The Purchase Intention of Green Personal-Care Products: An Artificial Neural Networks Approach
Saveendra Perera & Arfian Zudana
What the paper says
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.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.