprise cooperation system, deep learning framework platform, deep learning framework work tool service. In order for an organization to successfully adopt a deep learning open source framework, at the stage of using the framework, first, the hardware (GPU) environment for AI R&D group must support the knowledge and expertise of the developers in the team. Second, it is necessary to support the use of deep learning frameworks by research developers through collecting and managing data inside and outside the company with a data enterprise cooperation system. Third, deep learning research expertise must be supplemented through cooperation with researchers from academic institutions such as universities and research institutes. Satisfying three procedures in the stage of using the deep learning framework, companies will increase the number of deep learning research developers, the ability to use the deep learning framework, and the support of GPU resource. In the proliferation stage of the deep learning framework, fourth, a company makes the deep learning framework platform that improves the research efficiency and effectiveness of the developers, for example, the optimization of the hardware (GPU) environment automatically. Fifth, the deep learning framework tool service team complements the developers' expertise through sharing the information of the external deep learning open source framework community to the in-house community and activating developer retraining and seminars. To implement the identified five success factors, a step-by-step enterprise procedure for adoption of the deep learning framework was proposed: defining the project problem, confirming whether the deep learning methodology is the right method, confirming whether the deep learning framework is the right tool, using the deep learning framework by the enterprise, spreading the framework of the enterprise. The first three steps (i.e. defining the project problem, confirming whether the deep learning methodology is the right method, and confirming whether the deep learning framework is the right tool) are pre-considerations to adopt a deep learning open source framework. After the three pre-considerations steps are clear, next two steps (i.e. using the deep learning framework by the enterprise and spreading the framework of the enterprise) can be processed. In the fourth step, the knowledge and expertise of developers in the team are important in addition to hardware (GPU) environment and data enterprise cooperation system. In final step, five important factors are realized for a successful adoption of the deep learning open source framework. This study provides strategic implications for companies adopting or using deep learning framework according to the needs of each industry and business.

• Food Science of Animal Resources
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• v.27 no.1
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• pp.117-121
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• 2007

The number of internet users and the scale of electronic on-line trading are on the increase due to the development of information technology and the internet. The aim of this study was to monitor the accuracy of country-of-origin labels and the indicated contents of meat available by electronic on-line trading by using a structural interview sheet for 100 on-line meat product markets. The result of this investigation showed a 100% level of accuracy for business name and telephone number whereas the company address, meat manufacturer and supplier, and business registration were less reliable. We also investigated the accuracy of site policy, e-mail address, and fax number. The results showed that the accuracy of fax numbers was the lowest. The product name and the kind of meat actually in the product showed a 100% level of conformity, while the price (96.3%), place of origin (93.6%), capacity (90.4%), meat parts (80.9%) and contents of the product (73.4%) showed a relatively low level of conformity. Serious safety issues were exposed by the disturbingly low 20.2% accuracy of indicated expiration dates and 5.3% accuracy of indicated manufacturing dates. To ensure food safety, it is essential to improve consumer understanding and trust regarding food safety through continuous public relations. More education and information are needed to raise consumer awareness of the facts versus myths regarding food safety.

• Journal of The Korean Association For Science Education
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• v.31 no.1
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• pp.78-98
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• 2011

The purpose of this study was to analyze elementary school students' interpretation of data characteristics by cognitive style. Participants were elementary students in sixth grade who can use integrated inquiry process skills. The students were divided into two groups, analytic cognitive style and wholistic cognitive style according to their response to Cognitive Style Analysis. They performed scientific interpretation of data activity. To collect data for this study, participants recorded the result on scientific interpretation of data activity paper and researcher recorded the situation on videotape and interviewed with participants after the end of interpretation of data to get additional data. And the findings of this study were as follows: First, the study analyzed interpretation of data characteristics by the operator regarding different situations of interpreting data according to cognitive style. For example, in the intermediate state, analytic-cognitive style students showed high achievement in identifying variables, and wholistic-cognitive style students were active in using prior knowledge to interpret data. Second, the result of analysis on the direction of interpreting data and preference for data types in interpreting data activities according to cognitive style are as follows: Wholistic-cognitive style students showed relatively high perception of information through the top-down approach. On the other hand, analytic-cognitive style students usually used the bottom-up approach gradually expanding detailed information to the scientific question-related answer and showed a preference data of the table type. Through the result, this study aimed to help establish a data interpretation strategy for learners to solve problems based on understanding of interpretation of data characteristics according to learners' cognitive style, and purposed the instruction design suggesting the data requiring various data interpretation strategies to develop learners' data interpretation ability.

