Effects of the design of mobile security notifications and mobile app usability on users’ security perceptions and continued use intention

The explosive global adoption of mobile applications (i.e., apps) has been fraught with security and privacy issues. App users typically have a poor understanding of information security; worse, they routinely ignore security notifications designed to increase security on apps. By considering both mobile app interface usability and mobile security notification (MSN) design, we investigate how security perceptions of apps are formed and how these perceptions influence users’ intentions to continue using apps. Accordingly, we designed and conducted a set of controlled survey experiments with 317 participants in different MSN interface scenarios by manipulating the types of MSN interfaces (i.e., high vs. low disruption), the context (hedonic vs. utilitarian scenarios), and the degree of MSN intrusiveness (high vs. low intrusiveness). We found that both app interface usability and the design of MSNs significantly impacted users’ perceived security, which, in turn, has a positive influence on users’ intention to continue using the app. In addition, we identified an important conundrum: disruptive MSNs—a common approach to delivering MSNs—irritate users and negatively influence their perceptions of app security. Thus, our results directly challenge current practice. If these results hold, current practice should shift away from MSNs that interrupt task performance.     

Lifting inter-app data-flow analysis to large app sets

Mobile apps process increasing amounts of private data, giving rise to privacy concerns. Such concerns do not arise only from single apps, which might—accidentally or intentionally—leak private information to untrusted parties, but also from multiple apps communicating with each other. Certain combinations of apps can create critical data flows not detectable by analyzing single apps individually. While sophisticated tools exist to analyze data flows inside and across apps, none of these scale to large numbers of apps, given the combinatorial explosion of possible (inter-app) data flows. We present a scalable approach to analyze data flows across Android apps. At the heart of our approach is a graph-based data structure that represents inter-app flows efficiently. Following ideas from product-line analysis, the data structure exploits redundancies among flows and thereby tames the combinatorial explosion. Instead of focusing on specific installations of app sets on mobile devices, we lift traditional data-flow analysis approaches to analyze and represent data flows of all possible combinations of apps. We developed the tool Sifta and applied it to several existing app benchmarks and real-world app sets, demonstrating its scalability and accuracy.     

Studying the consistency of star ratings and the complaints in 1 & 2-star user reviews for top free cross-platform Android and iOS apps

How users rate a mobile app via star ratings and user reviews is of utmost importance for the success of an app. Recent studies and surveys show that users rely heavily on star ratings and user reviews that are provided by other users, for deciding which app to download. However, understanding star ratings and user reviews is a complicated matter, since they are influenced by many factors such as the actual quality of the app and how the user perceives such quality relative to their expectations, which are in turn influenced by their prior experiences and expectations relative to other apps on the platform (e.g., iOS versus Android). Nevertheless, star ratings and user reviews provide developers with valuable information for improving the overall impression of their app. In an effort to expand their revenue and reach more users, app developers commonly build cross-platform apps, i.e., apps that are available on multiple platforms. As star ratings and user reviews are of such importance in the mobile app industry, it is essential for developers of cross-platform apps to maintain a consistent level of star ratings and user reviews for their apps across the various platforms on which they are available. In this paper, we investigate whether cross-platform apps achieve a consistent level of star ratings and user reviews. We manually identify 19 cross-platform apps and conduct an empirical study on their star ratings and user reviews. By manually tagging 9,902 1 & 2-star reviews of the studied cross-platform apps, we discover that the distribution of the frequency of complaint types varies across platforms. Finally, we study the negative impact ratio of complaint types and find that for some apps, users have higher expectations on one platform. All our proposed techniques and our methodologies are generic and can be used for any app. Our findings show that at least 79% of the studied cross-platform apps do not have consistent star ratings, which suggests that different quality assurance efforts need to be considered by developers for the different platforms that they wish to support.     

