• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.2
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• pp.28-41
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• 2023

The objective of the study was to develop an Urban Windway Forest Creation Planning Technique for the Improvement of the Urban Environment using the case of Daejeon Metropolitan City. Through a spatial analysis of fine dust and heat waves, a basin zone, in which the concentration was relatively serious, was derived, and an area with the potential of cold air flow was selected as the target area for the windway forest development by analyzing the climate and winds in the relevant zone. Extreme fine dust areas included the areas of the Daejeon Industrial Complex Regeneration Business District in Daedeok-gu and Daedeok Techno Valley in Yuseong-gu. Heat wave areas included the areas of Daedeok industrial Complex in Moksang-dong, the Daejeon Industrial Complex Regeneration Business District in Daehwa-dong, and the high-density residential area in Ojeong-dong. As a result of measuring the wind speeds in Daejeon with an Automatic Weather System, the average wind speeds during the day and night were 0.1 to 1.7 m/s,, respectively. So, a plan of for a windway forest that smoothly induces the movement of cold air formed in outer forests at night is required. The fine dust/heat wave intensive management zones of Daejeon Metropolitan City were Daejeoncheon, Yudeungcheon, Gapcheon-Yudeungcheon, and Gapcheon. The windway forest formation plan case involved the old city center of Daejeon Metropolitan City among the four zones, the Gapcheon-Yudeungcheon area, in which the windway formation effect was presumed to be high. The Gapcheon-Yudeungcheon area is a downtown area that benefits from the cold and fresh air generated on Mt. Gyejok and Mt. Wuseong, which are outer forests. Accordingly, the windway forest was planned to spread the cold air to the city center by connecting the cold air generated in the Seosa-myeon forest of Mt. Gyejok and the Namsa-myeon forest of Mt. Wuseong through Gapcheon, Yudeungcheon, and street forests. After selecting the target area for the wind ventilation forest, a climate map and wind formation function evaluation map were prepared for the area, the status of variation wind profiles (night), the status of fine dust generation, and the surface temperature distribution status were grasped in detail. The wind ventilation forest planning concept and detailed target sites by type were identified through this. In addition, a detailed action plan was established according to the direction of creation and setting of the direction of creation for each type of wind ventilation forest.

• Asia Marketing Journal
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• v.15 no.4
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• pp.61-101
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• 2014

This study investigates when and how disagreements in online customer ratings prompt more favorable product evaluations. Among the three metrics of volume, valence, and variance that feature in the research on online customer ratings, volume and valence have exhibited consistently positive patterns in their effects on product sales or evaluations (e.g., Dellarocas, Zhang, and Awad 2007; Liu 2006). Ratings variance, or the degree of disagreement among reviewers, however, has shown rather mixed results, with some studies reporting positive effects on product sales (e.g., Clement, Proppe, and Rott 2007) while others finding negative effects on product evaluations (e.g., Zhu and Zhang 2010). This study aims to resolve these contradictory findings by introducing preference heterogeneity as a possible moderator and causal attribution as a mediator to account for the moderating effect. The main proposition of this study is that when preference heterogeneity is perceived as high, a disagreement in ratings is attributed more to reviewers' different preferences than to unreliable product quality, which in turn prompts better quality evaluations of a product. Because disagreements mostly result from differences in reviewers' tastes or the low reliability of a product's quality (Mizerski 1982; Sen and Lerman 2007), a greater level of attribution to reviewer tastes can mitigate the negative effect of disagreement on product evaluations. Specifically, if consumers infer that reviewers' heterogeneous preferences result in subjectively different experiences and thereby highly diverse ratings, they would not disregard the overall quality of a product. However, if consumers infer that reviewers' preferences are quite homogeneous and thus the low reliability of the product quality contributes to such disagreements, they would discount the overall product quality. Therefore, consumers would respond more favorably to disagreements in ratings when preference heterogeneity is perceived as high rather than low. This study furthermore extends this prediction to the various levels of average ratings. The heuristicsystematic processing model so far indicates that the engagement in effortful systematic processing occurs only when sufficient motivation is present (Hann et al. 2007; Maheswaran and Chaiken 1991; Martin and Davies 1998). One of the key factors affecting this motivation is the aspiration level of the decision maker. Only under conditions that meet or exceed his aspiration level does he tend to engage in systematic processing (Patzelt and Shepherd 2008; Stephanous and Sage 1987). Therefore, systematic causal attribution processing regarding ratings variance is likely more activated when the average rating is high enough to meet the aspiration level than when it is too low to meet it. Considering that the interaction between ratings variance and preference heterogeneity occurs through the mediation of causal attribution, this greater activation of causal attribution in high versus low average ratings would lead to more pronounced interaction between ratings variance and preference heterogeneity in high versus low average ratings. Overall, this study proposes that the interaction between ratings variance and preference heterogeneity is more pronounced when the average rating is high as compared to when it is low. Two laboratory studies lend support to these predictions. Study 1 reveals that participants exposed to a high-preference heterogeneity book title (i.e., a novel) attributed disagreement in ratings more to reviewers' tastes, and thereby more favorably evaluated books with such ratings, compared to those exposed to a low-preference heterogeneity title (i.e., an English listening practice book). Study 2 then extended these findings to the various levels of average ratings and found that this greater preference for disagreement options under high preference heterogeneity is more pronounced when the average rating is high compared to when it is low. This study makes an important theoretical contribution to the online customer ratings literature by showing that preference heterogeneity serves as a key moderator of the effect of ratings variance on product evaluations and that causal attribution acts as a mediator of this moderation effect. A more comprehensive picture of the interplay among ratings variance, preference heterogeneity, and average ratings is also provided by revealing that the interaction between ratings variance and preference heterogeneity varies as a function of the average rating. In addition, this work provides some significant managerial implications for marketers in terms of how they manage word of mouth. Because a lack of consensus creates some uncertainty and anxiety over the given information, consumers experience a psychological burden regarding their choice of a product when ratings show disagreement. The results of this study offer a way to address this problem. By explicitly clarifying that there are many more differences in tastes among reviewers than expected, marketers can allow consumers to speculate that differing tastes of reviewers rather than an uncertain or poor product quality contribute to such conflicts in ratings. Thus, when fierce disagreements are observed in the WOM arena, marketers are advised to communicate to consumers that diverse, rather than uniform, tastes govern reviews and evaluations of products.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.1
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• pp.67-83
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• 2023

