Henning Silber, Johannes Breuer, Barbara Felderer, Frederic Gerdon, Patrick Stammann, Jessica Daikeler, Florian Keusch, Bernd Weiß
{"title":"Asking for Traces: A Vignette Study on Acceptability Norms and Personal Willingness to Donate Digital Trace Data","authors":"Henning Silber, Johannes Breuer, Barbara Felderer, Frederic Gerdon, Patrick Stammann, Jessica Daikeler, Florian Keusch, Bernd Weiß","doi":"10.1177/08944393241305776","DOIUrl":null,"url":null,"abstract":"Digital trace data are increasingly used in the social sciences. Given the risks associated with data access via application programming interfaces (APIs) as well as ethical discussions around the use of such data, data donations have been proposed as a methodologically reliable and ethically sound way of collecting digital trace data. While data donations have many advantages, study participants may be reluctant to share their data, for example, due to privacy concerns. To assess which factors in a data donation request are relevant for participants’ acceptance and decisions, we conducted a vignette experiment investigating the general acceptability and personal willingness to donate various data types (i.e., data from GPS, web browsing, LinkedIn/Xing, Facebook, and TikTok) for research purposes. The preregistered study was implemented in the probability-based German Internet Panel (GIP) and gathered responses from n = 3821 participants. Results show that people rate the general acceptability of data donation requests higher than their own willingness to donate data. Regarding the different data types, respondents indicated that they would be more willing to donate their LinkedIn/Xing, TikTok, and GPS data compared to web browsing and Facebook data. In contrast, information about whether the donated data would be shared with other researchers and data security did not affect the responses to the respective donation scenarios. Based on these results, we discuss implications for studies employing data donations.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"83 1","pages":""},"PeriodicalIF":3.0000,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Social Science Computer Review","FirstCategoryId":"90","ListUrlMain":"https://doi.org/10.1177/08944393241305776","RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
Digital trace data are increasingly used in the social sciences. Given the risks associated with data access via application programming interfaces (APIs) as well as ethical discussions around the use of such data, data donations have been proposed as a methodologically reliable and ethically sound way of collecting digital trace data. While data donations have many advantages, study participants may be reluctant to share their data, for example, due to privacy concerns. To assess which factors in a data donation request are relevant for participants’ acceptance and decisions, we conducted a vignette experiment investigating the general acceptability and personal willingness to donate various data types (i.e., data from GPS, web browsing, LinkedIn/Xing, Facebook, and TikTok) for research purposes. The preregistered study was implemented in the probability-based German Internet Panel (GIP) and gathered responses from n = 3821 participants. Results show that people rate the general acceptability of data donation requests higher than their own willingness to donate data. Regarding the different data types, respondents indicated that they would be more willing to donate their LinkedIn/Xing, TikTok, and GPS data compared to web browsing and Facebook data. In contrast, information about whether the donated data would be shared with other researchers and data security did not affect the responses to the respective donation scenarios. Based on these results, we discuss implications for studies employing data donations.
期刊介绍:
Unique Scope Social Science Computer Review is an interdisciplinary journal covering social science instructional and research applications of computing, as well as societal impacts of informational technology. Topics included: artificial intelligence, business, computational social science theory, computer-assisted survey research, computer-based qualitative analysis, computer simulation, economic modeling, electronic modeling, electronic publishing, geographic information systems, instrumentation and research tools, public administration, social impacts of computing and telecommunications, software evaluation, world-wide web resources for social scientists. Interdisciplinary Nature Because the Uses and impacts of computing are interdisciplinary, so is Social Science Computer Review. The journal is of direct relevance to scholars and scientists in a wide variety of disciplines. In its pages you''ll find work in the following areas: sociology, anthropology, political science, economics, psychology, computer literacy, computer applications, and methodology.