• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.12
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• pp.4907-4921
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• 2010

The object of this descriptive survey research was to provide basic information source for building objective standards of DNR (Do Not Resuscitate) that can be clinically applied, by analyzing college students' awareness and attitude toward DNR. The participants of the study were 1,267 students from one college of Daegu, South Korea. The structured survey questionnaire was used for data collection, and the survey was conducted from 1-31 July, 2010. The error and percentage was estimated by SPSS 17.0 program, and analyzed with $x^2$-test. As a result of comparing the nursing students' and non-health care major students' awareness and attitude toward DNR, the significant differences were found in the necessity of DNR, reason for supporting DNR, reason for opposing DNR, and DNR decision-maker, among the awareness dimension; among the attitude dimension, significant differences were found in implication of family DNR and self-DNR. Comparing the nursing students' and non-health care major students' awareness toward DNR related information provision, researchers have found significant differences in the necessity of giving information on DNR, timing of the DNR information provision, result of the DNR-related information provision, and guidelines for the DNR information provision. In terms of the difference in DNR's necessity recognition by the demographic information, the significant differences existed based on the religion and the history of blood donation; in terms of the differences in attitude toward DNR decision-maker, the differences were found on the religion and the number of siblings. For the attitude toward family member's DNR, the significant differences existed for the sex, age, economic status, religion, the number of siblings, the history of familial illness and death, and experience of blood donation; the attitude toward the DNR for the self was significantly differed by the sex, economic status, the number of siblings, and the history of familial illness and death. To establish the standards for DNR based on the study, we suggest more well-designed future studies.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.1-23
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• 2014

Market forecasting aims to estimate the sales volume of a product or service that is sold to consumers for a specific selling period. From the perspective of the enterprise, accurate market forecasting assists in determining the timing of new product introduction, product design, and establishing production plans and marketing strategies that enable a more efficient decision-making process. Moreover, accurate market forecasting enables governments to efficiently establish a national budget organization. This study aims to generate a market growth curve for ICT (information and communication technology) goods using past time series data; categorize products showing similar growth patterns; understand markets in the industry; and forecast the future outlook of such products. This study suggests the useful and meaningful process (or methodology) to identify the market growth pattern with quantitative growth model and data mining algorithm. The study employs the following methodology. At the first stage, past time series data are collected based on the target products or services of categorized industry. The data, such as the volume of sales and domestic consumption for a specific product or service, are collected from the relevant government ministry, the National Statistical Office, and other relevant government organizations. For collected data that may not be analyzed due to the lack of past data and the alteration of code names, data pre-processing work should be performed. At the second stage of this process, an optimal model for market forecasting should be selected. This model can be varied on the basis of the characteristics of each categorized industry. As this study is focused on the ICT industry, which has more frequent new technology appearances resulting in changes of the market structure, Logistic model, Gompertz model, and Bass model are selected. A hybrid model that combines different models can also be considered. The hybrid model considered for use in this study analyzes the size of the market potential through the Logistic and Gompertz models, and then the figures are used for the Bass model. The third stage of this process is to evaluate which model most accurately explains the data. In order to do this, the parameter should be estimated on the basis of the collected past time series data to generate the models' predictive value and calculate the root-mean squared error (RMSE). The model that shows the lowest average RMSE value for every product type is considered as the best model. At the fourth stage of this process, based on the estimated parameter value generated by the best model, a market growth pattern map is constructed with self-organizing map algorithm. A self-organizing map is learning with market pattern parameters for all products or services as input data, and the products or services are organized into an $N{\times}N$ map. The number of clusters increase from 2 to M, depending on the characteristics of the nodes on the map. The clusters are divided into zones, and the clusters with the ability to provide the most meaningful explanation are selected. Based on the final selection of clusters, the boundaries between the nodes are selected and, ultimately, the market growth pattern map is completed. The last step is to determine the final characteristics of the clusters as well as the market growth curve. The average of the market growth pattern parameters in the clusters is taken to be a representative figure. Using this figure, a growth curve is drawn for each cluster, and their characteristics are analyzed. Also, taking into consideration the product types in each cluster, their characteristics can be qualitatively generated. We expect that the process and system that this paper suggests can be used as a tool for forecasting demand in the ICT and other industries.