The social venture start-up phenomenon is found from the perspectives of social enterprise and for-profit enterprise. This study aims to fundamentally explore the start-up phenomenon of social ventures from these two perspectives. Considering the lack of prior research that researched both social and commercial perspectives at the same time, this paper analyzed using grounded theory approach of Strauss & Corbin(1998), an inductive research method that analyzes based on prior research and interview data. In order to collect data for this study, eight corporate representatives currently operating social ventures were interviewed and data and phenomena were analyzed. This progressed to a theoretical saturation where no additional information was derived. The analysis results of this study using the grounded theory approach are as follows. As a result of open coding and axial coding, 147 concepts and 70 subcategories were derived, and 18 categories were derived through the final abstraction process. In the selective coding, 'expansion of social venture entry in the social domain' and 'expansion of social function of for-profit companies' were selected as key categories, and a story line was formed around this. In this study, we saw that it is necessary to conduct academic research and analysis on the competitive factors required for companies that pursue the values of two conflicting relationships, such as social ventures, to survive with competitiveness. In practice, concepts such as collaboration with for-profit companies, value combination, entrepreneurship competency and performance improvement, social value execution competency reinforcement, communication strategy, for-profit enterprise value investment, and entrepreneur management competency were derived. This study explains the social venture phenomenon for social enterprises, commercial enterprises, and entrepreneurs who want to enter the social venture field. It is expected to provide the implications necessary for successful social venture startups.

• Journal of Technology Innovation
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• v.11 no.1
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• pp.147-176
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• 2003

