• The Journal of Asian Finance, Economics and Business
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• v.7 no.11
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• pp.549-556
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• 2020

Real estate markets play an essential role in the economic development of both developed and developing countries. Investment decisions in private real estate demand the consideration of several qualitative and quantitative criteria. Especially in Vietnam, demand for housing, apartments are rising which has resulted because of the migration from rural to urban areas. This study aims to determine the influencing factors of the real estate purchasing behavior and then recommend a grey Multi-Criteria Decision Making (MCDM) support model to evaluate real estate alternatives based on a numerical example in Vietnam. A set of essential criteria are identified based on experts' opinion, and the proposed determinants are initial investment, maintenance cost, prestige location, distance to interesting places, parking lot, public transportation, property condition, total area size, number of rooms, and neighbors. The subjective weights were obtained by using the Decision-Making Trial and Evaluation Laboratory (DEMATEL) model, and the Grey Relational Analysis (GRA) technique is employed to prioritize and rank real estate alternatives. The results reveal that this approach can be useful to make purchasing decisions for many kinds of real estate property under uncertain business environments. These findings indicate that the presented hybrid model has advantages in granting flexibility to the preferences of decision makers.

• The Korean Journal of Food & Health Convergence
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• v.8 no.6
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• pp.19-29
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• 2022

Business intelligence (BI) is a process for turning data into insights that inform an organization's strategic and tactical decisions. BI aims to give decision-makers the information they need to make better decisions Patient safety analysis, illness surveillance, and fraud identification are just a few healthcare decision-making processes that can be supported by data mining. Thus, the purpose of the current research is to outline the need if BI as an essential factor in the healthcare sector by reviewing various scholarly materials and the findings. The present author conducted one of the most famous qualitative literature approach which has been called as PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) statement. The selecting criteria for eligible prior studies were estimated by whether studies are suitable for the current research, identifying they are peer-reviewed and issued by notable publishers between 2017 and 2022. According to the result based on the PRISMA analysis, BI plays a vital role in the healthcare sector and there are four business intelligence factors (Data, Analytic, Reporting, and Visualization) that will ensure that the healthcare sector provides the right healthcare services to the customers to be addressed in this section include; data, analytics, reporting, and visualization.

• East Asian Economic Review
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• v.17 no.2
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• pp.207-241
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• 2013

The notion of green growth emerged in 2009. Since then, policy makers and practitioners have largely adopted the term. Although rather intermittently, there have been academic observations on green growth, with the term often being cited as a paradigm and a policy guide for generating new sources of growth. The most important reasons for the surge in green growth today as a new trend and an international agenda item are the rather unsatisfactory results and pitfalls of sustainable development, which has failed at promoting a tangible international environmental principle or a concrete policy framework. Green growth has been proposed as an alternative simultaneously to foster the dynamics of global environmental governance and to reinvigorate the world economy. This study examines to what extent green growth plays a complementary role in existing global environmental governance. Available evidence provides reasonable grounds for arguing that a positive outcome may well be expected from the evolution of green growth architecture and followed by practical policies. It became a global agenda out of a few influential national governments' control. However, decision makers in the leading countries, both developed and developing must be willing to continue implementing what has been discussed and agreed thus far, beyond changes in political leadership and administrations.

• Architectural research
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• v.21 no.2
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• pp.49-57
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• 2019

Risk assessment during pre-construction phase is important due to the uncertainty of the risks that may exist in projects. Risk checklist is a method to systematically classify and organize the risks that have been experienced in the past, and to identify the risk factors that may be present in the future projects. In addition, risk value assessment based on checklists plays a key role in risk management, and various risk assessment researches have been conducted to carry out this systematically. However, previous approaches have limitations in common, this is because risk values are evaluated individually in risk checklists, which ignore interdependencies among risk factors and neglect the emergence of co-occurrence of risks. Hence, when multiple risk factors cooccur, they cannot be far off from the conventional method of summing the total risk value to establish the risk response strategy. Most of risk factors are interdependent and may have multiple effects if occurred than expected. In particular, specific cause can be overlapped if multiple risks co-occur, and this may result in overestimation of the risk response for the future project. Thus, the objective of this research is to propose a model to help decision makers to quantify the risk value reflecting the interdependency during the identification phase using existing risk checklist that is currently being practiced in actual construction projects. The proposed model will provide the guideline to support the prediction and identification of the interdependency of risks in practice. In addition, the better understanding and prediction of the exceeding risk response by co-occurring risks during the risk identification phase for decision makers.

