Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 189

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

Hautz, Julia, Füller, Johann, Hutter, Katja, Thürridl, Carina·2014·Journal of Interactive Marketing

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.intmar.2013.06.003

Cites

15 of 15 references verified0 citation errors · 0 not checkable

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.

A few free checks a day · papers already verified open instantly · results are public

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

Show all 15 references

Cited by

See all 189

Metadata

Authors
Hautz, J., Füller, J., Hutter, K., & Thürridl, C.
Venue
Journal of Interactive Marketing
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.