• Title, Summary, Keyword: Knowledge stock

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A Knowledge Stock and Flow Perspective for the Assimilation of Knowledge Management Innovation (지식관리혁신의 동화를 위한 지식의 축척과 흐름의 관점)

  • Lee, Jae Nam;Choi, Byoung-Gu
    • Knowledge Management Research
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    • v.11 no.5
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    • pp.1-23
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    • 2010
  • In order to provide a better understanding about the phenomenon of KM assimilation, this study attempts to conceptually develop and empirically compare two different models: (1) the first model, which considers the KM process as the flow of knowledge that plays an intervening role between knowledge stocks (i.e., knowledge worker, technical knowledge infrastructure, external knowledge linkage, knowledge strategy, and internal knowledge climate) and the level of KM assimilation; and (2) the second model is a simple direct effect formulation without any distinction between knowledge stock and flow. These two models were then tested and compared using the responses of 187 Korean organizations that had already implemented enterprise-wide KM systems. The findings indicate that the two models are useful in explaining successful KM assimilation. However, the first causal model with the distinction between knowledge stock and flow assesses the effectiveness of KM more accurately than the second model without the distinction. Interestingly, the KM process was shown to be the most critical factor for the proliferation of KM activities across an organization. The findings of this study are expected to serve not only as early groundwork for researchers hoping to understand KM and its effective assimilation in organizations, but should also provide practitioners with guidelines as to how they can enhance their KM assimilation level so as to improve their organizational performance.

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Quantitative Causal Reasoning in Stock Price Index Prediction Model

  • Kim, Myoung-Joon;Ingoo Han
    • Proceedings of the Korean Operations and Management Science Society Conference
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    • pp.228-231
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    • 1998
  • Artificial Intelligence literatures have recognized that stock market is a highly unstructured and complex domain so that it is difficult to find knowledge that belongs to that domain. This paper demonstrates that the proposed QCOM can derive global knowledge about stock market on the basis of a set of local knowledge and express it as a digraph representation. In addition, inference mechanism using quantitative causal reasoning can describe the qualitative and quantitative effects of exogenous variables on stock market.

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Synthesis of Machine Knowledge and Fuzzy Post-Adjustment to Design an Intelligent Stock Investment System

  • Lee, Kun-Chang;Kim, Won-Chul
    • Journal of the Korean Operations Research and Management Science Society
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    • v.17 no.2
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    • pp.145-162
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    • 1992
  • This paper proposes two design principles for expert systems to solve a stock market timing (SMART) problems : machine knowledge and fuzzy post-adjustment, Machine knowledge is derived from past SMART instances by using an inductive learning algorithm. A knowledge-based solution, which can be regarded as a prior SMART strategy, is then obtained on the basis of the machine knowledge. Fuzzy post-adjustment (FPA) refers to a Bayesian-like reasoning, allowing the prior SMART strategy to be revised by the fuzzy evaluation of environmental factors that might effect the SMART strategy. A prototype system, named K-SISS2 (Knowledge-based Stock Investment Support System 2), was implemented using the two design principles and tested for solving the SMART problem that is aimed at choosing the best time to buy or sell stocks. The prototype system worked very well in an actual stock investment situation, illustrating basic ideas and techniques underlying the suggested design principles.

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An Empirical Analysis of the Railroad R&D Stock (철도 R&D Stock에 대한 실증적 분석)

  • Park, Man-Soo;Moon, Dae-Seop;Lee, Hi-Sung
    • Journal of the Korean Society for Railway
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    • v.13 no.5
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    • pp.528-534
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    • 2010
  • In the new growth theory, R&D stock is the third factor of production excluding a labor and capital. In this point, a R&D stock is located in a capital which is accumulated by money like existing capital and this is a knowledge capital. The effort for escalating this knowledge capital is R&D investment and R&D stock is an accumulation of this. A contribution degree of the economic growth and a return of R&D investments are analyzed by an estimation of relation R&D stock and a total factor of productivity. This study analyzed R&D stock of railroad R&D investments and compared R&D stock with a technical level. So, a technical level is proportionally escalated following escalation of R&D stock. and compared railroad industry weight on the GDP with a railroad R&D stock weight on whole industries R&D stock. According to a relatively small railroad R&D stock weight against the railroad industry weight, a continuous railroad R&D investment is needed.

The analysis of the railroad R&D investment and R&D Stock (철도 연구개발투자와 지식축적량 분석)

  • Park, Man-Soo;Lee, Hi-Sung;Moon, Dae-Seop
    • Proceedings of the KSR Conference
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    • pp.791-794
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    • 2009
  • Each nation of the world is intensively propelling the R&D investment to solve the financial crisis and worldwide economic recession occurred from last year. This means the world economic is under economic system based on the knowledge. So, The R&D is continuously propelled for possession of the technology through the R&D stock and which is core in the knowledge based economic system. In this world stream, our government is also increasing the R&D investment and checked the technology level through surveying the R&D stock and corn parison of each industry or world. The R&D investment of the railroad is continued but there is no data of the R&D stock. So, surveying the railroad R&D stock and comparing with korea industry is processed.

