Predicting purchase intentions in online food delivery using deep learning and AIDA model: Insights from sentiment analysis of user reviews
Le Li et al.
What the paper says
This study investigates how customer sentiments and engagement stages, as conceptualized by the AIDA (Awareness, Interest, Desire, Action) model, influence purchase intention in the context of online food delivery services. Leveraging over 1.5 million English-language reviews from Uber Eats and DoorDash collected via the Google Play Store, we developed a robust machine learning pipeline to classify sentiments, detect AIDA stage keywords, and predict purchase intention using deep learning models. A Sequential Neural Network and a Bidirectional LSTM model with pre-trained GloVe embeddings were trained on processed review text to predict purchase intentions, achieving high accuracy across both datasets (Uber Eats: 94.69%, DoorDash: 95.87%). Regression analyses using Ordinary Least Squares (OLS) further validated the predictive power of AIDA stages and sentiment, yielding R-squared values of 0.947 for Uber Eats and 0.915 for DoorDash. All AIDA stages and sentiment showed statistically significant positive effects on purchase intention. Variance Inflation Factor (VIF) analysis confirmed the absence of multicollinearity, reinforcing the reliability of the findings. This integrated framework offers valuable insights for digital platform operators and marketers by demonstrating how user-generated content can be harnessed to understand and influence consumer behavior in service ecosystems.
2 citations
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.25 × 0.4 = 0.10 |
| M · momentum | 0.55 × 0.15 = 0.08 |
| 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.