The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Emotion-Induced Engagement in Internet Video Advertisements
Abstract
This study shows how advertisers can leverage emotion and attention to engage consumers in watching Internet video advertisements. In a controlled experiment, the authors assessed joy and surprise through automated facial expression detection for a sample of advertisements. They assessed concentration of attention through eye tracking and viewer retention by recording zapping behavior. This allows tests of predictions about the interplay of these emotions and interperson attention differences at each point in time during exposure. Surprise and joy effectively concentrate attention and retain viewers. However, importantly, the level rather than the velocity of surprise affects attention concentration most, whereas the velocity rather than the level of joy affects viewer retention most. The effect of joy is asymmetric, with higher gains for increases than losses for decreases. Using these findings, the authors develop representative emotion trajectories to support ad design and testing.
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Cites
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The effects of emotion on attention: A review of attentional processing of emotional information
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“The GPR2 for the first level of the full model is 63% and confirms that the sets of variables jointly explain a significant portion of the variance in observed zapping.”
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Cited by
Hollebeek, L. D., & Macky, K. (2019) · Journal of Interactive Marketing · doi:10.1016/j.intmar.2018.07.003 · Cited by 978
Psychological targeting as an effective approach to digital mass persuasion
Matz, S. C., Kosinski, M., Nave, G., & Stillwell, D. J. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1710966114 · Cited by 892
Unstructured data in marketing
Balducci, B., & Marinova, D. (2018) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-018-0581-x · Cited by 300
From Emotion AI to Cognitive AI
Zhao, G., Li, Y., & Xu, Q. (2022) · International Journal of Network Dynamics and Intelligence · doi:10.53941/ijndi0101006 · Cited by 100
Automatic measurement of ad preferences from facial responses gathered over the Internet
McDuff, D., El Kaliouby, R., Senechal, T., Demirdjian, D., & Picard, R. (2014) · Image and Vision Computing · doi:10.1016/j.imavis.2014.01.004 · Cited by 52
Metadata
- Authors
- Teixeira, T., Wedel, M., & Pieters, R.
- Venue
- Journal of Marketing Research
- Publisher
- SAGE Publications
- Published
- 2012-04
- Volume
- 49
- Issue
- 2
- Pages
- 144-159
- ISSN
- 0022-2437 (ISSN-L)
- 1547-7193
- OpenAlex
- W2020911841
- Cited by
- 432
- References
- 8
- FWCI
- 45.01
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
