• Journal of Smart Tourism
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• v.1 no.4
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• pp.3-7
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• 2021

Although Geographic Information Systems (GISs) have commonly been employed as powerful tools for manipulating and displaying spatial data in community-based tourism, a variety of GIS functions still lack the capabilities required to assist multiple decision makers to come to consensual decisions. In this study, I propose an alternative approach: spatial multicriteria decision analysis (SMCDA) that could reflect diverse decision makers' preferences by integrating GISs and multicriteria decision analysis (MCDA). I review the small number of case studies that have employed SMCDA, with a focus on the roles of GISs and MCDA. The methodological integration of GISs and MCDA into multi-spatial decision support systems offers the potential to implement participatory decision-making to solve complex spatial problems in community-based tourism planning, development, and management.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.10a
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• pp.355-361
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• 1999

An application of case-based reasoning is proposed to build an influence diagram for identifying successful new ventures. The decision to invest in new ventures in characterized by incomplete information and uncertainty, where some measures of firm performance are quantitative, while some others are substituted by qualitative indicators. Influence diagrams are used as a model for representing investment decision problems based on incomplete and uncertain information from a variety of sources. The building of influence diagrams needs much time and efforts and the resulting model such as a decision model is applicable to only one specific problem. However, some prior knowledge from the experience to build decision model can be utilized to resolve other similar decision problems. The basic idea of case-based reasoning is that humans reuse the problem solving experience to solve a new decision. In this paper, we suggest a case-based reasoning approach to build an influence diagram for the class of investment decision problems. This is composed of a retrieval procedure and an adaptation procedure. The retrieval procedure use two suggested measures, the fitting ratio and the garbage ratio. An adaptation procedure is based on a decision-analytic knowledge and decision participants knowledge. Each step of procedure is explained step by step, and it is applied to the investment decision problem in new ventures.

• Journal of Construction Engineering and Project Management
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• v.7 no.2
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• pp.19-33
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• 2017

Various forms of procurement are open to companies when constructing new owner-occupied properties. The selection of a form of procurement is an important decision-making problem for companies and their Corporate Real Estate Management (CREM) departments. With this in mind, a decision support tool has been developed to optimise the outcome and success of company's proposed real estate development projects and is presented in this paper. This model can also be used for current real estate portfolios to optimise returns in the long term. In pursuit of this objective and in order to provide an academic basis for this study, decision-relevant goals and parameters were initially identified from the referenced literature used in our research. These were subsequently evaluated in a case study of a corporate development project and identified as specific decision criteria from the perspective of the CREM. The investigation identified 15 criteria that are relevant to the corporate decision-making process in procuring and/or developing property. A model for supporting and resolving decision-making problems of this type was derived from decision theory. In this paper, a decision-making tool is presented that takes into account all the decision criteria from the CREM point of view for the first time. This model should therefore serve as an aid in implementing the decision-making process for the development of real estate projects in a more structured and transparent manner.

• Journal of Korean Academy of Nursing
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• v.40 no.6
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• pp.892-902
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• 2010

Purpose: This study was done to identify participation by home healthcare nurses in clinical decision making and factors influencing clinical decision making. Methods: A descriptive survey was used to collect data from 68 home healthcare nurses in 22 hospital-based home healthcare services in Korea. To investigate participation, the researcher developed 3 scenarios through interviews with 5 home healthcare nurses. A self-report questionnaire composed of tools for characteristics, factors of clinical decision making, and participation was used. Results: Participation was relatively high, but significantly lower in the design phase (F=3.51, p=.032). Competency in clinical decision making (r=.45, p<.001), perception of the decision maker role (r=.47, p<.001), and perception of the utility of clinical practice guidelines (r=.25, p=.043) were significantly correlated with participation. Competency in clinical decision making (Odds ratio [OR]=41.79, p=.007) and perception of the decision maker role (OR=15.09, p=.007) were significant factors predicting participation in clinical decision making by home healthcare nurses. Conclusion: In order to encourage participation in clinical decision making, education programs should be provided to home healthcare nurses. Official clinical practice guidelines should be used to support home healthcare nurses’ participation in clinical decision making in cases where they can identify and solve the patient health problems.

