All participants gave informed consent that their data will be published in anonymised and aggregated form in a data repository for other researchers. 
You can find the aggregated data set (1) for Study 1 (appdata_study1.csv) and (2) for Study 2 (appdata_study2.csv) in this data repository. 

If you have questions regarding this data set, please contact kluetsch@psych.rwth-aachen.de.
If you use our data set, please cite this data set in the following way: 
Klütsch, J., Kreuder, A., Haehn, L., Kuck, I., Böffel, C., Frick, U., & Schlittmeier, S. J. (2026). Dataset to "Factors shaping privacy trade-offs in app downloads: The role of privacy ratings, friends' recommendation and resignation among young adults and adults" [Dataset]. RWTH Publications. https://doi.org/10.18154/RWTH-2026-03640

############ Study 1 ######################################
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###More information about the variable names and coding:###
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## Identification variables ##
# running number --> global continuous trial index running spanning all participants
# vpn --> participant ID code
# trial number --> trial index within each participant

## Friends Recommendation IV ##
#peer_group => 1 = friends recommendation, 0 = social media platform recommendation

## Privacy Rating IV ##
#privacy => 1 = high privacy , 0 = low privacy

## App alignment with developmental opportunity IV ##
4 types of apps: App_Type --> 1 = partner app, 2 = job app, 3 = messenger app, 4 = self-discovery app 
dummy coded for analysis 
partner --> 1 = partner app, 0 = not partner app
job --> 1 = job app, 0 = not job app
socRel --> 1 = messenger app, 0 = not messenger app
self --> 1 = self-discovery app, 0 = not self-discovery app

## Design as control ##
4 types of color-balanced app design: Design_App --> 1 = purple, 2 = orange, 3 = blue, 4 = brown (see also Figure 3 and 4 in the paper)
dummy coded for analysis 
design1 --> 1 = purple, 0 = not purple
design2 --> 1 = orange, 0 = not orange
design3 --> 1 = blue, 0 = not blue
design4 --> 1 = brown, 0 = not brown

## Download DV ##
# download_probability--> reported download probability for one app ( in % )

## Demographics ##
# age --> continuous variable (above 29 years old were excluded in the analysis)

# female --> 1 = female, 0 = not female
# no_gender --> 1 = no gender, 0 = gender specified

# occupation --> 1 = pupil, 2 = apprenticeship, 3 = student, 4 = employee, 5 = civil servant, 6 = self-employed, 7 = unemployed, 8 = other
# education --> 1 = secondary school diploma (Hauptschule), 2 = secondary school diploma (Realschule), 3 = apprenticeship, 4 = A-Level, 5 = University degree, 6 = Other, 7 = No degree

## Control Items ##
# Participants who failed control items were excluded in the analysis
# control_1_development --> 3-point scale with three response options: (1) “not important”, (2) “somewhat important”, (3) “very important” --> To make sure you are fully engaged, please select “not important” here.
# control item failed if not 0

# control_2_digcom --> 6-point Likert scale ranging from 1 “I do not agree at all” to 6 “I strongly agree” --> To make sure you are fully on board, please select "strongly agree" here.
# control item failed if not 4

## Developmental Importance and Fulfilment IV ##
# DevTask--> refers to developmental task addressed
# job = developmental task of entering professional life
# socRel = developmental task of belonging to one's peer group
# partner = developmental task of forming a committed relationship
# self = developmental task of developing one's hobbies and interests

# dev_requirement_DevTask_fulfilment --> 0 = not reached yet, 1 = started, 2 = already reached
# dev_requirement_DevTask_importance --> 0 = not important at all, 1 = somehow important, 2 = very important

# dev_fulfillment - calculated variable from dev_requirement_DevTask_fulfilment: app represents developmental opportunity that is not fulfilled (0) or fulfilled (1)
# dev_importance - calculated variable from dev_requirement_DevTask_fulfilment: app represents developmental opportunity that is not important (0) or important (1)
# dev_relevance -  calculated variable from dev_requirement_DevTask_fulfilment and dev_requirement_DevTask_importance: app represents developmental opportunity that is not fulfilled and important (1), otherwise (0)

############ Study 2 ######################################
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###More information about the variable names and coding:###
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## Identification variables ##
# running number --> global continuous trial index running spanning all participants
# vpn --> participant ID code
# trial number --> trial index within each participant

## Privacy Rating IV ##
#privacy => 1 = high privacy , 0 = low privacy

## Friends Recommendation IV ##
#social norm => 1 = friends recommendation, 0 = browsing; no recommendation

## App as control ##
# 8 types of color-balanced cooking apps: App --> Recipe Ventures, Cook Essentials, Recip-E, Tastery, Cookot, Remarkable Cook, CooKING, CookIt

