An assessment of the use of partial least squares structural equation modeling in marketing research

Most methodological fields undertake regular critical reflections to ensure rigorous research and publication practices, and, consequently, acceptance in their domain. Interestingly, relatively little attention has been paid to assessing the use of partial least squares structural equation modeling...

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Veröffentlicht in:Journal of the Academy of Marketing Science Jg. 40; H. 3; S. 414 - 433
Hauptverfasser: Hair, Joe F., Sarstedt, Marko, Ringle, Christian M., Mena, Jeannette A.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Boston Springer US 01.05.2012
Springer Science + Business Media LLC
Springer
Springer Nature B.V
Schlagworte:
Analysis
Business and Management
Business enterprises
Forecasts and trends
Management
Market share
Marketing
Marketing research
Marktforschung
Methodological Paper
Partielle kleinste Quadrate
Research methodology
Researchers
Social Sciences
Statistical methods
Strategic management
Structural equation modeling
Strukturgleichungsmodell
Studies
Variables
Wissenschaftliche Methode
United States
Empirical research methods
Path modeling
Structural equation modeling
Partial least squares
ISSN:0092-0703, 1552-7824
Online-Zugang:Volltext
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Zusammenfassung:Most methodological fields undertake regular critical reflections to ensure rigorous research and publication practices, and, consequently, acceptance in their domain. Interestingly, relatively little attention has been paid to assessing the use of partial least squares structural equation modeling (PLS-SEM) in marketing research—despite its increasing popularity in recent years. To fill this gap, we conducted an extensive search in the 30 top ranked marketing journals that allowed us to identify 204 PLS-SEM applications published in a 30-year period (1981 to 2010). A critical analysis of these articles addresses, amongst others, the following key methodological issues: reasons for using PLS-SEM, data and model characteristics, outer and inner model evaluations, and reporting. We also give an overview of the interdependencies between researchers’ choices, identify potential problem areas, and discuss their implications. On the basis of our findings, we provide comprehensive guidelines to aid researchers in avoiding common pitfalls in PLS-SEM use. This study is important for researchers and practitioners, as PLS-SEM requires several critical choices that, if not made correctly, can lead to improper findings, interpretations, and conclusions.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0092-0703
1552-7824
DOI:10.1007/s11747-011-0261-6