... use the results of SEM as input to the ANN model to develop a predictive model ...

Article A hybrid SEM-neural network method for modeling the academic...

The hybrid SEM-ANN approach refers to a modeling technique that combines Structural Equation Modeling (SEM) with Artificial Neural Networks (ANN) to leverage the strengths of both methods. This approach is especially useful in social sciences, behavioral studies, marketing, engineering, and sustainability research, where complex relationships among variables are involved.

🔍 What Is SEM (Structural Equation Modeling)?

SEM is a statistical technique that:

• Models causal relationships between observed and latent variables.
• Includes both measurement models (like confirmatory factor analysis) and structural models (like regression).
• Is based on linear assumptions and requires model fit statistics.

✅ Strengths of SEM:

• Good for theory testing and hypothesis validation.
• Handles complex multivariate relationships.
• Provides path coefficients, R² values, and model fit indices.

🤖 What Is ANN (Artificial Neural Network)?

ANN is a machine learning algorithm inspired by the human brain that:

• Models nonlinear and complex relationships.
• Learns from data through training and backpropagation.
• Can handle large datasets and high-dimensional inputs.

✅ Strengths of ANN:

• Good for prediction and pattern recognition.
• Captures nonlinear interactions automatically.
• Not limited by distributional assumptions.

🔗 Hybrid SEM-ANN Approach

This hybrid approach combines both:

🔄 Step-by-step Concept:

🧠 Use Cases:

• Understanding factors influencing user satisfaction, behavioral intention, or customer loyalty.
• Sustainability studies (e.g., environmental behavior, green practices).
• Modeling energy systems or financial risks.

⚖️ Benefits of the Hybrid Model:

SEM ANN

Theory-driven = Data-driven

Explains causal relationships = Excellent for prediction

Linear modeling = Nonlinear modeling

Requires distributional assumptions = No strict assumptions