Sharing is caring: willingness to share personal data through contact tracing apps in China, Germany, and the US

Figures & data

Table 1. Cross-country comparison of data collected through CTAs.

CTA type	Release date	Type of data collected
		Personal identity	Residential address	Phone number	Manual activity tracking	Network and online transaction data	Identification data of the phone	Real-time location (exact location)/ Location data	Relative location (not exact location)/ Encounter data	Type of health information (symptoms, etc.)	Transparency of data collection	Usage data
China (Health Code)	11 February 2020	Full name, profile picture, gender, and ID number are required at registration.	Required at registration.	Required at registration.	Required at registration.	Collection of network & transaction data (i.e., shopping history), & user’s social networks	No information available.	App collects the location of the phone (GPS or triangulation from cell towers nearby)	Not collected.	General health information required at registration.	Citizens’ informed consent for the collection, use, sharing, disclosure, and storage of data not required	No information available.
Germany (Corona-Warn-App)	16 June 2020	Not required.	Not required.	Not required.	Voluntary.	Not required.	Phone’s data is identified by an anonymous, secure, random code.*	No collection of location data.	Phones use Bluetooth signal to swap encrypted token with other phones nearby.**	Voluntary symptoms information.	Regularly updated privacy policy on the app’s official website.	No collection of usage data without explicit prior consent.
US - Type a (e.g., New York’s ‘Covid Alert NY’)	Different dates. Covid Alert NY: 1 October 2020, others: August 2020 and later.	Voluntary demographic information: country, gender, age-range, race and ethnicity.	Not required.	Not required.	Not required.	Not required.	Phone’s data is identified by an anonymous, secure, random code.*	No collection of location data.	Phones use Bluetooth signal to swap encrypted token with other phones nearby.**	Voluntary symptoms information.	Data and privacy policy available on the New York State’s official website.	Though anonymized at all times, data related to app adoption and usage are being collected.
US - Type b (e.g., North and South Dakota’s ‘Care19 Diary’)		Not required.	Not required.	Not required.	Voluntary.	Not required.	Phone’s data is identified by an anonymous ID number that is assigned to users upon first-time use of the app.	App collects the location of the phone (GPS or triangulation from cell towers nearby).***	No collection of direct user-to-user encounter data. Possible contacts are being traced via overlapping location data.	Not required.	Link to privacy policy available on the official states’ websites.	Usage data are collected at a minimum and only after user opt-in.

Notes: *The code is changed regularly in order to make it impossible to identify a phone through the code. Based on Apple and Google’s exposure notification system (GAEN). ** The code contains information about the encounter itself, its duration, and the distance between the two people. *** The location data are used to identify the places a person has visited for at least 10 min.

Sources: QQ News, 2021; NASHP, 2022; Hu et al., 2020; Coronawarn, 2021; Johnson, 2020; Liang, 2020; Mozur et al., 2020.

Figure 1. Conceptual framework.

Table 2. Demographic composition of the sample and the total population.

	Three countries		China		Germany		United States
	Total	Sample	Total	Sample	Total	Sample	Total	Sample
Population
	1,821 Mill.	6,464	1,410 Mill.	2,201	83 Mill.	2,083	328 Mill.	2,180
Gender
Female	50.03%	48.7%	48.9%	46.3%	50.7%	49.1%	50.5%	50.7%
Male	49.97%	51.3%	51.1%	53.7%	49.3%	50.9%	49.5%	49.3%
Age
Population median in yrs	41	36	38	36	46	41	38	40
Pop. Online
	80.7%	100%	64.6%	100%	88.1%	100%	89.4%	100%

Sources: Barro & Lee, 2013 http://www.barrolee.com; Pew Global Attitudes Survey. (2017). Spring 2017 Survey Data. https://www.pewresearch.org/global/dataset/spring-2017-survey-data/; Statista (2016). Population in China in 2016, by Region (in Million Inhabitants). https://www.statista.com/statistics/279013/population-in-china-by-region

Table 3. Summary of independent variables.

