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

Journal re
Journal of Knowledge Management
  • Volumen: 23
  • Número: 1
  • Fecha: 14 enero 2019
  • Páginas: 67-89
  • ISSN: 17587484 13673270
  • Tipo de fuente: Revista
  • DOI: 10.1108/JKM-05-2018-0322
  • Tipo de documento: Crítica
  • Editorial: Emerald Group Publishing Ltd. Howard House Wagon Lane, Bingley BD16 1WA
© 2018, Emerald Publishing Limited. 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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