• Title/Summary/Keyword: computer-based technology

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A Combat Effectiveness Evaluation Algorithm Considering Technical and Human Factors in C4I System (NCW 환경에서 C4I 체계 전투력 상승효과 평가 알고리즘 : 기술 및 인적 요소 고려)

  • Jung, Whan-Sik;Park, Gun-Woo;Lee, Jae-Yeong;Lee, Sang-Hoon
    • Journal of Intelligence and Information Systems
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    • v.16 no.2
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    • pp.55-72
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    • 2010
  • Recently, the battlefield environment has changed from platform-centric warfare(PCW) which focuses on maneuvering forces into network-centric warfare(NCW) which is based on the connectivity of each asset through the warfare information system as information technology increases. In particular, C4I(Command, Control, Communication, Computer and Intelligence) system can be an important factor in achieving NCW. It is generally used to provide direction across distributed forces and status feedback from thoseforces. It can provide the important information, more quickly and in the correct format to the friendly units. And it can achieve the information superiority through SA(Situational Awareness). Most of the advanced countries have been developed and already applied these systems in military operations. Therefore, ROK forces also have been developing C4I systems such as KJCCS(Korea Joint Command Control System). And, ours are increasing the budgets in the establishment of warfare information systems. However, it is difficult to evaluate the C4I effectiveness properly by deficiency of methods. We need to develop a new combat effectiveness evaluation method that is suitable for NCW. Existing evaluation methods lay disproportionate emphasis on technical factors with leaving something to be desired in human factors. Therefore, it is necessary to consider technical and human factors to evaluate combat effectiveness. In this study, we proposed a new Combat Effectiveness evaluation algorithm called E-TechMan(A Combat Effectiveness Evaluation Algorithm Considering Technical and Human Factors in C4I System). This algorithm uses the rule of Newton's second law($F=(m{\Delta}{\upsilon})/{\Delta}t{\Rightarrow}\frac{V{\upsilon}I}{T}{\times}C$). Five factors considered in combat effectiveness evaluation are network power(M), movement velocity(v), information accuracy(I), command and control time(T) and collaboration level(C). Previous researches did not consider the value of the node and arc in evaluating the network power after the C4I system has been established. In addition, collaboration level which could be a major factor in combat effectiveness was not considered. E-TechMan algorithm is applied to JFOS-K(Joint Fire Operating System-Korea) system that can connect KJCCS of Korea armed forces with JADOCS(Joint Automated Deep Operations Coordination System) of U.S. armed forces and achieve sensor to shooter system in real time in JCS(Joint Chiefs of Staff) level. We compared the result of evaluation of Combat Effectiveness by E-TechMan with those by other algorithms(e.g., C2 Theory, Newton's second Law). We can evaluate combat effectiveness more effectively and substantially by E-TechMan algorithm. This study is meaningful because we improved the description level of reality in calculation of combat effectiveness in C4I system. Part 2 will describe the changes of war paradigm and the previous combat effectiveness evaluation methods such as C2 theory while Part 3 will explain E-TechMan algorithm specifically. Part 4 will present the application to JFOS-K and analyze the result with other algorithms. Part 5 is the conclusions provided in the final part.

Real-time CRM Strategy of Big Data and Smart Offering System: KB Kookmin Card Case (KB국민카드의 빅데이터를 활용한 실시간 CRM 전략: 스마트 오퍼링 시스템)