This Paper Presents a new measurement technique to derive the level of BSRC (Basic Science and Research Capacity) index by use of the factor analysis which is extended with the assumption of the standard normal probability distribution of the selected explanatory variables. The new measurement method is used to forecast the gap of Korea's BSRC level compared with those of major OECD countries in terms of time lag and to make their international comparison during the time period of 1981∼1999, based on the assumption that the BSRC progress function of each country takes the form of the logistic curve. The US BSRC index is estimated to be 0.9878 in 1981, 0.9996 in 1990 and 0.99991 in 1999, taking the 1st place. The US BSRC level has been consistently the top among the 16 selected variables, followed by Japan, Germany, France and the United Kingdom, in order. Korea's BSRC is estimated to be 0.2293 in 1981, taking the lowest place among the 16 OECD countries. However, Korea's BSRC indices are estimated to have been increased to 0.3216 (in 1990) and 0.44652 (in 1999) respectively, taking 10th place. Meanwhile, Korea's BSRC level in 1999 (0.44652) is estimated to reach those of the US and Japan in 2233 and 2101, respectively. This means that Korea falls 234 years behind USA and 102 years behind Japan, respectively. Korea is also estimated to lag 34 years behind Germany, 16 years behind France and the UK, 15 years behind Sweden, 11 years behind Canada, 7 years behind Finland, and 5 years behind the Netherlands. For the period of 1981∼1999, the BSRC development speed of the US is estimated to be 0.29700. Its rank is the top among the selected OECD countries, followed by Japan (0.12800), Korea (0.04443), and Germany (0.04029). the US BSRC development speed (0.2970) is estimated to be 2.3 times higher than that of Japan (0.1280), and 6.7 times higher than that of Korea. German BSRC development speed (0.04029) is estimated to be fastest in Europe, but it is 7.4 times slower than that of the US. The estimated BSRC development speeds of Belgium, Finland, Italy, Denmark and the UK stand between 0.01 and 0.02, which are very slow. Particularly, the BSRC development speed of Spain is estimated to be minus 0.0065, staying at the almost same level of BSRC over time (1981 ∼ 1999). Since Korea shows BSRC development speed much slower than those of the US and Japan but relative]y faster than those of other countries, the gaps in BSRC level between Korea and the other countries may get considerably narrower or even Korea will surpass possibly several countries in BSRC level, as time goes by. Korea's BSRC level had taken 10th place till 1993. However, it is estimated to be 6th place in 2010 by catching up the UK, Sweden, Finland and Holland, and 4th place in 2020 by catching up France and Canada. The empirical results are consistent with OECD (2001a)'s computation that Korea had the highest R&D expenditures growth during 1991∼1999 among all OECD countries ; and the value-added of ICT industries in total business sectors value added is 12% in Korea, but only 8% in Japan. And OECD (2001b) observed that Korea, together with the US, Sweden, and Finland, are already the four most knowledge-based countries. Hence, the rank of the knowledge-based country was measured by investment in knowledge which is defined as public and private spending on higher education, expenditures on R&D and investment in software.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.187-204
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• 2016

Document classification based on emotional polarity has become a welcomed emerging task owing to the great explosion of data on the Web. In the big data age, there are too many information sources to refer to when making decisions. For example, when considering travel to a city, a person may search reviews from a search engine such as Google or social networking services (SNSs) such as blogs, Twitter, and Facebook. The emotional polarity of positive and negative reviews helps a user decide on whether or not to make a trip. Sentiment analysis of customer reviews has become an important research topic as datamining technology is widely accepted for text mining of the Web. Sentiment analysis has been used to classify documents through machine learning techniques, such as the decision tree, neural networks, and support vector machines (SVMs). is used to determine the attitude, position, and sensibility of people who write articles about various topics that are published on the Web. Regardless of the polarity of customer reviews, emotional reviews are very helpful materials for analyzing the opinions of customers through their reviews. Sentiment analysis helps with understanding what customers really want instantly through the help of automated text mining techniques. Sensitivity analysis utilizes text mining techniques on text on the Web to extract subjective information in the text for text analysis. Sensitivity analysis is utilized to determine the attitudes or positions of the person who wrote the article and presented their opinion about a particular topic. In this study, we developed a model that selects a hot topic from user posts at China's online stock forum by using the k-means algorithm and self-organizing map (SOM). In addition, we developed a detecting model to predict a hot topic by using machine learning techniques such as logit, the decision tree, and SVM. We employed sensitivity analysis to develop our model for the selection and detection of hot topics from China's online stock forum. The sensitivity analysis calculates a sentimental value from a document based on contrast and classification according to the polarity sentimental dictionary (positive or negative). The online stock forum was an attractive site because of its information about stock investment. Users post numerous texts about stock movement by analyzing the market according to government policy announcements, market reports, reports from research institutes on the economy, and even rumors. We divided the online forum's topics into 21 categories to utilize sentiment analysis. One hundred forty-four topics were selected among 21 categories at online forums about stock. The posts were crawled to build a positive and negative text database. We ultimately obtained 21,141 posts on 88 topics by preprocessing the text from March 2013 to February 2015. The interest index was defined to select the hot topics, and the k-means algorithm and SOM presented equivalent results with this data. We developed a decision tree model to detect hot topics with three algorithms: CHAID, CART, and C4.5. The results of CHAID were subpar compared to the others. We also employed SVM to detect the hot topics from negative data. The SVM models were trained with the radial basis function (RBF) kernel function by a grid search to detect the hot topics. The detection of hot topics by using sentiment analysis provides the latest trends and hot topics in the stock forum for investors so that they no longer need to search the vast amounts of information on the Web. Our proposed model is also helpful to rapidly determine customers' signals or attitudes towards government policy and firms' products and services.

