Marker Variables in SmartPLS: A Practical Guide to Common Method Variance
Noor Ul Hadi, Balqees Naser Almessabi, Muhammad Imran Khan
Prince Mohammad bin Fahd University Zayed University Imperial College London
阅读操作
确认中在文库中上传 PDF 后可生成中文音频讲解。
摘要与影响
Common method variance (CMV) remains a persistent concern in survey-based structural equation modeling (SEM), yet practical guidance for implementing marker-variable procedures in partial least squares structural equation modeling (PLS-SEM) remains fragmented. Existing studies differ in how marker variables are selected, specified, and interpreted, creating inconsistent applications in SmartPLS, particularly for mediation, moderated mediation, and higher-order construct models. This paper consolidates existing methodological recommendations into a practical workflow covering marker selection, model specification, implementation, interpretation, and reporting. The proposed guidance clarifies the distinction between dependent-variable-only and broader measurement-level specifications, provides practical criteria for selecting and specifying marker variables, and demonstrates their implementation across direct-effect, mediation, moderated mediation, and hierarchical component models estimated using the repeated-indicators approach. Across the worked models, substantive relationships remained stable after marker inclusion, while marker paths were consistently unsupported. Particular attention is given to hierarchical component models, where marker paths are specified at the lower-order construct level at which the indicators are represented. The paper further provides practical decision rules for interpreting marker-variable results based on substantive path stability and marker-path behavior while emphasizing appropriate reporting practices and the limitations of marker-variable diagnostics. Rather than introducing a new statistical method, the paper provides an applied SmartPLS guide intended to improve the consistency, transparency, and rigor of marker-variable applications in PLS-SEM research.
逐年被引趋势
暂无年度引用数据
关键指标
同类平均 = 1
同领域 · 同年份 · 同类型
Google Scholar 与 OpenAlex 的被引统计范围不同,数值存在差异属正常。
AI 辅助阅读
依据:摘要
可就本文提问;依据不足时会说明。
学术脉络
学科主题
计算机 / AIPsychometric Methodologies and Testing
Business Strategies and Management Research · Advanced Statistical Methods and Models
参考文献 34
此处列出前 3 条