• International Commerce and Information Review
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• v.14 no.2
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• pp.249-269
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• 2012

The focus of our work is to identify and understand the drivers of alliance performance so that businesses can maximize their chances of a successful alliance-an area that has received little attention in empirical modeling. Although both conceptual and applied research on alliances has increased, an empirically tested comprehensive theoretical model that explains alliance performance has yet to be developed. Using five salient perspective, namely market power theory, transaction cost theory, the resource-based view, institutional theory, real option theory, this paper attempts to provide a theoretical rationale linking motives of global alliance networks on firm's capabilities, partner's capabilities, operating structures, and performances of Korean companies. The key contribution of this study is that it paints a picture of what matters in driving alliance performance. Our work shows the complex nature of driving performance and the interplay of firm's capabilities, partner's capabilities, and operating structures for understanding alliance performances. This study has given us a small but significant step forward towards understanding the intricacies of alliance performance. We are now better able to understand the respective roles played by various alliance factors and derive insights that lead to improved alliance performance.

• Korea Journal of Hospital Management
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• v.7 no.4
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• pp.57-101
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• 2002

These days, the environment of hospital marketing is changing rapidly. The level of expectation and demand of patients have become greater and more diversified, and patients have more alternatives in selecting hospitals. The standard of hospital selection and the type of using hospital have been changed, and competition among hospitals has been accelerated due to the opening of the medical market through globalization. Accordingly, differentiation strategies are critical in hospital marketing. The quality of medical service oriented toward patient satisfaction becomes a strong strategic weapon to secure a hospital's competitive advantage. Therefore, marketing and communication strategies should be focused on patient-oriented, rather than hospital-oriented. Considering the changes in the hospital environment and the increase in the patients' expectation level, this study categorizes doctors' communication styles into four different ones: trust-type, professional-type, cooperation-type, and control-type. The effects of these communication styles on patient satisfaction were empirically examined. The moderating roles of the patient's characteristics and clinical characteristics between the doctors' communication styles and patient satisfaction were also investigated to find out managerial implications for hospital management. To achieve such goals, data were collected from patients of 12 general hospitals in Busan. The data were analyzed to test research hypotheses that examine 1) the relationships between doctors' communication styles and patient satisfaction, 2) the moderating roles of the patient characteristics and clinical characteristics in the research model, and 3) the impact of patient satisfaction on positive word-of-mouth and repurchase. The following summarizes the major results of this research. First, the data showed that patient satisfaction varied across doctors' communication styles. Trust-type style had the strongest impact on patient satisfaction while control-type style had the weakest influence on patient satisfaction. Professional-type style and cooperation-type style also had positive effects on patient satisfaction but the impact of the two are not statistically different. Second, significant differences in terms of patient satisfaction were found depending upon demographic variables such as gender, marital status, age, occupation, and education. Patient satisfaction, however, was consistent across varying income groups. Third, patients' medical insurance types were also related to patient satisfaction. It implies that a doctor may need to use different communication styles depending on a patient's medical insurance type. Fourth, out-patient and in-patient showed a different level of satisfaction with varying communication styles. Fifth, highly professional knowledge and strong control can influence patient satisfaction depending on the characteristics of the patient treatment field. Sixth, patient satisfaction were proved to have significantly positive effects on word-of-mouth and repurchase. The implications drawn from this study must be tempered by its limitations. First of all, the subjects used in this study were patients in Busan and small- and medium-size hospitals were excluded from the research. Therefore, future research should examine the research model by using a variety of hospitals and clinics throughout Korea. Another research agenda has to do with finding more determinant and moderating variables which will increase an explanatory power of the model. In short, this study may be the first empirical research that investigates the effects of doctors' communication styles on patient satisfaction. Interestingly enough, the results showed that each communication style had a unique impact on patient satisfaction. The findings from this research can be very useful in developing hospital marketing strategies.