• The Korean Journal of Financial Management
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• v.24 no.4
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• pp.75-106
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• 2007

In this study, it is analyzed whether oil price plays a major role in the pricing return on Koran stock market and examined why the covariance risk between oil and return on stock is different in each industry. Firstly, this study explores whether the expected rate of return on stock is pricing due to global oil price factors as a function of risk premium by using a two-factor APT. Also, it is examined whether spill-over effects of oil price volatility affect the beta risk to oil price. Considering the asymmetry of oil price volatility, we use the GJR model. As a result, it shows that oil price is an independent pricing factor and oil price volatility transmits to stock return in only electricity and electrical equipment. Secondly, the two step-analyzing process is introduced to find why the covariance between oil price factor and stock return is different in each industry. The first step is to study whether beta risk exists in each industry by using two proxy variables like size and liquidity as control variables. The second step is to grasp the systematic relationship between the difference of liquidity and size and beta to oil price factor by using the panel-data model which can be analyzed efficiently using the cross-sectional data formed with time series. Through the analysis, we can argue that oil price factor is an independent pricing factor in only electricity and electrical equipment having the greatest market capitalization, and know that beta risk to oil price factor is a proxy of size in the other industries. According to the result of panel-data model, it is argued that the beta to oil price factor augments when market capitalization increases and this fact supports the first assertion. In conclusion, the expected rate of return of electricity and electrical equipment works as a function of risk premium to market portfolio and oil price, and the reason to make beta risk power differentiated in each industry attributes to the size.

• 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.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.121-139
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• 2014

Predicting corporate failure has been an important topic in accounting and finance. The costs associated with bankruptcy are high, so the accuracy of bankruptcy prediction is greatly important for financial institutions. Lots of researchers have dealt with the topic associated with bankruptcy prediction in the past three decades. The current research attempts to use ensemble models for improving the performance of bankruptcy prediction. Ensemble classification is to combine individually trained classifiers in order to gain more accurate prediction than individual models. Ensemble techniques are shown to be very useful for improving the generalization ability of the classifier. Bagging is the most commonly used methods for constructing ensemble classifiers. In bagging, the different training data subsets are randomly drawn with replacement from the original training dataset. Base classifiers are trained on the different bootstrap samples. Instance selection is to select critical instances while deleting and removing irrelevant and harmful instances from the original set. Instance selection and bagging are quite well known in data mining. However, few studies have dealt with the integration of instance selection and bagging. This study proposes an improved bagging ensemble based on instance selection using genetic algorithms (GA) for improving the performance of SVM. GA is an efficient optimization procedure based on the theory of natural selection and evolution. GA uses the idea of survival of the fittest by progressively accepting better solutions to the problems. GA searches by maintaining a population of solutions from which better solutions are created rather than making incremental changes to a single solution to the problem. The initial solution population is generated randomly and evolves into the next generation by genetic operators such as selection, crossover and mutation. The solutions coded by strings are evaluated by the fitness function. The proposed model consists of two phases: GA based Instance Selection and Instance based Bagging. In the first phase, GA is used to select optimal instance subset that is used as input data of bagging model. In this study, the chromosome is encoded as a form of binary string for the instance subset. In this phase, the population size was set to 100 while maximum number of generations was set to 150. We set the crossover rate and mutation rate to 0.7 and 0.1 respectively. We used the prediction accuracy of model as the fitness function of GA. SVM model is trained on training data set using the selected instance subset. The prediction accuracy of SVM model over test data set is used as fitness value in order to avoid overfitting. In the second phase, we used the optimal instance subset selected in the first phase as input data of bagging model. We used SVM model as base classifier for bagging ensemble. The majority voting scheme was used as a combining method in this study. This study applies the proposed model to the bankruptcy prediction problem using a real data set from Korean companies. The research data used in this study contains 1832 externally non-audited firms which filed for bankruptcy (916 cases) and non-bankruptcy (916 cases). Financial ratios categorized as stability, profitability, growth, activity and cash flow were investigated through literature review and basic statistical methods and we selected 8 financial ratios as the final input variables. We separated the whole data into three subsets as training, test and validation data set. In this study, we compared the proposed model with several comparative models including the simple individual SVM model, the simple bagging model and the instance selection based SVM model. The McNemar tests were used to examine whether the proposed model significantly outperforms the other models. The experimental results show that the proposed model outperforms the other models.

