Understanding the in-app advertisement effect on mobile user ad accessibility

With the growth of mobile advertising, in-app advertising has become the next revolution in online advertising. However, most of the studies that are conducted on online advertising cannot be applied directly to understand the in-app advertising; for that, we need specific and targeted research. Following this research gap, in this study, we look at inapp advertisement effect on mobile user behavior in respect to its ad features namely, ad size, ad position, and ad vividness. Also, we study how meta-motivations moderate the effect of vividness on the user intentions. Our hypotheses are developed to analyze how ad features will achieve the actual product knowledge and perceived ad diagnosticity and then how they lead to the actual ad accessibility. The model will be tested using data from a laboratory experiment. This study is one of the pioneer studies that examine the role of user interaction effect on mobile ad features. The study will contribute to enhancing the goals of the both advertisers and publishers of online ad ecosystem and it introduces new concepts and measurement techniques for the mobile marketing literature

Assessing the Impact of Usability from Evaluating Mobile Health Applications

Software applications that are used to monitor, track, and improve health are called Mobile Health Applications or mHAs. They are developed with or without the help of medical professionals to potentially aid health, achieve health goals and improve lifestyle or behavior. Although mobile Health Applications have been on the market since the advent of mobile applications, the pandemic saw to a 25% increase in the number of mobile health applications available on the app stores. This indicates the growing demand for mHAs. This research was conducted to evaluate the impact of usability of mobile health applications. The dataset used to carry out this research is a review data set of health-condition management focused apps. These apps managed conditions like Diabetes, Depression, Hypertension, etc. System Usability Score, Net Promoter Score, App Ratings, Patient engagement was some of the features that were used to conduct the research. There were low correlations between App’s reaction to dangerous information and usability score (0.17), Existence of privacy policy and usability score (-0.032), IOS App Rating and Usability Score (0.053), Android App Rating and Usability Score (-0.029). Patient, Caregiver/Clinicians engagement-based variables like ‘does the app makes reference to specific disease guidelines’, ‘in what way does the app engage patients’, ‘does the app provide support through social media’ showed higher correlations with usability scores and clinical utility. It is recommended that to evaluate the usability of mobile health applications, a combination of usability measuring methods be used

Factors Related to User Ratings and User Downloads of Mobile Apps for Maternal and Infant Health: Cross-Sectional Study

Background: Mobile health apps related to maternal and infant health (MIH) are prevalent and frequently used. Some of these apps are extremely popular and have been downloaded over 5 million times. However, the understanding of user behavior and user adoption of these apps based on consumer preferences for different app features and categories is limited. Objective: This study aimed to examine the relationship between MIH app characteristics and users’ perceived satisfaction and intent to use. Methods: The associations between app characteristics, ratings, and downloads were assessed in a sample of MIH apps designed to provide health education or decision-making support to pregnant women or parents and caregivers of infants. Multivariable linear regression was used to assess the relationship between app characteristics and user ratings, and ordinal logistic regression was used to assess the relationship between app characteristics and user downloads. Results: The analyses of user ratings and downloads included 421 and 213 apps, respectively. The average user rating was 3.79 out of 5. Compared with the Apple App Store, the Google Play Store was associated with high user ratings (beta=.33; P =.005). Apps with higher standardized user ratings (beta=.80; P <.001), in-app purchases (beta=1.12; P =.002), and in-app advertisements (beta=.64; P =.02) were more frequently downloaded. Having a health care organization developer as part of the development team was neither associated with user ratings (beta=−.20; P =.06) nor downloads (beta=−.14; P =.63). Conclusions: A majority of MIH apps are developed by non–health care organizations, which could raise concern about the accuracy and trustworthiness of in-app information. These findings could benefit app developers in designing better apps and could help inform marketing and development strategies. Further work is needed to evaluate the clinical accuracy of information provided within the apps. [JMIR Mhealth Uhealth 2020;8(1):e15663

Architecture of in-app ad recommender system

Increased adoption of smartphones has caused mobile advertising to be the secondmost revenue-generating medium among all forms of existing online advertising. Application (henceforth called app) developers try to monetize their apps by selling in-app ad-spaces to the advertisers (or ad-agencies) through various intermediaries such as ad-networks. Surveys, however, indicate that mobile ad campaigns are not as successful as they can be, in part due to inappropriate audience targeting, and in turn, user-apathy toward such ads. This motivates the need for a system, where both advertisers and mobile-app developers gain from the in-app advertising eco-system. In this paper, we propose an architecture of design-science artifacts for an ad-network, to meet the objectives of both these stakeholders

In-Vivo Bytecode Instrumentation for Improving Privacy on Android Smartphones in Uncertain Environments

In this paper we claim that an efficient and readily applicable means to improve privacy of Android applications is: 1) to perform runtime monitoring by instrumenting the application bytecode and 2) in-vivo, i.e. directly on the smartphone. We present a tool chain to do this and present experimental results showing that this tool chain can run on smartphones in a reasonable amount of time and with a realistic effort. Our findings also identify challenges to be addressed before running powerful runtime monitoring and instrumentations directly on smartphones. We implemented two use-cases leveraging the tool chain: BetterPermissions, a fine-grained user centric permission policy system and AdRemover an advertisement remover. Both prototypes improve the privacy of Android systems thanks to in-vivo bytecode instrumentation.Comment: ISBN: 978-2-87971-111-

Who you gonna call? Analyzing Web Requests in Android Applications

Relying on ubiquitous Internet connectivity, applications on mobile devices frequently perform web requests during their execution. They fetch data for users to interact with, invoke remote functionalities, or send user-generated content or meta-data. These requests collectively reveal common practices of mobile application development, like what external services are used and how, and they point to possible negative effects like security and privacy violations, or impacts on battery life. In this paper, we assess different ways to analyze what web requests Android applications make. We start by presenting dynamic data collected from running 20 randomly selected Android applications and observing their network activity. Next, we present a static analysis tool, Stringoid, that analyzes string concatenations in Android applications to estimate constructed URL strings. Using Stringoid, we extract URLs from 30, 000 Android applications, and compare the performance with a simpler constant extraction analysis. Finally, we present a discussion of the advantages and limitations of dynamic and static analyses when extracting URLs, as we compare the data extracted by Stringoid from the same 20 applications with the dynamically collected data

An M-Learning Application to Enhance Children’s Learning Experience

eXtension.org is an interactive learning environment offers reliable educational and information resources on a variety of topics. For Youth, For Life Learning Network community works with eXtension.org to produce youth oriented content for the base of eXtension.org. However, there is no particular software or application designed for children to gather information from extension.org so far. The target user of eXtension.org is the general public, which means it is not child-friendly. In this project, we developed a child-friendly android version mobile app to draw children’s attention in exploring science knowledge from eXtension.org. The app is an educational tool designed to provide learning opportunities to children. It provides several articles to its users in a systematically categorized and prioritized topic. The application is a joint collaboration from eXtension.org and For Youth, For Life (FYFL)

Enabling poll surveys via in-app advertisements

Pollsters often seek to cost-effectively obtain data on issues of immediate relevance. App developers have available ad inventory that can allow pollsters to reach users of their apps. Advertising networks benefit from being able to uniquely identify users to serve more relevant ads. The techniques of this disclosure expand the scope of typical ad experience to include polls. A pollster creates a poll, e.g., using automated template tools provided by an ad network. The poll is served to app users in formats such as banner, native, interstitial, rewarded ads, etc. Users are provided with rewards for responding to the poll