Hi, i'm Jack Jamieson. I'm presenting on behalf
of my co-authors Daniel A. Epstein, Yunan Chen and Naomi Yamashita.
Our research was focused on unpacking intention and behavior to explain contact tracing app
adoption and hesitancy in the United States. Contact tracing apps are smartphone applications
that alert people if they've come into contact with someone who's tested positive for COVID-19.
They've been found to be effective at reducing the number of COVID-19 infections. Essentially,
a higher number of people who use these apps leads to a greater reduction in cases. Importantly,
they enlist individuals to address a collective challenge, so the lessons here are important not
just for contact tracing apps or for the pandemic but for future collective social problems.
Most governments and public health agencies targeted an adoption rate of about 60 percent
and early survey research suggested that over 70 percent of people were willing to install a
contact tracing app. The outcome has not reached those goals, however. Globally,
most countries have had about 20 percent adoption. In the United States, it's been about 14 percent.
Individual's decisions about whether or not to use a contact tracing app are quite complex,
so we set out to better understand those. Specifically, our analysis focused on
whether contact tracing app adoption can be characterized by an intention-behavior gap.
When people say they will do something, it's a quite good indicator of whether they will
actually take that action. It may be the best indicator that we have. But, it's not perfect.
Various studies put the correlation between intention and behavior between 0.3 or 0.6.
We investigated this with a survey of 290 USA residents in December 2020.
Our survey design basically entailed asking our participants about their attitudes towards
contact tracing apps, whether they had installed an app or intended to do so,
as well as their trust in society, their everyday life conditions ,attitudes towards the pandemic
and so on. Now, not every state has a contact tracing app so we described a hypothetical example
to our participants that match the kind of overall common design in most states.
Our first research question investigated people's attitudes towards these apps. They generally had
positive impressions, noting that this would be useful, they thought, for containing the
spread of the pandemic. However, many people said this would have a minimal impact on daily
life. There were some concerns about relying on other people, such as this person who said
the app depends too much on the goodwill of other people and that's not working currently. And some
participants had surveillance and privacy concerns thinking that these apps might be monitoring them.
At the time of the study contact tracing apps were available in 22 states,
so our second research question investigated whether intentions to install an app was different
between people in states where an app is available or where an app was not available. In other words,
could it be possible that people have a different opinion when they've seen an actual app become
available in their area? To answer this question, we divided our participants into
these two groups and then we looked at their intention. We found that in both groups about
55 percent said they would probably install, definitely install, or had already installed.
The main difference was, of course, that people in a state where a contact tracing app was available,
there's a significant group of people who had already installed the app.
The main takeaway though is that there was not a major difference in intention -- overall
intention -- between states where an app is available or where one is not available.
Our next question then focused on participants in a state where a contact tracing app
is available. We asked, "what are the differences between people who say they would install an app
but have not done so and between people who actually installed the app?" We investigated
this with two regression models. Here i'm going to present simplified results. The full results
and discussion are of course in the paper. First, let's look at variables associated with
intention to install among people who did not actually install the app. The figure is showing
odds ratios, so if a point is to the right of the orange line the variable is associated with being
more likely to have a positive intention. Left of the orange line means less likely.
What we see is that knowing at least one person who uses a contact tracing app
is associated with a higher likelihood of positive intention. Believing that everyone
should install the app also is associated with a higher intention. Worry about information leaking
to third parties -- so a privacy concern -- is associated with a lower intention to install.
And stress about oneself or one's family being infected is associated with a higher intention.
Our second figure focuses on whether or not people had actually installed among all participants
who had an app available in their state. What we see is that knowing at least one app user
is associated with being more likely to have installed as is believing that everyone should
install the app. And, the other two variables are no longer significant in this model.
Based on this result I'll briefly discuss implications for design. We found that privacy
concerns were negatively associated with intention but not with the behavior of actually installing.
There's two sides this result. The first is that a lack of privacy concerns was
not sufficient to drive adoption. So, even if reassuring people about privacy is important,
they need to have another motivation to install.
The second is that people may be installing these apps despite having privacy concerns.
In this case, they could have latent concerns which could make them more likely to abandon these
apps in the future. If their trust is misplaced this could lead to even further distrust of
future technologies. In sum, if people do install these apps despite having privacy concerns it's
very important for designers governments and health agencies to do justice to that trust.
Additionally social norms were a strong predictor of both intention and behavior. This came in the
form of a descriptive norm, knowing at least one app user, and an injunctive norm, believing that
everyone should install the app. Based on this finding we argue that increasing social visibility
of the apps could help increase adoption. This could be pursued through things like
social sharing features and social media badges. Additionally, designers could use messaging to
reinforce injunctive norms in the app, such as asserting to users, "you're keeping your friends
family and community safe," as a way to encourage them to believe that they're doing the right thing
and to think that they should recommend this to other people because everyone should install.
Now, we're presenting these suggestions for bridging an intention-behavior gap
to increase adoption, but we want to acknowledge that this presumes the apps are ultimately
beneficial for individuals. HCI research has identified valid concerns about accuracy,
privacy, and access of contact tracing apps so it's very important to avoid
tech solutionism. Thus, understanding how and why people make decisions about these technologies,
including non-use decisions, can help us understand benefits
and shortcomings so that we can address those in future designs. Thank you very much.