Category	Measurement
Crisis perception
Health concern At the peak of the COVID-19 pandemic, have you worried about any of the following?	1 = catching the virus myself, 2 = family members catching the virus, 3 = friends catching the virus, 4 = I have not been worried about any of these. Three dummies: 1-3.
Financial situation To what extent, if at all, has the status of your monthly household income been impacted from before the COVID-19 virus to now?	1 = improved slightly, 2 = improved somewhat, 3 = stayed the same, 4 = worsened somewhat, 5 = worsened significantly, 99 = don’t know/ prefer not to answer; regression dummy variable created: 1 = worsened significantly, 0 = improved slightly, improved somewhat, stayed the same, worsened somewhat, don’t know/ prefer not to answer. One dummy that combines 4-5.
Psychological effect Which of the following feelings, if any, have you felt an increase in throughout the COVID-19 pandemic?	1 = panic, 2 = anxiety, 3 = loneliness, 4 = sadness, 5 = tiredness, 6 = boredom, 7 = impatience, 8 = anger, 9 = inspiration, 10 = happiness, 11 = other, 99 = none. Two dummies: 1-2.
Disclosure benefits
Perceived positive CTA consequences Do you believe that the use of COVID-19 tracing apps would result in any of the following?	1 = fewer COVID-19 infections, 2 = isolating infected people, 3 = making it safer to go out, 4 = better health information, 5 = privacy violations, 6 = discrimination against people who test positive for covid-19, 7 = government surveillance, 8 = use of data for commercial purposes, 9 = other, 10 = none. Two dummies for 1, 4.
Use reasons Which of the following, if any, would motivate you (or has motivated you) to use COVID-19 tracing apps?	1 = to protect myself, 2 = to protect others, 3 = to prevent further economic loss, 4 = to move around outside freely again, 5 = the government enforcing it, 6 = I would never use the app, 7 = other, 8 = none. Four dummies for 1-4.
Disclosure risks
Perceived negative CTA consequences Do you believe that the use of COVID-19 tracing apps would result in any of the following?	1 = fewer COVID-19 infections, 2 = isolating infected people, 3 = making it safer to go out, 4 = better health information, 5 = privacy violations, 6 = discrimination against people who test positive for COVID-19, 7 = government surveillance, 8 = use of data for commercial purposes, 9 = other, 10 = none. Four dummies: 5-8.
Socio-political beliefs
Capacity In general, who do you think has the most capability in managing the COVID-19 pandemic?	1 = central government, 2 = local government, 3 = private companies, 4 = non – government organizations, 5 = international organizations, 6 = individual citizens, 7 = scientific expert community, 8 = other, 9 = none. Four dummies:1, 2, 3, 6.
Responsibility In general, who do you think holds the most responsibility in managing the COVID-19 pandemic?	1 = central government, 2 = local government, 3 =private companies, 4 = non-government organizations, 5 = international organizations, 6 = individual citizens, 7 = scientific expert community, 8 = other, 9 = none. Four dummies: 1, 2, 3, 6.
Trust in government institutions How much do you trust the governmental institutions of your country?	1 = a lot, 2 = somewhat, 3 = neither trust nor distrust, 4 = not much, 5 = not at all, 6 = Prefer not to answer. Two dummies:4 & 5.
Data management Who should have the rights to access and manage the data collected?	1 = local government, 2 = central government, 3 =private companies, 4 = non-government organizations, 5 = international organizations, 6 = medical staff, 7 = scientific expert community, 8 = individual citizens, 9 = other, 10 = none. Four dummies: 1, 2, 3, 7
Control Variables
Age	In years (open box)
Gender	0 = male, 1 = female
Income	Germany, US: 1 = below 250, 2 = 250-500, 3 = 500-1,000 … 12 = more than 15,000, 99 = prefer not to say (in local currency); China: 1 = below 700, 2 = 700–1,400, 3 = 1,400–2,100 … 12 = more than 28,000, 99 = prefer not to say (in CNY); regrouped: 1 = low (1-3), 2 = medium (4-6), 3 = high (7-12), 99 = prefer not to say (99)
Education	1 = no formal education, 2 = high school diploma or equivalent, 3 = vocational training, 4 = Bachelor’s degree, 5 = Master’s or Doctorate degree
CTA understanding	1 = fully understand, 2 = somewhat understand, 3 = not understand at all One dummy for each category.
CTA acceptance	1 = strongly agree, 2 = somewhat agree, 3 = neither agree nor disagree, 4 = somewhat disagree, 5 = strongly disagree One dummy for each category.
Similar app use	1 = several times per week, 2 = several times per month, 3 = yes, but not often, 4 = only once, 5 = never used before. Dummy 1 = Regular use (categories 1–2). Dummy 2 = Irregular use (categories 3–4). Dummy 3 = No use (category 5)

Figure 2. Willingness to share different types of data through CTAs by country.

