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      Tips to use partial least squares structural equation modelling (PLS-SEM) in knowledge management

      , ,
      Journal of Knowledge Management
      Emerald

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          Abstract

          Purpose

          Structural equation modelling (SEM) has been defined as the combination of latent variables and structural relationships. The partial least squares SEM (PLS-SEM) is used to estimate complex cause-effect relationship models with latent variables as the most salient research methods across a variety of disciplines, including knowledge management (KM). Following the path initiated by different domains in business research, this paper aims to examine how PLS-SEM has been applied in KM research, also providing some new guidelines how to improve PLS-SEM report analysis.

          Design/methodology/approach

          To ensure an objective way to analyse relevant works in the field of KM, this study conducted a systematic literature review of 63 publications in three SSCI-indexed and specific KM journals between 2015 and 2017.

          Findings

          Our results show that over the past three years, a significant amount of KM works has empirically used PLS-SEM. The findings also suggest that in light of recent developments of PLS-SEM reporting, some common misconceptions among KM researchers occurred mainly related to the reasons for using PLS-SEM, the purposes of PLS-SEM analysis, data characteristics, model characteristics and the evaluation of the structural models.

          Originality/value

          This study contributes to that vast KM literature by documenting the PLS-SEM-related problems and misconceptions. Therefore, it will shed light for better reports in PLS-SEM studies in the KM field.

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          Most cited references99

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          Evaluating Structural Equation Models with Unobservable Variables and Measurement Error

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            The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations.

            In this article, we attempt to distinguish between the properties of moderator and mediator variables at a number of levels. First, we seek to make theorists and researchers aware of the importance of not using the terms moderator and mediator interchangeably by carefully elaborating, both conceptually and strategically, the many ways in which moderators and mediators differ. We then go beyond this largely pedagogical function and delineate the conceptual and strategic implications of making use of such distinctions with regard to a wide range of phenomena, including control and stress, attitudes, and personality traits. We also provide a specific compendium of analytic procedures appropriate for making the most effective use of the moderator and mediator distinction, both separately and in terms of a broader causal system that includes both moderators and mediators.
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              Towards a Methodology for Developing Evidence-Informed Management Knowledge by Means of Systematic Review

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                Author and article information

                Journal
                Journal of Knowledge Management
                JKM
                Emerald
                1367-3270
                January 14 2019
                January 14 2019
                : 23
                : 1
                : 67-89
                Article
                10.1108/JKM-05-2018-0322
                8327dd53-c69b-4085-ba94-7affd373299b
                © 2019

                https://www.emerald.com/insight/site-policies

                History

                Quantitative & Systems biology,Biophysics
                Quantitative & Systems biology, Biophysics

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