# What Went Wrong? Predictors of Contact Tracing Adoption in Italy during COVID-19 Pandemic
## 1 Introduction
In the case of a COVID-19 contagion, a national health operator can, if the user approves, upload a random exposure key on Immuni's server, thus informing the other users who met the infected person that may have been exposed to infection [13]. Generally, the app detects and transmits a risky exposure notification when the Immuni's system, which periodically compares all the exposure keys with locally saved rolling proximity identifiers, when certain conditions are met (e.g., the length of the contact and the distance between the devices is above certain thresholds). At this point, Immuni advises the user to consult a general practitioner [13]. Studies have shown that cultural attitudes and social norms towards privacy have influenced the use of these systems to combat the pandemic [14; 15; 16]. In the case of Immuni, GDPR legal requirements are met by design. Indeed, Immuni does not allow direct identification of users or devices, and the data exchanged via BLE are exclusively those necessary  
Figure 1: Level of adoption of the CT systems (%) in the 30 countries that made the download CT app data available. to warn people that they have been exposed to a risk of contagion. The communication of the identification code in the case of positivity is not mandatory and will happen only with a health worker who enters the code on the Immuni server to identify the devices with which the infected person has come into contact in the last few hours, but without matching the person's identity to the entered code. In no case are the movements of users tracked, thus excluding any form of geolocation. The failure of such systems was individuated by the scientific literature in several factors: skepticism about the effectiveness of the app itself [15], concerns over potential privacy and cybersecurity implications [12], inability for some to download the app [16], and lack of trust in the government [17]. The analysis of classical and integrated psychological models can help us identify the factors that contributed to the failure of these systems [18]. [PERSON] and colleagues [19] argue that adoption models in the context of mHealth have largely been based on traditional adoption theories such as reasoned action theory--TRA [20; 21], Technology Acceptance Model--TAM [22; 23], and Unified Theory of Acceptance and Use of Technology--UTAUT [24; 25]. Based on these models, several important factors should be considered to adequately model the adoption of CT systems. Each of the models could theoretically be adequate to explain our specific condition. However, all the models presented pre-date the pandemic. For this reason, we integrated traditional adoption theories with other psychological factors that emerged from the literature as associated with CTS adoption to understand if there is a model that fits better than others to explain Immuni adoption or if the need for a new, more specific model arises. Towards Comprehensive Modeling of CT Systems Adoption: Factors Potentially Affecting Technological Adoption  
The adoption and use of innovation are influenced by the perception of efficacy that people have towards the new proposal. In this regard, the literature shows that performance expectancy is, in fact, one of the variables that best predicts the adoption of new technology [26; 27; 28]. [PERSON] et al. [26] defined performance expectancy as the degree to which using technology will provide benefits to consumers in performing certain activities. Thus, the higher people's perception of the benefits of a new proposal, the greater the probability that the proposal will be adopted. Regarding the new contact tracing apps proposed in recent years, the literature showed that performance expectancy is a core predictor of COVID-19 app adoption and usage. Indeed, the intention to use the app is higher if people are convinced about the app's efficacy to protect them from potential exposure to the virus [27]. As underlined by psychological models [22; 23; 24; 25], people's attitudes are an important element for behavioral change.