• Journal of Home Health Care Nursing
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• v.30 no.2
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• pp.163-173
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• 2023

Purpose: The aim of this descriptive research study was to identify the factors affecting the ethical decision-making of nursing students. Methods: A convenience sample of 193 nursing students from three nursing colleges in D city who were engaged in clinical practice completed an online Google Forms questionnaire from September 9 to September 20, 2021. Using SPSS 23.0, data were analyzed with descriptive statistics, an independent t-test, a one-way ANOVA, Scheffé's test, Pearson's correlation coefficient, and a multiple regression analysis. Results: The influencing factors of ideal ethical decision-making were guilt (β=.38, p<.001), awareness of the nurses' Code of Ethics (β=.18, p=.023) and motivation for entering school, among general characteristics (β=-.18, p=.033). The explanatory power of the model was 22.2%. Further, the influencing factors of realistic ethical decision-making were ideal ethical decision-making (β=.26, p=.001) and grade (among general characteristics) (β=.15, p=.029); the explanatory power of the model was 17.9%. Conclusion: Various educational tools and programs pertaining to making ideal and ethical decisions must be enhanced to promote ethical choices in clinical areas and realistic ethical decision-making ability to actually make such choices. This focus may enable nurses to improve their nursing professionalism in the future.

• Journal of the Korea Safety Management & Science
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• v.3 no.2
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• pp.133-144
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• 2001

This article deals with the multiple alternative proposal of Strategy. when Decision makers meet a very complex and important problems to take a good choice. It might not be easy that we make a decision and accept the decision as an exact result of analysis at a complication and uncertain situation. Although the decision under unpredictable state is many existence and each field is classified to support it. he can not provide exact estimations and be able to specify a result and forecasting. This is the reason why the original research use Statistical Survey method and Visual Decision Making Process(VDMP) to improve decision analysis method. Therefore, Our research suggests that the VDMP utilized in the strategic decision making situation as a group decision adding tool, can be applied in the development of a process vision and implementation plan. as a result, researcher describe step by step the process of VDMP.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.05a
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• pp.243-246
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• 2003

Data mining is a process of discovering useful patterns for decision making from an amount of data. It has recently received much attention in a wide range of business and engineering fields Classifying a group into subgroups is one of the most important subjects in data mining Tree-based methods, known as decision trees, provide an efficient way to finding classification models. The primary concern in tree learning is to minimize a node impurity, which is evaluated using a target variable in the data set. However, there are situations where multiple target variables should be taken into account, for example, such as manufacturing process monitoring, marketing science, and clinical and health analysis. The purpose of this article is to present several methods for measuring the node impurity, which are applicable to data sets with multiple target variables. For illustrations, numerical examples are given with discussion.

• Proceedings of the Safety Management and Science Conference
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• 2003.05a
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• pp.1-5
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• 2003

We purpose a decision model to select the optimal facilities for the Decision Making problems with multiple characteristics(nominal-is-best characteristics, larger-is -better characteristics, smaller- is -better characteristics). Using this model, concept of the loss function is used in this comprehensive method of for select the optimal preferred facilities. To solve the issue on the optimal preferred facilities for multiple characteristics, this study propose the loss function with cross-product terms among the characteristics and derived range of the coefficients of the terms.

• Journal of applied mathematics & informatics
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• v.42 no.2
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• pp.257-281
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• 2024

In real life, a decision-maker can assign multiple values for pairwise comparison with a certain confidence level. Studies incorporating multi-choice parameters in multi-criteria decision-making methods are lacking in the literature. So, In this work, an extension of the Best-Worst Method (BWM) with multi-choice pairwise comparisons and multi-choice confidence parameters has been proposed. This work incorporates an extension to the original BWM with multi-choice uncertainty and confidence level. The BWM presumes the Decision-Maker to be fully confident about preference criteria vectors best to others & others to worst. In the proposed work, we consider uncertainty by giving decision-makers freedom to have multiple choices for preference comparison and having a corresponding confidence degree for each choice. This adds one more parameter corresponding to the degree of confidence of each choice to the already existing MCDM, i.e. multi-choice BWM and yields acceptable results similar to other studies. Also, the consistency ratio remained low within the acceptable range. Two real-life case studies are presented to validate our study on proposed models.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.3
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• pp.1223-1233
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• 2011