• Journal of Advanced Navigation Technology
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• v.17 no.5
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• pp.516-523
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• 2013

In this paper, The path loss model of Air Traffic Control(ATC) telecommunication radio channel has been studied at the Incheon International Airport(IIA) with the terminal with two antlers. We measured two frequencies among VHF/UHF channel bands. The transmitting site radiated the Continuous Wave(CW). The propagation measurement was taken using the moving vehicle equipped with receiver and antenna. The transmitting power, frequency and antenna height are the same as the current operating condition. The path loss exponent and intercept parameters were extracted by the basic path loss model and hata model. The path loss exponents at passager terminal areas were 3.32 and 3.10 respectively in 128.2 MHz and 269.1 MHz. The deviation of prediction error is 9.69 and 9.65. The new path loss equation at the terminal area was also developed using the derived path loss parameters. The new path loss was compared with other models. This result will be helpful for the ATC site selection and service quality evaluation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.4
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• pp.232-241
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• 2019

This study examines one of the conventional and controversial issues in modern finance. Specifically, this study identifies financial determinants of corporate R&D intensity for firms belonging to Korean Chaebols. Empirical estimation procedures are applied to derive more robust results of each hypothesis test. Static panel data, Tobit regression and stepwise regression models are employed to obtain significant financial factors of R&D expenditures, while logit, probit and complementary log-log regression models are used to detect financial differences between Chaebol firms and their counterparts not classified as Chaebols. Study results found the level of R&D intensity in the prior fiscal year, market-value based leverage ratio and firm size empirically showed their significance to account for corporate R&D intensity in the first hypothesis test, whereas the majority of explanatory variables had important power on a relative basis. Assuming that the current circumstances in the domestic capital market may necessitate gradual changes of Korean Chaebols in terms of their socio-economic function, the results of this study are expected to contribute to identifying financial antecedents that can be beneficial to attain optimal level of corporate R&D expenditures for Chaebol firms on a virtuous cycle.

• Korea Trade Review
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• v.43 no.2
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• pp.87-105
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• 2018

We investigate the effects of inward foreign direct investment on innovation in Korean industries from 1998 to 2015 by first dividing FDI into greenfield and M&A (mergers and acquisitions). Furthermore, we use the number of patent applications as the proxy of innovation. Our empirical results are as follows: First, inward foreign direct investment has a significantly positive effect on the number of patent applications. This result suggests that the transfer of technology or knowledge through the inward foreign direct investment has a positive impact on innovation in Korean industries. Second, the greenfield investment has a positive impact on patent applications. This result is consistent with Liu and Zou (2008)'s assertion that greenfield investment has a positive impact on innovation by increasing facilities or plants. The M&A investment, however, has no significant effect on patent applications. This result is consistent with Stiebale and Reize (2011) who argue that the host countries do not benefit from technology transfer through M&A investments. In addition, this supports Liu and Zou (2008) and Garcia et al. (2013)'s hypothesis that foreign parent firms do not influence the innovation of host countries by employing strategies to increase market power rather than R&D activities through M&A investments. It is meaningful that this study first analyzes the impact of foreign direct investment on innovation in Korean industries and uses the number of patent applications as a proxy of innovation. Our empirical evidence provides policy implications for innovation and attraction of inward foreign direct investments.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.25-38
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• 2019

