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Media effects revisited: corporate scandals, partisan narratives, and attitudes toward cryptocurrency regulation

Published online by Cambridge University Press:  02 January 2026

Pepper D. Culpepper*
Affiliation:
Blavatnik School of Government, University of Oxford, Oxford, UK
Taeku Lee
Affiliation:
Government, Harvard University, Cambridge, MA, USA
Ryan Shandler
Affiliation:
School of Cybersecurity and Privacy, Georgia Institute of Technology, Atlanta, USA
*
Corresponding author: Pepper D. Culpepper; Email: pepper.culpepper@bsg.ox.ac.uk
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Abstract

This article advances the literature on media effects by examining how contrasting partisan narratives influence support for regulation after a real-world corporate scandal. Using both multi-wave observational and randomized experimental data, we show that self-selected media exposure and experimentally assigned information shape public opinion in distinct ways. While scandals are narratives of regulatory failure, partisan media environments differently attribute blame for that failure. In two separate observational waves, only Democrats exposed to news about the FTX bankruptcy increased their support for crypto regulation. In the experiment, only Republicans shifted in favor of regulation. Research on media effects needs to take into account not only media content, but also the partisan information environments that expose citizens to that content.

Information

Type
Original Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of EPS Academic Ltd.
Figure 0

Figure 1. Distribution of depth of exposure to FTX scandal.

Note: A score of 0 on the x-axis indicates participants who exhibited no knowledge of the scandal. A score of 1 denotes participants who accurately identified the scandal from among possible news items but were unfamiliar with any further details about the scandal. A score of 4 denotes people who correctly answered every question we asked about details of the scandal.
Figure 1

Table 1. Balance checks for scandal awareness

Figure 2

Figure 2. Effect of scandal awareness on support for crypto regulation by partisan identity.

Note: The panel shows the marginal effect of scandal awareness (with 95% CIs) on crypto regulation attitudes along the values of partisan identity. The regression analyses for each model are produced in Online Appendix F, and the results are robust to the inclusion of covariates. The histogram reflects the distributions of partisan ID (1 = strong Democrat, 7 = strong Republican).
Figure 3

Table 2. Perceived causes of FTX scandal

Figure 4

Figure 3. Agreement with FTX scandal causes by partisan identity.

Note: Each dot reflects the mean level of agreement with the offered scandal cause among respondents at each level of partisan identity. On the x-axis, 1 denotes strong Democrats, and 7 denotes strong Republicans.
Figure 5

Figure 4. Proportion of morality and regulation frames in FTX coverage among left- and right-leaning news outlets.

Figure 6

Table 3. Cause of the scandal due to the partisanship of respondent and news source

Figure 7

Figure 5. Screenshot of the scandal article.

Note: This is an abridged version of the experimental treatment used in the US. Full copies of this article and the control appear in Online Appendix L.
Figure 8

Figure 6. Effect of scandal treatment on support for crypto regulation by partisan identity (US).

Note: The panel shows the marginal effect of the scandal treatment (with 95% CIs) on crypto regulation attitudes along the values of partisan identity. The regression analysis for the model is produced in full in Online Appendix N. We show in the appendix that the treatment effect is robust to the inclusion of covariates (age, gender, education, and income). The histogram at the bottom of the plot reflects the distributions of partisan ID (1 = strong Democrat, 7 = strong Republican).
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