Evaluating mobile app usage by service sector micro and small enterprises in Nigeria: an abductive approach

ABSTRACT

This research investigated how mobile apps influence the dynamic capabilities of service sector micro and small enterprises (MSEs) in Lagos, Nigeria. Using an abductive method, data from 388 service sector MSEs was examined through exploratory factor analysis. The resultant model suggests that mobile app usage barely increases the absorptive capability (integrating new learning into the organization) of MSEs; rather, it strongly influences the ability to seize opportunities. The result implies that mobile app usage by service sector MSEs in Lagos deviates from the conventional views on the micro foundations of the dynamic capability framework, which argues that sensed opportunities are first analysed (shaped) before resources are deployed towards their maximization. These findings suggest that the service sector MSEs in Lagos seldom scrutinize opportunities before deploying resources to seize them. This study extends IS literature on how mobile apps help MSEs to exploit business opportunities in resource-constrained contexts.     

Detecting plagiarized mobile apps using API birthmarks

This paper addresses the problem of detecting plagiarized mobile apps. Plagiarism is the practice of building mobile apps by reusing code from other apps without the consent of the corresponding app developers. Recent studies on third-party app markets have suggested that plagiarized apps are an important vehicle for malware delivery on mobile phones. Malware authors repackage official versions of apps with malicious functionality, and distribute them for free via these third-party app markets. An effective technique to detect app plagiarism can therefore help identify malicious apps. Code plagiarism has long been a problem and a number of code similarity detectors have been developed over the years to detect plagiarism. In this paper we show that obfuscation techniques can be used to easily defeat similarity detectors that rely solely on statically scanning the code of an app. We propose a dynamic technique to detect plagiarized apps that works by observing the interaction of an app with the underlying mobile platform via its API invocations. We propose API birthmarks to characterize unique app behaviors, and develop a robust plagiarism detection tool using API birthmarks.     

App store mining is not enough for app improvement

The rise in popularity of mobile devices has led to a parallel growth in the size of the app store market, intriguing several research studies and commercial platforms on mining app stores. App store reviews are used to analyze different aspects of app development and evolution. However, app users’ feedback does not only exist on the app store. In fact, despite the large quantity of posts that are made daily on social media, the importance and value that these discussions provide remain mostly unused in the context of mobile app development. In this paper, we study how Twitter can provide complementary information to support mobile app development. By analyzing a total of 30,793 apps over a period of six weeks, we found strong correlations between the number of reviews and tweets for most apps. Moreover, through applying machine learning classifiers, topic modeling and subsequent crowd-sourcing, we successfully mined 22.4% additional feature requests and 12.89% additional bug reports from Twitter. We also found that 52.1% of all feature requests and bug reports were discussed on both tweets and reviews. In addition to finding common and unique information from Twitter and the app store, sentiment and content analysis were also performed for 70 randomly selected apps. From this, we found that tweets provided more critical and objective views on apps than reviews from the app store. These results show that app store review mining is indeed not enough; other information sources ultimately provide added value and information for app developers.     

Seeing new in the familiar: intensifying aesthetic engagement with the city through new location-based technologies

ABSTRACT

Understanding better the effects of the use of mobile apps to the use and appreciation of urban environments has been gaining more prominence as a research topic recently due to the increasing everyday use of these apps. Whether this type of digital mediation changes the lived experience is of interest in this article. The intention is to show that besides changing the prevailing practices and behaviour, new technologies also enhance and add positive value to the everyday urban experience. This positive experiential value is approached with the framework consisting of recent advances in philosophical urban and everyday aesthetics, which put emphasis on both familiarity and fun as important qualities that describe the everyday experience in urban environments. We claim that new digital tools increase the quality of fun when moving in familiar surroundings. Fun, understood through the lens of the aesthetic, precedes the experienced quality of playfulness. It alters the existing affordances of the urban environment in a way that make more complex aesthetic qualities emerge. The case examples are GPS-based wayfinding applications such as route planners and navigation tools for pedestrian use and related AR applications such as the popular game app Pokémon GO.     

