The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Let Users Generate Your Video Ads? The Impact of Video Source and Quality on Consumers’ Perceptions and Intended Behaviors
Abstract
The advent of social media has challenged companies as sole creators of marketing messages. Whether intentionally branded or subconsciously promoted, users become ambassadors for products and brands by producing and disseminating user-generated content (UGC). Whereas previous studies on UGC have mainly considered verbal peer recommendations or written reviews, our research focuses on user-generated videos (UGVs). In our 2 (source: user vs. agency) × 2 (technical quality: low vs. high) experimental study, we explore the impact of user-generated videos (UGVs) vs. agency-generated videos (AGVs) on spectators’ perceived source credibility and their intention to visit the tourist destination Tyrol in Austria, as promoted in the video. In addition, we explore the moderating role of video quality. In contrast to conventional wisdom, our results indicate that there is no general superiority of UGVs over AGVs. Rather, the influence of different video generators (user vs. agency) on spectators’ perception and intended behavior depends on the technical quality of the video. In the case of low technical quality, users as generators have a significantly stronger positive effect on source trustworthiness and expertise – and thereby on consumers’ intended behaviors – than AGVs. However, no impact from the generating source on trustworthiness has been found under the condition of highly technical quality. Concerning source expertise, videos generated by users are rated more highly than agency-generated videos under both low and high technical qualities, but the advantage is significantly lower under high technical quality. Our findings contribute to a better understanding of user-generated content overall and relativize the widely assumed general superiority of user-generated content over commercial, firm-generated content, at least in the case of videos.
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
A Cognitive Model of the Antecedents and Consequences of Satisfaction Decisions
Oliver, R. L. (1980) · Journal of Marketing Research · doi:10.1177/002224378001700405 · Cited by 14,494
Reconsidering Baron and Kenny: myths and truths about mediation analysis
Zhao, X., Lynch, J. G., & Chen, Q. (2010) · Journal of Consumer Research · doi:10.1086/651257 · Cited by 12,583
Mediation in experimental and nonexperimental studies: New procedures and recommendations.
Shrout, P. E., & Bolger, N. (2002) · Psychological Methods · doi:10.1037/1082-989x.7.4.422 · Cited by 11,335
Beyond Baron and Kenny: Statistical Mediation Analysis in the New Millennium
Hayes, A. F. (2009) · Communication Monographs · doi:10.1080/03637750903310360 · Cited by 9,757
A comparison of methods to test mediation and other intervening variable effects
MacKinnon, D. P., Lockwood, C. M., Hoffman, J. M., West, S. G., & Sheets, V. (2002) · Psychological Methods · doi:10.1037/1082-989x.7.1.83 · Cited by 9,406
Confidence Limits for the Indirect Effect: Distribution of the Product and Resampling Methods
MacKinnon, D. P., Lockwood, C. M., & Williams, J. (2004) · Multivariate Behavioral Research · doi:10.1207/s15327906mbr3901_4 · Cited by 7,806
Hennig-Thurau, T., Gwinner, K. P., Walsh, G., & Gremler, D. D. (2004) · Journal of Interactive Marketing · doi:10.1002/dir.10073 · Cited by 5,916
Required sample size to detect the mediated effect
Fritz, M. S., & MacKinnon, D. P. (2007) · Psychological Science · doi:10.1111/j.1467-9280.2007.01882.x · Cited by 4,810
Ohanian, R. (1990) · Journal of Advertising · doi:10.1080/00913367.1990.10673191 · Cited by 4,043
The Persuasion Knowledge Model: How People Cope with Persuasion Attempts
Friestad, M., & Wright, P. (1994) · Journal of Consumer Research · doi:10.1086/209380 · Cited by 3,811
Cited by
Sokolova, K., & Kefi, H. (2020) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2019.01.011 · Cited by 1,650
The effect of social media communication on consumer perceptions of brands
Schivinski, B., & Dabrowski, D. (2016) · Journal of Marketing Communications · doi:10.1080/13527266.2013.871323 · Cited by 940
Facebook use, envy, and depression among college students: is facebooking depressing?
Tandoc, E. C., Ferrucci, P., & Duffy, M. (2015) · Computers in Human Behavior · doi:10.1016/j.chb.2014.10.053 · Cited by 781
Effects of Social Shopping Behavior from the Perspective of Collective Intelligence and Dissonance
Chiang, I. P., Liang, C. C., & Yang, W. L. (2021) · International Journal of Electronic Commerce Studies · doi:10.7903/ijecs.1737 · Cited by 6
Metadata
- Authors
- Hautz, J., Füller, J., Hutter, K., & Thürridl, C.
- Venue
- Journal of Interactive Marketing
- Publisher
- SAGE Publications
- Published
- 2014-02
- Volume
- 28
- Issue
- 1
- Pages
- 1-15
- ISSN
- 1094-9968 (ISSN-L)
- 1520-6653
- OpenAlex
- W2081761528
- Cited by
- 189
- References
- 15
- FWCI
- 30.91
- 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: Let Users Generate Your Video Ads? The Impact of Video Source and Quality on Consumers’ Perceptions and Intended Behaviors. https://app.cochat.ai/papers/10.1016/j.intmar.2013.06.003
- The paper
- Hautz, J., Füller, J., Hutter, K., & Thürridl, C. (2014). Let Users Generate Your Video Ads? The Impact of Video Source and Quality on Consumers’ Perceptions and Intended Behaviors. Journal of Interactive Marketing. https://doi.org/10.1016/j.intmar.2013.06.003
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.