The Purchase Intention of Green Personal-Care Products: An Artificial Neural Networks Approach
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Abstract
This study explores the use of deep learning (DL) to predict consumer purchase intentions for green personal-care products. Using primary data collected from 110 consumers via online questionnaires, the study applies artificial neural networks (ANNs) to analyse purchasing behaviour, diverging from traditional statistical methods. Through a rigorous optimisation process, the ANN model achieves outstanding performance, with 90% Accuracy, 100% Recall, and F1 and Precision scores of 95%. These results outperform other machine learning models tested, including random forest, support vector machine (SVM), gradient boosting, XGBoost and decision tree, highlighting the ANN’s superior ability to handle complex, non-linear relationships within small datasets. This study demonstrates that deep learning models are highly adaptable to business requirements in the green personal-care sector, whether in traditional retail or online settings, and can be applied effectively to forecast consumer trends regardless of whether the datasets are complex or relatively simple.
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References
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