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.
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.
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.
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.
The authors gratefully acknowledge the Ministerio de Economía, Industria y Competitividad of the Spanish Government for financing the research project ECO2017-88987-R (MINECO/FEDER;UE), co-financed from the European Union FEDER funds.
Cepeda-Carrion, G., Cegarra-Navarro, J.-G. and Cillo, V. (2019), "Tips to use partial least squares structural equation modelling (PLS-SEM) in knowledge management", Journal of Knowledge Management, Vol. 23 No. 1, pp. 67-89. https://doi.org/10.1108/JKM-05-2018-0322
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