• Title/Summary/Keyword: network value

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An Effect of SNS Tourism Information Service Quality on User Satisfaction and Reuse Intention: Focusing on Mediating Effect of Value (SNS 관광정보 서비스품질이 사용자 만족과 재이용의도에 미치는 영향: 가치의 매개효과를 중심으로)

  • Kim, Tae-Kyung;Cho, Chul-Ho
    • Journal of Korean Society for Quality Management
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    • v.43 no.2
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    • pp.185-200
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    • 2015
  • Purpose: Present study was designed to examine the casual relationships among tourism information service quality, value, user satisfaction, and reuse intention in social network service(SNS). Also, we intended to testify the mediating role of value in causal model. We applied path analysis model in order to test the hypotheses and research model. Methods: Survey tool, that is, questionnaire has obtained validity through literature survey, exploratory survey and pretest and sample 272 was selected. For statistical treatment of pretest and main analysis, SPSS18.0 and AMOS18.0 were employed and structural equation model was employed as analysis method. Results: Result of this study shows as follows. Two factors(ease of understanding and structure) have an effect on user satisfaction and reuse intention, and we found that value played a significant and important role in causal relationship. Therefore, value was empirically confirmed as t he import ant fact or preceding user satisfaction and reuse intention. Conclusion: Present study shows that two factors(ease of understanding and structure) in via of value, were important factors that related business companies have to emphasize to raise performance. However, present study has some limitations to additionally research in the future.

The Sharing Economy Business Model per the Analysis of Value Attributes (공유경제 비즈니스 모델의 가치 요인 분석)

  • Lee, Junmin;Hwang, Junseok;Kim, Jonglip
    • Journal of Information Technology Services
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    • v.15 no.4
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    • pp.153-174
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    • 2016
  • On account of multiple causes, including prolonged global economic crisis, addressing environmental pollution and the advent of hyper-connected society, a new paradigm called 'sharing economy' has rapidly emerged. Many startups have attempted to build promising business model based on the sharing economy concept. Nevertheless, successful cases are still very rare in the global level, except for Uber and Airbnb cases. Therefore, this study analyzes necessary causes and sufficient causes for successful settlements in the market through a comparative case analysis on digital matching firms in the sharing economy businesses. For the case study, we compare five successful cases (Uber, Airbnb, Kickstarter, TaskRabbit and DogVacay), three failure cases (Homejoy, Ridejoy and Tuterspree) and a platform cooperativism case (Juno) in accordance with six value attributes of business model including value proposition, market segment, value chain, cost structure and profit potential, value network and competitive strategy. We apply Boolean method to support controlled comparison and eliminate unnecessary attributes. The Boolean analysis result shows that value proposition, cost structure and profit potential, value network and competitive strategy are the essential attributes. Furthermore, the result indicates that each attribute is a necessary condition, where all four conditions should be met simultaneously in order to be successful. With this result, we discuss essential consideration for those who are planning startup based on the sharing economy business model.

A Study on Intelligent Value Chain Network System based on Firms' Information (기업정보 기반 지능형 밸류체인 네트워크 시스템에 관한 연구)