  • Choi, Jaewon;Sohn, Bongjin;Lim, Hyuna
    • Journal of Intelligence and Information Systems
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    • v.25 no.2
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    • pp.1-23
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    • 2019
  • Big data refers to data that is difficult to store, manage, and analyze by existing software. As the lifestyle changes of consumers increase the size and types of needs that consumers desire, they are investing a lot of time and money to understand the needs of consumers. Companies in various industries utilize Big Data to improve their products and services to meet their needs, analyze unstructured data, and respond to real-time responses to products and services. The financial industry operates a decision support system that uses financial data to develop financial products and manage customer risks. The use of big data by financial institutions can effectively create added value of the value chain, and it is possible to develop a more advanced customer relationship management strategy. Financial institutions can utilize the purchase data and unstructured data generated by the credit card, and it becomes possible to confirm and satisfy the customer's desire. CRM has a granular process that can be measured in real time as it grows with information knowledge systems. With the development of information service and CRM, the platform has change and it has become possible to meet consumer needs in various environments. Recently, as the needs of consumers have diversified, more companies are providing systematic marketing services using data mining and advanced CRM (Customer Relationship Management) techniques. KB Kookmin Card, which started as a credit card business in 1980, introduced early stabilization of processes and computer systems, and actively participated in introducing new technologies and systems. In 2011, the bank and credit card companies separated, leading the 'Hye-dam Card' and 'One Card' markets, which were deviated from the existing concept. In 2017, the total use of domestic credit cards and check cards grew by 5.6% year-on-year to 886 trillion won. In 2018, we received a long-term rating of AA + as a result of our credit card evaluation. We confirmed that our credit rating was at the top of the list through effective marketing strategies and services. At present, Kookmin Card emphasizes strategies to meet the individual needs of customers and to maximize the lifetime value of consumers by utilizing payment data of customers. KB Kookmin Card combines internal and external big data and conducts marketing in real time or builds a system for monitoring. KB Kookmin Card has built a marketing system that detects realtime behavior using big data such as visiting the homepage and purchasing history by using the customer card information. It is designed to enable customers to capture action events in real time and execute marketing by utilizing the stores, locations, amounts, usage pattern, etc. of the card transactions. We have created more than 280 different scenarios based on the customer's life cycle and are conducting marketing plans to accommodate various customer groups in real time. We operate a smart offering system, which is a highly efficient marketing management system that detects customers' card usage, customer behavior, and location information in real time, and provides further refinement services by combining with various apps. This study aims to identify the traditional CRM to the current CRM strategy through the process of changing the CRM strategy. Finally, I will confirm the current CRM strategy through KB Kookmin card's big data utilization strategy and marketing activities and propose a marketing plan for KB Kookmin card's future CRM strategy. KB Kookmin Card should invest in securing ICT technology and human resources, which are becoming more sophisticated for the success and continuous growth of smart offering system. It is necessary to establish a strategy for securing profit from a long-term perspective and systematically proceed. Especially, in the current situation where privacy violation and personal information leakage issues are being addressed, efforts should be made to induce customers' recognition of marketing using customer information and to form corporate image emphasizing security.

The Precision Test Based on States of Bone Mineral Density (골밀도 상태에 따른 검사자의 재현성 평가)

