Protecting Privacy on Mobile Apps: A Principal–Agent Perspective

Although individuals increasingly use mobile applications (apps) in their daily lives, uncertainty exists regarding how the apps will use the information they request, and it is necessary to protect users from privacy-invasive apps. Recent literature has begun to pay much attention to the privacy issue in the context of mobile apps. However, little attention has been given to designing the permission request interface to reduce individuals’ perceived uncertainty and to support their informed decisions. Drawing on the principal–agent perspective, our study aims to understand the effects of permission justification, certification, and permission relevance on users’ perceived uncertainty, which in turn influences their permission authorization. Two studies were conducted with vignettes. Our results show that certification and permission relevance indeed reduce users’ perceived uncertainty. Moreover, permission relevance moderates the relationship between permission justification and perceived uncertainty. Implications for theory and practice are discussed.

Classifying Mobile Applications Using Word Embeddings

Modern application stores enable developers to classify their apps by choosing from a set of generic categories, or genres, such as health, games, and music. These categories are typically static—new categories do not necessarily emerge over time to reflect innovations in the mobile software landscape. With thousands of apps classified under each category, locating apps that match a specific consumer interest can be a challenging task. To overcome this challenge, in this article, we propose an automated approach for classifying mobile apps into more focused categories of functionally related application domains. Our aim is to enhance apps visibility and discoverability. Specifically, we employ word embeddings to generate numeric semantic representations of app descriptions. These representations are then classified to generate more cohesive categories of apps. Our empirical investigation is conducted using a dataset of 600 apps, sampled from the Education, Health&Fitness, and Medical categories of the Apple App Store. The results show that our classification algorithms achieve their best performance when app descriptions are vectorized using GloVe, a count-based model of word embeddings. Our findings are further validated using a dataset of Sharing Economy apps and the results are evaluated by 12 human subjects. The results show that GloVe combined with Support Vector Machines can produce app classifications that are aligned to a large extent with human-generated classifications.

What and How long: Prediction of Mobile App Engagement

User engagement is crucial to the long-term success of a mobile app. Several metrics, such as dwell time, have been used for measuring user engagement. However, how to effectively predict user engagement in the context of mobile apps is still an open research question. For example, do the mobile usage contexts (e.g., time of day) in which users access mobile apps impact their dwell time? Answers to such questions could help mobile operating system and publishers to optimize advertising and service placement. In this article, we first conduct an empirical study for assessing how user characteristics, temporal features, and the short/long-term contexts contribute to gains in predicting users’ app dwell time on the population level. The comprehensive analysis is conducted on large app usage logs collected through a mobile advertising company. The dataset covers more than 12K anonymous users and 1.3 million log events. Based on the analysis, we further investigate a novel mobile app engagement prediction problem—can we predict simultaneously what app the user will use next and how long he/she will stay on that app? We propose several strategies for this joint prediction problem and demonstrate that our model can improve the performance significantly when compared with the state-of-the-art baselines. Our work can help mobile system developers in designing a better and more engagement-aware mobile app user experience.

Factors Associated With the Experience of Cognitive Training Apps for the Prevention of Dementia: Cross-sectional Study Using an Extended Health Belief Model

Background The prevalence and economic burden of dementia are increasing dramatically. Using information communication technology to improve cognitive functions is proven to be effective and holds the potential to serve as a new and efficient method for the prevention of dementia. Objective The aim of this study was to identify factors associated with the experience of mobile apps for cognitive training in middle-aged adults. We evaluated the relationships between the experience of cognitive training apps and structural variables using an extended health belief model. Methods An online survey was conducted on South Korean participants aged 40 to 64 years (N=320). General characteristics and dementia knowledge were measured along with the health belief model constructs. Statistical analysis and logistic regression analysis were performed. Results Higher dementia knowledge (odds ratio [OR] 1.164, P=.02), higher perceived benefit (OR 1.373, P<.001), female gender (OR 0.499, P=.04), and family history of dementia (OR 1.933, P=.04) were significantly associated with the experience of cognitive training apps for the prevention of dementia. Conclusions This study may serve as a theoretical basis for the development of intervention strategies to increase the use of cognitive training apps for the prevention of dementia.

