Publication
Predicting consumer ad preferences: leveraging a machine learning approach for EDA and FEA neurophysiological metrics
| dc.contributor.author | Marques, João Alexandre Lobo | |
| dc.contributor.author | Neto, Andreia C. | |
| dc.contributor.author | Silva, Susana C. | |
| dc.contributor.author | Bigne, Enrique | |
| dc.date.accessioned | 2024-09-19T17:14:40Z | |
| dc.date.available | 2024-09-19T17:14:40Z | |
| dc.date.issued | 2025-01-01 | |
| dc.description.abstract | This research unveils to predict consumer ad preferences by detecting seven basic emotions, attention and engagement triggered by advertising through the analysis of two specific physiological monitoring tools, electrodermal activity (EDA), and Facial Expression Analysis (FEA), applied to video advertising, offering a twofold contribution of significant value. First, to identify the most relevant physiological features for consumer preference prediction. We integrated a statistical module encompassing inferential and exploratory analysis tools, which identified emotions such as Joy, Disgust, and Surprise, enabling the statistical differentiation of preferences concerning various advertisements. Second, we present an artificial intelligence (AI) system founded on machine learning techniques, encompassing k-Nearest Neighbors, Support Vector Machine, and Random Forest (RF). Our findings show that the RF technique emerged as the top performer, boasting an 81% Accuracy, 84% Precision, 79% Recall, and an F1-score of 81% in predicting consumer preferences. In addition, our research proposes an eXplainable AI module based on feature importance, which discerned Attention, Engagement, Joy, and Disgust as the four most pivotal features influencing consumer ad preference prediction. The results indicate that computerized intelligent systems based on EDA and FEA data can be used to predict consumer ad preferences based on videos and effectively used as supporting tools for marketing specialists. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.doi | 10.1002/mar.22118 | pt_PT |
| dc.identifier.eid | 85203550427 | |
| dc.identifier.issn | 0742-6046 | |
| dc.identifier.uri | http://hdl.handle.net/10400.14/46620 | |
| dc.identifier.wos | 001310382800001 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | Advertising | pt_PT |
| dc.subject | Consumer ad preferences | pt_PT |
| dc.subject | Consumer neuroscience | pt_PT |
| dc.subject | Emotions | pt_PT |
| dc.subject | Explainable artificial intelligence | pt_PT |
| dc.subject | Machine learning | pt_PT |
| dc.title | Predicting consumer ad preferences: leveraging a machine learning approach for EDA and FEA neurophysiological metrics | pt_PT |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.citation.title | Psychology & Marketing | pt_PT |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | article | pt_PT |