  • Yoo, Jae-Sook;Kim, Eun-Hye;Kim, Ho-Seong;Shin, Sang-Ki;Cho, Si-Man
    • The Korean Journal of Nuclear Medicine Technology
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    • v.13 no.1
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    • pp.67-72
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    • 2009
  • Purpose: ISCD (International Society for Clinical Densitometry) requests that users perform mandatory Precision test to raise their quality even though there is no recommendation about patient selection for the test. Thus, we investigated the effect on precision test by measuring reproducibility of 3 bone density groups (normal, osteopenia, osteoporosis). Materials and Methods: 4 users performed precision test with 420 patients (age: $57.8{\pm}9.02$) for BMD in Asan Medical Center (JAN-2008 ~ JUN-2008). In first group (A), 4 users selected 30 patient respectively regardless of bone density condition and measured 2 part (L-spine, femur) in twice. In second group (B), 4 users measured bone density of 10 patients respectively in the same manner of first group (A) users but dividing patient into 3 stages (normal, osteopenia, osteoporosis). In third group (C), 2 users measured 30 patients respectively in the same manner of first group (A) users considering bone density condition. We used GE Lunar Prodigy Advance (Encore. V11.4) and analyzed the result by comparing %CV to LSC using precision tool from ISCD. Check back was done using SPSS. Results: In group A, the %CV calculated by 4 users (a, b, c, d) were 1.16, 1.01, 1.19, 0.65 g/$cm^2$ in L-spine and 0.69, 0.58, 0.97, 0.47 g/$cm^2$ in femur. In group B, the %CV calculated by 4 users (a, b, c, d) were 1.01, 1.19, 0.83, 1.37 g/$cm^2$ in L-spine and 1.03, 0.54, 0.69, 0.58 g/$cm^2$ in femur. When comparing results (group A, B), we found no considerable differences. In group C, the user_1's %CV of normal, osteopenia and osteoporosis were 1.26, 0.94, 0.94 g/$cm^2$ in L-spine and 0.94, 0.79, 1.01 g/$cm^2$ in femur. And the user_2's %CV were 0.97, 0.83, 0.72 g/$cm^2$ L-spine and 0.65, 0.65, 1.05 g/$cm^2$ in femur. When analyzing the result, we figured out that the difference of reproducibility was almost not found but the differences of two users' several result values have effect on total reproducibility. Conclusions: Precision test is a important factor of bone density follow up. When Machine and user's reproducibility is getting better, it’s useful in clinics because of low range of deviation. Users have to check machine's reproducibility before the test and keep the same mind doing BMD test for patient. In precision test, the difference of measured value is usually found for ROI change caused by patient position. In case of osteoporosis patient, there is difficult to make initial ROI accurately more than normal and osteopenia patient due to lack of bone recognition even though ROI is made automatically by computer software. However, initial ROI is very important and users have to make coherent ROI because we use ROI Copy function in a follow up. In this study, we performed precision test considering bone density condition and found LSC value was stayed within 3%. There was no considerable difference. Thus, patient selection could be done regardless of bone density condition.

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Development of New Device for the Rapid Measurement of the freshness of Wet Fish by Using Micro Computer (마이크로 컴퓨터를 이용한 어육의 신선도 측정장치의 개발)

  • CHO Young-Je;LEE Nam-Geoul;KIM Sang-Bong;CHOI Young-Joon;LEE Keun-Woo;KIM Geon-Bae
    • Korean Journal of Fisheries and Aquatic Sciences
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    • v.28 no.3
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    • pp.253-262
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    • 1995
  • To develop a device for measuring fish freshness which could be move accurate and reliable than used freshness measuring systems. A new device based on digital circuit was designed using a microcomputer. The device was composed of a sensor part, 8096 microprocessor and a segment display. The effectiveness of device has been evaluated by the coefficient of correlation among the measured freshness stores such as electrical Q-value, K-value and amount of volatile basic nitrogen (VBN) of plaice, Paralichthys Olivaceus, during storage at $-3^{\circ}C,\;0^{\circ}C,\;5^{\circ}C,\;10^{\circ}C,\;and\;25^{\circ}C$. Q-values measured by a new device were more closely correlated with K-value (r=-0.978-\;-0.962,\;p<0.05) and VBN (r=-0.888-\;-0.988,\;p<0.05) in case of plaice meat. If more data would achieve using various fishes, this new designed device could be a valuable kit in fish market by its compact portability.

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Efficient Topic Modeling by Mapping Global and Local Topics (전역 토픽의 지역 매핑을 통한 효율적 토픽 모델링 방안)