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An Evolutionary Approach to Inferring Decision Rules from Stock Price Index Predictions of Experts

  • Kim, Myoung-Jong
    • Management Science and Financial Engineering
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    • v.15 no.2
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    • pp.101-118
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    • 2009
  • In quantitative contexts, data mining is widely applied to the prediction of stock prices from financial time-series. However, few studies have examined the potential of data mining for shedding light on the qualitative problem-solving knowledge of experts who make stock price predictions. This paper presents a GA-based data mining approach to characterizing the qualitative knowledge of such experts, based on their observed predictions. This study is the first of its kind in the GA literature. The results indicate that this approach generates rules with higher accuracy and greater coverage than inductive learning methods or neural networks. They also indicate considerable agreement between the GA method and expert problem-solving approaches. Therefore, the proposed method offers a suitable tool for eliciting and representing expert decision rules, and thus constitutes an effective means of predicting the stock price index.

A Knowledge-Based Fuzzy Post-Adjustment Mechanism:An Application to Stock Market Timing Analysis

  • Lee, Kun-Chang
    • Journal of the Korean Operations Research and Management Science Society
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    • v.20 no.1
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    • pp.159-177
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    • 1995
  • The objective of this paper is to propose a knowledge-based fuzzy post adjustment so that unstructured problems can be solved more realistically by expert systems. Major part of this mechanism forcuses on fuzzily assessing the influence of various external factors and accordingly improving the solutions of unstructured problem being concerned. For this purpose, three kinds of knowledge are used : user knowledge, expert knowledge, and machine knowledge. User knowledge is required for evaluating the external factors as well as operating the expert systems. Machine knowledge is automatically derived from historical instances of a target problem domain by using machine learning techniques, and used as a major knowledge source for inference. Expert knowledge is incorporate dinto fuzzy membership functions for external factors which seem to significantly affect the target problems. We applied this mechanism to a prototyoe expert system whose major objective is to provide expert guidance for stock market timing such as sell, buty, or wait. Experiments showed that our proposed mechanism can improve the solution quality of expert systems operating in turbulent decision-making environments.

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Estimation of S&T Knowledge Production Function Using Principal Component Regression Model (주성분 회귀모형을 이용한 과학기술 지식생산함수 추정)

  • Park, Su-Dong;Sung, Oong-Hyun
    • Journal of Korea Technology Innovation Society
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    • v.13 no.2
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    • pp.231-251
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    • 2010
  • The numbers of SCI paper or patent in science and technology are expected to be related with the number of researcher and knowledge stock (R&D stock, paper stock, patent stock). The results of the regression model showed that severe multicollinearity existed and errors were made in the estimation and testing of regression coefficients. To solve the problem of multicollinearity and estimate the effect of the independent variable properly, principal component regression model were applied for three cases with S&T knowledge production. The estimated principal component regression function was transformed into original independent variables to interpret properly its effect. The analysis indicated that the principal component regression model was useful to estimate the effect of the highly correlate production factors and showed that the number of researcher, R&D stock, paper or patent stock had all positive effect on the production of paper or patent.

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The Mediating Effects of Bidirectional Knowledge Transfer on System Implementation Success

  • Kim, Jong Uk;Kim, Hyo Sin;Park, Sang Cheol
    • Asia pacific journal of information systems
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    • v.25 no.3
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    • pp.445-472
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    • 2015
  • Although knowledge transfer between two different parties occurs in IS development projects, the majority of prior studies focused on knowledge transfer from IT consultants to clients. Considering two parts of knowledge transfer in IS development projects, we must consider both 'where knowledge is transferred from' and 'where it is transferred to'. Therefore, in this study, we attempt to describe two different routes of knowledge transfer, such as knowledge transfer from an IT consultant to a client and knowledge transfer from a client to an IT consultant. In this regard, we have examined the effect of two different routes of knowledge transfer on system implementation success in IS development project. Specifically, we adopted the knowledge stock-flow theory to examine the causal relationship between IT consulting firms and clients in terms of knowledge transfer and eventual system implementation success. Survey data collected from 213 pairs of individuals (both clients and IT consultants) were used to test the model using three different analytic approaches such as PLS (partial least squares) and two types of mediated regression techniques. We found that knowledge transfers partially mediated both the relationships between IT consultants' IT skills (project members' business knowledge) and system implementation success. Furthermore, the effects of each knowledge transfer were distinguished by depending on the types of system, such as ERP or groupware. Our attempts have significant implications for both research and practice given the importance of effective knowledge transfer to IT consulting.

Knowledge Capital in Economic Growth: A Panel Analysis of 120 Countries

  • Lim, Dong-Geon;Jung, Jin Hwa
    • Asian Journal of Innovation and Policy
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    • v.6 no.1
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    • pp.94-110
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    • 2017
  • This paper approaches knowledge capital as social infrastructure and analyzes its impact on economic growth. To this end, we constructed a panel dataset for 120 countries for the years 2000-2014 and estimated the economic growth function using the panel analysis. As proxies for knowledge capital, we used the R&D expenditure per capita and the number of patent applications per thousand people in each country, both measured in stock. Economic growth was measured in terms of real GDP per capita and real value added per capita at the industry level. The empirical findings demonstrate that knowledge capital accumulated in a society significantly promotes economic growth. Especially R&D stock increases real value added per capita in all industries-not only manufacturing, but also services and agriculture-implying substantial inter-industry spillover effects. The findings of this study suggest that knowledge capital boosts economic growth as core social infrastructure.