Partial least squares structural equation modeling (PLS-SEM) has evolved from a niche methodological approach to a central tool for researchers across disciplines. In their recent bibliometric study, Angelelli et al. (2025) map the conceptual structure and thematic evolution of PLS-SEM research from 1985 to 2022, analyzing 9,150 Web of Science documents. Their work offers a panoramic view of how PLS-SEM has grown, shifted focus, and intertwined methodological innovation with diverse applications (Angelelli et al., 2025).
The authors begin by segmenting the literature into four periods—1995–2013, 2014–2017, 2018–2020, and 2021–2022—to account for changes in publication volume and research trends. In the first period, “Indicators” and “Management studies” emerge as motor themes, reflecting PLS-SEM’s early applications in performance measurement and strategic management. Methodological topics such as heterogeneity, formative indicators, and Monte Carlo simulations also dominated, laying a foundation for later semantic diversification.
During 2014–2017, the consolidation of PLS and SEM into the unified “PLS-SEM” concept became a motor theme alongside “Satisfaction models.” This shift illustrates the field’s maturation: methodological refinements—such as discriminant validity testing and mediating/moderating analyses—coalesced with applications in technology acceptance and business research. Notably, PLS-SEM tools like SmartPLS and R packages (e.g., SEMinR) gained prominence, democratizing the method’s use beyond specialized statistics communities.
The 2018–2020 window witnessed an exponential increase in publications, with “Performance measurement” joining “PLS-SEM” and “Satisfaction models” as motor themes. Here, PLS-SEM not only continued to refine its predictive and evaluation metrics (e.g., Shmueli et al.’s focus on predictive power) but also extended into environmental and social contexts. Studies on green supply chain management, circular economy, and community-level analyses underscored PLS-SEM’s versatility in addressing sustainability and societal challenges.
In the most recent period (2021–2022), Angelelli et al. document an even steeper publication surge, driven in part by COVID-19–related research on behavioral intentions and digital service adoption. “Technology acceptance” reemerged as a motor theme, reflecting intense interest in emerging technologies—from blockchain to telemedicine—and their adoption drivers. Smaller clusters in ecological and environmental sciences signal PLS-SEM’s continued penetration into new domains, though their niche status suggests ongoing opportunities for interdisciplinary synthesis.
Angelelli and colleagues enrich traditional syntactic bibliometric analyses by integrating co-word network analysis, identifying thematic clusters through the Walktrap algorithm and quantifying their centrality and density using Callon’s metrics. This dual syntactic–semantic approach reveals not only which topics dominate PLS-SEM research, but also how methodological and application themes interact and evolve. For instance, the absorption of “Attitude” into “Satisfaction models,” and the merging of “Validity” into broader clusters, highlight dynamic theme convergence over time.
For health management scholars and practitioners, this study offers a roadmap for leveraging PLS-SEM in patient safety, quality improvement, and risk management research. By understanding the method’s thematic trajectory—from foundational performance models to cutting-edge applications in pandemic response—researchers can situate their work within current trends and anticipate emerging opportunities.
Ultimately, this comprehensive analysis underscores PLS-SEM’s transition from a specialized estimator to a broadly adopted framework that bridges theory and practice across fields. As scientific production accelerates, the study highlights the importance of semantic analyses in bibliometrics, encouraging scholars to consider both the structural and thematic dimensions of methodological evolution.
Article citation (APA style)
Angelelli, M., Ciavolino, E., Ringle, C. M., Sarstedt, M., & Aria, M. (2025). Conceptual structure and thematic evolution in partial least squares structural equation modeling research. Quality & Quantity. Advance online publication. https://doi.org/10.1007/s11135-025-02071-4
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