Note: Sample size = 6464; weighted sample, numbers = percent.

Figure 3. Odds ratios of effects on willingness to share any type of data through CTAs.

Note: For brevity and clarity reasons, control variables are not shown in the graph, but have been included in the model. *p .05; **p < .01; ***p < .001.

Figure 4. Odds ratios of effects on willingness to share health information.

Note: For brevity and clarity reasons, control variables are not shown in the graph, but have been included in the model. p < .05; **p < .01; ***p < .001.

Figure 5. Odds ratios of effects on willingness to share relative location data.

Note: For brevity and clarity reasons, control variables are not shown in the graph, but have been included in the model. *p < .05; **p < .01; ***p < .001.

Figure 6. Odds ratios of effects on willingness to share real location data.

Note: For brevity and clarity reasons, control variables are not shown in the graph, but have been included in the model. p < .05; **p < .01; ***p < .001.

Figure 7. Odds ratios of effects on willingness to share personal identity.

Note: For brevity and clarity reasons, control variables are not shown in the graph, but have been included in the model. *p < .05; p < .01; ***p < .001.

Table 4. Overview of hypotheses for willingness to share any type of data through CTAs.

Hypothesis number	China	Germany	US
H1a own health	X	X	X
H1b family health	X	X	X
H1c friends’ health	X	X	X
H1d financial situation	X	X	X
H2a anxiety	✓	X	X
H2b panic	X	X	X
H3a fewer infections	✓	✓	✓
H3b prevent economic loss	X	X	X
H3c freely walk outside	X	✓	✓
H3d health information	✓	✓	✓
H3e protect me	✓	✓	✓
H3f protect others	X	✓	✓
H4a privacy violations	X	X	X
H4b government surveillance	X	X	X
H4c discrimination	X	X	X
H4d commercial data use	X	X	X
H5a capability individuals	X	X	X
H5b responsibility individuals	X	X	X
H6 trust in government institutions	X	✓	X
H7a data management central government	✓	✓	X
H7b data management local government	✓	✓	X
H7c data management private enterprises	X	✓	X
H7d data management experts	✓	✓	✓

Note: ‘X’ refers to failure to reject the null hypothesis, ‘✓’ refers to a rejected null hypothesis.

Figure A1. Odds ratios of effects on willingness to share phone number and email address.

Figure A2. Full sample – Odds ratios of effects on willingness to share any data.

Table A1. Hosmer-Lemeshow and AUC tests

Hosmer-Lemeshow and AUC tests
	Model name	X-squared	df	p-value	AUC
China	Health information	13.837	8	0.086	0.804
	Relative location	26.726	8	0.001	0.716
	Real location	2.648	8	0.954	0.720
	Personal identity	7.036	8	0.533	0.705
	Phone no./ email	5.497	8	0.703	0.697
	None	7.1406	8	0.522	0.903
Germany	Health information	11.467	8	0.176	0.815
	Relative location	10.14	8	0.255	0.770
	Real location	6.617	8	0.578	0.825
	Personal identity	3.690	8	0.884	0.793
	Phone no./ email	4.047	8	0.853	0.769
	None	15.555	8	0.049	0.929
US	Health information	22.009	8	0.005	0.828
	Relative location	10.122	8	0.257	0.802
	Real location	11.04	8	0.199	0.809
	Personal identity	16.928	8	0.031	0.798
	Phone no./ email	5.776	8	0.672	0.769
	None	13.774	8	0.088	0.891

Table A2. Variance inflation factors (VIFs)