• Journal of KIISE:Software and Applications
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• v.29 no.3
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• pp.156-166
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• 2002

Decision tree induction is a kind of useful machine learning approach for extracting classification rules from a set of feature-based examples. According to the partitioning style of the feature space, decision trees are categorized into univariate decision trees and multivariate decision trees. Due to observation error, uncertainty, subjective judgment, and so on, real-world data are prone to contain some errors in their feature values. For the purpose of making decision trees robust against such errors, there have been various trials to incorporate fuzzy techniques into decision tree construction. Several researches hove been done on incorporating fuzzy techniques into univariate decision trees. However, for multivariate decision trees, few research has been done in the line of such study. This paper proposes a fuzzy decision tree induction method that builds fuzzy multivariate decision trees named fuzzy oblique decision trees, To show the effectiveness of the proposed method, it also presents some experimental results.

• Journal of Korean Society for Quality Management
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• v.17 no.2
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• pp.113-120
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• 1989

The purposes of this paper are to compare expert systems and decision support systems, and illustrate the possible benefits when expert systems are integrated into the model base of a decision support systems for supporting decision-makers. Integrating expert systems capability into decision support systems may enhance the quality and efficiency of both computerized systems. This integration can improve selection of model, analysis, model management, judgement, and modeling. Thus the results are much more powerful decision support systems than are presently available.

• Journal of information and communication convergence engineering
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• v.9 no.3
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• pp.276-281
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• 2011

This paper presents a method of constructing the switching function using edge-valued decision diagrams. The proposed method is as following. The edge-valued decision diagram is a new data structure type of decision diagram which is recently used in constructing the digital logic systems based on the graph theory. Next, we apply edge-valued decision diagram to function minimization of digital logic systems. The proposed method has the visible, schematic and regular properties.

• Journal of the Korean Data and Information Science Society
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• v.18 no.4
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• pp.985-994
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• 2007

We note that the time of adolescents is very important time for obtaining informations about their jobs, exploring and making appropriate their career decision. In order to understand the career decision level of adolescents, we needed a study on effects of personal variables and family variables affecting the career decision level. For this, we provide direct, indirect and total effects of family variables and personal variables on the career decision level using the path analysis. Therefore, in this study, we give the real usefulness for making a different diagnosis and strategy solving some problems of career decision level.

• Journal of Agricultural Extension & Community Development
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• v.12 no.2
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• pp.257-270
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• 2005

The purposes of this study were to explore the differences of decision making in farming settlement and to analyze attributes for vocational decision making of rural youth. The study was carried out by literature review and questionnaire. To analyze vocational decision making of rural youth, the data were collected from 196 rural youth by stratified random sampling. The SPSSWIN/ver10 was used for analyzing data with frequency, percentage, t-test ANOVA and factor analysis. The major findings of this study were as follows; 1) Rural youth decided to become farmers at the middle and high school stage for farming succession and eco-friendly life orientation. 2) Youth parents were the major source of influence on their rural youth to settle in farming. 3) Rural youth with high economic status, upper academic achievement and university graduation showed higher autonomous decision making and lower heteronomous decision making. 4) Rural youth with low economic status and low academic achievement showed unstable vocational decision making. 5) The study suggested that rural youth with unstable decision making should get more educational opportunities and supports.

• Journal of Engineering Education Research
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• v.19 no.6
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• pp.20-31
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• 2016

The purpose of this study was to explore factors predicting career exploration behavior of engineering students. Career decision making types(rational type, intuitive type, and dependent type) and career decision making self-efficacy were included as predictors for this study. 249 engineering students in Gyeonggi-do of South Korea participated in this study and 231 data analyzed as final samples through multiple regression and mediation analysis. Results showed as follows: first, rational and intuitive decision making significantly predicted career exploration behavior. Also, career decision making self-efficacy completely mediated between these career decision making types(rational and intuitive) and career exploration behavior. The implication of the findings on practice for career education are discussed in the paper.