• Environmental and Resource Economics Review
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• v.23 no.4
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• pp.719-746
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• 2014

This study attempts to estimate the value of the water quality improvement by deriving the equilibrium price of the water pollutant emission permit for the imaginary water pollutant emission trading market. It is reasonable to say that there is already an implicit social agreement for the unit value of water pollutant, when the government set the Total Water Pollutant Loading System for the major river basin as a part of the Comprehensive Measures for Water Management, particularly for the Nakdong River Basin. Therefore, we can derive the unit value of water pollutant emission, which is already implied in the pollution allowance for each city or county by the Total Water Pollutant Loading System. Once estimated, it will be useful to the economic assessment of the water quality related projects. An imaginary water pollutant emission trading system for the Nakdong River Basin, where Total Water Pollutant Loading System is already effective, is constructed for the estimation of the equilibrium price of water pollutant permit. By estimating marginal abatement cost curve or each city or county, we can compute the equilibrium price of the permit and then it is regarded as the economic value of the water pollutant. The marginal net benefit function results from the relationship between the emission and the benefit, and then the equilibrium price of permit comes from constructing the excess demand function of the permit by using the total allowable permit of the local government entity. The equilibrium price of the permit would be estimated to be $1,409.3won/kg{\cdot}BOD$. This is within reasonable boundary compared for the permit price compared to foreign example. This permit price would be applied to calculate for the economic value of the water quality pollutants, and also be expected to use directly for the B/C analysis of the business involved with water quality change.

• Journal of Korean Public Health Nursing
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• v.4 no.1
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• pp.37-55
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• 1990

Every country in the world has been trying to expand the basic health right for the peoples as W.H.O has established the goal 'health for AU' by the year of 2000. Related to this goal, our government authority has establish the policy 'the insurance of health for all' into effect from July 198\). Recently hospitalshave been making a ceaseless effort for the plan for the rationalization of its management the academic World is making it a subject of discussion by doing the secure of manpower at a resonable level and the increase of productivity by the manpower. As a result of the efforts the study was established to secure the numbers of nursing manpower at a resonable level and use the unskilled persons at the utilizing field and seek the possible area of their activity for more efficient service through the investigation of ablity of simple nursing activities of regiestered nurses and nurse aides for rational function according to the educational levels and talents. The method of study was established by the registered nurses and nurse and nurse aides(R.N 229, N.A 226) who are working in 15 hospitals with over 200 beds. This surrey was conducted from Mar 29, 1989 to April 8, 1989. The method to test the degree of importance, difficulties, and the abillity of performance of a simple nursing activities was classified into 35 activities on the basis of references on this field. The degree of importance was composed from point l(Not so important) to 5(Very important). the degree of difficulties. was composed as follows; very easy - Point 1 very difficult and complicated - Point 5. and the ability of performance was composed from point 1 to 5. The materials gathered through the survey were analyzed with frequency, mean standard deviation, percentage. t-test, Anova, pearson's coefficient of correlation, stepwise multiple regression. factor analysis, discriminant analysis. The obtained results are summarized as follows: 1. The recognition values of the simple nursing activities of each group of registered nurse and nurse aides show; The degree of importance; 4.04 and 4.26 The degree of difficulties; 2.72 and 2.94 The ability of performance; 2.07 and 2.38 The brief summary shows there are little differences between who two groups the simple nursing activities turned out to be easy and simple work. 2. Regardless of the degree of importance, and difficulties, the ability of performance the important in fluencing of the degree of the simple nursing activities between the registered nurses and nurse aides was the order of educational level, hospital career, working career in wards and ages of the registered nurses and ages and hospital creer of nurse aides. The result was that the simple nursing activities could easily be familiar through the training of their working environment and period of experience. 3. Among the 35 simple nursing activities the items capable of resonable entrusting to the nurse aides are 5 that is helping bed-bathing, 8itz Bath, using bed pan, care while delivering patient, accompaying patient when visitor's check. There wasn't and differences between RN and nurse aides in performing the above 5 items. In anywhere. so we can say obviosuly that this nursing activities should be performed under the nursing system of which chief of nurse are supposed to supervise nurse aides as a possible function to be entrusted. In view of the above mentioned results, therefore, this partial functional job of the simple nursing activities can able be entrusted to the nurse aides through the regular training course. In case of these functional activities could be entrusted under, the responsibility of registered nurse, we can able suggest to for that there are the following advantages: 1.. In the nursing activities-affairs, the qualified guarantee of the nursing services can be kept and increased or promotoed with accommodation of the required nursing service and roles being expanded presently. 2. In the productivity of the hospital manpower, therefore, we have comt to view and consider in favourly that when an automational administration times would be come in the near future time to hospital affairs as a reality, to utilize the existing nures aides is better rather than investing so as to develop the other source manpowers or seek its for the efficient business management in the operational strategy or its policy.