• Journal of the Korean Institute of Landscape Architecture
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• v.48 no.5
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• pp.80-88
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• 2020

With the issuance of one-week fine dust emergency reduction measures in March 2019, the public's anxiety about fine dust is increasingly growing. In order to assess the application of air purifying plant-based bio-filters to public facilities, this study presented a method for measuring pollutant reduction effects by creating an indoor environment for continuous discharge of particle pollutants and conducted basic studies to verify whether indoor air quality has improved through the system. In this study conducted in a lecture room in spring, the background concentration was created by using mosquito repellent incense as a pollutant one hour before monitoring. Then, according to the schedule, the fine dust reduction capacity was monitored by irrigating for two hours and venting air for one hour. PM₁₀, PM_2.5, and temperature & humidity sensors were installed two meters front of the bio-filters, and velocity probes were installed at the center of the three air vents to conduct time-series monitoring. The average face velocity of three air vents set up in the bio-filter was 0.38±0.16 m/s. Total air-conditioning air volume was calculated at 776.89±320.16㎥/h by applying an air vent area of 0.29m×0.65m after deducing damper area. With the system in operation, average temperature and average relative humidity were maintained at 21.5-22.3℃, and 63.79-73.6%, respectively, which indicates that it satisfies temperature and humidity range of various conditions of preceding studies. When the effects of raising relatively humidity rapidly by operating system's air-conditioning function are used efficiently, it would be possible to reduce indoor fine dust and maintain appropriate relative humidity seasonally. Concentration of fine dust increased the same in all cycles before operating the bio-filter system. After operating the system, in cycle 1 blast section (C-1, β=-3.83, β=-2.45), particulate matters (PM₁₀) were lowered by up to 28.8% or 560.3㎍/㎥ and fine particulate matters (PM_2.5) were reduced by up to 28.0% or 350.0㎍/㎥. Then, the concentration of find dust (PM₁₀, PM_2.5) was reduced by up to 32.6% or 647.0㎍/㎥ and 32.4% or 401.3㎍/㎥ respectively through reduction in cycle 2 blast section (C-2, β=-5.50, β=-3.30) and up to 30.8% or 732.7㎍/㎥ and 31.0% or 459.3㎍/㎥ respectively through reduction in cycle 3 blast section (C-3, β=5.48, β=-3.51). By referring to standards and regulations related to the installation of vegetation bio-filters in public facilities, this study provided plans on how to set up objective performance evaluation environment. By doing so, it was possible to create monitoring infrastructure more objective than a regular lecture room environment and secure relatively reliable data.

• Korean Journal of Soil Science and Fertilizer
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• v.23 no.2
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• pp.87-93
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• 1990

A statistical model to predict soil temperature from the ambient meteorological factors including mean, maximum and minimum air temperatures, precipitation, wind speed and snow depth combined with Fourier time series expansion was developed with the data measured at the Suwon Meteorolical Service from 1979 to 1988. The stepwise elimination technique was used for statistical analysis. For the yearly oscillation model for soil temperature with 8 terms of Fourier expansion, the mean square error was decreased with soil depth showing 2.30 for the surface temperature, and 1.34-0.42 for 5 to 500-cm soil temperatures. The $r^2$ ranged from 0.913 to 0.988. The number of lag days of air temperature by remainder analysis was 0 day for the soil surface temperature, -1 day for 5 to 30-cm soil temperature, and -2 days for 50-cm soil temperature. The number of lag days for precipitaion, snow depth and wind speed was -1 day for the 0 to 10-cm soil temperatures, and -2 to -3 days for the 30 to 50-cm soil teperatures. For the statistical soil temperature prediction model combined with the yearly oscillation terms and meteorological factors as remainder terms considering the lag days obtained above, the mean square error was 1.64 for the soil surfac temperature, and ranged 1.34-0.42 for 5 to 500cm soil temperatures. The model test with 1978 data independent to model development resulted in good agreement with $r^2$ ranged 0.976 to 0.996. The magnitudes of coeffcicients implied that the soil depth where daily meteorological variables night affect soil temperature was 30 to 50 cm. In the models, solar radiation was not included as a independent variable ; however, in a seperated analysis on relationship between the difference(${\Delta}Tmxs$) of the maximum soil temperature and the maximum air temperature and solar radiation(Rs ; $J\;m^{-2}$) under a corn canopy showed linear relationship as $${\Delta}Tmxs=0.902+1.924{\times}10^{-3}$$ Rs for leaf area index lower than 2 $${\Delta}Tmxs=0.274+8.881{\times}10^{-4}$$ Rs for leaf area index higher than 2.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.69-92
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• 2015

