• 유통과학연구
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• 제17권11호
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• pp.53-62
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• 2019

Purpose: This research aims to study the internal and external social practices of supply chain management along with economic and social performance of eight Portuguese companies from different industrial sectors. Through empirical data derived from eight case studies, five research propositions are suggested and tested. Research, design, data and methodology: The data was collected through 22 semi-structured interviews with general, procurement, and environmental/safety managers from eight companies from different industrial sectors. Secondary data was collected from reports, websites, and companies' internal documentation. Results: The analysis identifies the most important social practices considered by managers, as well as the performance measures that are most appropriate and most widely used to evaluate the influence of social practices on corporate economic and social performance. The results support four of the five propositions of this research. Companies' economic and social performance are affected by the implementation of social practices into the supply chain, namely the internal social practices. Conclusions: The findings confirmed that there is a positive relationship between internal social practices and economic performance. Internal social supply chain practices contribute to improve social performance. It also identifies the social practices which have negative effects on focal company performance.

• Journal of Contemporary Eastern Asia
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• 제14권2호
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• pp.87-96
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• 2015

Social media comprises a vast and consequential landscape that has been poorly mapped and understood. Hundreds of millions of people have eagerly moved many of the conversations and discussions that compose civil society into these services and platforms. There is a need to document and analyze these social spaces for many academic and commercial purposes. The Social Media Research Foundation has engaged a strategy to cultivate better research into the structure and dynamics of social media. The foundation is dedicated to the creation of open tools, open data, and open scholarship related to social media. It has implemented a free and open network collection, analysis, and visualization tool called NodeXL to facilitate social media network research. Using NodeXL a group of researchers has collectively authored a publicly available archive, called the NodeXL Graph Gallery, composed of network data sets and visualizations from users around the world. This site has enabled the aggregation of tens of thousands of network datasets and images. Use of the archive has led to scholarly research results that are based on the wide range and scope of social media data sets available.

• 한국CDE학회논문집
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• 제20권3호
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• pp.254-262
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• 2015

This study proposes an approach to Product Data Management (PDM) education for collaborative product data management, which can support collaborative product development process. This approach introduces social media and a PDM system into a framework for PDM education supported by consistent product development process and product data model. It has been applied to two PDM classes and the result shows that the social media in PDM education can support not only experiences of the collaborative product data management but also interactive and informal communications among instructors and participants using integrated social media with product data during courses.

• 한국사회복지학
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• 제54권
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• pp.245-272
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• 2003

본 연구는 종합사회복지관의 프로그램 개발과정에 있어서 프로그램 개발자들의 정보수집정도 및 유형을 파악하고, 이러한 정보수집정도에 영향을 미치는 예측 변인이 무엇인지를 밝히는 데 조사의 목적이 있다. 조사방법으로는 전국의 335개의 종합사죄복지관의 청소년(아동)부서의 프로그램 개발을 책임지고 있는 사회복지사를 응답자로 하여 우편으로 설문조사를 실시하였다. 연구의 주요한 결과를 살펴보면, 우선 기술분석결과에서 첫째, 현장에서 프로그램 개발활동이 활발하다는 것이 밝혀졌다. 둘째, 복지관에서 프로그램 개발을 주로 나이와 기관의 근무경험이 부족한 직원들이 개발을 맡고 있다. 셋째, 개발자들이 비공식적인 정보보다는 공식적 정보들을 더 많이 수집하고 있다. 넷째, 개발자들이 프로그램 개발관련지식을 공식적 교육프로그램을 통해서 획득한 '공식적' 훈련보다는 '비공식적' 훈련으로 통해서 더 많은 지식을 습득하고 있는 것으로 나타나고 있다. 회귀분석결과를 살펴보면, '전체적인 정보수집활동'에 영향을 미치는 요인으로서 업무의 자율성, 업무의 부담감, 공식적 교육정도, 개방성 등이 중요한 변수로서 나타났다. 특히, '전체적인 정보수집활동'에서 중요하게 영양을 미치는 업무의 부담감의 변수는 흥미롭게도 '전체적인 정보수집활동'을 세부적으로 구분한 '공식적 정보수집활동'과 '비공식적 정보수집활동'으로 구분하여 살펴본 회귀분석결과에 유일하게 다르게 영양을 미치는 요인으로 나타났다. 주요 결과를 바탕으로 이론적 및 실천적인 함의를 논하였다.

• 한국유기농업학회지
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• 제29권3호
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• pp.317-332
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• 2021

This study estimated the predictive onion yield through Stepwise regression of big data and weather variables by onion growing season. The economic feasibility of onion observations using big data was analyzed using estimated predictive data. The social welfare effect was estimated through the model of Harberger's triangle using onion yield prediction with big data and it without big data. Predicted yield using big data showed a deviation of -9.0% to 4.2%. As a result of estimating the social welfare effect, the average annual value was 23.3 billion won. The average annual value of social welfare effects if big data was not used was measured at 22.4 billion won. Therefore, it was estimated that the difference between the social welfare effect when the prediction using big data was used and when it was not was about 950 million won. When these results are applied to items other than onion items, the effect will be greater. It is judged that it can be used as basic data to prove the justification of the agricultural observation project. However, since the simple Harberger's triangle theory has the limitation of oversimplifying reality, it is necessary to evaluate the economic value through various methods such as measuring the effect of agricultural observation under a more realistic rational expectation hypothesis in future studies.

