Going Against a Pandemic: Persuasive Messages on Benefits for the Common Good or Individuals to Encourage Data Disclosure to Disease Spread Apps

Abstract

Currently, the world is experiencing the COVID-19 pandemic. The widespread virus has motivated the development of monitoring systems that identify those infected by COVID-19 to warn whoever has been in contact with infected individuals. A much-discussed monitoring system in the fight against the virus has been mobile disease spread applications. The apps are succeeding in various countries but failing in others. Predicted causes to the failure are reactance and privacy concerns. Thus, this study aims to understand how countries can overcome privacy concerns and reactance to develop persuasive messages which increase attitudinal- and behavioural intention to adopt a disease spread app. In the first part of the thesis are the hypotheses developed, and the conceptual framework is set up. It is predicted that behavioural intention to disclose personal data to a disease spread app is larger when an individual is presented with a persuasive message on the common good benefits of disclosing the data, relative to a focus on personal benefits of disclosing. A chain-of-effects driving behavioural intention are predicted, which begins with threat to freedom or trait proneness that increases reactance. These chains are moderated and increased by fear. Next, reactance decreases attitudinal intention to disclose. These mentioned chains’ magnitudes are larger for those presented the personal benefits message, compared to those presented with the common good benefits message. Lastly, attitudinal intention increases behavioural intention to disclose. This final chain is larger for those presented the common good benefits message. Except, this final chain could be negative when the chain-of-effects starts from trait proneness. Thus, it would be smaller for the common good benefits message. All predictions were supported, except fear and the final chain was found to be positive for trait proneness. In the final part of the thesis is the methods section. An experimental design was used with two conditions and persuasive messages, which solely differed in message topic: common good or personal benefits of disclosing personal data to a disease spread app. After participants were presented a message, they were shown questions and measures to test the predictions and hypotheses. A convenience sampling method was used, and after data cleaning, there were 296 participants. To decide when behavioural intention is larger, a one-way analysis of variance was run, and to find the chains’ magnitudes and directions, PROCESS macro was used.