Effects of the aesthetic design of icons on app downloads: evidence from an android market

With the rapid development of the mobile app market, understanding the determinants of mobile app success has become vital to researchers and mobile app developers. Extant research on mobile applications primarily focused on the numerical and textual attributes of apps. Minimal attention has been provided to how the visual attributes of apps affect the download behavior of users. Among the features of app “appearance”, this study focuses on the effects of app icon on demand. With aesthetic product and interface design theories, we analyze icons from three aspects, namely, color, complexity, and symmetry, through image processing. Using a dataset collected from one of the largest Chinese Android websites, we find that icon appearance influences the download behavior of users. Particularly, apps with icons featuring higher colorfulness, proper complexity, and slight asymmetry lead to more downloads. These findings can help developers design their apps.     

An Efficient Memory Management for Mobile Operating Systems Based on Prediction of Relaunch Distance

Recently, various mobile apps have included more features to improve user convenience. Mobile operating systems load as many apps into memory for faster app launching and execution. The least recently used (LRU)-based termination of cached apps is a widely adopted approach when free space of the main memory is running low. However, the LRU-based cached app termination does not distinguish between frequently or infrequently used apps. The app launch performance degrades if LRU terminates frequently used apps. Recent studies have suggested the potential of using users’ app usage patterns to predict the next app launch and address the limitations of the current least recently used (LRU) approach. However, existing methods only focus on predicting the probability of the next launch and do not consider how soon the app will launch again. In this paper, we present a new approach for predicting future app launches by utilizing the relaunch distance. We define the relaunch distance as the interval between two consecutive launches of an app and propose a memory management based on app relaunch prediction (M2ARP). M2ARP utilizes past app usage patterns to predict the relaunch distance. It uses the predicted relaunch distance to determine which apps are least likely to be launched soon and terminate them to improve the efficiency of the main memory.     

Leveraging app usage contexts for app recommendation: a neural approach

The large volume and variety of apps pose a great challenge for people to choose appropriate apps. As a consequence, app recommendation is becoming increasingly important. Recently, app usage data which record the sequence of apps being used by a user have become increasingly available. Such data record the usage context of each instance of app use, i.e., the app instances being used together with this app (within a short time window). Our empirical data analysis shows that a user has a pattern of app usage contexts. More importantly, the similarity in the two users’ preferences over mobile apps is correlated with the similarity in their app usage context patterns. Inspired by these important observations, this paper tries to leverage the predictive power of app usage context patterns for effective app recommendation. To this end, we propose a novel neural approach which learns the embeddings of both users and apps and then predicts a user’s preference for a given app. Our neural network structure models both a user’s preference over apps and the user’s app usage context pattern in a unified way. To address the issue of unbalanced training data, we introduce several sampling methods to sample user-app interactions and app usage contexts effectively. We conduct extensive experiments using a large real app usage data. Comparative results demonstrate that our approach achieves higher precision and recall, compared with the state-of-the-art recommendation methods.

     

The Dating and Domestic Violence App Rubric: Synthesizing Clinical Best Practices and Digital Health App Standards for Relationship Violence Prevention Smartphone Apps

ABSTRACT

Smartphones are used to both perpetrate and intervene in dating and domestic violence (DV). However, existing DV literature primarily evaluates technology as a tool for perpetration and emerging frameworks that measure eHealth app interventions have not yet considered DV.

To address this gap, the Dating and Domestic Violence App Rubric assesses smartphone-based DV intervention apps along common eHealth app measures such as user responsiveness and security as well as DV-appropriateness – categories derived from eHealth intervention theory and evidence-based DV interventions. As proof of concept, 38 DV intervention apps for iPhone were measured using this rubric.

K-means cluster analysis identified three clusters (high, medium, low). Apps targeting specific users or a specific intervention strategy tended to score higher overall. Findings suggest high-quality DV intervention apps may depend on active collaboration between stakeholders including app developers, DV advocates, and other professionals. Future research should expand this research to include additional DV apps and explore how individuals use smartphone apps to prevent or intervene in DV.     