  • Choi, Hochang;Kim, Namgyu
    • Journal of Intelligence and Information Systems
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    • v.23 no.3
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    • pp.69-94
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    • 2017
  • Recently, increase of demand for big data analysis has been driving the vigorous development of related technologies and tools. In addition, development of IT and increased penetration rate of smart devices are producing a large amount of data. According to this phenomenon, data analysis technology is rapidly becoming popular. Also, attempts to acquire insights through data analysis have been continuously increasing. It means that the big data analysis will be more important in various industries for the foreseeable future. Big data analysis is generally performed by a small number of experts and delivered to each demander of analysis. However, increase of interest about big data analysis arouses activation of computer programming education and development of many programs for data analysis. Accordingly, the entry barriers of big data analysis are gradually lowering and data analysis technology being spread out. As the result, big data analysis is expected to be performed by demanders of analysis themselves. Along with this, interest about various unstructured data is continually increasing. Especially, a lot of attention is focused on using text data. Emergence of new platforms and techniques using the web bring about mass production of text data and active attempt to analyze text data. Furthermore, result of text analysis has been utilized in various fields. Text mining is a concept that embraces various theories and techniques for text analysis. Many text mining techniques are utilized in this field for various research purposes, topic modeling is one of the most widely used and studied. Topic modeling is a technique that extracts the major issues from a lot of documents, identifies the documents that correspond to each issue and provides identified documents as a cluster. It is evaluated as a very useful technique in that reflect the semantic elements of the document. Traditional topic modeling is based on the distribution of key terms across the entire document. Thus, it is essential to analyze the entire document at once to identify topic of each document. This condition causes a long time in analysis process when topic modeling is applied to a lot of documents. In addition, it has a scalability problem that is an exponential increase in the processing time with the increase of analysis objects. This problem is particularly noticeable when the documents are distributed across multiple systems or regions. To overcome these problems, divide and conquer approach can be applied to topic modeling. It means dividing a large number of documents into sub-units and deriving topics through repetition of topic modeling to each unit. This method can be used for topic modeling on a large number of documents with limited system resources, and can improve processing speed of topic modeling. It also can significantly reduce analysis time and cost through ability to analyze documents in each location or place without combining analysis object documents. However, despite many advantages, this method has two major problems. First, the relationship between local topics derived from each unit and global topics derived from entire document is unclear. It means that in each document, local topics can be identified, but global topics cannot be identified. Second, a method for measuring the accuracy of the proposed methodology should be established. That is to say, assuming that global topic is ideal answer, the difference in a local topic on a global topic needs to be measured. By those difficulties, the study in this method is not performed sufficiently, compare with other studies dealing with topic modeling. In this paper, we propose a topic modeling approach to solve the above two problems. First of all, we divide the entire document cluster(Global set) into sub-clusters(Local set), and generate the reduced entire document cluster(RGS, Reduced global set) that consist of delegated documents extracted from each local set. We try to solve the first problem by mapping RGS topics and local topics. Along with this, we verify the accuracy of the proposed methodology by detecting documents, whether to be discerned as the same topic at result of global and local set. Using 24,000 news articles, we conduct experiments to evaluate practical applicability of the proposed methodology. In addition, through additional experiment, we confirmed that the proposed methodology can provide similar results to the entire topic modeling. We also proposed a reasonable method for comparing the result of both methods.

Animal Infectious Diseases Prevention through Big Data and Deep Learning (빅데이터와 딥러닝을 활용한 동물 감염병 확산 차단)

