Subtopic Deep Dive
PLS-SEM in Organizational Research
Research Guide
What is PLS-SEM in Organizational Research?
PLS-SEM (Partial Least Squares Structural Equation Modeling) in organizational research applies variance-based SEM to model complex latent variable relationships in non-normal survey data for predicting employee and organizational performance outcomes.
PLS-SEM excels in composite models with formative constructs common in organizational surveys on employee satisfaction, retention, and performance. Studies like Mailizar et al. (2021) extended TAM using PLS-SEM to predict e-learning intentions (337 citations). Over 10 provided papers from 2019-2022 demonstrate its use in analyzing IoT adoption, loyalty programs, and workplace environments.
Why It Matters
PLS-SEM enables predictive modeling of employee retention on organizational performance, as shown in Al Kurdi et al. (2020) with banking data (223 citations). In supply chain contexts, Lee et al. (2022) used PLS-SEM to link IoT benefits to performance improvements (271 citations). Alshurideh et al. (2020) applied it to loyalty programs enhancing customer-supplier ties (271 citations), supporting business decisions in non-experimental settings.
Key Research Challenges
Heteroscedasticity in Organizational Data
Survey data from employee performance studies often exhibits heteroscedasticity, violating OLS assumptions in PLS-SEM. Hair et al. (pre-2015 foundational works) emphasize robust standard errors, but applications like Gu et al. (2022) require advanced corrections for multi-mediation models (264 citations).
Composite Model Assessment
Distinguishing reflective vs. formative constructs in organizational climates challenges PLS-SEM validity, as in Al Kurdi et al. (2019) on knowledge sharing (265 citations). Recent papers demand HTMT ratios and VIF checks for reliable path modeling.
ML Integration with PLS-SEM
Hybrid SEM-ML approaches for predictive accuracy in metaverse adoption face interpretability trade-offs, per Al Marzouqi et al. (2022) (326 citations). Organizational researchers struggle with combining PLS paths and ML algorithms without causal loss.
Essential Papers
Examining university students’ behavioural intention to use e-learning during the COVID-19 pandemic: An extended TAM model
Mailizar Mailizar, Damon Burg, Suci Maulina · 2021 · Education and Information Technologies · 337 citations
Prediction of User’s Intention to Use Metaverse System in Medical Education: A Hybrid SEM-ML Learning Approach
Amina Al Marzouqi, Ahmad Aburayya, Said A. Salloum · 2022 · IEEE Access · 326 citations
Metaverse (MS) is a digital universe accessible through a virtual environment. It is established \nthrough the merging of virtually improved physical and digital reality. Metaverse (MS) offers ...
Does BLE technology contribute towards improving marketing strategies, customers’ satisfaction and loyalty? The role of open innovation
Haitham M. Alzoubi, Muhammad Turki Alshurideh, Barween Al Kurdi et al. · 2022 · International Journal of Data and Network Science · 310 citations
The purpose of this study is to explore the marketing strategies for the introduction of Beacons technology applications (BLE) technology in businesses and how it can convert potential clients into...
Investigating the impact of benefits and challenges of IOT adoption on supply chain performance and organizational performance: An empirical study in Malaysia
Khai Loon Lee, Puteri Nurhazira Romzi, Jalal Rajeh Hanaysha et al. · 2022 · Uncertain Supply Chain Management · 271 citations
In Malaysia, manufacturing industry is a major contributor to the economic advancement. As a result, cutting-edge technology like the internet of things (IoT) is projected to have a significant imp...
Loyalty program effectiveness: Theoretical reviews and practical proofs
Muhammad Turki Alshurideh, Anwar Gasaymeh, Gouher Ahmed et al. · 2020 · Uncertain Supply Chain Management · 271 citations
Loyalty programs are widely used by organizations as a structured customer relationship management (CRM) tool to build and extend customer-supplier relationship.Although a large number of benefits ...
The role of organisational climate in managing knowledge sharing among academics in higher education
Osama F. Al Kurdi, Ramzi El‐Haddadeh, Tillal Eldabi · 2019 · International Journal of Information Management · 265 citations
Impact of Employees' Workplace Environment on Employees' Performance: A Multi-Mediation Model
Zhenjing Gu, Supat Chupradit, Kuo Yen Ku et al. · 2022 · Frontiers in Public Health · 264 citations
This study examined the impact of workplace environment on employee task performance under the mediating role of employee commitment and achievement-striving ability. For this purpose, data were co...
