ABSTRACT This study examined elaboration likelihood model (ELM) processing routes used by audiences to evaluate image repair messages for two organizations that often provoke polarizing dispositions in audiences: the National Rifle Association (NRA) and the New England Patriots football team. Involvement is a key concept in the ELM paradigm, yet studies have overlooked issues on the extreme ends of the dispositional spectrum, especially where ego-involvement motivates individuals. In order to test this largely unexplored idea, participants in the study were presented with post-incident message responses for the Columbine and Sandy Hook school shootings and the Spygate and Deflategate cheating scandals. Results indicated that the relationship between disposition and peripheral processing was curvilinear, with an increased use of peripheral processing at dispositional extremes. Ego-involvement fully mediated this relationship for most messages. Study items measuring reaction to the message source also showed statistical similarities to central processing variables. Although message source is often treated as a peripheral cue in ELM studies, the results of this analysis suggested that the message source was not used as a peripheral cue here. These findings are consistent with several other past studies on dual process models that align message source more closely with a likelihood to elaborate.

中文翻译：

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极化组织和形象修复：极端性格和自我介入对组织响应的 ELM 处理途径的影响

摘要 本研究检查了观众用来评估图像修复信息的精细化可能性模型 (ELM) 处理路线，这两个组织经常引起观众的两极分化倾向：全国步枪协会 (NRA) 和新英格兰爱国者足球队。参与是 ELM 范式中的一个关键概念，但研究忽略了性格谱极端的问题，尤其是在自我参与激励个人的情况下。为了测试这个很大程度上未经探索的想法，研究的参与者收到了对 Columbine 和 Sandy Hook 学校枪击事件以及 Spygate 和 Deflategate 作弊丑闻的事后消息回复。结果表明，性格与外周加工之间呈曲线关系，随着在性格极端情况下更多地使用外围处理。对于大多数信息，自我参与完全调节了这种关系。测量对消息源的反应的研究项目也显示出与中央处理变量的统计相似性。尽管在 ELM 研究中消息源通常被视为外围线索，但该分析的结果表明，消息源在此处并未用作外围线索。这些发现与过去关于双重过程模型的其他几项研究一致，这些模型将消息源与详细说明的可能性更紧密地结合起来。尽管在 ELM 研究中消息源通常被视为外围线索，但该分析的结果表明，消息源在此处并未用作外围线索。这些发现与过去关于双重过程模型的其他几项研究一致，这些模型将消息源与详细说明的可能性更紧密地结合起来。尽管在 ELM 研究中消息源通常被视为外围线索，但该分析的结果表明，消息源在此处并未用作外围线索。这些发现与过去关于双重过程模型的其他几项研究一致，这些模型将消息源与详细说明的可能性更紧密地结合起来。