• Korean journal of communication and information
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• v.17
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• pp.145-178
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• 2001

Bourdieu is one of the few social science researchers who were interested in photography. Bourdieu's work on photography appears principally in two books: Un art moyen: Essai sur les usagrs sociaux de la photographie(1965) and La distinction(1979). In these books, Bourdieu analyzes the role of photography in the family life of peasants and small town and urban dwellers. He shows how different classes and groups express their esthetic worldview in response to different photographs and photographic styles. What Bourdieu analyzed is not just photography but ways of photographing and ways of looking at pictures. Through these analyses, Bourdieu explores the social definition of photography. Bourdieu's ideas on photographic practice in social life are as follows. First, the photography, especially family photography generally practiced, has the integrative function. It recreates the group by ritualizing and solemnizing the important moments of social life in which the group reaffirms its unity. Second, the photography as esthetic practice in search of legitimacy as a fine art becomes a means by which different classes are pitted against each other. Each of classes gives its own meaning to photographic practice. Despite its originality and persuasive power, Bourdieu's work on photography has its own limits. The data used by Bourdieu are 35 years old and relevant to European social life. Things has changed since. First, the technological improvement and innovation in photography was considerable. Cheap and good photographic materials, easy to operate, made photographic practice everybody's everyday activity. New media like camcorder and digital camera made photography one of the industrial discards like jukebox. It means that photography does not function as important means of distinction between classes any longer. The integrative function of the photography becomes more ambiguous too. Second, the esthetic status of the photography has changed. The family photography was already integraed into fine art. Photography is not a middle-brow art any more. Bourdieu's work on photography shows how photography was used by different social classes in European social life of the 1960's. His work is historically and geographically limited. Moreover, his work was ordered by the french affiliate of Eastman Kodak Company. And all along the analysis, Bourdieu didn't hide his intention of distinguishing his sociological method from the other methods, especially psychological one. These mean that Bourdieu's work was done in a specific context, for specific purposes. In this respect, Bourdieu's work on photography, like every sociological work, can not claim to be universal.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.23-45
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• 2020

Big data is creating in a wide variety of fields such as medical care, manufacturing, logistics, sales site, SNS, and the dataset characteristics are also diverse. In order to secure the competitiveness of companies, it is necessary to improve decision-making capacity using a classification algorithm. However, most of them do not have sufficient knowledge on what kind of classification algorithm is appropriate for a specific problem area. In other words, determining which classification algorithm is appropriate depending on the characteristics of the dataset was has been a task that required expertise and effort. This is because the relationship between the characteristics of datasets (called meta-features) and the performance of classification algorithms has not been fully understood. Moreover, there has been little research on meta-features reflecting the characteristics of multi-class. Therefore, the purpose of this study is to empirically analyze whether meta-features of multi-class datasets have a significant effect on the performance of classification algorithms. In this study, meta-features of multi-class datasets were identified into two factors, (the data structure and the data complexity,) and seven representative meta-features were selected. Among those, we included the Herfindahl-Hirschman Index (HHI), originally a market concentration measurement index, in the meta-features to replace IR(Imbalanced Ratio). Also, we developed a new index called Reverse ReLU Silhouette Score into the meta-feature set. Among the UCI Machine Learning Repository data, six representative datasets (Balance Scale, PageBlocks, Car Evaluation, User Knowledge-Modeling, Wine Quality(red), Contraceptive Method Choice) were selected. The class of each dataset was classified by using the classification algorithms (KNN, Logistic Regression, Nave Bayes, Random Forest, and SVM) selected in the study. For each dataset, we applied 10-fold cross validation method. 10% to 100% oversampling method is applied for each fold and meta-features of the dataset is measured. The meta-features selected are HHI, Number of Classes, Number of Features, Entropy, Reverse ReLU Silhouette Score, Nonlinearity of Linear Classifier, Hub Score. F1-score was selected as the dependent variable. As a result, the results of this study showed that the six meta-features including Reverse ReLU Silhouette Score and HHI proposed in this study have a significant effect on the classification performance. (1) The meta-features HHI proposed in this study was significant in the classification performance. (2) The number of variables has a significant effect on the classification performance, unlike the number of classes, but it has a positive effect. (3) The number of classes has a negative effect on the performance of classification. (4) Entropy has a significant effect on the performance of classification. (5) The Reverse ReLU Silhouette Score also significantly affects the classification performance at a significant level of 0.01. (6) The nonlinearity of linear classifiers has a significant negative effect on classification performance. In addition, the results of the analysis by the classification algorithms were also consistent. In the regression analysis by classification algorithm, Naïve Bayes algorithm does not have a significant effect on the number of variables unlike other classification algorithms. This study has two theoretical contributions: (1) two new meta-features (HHI, Reverse ReLU Silhouette score) was proved to be significant. (2) The effects of data characteristics on the performance of classification were investigated using meta-features. The practical contribution points (1) can be utilized in the development of classification algorithm recommendation system according to the characteristics of datasets. (2) Many data scientists are often testing by adjusting the parameters of the algorithm to find the optimal algorithm for the situation because the characteristics of the data are different. In this process, excessive waste of resources occurs due to hardware, cost, time, and manpower. This study is expected to be useful for machine learning, data mining researchers, practitioners, and machine learning-based system developers. The composition of this study consists of introduction, related research, research model, experiment, conclusion and discussion.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.99-120
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• 2010

