• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2002.11a
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• pp.3-13
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• 2002

고석하 등(2002)은 인터넷 소매상이 상품 품목의 명목 가격과 배송료를 이용해서 고객의 일회 총 구매 비용을 조절한다는 것을 밝혔다. 고석하 등(2002)은 같은 내용의 상품 조합을 인터넷 시장에서 구매하기 위한 비용과 전통 시장에서 구매하기 위한 비용을 비교하였다. 분석 결과, 그 교호작용과 함께, 상품 종류와 일회 구매액/가격의 크기의 두 요소가 인터넷 시장의 전통 시장에 대한 총 구매비용 할인율의 변동의 약 60％내지 80％를 설명할 수 있다는 것을 보여주었다. 한편, 구매액/가격은 인터넷 시장에서의 해당 산포도(전통 시장의 그것에 대비한)에는 거의 영향을 미31지 못하며, 상품의 종류도 산포도에는 할인율에서와 같이 큰 영향을 미치지 않았다. 인터넷 시장의 가격이나 구매비용 산포도는 상품 특성이나 구매액 크기 이외의 다른 요인에 의해서 주로 영향을 받는 것으로 나타났다. 따라서, 본 논문에서는 가격 요인 이외의 경제적 경쟁요인에 관한 실증연구로서, 2002년 6월 17일부터 20일까지, 소프트웨어, PC와 주변기기, 휴대폰, 가전제품, CD, 화장품, 그리고 책의 7가지 산업 전문 쇼핑몰과 종합 쇼핑몰을 대상으로, 인터넷 시장에서 수행되고 있는 경제적인 비가격 경쟁요인에 관한 실증 조사를 실시하였다. 조사 결과, 인터넷 시장에서 수행되고 있는 경제적인 비가격 경쟁요인은 매우 다양하며, 상품별로도 다른 특성을 보이고 있는 것으로 밝혀졌다. 인터넷 소매상의 경제적인 비가격 경쟁요인은 크게 배송료 면제와 배송료 외 인센티브 제도로 구분된다. 본 논문에서는 경제적인 비가격 경쟁요인의 모든 경우의 수를 고려할 수 있도록, 코드표를 작성하여 정리하고 분석하였다.기호로 인식하였다. 실험결과, 표준패턴을 음표와 비음표의 두개의 그룹으로 나누어 인식함으로써 DP 매칭의 처리 속도를 개선시켰고, 국소적인 변형이 있는 패턴과 특징의 수가 다른 패턴의 경우에도 좋은 인식률을 얻었다.리되고 이원화된 코드체계와 데이터 형태의 이질화를 통일하는 방법으로 데이터웨어하우스 시스템을 제시하였다. 결국 병원에서 데이터웨어하우스 시스템의 구축은 임상, 연구, 교육의 유기적 순환관계를 정립하여 지식의 순환적 고리인 수집, 공유, 확산, 재창출을 지속적 유지할 수 있는 인프라를 구축해 준다. 반면 상이한 정보들간의 충돌과 이에 따른 해석의 오류로 잘못된 의사결정을 위한 정보를 제공할 수 있고 기초정보의 접근 및 추출의 유용성에 의해서 정보유출에 대한 문제가 한계점으로 나타났다.로세스 개선을 위해서 무엇을 정말로 필요로 하는지를 밝힘으로써, 한국 소프트웨어 산업의 현실적인 특수성을 고려한 소프트웨어 프로세스 평가와 개선 모델의 개발을 위한 기초적인 자료를 제공할 것으로 예상된다. 또한, 본 연구 결과는, 우리나라 소프트웨어 조직들이 실제로 무엇을 필요로 하는지를 밝힘으로써, 우리나라의 소프트웨어 산업을 육성하기 위한 실효성 있는 정책 입안을 위한 기초 자료를 제공할 것으로 예상된다.를 검증하려고 한다. 협력체계 확립, ${\circled}3$ 전문인력 확보 및 인력구성 조정, 그리고 ${\circled}4$ 방문보건사업의 강화 등이다., 대사(代謝)와 관계(關係)있음을 시사(示唆)해 주고 있다.ble nutrient (TDN) was highest in booting stage (59.7%); however no sig

• Information Systems Review
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• v.11 no.1
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• pp.107-132
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• 2009

The purpose of this research is to define the consulting competence of IT consultants and empirically analyze the perceptual differences between the IT consultant group and the client group. Based on the previous researches and the opinion of the actual IT consultants, the consulting capability model has been established, which consists of six categories and eighteen factors. Six categories are (1) IT domain expertise, (2) problem solving ability, (3) project management capability, (4) communication skills, (5) human relations skills, and (6) professional ethics and attitude. Two field surveys have been performed and the responses of 174 IT consultants 116 clients have been acquired. It is shown that the level of possessed proficiency of IT consulting capability is far lower than the level of the required proficiency. And there exist the perceptual difference between two responding groups with respect to the level required proficiency but no difference exists in terms of the level of possessed proficiency. The findings of this research can provide some useful information in order to fully understand the differences between the IT consultant group and the client group.

