Dukalang Hendra H, Otok Bambang Widjanarko
Department of Statistics, Institut Teknologi Sepuluh Nopember, Surabaya, 60111 Indonesia.
Department of Islamic Banking, IAIN Sultan Amai Gorontalo, Gorontalo 96215 Indonesia.
MethodsX. 2025 May 29;14:103381. doi: 10.1016/j.mex.2025.103381. eCollection 2025 Jun.
Partial Least Squares Structural Equation Modelling (PLS-SEM) struggles with nonlinear relationships between latent variables, leading to biased results. To address this limitation, this study proposes a new model, Multivariate Adaptive Regression Splines Partial Least Square (MARSPLS), which is based on Multivariate Adaptive Regression Splines (MARS) using the PLS-SEM framework. The innovation lies in its ability to capture nonlinear and interaction effects between latent variables by leveraging the flexibility of MARS while retaining the latent structure estimation through PLS. The article elaborates the steps of Maximum Likelihood Estimator (MLE) and Ordinary Least Squares (OLS) to estimate values of MARSPLS parameters. The model is evaluated using both simulated and empirical data on e-wallet behavioural intention from 385 Indonesian respondents. Results show that MARSPLS with interaction achieves superior predictive accuracy, as indicated by higher R² value 54.08 % and lower AIC, AICc, and RMSE values. The primary characteristics of the recommended method involve the following:•A novel approach to PLS-SEM that assumes the relationship between latent is nonlinear or unknown.•The model involves four exogenous and one endogenous latent variable, without moderation and mediation effects.
偏最小二乘结构方程模型（PLS-SEM）在处理潜在变量之间的非线性关系时存在困难，会导致结果有偏差。为解决这一局限性，本研究提出了一种新模型，即基于多元自适应回归样条（MARS）并使用PLS-SEM框架的多元自适应回归样条偏最小二乘法（MARSPLS）。其创新之处在于，它能够利用MARS的灵活性捕捉潜在变量之间的非线性和交互效应，同时通过PLS保留潜在结构估计。本文详细阐述了最大似然估计器（MLE）和普通最小二乘法（OLS）来估计MARSPLS参数值的步骤。使用来自385名印度尼西亚受访者的电子钱包行为意向的模拟数据和实证数据对该模型进行了评估。结果表明，具有交互作用的MARSPLS具有更高的预测准确性，其R²值为54.08%，AIC、AICc和RMSE值更低。推荐方法的主要特点如下：
•一种新的PLS-SEM方法，假设潜在变量之间的关系是非线性或未知的。
•该模型涉及四个外生潜在变量和一个内生潜在变量，不存在调节和中介效应。
