ABSTRACT

COVID-19 mitigation strategies have largely relied on persuading populations to adopt behavioural changes, so it is critical to understand how such persuasive efforts can be made more effective. The Elaboration Likelihood Model (ELM) of persuasion allows for the integration of a variety of seemingly disparate effects into one overarching framework. This allows for prediction of which effects are more likely to lead to subsequent behaviour change than others and for generation of novel predictions. We review several recent investigations into persuasive effects of variables related to the source of a persuasive message, features of the message itself, the recipient, and interactive effects between variables across these categories. Each investigation is situated within the ELM framework, and future directions derived from the ELM perspective are discussed. Finally, the implications of each piece of research for COVID-19 persuasive messaging are unpacked and evidence-based recommendations are made.

摘要

COVID-19 缓解策略在很大程度上依赖于说服人们采取行为改变，因此了解如何使这种说服工作更有效至关重要。说服的精化似然模型 (ELM) 允许将各种看似不同的效果整合到一个总体框架中。这允许预测哪些影响比其他影响更可能导致随后的行为变化，并产生新的预测。我们回顾了最近对与说服性信息的来源、信息本身的特征、接收者以及这些类别变量之间的交互效应相关的变量的说服力影响的几项调查。每项调查都位于 ELM 框架内，并讨论了从 ELM 角度得出的未来方向。最后，每项研究对 COVID-19 有说服力的信息的影响都得到了解读，并提出了基于证据的建议。