Literature DB >> 22050442

Predicting psychological ripple effects: the role of cultural identity, in-group/out-group identification, and attributions of blame in crisis communication.

Deepa Anagondahalli1, Monique Mitchell Turner.   

Abstract

Incidents of intentional food contamination can produce ripple effects in consumers such as reduced trust and increased anxiety. In their postcrisis communication, food companies often direct the blame at the perpetrator in an effort to mitigate potential losses and regain consumer trust. The attempt to placate consumers may, in itself, potentially create psychological ripple effects in message readers. This study examined the interacting influence of two message characteristics: identity of the perpetrator of the crime (in-group/out-group membership), and the attribution of blame (reason why the perpetrator committed the crime), with message receiver characteristic (cultural identity) on psychological ripple effects such as blame, trust, anxiety, and future purchase intention. Results indicated that although group membership of the perpetrator was not significant in predicting outcomes for the organization, the attribution communicated in the message was. American message receivers blamed the organization more and trusted it less when personal dispositional attributions were made about the perpetrator. Asian message receivers blamed the organization more and trusted it less when situational attributions were made about the perpetrator. Lowered trust in the company and increased anxiety correlated with lower purchase intent for both American and Asian message receivers. Implications for crisis message design are discussed.
© 2011 Society for Risk Analysis.

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Year:  2011        PMID: 22050442     DOI: 10.1111/j.1539-6924.2011.01727.x

Source DB:  PubMed          Journal:  Risk Anal        ISSN: 0272-4332            Impact factor:   4.000


  1 in total

1.  Communicative Blame in Online Communication of the COVID-19 Pandemic: Computational Approach of Stigmatizing Cues and Negative Sentiment Gauged With Automated Analytic Techniques.

Authors:  Angela Chang; Peter Johannes Schulz; ShengTsung Tu; Matthew Tingchi Liu
Journal:  J Med Internet Res       Date:  2020-11-25       Impact factor: 5.428

  1 in total

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