• The Journal of Industrial Distribution & Business
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• v.13 no.6
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• pp.9-18
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• 2022

Purpose: The government around the world is still highlighting the effect of the new variant of Covid-19. The government continues to make efforts to restore the economy through several programs, one of them is National Economic Recovery. This program is expected to increase public and investor confidence in handling Covid-19. This study aims to capture public sentiment on the economic growth rate in Indonesia, especially during the third wave of the omicron variant of the covid-19 virus, that is at the time in the fourth quarter of 2021. Research design, data, and methodology: The approach used in this research is to collect crowdsourcing data from twitter, in the range of 1st to 10th October 2021. The analysis is done by building model using Deep Learning Neural Network method. Results: The result of the sentiment analysis is that most of the tweets have a neutral sentiment on the Economic Growth discussion. Several central figures who discussed were Minister of Coordinating for the Economy of Indonesia, Minister of State-Owned Enterprises. Conclusions: Data from social media can be used by the government to capture public responses, especially public sentiment regarding economic growth. This can be used by policy makers, for example entrepreneurs to anticipate economic movements under certain conditions.

• Asian Journal for Public Opinion Research
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• v.5 no.4
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• pp.266-284
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• 2018

This paper adopts the big data research method, and draws 491 data from the Tianya Forum about the Simultaneous Renting and Purchasing policy of Guangzhou. The qualitative analysis software Nvivo11 is used to cluster the main questions about the Simultaneous Renting and Purchasing policy in the forum. The 36 high-frequency word frequencies are obtained through text clustering. Through rooted theory analysis, the main driving factors for summarizing people's doubts are 9 main categories, 3 core categories, and the model of driving factors for online forums is established. The study finds that resource factors are the most key factor, economic factors are the important drivers, and policy guiding factors are sub-important drivers.

• Journal of Distribution Science
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• v.22 no.4
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• pp.91-104
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• 2024

Purpose: The study aims to assess the current status of domestic public food delivery apps and analyze the process through which sellers choose between private delivery apps and public delivery apps. This involves exploring strategiesto achieve the original purpose of public food delivery apps, which is to enhance the small business owners income and promote consumer welfare by preventing the monopoly of private food delivery apps. Research design, data and methodology: the research methodology is based on a model that introduces adjustments for non-economic effects, considering the preferences of multi-homing consumers, to more realistically reflect the benefits of sellers' choices. For data analysis, real business performance data from 'Daeguro', 'Meokkaebi', and 'Somunnan Shop' were used. Results: The study revealed that if the market share of public delivery apps within a specific region increases beyond a certain level, the benefits for small-business sellers also increase. This leads to the strategic advantage of simultaneously using both delivery apps. Furthermore, the results exhibit a tendency similar to real social phenomena. Conclusions: This analysis confirmed the role of public food delivery apps in the domestic delivery app market and presents policy recommendations, including application integration and the implementation of exclusive public interest functions, to effectively fulfill this role.

• Journal of Korean Society of Archives and Records Management
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• v.14 no.1
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• pp.53-73
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• 2014

The study aims to analyze the Public Data Act and provide alternative strategies for public data management. It conducts an extensive literature review based on a multidisciplinary approach and discusses the terms, public data and synonyms from the Public Data Act, and other related laws while also studies and traces the history of related regulations. The significance of the Public Data Act is analyzed and the major contents of the Act are examined, particularly, the contents that describe relevant committees. As a result, the article discusses five issues: relation between regulations, ambiguity of decision-making standards, 'professionality of a public data supply officer, low quality of public data, and lack of records and archives management.

• Journal of the Korean Data and Information Science Society
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• v.24 no.5
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• pp.1029-1041
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• 2013

Recently, along with emergence of big data, there are incresing demands for releasing information and micro data for public use so that protecting privacy and measuring risk of disclosure for released database become important issues in goverment and business sector as well as academic community. This paper reviews statistical methods for protecting privacy and measuring risk of disclosure when micro data or data analysis sever is released for public use.

• Health Policy and Management
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• v.20 no.4
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• pp.74-96
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• 2010

The purpose of this empirical study is to evaluate efficiency of the regional public hospitals, using DEA(Data Envelopment Analysis). to do this, we design a DEA-AR/AHP Hybrid model to evaluate efficiency of 34 Regional Public Hospitals. the proposed model is developed by adding Acceptance Region(AR). using analytical hierarchy process(AHP). this model is compared with those of typical DEA models. Financial data used in this study were obtained from Database of the Korea Association Regional Public Hospital and analyzed using DEA model. As a result of analysis, This study found that the DEA-AR/AHP Hybrid model was superior to those typical DEA models in determining the priority among efficient hospitals. the result of this study can provide helpful information to evaluate the efficiency of public hospitals for efficient operational management, to develop more precise measurement for the priority of the efficient hospitals.