## Data Sharing DV ##

# partial_probability--> probability of disclosing different types of data in order to use the app for free (in %)
# calculated from probability to disclose ten different data types 
	# --> residence town --> probability of disclosing information about residence town (in %) 
	# --> entered search terms --> probability of disclosing information about entered search terms (in %) 
	# --> gender --> probability of disclosing information about gender (in %) 
	# --> standard web browser --> probability of disclosing information about standard web browser (in %) 
	# --> country --> probability of disclosing information about country (in %) 
	# --> webpages visited --> probability of disclosing information about webpages visited (in %) 
	# --> family status --> probability of disclosing information about family status (in %) 
	# --> level of education --> probability of disclosing information about level of education (in %) 
	# --> height --> probability of disclosing information about height (in %) 
	# --> hobbies --> probability of disclosing information about hobbies (in %) 

## Privacy Cynicism ##
Items retrieved from Lutz et al. (2020)
on a scale from 1 (strongly disagree) to 7 (strongly agree)

# Mistrust: M01 --> M06
 M01 --> I think that Internet companies are unreliable.
 M02 --> Internet companies can’t be trusted.
 M03 --> I think that Internet companies are not honest.
 M04 --> I think that Internet companies don’t have my best interests in mind.
 M05 --> In the end, Internet companies only want to make money with our data.
 M06 --> I assume that Internet companies are only interested in their own benefit but not in mine.

# Uncertainty: US01 --> US06
 US01 --> It is difficult to keep up-to-date with the many things that happen online.
 US02 --> I am uncertain about what happens with my personal data on the Internet.
 US03 --> I am uncertain about what the services I use do with my personal data.
 US04 --> I am not sure if I do everything right when I use the Internet.
 US05 --> It is difficult to understand all the risks of using the Internet.
 US06 --> I am uncertain about what the other users I encounter online do with my personal data.

# Powerlessness: ML01 --> ML05
 ML01 --> Even if I try to protect my data, I can’t prevent others from accessing them.
 ML02 --> In the end, I can’t prevent others from accessing my data.
 ML03 --> I don’t have the power to protect my personal data effectively from all the possible dangers on the Internet.
 ML04 --> It would be naïve to think that I can protect my personal data online reliably.
 ML05 --> If someone is determined to access my personal data, there is nothing I can do to stop them.

# Resignation: R01 --> R05 

 R01 --> There is no point in dedicating too much attention to the protection of my personal data online.
 R02 --> I can’t be bothered to spend much time on data protection on the Internet.
 R03 --> I have given up trying to keep up-to-date with current solutions for protecting my personal data online.
 R04 --> I am careless with my personal data online because it is impossible to protect them effectively.
 R05 --> It doesn’t make a difference whether I try to protect my personal data online or not.


## Information about online behavior ###

# Importance of App Information during Data Sharing: KF02_01 --> KF02_11 ; on a scale from 1 (not at all) to 7 (strongly)

 KF02_01 --> Privacy through locks
 KF02_02 --> App Size
 KF02_03 --> App rating
 KF02_04 --> App category
 KF02_05 --> App language
 KF02_06 --> App Icon
 KF02_07 --> App name
 KF02_08 --> App manufacturer
 KF02_09 --> App preview
 KF02_10 --> Friends
 KF02_11 --> Advertisement online

# Sensitivity of Information requested: KF03_01 --> KF03_10 ; on a scale from 1 (not at all) to 7 (strongly)

  KF03_01 --> residence town
  KF03_02 --> entered search terms 
  KF03_03 --> gender
  KF03_04 --> standard web browser
  KF03_05 --> country
  KF03_06 --> webpages visited 
  KF03_07 --> family status 
  KF03_08 --> level of education 
  KF03_09 --> height 
  KF03_10 --> hobbies 

## Demographics ##
# age --> continuous variable 
# gender --> 1 = female, 0 = male

# education --> 1 = Secondary school diploma (Hauptschule), 2 = Secondary school diploma (Realschule), 3 = A-Level, 4 = Bachelor, 5 = Master/Diploma, 6 = Promotion, 7 = Other
# occupation --> 1 = pupil, 2 = apprenticeship, 3 = student, 4 = employee, 5 = self-employed, 6 = Renter, 7 = unemployed, 8 = Other
# appdownload_month --> 0 = less than one time, 1 = once, 2 = twice, 3 = three-times, 4 = 4 times or more

Open answers are provided upon request.