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Georgiadis, K, Kalaganis, FP, Riskos, K, Matta, E, Oikonomou, VP, Yfantidou, I, Chantziaras, D, Pantouvakis, K, Nikolopoulos, S, Laskaris, NA and Kompatsiaris, I (2023) NeuMa - the absolute Neuromarketing dataset en route to an holistic understanding of consumer behaviour. Scientific Data, 10 (1). ISSN 2052-4463
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NeuMa the absolute Neuromarketing dataset en route to an holistic understanding of consumer behaviour.pdf - Published Version Available under License Creative Commons Attribution. Download (2MB) | Preview |
Abstract
Neuromarketing is a continuously evolving field that utilises neuroimaging technologies to explore consumers’ behavioural responses to specific marketing-related stimulation, and furthermore introduces novel marketing tools that could complement the traditional ones like questionnaires. In this context, the present paper introduces a multimodal Neuromarketing dataset that encompasses the data from 42 individuals who participated in an advertising brochure-browsing scenario. In more detail, participants were exposed to a series of supermarket brochures (containing various products) and instructed to select the products they intended to buy. The data collected for each individual executing this protocol included: (i) encephalographic (EEG) recordings, (ii) eye tracking (ET) recordings, (iii) questionnaire responses (demographic, profiling and product related questions), and (iv) computer mouse data. NeuMa dataset has both dynamic and multimodal nature and, due to the narrow availability of open relevant datasets, provides new and unique opportunities for researchers in the field to attempt a more holistic approach to neuromarketing.
| Item Type: | Article |
|---|---|
| Subjects: | H Social Sciences > HF Commerce > HF5001 Business H Social Sciences > HF Commerce > HF5001 Business > HF5410 Marketing. Distribution of Products T Technology > T Technology (General) |
| Divisions: | Business & Management (from Sep 19) |
| Publisher: | Nature Research |
| SWORD Depositor: | A Symplectic |
| Date Deposited: | 08 Aug 2023 08:57 |
| Last Modified: | 08 Aug 2023 09:00 |
| DOI or ID number: | 10.1038/s41597-023-02392-9 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/20668 |
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