Study maps how quality labels actually sway shoppers—with mixed results
Researchers built an AI system to predict when food quality labels influence purchasing decisions, finding they work best on already-engaged consumers but struggle with shoppers who haven't heard of them. The 79% accuracy rate suggests retailers and regulators can use similar techniques to design more effective labeling—but only if they acknowledge the approach has blind spots.
Originaltitel: Extraction of Consumer Behavior Patterns Toward Quality Labels Using a Fuzzy Inference System Based on the Hierarchy of Effects Model
Kvalitetsmärkningar styr konsumentbeteenden olika beroende på medvetandestadium — en upptäckt som tillverkare och märkesägare inom biovetenskaper bör integrera i sitt etikettkommunikation. Svenska forskare från Sustainable Innovation utvecklade en fuzzy inference-modell baserad på effekthierarkin för att kartlägga detta samband. Modellen klassificerade 76 beteendemönster över fyra stadier: medvetandefrånvaro, kognitiv, affektiv och köpintention. Noggrannheten låg på 78,59 procent totalt, men varierade markant — från 45,5 procent i medvetandefrånvaro till över 80 procent i senare stadier. Analys bekräftade att förtroende, upplevd kvalitet och upplevd risk är kritiska omställningsfaktorer mellan stadier. Resultaten möjliggör målriktad märkesdesign och kommunikationsstrategi. Klassimbalansen antyder behov av utvidgat datamaterial för högre tillförlitlighet vid framtida kommersiell tillämpning.
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.