An Empirical Study on the Reciprocity and Acceptance of Contact-tracing Apps

The "Health Code," a contact-tracing application deployed in China, was instrumental in managing the COVID-19 outbreak. Its success hinged on mutual acceptance and data sharing among users: individuals contributed and accessed personal information to assess and mitigate viral exposure risks. This study integrates the Technology Acceptance Model (TAM) and Social Exchange Theory to explore the psychological drivers of public endorsement of the "Health Code." We administered a survey to 3000 Chinese internet users to validate a moderated mediation model. The findings reveal that reciprocal behavior indirectly shapes attitudes toward the "Health Code" through the perceived risk and utility. However, the perception of COVID-19 risk partially attenuates this effect. The endorsement of the "Health Code" emerges from a symbiotic process, underpinned by a system of reciprocal data exchange. Findings confer theoretical and practical implications on propagating public health technologies, highlighting the motivating potential of reciprocal appeals and data transparency, while adapting strategies to evolving pandemic threats. Limitations concerning sample representativeness and model generalizability are discussed alongside prospective avenues exploring changing attitudes and automated tracing protocols.