How is common method bias addressed using partial least squares structural equation modeling in hospitality and tourism research?

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.