• 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.

• Korea Journal of Hospital Management
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• v.9 no.1
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• pp.66-94
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• 2004

The objective of this study is to examine how to maximize the efficiency of hospital management by minimizing the unit cost of hospital operation. For this purpose, this paper proposes to develop a model of the profit maximization based on the cost minimization dictum using the statistical tools of arriving at the maximum likelihood values. The preliminary survey data are collected from the annual statistics and their analyses published by Korea Health Industry Development Institute and Korean Hospital Association. The maximum likelihood value statistical analyses are conducted from the information on the cost (function) of each of 36 hospitals selected by the random stratified sampling method according to the size and location (urban or rural) of hospitals. We believe that, although the size of sample is relatively small, because of the sampling method used and the high response rate, the power of estimation of the results of the statistical analyses of the sample hospitals is acceptable. The conceptual framework of analyses is adopted from the various models of the determinants of hospital costs used by the previous studies. According to this framework, the study postulates that the unit cost of hospital operation is determined by the size, scope of service, technology (production function) as measured by capacity utilization, labor capital ratio and labor input-mix variables, and by exogeneous variables. The variables to represent the above cost determinants are selected by using the step-wise regression so that only the statistically significant variables may be utilized in analyzing how these variables impact on the hospital unit cost. The results of the analyses show that the models of hospital cost determinants adopted are well chosen. The various models analyzed have the (goodness of fit) overall determination (R2) which all turned out to be significant, regardless of the variables put in to represent the cost determinants. Specifically, the size and scope of service, no matter how it is measured, i. e., number of admissions per bed, number of ambulatory visits per bed, adjusted inpatient days and adjusted outpatients, have overall effects of reducing the hospital unit costs as measured by the cost per admission, per inpatient day, or office visit implying the existence of the economy of scale in the hospital operation. Thirdly, the technology used in operating a hospital has turned out to have its ramifications on the hospital unit cost similar to those postulated in the static theory of the firm. For example, the capacity utilization as represented by the inpatient days per employee tuned out to have statistically significant negative impacts on the unit cost of hospital operation, while payroll expenses per inpatient cost has a positive effect. The input-mix of hospital operation, as represented by the ratio of the number of doctor, nurse or medical staff per general employee, supports the known thesis that the specialized manpower costs more than the general employees. The labor/capital ratio as represented by the employees per 100 beds is shown to have a positive effect on the cost as expected. As for the exogeneous variable's impacts on the cost, when this variable is represented by the percent of urban 100 population at the location where the hospital is located, the regression analysis shows that the hospitals located in the urban area have a higher cost than those in the rural area. Finally, the case study of the sample hospitals offers a specific information to hospital administrators about how they share in terms of the cost they are incurring in comparison to other hospitals. For example, if his/her hospital is of small size and located in a city, he/she can compare the various costs of his/her hospital operation with those of other similar hospitals. Therefore, he/she may be able to find the reasons why the cost of his/her hospital operation has a higher or lower cost than other similar hospitals in what factors of the hospital cost determinants.

• Korean Journal of Soil Science and Fertilizer
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• v.11 no.4
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• pp.213-233
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• 1979

It is generally agreed that the country's population will grow up to the level of 52 million by the year of 2000 and that due the active growth of industry, urbanization and road constructions, sizable portion of existing arable lands will be utilized for other purposes than agriculture in near future. From 1966 to 1977, it was estimated that, the average annual conversion of arable lands to other uses, was 12,909 ha. If this trend persists, it is predicted that from 1978 to 1991 when the 6th Five Years Economic Development Plan will terminate, approximately 181,000 ha of arable lands will be converted for other uses again. On the other hand, it is certain that the increased population (39 million in 1981, 45 million in 1991, 52 million in 2001) and the changes in food pattern along with the enhancement of living standards will bring about the phenomenal increase in demands for not only the staple food but also the livestock products such as meat, milk and eggs, vegetables and fruits. These future increased demands for various foods, naturally mean the increased needs for the expansion of arable lands at the same time. It is predicted that, if more activities than present scale are not taken for the expansion of arable lands, the national food self sufficiency level will drop from 79% in 1977 down to 62% in 1991. To meet the increased food demands in future, there are several ways and means. These will include the increased land use intensity, elevation of unit area yield levels, minimization of conversion of arable lands to other uses and expansion of arable lands through the reclamations of idle hillside lands and tidal lands. Among these, the expansion of arable lands through reclamations of idle hillside lands and tidal lands are more positive measures to cope with the increased production of foods in future. The reclamation of hillside lands demands more attention because it needs more advanced technologies in agronomical and engineering aspects, larger scale fundings and integrated socioeconomic considerations. In agromical aspects, the thechniques for early improvement of chemical and physical properties of soils, proper soil conservation measures and rational cropping systems are of particular importance. As to the financial supports to encourage the farmings in hillside land, much bold fund inputs are essential for the construction of roads, installation of irrigation and drainage facilities, soil conservation mechanisms, which will ensure the stabilized farming with reasonable incomes in the newly reclaimed lands.