• Nuclear Engineering and Technology
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• v.39 no.5
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• pp.655-662
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• 2007

Most decision makers in the electricity industry plan their electric power expansion program by considering only a least cost operation, even when circumstances change with differing complexities. It is necessary, however, to analyze a long-term power expansion plan from various points of view, such as environmental friendliness, benefit of a carbon reduction, and system reliability, as well as least cost operation. The objective and approach of this study is to analyze the proper role of nuclear power in a long-term expansion plan by comparing different scenarios in terms of the system cost changes, $CO_2$ emission reduction, and system reliability in relation to the Business-As-Usual (BAU). The conclusion of this paper makes it clear that the Korean government cannot but expand the nationwide nuclear power program, because an increased energy demand is inevitable and other energy resources will not provide an adequate solution from an economic and sustainability point of view. The results of this analysis will help the Korean government in its long-term resource planning of what kinds of role each electric resource can play in terms of a triangular dilemma involving economics, environmental friendliness, and a stable supply of electricity.

• Journal of Korea Spatial Information System Society
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• v.2 no.2 s.4
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• pp.59-74
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• 2000

Because efficiency of the 1st-phase NGIS investment(1995-2000) has not been clearly measured, the action taken in the 2nd-phase NGIS project requires NGIS budgets to be evaluated in terms of effectiveness. Especially, the effective investments in local governments are critical for the NGIS projects, because they execute the much larger amount of budgets in total than other GIS projects. As indicated, for the successful NGIS implementation, it is important to obtain continuous political and financial supports from decision makers. As a persuasion measure for the budget appropriation, CBA(Cost-Benefit Analysis) and CEA(Cost-Efficiency Analysis) can play an important role for the decision makers. The major concern of this paper is how to measure the costs and benefits of the GIS implementation by considering important characteristics of the GIS projects in local governments, and existing theories are reviewed for this concern. The GISs in local governments can have different stages in terms of its evolution and the effectiveness of the applications can be represented variously. To identify categories for measuring costs and benefits of the various GISs, case studies and success stories are reviewed from both the foreign and domestic research. The categories of costs and benefits are determined from the tangible and intangible aspects. The categories for the quantitative and qualitative measure are proposed to evaluate the GISs in local governments. After measuring costs and benefits, three key evaluation methods in cost-benefit analysis are suggested as follows: 1) the benefit: cost ratio (B/C), 2) Internal Rate of Return (IRR), and 3) the net present values (NPV) of the costs and benefits. The sensitivity and uncertainty analysis are also helpful to make a decision for the GIS budget appropriation in local governments. In conclusion, although cost-benefit analysis is not an easy undertaking, it is certain that it can play an important role in the future for the GIS funding decisions in local governments.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.155-169
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• 2014

With the rapid advances in technologies, organizations are more likely to depend on information systems in their decision-making processes. Business Intelligence (BI) systems, in particular, have become a mainstay in dealing with complex problems in an organization, partly because a variety of advanced computational methods from statistics, machine learning, and artificial intelligence can be applied to solve business problems such as demand forecasting. In addition to the ability to analyze past and present trends, these predictive analytics capabilities provide huge value to an organization's ability to respond to change in markets, business risks, and customer trends. While the performance effects of BI system use in organization settings have been studied, it has been little discussed on the use of predictive analytics technologies embedded in BI systems for forecasting tasks. Thus, this study aims to find important factors that can help to take advantage of the benefits of advanced technologies of a BI system. More generally, a BI system can be viewed as an advisor, defined as the one that formulates judgments or recommends alternatives and communicates these to the person in the role of the judge, and the information generated by the BI system as advice that a decision maker (judge) can follow. Thus, we refer to the findings from the advice-giving and advice-taking literature, focusing on the role of explanations of the system in users' advice taking. It has been shown that advice discounting could occur when an advisor's reasoning or evidence justifying the advisor's decision is not available. However, the majority of current BI systems merely provide a number, which may influence decision makers in accepting the advice and inferring the quality of advice. We in this study explore the following key factors that can influence users' advice taking within the setting of a BI system: explanations on how the box-office grosses are predicted, types of advisor, i.e., system (data mining technique) or human-based business advice mechanisms such as prediction markets (aggregated human advice) and human advisors (individual human expert advice), users' evaluations of the provided advice, and individual differences in decision-makers. Each subject performs the following four tasks, by going through a series of display screens on the computer. First, given the information of the given movie such as director and genre, the subjects are asked to predict the opening weekend box office of the movie. Second, in light of the information generated by an advisor, the subjects are asked to adjust their original predictions, if they desire to do so. Third, they are asked to evaluate the value of the given information (e.g., perceived usefulness, trust, satisfaction). Lastly, a short survey is conducted to identify individual differences that may affect advice-taking. The results from the experiment show that subjects are more likely to follow system-generated advice than human advice when the advice is provided with an explanation. When the subjects as system users think the information provided by the system is useful, they are also more likely to take the advice. In addition, individual differences affect advice-taking. The subjects with more expertise on advisors or that tend to agree with others adjust their predictions, following the advice. On the other hand, the subjects with more knowledge on movies are less affected by the advice and their final decisions are close to their original predictions. The advances in predictive analytics of a BI system demonstrate a great potential to support increasingly complex business decisions. This study shows how the designs of a BI system can play a role in influencing users' acceptance of the system-generated advice, and the findings provide valuable insights on how to leverage the advanced predictive analytics of the BI system in an organization's forecasting practices.

