[Paper Review] What Makes People Install a COVID-19 Contact-Tracing App? Understanding the Influence of App Design and Individual Difference on Contact-Tracing App Adoption Intention

Tianshi Li, Camille Cobb+7 more|arXiv (Cornell University)|Dec 22, 2020

COVID-19 Digital Contact Tracing49 references12 citations

TL;DR

This study uses a national U.S. survey experiment (N=1963) to examine how app design and individual differences affect intentions to install COVID-19 contact-tracing apps. It finds that individual factors like prosocialness and perceived public health benefits matter more than design choices, with perceived benefits and community adoption driving adoption more than privacy concerns.

ABSTRACT

Smartphone-based contact-tracing apps are a promising solution to help scale up the conventional contact-tracing process. However, low adoption rates have become a major issue that prevents these apps from achieving their full potential. In this paper, we present a national-scale survey experiment ($N = 1963$) in the U.S. to investigate the effects of app design choices and individual differences on COVID-19 contact-tracing app adoption intentions. We found that individual differences such as prosocialness, COVID-19 risk perceptions, general privacy concerns, technology readiness, and demographic factors played a more important role than app design choices such as decentralized design vs. centralized design, location use, app providers, and the presentation of security risks. Certain app designs could exacerbate the different preferences in different sub-populations which may lead to an inequality of acceptance to certain app design choices (e.g., developed by state health authorities vs. a large tech company) among different groups of people (e.g., people living in rural areas vs. people living in urban areas). Our mediation analysis showed that one's perception of the public health benefits offered by the app and the adoption willingness of other people had a larger effect in explaining the observed effects of app design choices and individual differences than one's perception of the app's security and privacy risks. With these findings, we discuss practical implications on the design, marketing, and deployment of COVID-19 contact-tracing apps in the U.S.

Motivation & Objective

• To investigate the relative influence of app design choices and individual differences on adoption intentions for COVID-19 contact-tracing apps.
• To understand how perceptions of security, privacy, public health benefits, and community adoption mediate the effects of design and individual factors.
• To identify design and messaging strategies that can improve adoption rates, especially among vulnerable populations.
• To address the gap in quantitative understanding of how risk-benefit perceptions vary across app designs and user subgroups.
• To provide actionable insights for designing, marketing, and deploying effective contact-tracing apps in real-world settings.

Proposed method

• Conducted a between-subjects factorial survey experiment with 1,963 U.S. adults to simulate real-world app choice scenarios.
• Varied four key design factors: contact-tracing architecture (decentralized vs. centralized), location data usage, app provider (state health vs. tech company), and presentation of security risks.
• Collected data on adoption intentions, perceived risks, perceived benefits, and community adoption expectations.
• Used mediation analysis to assess how perceptions of public health benefits and community adoption rate explain the effects of design and individual differences.
• Applied quantitative analysis to compare the relative impact of design choices versus individual differences on adoption intentions.
• Controlled for demographic variables and used statistical modeling to isolate the effects of each factor.

Experimental results

Research questions

• RQ1 To what extent do app design choices affect people’s adoption intentions about a COVID-19 contact-tracing app?
• RQ2 To what extent do individual differences affect people’s adoption intentions about a COVID-19 contact-tracing app?
• RQ3 How do people’s perceived risks and benefits about a contact-tracing app mediate the influence of app design choices and individual differences on adoption intention?
• RQ4 Which combinations of design and individual factors produce the highest adoption intention, and what are the key leverage points for increasing uptake?
• RQ5 How do perceptions of public health benefit and community adoption rate compare to privacy and security risk perceptions in shaping adoption decisions?

Key findings

• Individual differences—such as prosocialness, perceived COVID-19 risk, technology readiness, and demographic factors—had a stronger influence on adoption intentions than app design choices.
• Perceived public health benefits and the belief that others will adopt the app were stronger mediators of adoption intention than perceived security and privacy risks.
• Certain app designs exacerbated differences in preferences across sub-populations, potentially increasing inequality in acceptance (e.g., rural vs. urban users, or those trusting state vs. tech company providers).
• Decentralized architecture and presenting security risks did not significantly improve adoption intentions when compared to centralized designs or no risk presentation.
• The combination of high perceived public health benefit and high expected community adoption rate significantly increased adoption intentions across diverse user groups.
• Essential workers and individuals in rural areas showed lower adoption intentions in some design conditions, highlighting the need for targeted design and outreach strategies.