Extraction of Consumer Behavior Patterns Toward Quality Labels Using a Fuzzy Inference System Based on the Hierarchy of Effects Model

This study explores the influence of quality labels on consumer purchasing behavior using a Fuzzy Inference System (FIS) aligned with the Hierarchy of Effects (HoE) model. The methodology involved four main phases: primary data collection, fuzzy rule extraction from HoE-based attributes, model validation, and pattern interpretation. A total of 76 fuzzy rules were generated to classify consumer behavior across four HoE stages: Not Aware, Cognitive, Affective, and Conative. The overall model accuracy reached 78.59%. However, performance was uneven across stages, particularly in the not aware stage, which achieved only 45.5% accuracy, with 36.4% of its cases not covered by any rule. In contrast, the Cognitive, Affective, and Conative stages exceeded 80% accuracy. Statistical validation through literature review and multinomial logistic regression confirmed the significant roles of trust, perceived quality, and perceived risk as predictors of consumer transitions across HoE stages. At the same time, the model offers interpretable insights for strategic communication and label design. Limitations of methodology such as imbalanced class representation that causing local overfitting highlight the need for parameter simplification and future integration with adaptive learning models to enhance generalizability.