Reading Guide
Foundational Papers
Start with Hsiao and Chang (2011, 97 citations) for PLS-SEM in leadership-innovation links; Al Dhaafri and Al-Swidi (2013, 24 citations) for ERP mediation in performance.
Recent Advances
Prioritize Mailizar et al. (2021, 337 citations) for TAM-PLS extensions; Al Marzouqi et al. (2022, 326 citations) for SEM-ML hybrids; Lee et al. (2022, 271 citations) for IoT performance models.
Core Methods
Core techniques include PLS path modeling, bootstrapping (5000 resamples), f² effect sizes, and predictive relevance Q²; hybrids add ML feature selection (Al Marzouqi et al., 2022).
How PapersFlow Helps You Research PLS-SEM in Organizational Research
Discover & Search
Research Agent uses searchPapers('PLS-SEM employee performance') to retrieve 250M+ OpenAlex papers, then citationGraph on Mailizar et al. (2021) reveals 337-citation extensions of TAM in organizational contexts, and findSimilarPapers uncovers Al Kurdi et al. (2020) retention studies.
Analyze & Verify
Analysis Agent employs readPaperContent on Lee et al. (2022) to extract PLS-SEM path coefficients, verifies heteroscedasticity claims via runPythonAnalysis (pandas normality tests, matplotlib Q-Q plots), and applies GRADE grading for evidence strength in multi-mediation models like Gu et al. (2022).
Synthesize & Write
Synthesis Agent detects gaps in ML-PLS integration from Al Marzouqi et al. (2022), flags contradictions in loyalty models (Alshurideh et al., 2020), while Writing Agent uses latexEditText for path diagrams, latexSyncCitations for 10+ papers, and latexCompile for publication-ready reports with exportMermaid for model flows.
Use Cases
"Run PLS-SEM simulation on employee retention data from Al Kurdi et al. (2020)"
Research Agent → searchPapers → Analysis Agent → runPythonAnalysis (pandas SEM simulation with statsmodels, matplotlib path plots) → researcher gets verified R² predictions and bootstrapped CIs.
"Generate LaTeX manuscript on PLS-SEM in IoT supply chains citing Lee et al. (2022)"
Synthesis Agent → gap detection → Writing Agent → latexEditText (add methods) → latexSyncCitations (10 papers) → latexCompile → researcher gets PDF with formatted equations and figures.
"Find GitHub code for hybrid PLS-SEM ML models like Al Marzouqi et al. (2022)"
Research Agent → citationGraph → Code Discovery (paperExtractUrls → paperFindGithubRepo → githubRepoInspect) → researcher gets plspm Python repos with metaverse prediction scripts.
Automated Workflows
Deep Research workflow scans 50+ PLS-SEM papers via searchPapers → citationGraph → structured report on organizational applications like Alshurideh et al. (2020). DeepScan's 7-step chain applies CoVe verification to heteroscedasticity fixes in Gu et al. (2022), with runPythonAnalysis checkpoints. Theorizer generates theory on PLS-SEM's predictive superiority from foundational Hsiao (2011) to recent hybrids.
Frequently Asked Questions
What is PLS-SEM in organizational research?
PLS-SEM models latent variables via variance-based paths for non-normal data, suiting surveys on employee performance and retention (Mailizar et al., 2021).
What are common methods in PLS-SEM organizational studies?
Bootstrapping for significance, HTMT for discriminant validity, and hybrid ML extensions as in Al Marzouqi et al. (2022) and Lee et al. (2022).
What are key papers on PLS-SEM for employee performance?
Mailizar et al. (2021, 337 citations) on TAM extensions; Al Kurdi et al. (2020, 223 citations) on retention; Gu et al. (2022, 264 citations) on workplace mediation.
What open problems exist in PLS-SEM organizational applications?
Handling heteroscedasticity in large surveys, integrating ML without losing interpretability, and validating formative constructs in dynamic climates (Al Kurdi et al., 2019).
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