Modeling the predictors of health information avoidance behavior: Implications from the stimulus organism response (S-O-R) theory

Abstract

The study aimed at modeling the factors that influence health information avoidance behavior, as well as measuring and validating the stimulus organism response (S-O-R) theory. A seven-factor (information overload, information sources exposure, risk perception, health information anxiety, cognitive dissonance, sadness, and health information avoidance) measurement model was used to estimate the health information avoidance behavior using the structural equation modeling (SEM). The findings show that risk perception had a significant positive influence on sadness (β = 0.492, CR = 7.445, p < 0.05), information overload exerts a significant positive impact on cognitive dissonance (β = 0.174, CR = 2.192, p < 0.05) and sadness significantly influence health information avoidance (β = 0.174, CR = 2.342, p < 0.05). Information overload exhibits a positive, but statistically non-significant influence on health information anxiety (β = 0.83, CR = 1.094, p > 0.05). The findings of SEM demonstrate acceptable model fit indices: χ² = 1.493, DF = 732; p = 0.000; IFI = 0.931; and TLI = 0.925, CFI = 0.930, SRMR = 0.045, RMSEA = 0.044. The study concludes that risk perception, sadness, and information overload are the main predictors of health information avoidance behavior. Other factors such as health information anxiety, exposure to different information sources, and cognitive dissonance had a non-significant impact on information avoidance behavior. The findings hold significant global relevance, potentially contributing to improved information-seeking behavior research. Our study also contributes to the advancement of the S-O-R (Stimulus-Organism-Response) framework