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

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.