Due to the wide spread of customers' frequent access of non face-to-face services, there have been many attempts to improve customer satisfaction using huge amounts of data accumulated throughnon face-to-face channels. Usually, a call center is regarded to be one of the most representative non-faced channels. Therefore, it is important that a call center has enough agents to offer high level customer satisfaction. However, managing too many agents would increase the operational costs of a call center by increasing labor costs. Therefore, predicting and calculating the appropriate size of human resources of a call center is one of the most critical success factors of call center management. For this reason, most call centers are currently establishing a department of WFM(Work Force Management) to estimate the appropriate number of agents and to direct much effort to predict the volume of inbound calls. In real world applications, inbound call prediction is usually performed based on the intuition and experience of a domain expert. In other words, a domain expert usually predicts the volume of calls by calculating the average call of some periods and adjusting the average according tohis/her subjective estimation. However, this kind of approach has radical limitations in that the result of prediction might be strongly affected by the expert's personal experience and competence. It is often the case that a domain expert may predict inbound calls quite differently from anotherif the two experts have mutually different opinions on selecting influential variables and priorities among the variables. Moreover, it is almost impossible to logically clarify the process of expert's subjective prediction. Currently, to overcome the limitations of subjective call prediction, most call centers are adopting a WFMS(Workforce Management System) package in which expert's best practices are systemized. With WFMS, a user can predict the volume of calls by calculating the average call of each day of the week, excluding some eventful days. However, WFMS costs too much capital during the early stage of system establishment. Moreover, it is hard to reflect new information ontothe system when some factors affecting the amount of calls have been changed. In this paper, we attempt to devise a new model for predicting inbound calls that is not only based on theoretical background but also easily applicable to real world applications. Our model was mainly developed by the interactive decision tree technique, one of the most popular techniques in data mining. Therefore, we expect that our model can predict inbound calls automatically based on historical data, and it can utilize expert's domain knowledge during the process of tree construction. To analyze the accuracy of our model, we performed intensive experiments on a real case of one of the largest car insurance companies in Korea. In the case study, the prediction accuracy of the devised two models and traditional WFMS are analyzed with respect to the various error rates allowable. The experiments reveal that our data mining-based two models outperform WFMS in terms of predicting the amount of accident calls and fault calls in most experimental situations examined.

• Journal of Hospice and Palliative Care
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• v.6 no.1
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• pp.22-33
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• 2003

Purpose : The purpose of this study was to evaluate the present status of patients' satisfaction and the reasons for any satisfaction or dissatisfaction in cancer pain management Methods : A cross-sectional survey was used to obtain the feedback about pain management. The results of the survey were collected from 59 in- or out-patient who had cancer treatment at two of the teaching hospitals in Seoul from July, 2002 to November, 2002. The data was obtained by a structured questionnaire based on the American Cancer Society Patient Outcome Questionnaire(APS-POQ) and other previous research. The clinical information for all patients were compiled by reviewing their medical records. Resuts : 1) The subjects' mean score of the worst pain was 6.77, the average pain score was 3.80, and the pain score after management was 2.93 for the past 24 hours. The mean score of total pain interference was $25.03{\pm}12.82$. Many of the subjects had false beliefs about pain such as 'the experience of pain is a sign that the illness has gotten worse', 'pain medicine should be 'saved' in case the pain gets worse' and 'people get addicted to pain medicine easily'. 2) 66.1% of the subjects were properly medicated with analgesics. 33.9% of the subjects reported use of various methods in controlling pain other than the prescribed medication. Only 33.9% of the subjects had a chance to be educated about pain management by doctors or nurses. 3) The mean score of patients' satisfaction with pain management was $4.19{\pm}1.14$. 72.9% of the subjects answered 'satisfied' with pain management. The reasons for dissatisfaction were 'the pain was not relieved even after the pain management', 'I was not quickly and promptly treated when I complained of pain', 'doctors and nurses didn't pay much attention to my complaints of pain.', and 'there was no appropriate information given on the methods of administration, effect duration and side effects of pain medicine.' The reasons for satisfaction were: 'the pain was relieved after the pain management.', 'doctors and nurses quickly and promptly controlled my pain.', 'doctors and nurses paid enough attention to my complaints of pain.' and 'trust in my physician'. 4) In pain severity or pain interference, no significant difference was found between the satisfied group and dissatisfied group. On the belief 'good patients avoid talking about pain', a significant difference was found between the satisfied group and dissatisfied group. Conclusions : The patients' satisfaction with cancer pain management has increased over the years but still about 30% of patients reported to be 'not satisfied' for various reasons. The results of this study suggest that patients' education should be done to improve satisfaction in the pain management program.