  • Sung, Tae-Eung;Kim, Kang-Hoe;Moon, Young-Su;Lee, Ho-Shin
    • Journal of Intelligence and Information Systems
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    • v.24 no.3
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    • pp.67-88
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    • 2018
  • Until recently, as we recognize the significance of sustainable growth and competitiveness of small-and-medium sized enterprises (SMEs), governmental support for tangible resources such as R&D, manpower, funds, etc. has been mainly provided. However, it is also true that the inefficiency of support systems such as underestimated or redundant support has been raised because there exist conflicting policies in terms of appropriateness, effectiveness and efficiency of business support. From the perspective of the government or a company, we believe that due to limited resources of SMEs technology development and capacity enhancement through collaboration with external sources is the basis for creating competitive advantage for companies, and also emphasize value creation activities for it. This is why value chain network analysis is necessary in order to analyze inter-company deal relationships from a series of value chains and visualize results through establishing knowledge ecosystems at the corporate level. There exist Technology Opportunity Discovery (TOD) system that provides information on relevant products or technology status of companies with patents through retrievals over patent, product, or company name, CRETOP and KISLINE which both allow to view company (financial) information and credit information, but there exists no online system that provides a list of similar (competitive) companies based on the analysis of value chain network or information on potential clients or demanders that can have business deals in future. Therefore, we focus on the "Value Chain Network System (VCNS)", a support partner for planning the corporate business strategy developed and managed by KISTI, and investigate the types of embedded network-based analysis modules, databases (D/Bs) to support them, and how to utilize the system efficiently. Further we explore the function of network visualization in intelligent value chain analysis system which becomes the core information to understand industrial structure ystem and to develop a company's new product development. In order for a company to have the competitive superiority over other companies, it is necessary to identify who are the competitors with patents or products currently being produced, and searching for similar companies or competitors by each type of industry is the key to securing competitiveness in the commercialization of the target company. In addition, transaction information, which becomes business activity between companies, plays an important role in providing information regarding potential customers when both parties enter similar fields together. Identifying a competitor at the enterprise or industry level by using a network map based on such inter-company sales information can be implemented as a core module of value chain analysis. The Value Chain Network System (VCNS) combines the concepts of value chain and industrial structure analysis with corporate information simply collected to date, so that it can grasp not only the market competition situation of individual companies but also the value chain relationship of a specific industry. Especially, it can be useful as an information analysis tool at the corporate level such as identification of industry structure, identification of competitor trends, analysis of competitors, locating suppliers (sellers) and demanders (buyers), industry trends by item, finding promising items, finding new entrants, finding core companies and items by value chain, and recognizing the patents with corresponding companies, etc. In addition, based on the objectivity and reliability of the analysis results from transaction deals information and financial data, it is expected that value chain network system will be utilized for various purposes such as information support for business evaluation, R&D decision support and mid-term or short-term demand forecasting, in particular to more than 15,000 member companies in Korea, employees in R&D service sectors government-funded research institutes and public organizations. In order to strengthen business competitiveness of companies, technology, patent and market information have been provided so far mainly by government agencies and private research-and-development service companies. This service has been presented in frames of patent analysis (mainly for rating, quantitative analysis) or market analysis (for market prediction and demand forecasting based on market reports). However, there was a limitation to solving the lack of information, which is one of the difficulties that firms in Korea often face in the stage of commercialization. In particular, it is much more difficult to obtain information about competitors and potential candidates. In this study, the real-time value chain analysis and visualization service module based on the proposed network map and the data in hands is compared with the expected market share, estimated sales volume, contact information (which implies potential suppliers for raw material / parts, and potential demanders for complete products / modules). In future research, we intend to carry out the in-depth research for further investigating the indices of competitive factors through participation of research subjects and newly developing competitive indices for competitors or substitute items, and to additively promoting with data mining techniques and algorithms for improving the performance of VCNS.

The Classification of Roughness fir Machined Surface Image using Neural Network (신경회로망을 이용한 가공면 영상의 거칠기 분류)

  • 사승윤
    • Journal of the Korean Society of Manufacturing Technology Engineers
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    • v.9 no.2
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    • pp.144-150
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    • 2000
  • Surface roughness is one of the most important parameters to estimate quality of products. As this reason so many studies were car-ried out through various attempts that were contact or non-contact using computer vision. Even through these efforts there were few good results in this research., however texture analysis making a important role to solve these problems in various fields including universe aviation living thing and fibers. In this study feature value of co-occurrence matrix was calculated by statistic method and roughness value of worked surface was classified, of it. Experiment was carried out using input vector of neural network with characteristic value of texture calculated from worked surface image. It's found that recognition rate of 74% was obtained when adapting texture features. In order to enhance recogni-tion rate combination type in characteristics value of texture was changed into input vector. As a result high recognition rate of 92.6% was obtained through these processes.

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Error Correction Technique of Distance Measurement for ToF LIDAR Sensor

  • Moon, Yeon-Kug;Shim, Young Bo;Song, Hyoung-Kyu
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.12 no.2
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    • pp.960-973
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    • 2018
  • This paper presents design for error correcting algorithm of the time of flight (ToF) detection value in the light detection and ranging (LIDAR) system sensor. The walk error of ToF value is generated by change of the received signal power depending on distance between the LIDAR sensor and object. The proposed method efficiently compensates the ToF value error by the independent ToF value calculation from the received signal using both rising point and falling point. A constant error of ~0.05 m is obtained after the walk error correction while an increasing error up to ~1 m is obtained with conventional method.