The explosion of social media data has led to apply text-mining techniques to analyze big social media data in a more rigorous manner. Even if social media text analysis algorithms were improved, previous approaches to social media text analysis have some limitations. In the field of sentiment analysis of social media written in Korean, there are two typical approaches. One is the linguistic approach using machine learning, which is the most common approach. Some studies have been conducted by adding grammatical factors to feature sets for training classification model. The other approach adopts the semantic analysis method to sentiment analysis, but this approach is mainly applied to English texts. To overcome these limitations, this study applies the Word2Vec algorithm which is an extension of the neural network algorithms to deal with more extensive semantic features that were underestimated in existing sentiment analysis. The result from adopting the Word2Vec algorithm is compared to the result from co-occurrence analysis to identify the difference between two approaches. The results show that the distribution related word extracted by Word2Vec algorithm in that the words represent some emotion about the keyword used are three times more than extracted by co-occurrence analysis. The reason of the difference between two results comes from Word2Vec's semantic features vectorization. Therefore, it is possible to say that Word2Vec algorithm is able to catch the hidden related words which have not been found in traditional analysis. In addition, Part Of Speech (POS) tagging for Korean is used to detect adjective as "emotional word" in Korean. In addition, the emotion words extracted from the text are converted into word vector by the Word2Vec algorithm to find related words. Among these related words, noun words are selected because each word of them would have causal relationship with "emotional word" in the sentence. The process of extracting these trigger factor of emotional word is named "Emotion Trigger" in this study. As a case study, the datasets used in the study are collected by searching using three keywords: professor, prosecutor, and doctor in that these keywords contain rich public emotion and opinion. Advanced data collecting was conducted to select secondary keywords for data gathering. The secondary keywords for each keyword used to gather the data to be used in actual analysis are followed: Professor (sexual assault, misappropriation of research money, recruitment irregularities, polifessor), Doctor (Shin hae-chul sky hospital, drinking and plastic surgery, rebate) Prosecutor (lewd behavior, sponsor). The size of the text data is about to 100,000(Professor: 25720, Doctor: 35110, Prosecutor: 43225) and the data are gathered from news, blog, and twitter to reflect various level of public emotion into text data analysis. As a visualization method, Gephi (http://gephi.github.io) was used and every program used in text processing and analysis are java coding. The contributions of this study are as follows: First, different approaches for sentiment analysis are integrated to overcome the limitations of existing approaches. Secondly, finding Emotion Trigger can detect the hidden connections to public emotion which existing method cannot detect. Finally, the approach used in this study could be generalized regardless of types of text data. The limitation of this study is that it is hard to say the word extracted by Emotion Trigger processing has significantly causal relationship with emotional word in a sentence. The future study will be conducted to clarify the causal relationship between emotional words and the words extracted by Emotion Trigger by comparing with the relationships manually tagged. Furthermore, the text data used in Emotion Trigger are twitter, so the data have a number of distinct features which we did not deal with in this study. These features will be considered in further study.

• Journal of the Korean Institute of Landscape Architecture
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• v.43 no.6
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• pp.73-85
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• 2015

