Conscious and non-conscious responses to branded narrative advertising: Investigating narrativity level and device type

Simonetti, Aline; Dini, Hossein; Bruni, Luis Emilio; Bigne, Enrique

doi:10.1177/23409444241248191

Cited by 2 publications

References 84 publications

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Predicting consumer ad preferences: Leveraging a machine learning approach for EDA and FEA neurophysiological metrics

Marques,

Neto,

Silva

et al. 2024

Psychology and Marketing

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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.

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Predicting consumer ad preferences: Leveraging a machine learning approach for EDA and FEA neurophysiological metrics

Marques,

Neto,

Silva

et al. 2024

Psychology and Marketing

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Bibliometric Exploration of Neuromarketing Research

Singh,

Bansal,

Mitali

et al. 2024

Advances in Business Strategy and Competitive Advantage

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This bibliometric study aims to comprehensively analyze the field of neuromarketing by utilizing data from the Scopus database. The study aims to identify research patterns, prominent researchers, and impactful studies in the field. Specifically, it focuses on the division of research, ethical considerations, and discrepancies in methodology. The study used co-authorship, co-citation, and co-occurrence analysis to examine the interdisciplinary nature of neuromarketing, focusing on integrating neuroscience, psychology, and marketing. The Vos Viewer tool was employed to analyze the performance and investigate topic clusters for this investigation. The findings reveal significant collaboration networks and fundamental research investigations, offering valuable insights into the evolution of research themes.

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Conscious and non-conscious responses to branded narrative advertising: Investigating narrativity level and device type

Cited by 2 publications

References 84 publications

Predicting consumer ad preferences: Leveraging a machine learning approach for EDA and FEA neurophysiological metrics

Predicting consumer ad preferences: Leveraging a machine learning approach for EDA and FEA neurophysiological metrics

Bibliometric Exploration of Neuromarketing Research

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