  • Kim, Sung Hyun;Choi, Joon Ki;Kim, Jae Seok;Jang, Ah Reum;Lee, Jae Ho;Cha, Kyung Jin;Lee, Sang Won
    • Journal of Intelligence and Information Systems
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    • v.24 no.4
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    • pp.137-154
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    • 2018
  • Animal infectious diseases, such as avian influenza and foot and mouth disease, occur almost every year and cause huge economic and social damage to the country. In order to prevent this, the anti-quarantine authorities have tried various human and material endeavors, but the infectious diseases have continued to occur. Avian influenza is known to be developed in 1878 and it rose as a national issue due to its high lethality. Food and mouth disease is considered as most critical animal infectious disease internationally. In a nation where this disease has not been spread, food and mouth disease is recognized as economic disease or political disease because it restricts international trade by making it complex to import processed and non-processed live stock, and also quarantine is costly. In a society where whole nation is connected by zone of life, there is no way to prevent the spread of infectious disease fully. Hence, there is a need to be aware of occurrence of the disease and to take action before it is distributed. Epidemiological investigation on definite diagnosis target is implemented and measures are taken to prevent the spread of disease according to the investigation results, simultaneously with the confirmation of both human infectious disease and animal infectious disease. The foundation of epidemiological investigation is figuring out to where one has been, and whom he or she has met. In a data perspective, this can be defined as an action taken to predict the cause of disease outbreak, outbreak location, and future infection, by collecting and analyzing geographic data and relation data. Recently, an attempt has been made to develop a prediction model of infectious disease by using Big Data and deep learning technology, but there is no active research on model building studies and case reports. KT and the Ministry of Science and ICT have been carrying out big data projects since 2014 as part of national R &D projects to analyze and predict the route of livestock related vehicles. To prevent animal infectious diseases, the researchers first developed a prediction model based on a regression analysis using vehicle movement data. After that, more accurate prediction model was constructed using machine learning algorithms such as Logistic Regression, Lasso, Support Vector Machine and Random Forest. In particular, the prediction model for 2017 added the risk of diffusion to the facilities, and the performance of the model was improved by considering the hyper-parameters of the modeling in various ways. Confusion Matrix and ROC Curve show that the model constructed in 2017 is superior to the machine learning model. The difference between the2016 model and the 2017 model is that visiting information on facilities such as feed factory and slaughter house, and information on bird livestock, which was limited to chicken and duck but now expanded to goose and quail, has been used for analysis in the later model. In addition, an explanation of the results was added to help the authorities in making decisions and to establish a basis for persuading stakeholders in 2017. This study reports an animal infectious disease prevention system which is constructed on the basis of hazardous vehicle movement, farm and environment Big Data. The significance of this study is that it describes the evolution process of the prediction model using Big Data which is used in the field and the model is expected to be more complete if the form of viruses is put into consideration. This will contribute to data utilization and analysis model development in related field. In addition, we expect that the system constructed in this study will provide more preventive and effective prevention.

Electronic Word-of-Mouth in B2C Virtual Communities: An Empirical Study from CTrip.com (B2C허의사구중적전자구비(B2C虚拟社区中的电子口碑): 관우휴정려유망적실증연구(关于携程旅游网的实证研究))

