The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Emotional Response to Television Commercials: Facial EMG vs. Self-Report
Abstract
As television commercials increasingly contain emotional elements designed both to get the viewer's attention and to communicate the advertising message, copy pretesting is Challenged to evaluate the potential effectiveness of these emotionally stimulating commercials and their success at eliciting the intended emotional responses. Standard copy measures, however, do not yield such informative results about emotional responses to commercials. In order to meet this challenge for copy pretesting, we measured the emotional responses to a series of television commercials of both females and males using self-report and facial EMG, a validated emotion measure used in academic research. We hypothesized that facial EMG, as compared to self-report, would be a more sensitive discriminator between commercials, would be more strongly related to recall, and peaks in facial EMG responses elicited during the commercial would be temporally related to specific emotion-congruent events in the commercial. The results strongly supported all of our hypotheses and illustrated the promise of facial EMG measures in advertising research and copy pretesting in particular. Facial EMG measures can reflect a qualitative richness and complexity of the viewer's emotional response that self-report measures cannot and, at the same time, offer precise and continuous quantitative data.
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
Russell, J. A. (1980) · Journal of Personality and Social Psychology · doi:10.1037/h0077714 · Cited by 16,334
Feeling and thinking: Preferences need no inferences.
Zajonc, R. B. (1980) · American Psychologist · doi:10.1037/0003-066x.35.2.151 · Cited by 8,094
Hedonic Consumption: Emerging Concepts, Methods and Propositions
Hirschman, E. C., & Holbrook, M. B. (1982) · Journal of Marketing · doi:10.1177/002224298204600314 · Cited by 5,616
Looking at pictures: Affective, facial, visceral, and behavioral reactions
LANG, P. J., GREENWALD, M. K., BRADLEY, M. M., & HAMM, A. O. (1993) · Psychophysiology · doi:10.1111/j.1469-8986.1993.tb03352.x · Cited by 3,190
Affective responses mediating acceptance of advertising.
Batra, R., & Ray, M. L. (1986) · Journal of Consumer Research · doi:10.1086/209063 · Cited by 1,076
Fear reduction and fear behavior: Problems in treating a construct
Lang, P. J. (1968) · Research in psychotherapy. · doi:10.1037/10546-004 · Cited by 989
An Examination of Consumer Decision Making for a Common Repeat Purchase Product
Hoyer, W. D. (1984) · Journal of Consumer Research · doi:10.1086/209017 · Cited by 776
ÖHman, A. (1986) · Psychophysiology · doi:10.1111/j.1469-8986.1986.tb00608.x · Cited by 738
Cited by
How to Capture the Heart? Reviewing 20 Years of Emotion Measurement in Advertising
Poels, K., & Dewitte, S. (2006) · SSRN Electronic Journal · doi:10.2139/ssrn.944401 · Cited by 447
Contributions of Psychophysiology to Media Research: Review and Recommendations
Ravaja, N. (2004) · Media Psychology · doi:10.1207/s1532785xmep0602_4 · Cited by 383
Current and potential methods for measuring emotion in tourism experiences: a review
Li, S., Scott, N., & Walters, G. (2015) · Current Issues in Tourism · doi:10.1080/13683500.2014.975679 · Cited by 266
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
- Hazlett, R. L., & Hazlett, S. Y.
- Venue
- Journal of Advertising Research
- Publisher
- Informa UK Limited
- Published
- 1999-03
- Volume
- 39
- Issue
- 2
- Pages
- 7-23
- ISSN
- 0021-8499 (ISSN-L)
- 1740-1909
- OpenAlex
- W2594496087
- Cited by
- 203
- References
- 8
- FWCI
- 2.12
- 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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Emotional Response to Television Commercials: Facial EMG vs. Self-Report. https://app.cochat.ai/papers/10.1080/00218499.1999.12466597
- The paper
- Hazlett, R. L., & Hazlett, S. Y. (1999). Emotional Response to Television Commercials: Facial EMG vs. Self-Report. Journal of Advertising Research. https://doi.org/10.1080/00218499.1999.12466597
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.