RESUMO
Governments worldwide are using digital contact tracing (DCT) apps as a critical element in their COVID-19 pandemic lockdown exit strategy. Despite substantial investment in research and development, the public's acceptance of DCT apps has been phenomenally low, signaling resistance among potential users. Little is known about why people would resist using the DCT app, a useful innovation that can potentially save millions of human lives. This study explores the determinants and consequences of citizens' resistance to use DCT apps using a sequential two-stage mixed-methods approach. The preliminary qualitative study analyzed interviews of 24 Indian smartphone users who chose not to use or discontinued the DCT app after an initial trial. In the quantitative stage, an integrated model based on innovation resistance theory and distrust theory was tested using the survey data collected from 194 non-adopters of the DCT app from India. The findings revealed that the factors, distrust, value barrier, information privacy concerns, and usage barrier predicted the resistance to the DCT app, and resistance, in turn, predicted intention to use. Additionally, distrust was found to be a key mediator between innovation barriers and resistance. The insights from this study could help the developers and policymakers formulate strategies for implementing DCT interventions during future disease outbreaks.
RESUMO
OBJECTIVES: Our study investigates the extent to which uptake of a COVID-19 digital contact-tracing (DCT) app among the Dutch population is affected by its configurations, its societal effects, and government policies toward such an app. METHODS: We performed a discrete choice experiment among Dutch adults including 7 attributes, that is, who gets a notification, waiting time for testing, possibility for shops to refuse customers who have not installed the app, stopping condition for contact tracing, number of people unjustifiably quarantined, number of deaths prevented, and number of households with financial problems prevented. The data were analyzed by means of panel mixed logit models. RESULTS: The prevention of deaths and financial problems of households had a very strong influence on the uptake of the app. Predicted app uptake rates ranged from 24% to 78% for the worst and best possible app for these societal effects. We found a strong positive relationship between people's trust in government and people's propensity to install the DCT app. CONCLUSIONS: The uptake levels we find are much more volatile than the uptake levels predicted in comparable studies that did not include societal effects in their discrete choice experiments. Our finding that the societal effects are a major factor in the uptake of the DCT app results in a chicken-or-the-egg causality dilemma. That is, the societal effects of the app are severely influenced by the uptake of the app, but the uptake of the app is severely influenced by its societal effects.