A Consumer Behavior Perspective of Adopting Mobile Contact Tracing Apps in a Public Health Crisis: Lessons from ABTraceTogether for COVID-19 Pandemic
Bibliographic record
Abstract
Responses to the COVID-19 pandemic included m-Health innovations, such as contact tracing and exposure notification applications to track virus exposure. Such apps were released by over 45 international governments throughout 2020, becoming the first m-Health innovation with such widescale deployment. Most regions relied on voluntary adoption, and many failed to receive a critical mass of users. Some of these apps can track and share user’s locations, social contacts, and health information, which sparked concerns and misperceptions about the privacy and security of user data. It is important to understand consumer behavior and adoption challenges based on people’s perceptions of benefits, barriers, and risks. To investigate this, we sent an online questionnaire to over 600 participants with open-ended questions asking about their experience with one such app, ABTraceTogether. This chapter covers qualitative findings regarding device and application-level issues participants identified as barriers to their adoption and continued usage of the app, which are accessibility, battery life, downloading challenges, device memory, network connectivity and costs, operating system compatibility, performance issues, and usability. Insight on consumer behavior gained from this study can guide m-Health design and promotion to aid future health crises and personal wellbeing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".