• Journal of the Korean Society for information Management
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• v.38 no.1
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• pp.53-69
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• 2021

This study aims to understand the research landscape of South Korea using the data of 2020 Google Scholar Metrics. To achieve the goal, we constructed and analyzed four types of networks including the university collaboration network, the keyword co-occurrence network, the journal citation network, and the discipline citation network. Through the analysis of the university collaboration network, we found major universities such as Seoul National University, Keimyung University, and Sungkyunkwan University that have led collaborative research. Job related keywords such as job change intention and job satisfaction have been frequently studied with other keywords. Through the analysis of the journal citation network, we found multiple journals such as The Journal of the Korea Contents Association, Korean Journal of Sociology, and Korean Journal of Culture and Social Issues that have been widely cited by the other journals and influenced them. Finally, Education, Business administration, and Social welfare were identified as the top influential disciplines that have influenced other disciplines through the knowledge diffusion. The study is the first of its kind to use the data of Google Scholar Metrics and conduct a stepwise network analysis (e.g., keyword, journal, and discipline) to broadly understand the research landscape of South Korea. Our results can be used by government agencies and universities to develop effective strategies of promoting university collaboration and interdisciplinary research.

• Journal of Practical Agriculture & Fisheries Research
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• v.12 no.1
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• pp.137-150
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• 2010

Traditionally, like many people in mountain region of the Himalaya, the Lwang communities depend on mix of subsistence agriculture, animal husbandry, and seasonal migrant labor for their livelihoods. These traditional systems are characterized by low productivity, diverse use of available natural resources (largely for home consumption), limited markets, and some aversion for innovation. The potential to generate wealth through commerce has largely been untapped by these mountain residents and thus is undervalued in local and national economies. Introduction of organic tea farming is a part of Lwang community's several initiatives to break the vicious poverty cycle Annapurna Conservation Area Project (ACAP) played facilitating roles in all their efforts since beginning. In five years, the tea plantation emerged as a new means for secured a livelihood. This study aims to analyze the current practices in tea farming both in terms of farm management and soil nutrient status(technical) and the prosperity of the tea farmers (social). The technical aspect covers the soil and tea leaf analysis of various nutrients contents in the soil and tea leaf. Originally, the technical aspect of the study was not planned but later during the consultation with the advisor it was taken into consideration which added value to the research study. The sample were collected from different locations and analyzed on the field itself. The other part of the study i.e. the social aspect was done through questionnaire survey and focus group discussion. the tea farming provided them not only a new opportunity but also earned an identity in the region. This initiative was undertaken as a piloting measure. Now that the tea is in production with processing unit established locally, more serious consideration has to be given for better yield and economic prosperity. This research finding will help the community to analyze their efforts and make correction measures in tea garden management and application of fertilizer. It is also expected to fill up the gaps of knowledge and information required to reduce economic stresses and enhance capacity of farmers to make the tea farming a sustainable and beneficial business. The findings are expected to Sustainability of organic tea farming has direct impacts on biodiversity conservation compared to the other traditional farming practices that are more resource intensive. The study will also contribute to identify key action points required for reducing poverty while conserving environment and enhancing livelihoods

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.113-125
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• 2013

Every company wants to know customer's requirement and makes an effort to meet them. Cause that, communication between customer and company became core competition of business and that important is increasing continuously. There are several strategies to find customer's needs, but VOC (Voice of customer) is one of most powerful communication tools and VOC gathering by several channels as telephone, post, e-mail, website and so on is so meaningful. So, almost company is gathering VOC and operating VOC system. VOC is important not only to business organization but also public organization such as government, education institute, and medical center that should drive up public service quality and customer satisfaction. Accordingly, they make a VOC gathering and analyzing System and then use for making a new product and service, and upgrade. In recent years, innovations in internet and ICT have made diverse channels such as SNS, mobile, website and call-center to collect VOC data. Although a lot of VOC data is collected through diverse channel, the proper utilization is still difficult. It is because the VOC data is made of very emotional contents by voice or text of informal style and the volume of the VOC data are so big. These unstructured big data make a difficult to store and analyze for use by human. So that, the organization need to automatic collecting, storing, classifying and analyzing system for unstructured big VOC data. This study propose an intelligent VOC analyzing system based on opinion mining to classify the unstructured VOC data automatically and determine the polarity as well as the type of VOC. And then, the basis of the VOC opinion analyzing system, called domain-oriented sentiment dictionary is created and corresponding stages are presented in detail. The experiment is conducted with 4,300 VOC data collected from a medical website to measure the effectiveness of the proposed system and utilized them to develop the sensitive data dictionary by determining the special sentiment vocabulary and their polarity value in a medical domain. Through the experiment, it comes out that positive terms such as "칭찬, 친절함, 감사, 무사히, 잘해, 감동, 미소" have high positive opinion value, and negative terms such as "퉁명, 뭡니까, 말하더군요, 무시하는" have strong negative opinion. These terms are in general use and the experiment result seems to be a high probability of opinion polarity. Furthermore, the accuracy of proposed VOC classification model has been compared and the highest classification accuracy of 77.8% is conformed at threshold with -0.50 of opinion classification of VOC. Through the proposed intelligent VOC analyzing system, the real time opinion classification and response priority of VOC can be predicted. Ultimately the positive effectiveness is expected to catch the customer complains at early stage and deal with it quickly with the lower number of staff to operate the VOC system. It can be made available human resource and time of customer service part. Above all, this study is new try to automatic analyzing the unstructured VOC data using opinion mining, and shows that the system could be used as variable to classify the positive or negative polarity of VOC opinion. It is expected to suggest practical framework of the VOC analysis to diverse use and the model can be used as real VOC analyzing system if it is implemented as system. Despite experiment results and expectation, this study has several limits. First of all, the sample data is only collected from a hospital web-site. It means that the sentimental dictionary made by sample data can be lean too much towards on that hospital and web-site. Therefore, next research has to take several channels such as call-center and SNS, and other domain like government, financial company, and education institute.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.2
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• pp.197-214
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• 2024

