Country- and app-level factors affecting the adoption and evaluation of COVID-19 mobile apps

BACKGROUND: Countries across the globe have released many COVID-19 mobile apps. However, there is a lack of systematic empirical investigation into the factors affecting the adoption and evaluation of COVID-related apps. This study explores what factors at the country level and the app levels would influence the adoption and evaluation of COVID-19 apps. METHODS: We collected data on 267 COVID-19 apps in App Store and Google Play. The number of installs, ratings, reviews and rating scores were used as indicators of adoption and evaluation. Country-level predictors include the number of infected cases and the political system (i.e., democratic vs. non-democratic). App-level predictors include developer (i.e., government vs. non-government) and functions. Four app functions were coded for analysis: providing health information, contact tracing, home monitoring, and consultation. Negative binomial regression and OLS (Ordinary Least Square) regression were used to analyze the data. RESULTS: Our analyses show that apps developed by countries with more infected cases (B = 0.079, CI (Confidence Interval) = 0.000, 0.158; P = .049) and by non-governmental institutions (B=-0.369, CI=-0.653, -0.083; P = .01) received more positive rating scores. Apps with home monitoring function received lower rating scores (B=-0.550, CI=-0.971, -0.129; P = .01). Regarding adoption, apps developed by governments were more likely to be installed (IRR (Incident Rate Ratio) = 8.156, CI = 3.389, 19.626; P < .001), to be rated (IRR = 26.036, CI = 7.331, 92.468; P < .001), and to receive user comments (IRR = 12.080, CI = 3.954, 37.568; p < .001). Apps with functions of contact tracing or consultation were more likely to be installed (IRR = 4.533, CI = 2.072, 9.918; p < .001; IRR = 4.885, CI = 1.970, 12.111; p < .001), to be rated (IRR = 11.634, CI = 3.486, 38.827; p < .001; IRR = 17.194, CI = 5.309, 55.680; p < .001), and to receive user comments (IRR = 5.688, CI = 2.052, 5.770; p < .001; IRR = 16.718, CI = 5.363, 52.113; p < .001). Apps with home monitoring functions were less likely to be rated (IRR = 0.206, CI = 0.047, 0.896; P = .04) but more likely to receive user comments (IRR = 3.874, CI = 1.044, 14.349; P = .04). Further analysis shows that apps developed in democratic countries (OR (Odd Ratio) = 3.650, CI = 1.238, 10.758; P = .02) or by governments (OR = 7.987, CI = 4.106, 15.534, P < .001) were more likely to include the function of contact tracing. CONCLUSION: This study systematically investigates factors affecting the adoption and evaluation of COVID-19 apps. Evidence shows that government-developed apps and the inclusion of contact tracing and consultation app functions strongly predict app adoption.