This study investigated the extent and magnitude of the woodland edge effects on users' thermal environments according to distance from woodland border. A series of experiments to measure air temperature, relative humidity, wind velocity, MRT and UTCI were conducted over six days between July 31 and August 5, 2015, which corresponded with extremely hot weather, at the south-facing edge of Hadongsongrim(pure Pinus densiflora stands, tree age: $100{\pm}33yr$, tree height: $12.8{\pm}2.7m$, canopy closure: 75%, N $35^{\circ}03^{\prime}34.7^{{\prime}{\prime}}$, E $127^{\circ}44^{\prime}43.3^{{\prime}{\prime}}$, elevation 7~10m) and east-facing edge of Hamyangsangrim (Quercus serrata-Carpinus tschonoskii community, tree age: 102~125yr/58~123yr, tree height: tree layer $18.6{\pm}2.3m/subtree$ layer $5.9{\pm}3.2m/shrub$ layer $0.5{\pm}0.5m$, herbaceous layer coverage ratio 60%, canopy closure: 96%, N $35^{\circ}31^{\prime}28.1^{{\prime}{\prime}}$, E $127^{\circ}43^{\prime}09.8^{{\prime}{\prime}}$, elevation 170~180m) in rural villages of Hadong and Hamyang, Korea. The minus result value of depth means woodland's outside. The depth of edge influence(DEI) on the maximum air temperature, minimum relative humidity and wind speed at maximum air temperature time during the daytime(10:00~17:00) were detected to be $12.7{\pm}4.9$, $15.8{\pm}9.8$ and $23.8{\pm}26.2m$, respectively, in the mature evergreen conifer woodland of Hadongsongrim. These were detected to be $3.7{\pm}2.2$, $4.9{\pm}4.4$ and $2.6{\pm}7.8m$, respectively, in the deciduous broadleaf woodland of Hamyansangrim. The DEI on the maximum 10 minutes average MRT, UTCI from the three-dimensional environment absorbed by the human-biometeorological reference person during the daytime(10:00~17:00) were detected to be $7.1{\pm}1.7$ and $4.3{\pm}4.6m$, respectively, in the relatively sparse woodland of Hadongsongrim. These were detected to be $5.8{\pm}4.9$ and $3.5{\pm}4.1m$, respectively, in the dense and closed woodland of Hadongsongrim. Edge effects on the thermal environments of air temperature, relative humidity, wind speed, MRT and UTCI in the sparse woodland of Hadongsongrim were less pronounced than those recorded in densed and closed woodland of Hamyansangrim. The gradient variation was less steep for maximum 10 minutes average UTCI with at least $4.3{\pm}4.6m$(Hadongsongrim) and $3.5{\pm}4.1m$(Hamyansangrim) being required to stabilize the UTCI at mature woodlands. Therefore it is suggested that the woodlands buffer widths based on the UTCI values should be 3.5~7.6 m(Hamyansangrim) and 4.3~8.9(Hadongsongrim) m on each side of mature woodlands for users' thermal comfort environments. The woodland edge structure should be multi-layered canopies and closed edge for the buffer effect of woodland edge on woodland users' thermal comfort.

• Journal of Preventive Medicine and Public Health
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• v.24 no.3 s.35
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• pp.441-458
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• 1991

The objective of this study was to provide basic reference data for stabilization scheme of medical insurance benefits through forecasting of the medical care utilization rate, the medical expence per case, and the treatment days per case in medical insurance program for government employees & private school teachers and for industrial workers. For the achievement of above objective, this study was carried out by Box-Jenkins time series analysis (ARIMA Model), using monthly statistical data from Jan. 1979 to Dec. 1989, of medical insurance program for government employees & private school teachers and for industrial workers. The results are as follows ; ARIMA model of the medical care utilization rate in medical insurance program for government employees & private school teachers was ARIMA (1, 1, 1) and it for outpatient in medical insurance program for industrial workers was ARIMA (1, 1, 1), while it for inpatient in medical insurance program for industrial workers was ARIMA (1, 0, 1). ARIMA model of the medical expense per case in medical insurance program for government employees & private school teachers and for outpatient in medical insurance program for industrial workers were ARIMA (1, 1, 0), while it for inpatient in medical insurance program for industrial workers was ARIMA (1, 0, 1). ARIMA model of the treatment days per case of both medical insurance program for government employees & private school teachers and industrial workers were ARIMA (1, 1, 1). Forecasting value of the medical care utilzation rate for inpatient in medical insurance program for government employees & private school teachers was 0.0061 at dec. 1989, 0.0066 at dec. 1994 and it for outpatient was 0.280 at dec. 1989, 0.294 at dec. 1994, while it for inpatient in medical insurance program for industrial workers was 0.0052 at dec. 1989, 0.0056 at dec. 1994 and it for outpatient was 0.203 at dec. 1989, 0.215 at 1994. Forecasting value of the medical expense per case for inpatient in medical insurance program for government employees & private school teachers was 332,751 at dec. 1989, 354,511 at dec. 1994 and it for outpatient was 11,925 at dec. 1989, 12,904 at dec. 1994, while it for inpatient in medical insurance program for industrial workers was 281,835 at dec. 1989, 293,973 at dec. 1994 and it for outpatient was 11,599 at dec. 1989, 11,585 at 1994. Forecasting value of the treatment days per case for inpatient in medical insurance program for government employees & private school teachers was 13.79 at dec. 1989,13.85 at an. 1994 and in for outpatient was 5.03 at dec. 1989, 5.00 at dec. 1994, while it for inpatient in medical insurance program for industrial workers was 12.23 at dec. 1989, 12.85 at dec. 1994 and it for outpatient was 4.61 at dec. 1989, 4.60 at 1994.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.127-146
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• 2017