Selecting high-quality information that meets the interests and needs of users among the overflowing contents is becoming more important as the generation continues. In the flood of information, efforts to reflect the intention of the user in the search result better are being tried, rather than recognizing the information request as a simple string. Also, large IT companies such as Google and Microsoft focus on developing knowledge-based technologies including search engines which provide users with satisfaction and convenience. Especially, the finance is one of the fields expected to have the usefulness and potential of text data analysis because it's constantly generating new information, and the earlier the information is, the more valuable it is. Automatic knowledge extraction can be effective in areas where information flow is vast, such as financial sector, and new information continues to emerge. However, there are several practical difficulties faced by automatic knowledge extraction. First, there are difficulties in making corpus from different fields with same algorithm, and it is difficult to extract good quality triple. Second, it becomes more difficult to produce labeled text data by people if the extent and scope of knowledge increases and patterns are constantly updated. Third, performance evaluation is difficult due to the characteristics of unsupervised learning. Finally, problem definition for automatic knowledge extraction is not easy because of ambiguous conceptual characteristics of knowledge. So, in order to overcome limits described above and improve the semantic performance of stock-related information searching, this study attempts to extract the knowledge entity by using neural tensor network and evaluate the performance of them. Different from other references, the purpose of this study is to extract knowledge entity which is related to individual stock items. Various but relatively simple data processing methods are applied in the presented model to solve the problems of previous researches and to enhance the effectiveness of the model. From these processes, this study has the following three significances. First, A practical and simple automatic knowledge extraction method that can be applied. Second, the possibility of performance evaluation is presented through simple problem definition. Finally, the expressiveness of the knowledge increased by generating input data on a sentence basis without complex morphological analysis. The results of the empirical analysis and objective performance evaluation method are also presented. The empirical study to confirm the usefulness of the presented model, experts' reports about individual 30 stocks which are top 30 items based on frequency of publication from May 30, 2017 to May 21, 2018 are used. the total number of reports are 5,600, and 3,074 reports, which accounts about 55% of the total, is designated as a training set, and other 45% of reports are designated as a testing set. Before constructing the model, all reports of a training set are classified by stocks, and their entities are extracted using named entity recognition tool which is the KKMA. for each stocks, top 100 entities based on appearance frequency are selected, and become vectorized using one-hot encoding. After that, by using neural tensor network, the same number of score functions as stocks are trained. Thus, if a new entity from a testing set appears, we can try to calculate the score by putting it into every single score function, and the stock of the function with the highest score is predicted as the related item with the entity. To evaluate presented models, we confirm prediction power and determining whether the score functions are well constructed by calculating hit ratio for all reports of testing set. As a result of the empirical study, the presented model shows 69.3% hit accuracy for testing set which consists of 2,526 reports. this hit ratio is meaningfully high despite of some constraints for conducting research. Looking at the prediction performance of the model for each stocks, only 3 stocks, which are LG ELECTRONICS, KiaMtr, and Mando, show extremely low performance than average. this result maybe due to the interference effect with other similar items and generation of new knowledge. In this paper, we propose a methodology to find out key entities or their combinations which are necessary to search related information in accordance with the user's investment intention. Graph data is generated by using only the named entity recognition tool and applied to the neural tensor network without learning corpus or word vectors for the field. From the empirical test, we confirm the effectiveness of the presented model as described above. However, there also exist some limits and things to complement. Representatively, the phenomenon that the model performance is especially bad for only some stocks shows the need for further researches. Finally, through the empirical study, we confirmed that the learning method presented in this study can be used for the purpose of matching the new text information semantically with the related stocks.

• Asia pacific journal of information systems
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• v.19 no.4
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• pp.47-75
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• 2009

Recently, researches on Management Information Systems (MIS) have laid out theoretical foundation and academic paradigms by introducing diverse theories, themes, and methodologies. Especially, academic paradigms of MIS encourage a user-friendly approach by developing the technologies from the users' perspectives, which reflects the existence of strong causal relationships between information systems and user's behavior. As in other areas in social science the use of structural equation modeling (SEM) has rapidly increased in recent years especially in the MIS area. The SEM technique is important because it provides powerful ways to address key IS research problems. It also has a unique ability to simultaneously examine a series of casual relationships while analyzing multiple independent and dependent variables all at the same time. In spite of providing many benefits to the MIS researchers, there are some potential pitfalls with the analytical technique. The research objective of this study is to provide some guidelines for an appropriate use of SEM based on the assessment of current practice of using SEM in the MIS research. This study focuses on several statistical issues related to the use of SEM in the MIS research. Selected articles are assessed in three parts through the meta analysis. The first part is related to the initial specification of theoretical model of interest. The second is about data screening prior to model estimation and testing. And the last part concerns estimation and testing of theoretical models based on empirical data. This study reviewed the use of SEM in 164 empirical research articles published in four major MIS journals in Korea (APJIS, ISR, JIS and JITAM) from 1991 to 2007. APJIS, ISR, JIS and JITAM accounted for 73, 17, 58, and 16 of the total number of applications, respectively. The number of published applications has been increased over time. LISREL was the most frequently used SEM software among MIS researchers (97 studies (59.15%)), followed by AMOS (45 studies (27.44%)). In the first part, regarding issues related to the initial specification of theoretical model of interest, all of the studies have used cross-sectional data. The studies that use cross-sectional data may be able to better explain their structural model as a set of relationships. Most of SEM studies, meanwhile, have employed. confirmatory-type analysis (146 articles (89%)). For the model specification issue about model formulation, 159 (96.9%) of the studies were the full structural equation model. For only 5 researches, SEM was used for the measurement model with a set of observed variables. The average sample size for all models was 365.41, with some models retaining a sample as small as 50 and as large as 500. The second part of the issue is related to data screening prior to model estimation and testing. Data screening is important for researchers particularly in defining how they deal with missing values. Overall, discussion of data screening was reported in 118 (71.95%) of the studies while there was no study discussing evidence of multivariate normality for the models. On the third part, issues related to the estimation and testing of theoretical models on empirical data, assessing model fit is one of most important issues because it provides adequate statistical power for research models. There were multiple fit indices used in the SEM applications. The test was reported in the most of studies (146 (89%)), whereas normed-test was reported less frequently (65 studies (39.64%)). It is important that normed- of 3 or lower is required for adequate model fit. The most popular model fit indices were GFI (109 (66.46%)), AGFI (84 (51.22%)), NFI (44 (47.56%)), RMR (42 (25.61%)), CFI (59 (35.98%)), RMSEA (62 (37.80)), and NNFI (48 (29.27%)). Regarding the test of construct validity, convergent validity has been examined in 109 studies (66.46%) and discriminant validity in 98 (59.76%). 81 studies (49.39%) have reported the average variance extracted (AVE). However, there was little discussion of direct (47 (28.66%)), indirect, and total effect in the SEM models. Based on these findings, we suggest general guidelines for the use of SEM and propose some recommendations on concerning issues of latent variables models, raw data, sample size, data screening, reporting parameter estimated, model fit statistics, multivariate normality, confirmatory factor analysis, reliabilities and the decomposition of effects.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.157-177
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• 2022

