When blame avoidance backfires: Responses to performance framing and outgroup scapegoating during the COVID-19 pandemic

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

When blame avoidance backfires : Responses to performance framing and outgroup scapegoating during the COVID-19 pandemic. / Porumbescu, Gregory; Moynihan, Donald; Anastasopoulos, Jason; Olsen, Asmus Leth.

In: Governance, Vol. 36, No. 3, 2023, p. 779-803.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Porumbescu, G, Moynihan, D, Anastasopoulos, J & Olsen, AL 2023, 'When blame avoidance backfires: Responses to performance framing and outgroup scapegoating during the COVID-19 pandemic', Governance, vol. 36, no. 3, pp. 779-803. https://doi.org/10.1111/gove.12701

APA

Porumbescu, G., Moynihan, D., Anastasopoulos, J., & Olsen, A. L. (2023). When blame avoidance backfires: Responses to performance framing and outgroup scapegoating during the COVID-19 pandemic. Governance, 36(3), 779-803. https://doi.org/10.1111/gove.12701

Vancouver

Porumbescu G, Moynihan D, Anastasopoulos J, Olsen AL. When blame avoidance backfires: Responses to performance framing and outgroup scapegoating during the COVID-19 pandemic. Governance. 2023;36(3):779-803. https://doi.org/10.1111/gove.12701

Author

Porumbescu, Gregory ; Moynihan, Donald ; Anastasopoulos, Jason ; Olsen, Asmus Leth. / When blame avoidance backfires : Responses to performance framing and outgroup scapegoating during the COVID-19 pandemic. In: Governance. 2023 ; Vol. 36, No. 3. pp. 779-803.

Bibtex

@article{4b77c02042d54b50ac3fd2f86cd1512d,
title = "When blame avoidance backfires: Responses to performance framing and outgroup scapegoating during the COVID-19 pandemic",
abstract = "Public officials use blame avoidance strategies when communicating performance information. While such strategies typically involve shifting blame to political opponents or other governments, we examine how they might direct blame to ethnic groups. We focus on the COVID-19 pandemic, where the Trump administration sought to shift blame by scapegoating (using the term {"}Chinese virus{"}) and mitigate blame by positively framing performance information on COVID-19 testing. Using a novel experimental design that leverages machine learning techniques, we find scapegoating outgroups backfired, leading to greater blame of political leadership for the poor administrative response, especially among conservatives. Backlash was strongest for negatively framed performance data, demonstrating that performance framing shapes blame avoidance outcomes. We discuss how divisive blame avoidance strategies may alienate even supporters.",
keywords = "ATTRIBUTIONS, POLITICS, RACE",
author = "Gregory Porumbescu and Donald Moynihan and Jason Anastasopoulos and Olsen, {Asmus Leth}",
year = "2023",
doi = "10.1111/gove.12701",
language = "English",
volume = "36",
pages = "779--803",
journal = "Governance",
issn = "0952-1895",
publisher = "Wiley Online",
number = "3",

}

RIS

TY - JOUR

T1 - When blame avoidance backfires

T2 - Responses to performance framing and outgroup scapegoating during the COVID-19 pandemic

AU - Porumbescu, Gregory

AU - Moynihan, Donald

AU - Anastasopoulos, Jason

AU - Olsen, Asmus Leth

PY - 2023

Y1 - 2023

N2 - Public officials use blame avoidance strategies when communicating performance information. While such strategies typically involve shifting blame to political opponents or other governments, we examine how they might direct blame to ethnic groups. We focus on the COVID-19 pandemic, where the Trump administration sought to shift blame by scapegoating (using the term "Chinese virus") and mitigate blame by positively framing performance information on COVID-19 testing. Using a novel experimental design that leverages machine learning techniques, we find scapegoating outgroups backfired, leading to greater blame of political leadership for the poor administrative response, especially among conservatives. Backlash was strongest for negatively framed performance data, demonstrating that performance framing shapes blame avoidance outcomes. We discuss how divisive blame avoidance strategies may alienate even supporters.

AB - Public officials use blame avoidance strategies when communicating performance information. While such strategies typically involve shifting blame to political opponents or other governments, we examine how they might direct blame to ethnic groups. We focus on the COVID-19 pandemic, where the Trump administration sought to shift blame by scapegoating (using the term "Chinese virus") and mitigate blame by positively framing performance information on COVID-19 testing. Using a novel experimental design that leverages machine learning techniques, we find scapegoating outgroups backfired, leading to greater blame of political leadership for the poor administrative response, especially among conservatives. Backlash was strongest for negatively framed performance data, demonstrating that performance framing shapes blame avoidance outcomes. We discuss how divisive blame avoidance strategies may alienate even supporters.

KW - ATTRIBUTIONS

KW - POLITICS

KW - RACE

U2 - 10.1111/gove.12701

DO - 10.1111/gove.12701

M3 - Journal article

C2 - 35942431

VL - 36

SP - 779

EP - 803

JO - Governance

JF - Governance

SN - 0952-1895

IS - 3

ER -

ID: 342569015