@misc{10481/109711,
year = {2025},
month = {11},
url = {https://hdl.handle.net/10481/109711},
abstract = {Purpose – The purpose of this study is to investigate how common method bias (CMB) is addressed using partial least squares structural equation modeling (PLS-SEM) in hospitality and tourism research.
Design/methodology/approach – A systematic literature review was conducted on empirical studies published in 2023 and 2024 across 11 high-ranking hospitality and tourism journals. After applying exclusion criteria, 227 articles using PLS-SEM and addressing CMB were somehow analyzed, focusing on the extent and quality of reported CMB controls. The review included both procedural and statistical techniques.
Findings – While awareness of CMB has increased, many studies either fail to address it or apply methods incorrectly, especially the full collinearity test. Most rely on basic procedures or Harman’s single-factor test, with limited use of more advanced techniques. A practical demonstration of the random dependent variable (RDV) technique is provided to guide proper implementation.
Originality/value – Given the field’s reliance on self-reported data, the potential for CMB is high, yet methodological rigor in its detection and control remains underexplored. This paper’s main contribution highlights the RDV technique as a critical field-appropriate tool to address CMB within PLS-SEM applications.},
publisher = {Emerald},
keywords = {Common method bias},
keywords = {CMB},
keywords = {Hospitality},
keywords = {Partial least squares},
keywords = {PLS},
keywords = {Structural equation modeling},
keywords = {SEM},
keywords = {Tourism},
title = {How is common method bias addressed using partial least squares structural equation modeling in hospitality and tourism research?},
doi = {10.1108/TR-07-2025-0762},
author = {Castillo López, Ana and Rescalvo Martín, Silvia Elisa and Karatepe, Osman M.},
}