Services using artificial intelligence have begun to emerge in daily life. Artificial intelligence is applied to products in consumer electronics and communications such as artificial intelligence refrigerators and speakers. In the financial sector, using Kensho's artificial intelligence technology, the process of the stock trading system in Goldman Sachs was improved. For example, two stock traders could handle the work of 600 stock traders and the analytical work for 15 people for 4weeks could be processed in 5 minutes. Especially, big data analysis through machine learning among artificial intelligence fields is actively applied throughout the financial industry. The stock market analysis and investment modeling through machine learning theory are also actively studied. The limits of linearity problem existing in financial time series studies are overcome by using machine learning theory such as artificial intelligence prediction model. The study of quantitative financial data based on the past stock market-related numerical data is widely performed using artificial intelligence to forecast future movements of stock price or indices. Various other studies have been conducted to predict the future direction of the market or the stock price of companies by learning based on a large amount of text data such as various news and comments related to the stock market. Investing on commodity asset, one of alternative assets, is usually used for enhancing the stability and safety of traditional stock and bond asset portfolio. There are relatively few researches on the investment model about commodity asset than mainstream assets like equity and bond. Recently machine learning techniques are widely applied on financial world, especially on stock and bond investment model and it makes better trading model on this field and makes the change on the whole financial area. In this study we made investment model using Support Vector Machine among the machine learning models. There are some researches on commodity asset focusing on the price prediction of the specific commodity but it is hard to find the researches about investment model of commodity as asset allocation using machine learning model. We propose a method of forecasting four major commodity indices, portfolio made of commodity futures, and individual commodity futures, using SVM model. The four major commodity indices are Goldman Sachs Commodity Index(GSCI), Dow Jones UBS Commodity Index(DJUI), Thomson Reuters/Core Commodity CRB Index(TRCI), and Rogers International Commodity Index(RI). We selected each two individual futures among three sectors as energy, agriculture, and metals that are actively traded on CME market and have enough liquidity. They are Crude Oil, Natural Gas, Corn, Wheat, Gold and Silver Futures. We made the equally weighted portfolio with six commodity futures for comparing with other commodity indices. We set the 19 macroeconomic indicators including stock market indices, exports & imports trade data, labor market data, and composite leading indicators as the input data of the model because commodity asset is very closely related with the macroeconomic activities. They are 14 US economic indicators, two Chinese economic indicators and two Korean economic indicators. Data period is from January 1990 to May 2017. We set the former 195 monthly data as training data and the latter 125 monthly data as test data. In this study, we verified that the performance of the equally weighted commodity futures portfolio rebalanced by the SVM model is better than that of other commodity indices. The prediction accuracy of the model for the commodity indices does not exceed 50% regardless of the SVM kernel function. On the other hand, the prediction accuracy of equally weighted commodity futures portfolio is 53%. The prediction accuracy of the individual commodity futures model is better than that of commodity indices model especially in agriculture and metal sectors. The individual commodity futures portfolio excluding the energy sector has outperformed the three sectors covered by individual commodity futures portfolio. In order to verify the validity of the model, it is judged that the analysis results should be similar despite variations in data period. So we also examined the odd numbered year data as training data and the even numbered year data as test data and we confirmed that the analysis results are similar. As a result, when we allocate commodity assets to traditional portfolio composed of stock, bond, and cash, we can get more effective investment performance not by investing commodity indices but by investing commodity futures. Especially we can get better performance by rebalanced commodity futures portfolio designed by SVM model.