• Journal of the Korean Society of School Health
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• v.28 no.3
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• pp.139-149
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• 2015

Purpose: This study aims to analyze the factors affecting the agenda-setting process and the formation process of school-based mental health policies by applying a policy stream model. Methods: For this purpose, Kingdon's policy stream model was used as the analytical framework. Results: First, when establishing a school-based mental health policy, the agenda was set going through unpredictable and nonlinear changes. Second, for the school-based mental health policy to be selected onto the agenda and to be developed and implemented as an actual policy, the role of policy makers was considered most important in the process. Third, the policy window for school-based mental health policy was closed around the year 2013. Finally, an analysis of the school-based mental health policy stream identified two key features. One is that the school-based mental health policy first emerged when school violence prevention policy expanded its scope into relevant neighboring policies. The other is that the school-based mental health policy has taken shape through a linear decision-making process (being put on the government's agenda, searching for an alternative, selection, and implementation) during the policy implementation period after it has been selected as an alternative policy. Conclusion: Conclusions can be summed up as follows. The school-based mental health policy needs continuous development and improvement in case the window for the policy may open in the coming future. The government's support is needed to draw policy makers' interest and participation who play the biggest role in establishing policies.

• Journal of Information Technology Services
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• v.22 no.4
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• pp.123-140
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• 2023

In recent times, the global economy has been subject to increasing volatility, which has made it considerably more difficult to accurately predict economic indicators compared to previous periods. In response to this challenge, the present study conducts an exploratory investigation that aims to predict the Business Survey Index (BSI) by leveraging data mining techniques on both structured and unstructured data sources. For the structured data, we have collected information regarding foreign, domestic, and industrial conditions, while the unstructured data consists of content extracted from newspaper articles. By employing an extensive set of 44 distinct data mining techniques, our research strives to enhance the BSI prediction accuracy and provide valuable insights. The results of our analysis demonstrate that the highest predictive power was attained when using data exclusively from the t-1 period. Interestingly, this suggests that previous timeframes play a vital role in forecasting the BSI effectively. The findings of this study hold significant implications for economic decision-makers, as they will not only facilitate better-informed decisions but also serve as a robust foundation for predicting a wide range of other economic indicators. By improving the prediction of crucial economic metrics, this study ultimately aims to contribute to the overall efficacy of economic policy-making and decision processes.

• Smart Media Journal
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• v.2 no.2
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• pp.20-27
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• 2013

The concept of social media is top of the agenda for many business executives and decision makers, as well as consultants try to identify ways where companies can make profitable use of applications such as Netflix, Flixster. The social media is playing an increasingly important role as the information sources for customers making product choices etc. With the flourish of Web 2.0 technology, customer reviews are becoming more and more useful and important information resources for people to save their time and energy on purchasing products that they want. This paper proposes the Bayesian Probabilistic Classification algorithm to mine the social media review, and evaluates it by different splits and cross validation mechanism from the real data set. The explored study experimental results show the robustness and effectiveness of proposed approach for mining the social media review.