Investors are trading stocks by keeping a close watch on the order information submitted by domestic and foreign investors in real time through Limit Order Book information, so-called price current provided by securities firms. Will order information released in the Limit Order Book be useful in stock price prediction? This study analyzes whether it is significant as a predictor of future stock price up or down when order imbalances appear as investors' buying and selling orders are concentrated to one side during intra-day trading time. Using classification algorithms, this study improved the prediction accuracy of the order imbalance information on the short-term price up and down trend, that is the closing price up and down of the day. Day trading strategies are proposed using the predicted price trends of the classification algorithms and the trading performances are analyzed through empirical analysis. The 5-minute KOSPI200 Index Futures data were analyzed for 4,564 days from January 19, 2004 to June 30, 2022. The results of the empirical analysis are as follows. First, order imbalance information has a significant impact on the current stock prices. Second, the order imbalance information observed in the early morning has a significant forecasting power on the price trends from the early morning to the market closing time. Third, the Support Vector Machines algorithm showed the highest prediction accuracy on the day's closing price trends using the order imbalance information at 54.1%. Fourth, the order imbalance information measured at an early time of day had higher prediction accuracy than the order imbalance information measured at a later time of day. Fifth, the trading performances of the day trading strategies using the prediction results of the classification algorithms on the price up and down trends were higher than that of the benchmark trading strategy. Sixth, except for the K-Nearest Neighbor algorithm, all investment performances using the classification algorithms showed average higher total profits than that of the benchmark strategy. Seventh, the trading performances using the predictive results of the Logical Regression, Random Forest, Support Vector Machines, and XGBoost algorithms showed higher results than the benchmark strategy in the Sharpe Ratio, which evaluates both profitability and risk. This study has an academic difference from existing studies in that it documented the economic value of the total buy & sell order volume information among the Limit Order Book information. The empirical results of this study are also valuable to the market participants from a trading perspective. In future studies, it is necessary to improve the performance of the trading strategy using more accurate price prediction results by expanding to deep learning models which are actively being studied for predicting stock prices recently.

• Journal of Digital Convergence
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• v.12 no.4
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• pp.103-113
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• 2014

Bargain position and trust are core issues in supply chain management, yet the effect of bargain position on trust remains to be undetermined. The purpose of this research is to present theoretical and methodological hurdles for the relationship among various bargain positions and trust, and develops a set of hypotheses about the asymmetric effect of bargain position on trust in supply chain network. An analytical tool to analyze nonlinear effects on a response surface is introduced. Based on the data collected through a survey of firms participating in Project Supply chain, a set of hypotheses is tested. The analysis results support the prediction that the bargain position perceived by the buyers have asymmetric effects on trust toward supplies, and provide more fine-grained accounts on the relationships among bargain power, bargain position and trust in a supply chain network.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.34 no.2
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• pp.129-136
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• 2010

Axial piston pumps have been widely used as power sources for hydraulic systems, but studies on the fluid flow within the pump have been usually performed using 1-D analysis because of the difficulties in considering the fluid compressibility, high-speed revolution, variation of the flow rate, and complicated geometry. The goal of this study was to understand the hydraulic fluid flow within axial piston pumps by using the 3-D numerical method and the process of generating discharge pressure ripples. To improve the convergence and robustness of the simulation model, a grid system was constructed with hexahedron-type grids around the valve plate. Furthermore, we employed an empirical formula to describe the relationship between the oil density and pressure. The CFD (computational fluid dynamics) results compared well with the experimental data.