A Value-oriented System Integration Project Sizing and Cost Estimation Model (가치중심의 SI (System Integration) 사업 규모 및 비용산정 모형 구축 연구)

  • Kim, Hyun-Soo
    • Asia pacific journal of information systems
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    • v.8 no.3
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    • pp.101-118
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    • 1998
  • The purpose of this study is to construct a value-oriented sizing and cost estimation model for system integration projects. In particular, this study is to build a system architecture design and integration cost model, and a network design and implementation cost model. Unlike software development projects, system integration projects include knowledge-intensive professional services on system architecture and network design areas. Because of these work's high invisibility, the cost of these services is hard to estimate and measure. Therefore, we need to develop value-oriented cost models. This study presents 6 value-oriented cost models, and tests statistical significance of these models with real system integration project data. The results show that cost factors on these models are valid, and models are statistically significant. Future work is needed to integrate various cost models and apply the whole model to field projects to increase model's prediction accuracy.

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퍼지신경망에 의한 퍼지 회귀분석: 품질 평가 문제에의 응용

  • 권기택
    • Proceedings of the Korea Association of Information Systems Conference
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    • 1996.11a
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    • pp.211-216
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    • 1996
  • This paper propose a fuzzy regression method using fuzzy neural networks when a membership value is attached to each input-output pair. First, an architecture o fuzzy neural networks with fuzzy weights and fuzzy biases is shown. Next, a cost function is defined using the fuzzy output from the fuzzy neural network and the corresponding target output with a membership value. A learning algorithm is derived from the cost function. The derived learning algorithm trains the fuzzy neural network so 솜 t the level set of the fuzzy output includes the target output. Last, the proposed method is applied to the quality evaluation problem of injection molding

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공급사슬네트워크

  • Ahn, Ung
    • Proceedings of the Korean Operations and Management Science Society Conference
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    • 2003.11a
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    • pp.243-246
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    • 2003
  • A radical environmental change of enterprise and varieties of customer needs demand that the value-added activities of enterprise should be restructured and coordinated. But it is not sufficient that the reengineering processes are restricted withen a firm, so the value-added processes should be expanded into intercompany. The integrated organizational structure between enterprises refer to supply chain network. In this paper we present characteristics, structure concepts, and axiomatic model of supply chain network.

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퍼지신경망에 의한 퍼지회귀분석 : 품질평가 문제에의 응용

  • 권기택
    • Proceedings of the Korea Society for Industrial Systems Conference
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    • 1996.10a
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    • pp.211-216
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    • 1996
  • This paper propose a fuzzy regression method using fuzzy neural networks when a membership value is attached to each input-output pair. First, an architecture of fuzzy nerual networks with fuzzy weights and fuzzy biases is shown. Next a cost function is defined using the fuzzy output from the fuzzy neural network and the corresponding target output with a membership value.A learning algorithm is derived from the cost function. The derived learning algorithm trains the fuzzy neural network so that the level set of the fuzzy output includes the target output. Last, the proposed method is applied to the quality evaluation problem of injection molding.

Outlier prediction in sensor network data using periodic pattern (주기 패턴을 이용한 센서 네트워크 데이터의 이상치 예측)

  • Kim, Hyung-Il
    • Journal of Sensor Science and Technology
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    • v.15 no.6
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    • pp.433-441
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    • 2006
  • Because of the low power and low rate of a sensor network, outlier is frequently occurred in the time series data of sensor network. In this paper, we suggest periodic pattern analysis that is applied to the time series data of sensor network and predict outlier that exist in the time series data of sensor network. A periodic pattern is minimum period of time in which trend of values in data is appeared continuous and repeated. In this paper, a quantization and smoothing is applied to the time series data in order to analyze the periodic pattern and the fluctuation of each adjacent value in the smoothed data is measured to be modified to a simple data. Then, the periodic pattern is abstracted from the modified simple data, and the time series data is restructured according to the periods to produce periodic pattern data. In the experiment, the machine learning is applied to the periodic pattern data to predict outlier to see the results. The characteristics of analysis of the periodic pattern in this paper is not analyzing the periods according to the size of value of data but to analyze time periods according to the fluctuation of the value of data. Therefore analysis of periodic pattern is robust to outlier. Also it is possible to express values of time attribute as values in time period by restructuring the time series data into periodic pattern. Thus, it is possible to use time attribute even in the general machine learning algorithm in which the time series data is not possible to be learned.