Seeking medical advice in mobile applications: How social cue design and privacy concerns influence trust and behavioral intention in impersonal patient–physician interactions

2022 ◽  
pp. 107178
Author(s):  
Jiaxin Zhang ◽  
Yan Luximon ◽  
Qingchuan Li
Keyword(s):  
Mobile Applications ◽  
Behavioral Intention ◽  
Medical Advice ◽  
Privacy Concerns ◽  
Social Cue

Assessment and Prediction of Privacy Concerns on Health Apps (Preprint)

2020 ◽  
Author(s):  
Reham AlTamime ◽  
Vincent Marmion ◽  
Wendy Hall
Keyword(s):  
Data Sharing ◽  
Mobile Applications ◽  
Retention Rate ◽  
Mobile Apps ◽  
Third Party ◽  
Privacy Concerns ◽  
Health Apps ◽  
Mobile Health Apps ◽  
Specific Factors ◽  
And Control

BACKGROUND Mobile apps and IoT-enabled smartphones technologies facilitate collecting, sharing, and inferring from a vast amount of data about individuals’ location, health conditions, mobility status, and other factors. The use of such technology highlights the importance of understanding individuals’ privacy concerns to design applications that integrate their privacy expectations and requirements. OBJECTIVE This paper explores, assesses, and predicts individuals’ privacy concerns in relation to collecting and disclosing data on mobile health apps. METHODS We designed a questionnaire to identify participants’ privacy concerns pertaining to a set of 432 mobile apps’ data collection and sharing scenarios. Participants were presented with 27 scenarios that varied across three categorical factors: (1) type of data collected (e.g. health, demographic, behavioral, and location); (2) data sharing (e.g., whether it is shared, and for what purpose); and, (3) retention rate (e.g., forever, until the purpose is satisfied, unspecified, week, or year). RESULTS Our findings show that type of data, data sharing, and retention rate are all factors that affect individuals’ privacy concerns. However, specific factors such as collecting and disclosing health data to a third-party tracker play a larger role than other factors in triggering privacy concerns. CONCLUSIONS Our findings suggest that it is possible to predict privacy concerns based on these three factors. We propose design approaches that can improve users’ awareness and control of their data on mobile applications

Why Are Filipino Consumers Strong Adopters of Mobile Applications?

2015 ◽  
pp. 236-245 ◽  
Author(s):  
Donald L. Amoroso ◽  
Ricardo Lim
Keyword(s):  
Mobile Applications ◽  
Behavioral Intention ◽  
The Philippines ◽  
Ease Of Use ◽  
Mobile Technologies ◽  
Intention To Use ◽  
Key Factors ◽  
Personal Innovativeness ◽  
Research Questions ◽  
Behavioral Intention To Use

In this chapter, the authors study factors such as ease of use and personal innovativeness in order to understand the consumer adoption of mobile technologies in the Philippines in order to build on existing adoption theories for academics and make recommendations to practitioners. The research questions include: (1) What key factors drive adoption of mobile technologies by Filipino consumers? (2) How are Filipino mobile consumers personally innovative in their use of mobile technologies? The authors surveyed 725 mobile Filipino consumers. The resulting linear regression model shows a significant amount of variance explained for behavioral intention to use mobile applications. Personal innovation had a strong statistical impact on both attitude toward using and behavioral intention to use.

A structural model of information privacy concerns toward hospital websites

2015 ◽  
Vol 49 (3) ◽  
pp. 305-324 ◽  
Author(s):  
Kuang-Ming Kuo ◽  
Paul C. Talley ◽  
Chen-Chung Ma
Keyword(s):  
Web Sites ◽  
Behavioral Intention ◽  
Information Privacy ◽  
Online Privacy ◽  
Equation Modeling ◽  
Specific Information ◽  
Privacy Policy ◽  
Content Type ◽  
Privacy Concerns ◽  
Information Privacy Concerns

Purpose – The purpose of this paper is to propose and empirically test a theoretical model that considers the predictors of an individual’s perceptions of information privacy, and also how it relates to his/her behavioral intention toward approaching hospital web sites. Design/methodology/approach – This paper collects data using survey methodology. A total of 331 usable participants are gathered and analyzed via structural equation modeling. Findings – Significant predictors of information privacy concerns include a stated online privacy policy and a hospital’s reputation. Further, online privacy policy predicts a hospital’s reputation. Finally, hospital reputation and information privacy concerns significantly predict an individual’s behavioral intention toward approaching hospital web sites. Research limitations/implications – The study confirmed that an online privacy policy and reputation can effectively alleviate specific information privacy concerns; therefore, this may indicate that these two factors should be considered whenever investigating individuals’ information privacy concerns. Practical implications – To acquire a good reputation and to diminish individuals’ information privacy concerns toward hospital web sites, hospitals should pay attention to the posting of an online privacy policy and communicating such policies to given individuals. Originality/value – This paper fulfils the gap of exploring the relationship among online privacy policy, organization reputation, and information privacy concerns. Further, the hypothesized model and its findings could also provide useful information for managers who are intent on boosting hospital web site usage frequency patterns.