• The Journal of Dong Guk Oriental Medicine
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• v.8 no.2
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• pp.115-129
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• 2000

The cause that the increase of animality fat intakes, under exercise, fatness, adding the stress, advanced age etc., the occurrence rate of the circulation system disease has been increased. And the thrombosis importantly came to the front as the risk factor of these circulation system's disease. Nowadays, the ischemic disease has especially discussed, for example the angina or myocardial infarction, originated in thrombosis that came from the platelet aggregation. In the western medicine, as the cure and prevention, using the aspirin or ticlopidine for platelet aggregation suppressant. But in the , the curing method must be used properly according to the pectoralgia or heartache's kind, state, grade. The platelet do not attache to the normal hemangioendothelial cell. But when it stimulated by endothelium peronia and so on, it attache to the injury endothelium or rise aggregation between the platelet. On this time, it secrete the platelet aggregation inducer as like ADP, thromboxane A2 from the inside of platelet. So it has first defensive function through the aggregation augment that prevent the celerity consumption of blood. But the activation of abnormal platelet occur the platelet grume and thrombogenesis. So it bring up the occlusive angiosis, so to speak, cardiovascular disease, cerebrovascular disease, arterial sclerosis. In oriental medicine, the thrombosis in the category of blood stasis and this blood stasis present the generalise or local blood circulation disturbance that generated by all kinds of pathological fact or blood stream retention accompanying with a series of syndrome. As the syndrome, stabbing pain fixed at certain region, squamous and dry skin, fullness and pain of the chest and hypochondrium, firmness and fullness of the lower abdomen, black stool, dark purple tongue or with ecchymoses and petechiae etc.. has been created. And it becomes the pathopoiesis cause that the convulsion and palpitation, severe palpitatiion, tympanites, the symtom complex with a mass or swelling in the abdomen, insanity, stricken by wind etc.. Moreover, it used the drugs for invigorating blood circulation and eliminating blood stasis or drugs for removing blood stasis for all kinds of syndrome through the blood stasis. And the drugs for activating the blood circulation, such as Salviae Radix, Angelicae Sinensis Radix, Persicae Semen, Achyranthis Radix, Cnidii Rhizoma, Carthami Flos are used for that. And it is used to the herbs of insects that has strong effect about the disintergrating blood stasis such as Hirudo, Scolopendrae Corpus, Buthus, Lumbricus etc.. On this study, It used Geijibokryunghwan(GBH) and the consisting herbs to investigate the influence of platelet aggregation about drugs that used to improvement various symptoms created by the thrombosis in oriental medicine. GBH formula has as formula recorded in the , action of 'eleminating the evil and not impairment of healthy energy' and 'promoting the flow of QI and cold and heat, so used for the expel blood stasis herbs from the ancient. Therefore we investigated the restraint effect of GBH and the consisting herbs about the platelet agregation induced to the ADP, AA or collagen. The conclusion is following. 1. When it added the aggregation inducer after that it added GBH and individual consisting herbs in the PRP, GBH showed the (+) inhibition effect on the platelet aggregation and it showed the (+) inhibition effect in the individual consisting herbs as like Paeoniae Radix and Moutan Cortex Radicis. 2. It showed the (+), (+,++) inhibition effect on the platelet aggregation in Paeoniae Radix Hoelen, Paeoniae Radix Moutan Cortex Radicis, Hoelen Moutan Cortex Radicis etc. 3. In the aggregation inhibition activating on the difference of density, GBH showed strong inhibition effect to the aggregation state induced to collagen, and it showed the inhibition effect in the individual consisting herbs as like Paeoniae Radix and Moutan Cortex Radicis about the aggregation induced by the collagen. 4. It showed the strong inhibition effect about the aggregation induced by the collagen in Paeoniae Radix Hoelen, Paeoniae Radix Moutan Cortex Radicis, Hoelen Moutan Cortex Radicis etc Like this, as confirm GBH and the individual consisting herb's inhibition effect of platelet aggregation, We considerated that GBH and the individual consisting herbs have practical applicational value of clinical trial in the thrombosis caused by platelet aggregation.

