• Journal of Distribution Science
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• v.22 no.2
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• pp.71-82
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• 2024

Purpose: Despite significant research on decision-making, researchers struggle to comprehend the decision-making process. This paper aims to not only examine the relationship between problem-solving skills, cognitive competency, and decision-making but also develop measurement instruments for cognitive competency and problem-solving skills to better model decision-making. Research Design, Methodology and Approach: A cross-sectional study was conducted by surveying 292 university students in HCM City, Vietnam, via email sent randomly by Google Forms. This study identifies the conceptual framework and tests the hypotheses using a deductive approach. The SPSS program was used to evaluate the scales' reliability, and the SmartPLS program was used to assess the measurement and structural models. Results: The results show that the research model better modelled the relationship between problem-solving skills, cognitive competency, and decision-making. Although thinking ability has no direct impact on decision-making, both creativity and problem-solving skills have a positive impact on decision-making. The mediating role of problem-solving skills is also determined by the positive relationship between cognitive competency and decision-making. Conclusions: This study highlights decision-making efficiency through the cognitive process from low to high levels and provides for policymakers and managers to explain the decision-making process in a variety of sectors, such as distribution chains, marketing, and human resource distribution.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.231-252
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• 2021

Artificial intelligence (AI) is a key technology that will change the future the most. It affects the industry as a whole and daily life in various ways. As data availability increases, artificial intelligence finds an optimal solution and infers/predicts through self-learning. Research and investment related to automation that discovers and solves problems on its own are ongoing continuously. Automation of artificial intelligence has benefits such as cost reduction, minimization of human intervention and the difference of human capability. However, there are side effects, such as limiting the artificial intelligence's autonomy and erroneous results due to algorithmic bias. In the labor market, it raises the fear of job replacement. Prior studies on the utilization of artificial intelligence have shown that individuals do not necessarily use the information (or advice) it provides. Algorithm error is more sensitive than human error; so, people avoid algorithms after seeing errors, which is called "algorithm aversion." Recently, artificial intelligence has begun to be understood from the perspective of the augmentation of human intelligence. We have started to be interested in Human-AI collaboration rather than AI alone without human. A study of 1500 companies in various industries found that human-AI collaboration outperformed AI alone. In the medicine area, pathologist-deep learning collaboration dropped the pathologist cancer diagnosis error rate by 85%. Leading AI companies, such as IBM and Microsoft, are starting to adopt the direction of AI as augmented intelligence. Human-AI collaboration is emphasized in the decision-making process, because artificial intelligence is superior in analysis ability based on information. Intuition is a unique human capability so that human-AI collaboration can make optimal decisions. In an environment where change is getting faster and uncertainty increases, the need for artificial intelligence in decision-making will increase. In addition, active discussions are expected on approaches that utilize artificial intelligence for rational decision-making. This study investigates the impact of artificial intelligence on decision-making focuses on human-AI collaboration and the interaction between the decision maker personal traits and advisor type. The advisors were classified into three types: human, artificial intelligence, and human-AI collaboration. We investigated perceived usefulness of advice and the utilization of advice in decision making and whether the decision-maker's personal traits are influencing factors. Three hundred and eleven adult male and female experimenters conducted a task that predicts the age of faces in photos and the results showed that the advisor type does not directly affect the utilization of advice. The decision-maker utilizes it only when they believed advice can improve prediction performance. In the case of human-AI collaboration, decision-makers higher evaluated the perceived usefulness of advice, regardless of the decision maker's personal traits and the advice was more actively utilized. If the type of advisor was artificial intelligence alone, decision-makers who scored high in conscientiousness, high in extroversion, or low in neuroticism, high evaluated the perceived usefulness of the advice so they utilized advice actively. This study has academic significance in that it focuses on human-AI collaboration that the recent growing interest in artificial intelligence roles. It has expanded the relevant research area by considering the role of artificial intelligence as an advisor of decision-making and judgment research, and in aspects of practical significance, suggested views that companies should consider in order to enhance AI capability. To improve the effectiveness of AI-based systems, companies not only must introduce high-performance systems, but also need employees who properly understand digital information presented by AI, and can add non-digital information to make decisions. Moreover, to increase utilization in AI-based systems, task-oriented competencies, such as analytical skills and information technology capabilities, are important. in addition, it is expected that greater performance will be achieved if employee's personal traits are considered.

• Korean Journal of Construction Engineering and Management
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• v.9 no.2
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• pp.125-135
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• 2008

A controller who is responsible for visiter's safety makes a decision about measures for visiter safety in human-based decision making process. Many potential accidents that are caused by human error lurk in results of the process. The accidents can be decreased by changing the decision making process from human-based into technology-based. Technology-based decision making process can catch a controller's attention through data filtering, alarm filtering, and so on. So, the controller can get information on occurrence of an unforeseen accident pro-actively. The objective of this study is to suggest a concept of 3D spatial information control system for visitor flow control in multi complex building using technology-based decision making process. This study shows utilization of the system and contribution.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.27 no.2
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• pp.16-25
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• 2019

While various efforts are being made to ensure aviation safety, air accident rate induced by pilot human factors is still high worldwide. In particular, among pilot human factors, it would be the most important issue for pilots to anticipate and recognize flight environmental factors beyond their control and to make a positive decision making(ADM). In the Republic of Korea Air Force(ROKAF), there were many dizzying experiences induced by bird strike incidents and developed into dangerous moments such as damage to the aircraft and pilots' increased mental stress. It is a matter of serious concern in terms of safety management and human factors to dismiss bird strike incidents as inevitable misfortune due to environmental factors. In 2018, the ROKAF Aviation Safety Agency(ASA) conducted an experimental study to enhance pilots' ADM competence that can anticipate and avoid a bird strike. As the way of the study, 'Bird Strike Preventing Information' had been written and distributed every week by the ASA to flight units in the ROKAF during the period of the study. Through enhanced pilots' perceptual ADM competence, there was a noticeable number of reduction in bird strike incident compared to previous years of the experimental study.