The purpose of this study was to investigate the differences in university student's career maturity according to general characteristics(for example, gender, grade, and SES) and undergraduate student's career decision-making styles. A scale measuring the career decision-making styles(Harren, 1984) and career maturity(Crites, 1978) was administered to 223 university students around the capital city. The data were analyzed using Cronbach's ${\alpha}$ tests, means, standard deviations, One-way MANOVAs, and Multiple Discriminant Analyses. The results of this study were as follows: 1. There was not significant difference by gender in career maturity. But there were significant differences by grade and SES in career maturity. Freshmen and Senior exhibited higher career decision-making attitude than sophomores and juniors. High-SES group showed higher career decision-making attitude and lower career independence. 2. There were significant differences by career decision-making styles in career maturity. 51.1% of our samples were rational decision-making styles, 30.0% of our samples were intuitive decision-making styles, and 18.8% of our samples were dependent decision-making styles. Undergraduate students with rational decision-making styles showed more positive career choice behavior and higher career independence than undergraduate students dependent decision-making styles.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.1
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• pp.10-19
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• 2013

Dispatching rule plays an important role in a hybrid flow shop. Finding the appropriate dispatching rule becomes more challenging when there are multiple criteria, uncertain demands, and dynamic manufacturing environment. Using a single dispatching rule for the whole shop or a set of rules based on a single criterion is not sufficient. Therefore, a multi-criteria decision making technique using 'the order preference by similarity to ideal solution' (TOPSIS) and 'analytic hierarchy process' (AHP) is presented. The proposed technique is aimed to find the most suitable set of dispatching rules under different manufacturing scenarios. A simulation based case study on a PCB manufacturing process is presented to illustrate the procedure and effectiveness of the proposed methodology.

• Journal of the Korean Operations Research and Management Science Society
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• v.11 no.2
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• pp.51-68
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• 1986

The effects of goal setting in the context of Multiple Criteria Making (MCDM) are classified into two types : internal and external. In the internal models, the impact of the changed goal is limited only to the other goals in MCDM model. By contrast, in the external models, the impact is limited to the factors not included in the MCDM model. In fact, most real world examples of goal setting have the nature of mixed models. To assist in the goal setting process, the framework named Goal Setting Support (GSS) is developed. The GSS helps decision-makers for mixed models to 1) make internal trade-offs in a way that guarantees non-dominancy after the trade-ofs, and 2) evaluate achieved goals systematically. The GSS can be used in creating Decision Support Systems that will allow interactive goal setting.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.3
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• pp.181-187
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• 2014

Dealing with uncertainty is always a challenging problem. Intuitionistic fuzzy sets was presented to manage situations in which experts have some membership and non-membership value to assess an alternative. Hesitant fuzzy sets was used to handle such situations in which experts hesitate between several possible membership values to assess an alternative. In this paper, the concept of intuitionistic hesitant fuzzy set is introduced to provide computational basis to manage the situations in which experts assess an alternative in possible membership values and non-membership values. Distance measure is defined between any two intuitionistic hesitant fuzzy elements. Fuzzy technique for order preference by similarity to ideal solution is developed for intuitionistic hesitant fuzzy set to solve multi-criteria decision making problem in group decision environment. An example is given to illustrate this technique.

• Journal of the Korean Operations Research and Management Science Society
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• v.30 no.1
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• pp.75-94
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• 2005

Research in the decision sciences has continued to develop a variety of mathematical models as well as software tools supporting corporate decision-making. Yet. in spite of their potential usefulness, the models are little used in real-world decision making since the model solution processes are too complex for ordinary users to get accustomed. This paper proposes an intelligent and flexible model-solver integration framework that enables the user to solve decision problems using multiple models and solvers without having precise knowledge of the model-solution processes. Specifically, for intuitive model-solution, the framework enables a decision support system to suggest the compatible solvers of a model autonomously without direct user intervention and to solve the model by matching the model and solver parameters intelligently without any serious conflicts. Thus, the framework would improve the productivity of institutional model solving tasks by relieving the user from the burden of leaning model and solver semantics requiring considerable time and efforts.