Evaluating Knowledge Quality in Knowledge Management Systems

Chakrabarti, Deepankar; Arora, Monika; Sharma, Prayas

doi:10.18576/jsap/070107

Cited by 6 publications

(9 citation statements)

References 37 publications

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“…The knowledge quality is the quality of the information in KMS. The essential elements inherent in knowledge quality are accurate, complete, consistent, current, and relevant information (Chakrabarti et al, 2018;Gilang et al, 2017). The element is used as a questionnaire keyword and is described in Table 2.…”

Section: Knowledge Qualitymentioning

confidence: 99%

Creation of Questionnaire Keywords With the Cise Method for KMS User Satisfaction Evaluation

Tju

2022

jmika

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This study aims to obtain user satisfaction factors for a knowledge management system so that a questionnaire can be made for evaluation or measurement. The SECI method is used with the CISE sequence which consists of four knowledge creation steps, namely C-combination, I-nternalization, S-socialization, and ending E-externalization. The stage begins with literature studies and then modifications are made with the selection, addition, and incorporation of existing models. From understanding and analyzing several models, discussions or brainstorming with colleagues were then carried out so that a final model was obtained to compile a list of keywords and statements as a questionnaire based on indicators related to knowledge management and the satisfaction of knowledge management system users. The results obtained there are eight user satisfaction factors divided into technical aspects (knowledge quality, knowledge sharing, system quality, service quality) and social aspects (management support, system use, perceived usefulness, user attribute). Five attributes were obtained from each of these factors as keywords resulting in 40 questionnaire statements. It can be concluded that the factors of user satisfaction knowledge management system can be built from the satisfaction of information system users in general by adding distinctive factors including the creation, transfer, and documentation of knowledge.

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Section: Knowledge Qualitymentioning

confidence: 99%

Creation of Questionnaire Keywords With the Cise Method for KMS User Satisfaction Evaluation

Tju

2022

jmika

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“…Knowledge quality is defined as created knowledge which is relevant to knowledge workers and is valuable in content [8,38]. Knowledge quality is a significant factor when it comes to the success of knowledge intensive systems [6][7][8][9][10][11][12]. Knowledge quality has its own attributes and are as follows; intrinsic knowledge quality, contextual knowledge quality, actionable knowledge quality and accessibility knowledge quality [8,38,46].…”

Section: Knowledge Qualitymentioning

confidence: 99%

“…The literature mentioned that knowledge intensive system usage is affected by quality of knowledge [9,[13][14][15]. Knowledge residing in an organisation must be preserved to ensure knowledge quality [7,37]. Lastly, knowledge quality is important as it was identified that it is a catalyst for innovation within an organisation [38,54,55].…”

Section: Identified Research Gapmentioning

confidence: 99%

“…Without an effective knowledge-intensive system, knowledge could not be shared effectively [2][3][4][5]. Furthermore, knowledge quality itself is integral in the success of a knowledge-intensive system [6][7][8][9][10][11][12]. The usage of knowledge-intensive systems is affected by the quality of knowledge present in the system [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

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Implementation of Decision Tree Algorithm to Classify Knowledge Quality in a Knowledge Intensive System

et al. 2021

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Knowledge is an important asset for an organisation as it facilitates organisational growth. To facilitate knowledge creation and sharing, this is where a knowledge-intensive system is required. One key area that hinders the effective use of knowledge-intensive systems in an organisation is the lack of knowledge quality. This causes the system to be underutilised, and as a result, knowledge will not be captured or shared effectively. Recent KM findings identified that machine learning could be beneficial to knowledge management. A literature review was conducted to identify knowledge of quality attributes and machine learning algorithms. From the findings, it was identified that the decision tree algorithm has a strong potential at classifying knowledge quality. An experiment was then devised to identify the training model required and measure its effectiveness using a pilot test. This involved using a knowledge-intensive system and mapping its variables to the respective knowledge quality attributes. From the experimentation result, the training model is then devised before implemented in a pilot test. The pilot test involved collecting knowledge using the same knowledge-intensive system before running the training model. From the results, it was identified that the decision tree could classify knowledge quality though the results yielded four different outputs at classifying knowledge quality. It was concluded that machine learning is beneficial in the area of knowledge management.

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“…In the past, scholars mentioned that measuring knowledge quality should include correctness, completeness, consistency, relevance, etc. [10]. Some studies mentioned that measuring knowledge quality should include certainty, accuracy, and operability [11], while Arora et al (2013) believed that measuring knowledge quality should include completeness, timeliness, accuracy, transparency, and relevancy [12].…”

Section: Introductionmentioning

confidence: 99%

Assessing Knowledge Quality Using Fuzzy MCDM Model

Wei,

Tai,

Lee

et al. 2023

Mathematics

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The purpose of knowledge management is to excavate the tacit knowledge accumulated by each enterprise member through the knowledge proposal system. Each knowledge proposal must be assessed, and after passing the quality assessment, the knowledge proposal will be stored in the knowledge repository and shared with other employees who need the knowledge at work. In the long run, the capabilities of all employees will gradually enhance and the competitiveness of enterprises will naturally increase. The correct assessment of knowledge quality is the key to the success of knowledge management. Some scholars propose to use the AHP (analytical hierarchical process) to determine the quality of knowledge. The problem with this approach is that the AHP can only obtain the relative quality of all knowledge, not the actual quality of knowledge. Therefore, this study proposes a fuzzy assessment model to measure knowledge quality, which includes a knowledge quality fuzziness index (KQFI) and a checking gate. First, experts conduct linguistic evaluation on the weight of criteria and knowledge quality. All linguistic evaluations are then integrated into a knowledge quality fuzziness index (KQFI), which is compared with a fuzzy threshold (FT); then, the level of goodness of KQFI to FT is obtained. If it is greater than 0.5, it means that the quality of the knowledge proposal is qualified; otherwise, it means that the quality of the knowledge proposal is unqualified. This study uses a case including five experts and nine knowledge proposals to demonstrate the applicability of the method. The results show that the method finally judges six knowledge instances as qualified and three as unqualified. The results show that the proposed method can indeed assist enterprises to effectively screen knowledge proposals.

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Evaluating Knowledge Quality in Knowledge Management Systems

Cited by 6 publications

References 37 publications

Creation of Questionnaire Keywords With the Cise Method for KMS User Satisfaction Evaluation

Creation of Questionnaire Keywords With the Cise Method for KMS User Satisfaction Evaluation

Implementation of Decision Tree Algorithm to Classify Knowledge Quality in a Knowledge Intensive System

Assessing Knowledge Quality Using Fuzzy MCDM Model

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