• Radiation Oncology Journal
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• v.26 no.4
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• pp.195-203
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• 2008

Purpose: To investigate the treatment outcome and failure patterns after definitive chemoradiation therapy in locally advanced, unresectable esophageal cancer. Materials and Methods: From February 1994 to December 2002, 168 patients with locally advanced unresectable or medically inoperable esophageal cancer were treated by definitive chemoradiation therapy. External beam radiation therapy (EBRT) ($42{\sim}46\;Gy$) was delivered to the region encompassing the primary tumor and involved lymph nodes, while the supraclavicular fossa and celiac area were included in the treatment area as a function of disease location. The administered cone-down radiation dose to the gross tumor went up to $54{\sim}66\;Gy$, while the fraction size of the EBRT was 1.8-2.0 Gy/fraction qd or 1.2 Gy/fraction bid. An optional high dose rate (HDR) intraluminal brachytherapy (BT) boost was also administered (Ir-192, $9{\sim}12\;Gy/3{\sim}4\;fx$). Two cycles of concurrent FP chemotherapy (5-FU $1,000\;mg/m^2$/day, days $2{\sim}6$, $30{\sim}34$, cisplatin $60\;mg/m^2$/day, days 1, 29) were delivered during radiotherapy with the addition of two more cycles. Results: One hundred sixty patients were analyzable for this review [median follow-up time: 10 months (range $1{\sim}149$ months)). The number of patients within AJCC stages I, II, III, and IV was 5 (3.1%), 38 (23.8%), 68 (42.5%), and 49 (30.6%), respectively. A HDR intraluminal BT was performed in 26 patients. The 160 patients had a median EBRT radiation dose of 59.4 Gy (range $44.4{\sim}66$) and a total radiation dose, including BT, of 60 Gy (range $44.4{\sim}72$), while 144 patients received a dose higher than 40 Gy. Despite the treatment, the disease recurrence rate was 101/160 (63.1%). Of these, the patterns of recurrence were local in 20 patients (12.5%), persistent disease and local progression in 61 (38.1%), distant metastasis in 15 (9.4%), and concomitant local and distant failure in 5 (3.1%). The overall survival rate was 31.8% at 2 years and 14.2% at 5 years (median 11.1 months). Disease-free survival was 29.0% at 2 years and 22.7% at 5 years (median 10.4 months). The response to treatment and N-stage were significant factors affecting overall survival. In addition, total radiation dose (${\geq}50\;Gy$ vs. < 50 Gy), BT and fractionation scheme (qd. vs. bid.) were not significant factors for overall survival and disease-free survival. Conclusion: Survival outcome after definitive chemoradiation therapy in unresectable esophageal cancer was comparable to those of other series. The main failure pattern was local recurrence. Survival rate did not improve with increased radiation dose over 50 Gy or the use of brachytherapy or hyperfractionation.

• Journal of Preventive Medicine and Public Health
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• v.30 no.2 s.57
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• pp.437-461
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• 1997

