Understanding the Impact of Information Representation on Willingness to Share Information

Schneegaß, Stefan; Poguntke, Romina; Machulla, Tonja

doi:10.1145/3290605.3300753

Cited by 21 publications

(19 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our finding that a willingness to share wearable device data may differ due to experience with using such devices is similar to the findings of others. For example, a survey found that those with a self-rated low or medium level of expertise with wearables were less willing to share such data [ 14 ]. Another survey of health and fitness app users found that “quantified-selfers” were significantly more willing to share their personal data on a public scientific database compared to “non–quantified-selfers” [ 8 ].…”

Section: Discussionmentioning

confidence: 99%

Willingness to Share Wearable Device Data for Research Among Mechanical Turk Workers: Web-Based Survey Study

Taylor¹,

Flaks‐Manov²,

Ramesh³

et al. 2021

J Med Internet Res

View full text Add to dashboard Cite

Background Wearable devices that are used for observational research and clinical trials hold promise for collecting data from study participants in a convenient, scalable way that is more likely to reach a broad and diverse population than traditional research approaches. Amazon Mechanical Turk (MTurk) is a potential resource that researchers can use to recruit individuals into studies that use data from wearable devices. Objective This study aimed to explore the characteristics of wearable device users on MTurk that are associated with a willingness to share wearable device data for research. We also aimed to determine whether compensation was a factor that influenced the willingness to share such data. Methods This was a secondary analysis of a cross-sectional survey study of MTurk workers who use wearable devices for health monitoring. A 19-question web-based survey was administered from March 1 to April 5, 2018, to participants aged ≥18 years by using the MTurk platform. In order to identify characteristics that were associated with a willingness to share wearable device data, we performed logistic regression and decision tree analyses. Results A total of 935 MTurk workers who use wearable devices completed the survey. The majority of respondents indicated a willingness to share their wearable device data (615/935, 65.8%), and the majority of these respondents were willing to share their data if they received compensation (518/615, 84.2%). The findings from our logistic regression analyses indicated that Indian nationality (odds ratio [OR] 2.74, 95% CI 1.48-4.01, P=.007), higher annual income (OR 2.46, 95% CI 1.26-3.67, P=.02), over 6 months of using a wearable device (OR 1.75, 95% CI 1.21-2.29, P=.006), and the use of heartbeat and pulse tracking monitoring devices (OR 1.60, 95% CI 0.14-2.07, P=.01) are significant parameters that influence the willingness to share data. The only factor associated with a willingness to share data if compensation is provided was Indian nationality (OR 0.47, 95% CI 0.24-0.9, P=.02). The findings from our decision tree analyses indicated that the three leading parameters associated with a willingness to share data were the duration of wearable device use, nationality, and income. Conclusions Most wearable device users indicated a willingness to share their data for research use (with or without compensation; 615/935, 65.8%). The probability of having a willingness to share these data was higher among individuals who had used a wearable for more than 6 months, were of Indian nationality, or were of American (United States of America) nationality and had an annual income of more than US $20,000. Individuals of Indian nationality who were willing to share their data expected compensation significantly less often than individuals of American nationality (P=.02).

show abstract

Section: Discussionmentioning

confidence: 99%

Willingness to Share Wearable Device Data for Research Among Mechanical Turk Workers: Web-Based Survey Study

Taylor¹,

Flaks‐Manov²,

Ramesh³

et al. 2021

J Med Internet Res

View full text Add to dashboard Cite

show abstract

“…There exists an array of prior works that shed light on users' remorse and struggle with understanding privacy risks associated with apps [43,45,47,50] and offer solutions to aid the user [2,31,32,36,46].…”

Section: Related Workmentioning

confidence: 99%

Nudging the User with Privacy Indicator: A Study on the App Selection Behavior of the User

Bock

Momen

2020

Proceedings of the 11th Nordic Conference on Human-Computer Interaction: Shaping Experiences, Shaping Society

View full text Add to dashboard Cite

This paper presents an empirical study on user behavior, decision making, and perception about privacy concern while selecting apps. An app store demo was presented to the user with a minor modification-a privacy indicator for each app. After carrying out several tasks using this modified mobile interface, participants were interviewed to document reasons behind their decisions, thought process, and perception regarding individual privacy. A total of 82 adults volunteered under the pretext of a usability study. A significant influence of the privacy indicator on their app selection behavior was observed, although this influence decreased in case of familiar apps. Furthermore, responses from questionnaires, data from eye-tracking device and documented interviews, with video confrontation showed coherence with respect to the corresponding app selection behavior.CCS Concepts: • Security and privacy → Privacy protections; Usability in security and privacy; • Human-centered computing → Empirical studies in ubiquitous and mobile computing.

show abstract

“…When it comes to data privacy, these end-users need to be supported by appropriate legislative infrastructure and treated fairly by the companies of wearables. In their study, Schneegass et al (2019) showed that there is a lack of understanding of the correlation between the sensors implemented in activity tracking technologies and the information derived from them. They state that data requests should be based on derived information instead of all data collected by the sensor.…”

mentioning

confidence: 99%

Data privacy-related behavior and concerns of activity tracking technology users from Germany and the USA

Ilhan

Fietkiewicz

2020

AJIM

View full text Add to dashboard Cite

PurposeThis investigation aims to examine the differences and similarities between activity tracking technology users from two regions (the USA and Germany) in their intended privacy-related behavior. The focus lies on data handling after hypothetical discontinuance of use, data protection and privacy policy seeking, and privacy concerns.Design/methodology/approachThe data was collected through an online survey in 2019. In order to identify significant differences between participants from Germany and the USA, the chi-squared test and the Mann–Whitney U test were applied.FindingsThe intensity of several privacy-related concerns was significantly different between the two groups. The majority of the participants did not inform themselves about the respective data privacy policies or terms and conditions before installing an activity tracking application. The majority of the German participants knew that they could request the deletion of all their collected data. In contrast, only 35% out of 68 participants from the US knew about this option.Research limitations/implicationsThis study intends to raise awareness about managing the collected health and fitness data after stopping to use activity tracking technologies. Furthermore, to reduce privacy and security concerns, the involvement of the government, companies and users is necessary to handle and share data more considerably and in a sustainable way.Originality/valueThis study sheds light on users of activity tracking technologies from a broad perspective (here, participants from the USA and Germany). It incorporates not only concerns and the privacy paradox but (intended) user behavior, including seeking information on data protection and privacy policy and handling data after hypothetical discontinuance of use of the technology.

show abstract

Understanding the Impact of Information Representation on Willingness to Share Information

Cited by 21 publications

References 17 publications

Willingness to Share Wearable Device Data for Research Among Mechanical Turk Workers: Web-Based Survey Study

Willingness to Share Wearable Device Data for Research Among Mechanical Turk Workers: Web-Based Survey Study

Nudging the User with Privacy Indicator: A Study on the App Selection Behavior of the User

Data privacy-related behavior and concerns of activity tracking technology users from Germany and the USA

Contact Info

Product

Resources

About