• International Journal of Human Ecology
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• v.7 no.1
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• pp.53-65
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• 2006

The purpose of this study was to examine the differences in the decision making styles of Korean and American consumers. Focus group interviews were used as the median to collect information related to their methods of approach to a market to buy a universal need such as clothing for personal use. Findings suggest that within each construct, there may be different factors that should be used to measure the decision making styles of Korean and American consumers. It also implies that the previously established Consumer Decision making styles instrument may not be a reliable measure cross-culturally. Recommendations for future research are suggested.

• Journal of the Korean Operations Research and Management Science Society
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• v.37 no.4
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• pp.19-35
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• 2012

The Semantic Web technology, purporting to share, to reuse and to process by machines data stored in the Web environment, incessantly evolves to help human decision making; in particular, decision making based on data, or quantitative decision making. This trend drives researchers to fill the gap with strenuous efforts between the current state of the technology and the terminus of this evolution. The Semantic Web Constraint Language (SWCL) together with SWRL is one of these endeavors to achieve the goal. This paper focuses particularly on how to express the piecewise linear form in the context of SWCL. The importance of this ingredient can be fortified by the fact that any nonlinear expression can be approximated in the piecewise linear form. This paper will also provide the information of how it will work in the decision making process through an example of the Internet shopping mall problem.

• Journal of the Ergonomics Society of Korea
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• v.25 no.3
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• pp.7-16
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• 2006

Although several theories and models have been proposed to explain the effects of cultural differences in team decision making, many aspects of team decision-making in multi-cultural contexts such as team performance, team communication, and team cognition still remain unclear. In particular, little attention has paid to the empirical studies on team processes multi-cultural team members use to interact with each other to accomplish the task in different task environments. To investigate the effects of culture and task characteristics on team decision making behavior in multi-cultural contexts, this study compared culturally homogenous and heterogeneous groups in the context of logistics decision making. Results of the study showed that cultural difference and task complexity may affect team performance as well as team interaction process to varying degree.

• Proceedings of the IEEK Conference
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• 2001.06c
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• pp.237-240
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• 2001

An image processing and decision making method for the Automatic Surveillance System is proposed. The aim of our Automatic Surveillance System is to detect a moving object and make a decision on whether it is human or not. Various object features such as the ratio of the width and the length of the moving object, the distance dispersion between the principal axis and the object contour, the eigenvectors, the symmetric axes, and the areas if the segmented region are used in this paper. These features are not the unique and decisive characteristics for representing human Also, due to the outdoor image property, the object feature information is unavoidably vague and inaccurate. In order to make an efficient decision from the information, we use a fuzzy rules base system ai an approximate reasoning method. The fuzzy rules, combining various object features, are able to describe the conditions for making an intelligent decision. The fuzzy rule base system is initially constructed by heuristic approach and then, trained and tasted with input/output data Experimental result are shown, demonstrating the validity of our system.

• Industrial Engineering and Management Systems
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• v.11 no.1
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• pp.123-132
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• 2012

Recently, global environmental problems have become serious due to human economic activities. Therefore, in order to build a sustainable society in which human economic activities coexist with nature, it is important to promote and enhance environmental education. As a preceding study, a board and computer game of "Ecopoly," which is the game for education on global environmental problems, were developed. This study further aims to develop algorithms which make decisions in Ecopoly, to automate decision-makings of opponents using the algorithms, and to develop the environmental educational game "Ecopoly V" which enables self-learning. In order to develop the algorithms, the board game of Ecopoly was played, and each player's decision-makings at the all points at which players make a decision were observed and analyzed. From the analyses, it became clear that the decision-makings were distinguished by 3 characteristics; Ecology type, Economy type, and Balance type. Based on the characteristics, the factors and standard values of each decision-making were made clear. Algorithms were developed based on the factors and standard values. Ecopoly V was developed by incorporating the algorithms into the computer game of Ecopoly. Experimental testing of the game was conducted and the validity of the game was verified.

• Journal of Internet Computing and Services
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• v.21 no.1
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• pp.237-246
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• 2020

this paper, a data modeling method based on decision-making conditions is proposed for making combat and battlefield management systems to be intelligent, which are also a decision-making support system. A picture of a robot seeing and perceiving like humans and arriving a point it wanted can be understood and be felt in body. However, we can't find an example of implementing a decision-making which is the most important element in human cognitive action. Although the agent arrives at a designated office instead of human, it doesn't support a decision of whether raising the market price is appropriate or doing a counter-attack is smart. After we reviewed a current situation and problem in control & command of military, in order to collect a big data for making a machine staff's advice to be possible, we propose a data modeling prototype based on decision-making conditions as a method to change a current control & command system. In addition, a decision-making tree method is applied as an example of the decision making that the reformed control & command system equipped with the proposed data modeling will do. This paper can contribute in giving us an insight of how a future AI decision-making staff approaches to us.