信息资源管理学报2026，Vol.16Issue(1)：89-100,12.DOI:10.13365/j.jirm.2026.01.089
基于CAC范式的突发公共卫生事件个体保护行为影响因素研究
Research on Influencing Factors of Individual Protective Behavior in Public Health Emergencies Based on CAC Paradigm
摘要
Abstract
Based on the Cognition-Affect-Conation(CAC)paradigm,we constructed a model of the influencing factors of individual protective behaviors in public health emergencies.379 questionnaires were collected through an online survey,and the Partial Least Squares Structural Equation Modeling(PLS-SEM)was used for empirical analysis to investigate the mechanisms of risk perception,negative emotions,information seeking behaviors,and individual pro-tective behaviors in public health emergencies.The results show that the perceived severity and uncontrollability of risk have a significant positive effect on fear and anxiety,and anxiety has a significant positive effect on individual protec-tive behaviors.Meanwhile,the perceived severity and uncontrollability of risk indirectly affect individual protective be-haviors through anxiety.Information seeking behaviors have a significant moderating effect on the mediating effects.This study establishes an analytical framework for the study of individual protective behaviors in public health emer-gencies from the perspective of the correlation paths of cognition,emotion,and conation,which is conducive to optimi-zing the risk communication mechanism for emergencies and enhancing the implementation effectiveness of risk gov-ernance measures.关键词
CAC范式/突发公共卫生事件/个体保护行为/风险感知/负面情感/信息搜寻行为Key words
CAC paradigm/Public health emergency/Individual protective behavior/Risk perception/Negative e-motion/Information seeking behavior分类
社会科学引用本文复制引用
王晓,林中惠,王玉阳,陈思菁,段尧清..基于CAC范式的突发公共卫生事件个体保护行为影响因素研究[J].信息资源管理学报,2026,16(1):89-100,12.基金项目
本文系国家自然科学基金青年项目"基于细粒度认知特征挖掘的在线学习交互关系演化规律研究"(62207016)的研究成果之一.(This work is supported by the National Natural Science Foundation of China Youth Program,project titled"The Evolution Rule Study of Interaction Relationship in Online Learning Based on the Mining of Fine-Grained Cognitive Features"(62207016).) （62207016）
