Li Jinhui
School of Journalism and Communication, Jinan University, 601 Huangpu Ave West, Guangzhou, Guangdong, China 510632.
National Media Experimental Teaching Demonstration Center, Jinan University, 601 Huangpu Ave West, Guangzhou, Guangdong, China 510632.
Inf Process Manag. 2023 Jan;60(1):103163. doi: 10.1016/j.ipm.2022.103163. Epub 2022 Nov 10.
Guided by three major theoretical frameworks, this meta-analysis synthesizes 17 empirical studies (15 articles with 18,297 participants, 13 of them are from non-representative samples) and quantifies the effect sizes of a list of antecedents (e.g., cognitive, affective, and social factors) on information avoidance during the COVID-19 context. Findings indicated that information-related factors including channel belief ( = -0.35, < .01) and information overload ( = 0.23, < .01) are more important in determining individual's avoidance behaviors toward COVID-19 information. Factors from the psychosocial aspects, however, had low correlations with information avoidance. While informational subjective norms released a negative correlation ( = -0.16, < .1) which was approaching significant, positive and negative risk responses were not associated with information avoidance. Moderator analysis further revealed that the impacts of several antecedents varied for people with different demographic characteristics (i.e., age, gender, region of origin), and under certain sampling methods. Theoretically, this meta-analysis may help determine the most dominant factors from a larger landscape, thus providing valuable directions to refine frameworks and approaches in health information behaviors. Findings from moderator analysis have also practically inspired certain audience segmentation strategies to tackle occurrence of information avoidance during the COVID-19 pandemic.
在三个主要理论框架的指导下,本荟萃分析综合了17项实证研究(15篇文章,共18297名参与者,其中13项来自非代表性样本),并对一系列前因(如认知、情感和社会因素)在新冠疫情期间对信息回避的影响大小进行了量化。研究结果表明,与信息相关的因素,包括渠道信念(β = -0.35,p <.01)和信息过载(β = 0.23,p <.01),在决定个体对新冠疫情信息的回避行为方面更为重要。然而,心理社会方面的因素与信息回避的相关性较低。虽然信息主观规范呈现出接近显著的负相关(β = -0.16,p <.1),但积极和消极风险反应与信息回避无关。调节效应分析进一步表明,在不同的人口统计学特征(即年龄、性别、原籍地区)人群以及特定抽样方法下,几个前因的影响有所不同。从理论上讲,本荟萃分析可能有助于从更宏观的层面确定最主要的因素,从而为完善健康信息行为的框架和方法提供有价值的方向。调节效应分析的结果在实践中也启发了某些受众细分策略,以应对新冠疫情期间信息回避的发生。