• 산업경영시스템학회지
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• 제40권2호
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• pp.121-128
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• 2017

This paper was to evaluate social enterprises' management efficiency with Data Envelope Analysis (DEA). The data was based on the 168 social enterprises' of annual performance reports published in 2015. The research focused on to measure both financial efficiency and social impact of the companies simultaneously. To apply DEA, the paper classified the enterprises into seven types based on types of socal impacts which each company provides before the estimation of the efficiency. The research results showed that group D, which employes disadvantaged people, provides social services and shares resources was the most efficient group and had higest net worths in Pure Technical Efficiency. In contrast, Group B, which only employs social advantage people and provides social service, was the least efficient one. The research suggests a practical and efficient framework in measuring social enterprises' management efficiency, including both the financial performance and social impacts simultaneously with their self-publishing reports. Because the Korea Social Enterprise Promotion Agency does not open business reports which social enterprises submit each year, there are basic limitations on researchers attempting to analyse with data from all social enterprises in Korea. Thus, this study dealt with only 10% of the social enterprises which self-published their performance report on the Korea Social Enterprise Promotion Agency's web site. Regardless of these limitations, this study suggested substantial methods to estimate management efficiency with the self-published reports. Because self-publishing is increasing each year, it will be the main source of information for researchers in examining and evaluating social enterprises' financial performance or social contribution. The research suggests a practical and efficient framework in measuring social enterprises' management efficiency, including both the financial performance and social impacts simultaneously with their self-publishing reports. The research results suggest not only list of efficient enterprises but also methods of improvement for less efficient enterprises.

• International Journal of Advanced Culture Technology
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• 제7권4호
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• pp.255-259
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• 2019

This study reviews the analysis and characteristics of databases from big data and then establishes representational strategy. Thus, analysis has continued for a long time in the quantity and quality of data, and there are changes in the location of data in the social sciences, past trends and the emergence of big data. The introduction of big data is presented as a prototype of new social science and is a useful practical example that empirically shows the need, basis, and direction of analysis through trend prediction services. Big data provides a future perspective as an important foundation for social change within the framework of basic social sciences.

• 한국BIM학회 논문집
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• 제13권4호
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• pp.106-114
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• 2023

In this study, we developed a method to quantify urban social costs using mobile sensing data, providing a novel approach to urban planning. By collecting and analyzing extensive mobile data over time, we transformed travel patterns into measurable social costs. Our findings highlight the effectiveness of big data in urban planning, revealing key correlations between transportation modes and their associated social costs. This research not only advances the use of mobile data in urban planning but also suggests new directions for future studies to enhance data collection and analysis methods.

• 정보과학회 논문지
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• 제42권6호
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• pp.781-790
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• 2015

인터넷과 모바일 환경의 발전에 따라 최근에는 마이크로블로그가 성행하고 있다. 마이크로블로그에는 부가적인 데이터가 담겨있다. 그 중 위치 정보에 대한 데이터를 포함하는 마이크로블로그 데이터를 공간 소셜 웹 객체라고 지칭한다. 이러한 마이크로블로그 데이터에 대한 일반 집계는 사용자별 데이터 집계 등이 있으나, 단일 정보에 대한 집계만 가능하다. 본 연구는 공간 소셜 웹 객체의 특성을 갖는 마이크로블로그 데이터의 공간 소셜 분석을 위해, 일반 집계와 공간 데이터를 결합하고 지오해시와 맵리듀스를 이용한 공간 집계에 대한 알고리즘을 제시한다. 이를 통해 의미있는 공간 소셜에 대한 분석의 기반을 마련하였다.

• International Journal of Computer Science & Network Security
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• 제22권3호
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• pp.396-404
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• 2022

Online social networks contain a large amount of data that can be converted into valuable and insightful information. Text mining approaches allow exploring large-scale data efficiently. Therefore, this study reviews the recent literature on text mining in online social networks in a way that produces valid and valuable knowledge for further research. The review identifies text mining techniques used in social networking, the data used, tools, and the challenges. Research questions were formulated, then search strategy and selection criteria were defined, followed by the analysis of each paper to extract the data relevant to the research questions. The result shows that the most social media platforms used as a source of the data are Twitter and Facebook. The most common text mining technique were sentiment analysis and topic modeling. Classification and clustering were the most common approaches applied by the studies. The challenges include the need for processing with huge volumes of data, the noise, and the dynamic of the data. The study explores the recent development in text mining approaches in social networking by providing state and general view of work done in this research area.