  • Li, Guoxin;Elliot, Statia;Choi, Chris
    • Journal of Global Scholars of Marketing Science
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    • v.20 no.3
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    • pp.262-268
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    • 2010
  • Virtual communities (VCs) have developed rapidly, with more and more people participating in them to exchange information and opinions. A virtual community is a group of people who may or may not meet one another face to face, and who exchange words and ideas through the mediation of computer bulletin boards and networks. A business-to-consumer virtual community (B2CVC) is a commercial group that creates a trustworthy environment intended to motivate consumers to be more willing to buy from an online store. B2CVCs create a social atmosphere through information contribution such as recommendations, reviews, and ratings of buyers and sellers. Although the importance of B2CVCs has been recognized, few studies have been conducted to examine members' word-of-mouth behavior within these communities. This study proposes a model of involvement, statistics, trust, "stickiness," and word-of-mouth in a B2CVC and explores the relationships among these elements based on empirical data. The objectives are threefold: (i) to empirically test a B2CVC model that integrates measures of beliefs, attitudes, and behaviors; (ii) to better understand the nature of these relationships, specifically through word-of-mouth as a measure of revenue generation; and (iii) to better understand the role of stickiness of B2CVC in CRM marketing. The model incorporates three key elements concerning community members: (i) their beliefs, measured in terms of their involvement assessment; (ii) their attitudes, measured in terms of their satisfaction and trust; and, (iii) their behavior, measured in terms of site stickiness and their word-of-mouth. Involvement is considered the motivation for consumers to participate in a virtual community. For B2CVC members, information searching and posting have been proposed as the main purpose for their involvement. Satisfaction has been reviewed as an important indicator of a member's overall community evaluation, and conceptualized by different levels of member interactions with their VC. The formation and expansion of a VC depends on the willingness of members to share information and services. Researchers have found that trust is a core component facilitating the anonymous interaction in VCs and e-commerce, and therefore trust-building in VCs has been a common research topic. It is clear that the success of a B2CVC depends on the stickiness of its members to enhance purchasing potential. Opinions communicated and information exchanged between members may represent a type of written word-of-mouth. Therefore, word-of-mouth is one of the primary factors driving the diffusion of B2CVCs across the Internet. Figure 1 presents the research model and hypotheses. The model was tested through the implementation of an online survey of CTrip Travel VC members. A total of 243 collected questionnaires was reduced to 204 usable questionnaires through an empirical process of data cleaning. The study's hypotheses examined the extent to which involvement, satisfaction, and trust influence B2CVC stickiness and members' word-of-mouth. Structural Equation Modeling tested the hypotheses in the analysis, and the structural model fit indices were within accepted thresholds: ${\chi}^2^$/df was 2.76, NFI was .904, IFI was .931, CFI was .930, and RMSEA was .017. Results indicated that involvement has a significant influence on satisfaction (p<0.001, ${\beta}$=0.809). The proportion of variance in satisfaction explained by members' involvement was over half (adjusted $R^2$=0.654), reflecting a strong association. The effect of involvement on trust was also statistically significant (p<0.001, ${\beta}$=0.751), with 57 percent of the variance in trust explained by involvement (adjusted $R^2$=0.563). When the construct "stickiness" was treated as a dependent variable, the proportion of variance explained by the variables of trust and satisfaction was relatively low (adjusted $R^2$=0.331). Satisfaction did have a significant influence on stickiness, with ${\beta}$=0.514. However, unexpectedly, the influence of trust was not even significant (p=0.231, t=1.197), rejecting that proposed hypothesis. The importance of stickiness in the model was more significant because of its effect on e-WOM with ${\beta}$=0.920 (p<0.001). Here, the measures of Stickiness explain over eighty of the variance in e-WOM (Adjusted $R^2$=0.846). Overall, the results of the study supported the hypothesized relationships between members' involvement in a B2CVC and their satisfaction with and trust of it. However, trust, as a traditional measure in behavioral models, has no significant influence on stickiness in the B2CVC environment. This study contributes to the growing body of literature on B2CVCs, specifically addressing gaps in the academic research by integrating measures of beliefs, attitudes, and behaviors in one model. The results provide additional insights to behavioral factors in a B2CVC environment, helping to sort out relationships between traditional measures and relatively new measures. For practitioners, the identification of factors, such as member involvement, that strongly influence B2CVC member satisfaction can help focus technological resources in key areas. Global e-marketers can develop marketing strategies directly targeting B2CVC members. In the global tourism business, they can target Chinese members of a B2CVC by providing special discounts for active community members or developing early adopter programs to encourage stickiness in the community. Future studies are called for, and more sophisticated modeling, to expand the measurement of B2CVC member behavior and to conduct experiments across industries, communities, and cultures.

The Effects of Environmental Dynamism on Supply Chain Commitment in the High-tech Industry: The Roles of Flexibility and Dependence (첨단산업의 환경동태성이 공급체인의 결속에 미치는 영향: 유연성과 의존성의 역할)

