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Knowledge quality: A review and a revised conceptual model

Author

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  • Mehwish Waheed

    (UM - University of Malaya = Universiti Malaya [Kuala Lumpur, Malaisie])

  • Kiran Kaur

    (UM - University of Malaya = Universiti Malaya [Kuala Lumpur, Malaisie])

Abstract

This paper presents the theoretical underpinnings and measures employed in the assessment of knowledge quality. It aims to identify a conceptual and operational measure of knowledge quality within the context of an e-learning environment. The review is conducted in two phases. First, the conceptual and operational measures of knowledge quality are explained based on the epistemic concept of the essence of knowledge and the knowledge hierarchy structure. Next, a detailed analysis of the dimensions measuring data quality (DQ), information quality (IQ), and knowledge quality (KQ) in 33 studies ranging from research on information systems (IS), to knowledge and information management, e-learning, and data warehouses is presented. Minimal biased methods were used to acquire the literature for this review. The findings reveal that there is an evident lack of research in measuring KQ based on a definite conceptual understanding of the hierarchical structure of knowledge. Furthermore, researchers' repeated use of the DQ and IQ dimensions to measure KQ has failed to develop a reliable KQ measure. Subsequently, an operational view of KQ is proposed with six distinct dimensions: Adaptable, Applicable, Expandable, True, Innovative, and Justified. This proposed KQ model that is based on the hierarchical structure of knowledge; it identifies the dimensions distinct to the KQ construct. It may be useful for the conceptualization and measure of KQ in various information environments, including e-learning and IS. The proposed model provides a more explicit way to conceptualize and measure the KQ construct by acknowledging that it is an extension of the IQ construct but with distinct and unique dimensions.

Suggested Citation

  • Mehwish Waheed & Kiran Kaur, 2016. "Knowledge quality: A review and a revised conceptual model," Post-Print hal-03882160, HAL.
  • Handle: RePEc:hal:journl:hal-03882160
    DOI: 10.1177/0266666914539694
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    Cited by:

    1. Chiu-Chi Wei & Chih-Chien Tai & Shun-Chin Lee & Meng-Ling Chang, 2023. "Assessing Knowledge Quality Using Fuzzy MCDM Model," Mathematics, MDPI, vol. 11(17), pages 1-16, August.

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