# What Went Wrong? Predictors of Contact Tracing Adoption in Italy during COVID-19 Pandemic
## 4 Discussion and Conclusions
Finally, we tried to identify the best possible predictive model for Immuni adoption, given the variables collected by our study. In general, each theoretical model (TAM, TRA, UTAUT) produced one or more predictors regarding the adoption of CTs; however, it is only through their integration into a new model specific to our condition that we reached a level with 52% variance explained. The best predictors of contact tracing adoption turned out to be, first of all, having a behavioral model to observe (i.e., observing a significant one who used the application), followed by the perception of Immuni as an effective way of dealing with the COVID-19 outbreak (i.e., acceptance), and a higher perceived likelihood of infection fear. Trust in Government and Trust in Government actions against COVID-19 variables did not fit into the best predictive model; nevertheless, their effect on Immuni adoption was highlighted by univariate analyses. The role of the political dimension is undoubtedly, and we should not be misled by the fact that this dimension does not fit into the final model, probably due to the shared variance between Trust in Government and the other variables in the model. Of course, this work is not without limits. The results of the present study are cross-sectional (no causation could be inferred) and not fully generalizable to all Italians, partly due to the high number of Tuscans. Moreover, our findings are supported by a biased sample of people that have technical skills and know how to operate with these apps. Thus, the described findings may not apply to the perceptions and behaviors of other people. Future research should further deepen the investigation of the factors related to people's adoption of contact tracing solutions. For instance, in our case, occupation did not associate with CT adoption; however, the type of profession could plausibly play a role. People that were allowed to work from home (i.e., remote working, home working) [58; 59] already limited their contact with other people by default, so we can assume that they would be less likely to download and use CTs (i.e., they may perceive less need to do it) than people who had to work on-site and interact with others. People's health status could be another valuable direction for future work, thus investigating whether the willingness to download and use the CT app is different between healthy individuals and those with COVID-19. Unfortunately, a recent publication based on a survey reported that just 0.8% referred to have been infected by Covid-19 [60], and thus a larger sample size would be needed to investigate such a difference. Despite the aforementioned limitations, our work contributed to defining the factors (extracted from several psychological models) capable of promoting the adoption of contact tracing systems. Encouraging the adoption of contact tracing systems can help to control virus spread together with other measures such as wearing masks, physical distancing, and handwashing [60], although the efficacy of these apps to control the pandemic is not fully demonstrated [61]. In general, many things appeared to contribute to making things go wrong with CT functioning and adoption around the world: (a) many individuals do not have smartphones, especially in poor and disadvantaged countries [62; 63]; (b) BLE technology was not originally designed to accurately estimate the distance between smartphones and thus is prone to errors [64]; (c) the efficacy of CT apps depend on user honesty (which is not always granted) and in the ease in reporting themselves as positive; (d) privacy and data security concerns [65; 66; 67; 68].