# Combination of partial least square structural equation modeling scheme of principal component analysis with importance performance analysis

## TL;DR

This paper introduces a new method combining PCA and PLS-SEM with IPA to better understand and prioritize factors affecting child malnutrition in East Java.

## Contribution

A novel PCA-based weighting scheme for PLS-SEM integrated with IPA for indicator prioritization.

## Key findings

- Socio-economic conditions most strongly influence food security, parenting, and health–environment services.
- Exclusive breastfeeding is identified as a priority for intervention through IPA.
- The method improves PLS estimation stability and provides actionable policy insights.

## Abstract

•PCA is used in the inner weighting scheme of the PLS model to obtain latent variable scores.•PLS-IPA explains the influence between latent variables and indicators while mapping indicators based on importance and performance.

PCA is used in the inner weighting scheme of the PLS model to obtain latent variable scores.

PLS-IPA explains the influence between latent variables and indicators while mapping indicators based on importance and performance.

Structural Equation Modeling (SEM) is widely used to assess causal relationships among latent variables, yet its strict assumptions often limit empirical applications. Partial Least Squares SEM (PLS-SEM) offers greater flexibility, but the choice of weighting scheme remains a methodological challenge. This study introduces a PCA-based weighting scheme to improve the stability and accuracy of PLS estimation. Importance-Performance Analysis (IPA) is further integrated to identify high-impact but underperforming indicators. Applied to child malnutrition in East Java, the approach reveals that socio-economic conditions most strongly influence food security, parenting, and health–environment services. IPA highlights exclusive breastfeeding as a priority for intervention. The proposed methodological approach strengthens PLS estimation and yields actionable insights for prioritizing policy measures.

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