• Asia pacific journal of information systems
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• v.22 no.3
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• pp.75-97
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• 2012

Knowledge residing in the heads of employees has always been regarded as one of the most critical resources within a firm. However, many tries to facilitate knowledge transfer among employees has been unsuccessful because of the motivational and cognitive problems between the knowledge source and the recipient. Social capital, which is defined as "the sum of the actual and potential resources embedded within, available through, derived from the network of relationships possessed by an individual or social unit [Nahapiet and Ghoshal, 1998]," is suggested to resolve these motivational and cognitive problems of knowledge transfer. In Social capital theory, there are two research streams. One insists that social capital strengthens group solidarity and brings up cooperative behaviors among group members, such as voluntary help to colleagues. Therefore, social capital can motivate an expert to transfer his/her knowledge to a colleague in need without any direct reward. The other stream insists that social capital provides an access to various resources that the owner of social capital doesn't possess directly. In knowledge transfer context, an employee with social capital can access and learn much knowledge from his/her colleagues. Therefore, social capital provides benefits to both the knowledge source and the recipient in different ways. However, prior research on knowledge transfer and social capital is mostly limited to either of the research stream of social capital and covered only the knowledge source's or the knowledge recipient's perspective. Social network theory which focuses on the structural dimension of social capital provides clear explanation about the in-depth mechanisms of social capital's two different benefits. 'Strong tie' builds up identification, trust, and emotional attachment between the knowledge source and the recipient; therefore, it motivates the knowledge source to transfer his/her knowledge to the recipient. On the other hand, 'weak tie' easily expands to 'diverse' knowledge sources because it does not take much effort to manage. Therefore, the real value of 'weak tie' comes from the 'diverse network structure,' not the 'weak tie' itself. It implies that the two different perspectives on strength of ties can co-exist. For example, an extroverted employee can manage many 'strong' ties with 'various' colleagues. In this regards, the individual-level structure of one's relationships as well as the dyadic-level relationship should be considered together to provide a holistic view of social capital. In addition, interaction effect between individual-level characteristics and dyadic-level characteristics can be examined, too. Based on these arguments, this study has following research questions. (1) How does the social capital of the knowledge source and the recipient influence knowledge transfer respectively? (2) How does the strength of ties between the knowledge source and the recipient influence knowledge transfer? (3) How does the social capital of the knowledge source and the recipient influence the effect of the strength of ties between the knowledge source and the recipient on knowledge transfer? Based on Social capital theory and Social network theory, a multi-level research model is developed to consider both the individual-level social capital of the knowledge source and the recipient and the dyadic-level strength of relationship between the knowledge source and the recipient. 'Cross-classified random effect model,' one of the multi-level analysis methods, is adopted to analyze the survey responses from 337 R&D employees. The results of analysis provide several findings. First, among three dimensions of the knowledge source's social capital, network centrality (i.e., structural dimension) shows the significant direct effect on knowledge transfer. On the other hand, the knowledge recipient's network centrality is not influential. Instead, it strengthens the influence of the strength of ties between the knowledge source and the recipient on knowledge transfer. It means that the knowledge source's network centrality does not directly increase knowledge transfer. Instead, by providing access to various knowledge sources, the network centrality provides only the context where the strong tie between the knowledge source and the recipient leads to effective knowledge transfer. In short, network centrality has indirect effect on knowledge transfer from the knowledge recipient's perspective, while it has direct effect from the knowledge source's perspective. This is the most important contribution of this research. In addition, contrary to the research hypothesis, company tenure of the knowledge recipient negatively influences knowledge transfer. It means that experienced employees do not look for new knowledge and stick to their own knowledge. This is also an interesting result. One of the possible reasons is the hierarchical culture of Korea, such as a fear of losing face in front of subordinates. In a research methodology perspective, multi-level analysis adopted in this study seems to be very promising in management research area which has a multi-level data structure, such as employee-team-department-company. In addition, social network analysis is also a promising research approach with an exploding availability of online social network data.