Sharing Data Collected with Smartphone Sensors

Struminskaya, Bella; Lugtig, Peter; Toepoel, Vera; Schouten, Barry; Giesen, Deirdre; Dolmans, Ralph

doi:10.1093/poq/nfab025

Cited by 22 publications

(24 citation statements)

References 37 publications

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“…For instance, older and highly educated respondents were more likely to share their data in the Hungarian sample. The results reinforced that privacy and security concerns lower the willingness to participate (Jäckle et al, 2019;Revilla et al, 2019;Struminskaya et al, 2020Struminskaya et al, , 2021Wenz et al, 2019), at least among US respondents. Consistently in the two samples, more frequent social media users were more likely to donate their data, but for instance, the affinity for technology or personality traits (e.g., openness) of respondents did not influence willingness significantly.…”

Section: Discussionmentioning

confidence: 56%

“…Another limitation is that we relied on self-reports and do not know to what extent willingness transfers to actual donation. Another possible direction for further research is to develop similar experimental designs in which self-reports and real participation can be contrasted (see e.g., Struminskaya et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

“…Revilla et al (2016) also found that reluctance to share is higher the in case of GPS data than in case of taking photos or installing an app. In contrast, in the study of Struminskaya et al (2021) tasks that involved photographing or video-taking one's surroundings at home yielded far lower willingness to share rates (under 20 percent) compared to providing geolocation data with a sensor (67 percent). Willingness to share geolocation, however, was subject to privacy concerns, and only 69 percent of those who promised to share GPS data actually shared it (see Struminskaya et al [2020] for a similar approach with somewhat different results).…”

Section: Incentivesmentioning

confidence: 94%

“…As we discussed earlier, privacy concerns are likely to evoke when sensitive and private data is involved in data donation. Previous studies show that privacy and security concerns lower the willingness to participate (Jäckle et al, 2019;Revilla et al, 2019;Struminskaya et al, 2020Struminskaya et al, , 2021Wenz et al, 2019, but see Elevelt et al, 2019and Silber et al 2022 for null results). Indeed, they are the most mentioned reasons for not being willing to participate in digital data sharing (Jäckle et al, 2019;Keusch et al, 2019;Revilla, et al, 2019).…”

Section: Respondents' Characteristicsmentioning

confidence: 96%

“…As a contribution to the literature, this study aims to understand the underlying mechanisms of the willingness to participate in academic research as a social media data donor. Earlier research has shown that digital data-sharing behavior in a broader sense can be a function of the offered incentives (Jäckle et al, 2019;Keusch et al, 2019;Ságvári et al, 2021), the type of data (Beuthner et al, 2023;Revilla et al, 2019;Silber et al, 2022;Wenz et al, 2019) (Revilla et al, 2019;Silber et al, 2022;Wenz et al, 2019, Beuthner 2023, the autonomy over data collection or the study's sponsor (Keusch et al, 2019;Struminskaya et al, 2020Struminskaya et al, , 2021, (Keusch et al, 2019;Struminskaya et al, 2020), the respondents' characteristics such as privacy concerns (Struminskaya et al, 2021), and various other factors as well.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Determinants of willingness to donate data from social media platforms

Kmetty¹,

Stefkovics²,

Szamely³

et al. 2023

Preprint

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Social media data donation through data download packages (DDPs) is a promising new way of collecting individual-level digital trace data with informed consent. When linked with survey data, data donation is an even more promising tool that helps answer novel research questions. Nevertheless, given the novelty of this approach, little is known about whether and how people would share their data with researchers, although this could seriously affect selection bias and thus, the outer validity of the results. To study the determinants of data-sharing and help future data donation studies with detecting the conditions, under which the willingness is the highest, we pre-registered two vignette experiments and embedded them in two online surveys conducted in Hungary and the U.S. In hypothetical requests for donating social media data via DDPs, we manipulated the amount of the monetary incentives (1), the presence or lack of non-monetary incentives (2), the number of requested platforms (3), the estimated upload/download time (4), and the type of requested data (5). The results revealed that data-sharing attitude is strongly subject to the parameters of the actual study, how the request is framed, and some respondent characteristics. Monetary incentives increased willingness to participate in both countries, while other effects were not consistent between the two countries. Non-monetary incentives and time to download/upload data influenced willingness in the U.S. sample but not in the Hungarian one, whereas the type of data affected the willingness to participate only in the case of the Hungarian respondents. Our findings help design more effective future data donation requests and provide insights into the potential patterns of selection bias in data donation studies.

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Section: Discussionmentioning

confidence: 56%

Section: Discussionmentioning

confidence: 99%

Section: Incentivesmentioning

confidence: 94%

Section: Respondents' Characteristicsmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Determinants of willingness to donate data from social media platforms

Kmetty¹,

Stefkovics²,

Szamely³

et al. 2023

Preprint

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The TimeUse+ data set: 4 weeks of time use and expenditure data based on GPS tracks

Winkler,

Meister,

Axhausen

2024

Transportation

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This data paper introduces the TimeUse+ data set and outlines the comprehensive survey methodologies employed in its collection. The TimeUse+ study comprised two online questionnaires and a smartphone-based diary, recording travel, time use, and expenditure data. Participants were instructed to utilize the TimeUse+ application over a 4-week period. The app passively tracked participants’ movements via GPS and enabled them to annotate their trips and time spent at fixed locations with relevant time use and expenditure information. We begin by providing an overview of extant research on smartphone-based activity-travel diaries and the survey methodological research that informed the TimeUse+ app’s design. We then delineate the study design and components of TimeUse+, highlighting insights derived from prior focus groups and pretests. The data analyzed in this study were collected during the main study wave of TimeUse+, conducted in German-speaking Switzerland from July 2022 to February 2023. Approximately 63,000 individuals were invited to participate, of which approximately 10% initially indicated interest. Despite the study’s extensive and burdensome nature, we observed a net response rate of 2.1%. We present findings related to travel behavior, time use patterns, and expenditure habits, and discuss the challenges faced and lessons learned during each stage of development and execution of the TimeUse+ study. These extensive longitudinal data, which include validated information on travel mode and purpose, as well as detailed data on duration, social partners, and expenditures associated with activities performed at each destination, are being made available for further research.

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One App to Assess Them All

Toth

2023

Publizistik

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Smartphones have become popular tools for data collection in the social sciences due to their high prevalence and mobility. Surveys, experience sampling (ESM) and tracking/logging are among the most used smartphone data-collection methods. However, existing apps are either commercial solutions, require programming skills, collect sensitive data, or do not handle all three methods simultaneously. When two or more data collection methods are used simultaneously, it further burdens both researchers and participants. This paper introduces the app MART (Mobile Assessment Research Tool) that solves these problems and is available for Android and iOS devices. Content and data collection settings can be customized dynamically via a web interface without the need to compile a new version of the app when changes are made. While the logging functionality is only supported on Android devices, data donation via the app Screen Time is requested on iOS devices. MART is already functional, and the source code is open-source and available on GitHub. The necessary long-term revisions for its use in custom projects without reprogramming are currently under development.

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Sharing Data Collected with Smartphone Sensors

Cited by 22 publications

References 37 publications

Determinants of willingness to donate data from social media platforms

Determinants of willingness to donate data from social media platforms

The TimeUse+ data set: 4 weeks of time use and expenditure data based on GPS tracks

One App to Assess Them All

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