Subtopic Deep Dive
Partial Least Squares Path Modeling
Research Guide
What is Partial Least Squares Path Modeling?
Partial Least Squares Path Modeling (PLS-PM) applies structural equation modeling with partial least squares estimation to analyze complex relationships among latent variables in quality and supply chain management models.
PLS-PM excels in handling small sample sizes, non-normal data, and formative constructs, contrasting with covariance-based SEM. Over 2,500 papers use PLS-SEM in management research, with 10 key supply chain studies exceeding 165 citations each since 2018. Applications span digital supply chains, green practices, and agility metrics.
Why It Matters
PLS-PM enables predictive modeling of supply chain agility and resilience under data scarcity, as in Lee et al. (2022) linking digital technologies to Malaysian manufacturing performance (307 citations). It quantifies green supply chain impacts on economic outcomes (Saeed et al., 2018; 174 citations) and lean practices on sustainability (Iranmanesh et al., 2019; 169 citations). Firms use it to validate strategic orientations for sustainable performance (Habib et al., 2021; 165 citations), informing decisions on AI-driven analytics (Dubey et al., 2022; 267 citations).
Key Research Challenges
Small Sample Handling
PLS-PM suits small samples but risks biased path estimates without bootstrapping validation. Hair et al. (pre-2015 foundational guidelines) emphasize minimum sample thresholds. Supply chain studies like Alzoubi and Ramakrishna (2020; 257 citations) highlight instability in formative constructs.
Formative vs Reflective Constructs
Distinguishing formative indicators in supply chain models leads to misspecification errors. Hussain et al. (2018; 235 citations) used PLS-SEM for quality factors but noted formative modeling challenges. Dubey et al. (2018; 199 citations) faced issues validating circular economy constructs.
Non-Normal Data Bias
Non-normal distributions in management data inflate Type I errors despite PLS robustness. Wamba and Akter (2019; 222 citations) addressed this in analytics capabilities. Chowdhury and Quaddus (2020; 169 citations) required robustness checks for sustainability risks.
Essential Papers
The effect of digital supply chain on organizational performance: An empirical study in Malaysia manufacturing industry
Khai Loon Lee, Nurul Ain Najiha Azmi, Jalal Rajeh Hanaysha et al. · 2022 · Uncertain Supply Chain Management · 307 citations
Nowadays, global technologies, especially digital things, have become an important tool for businesses to maintain feasible partnerships and build a great value connection with other companies. New...
Impact of artificial intelligence-driven big data analytics culture on agility and resilience in humanitarian supply chain: A practice-based view
Rameshwar Dubey, David Bryde, Yogesh K. Dwivedi et al. · 2022 · International Journal of Production Economics · 267 citations
Investigating the mediating role of information sharing strategy on agile supply chain
Haitham M. Alzoubi, Y. Ramakrishna · 2020 · Uncertain Supply Chain Management · 257 citations
Supply chains need to redesign their existing strategies and must develop new strategies to effectively face the challenges posed by certain disruptions, both man-made and natural.This requires the...
Structural Equation Model for Evaluating Factors Affecting Quality of Social Infrastructure Projects
Shahid Hussain, Fangwei Zhu, Ahmed Faisal Siddiqi et al. · 2018 · Sustainability · 235 citations
The quality of the constructed social infrastructure project has been considered a necessary measure for the sustainability of projects. Studies on factors affecting project quality have used vario...
Understanding supply chain analytics capabilities and agility for data-rich environments
Samuel Fosso Wamba, Shahriar Akter · 2019 · International Journal of Operations & Production Management · 222 citations
Purpose Big data-driven supply chain analytics capability (SCAC) is now emerging as the next frontier of supply chain transformation. Yet, very few studies have been directed to identify its dimens...
Supplier relationship management for circular economy
Rameshwar Dubey, Angappa Gunasekaran, Stephen J. Childe et al. · 2018 · Management Decision · 199 citations
Purpose With considerable international awareness of circular economy (CE), the purpose of this paper is to propose a theoretical framework, informed by institutional theory and upper echelon theor...
Institutional Pressures, Green Supply Chain Management Practices on Environmental and Economic Performance: A Two Theory View
Amer Saeed, Yun Jun, Saviour Ayertey Nubuor et al. · 2018 · Sustainability · 174 citations
The adoption of green practices within and outside organizational boundaries is imperative to ascertain environmental and economic performance goals. This article examined whether internal and exte...
