The DOI matches its Crossref record; 74 of the 74 references it cites were found in a registry or an index.
Campaigns, Reflection, and Deliberation: Advancing an O-S-R-O-R Model of Communication Effects
Abstract
Recent communication research concerning participatory politics has found that the effects of media, especially campaign ads, conventional news, and online political resources, are largely mediated through interpersonal discussion about politics. This article extends this line of theorizing about the role of political conversation in citizen competence by testing an O-S-R-O-R model of campaign communication mediation, a modification and extension of the longstanding O-S-O-R model of communication effects. This model combines insights from iterations of the communication mediation model (McLeod et al., 2001; Shah et al., 2007) and cognitive mediation model (Eveland, 2001; Eveland, Shah, & Kwak, 2003) to theorize a set of the interrelated reasoning (R) processes that channel the influences of campaign exposure and news consumption on political engagement. Three key mediators of campaign and news influence are postulated: face-to-face political conversation, online political messaging, and cognitive reflection. We provide empirical evidence to test this model by merging two datasets: (1) tracking of the content and placement of campaign messages in the 2000 and 2004 election cycles, and (2) surveys of traditional and digital media consumption and levels of campaign participation during these same elections. Findings reveal that political conversation, political messaging, and cognitive reflection mediate the effects of campaign advertising exposure and news consumption on political participation and knowledge, providing considerable support for our theory. This O-S-R-O-R model helps organize a large body of theorizing and research on campaigns and conversation in the communication sciences.
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
Bowling alone: the collapse and revival of American community
(2000) · Choice Reviews Online · doi:10.5860/choice.38-2454 · Cited by 28,789
(1990) · Choice Reviews Online · doi:10.5860/choice.27-6637 · Cited by 16,594
The Nature and Origins of Mass Opinion
Zaller, J. R. (1992) · doi:10.1017/cbo9780511818691 · Cited by 7,785
(2013) · Handbooks of Sociology and Social Research · doi:10.1007/978-94-007-6772-0 · Cited by 7,486
Voice and Equality: Civic Voluntarism in American Politics
VERBA, S., SCHLOZMAN, K. L., & BRADY, H. E. (1995) · Harvard University Press eBooks · doi:10.2307/j.ctv1pnc1k7 · Cited by 6,626
The Civic Culture: Political Attitudes and Democracy in Five Nations
Almond, G. A., & Verba, S. (1963) · Princeton University Press eBooks · doi:10.1515/9781400874569 · Cited by 5,936
Petty, R. E., & Cacioppo, J. T. (1986) · doi:10.1007/978-1-4612-4964-1 · Cited by 5,219
The Nature and Origins of Mass Opinion.
Kenski, H. C., & Zaller, J. R. (1993) · Contemporary Sociology · doi:10.2307/2074664 · Cited by 5,169
Communication and Persuasion: Central and Peripheral Routes to Attitude Change
Marquart, F., & Naderer, B. (2016) · Schlüsselwerke der Medienwirkungsforschung · doi:10.1007/978-3-658-09923-7_20 · Cited by 4,993
Internet paradox: A social technology that reduces social involvement and psychological well-being?
Kraut, R., Patterson, M., Lundmark, V., Kiesler, S., Mukophadhyay, T., & Scherlis, W. (1998) · American Psychologist · doi:10.1037/0003-066x.53.9.1017 · Cited by 4,793
Cited by
Curated Flows: A Framework for Mapping Media Exposure in the Digital Age
Thorson, K., & Wells, C. (2016) · Communication Theory · doi:10.1111/comt.12087 · Cited by 570
Automating the News: How Personalized News Recommender System Design Choices Impact News Reception
Beam, M. A. (2014) · Communication Research · doi:10.1177/0093650213497979 · Cited by 159
Bright, J., Hale, S., Ganesh, B., Bulovsky, A., Margetts, H., & Howard, P. (2020) · Communication Research · doi:10.1177/0093650219872394 · Cited by 104
Oser, J., & Boulianne, S. (2020) · Public Opinion Quarterly · doi:10.1093/poq/nfaa017 · Cited by 82
Zhao, X. (2012) · Communication Monographs · doi:10.1080/03637751.2012.672999 · Cited by 39
Metadata
- Authors
- Cho, J., Shah, D. V., McLeod, J. M., McLeod, D. M., Scholl, R. M., & Gotlieb, M. R.
- Venue
- Communication Theory
- Publisher
- Oxford University Press (OUP)
- Published
- 2009-02
- Volume
- 19
- Issue
- 1
- Pages
- 66-88
- ISSN
- 1050-3293 (ISSN-L)
- 1468-2885
- OpenAlex
- W2153653987
- Cited by
- 361
- References
- 74
- FWCI
- 43.37
- 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: Campaigns, Reflection, and Deliberation: Advancing an O-S-R-O-R Model of Communication Effects. https://app.cochat.ai/papers/10.1111/j.1468-2885.2008.01333.x
- The paper
- Cho, J., Shah, D. V., McLeod, J. M., McLeod, D. M., Scholl, R. M., & Gotlieb, M. R. (2009). Campaigns, Reflection, and Deliberation: Advancing an O-S-R-O-R Model of Communication Effects. Communication Theory. https://doi.org/10.1111/j.1468-2885.2008.01333.x
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.