Knowledge Transfer Activation Analysis: Knowledge Trade Perspective

Jeong, Soon-Ki; Ahn, Jongchang; Rhee, Byung Ho

doi:10.1080/08874417.2013.11645631

Cited by 6 publications

(4 citation statements)

References 46 publications

(56 reference statements)

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“…As prior research has shown, knowledge-sharing culture and knowledge trade markets are supporting mechanisms for knowledge transfer activation. Knowledge organization and strategy are positive factors for knowledge sharing; KMSs and knowledge rewards are positive factors for knowledge trade markets (Jeong et al 2013). Figure 2 shows how factors affect the empirically proved knowledge transfer platform (See Fig.…”

Section: Methodsmentioning

confidence: 99%

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Knowledge Trade and Sharing in Knowledge Management System

Ahn

Jeong

2015

Lecture Notes in Computer Science

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Abstract. The importance of knowledge management (KM) leads many companies to use a knowledge management system (KMS); however, users frequently do not make good use of KMS. There is a new view that knowledge market can be helpful to promote knowledge transfer by knowledge trade. Knowledge transfer could be activated through the items of knowledge organization, knowledge strategy, KMS, and knowledge reward via knowledge sharing culture and knowledge trade market. We have proposed that the framework be organically related to above factors in the prior research. This article examined various cases to analyse the effect of knowledge trade market and knowledge culture for knowledge transfer. We then considered real case researches of the Korean organizations and global firms in order to discuss each factor on how to activate knowledge transfer. This discussion suggests that organizations harmonize both knowledge culture and knowledge trade market for knowledge transfer.

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Section: Methodsmentioning

confidence: 99%

“…The framework for knowledge transfer activation is based on: (a) knowledge organization, (b) knowledge strategy, (c) KMS, (d) knowledge rewards, and (e) knowledge sharing culture and knowledge trade market. We want to examine potential gaps between evidence and practice in knowledge transfer framework (Jeong et al 2013).…”

Section: Introductionmentioning

confidence: 99%

Knowledge Trade and Sharing in Knowledge Management System

Ahn

Jeong

2015

Lecture Notes in Computer Science

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“…shares [33] of enterprises are positively related to knowledge transfer performance. In terms of the technical indicators, early research has demonstrated that technical and innovation capabilities would facilitate enterprise knowledge transfer performance [34].…”

Section: Plos Onementioning

confidence: 96%

The impact of knowledge transfer performance on the artificial intelligence industry innovation network: An empirical study of Chinese firms

Guo-Feng

Jiao

et al. 2020

PLoS ONE

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As a core driving force of the most recent round of industrial transformation, artificial intelligence has triggered significant changes in the world economic structure, profoundly changed our life and way of thinking, and achieved an overall leap in social productivity. This paper aims to examine the effect of knowledge transfer performance on the artificial intelligence industry innovation network and the path artificial intelligence enterprises can take to promote sustainable development through knowledge transfer in the above context. First, we construct a theoretical hypothesis and conceptual model of the innovation network knowledge transfer mechanism within the artificial intelligence industry. Then, we collect data from questionnaires distributed to Chinese artificial intelligence enterprises that participate in the innovation network. Moreover, we empirically analyze the impact of innovation network characteristics, organizational distance, knowledge transfer characteristics, and knowledge receiver characteristics on knowledge transfer performance and verify the hypotheses proposed in the conceptual model. The results indicate that innovation network centrality and organizational culture distance have a significant effect on knowledge transfer performance, with influencing factors including network scale, implicit knowledge transfer, receiver's willingness to receive, and receiver's capacity to absorb knowledge. For sustainable knowledge transfer performance on promoting Chinese artificial intelligence enterprises innovation, this paper finally delivers valuable insights and suggestions.

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“…This is because not every criterion is relevant to the specific enterprise's requirements (Chourides et al, 2003;Lewis et al, 2007;Wibowo and Deng, 2012). Much research has been done on identifying the relevant criteria for measuring and benchmarking the KM practices from different perspectives (Gottschalk, 2005;Kale and Karaman, 2012;Knutsson et al, 2012;Jeong et al, 2013).…”

Section: Benchmarking the Performance Of Km Practicesmentioning

confidence: 99%

Benchmarking knowledge management practices in small and medium enterprises

Wibowo

Grandhi

2017

BIJ

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Purpose The purpose of this paper is to formulate the process of measuring and benchmarking the performance of knowledge management (KM) practices as a multicriteria group decision-making problem and present a new multicriteria group decision-making approach for effectively evaluating the performance of KM practices to meet the interests of various stakeholders in small and medium enterprises (SMEs). Design/methodology/approach A new multicriteria group decision-making approach is developed for evaluating the performance of KM practices of individual SMEs. Intuitionistic fuzzy numbers are used for representing the subjective assessments of decision makers in evaluating the relative importance of the evaluation criteria and the performance of individual KM practices with respect to specific evaluation criteria. A fuzzy multicriteria group decision-making algorithm is developed for measuring and benchmarking the performance of alternative KM practices. Findings The proposed multicriteria group decision-making approach is capable of effectively evaluating the performance of KM practices through adequately considering the presence of multiple decision makers, the multi-dimensional nature of the evaluation problem, and appropriately modeling the subjectiveness and imprecision of the evaluation process. The presentation of an example shows that the proposed fuzzy multicriteria group decision-making algorithm is simple to use and efficient in computation. Research limitations/implications The outcome of the multicriteria group decision-making approach is highly dependent on the inputs provided by the decision maker. Practical implications The novelty from this research lies in the utilization of a multicriteria group decision-making approach for evaluating the performance of KM practices in an organization. The outcome from the performance evaluation process allows the enterprise to adopt appropriate KM practices for achieving competitive advantages. Social implications The proposed multicriteria group decision-making approach has a significant social implication as it can be used as a decision-making tool for providing various decision makers in SMEs with useful and strategic information concerning the performance of KM practices in a given situation. Originality/value The originality of this paper lies in the development of the multicriteria group decision-making approach for effectively measuring and benchmarking the performance of KM practices of individual SMEs.

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Knowledge Transfer Activation Analysis: Knowledge Trade Perspective

Cited by 6 publications

References 46 publications

Knowledge Trade and Sharing in Knowledge Management System

Knowledge Trade and Sharing in Knowledge Management System

The impact of knowledge transfer performance on the artificial intelligence industry innovation network: An empirical study of Chinese firms

Benchmarking knowledge management practices in small and medium enterprises

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