  • Kim, Sang-Deok;Ji, Seong-Goo
    • Journal of Global Scholars of Marketing Science
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    • v.17 no.2
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    • pp.31-54
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    • 2007
  • The exchange between buyers and sellers in the industrial market is changing from short-term to long-term relationships. Long-term relationships are governed mainly by formal contracts or informal agreements, but many scholars are now asserting that controlling relationship by using formal contracts under environmental dynamism is inappropriate. In this case, partners will depend on each other's flexibility or interdependence. The former, flexibility, provides a general frame of reference, order, and standards against which to guide and assess appropriate behavior in dynamic and ambiguous situations, thus motivating the value-oriented performance goals shared between partners. It is based on social sacrifices, which can potentially minimize any opportunistic behaviors. The later, interdependence, means that each firm possesses a high level of dependence in an dynamic channel relationship. When interdependence is high in magnitude and symmetric, each firm enjoys a high level of power and the bonds between the firms should be reasonably strong. Strong shared power is likely to promote commitment because of the common interests, attention, and support found in such channel relationships. This study deals with environmental dynamism in high-tech industry. Firms in the high-tech industry regard it as a key success factor to successfully cope with environmental changes. However, due to the lack of studies dealing with environmental dynamism and supply chain commitment in the high-tech industry, it is very difficult to find effective strategies to cope with them. This paper presents the results of an empirical study on the relationship between environmental dynamism and supply chain commitment in the high-tech industry. We examined the effects of consumer, competitor, and technological dynamism on supply chain commitment. Additionally, we examined the moderating effects of flexibility and dependence of supply chains. This study was confined to the type of high-tech industry which has the characteristics of rapid technology change and short product lifecycle. Flexibility among the firms of this industry, having the characteristic of hard and fast growth, is more important here than among any other industry. Thus, a variety of environmental dynamism can affect a supply chain relationship. The industries targeted industries were electronic parts, metal product, computer, electric machine, automobile, and medical precision manufacturing industries. Data was collected as follows. During the survey, the researchers managed to obtain the list of parts suppliers of 2 companies, N and L, with an international competitiveness in the mobile phone manufacturing industry; and of the suppliers in a business relationship with S company, a semiconductor manufacturing company. They were asked to respond to the survey via telephone and e-mail. During the two month period of February-April 2006, we were able to collect data from 44 companies. The respondents were restricted to direct dealing authorities and subcontractor company (the supplier) staff with at least three months of dealing experience with a manufacture (an industrial material buyer). The measurement validation procedures included scale reliability; discriminant and convergent validity were used to validate measures. Also, the reliability measurements traditionally employed, such as the Cronbach's alpha, were used. All the reliabilities were greater than.70. A series of exploratory factor analyses was conducted. We conducted confirmatory factor analyses to assess the validity of our measurements. A series of chi-square difference tests were conducted so that the discriminant validity could be ensured. For each pair, we estimated two models-an unconstrained model and a constrained model-and compared the two model fits. All these tests supported discriminant validity. Also, all items loaded significantly on their respective constructs, providing support for convergent validity. We then examined composite reliability and average variance extracted (AVE). The composite reliability of each construct was greater than.70. The AVE of each construct was greater than.50. According to the multiple regression analysis, customer dynamism had a negative effect and competitor dynamism had a positive effect on a supplier's commitment. In addition, flexibility and dependence had significant moderating effects on customer and competitor dynamism. On the other hand, all hypotheses about technological dynamism had no significant effects on commitment. In other words, technological dynamism had no direct effect on supplier's commitment and was not moderated by the flexibility and dependence of the supply chain. This study makes its contribution in the point of view that this is a rare study on environmental dynamism and supply chain commitment in the field of high-tech industry. Especially, this study verified the effects of three sectors of environmental dynamism on supplier's commitment. Also, it empirically tested how the effects were moderated by flexibility and dependence. The results showed that flexibility and interdependence had a role to strengthen supplier's commitment under environmental dynamism in high-tech industry. Thus relationship managers in high-tech industry should make supply chain relationship flexible and interdependent. The limitations of the study are as follows; First, about the research setting, the study was conducted with high-tech industry, in which the direction of the change in the power balance of supply chain dyads is usually determined by manufacturers. So we have a difficulty with generalization. We need to control the power structure between partners in a future study. Secondly, about flexibility, we treated it throughout the paper as positive, but it can also be negative, i.e. violating an agreement or moving, but in the wrong direction, etc. Therefore we need to investigate the multi-dimensionality of flexibility in future research.

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