Because of a significant improvement in the economic situation and development of scientific techniques in Korea during the last 30 years, the life expectancy of the Korean people has lengthened considerably and as a result, the number of the elderly has markedly increased. Such an increase of the number of aged population brought about many social, economic, and medical problems which were never seriously considered before. This study was conducted to assess the trend of medical care utilization and medical expenditure of the elderly. The data of each patient in the study were taken from computer database maintained for administrative purpose by the Korea Medical Insurance Corporation. The study population was 132,670 who were 60 years old or more and registered in Korean Medical Insurance Corporation from 1989 to 1993. The study subjects were predominantly female(56.3%) and 10,000-20,000 Won premium group(50.6%). The following are summaries of findings : The total increase of the number of inpatient cases was 40.5% from 1989 through 1993. The average annual increase was 3.7% in inpatient medical expenditures per case, 4.4% in inpatient medical expenditures per day and 0.08% in length of stay per case from 1989 through 1993. Cataract was the most prevalent disease of 10 leading frequent diseases in all ages from 1989 through 1993. The case mix in 1993 compared to 1989 revealed that cataract and ischemic cerebral disease were increased whereas essential hypertension and pulmonary tuberculosis were decreased . The average annual increase of medical expenditures was 3.8% in general hospitals, 6.3% in hospitals and 2.4% in clinics. From 1989 through 1993, medical expenditures used by high-cost patients accounted for about 14% to 20% of all expenditures for inpatient care, while they represented less than 2.5% of the elderly population. Time series analysis revealed that total medical expenditures and doctor's fee for inpatient will be progressively increased whereas drug expenditures for inpatient will be decreased. And there will be no change in length of stay. Based on the above results, the factors increasing medical cost and utilization should be identified and the method of cost containment for the elderly health care should be developed systematically.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.51-69
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• 2020

Now, it is a stylized fact that a small number of technology firms such as Apple, Alphabet, Microsoft, Amazon, Facebook and a few others have become larger and dominant players in an industry. Coupled with the rise of these leading firms, we have also observed that a large number of young firms have become an acquisition target in their early IPO stages. This indeed results in a sharp decline in the number of new entries in public exchanges although a series of policy reforms have been promulgated to foster competition through an increase in new entries. Given the observed industry trend in recent decades, a number of studies have reported increased concentration in most developed countries. However, it is less understood as to what caused an increase in industry concentration. In this paper, we uncover the mechanisms by which industries have become concentrated over the last decades by tracing the changes in industry concentration associated with a firm's status change in its early IPO stages. To this end, we put emphasis on the case in which firms are acquired shortly after they went public. Especially, with the transition to digital-based economies, it is imperative for incumbent firms to adapt and keep pace with new ICT and related intelligent systems. For instance, after the acquisition of a young firm equipped with AI-based solutions, an incumbent firm may better respond to a change in customer taste and preference by integrating acquired AI solutions and analytics skills into multiple business processes. Accordingly, it is not unusual for young ICT firms become an attractive acquisition target. To examine the role of M&As involved with young firms in reshaping the level of industry concentration, we identify a firm's status in early post-IPO stages over the sample periods spanning from 1990 to 2016 as follows: i) being delisted, ii) being standalone firms and iii) being acquired. According to our analysis, firms that have conducted IPO since 2000s have been acquired by incumbent firms at a relatively quicker time than those that did IPO in previous generations. We also show a greater acquisition rate for IPO firms in the ICT sector compared with their counterparts in other sectors. Our results based on multinomial logit models suggest that a large number of IPO firms have been acquired in their early post-IPO lives despite their financial soundness. Specifically, we show that IPO firms are likely to be acquired rather than be delisted due to financial distress in early IPO stages when they are more profitable, more mature or less leveraged. For those IPO firms with venture capital backup have also become an acquisition target more frequently. As a larger number of firms are acquired shortly after their IPO, our results show increased concentration. While providing limited evidence on the impact of large incumbent firms in explaining the change in industry concentration, our results show that the large firms' effect on industry concentration are pronounced in the ICT sector. This result possibly captures the current trend that a few tech giants such as Alphabet, Apple and Facebook continue to increase their market share. In addition, compared with the acquisitions of non-ICT firms, the concentration impact of IPO firms in early stages becomes larger when ICT firms are acquired as a target. Our study makes new contributions. To our best knowledge, this is one of a few studies that link a firm's post-IPO status to associated changes in industry concentration. Although some studies have addressed concentration issues, their primary focus was on market power or proprietary software. Contrast to earlier studies, we are able to uncover the mechanism by which industries have become concentrated by placing emphasis on M&As involving young IPO firms. Interestingly, the concentration impact of IPO firm acquisitions are magnified when a large incumbent firms are involved as an acquirer. This leads us to infer the underlying reasons as to why industries have become more concentrated with a favor of large firms in recent decades. Overall, our study sheds new light on the literature by providing a plausible explanation as to why industries have become concentrated.