• Health Policy and Management
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• v.6 no.2
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• pp.91-114
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• 1996

In this research, the Data Envelopment Analysis(DEA) was applied to measure production efficiency of Public Corporation Medical Centers(PCMCs) operating in Korea. The focus of this research is triple. First, identifing convenience and usefulness of DEA to measure the relative efficiency among PCMCs. Second, assessing magnitudes of the relative efficiency for each PCMC. Third, adding insights into some factors resulting inefficiency in PCMCs. Then, in this paper technical efficiency and scale efficiency measured by DEA[introduced by Charnes, Cooper, and Rhoides(1978) and Banker, Charnes, and Cooper(1984)] were analyzed and a new separate variable was introduced which makes it possible to determine whether operations were conducted in regions of increasing, constant or decresing returns to scale(in multiple input and output situations). And a multi-factor Tobit analysis was conducted to see which variables are associated with PCMC's efficiency.

• The Research Journal of the Costume Culture
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• v.23 no.5
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• pp.876-893
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• 2015

The purpose of this study is to identify the influences of psychological variables and fashion-related psychological variables on purchasing fashion items on the Internet. Boredom proneness and public self-consciousness were selected as psychological variables, and dressing style was selected as a fashion-related psychological variable. It was hypothesized that boredom proneness and public self-consciousness not only influence the purchasing frequency of fashion items on the Internet directly, but also indirectly through dressing style. Data were gathered by surveying university students in Seoul using convenience sampling. Two hundred and eighty-six questionnaires were used in the statistical analysis. SPSS was used for exploratory factor analysis, and AMOS was used for hypothesized relationship testing. The factor analysis of boredom proneness revealed five dimensions, "helplessness," "affective response," "lack of internal stimulation," "lack of external stimulation," and "perception of time." The factor analysis of public self-consciousness revealed two dimensions, "appearance-consciousness" and "style-consciousness," and the factor analysis of dressing style revealed one dimension. The overall fit of the hypothesized model suggests that the model fits the data well. The hypothesized relationship test proved that boredom proneness and public self-consciousness influence the purchasing frequency of fashion items on the Internet indirectly through dressing style. The results implicate effective strategies for Internet shopping malls and suggestions for future study.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.6
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• pp.835-841
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• 2018

In this study, we analyzed public health data analysis using the hadoop-based spack in the cloud environment using the data of the Community Health Survey from 2012 to 2014, and the factors affecting the quality of life and quality of life. In the proposed paper, we constructed a cloud manager for parallel processing support using Hadoop - based Spack for open medical big data analysis. And we analyzed the factors affecting the "quality of life" of the individual among open medical big data quickly without restriction of hardware. The effects of public health data on health - related quality of life were classified into personal characteristics and community characteristics. And multiple-level regression analysis (ANOVA, t-test). As a result of the experiment, the factors affecting the quality of life were 73.8 points for men and 70.0 points for women, indicating that men had higher health - related quality of life than women.

• Journal of dental hygiene science
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• v.23 no.4
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• pp.255-263
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• 2023

Background: This study used text mining techniques to determine public perceptions of dental fear, extracted keywords related to dental fear, identified the connection between the keywords, and categorized and visualized perceptions related to dental fear. Methods: Keywords in texts posted on Internet portal sites (NAVER and Google) between 1 January, 2000, and 31 December, 2022, were collected. The four stages of analysis were used to explore the keywords: frequency analysis, term frequency-inverse document frequency (TF-IDF), centrality analysis and co-occurrence analysis, and convergent correlations. Results: In the top ten keywords based on frequency analysis, the most frequently used keyword was 'treatment,' followed by 'fear,' 'dental implant,' 'conscious sedation,' 'pain,' 'dental fear,' 'comfort,' 'taking medication,' 'experience,' and 'tooth.' In the TF-IDF analysis, the top three keywords were dental implant, conscious sedation, and dental fear. The co-occurrence analysis was used to explore keywords that appear together and showed that 'fear and treatment' and 'treatment and pain' appeared the most frequently. Conclusion: Texts collected via unstructured big data were analyzed to identify general perceptions related to dental fear, and this study is valuable as a source data for understanding public perceptions of dental fear by grouping associated keywords. The results of this study will be helpful to understand dental fear and used as factors affecting oral health in the future.