SIG SEC - Information Security and Privacy
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Paper Type
Complete
Paper Number
1340
Description
Chronic pain has been identified as one of the most widespread health-related problems. Potential chronic pain apps users seek health communities for current and previous reviews to assess the quality of the apps and make a decision regarding disclosing their information to these apps. In this study, we present a multilevel perspective on how virtual health communities and environmental characteristics of chronic pain mobile health apps impact users’ privacy decisions. We used Exploratory Data Analysis and Machine Learning (ML) to operationalize the Theory of Multilevel Information Privacy. The results revealed that the most influential factors affecting users’ cost-benefit analysis are Chronic Pain MHA’s characteristics related to user’s information privacy. The ML results indicate that the existence of information privacy policy can be predicted through the ways the apps use to Collect Data, App's Category, Country, and Store Type, which in turn affect users' decisions.
Recommended Citation
Bantan, May; Alrefaei, Doaa; and Alharbi, Raed, "Assessing the Impact of Virtual Health Communities and Environmental Characteristics of Chronic Pain Mobile Health Apps on Users’ Privacy Decisions: A Multilevel Perspective" (2022). AMCIS 2022 Proceedings. 12.
https://aisel.aisnet.org/amcis2022/sig_sec/sig_sec/12
Assessing the Impact of Virtual Health Communities and Environmental Characteristics of Chronic Pain Mobile Health Apps on Users’ Privacy Decisions: A Multilevel Perspective
Chronic pain has been identified as one of the most widespread health-related problems. Potential chronic pain apps users seek health communities for current and previous reviews to assess the quality of the apps and make a decision regarding disclosing their information to these apps. In this study, we present a multilevel perspective on how virtual health communities and environmental characteristics of chronic pain mobile health apps impact users’ privacy decisions. We used Exploratory Data Analysis and Machine Learning (ML) to operationalize the Theory of Multilevel Information Privacy. The results revealed that the most influential factors affecting users’ cost-benefit analysis are Chronic Pain MHA’s characteristics related to user’s information privacy. The ML results indicate that the existence of information privacy policy can be predicted through the ways the apps use to Collect Data, App's Category, Country, and Store Type, which in turn affect users' decisions.
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SIG SEC