Decision process for digital contact tracing acceptance: a cross-sectional survey based on the elaboration likelihood model

Decision process for digital contact tracing acceptance: a cross-sectional survey based on the elaboration likelihood model

연구 분야: Strategies

학회: Multimedia Tools and Applications

Among various non-pharmaceutical control measures, Digital Contact Tracing (DCT) based on information technology is an effective tool to combat COVID-19, but the penetration rates in major countries of the world are not optimal. To better prepare for any future emerging infectious disease outbreaks, it is necessary to examine the decision process of DCT acceptance among the public. Guided by the Elaboration Likelihood Model (ELM), this study compares the central and peripheral routes that motivate DCT acceptance. The research also examines how these decision processes moderate individuals’ perceived susceptibility to COVID-19 and through self-efficacy. This research adopts a survey methodology for collecting 284 valid responses and utilizes structural equation modeling to verify the proposed hypotheses. The results demonstrate that perceived benefits develop along a dual route of argument quality and source credibility. Perceived benefits, in turn, enable individuals’ willingness to disclose personal information to DCT. Perceived susceptibility and self-efficacy also moderate the influences of argument quality on perceived benefits. The findings imply that healthcare authorities should employ differing strategies to foster individuals’ perceived benefits of DCT with the goal of boosting DCT penetration rates.