Dynamic Testing Techniques of Non-Functional Requirements in Mobile Apps: A Systematic Mapping Study

Context: The mobile app market is continually growing offering solutions to almost all aspects of people’s lives, e.g., healthcare, business, entertainment, as well as the stakeholders’ demand for apps that are more secure, portable, easy to use, among other non-functional requirements (NFRs). Therefore, manufacturers should guarantee that their mobile apps achieve high-quality levels. A good strategy is to include software testing and quality assurance activities during the whole life cycle of such solutions. Problem: Systematically warranting NFRs is not an easy task for any software product. Software engineers must take important decisions before adopting testing techniques and automation tools to support such endeavors. Proposal: To provide to the software engineers with a broad overview of existing dynamic techniques and automation tools for testing mobile apps regarding NFRs. Methods: We planned and conducted a Systematic Mapping Study (SMS) following well-established guidelines for executing secondary studies in software engineering. Results: We found 56 primary studies and characterized their contributions based on testing strategies, testing approaches, explored mobile platforms, and the proposed tools. Conclusions: The characterization allowed us to identify and discuss important trends and opportunities that can benefit both academics and practitioners.

Systematic Review of Mobile Applications to Monitor Symptoms of COVID-19 Using the Mobile Application Rating Scale (Preprint)

BACKGROUND Monitoring acute and long-term symptoms of COVID-19 is critical for personal and public health. Mobile health (mHealth) applications (apps) can be used to support symptom monitoring at the point of need for patients with COVID-19. OBJECTIVE To systematically review and evaluate mHealth apps for quality, functionality, and consistency with guidelines for monitoring symptoms of COVID-19. METHODS We conducted a systematic review of apps for COVID-19 symptom monitoring by searching in two major app stores. The final apps were independently assessed using the Mobile Application Rating Scale (MARS), IMS Institute for Healthcare Informatics functionality score, and guidelines from the Center for Disease Control and World Health Organization. Interrater reliability between the reviewers was calculated. RESULTS A total of 1,017 mobile apps were reviewed and 20 met the inclusion criteria. The majority of the apps (90%, n=18) were designed to track acute COVID-19 symptoms, and only two addressed long-term symptoms. Overall, the apps scored high on quality, with an overall MARS rating of 3.94. The most common functionality among all apps was the instruct function (95%, n=19). The most common symptoms included in the apps for tracking were: fever and dry cough (n=18), aches and pains (n=17), difficulty breathing (n=17), tiredness, sore throat, headache, loss of taste, or smell (n=16), and diarrhea (n=15). CONCLUSIONS mHealth apps designed to monitor symptoms of COVID-19 had high quality, but the majority of apps focused almost exclusively on acute symptoms. Future apps should also incorporate monitoring long-term symptoms of COVID-19. CLINICALTRIAL N/A

Mobile app users' privacy concerns: different heuristics for privacy assurance statements in the EU and China

PurposeAs mobile apps request permissions from users, protecting mobile users' personal information from being unnecessarily collected and misused becomes critical. Privacy regulations, such as General Data Protection Regulation in the European Union (EU), aim to protect users' online information privacy. However, one’s understanding of whether these regulations effectively make mobile users less concerned about their privacy is still limited. This work aims to study mobile users' privacy concerns towards mobile apps by examining the effects of general and specific privacy assurance statements in China and the EU.Design/methodology/approachDrawing on ecological rationality and heuristics theory, an online experiment and a follow-up validation experiment were conducted in the EU and China to examine the effects of privacy assurance statements on mobile users' privacy concerns.FindingsWhen privacy regulation is presented, the privacy concerns of Chinese mobile users are significantly lowered compared with EU mobile users. This indicates that individuals in the two regions react differently to privacy assurances. However, when a general regulation statement is used, no effect is observed. EU and Chinese respondents remain unaffected by general assurance statements.Originality/valueThis study incorporates notions from fast and frugal heuristics end ecological rationality – where seemingly irrational decisions may make sense in different societal contexts.

A survey of mobile app use among university students with visual impairment in India

Mobile phones/devices are an important part of our daily lives for sighted people and those with visual impairment (VI) in India. This study explores how Indian university students use and perceive mobile apps and identify the challenges in their usage. A paper-based survey was administered for 124 college students who were legally blind. The survey had items relating to sociodemographic information, use of mobile devices and apps, and the use of apps specifically designed for persons with VI. Results show that, on average, the participants with VI have been using a mobile device for more than 5 years. Many participants used Android devices and free apps. In addition, they found that the mobile apps were user-friendly and accessible. Results also found that Facebook and WhatsApp are more frequently used. Participants had a high level of self-efficacy and positive attitudes toward special apps for VI. Android devices are popular in India because of lower cost compared to iOS devices. Results are discussed, and implications for practice are provided.