	VIFs
Variable name	China	Germany	US
age	1.088062	1.170909	1.095378
gender	1.064115	1.055976	1.065824
income	1.137762	1.056325	1.055961
education	1.208909	1.112151	1.158757
healthConcern_me	2.148902	1.568971	1.667716
healthConcern_family	1.340397	1.290352	1.368872
healthConcern_friends	2.106080	1.499759	1.664983
finance	1.081724	1.074032	1.066793
panic	1.141004	1.129479	1.182932
anxiety	1.198297	1.303618	1.265119
understanding	1.566703	1.123402	1.185504
similarApp	1.209012	1.175318	1.250097
CTA_experience	1.379277	1.122378	1.172083
acceptance	1.288786	2.100664	1.748733
perceivedConsequences_fewerInfections	1.130961	1.543222	1.363320
perceivedConsequences_betterInfo	1.206696	1.232797	1.335416
perceivedConsequences_privacyViolations	1.274290	1.502195	1.632771
perceivedConsequences_govSurveillance	1.098133	1.489266	1.564161
perceivedConsequences_discrimination	1.214267	1.285045	1.406552
perceivedConsequences_commercialDataUse	1.227847	1.494107	1.435471
useReasons_protectMyself	1.295425	1.677685	1.650574
useReasons_protectOthers	1.321097	1.702126	1.570294
useReasons_preventEconLoss	1.111332	1.164109	1.240609
useReasons_moveOutside	1.127674	1.274635	1.298664
capability_individuals	1.120409	1.157516	1.256024
responsibility_individ	1.131418	1.172738	1.278367
dataManag_local	1.111716	1.188054	1.248559
dataManag_central	1.175308	1.277840	1.245941
dataManag_private	1.081500	1.062798	1.094688
dataManag_expert	1.135225	1.147731	1.170517
trust	1.169.762	1.434366	1.233182

Table A3. Hypotheses results for different types of data

Hypothesis	Country	Health information	Relative location	Real location	Personal identity
H1a own health	China	X	X	X	✓
	Germany	X	X	X	X
	US	X	X	X	X
H1b family health	China	X	X	X	X
	Germany	✓	X	X	X
	US	X	X	X	X
H1c friends’ health	China	X	X	✓	X
	Germany	X	X	X	✓
	US	X	X	X	X
H1d financial situation	China	X	X	X	X
	Germany	X	X	X	X
	US	X	X	X	X
H2a anxiety	China	X	✓	X	X
	Germany	X	X	X	✓
	US	X	X	X	X
H2b panic	China	X	X	X	✓
	Germany	X	X	✓	X
	US	X	X	X	X
H3a fewer infections	China	✓	✓	✓	✓
	Germany	✓	✓	✓	X
	US	✓	✓	✓	X
H3b prevent economic loss	China	X	X	X	X
	Germany	X	X	X	X
	US	X	X	X	X
H3c freely walk outside	China	✓	✓	X	X
	Germany	✓	✓	✓	X
	US	✓	✓	✓	✓
H3d health information	China	✓	✓	✓	X
	Germany	✓	X	X	✓
	US	✓	✓	X	✓
H3e protect me	China	✓	X	X	X
	Germany	✓	X	✓	X
	US	✓	✓	X	X
H3f protect others	China	✓	X	X	X
	Germany	✓	X	X	✓
	US	✓	X	X	X
H4a privacy violations	China	X	X	X	X
	Germany	X	X	X	X
	US	X	X	X	X
H4b government surveillance	China	X	X	X	X
	Germany	X	X	X	X
	US	X	X	X	X
H4c discrimination	China	X	X	✓	X
	Germany	✓	X	X	X
	US	X	X	X	X
H4d commercial data use	China	✓	X	X	X
	Germany	X	X	X	X
	US	✓	X	X	X
H5a capability individuals	China	X	X	X	X
	Germany	✓	X	X	X
	US	X	X	X	X
H5b responsibility individuals	China	✓	X	X	X
	Germany	X	X	X	X
	US	X	X	X	X
H6 trust in government institutions	China	X	X	X	X
	Germany	✓	X	X	Somewhat ✓
	US	X	X	X	✓
H7a data management central government	China	✓	✓	✓	X
	Germany	X	✓	X	X
	US	X	X	X	X
H7b data management local government	China	✓	X	✓	✓
	Germany	✓	✓	X	✓
	US	X	X	✓	✓
H7c data management private enterprises	China	X	✓	✓	X
	Germany	X	X	✓	X
	US	X	X	X	X
H7d data management experts	China	✓	✓	✓	✓
	Germany	✓	✓	✓	✓
	US	✓	✓	X	X

Note: ‘X’ refers to failure to reject the null hypothesis, ‘✓’ refers to a rejected null hypothesis.

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