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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Estimating the Number of Clusters in a Data Set Via the Gap Statistic
Tibshirani, R., Walther, G., & Hastie, T. (2001) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/1467-9868.00293 · Cited by 6,205
What good are positive emotions?
Fredrickson, B. L. (1998) · Review of General Psychology · doi:10.1037/1089-2680.2.3.300 · Cited by 6,066
Ekman, P., & Friesen, W. V. (2019) · PsycTESTS Dataset · doi:10.1037/t27734-000 · Cited by 6,019
How advertising works: what do we really know?
Vakratsas, D., & Ambler, T. (1999) · Journal of Marketing · doi:10.1177/002224299906300103 · Cited by 1,436
The effects of emotion on attention: A review of attentional processing of emotional information
Yiend, J. (2010) · Cognition and Emotion · doi:10.1080/02699930903205698 · Cited by 891
The persistence of attention to emotion: Brain potentials during and after picture presentation.
Hajcak, G., & Olvet, D. M. (2008) · Emotion · doi:10.1037/1528-3542.8.2.250 · Cited by 443
Measuring facial expressions by computer image analysis
Bartlett, M. S., Hager, J. C., Ekman, P., & Sejnowski, T. J. (1999) · Psychophysiology · doi:10.1017/s0048577299971664 · Cited by 424
Bayesian Measures of Explained Variance and Pooling in Multilevel (Hierarchical) Models
Gelman, A., & Pardoe, I. (2006) · Technometrics · doi:10.1198/004017005000000517 · Cited by 342 · cited 5×
“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.”
BackgroundInfluential
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
The dynamic effects of visual complexity and scene cuts on viewer attention
Wals, S., Grewal, D., Wilms, I., Hilken, T., Briassouli, A., & Wetzels, M. (2026) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-025-01137-x · Cited by 2
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
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.