This study aimed to investigate students' perceptions at science and technology specialized universities towards entrepreneurship support policies and to derive policy improvement measures by applying a bottom-up approach to reflect the requirements of the policy beneficiaries, i.e., the students. Specifically, the research explored effective execution strategies for student entrepreneurship support policies through a survey and analysis of KAIST students. The findings revealed that KAIST students recognize the urgent need for improvement in sharing policy objectives with the student entrepreneurship field, reflecting the opinions of the campus entrepreneurship scene in policy formulation, and constructing an entrepreneurship-friendly academic system for nurturing student entrepreneurs. Additionally, there was a highlighted need for enhancement in the capacity of implementing agencies, as well as in marketing and market development capabilities, and organizational management and practical skills as entrepreneurs within the educational curriculum. Consequently, this study proposes the following improvement measures: First, it calls for enhanced transparency and accessibility of entrepreneurship support policies, ensuring students clearly understand policy objectives and can easily access information. Second, it advocates for student-centered policy development, where students' opinions are actively incorporated to devise customized policies that consider their needs and the actual entrepreneurship environment. Third, there is a demand for improving entrepreneurship-friendly academic systems, encouraging more active participation in entrepreneurship activities by adopting or refining academic policies that recognize entrepreneurship activities as credits or expand entrepreneurship-related courses. Based on these results, it is expected that this research will provide valuable foundational data to actively support student entrepreneurship in science and technology specialized universities, foster an entrepreneurial spirit, and contribute to the creation of an innovation-driven entrepreneurship ecosystem that contributes to technological innovation and social value creation.

• 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.

• Korean Journal of Culture and Social Issue
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• v.15 no.3
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• pp.399-429
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• 2009

The purpose of this research examines the factors that influence happiness among Korean adolescents by focusing on psychological resource (as measured by self-efficacy), relational resource (as measured by social support) and financial resource (as measured by family's monthly income). In addition, the influence of academic achievement on happiness is examined. To examine the influence of socio-economic status and family's monthly income, adolescents living in three different districts in Seoul (from working to middle to upper class districts) were randomly selected and interviewed in their home. A total of 190 elementary school, middle school, high school and university students (male=83, female=107) completed the resiliency of efficacy scale developed by Bandura (1995) and emotional support and happiness scale developed by the present researchers, in addition to background information. The results of the path analysis are as follows. First, the most important predictor of happiness among Korean adolescents is relational resources. In other words, emotional support received from significant others was most predictive of happiness; more than 60 times the effect of family's monthly income, three times the effect of academic achievement, and two times the effect of resiliency of efficacy. The second most important factor that predicted the happiness of Korean adolescents was psychological resource (i.e., resiliency of efficacy), which had 30 times the effect of family's monthly income. In addition resiliency of efficacy played a mediating role between emotional support on one hand and happiness on the other. Third, those respondents who had higher academic achievement reported higher levels of happiness, which had 20 times the effect of family's monthly income. Fourth, family monthly income did not predict happiness among Korean adolescents. Fifth, socio-economic status and school level did not have direct influence on happiness but had mediating influence through their influence on emotional support. In other words, those respondents with higher socio-economic status and elementary school students were more likely to receive social support from significant others, which in turn increased their happiness. These results indicate that the most important predictor of happiness among Korean adolescents is emotional support, followed by resiliency of effic acy and academic achievement, indicating that those adolescents from wealthy families are not necessarily happier.