Internet Users' Information Privacy Concerns (IUIPC): The Construct, the Scale, and a Causal Model

2004 ◽  
Vol 15 (4) ◽  
pp. 336-355 ◽  
Author(s):  
Naresh K. Malhotra ◽  
Sung S. Kim ◽  
James Agarwal
Keyword(s):  
Behavioral Intention ◽  
Causal Model ◽  
Personal Information ◽  
Information Privacy ◽  
Second Order ◽  
Privacy Concerns ◽  
Internet Users ◽  
Information Privacy Concerns ◽  
Online Consumers ◽  
Order Construct

The lack of consumer confidence in information privacy has been identified as a major problem hampering the growth of e-commerce. Despite the importance of understanding the nature of online consumers' concerns for information privacy, this topic has received little attention in the information systems community. To fill the gap in the literature, this article focuses on three distinct, yet closely related, issues. First, drawing on social contract theory, we offer a theoretical framework on the dimensionality of Internet users' information privacy concerns (IUIPC). Second, we attempt to operationalize the multidimensional notion of IUIPC using a second-order construct, and we develop a scale for it. Third, we propose and test a causal model on the relationship between IUIPC and behavioral intention toward releasing personal information at the request of a marketer. We conducted two separate field surveys and collected data from 742 household respondents in one-on-one, face-to-face interviews. The results of this study indicate that the second-order IUIPC factor, which consists of three first-order dimensions—namely, collection, control, and awareness—exhibited desirable psychometric properties in the context of online privacy. In addition, we found that the causal model centering on IUIPC fits the data satisfactorily and explains a large amount of variance in behavioral intention, suggesting that the proposed model will serve as a useful tool for analyzing online consumers' reactions to various privacy threats on the Internet.

A SEM-Neural Network Approach for Predicting Antecedents of Factors Influencing Consumers' Intent to Install Mobile Applications

2018 ◽  
pp. 262-308 ◽  
Author(s):  
Yakup Akgül
Keyword(s):  
Neural Network ◽  
Mobile Applications ◽  
Behavioral Intention ◽  
Structural Equation ◽  
Equation Modeling ◽  
Second Phase ◽  
Intention To Use ◽  
Neural Network Approach ◽  
The Neural Network ◽  
Behavioral Intention To Use

This chapter explores the present gap in the literature regarding the acceptance of mobile applications by investigating the factors that affect users' behavioral intention to use apps in Turkey. First, structural equation modeling (SEM) was used to determine which variables had significant influence on intention to install. In a second phase, the neural network model was used to rank the relative influence of significant predictors obtained from SEM. The results reveal that habit, performance expectancy, trust, social influence, and hedonic motivation affect the users' behavioral intention to use apps.

Abstract P201: Evaluating Data Sharing And Privacy Policies Of Diabetes Mobile Apps: Where Is The Data Going?

Hypertension ◽  
2020 ◽  
Vol 76 (Suppl_1) ◽  
Author(s):  
Khaled Abdelrahman ◽  
Josh Bilello ◽  
Megna Panchbhavi ◽  
Mohammed S Abdullah
Keyword(s):  
Data Sharing ◽  
Mobile Applications ◽  
Mobile Apps ◽  
Third Party ◽  
Privacy Policy ◽  
Privacy Policies ◽  
Privacy Concerns ◽  
App Store ◽  
User Data ◽  
Do So

Introduction: Diabetes mobile applications (apps) that help patients monitor disease have led to privacy concerns. We aimed to assess privacy policies for diabetes mobile applications with a focus on data transmission to outside parties. Methods: The App Store was used to gather apps pertaining to diabetes by searching “diabetes” and “blood sugar”. Two readers evaluated privacy policies (PP) including data sharing and storing techniques for mention of 27 predetermined criteria. All network traffic generated while loading and using the app was intercepted by a man-in-the-middle attack to listen to data delivered between the sender and receiver of data transmissions. A packet analyzer determined contents of transmission, where data was sent, and if transmission contained user data. Results: Of 35 apps evaluated, 29 (83%) had PP. The most frequent transmission destinations were Google (n=130 transmissions), Kamai Technologies (n=53), Facebook (n=38) and Amazon (n=33). 35 of 35 apps (100%) were transmitting data to a third party. 2 of 2 (100%) of those who had a privacy policy without mention of a third party transmitted data to a third party. 8 of 8 (100%) apps who mentioned they would not transmit to a third party were found to do so. 19 of 19 (100%) apps who mentioned they would transmit data to a third party were found to do so. All apps (n=6) without a privacy policy were found to be transmitting data to a third party. Conclusion: Most diabetes apps on the App store have accessible PP. All apps evaluated transmitted data to a third party, even when the policy stated this would not occur. As mobile applications are increasingly utilized by patients, it is important to warn of privacy implications.

scholarly journals Privacy Enhanced Multimodal Neural Representations for Emotion Recognition

2020 ◽  
Vol 34 (05) ◽  
pp. 7985-7993
Author(s):  
Mimansa Jaiswal ◽  
Emily Mower Provost
Keyword(s):  
Emotion Recognition ◽  
Private Information ◽  
Mobile Applications ◽  
Primary Task ◽  
Sensitive Information ◽  
Conversational Agents ◽  
Demographic Information ◽  
Privacy Concerns ◽  
Multiple Datasets ◽  
Opt Out

Many mobile applications and virtual conversational agents now aim to recognize and adapt to emotions. To enable this, data are transmitted from users' devices and stored on central servers. Yet, these data contain sensitive information that could be used by mobile applications without user's consent or, maliciously, by an eavesdropping adversary. In this work, we show how multimodal representations trained for a primary task, here emotion recognition, can unintentionally leak demographic information, which could override a selected opt-out option by the user. We analyze how this leakage differs in representations obtained from textual, acoustic, and multimodal data. We use an adversarial learning paradigm to unlearn the private information present in a representation and investigate the effect of varying the strength of the adversarial component on the primary task and on the privacy metric, defined here as the inability of an attacker to predict specific demographic information. We evaluate this paradigm on multiple datasets and show that we can improve the privacy metric while not significantly impacting the performance on the primary task. To the best of our knowledge, this is the first work to analyze how the privacy metric differs across modalities and how multiple privacy concerns can be tackled while still maintaining performance on emotion recognition.

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