Reading Guide
Foundational Papers
Start with Yeung et al. (2005; 99 citations) for empirical QM models and Kamal and Irani (2014; 138 citations) for SCI frameworks; they establish PLS-SEM baselines for latent variable paths in management.
Recent Advances
Study Lee et al. (2022; 307 citations) for digital supply PLS applications and Dubey et al. (2022; 267 citations) for AI-resilience models to grasp predictive advances.
Core Methods
Core techniques: two-stage modeling (measurement then structural), consistent PLS (PLSc) for CB-SEM comparability, bootstrapping (5000 subsamples), f² effect sizes, and VAF for mediation.
How PapersFlow Helps You Research Partial Least Squares Path Modeling
Discover & Search
Research Agent uses searchPapers('PLS-SEM supply chain agility') to find Lee et al. (2022; 307 citations), then citationGraph reveals Dubey et al. (2022) clusters and findSimilarPapers uncovers Alzoubi (2020). exaSearch('PLS-PM formative constructs quality management') surfaces Hussain et al. (2018).
Analyze & Verify
Analysis Agent applies readPaperContent on Lee et al. (2022) to extract PLS path coefficients, verifyResponse with CoVe checks model fit claims against Hair guidelines, and runPythonAnalysis bootstraps SmartPLS outputs via pandas for R² validation. GRADE assigns A-grade to Dubey et al. (2022) for statistical rigor.
Synthesize & Write
Synthesis Agent detects gaps in green supply chain PLS models (e.g., missing AI integration post-Dubey 2022), flags contradictions between reflective measures in Saeed et al. (2018) and formatives in Habib et al. (2021). Writing Agent uses latexEditText for PLS path diagrams, latexSyncCitations for 10-paper bibliography, latexCompile for report PDF, and exportMermaid for causal loop diagrams.
Use Cases
"Replicate PLS-SEM bootstrap from Lee et al. 2022 digital supply chain paper"
Analysis Agent → readPaperContent (extracts latent variable scores) → runPythonAnalysis (NumPy bootstrap 5000 resamples, computes path significance) → researcher gets CSV of bias-corrected CIs and p-values.
"Write PLS-PM methods section comparing to CB-SEM for green supply chain review"
Synthesis Agent → gap detection (identifies CB-SEM limitations from foundational papers) → Writing Agent → latexEditText (drafts section) → latexSyncCitations (adds Saeed 2018, Dubey 2018) → latexCompile → researcher gets formatted LaTeX chapter.
"Find GitHub repos with PLS-SEM supply chain SmartPLS code"
Research Agent → paperExtractUrls (from Wamba 2019) → paperFindGithubRepo (matches analytics scripts) → githubRepoInspect (reviews R code for SCAC models) → researcher gets verified Python/SmartPLS implementations.
Automated Workflows
Deep Research workflow scans 50+ PLS-SEM supply papers via searchPapers → citationGraph → structured report ranking by citations (e.g., Lee 2022 top). DeepScan's 7-steps analyze Dubey et al. (2022): readPaperContent → runPythonAnalysis (agility metrics) → CoVe verification → GRADE report. Theorizer generates hypotheses linking PLS-PM to AI resilience from Dubey (2022) + foundational Kamal (2014).
Frequently Asked Questions
What defines Partial Least Squares Path Modeling?
PLS-PM estimates causal models with latent variables using partial least squares regression, prioritizing prediction over population parameter accuracy.
What are core PLS-PM methods in supply management?
Methods include mode A/B weighting, bootstrapping for significance, HTMT for discriminant validity, and PLS-Algorithm for path estimation, as applied in Hussain et al. (2018) quality models.
What are key papers on PLS-SEM in supply chains?
Top papers: Lee et al. (2022; 307 citations, digital chains), Dubey et al. (2022; 267 citations, AI agility), Hussain et al. (2018; 235 citations, infrastructure quality).
What open problems exist in PLS-PM for quality management?
Challenges include Bayesian PLS extensions for uncertainty, multi-group analysis for cross-cultural supply chains, and integration with machine learning for non-linear effects.
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Part of the Quality and Supply Management Research Guide