Modeling Adoption, Security, and Privacy of COVID-19 Apps: Findings and Recommendations From an Empirical Study Using the Unified Theory of Acceptance and Use of Technology

Abstract

Background The global health crisis caused by COVID-19 has drastically changed human society in a relatively short time. However, this crisis has offered insights into the different roles that such a worldwide virus plays in the lives of people and how those have been affected, as well as eventually proposing new solutions. From the beginning of the pandemic, technology solutions have featured prominently in virus control and in the frame of reference for international travel, especially contact tracing and passenger locator applications. Objective The objective of this paper is to study specific areas of technology acceptance and adoption following a unified theory of acceptance and use of technology (UTAUT) research model. Methods We presented a research model based on UTAUT constructs to study the determinants for adoption of COVID-19–related apps using a questionnaire. We tested the model via confirmatory factor analysis (CFA) and structural equation modeling (SEM) using travelers’ data from an insular tourist region. Results Our model explained 90.3% of the intention to use (N=9555) and showed an increased understanding of the vital role of safety, security, privacy, and trust in the usage intention of safety apps. Results also showed how the impact of COVID-19 is not a strong predictor of adoption, while age, education level, and social capital are essential moderators of behavioral intention. Conclusions In terms of scientific impact, the results described here provide important insights and contributions not only for researchers but also for policy and decision makers by explaining the reasons behind the adoption and usage of apps designed for COVID-19.

Developing and Evaluating a Measure of the Willingness to Use Pandemic-Related mHealth Tools Using National Probability Samples in the United States: Quantitative Psychometric Analyses and Tests of Sociodemographic Group Differences

“…Studies could adapt the CIS measure or test additional items based on whether participants would be willing to keep an automatically downloaded app. However, given that some may oppose digital health measures due to concerns about their rights and privacy [15,16,[53][54][55][56][57], assessing the willingness to use automatically downloaded opt-out apps should be assessed separately and compared against the willingness to use apps that require users to download. This study focused on voluntary access to digital health tools, which would likely be the most common scenario, and a prior study did not find marked differences in the willingness to use user-downloaded apps versus automatically downloaded apps [15].…”

“…Another study asked participants one question about how willing they would be to install an app for contact tracing and another question about how willing they would be to keep an app that was automatically downloaded to their smartphone [ 15 ]. An additional study used a 2-item measure of behavioral intentions to use digital health technologies for COVID-19 as a proxy of actual use, although the authors acknowledged that behavioral intentions and actual use are separate constructs [ 16 ]. A different study used a single-item measure of willingness or support for using a specific contact-tracing app being evaluated by the researchers, and participants’ responses were categorized as “app-supporting,” “app-willing,” and “app-reluctant” [ 17 ].…”

The German COVID-19 Digital Contact Tracing App: A Socioeconomic Evaluation

“…While manual contact tracing is considered an established response to infectious disease outbreaks [ 55 , 56 ], DCT apps were first developed in response to COVID-19 and have been studied extensively since then [ 57 ]. From the earliest stages, issues of privacy, security, and ethics were raised [ 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 ], with doubts about privacy identified as the biggest barrier to widespread use of such solutions [ 60 ]. Numerous studies investigated potentially promising DCT approaches from technical perspectives, including the currently mainly applied Bluetooth-based method [ 2 , 66 ] but also proximity tracing based on GPS [ 67 ], ultrasound [ 68 , 69 ], facial recognition approaches [ 61 ], blockchain-based approaches [ 70 ], or QR code scanning technology [ 71 ].…”

The role of uncertainty and affect in decision-making on the adoption of AI-based contact-tracing technology during the COVID-19 pandemic

“… 4 Despite their usefulness, risks related to privacy infringements and surveillance have been widely discussed in recent studies. 10 – 12 , 29 – 34 Although digital contact tracing based on smartphone applications has been endorsed by many governments and organizations across the world to help limit the spread of COVID-19, 26 , 30 , 31 public concerns regarding privacy intrusions (e.g., giving away one's movement and medical information) have hindered the wider adoption of contact-tracing technologies in many countries. 9 …”

“… 52 Similar results were found in studies conducted in other countries, such as Australia, India, and various European countries. 29 , 31 – 34 This suggests that achieving a balance between privacy and health data acquisition is one of the most urgent issues in the public adoption of contact tracing for technology-assisted health promotion.…”