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

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 v...

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Bibliographic Details
Published inJournal of knowledge management Vol. 23; no. 1; pp. 67 - 89
Main Authors Cepeda-Carrion, Gabriel, Cegarra-Navarro, Juan-Gabriel, Cillo, Valentina
Format Journal Article
LanguageEnglish
Published Kempston Emerald Publishing Limited 23.01.2019
Emerald Group Publishing Limited
Subjects
Absorptive capacity
Data analysis
Domains
Economic models
Hypothesis testing
Knowledge management
Knowledge sharing
Least squares
Literature reviews
Mathematical models
Modelling
Organizational learning
Social capital
Social networks
Software
Structural equation modeling
Systematic review
Tips
Variables
Knowledge management (KM)
Partial least squares (PLS)
Structural equation modelling (SEM)
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Summary: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.
ISSN:1367-3270
1758-7484
DOI:10.1108/JKM-05-2018-0322