Formal analysis of seamless application execution in mobile cloud computing

Mobile cloud computing augments the resource-constrained mobile devices to run rich mobile applications by leveraging the cloud resources and services. Compute-intensive mobile apps require significant communication resources for migrating the code from mobile devices to the cloud. For such apps, distributed application execution frameworks (DAEF) have been proposed in the literature. These frameworks either migrate the mobile app code during runtime or keep the app synchronized with another remotely executed app on the cloud. Frameworks also support mobile app live migration to cater for compute node mobility. One key research question arises is how successful are these DAEFs in achieving the seamless application execution under various network conditions? The answer to this question entails formal analysis of the DAEFs to determine the realistic bounds on propagation delay, bandwidth and application interaction with mobile device for various types and sizes of apps. In this research, we apply formal analysis techniques to define the execution time of the app and the time required for code migration. We also define three conditions for seamless application execution. Given realistic values for processor speed, application executable size, possible number of executed instructions, network propagation delay and transmission delay, we show what components of the mobile app need to be migrated during execution to the cloud. Finally, we compute realistic bounds for the app size (that can be executed seamlessly) based on important features which include cloud and device resources, bandwidth and latency profile.     

CSLabel: An Approach for Labelling Mobile App Reviews

Mobile apps (applications) have become a popular form of software, and the app reviews by users have become an important feedback resource. Users may raise some issues in their reviews when they use apps, such as a functional bug, a network lag, or a request for a feature. Understanding these issues can help developers to focus on users’ concerns, and help users to evaluate similar apps for download or purchase. However, we do not know which types of issues are raised in a review. Moreover, the amount of user reviews is huge and the nature of the reviews’ text is unstructured and informal. In this paper, we analyze 3 902 user reviews from 11 mobile apps in a Chinese app store — 360 Mobile Assistant, and uncover 17 issue types. Then, we propose an approach CSLabel that can label user reviews based on the raised issue types. CSLabel uses a cost-sensitive learning method to mitigate the effects of the imbalanced data, and optimizes the setting of the support vector machine (SVM) classifier’s kernel function. Results show that CSLabel can correctly label reviews with the precision of 66.5%, the recall of 69.8%, and the F₁ measure of 69.8%. In comparison with the state-of-the-art approach, CSLabel improves the precision by 14%, the recall by 30%, the F₁ measure by 22%. Finally, we apply our approach to two real scenarios: 1) we provide an overview of 1 076 786 user reviews from 1 100 apps in the 360 Mobile Assistant and 2) we find that some issue types have a negative correlation with users’ evaluation of apps.     

Understanding the impact of personality traits on mobile app adoption – Insights from a large-scale field study

The sheer amount of available apps allows users to customize smartphones to match their personality and interests. As one of the first large-scale studies, the impact of personality traits on mobile app adoption was examined through an empirical study involving 2043 Android users. A mobile app was developed to assess each smartphone user's personality traits based on a state-of-the-art Big Five questionnaire and to collect information about her installed apps. The contributions of this work are two-fold. First, it confirms that personality traits have significant impact on the adoption of different types of mobile apps. Second, a machine-learning model is developed to automatically determine a user's personality based on her installed apps. The predictive model is implemented in a prototype app and shows a 65% higher precision than a random guess. Additionally, the model can be deployed in a non-intrusive, low privacy-concern, and highly scalable manner as part of any mobile app.     

Understanding the evaluation of mHealth app features based on a cross-country Kano analysis

While mobile health (mHealth) apps play an increasingly important role in digitalized health care, little is known regarding the effects of specific mHealth app features on user satisfaction across different healthcare system contexts. Using personal health record (PHR) apps as an example, this study identifies how potential users in Germany and Denmark evaluate a set of 26 app features, and whether evaluation differences can be explained by the differences in four pertinent user characteristics, namely privacy concerns, mHealth literacy, mHealth self-efficacy, and adult playfulness. Based on survey data from both countries, we employed the Kano method to evaluate PHR features and applied a quartile-based sample-split approach to understand the underlying relationships between user characteristics and their perceptions of features. Our results not only reveal significant differences in 14 of the features between Germans and Danes, they also demonstrate which of the user characteristics best explain each of these differences. Our two key contributions are, first, to explain the evaluation of specific PHR app features on user satisfaction in two different healthcare contexts and, second, to demonstrate how to extend the Kano method in terms of explaining subgroup differences through user characteristic antecedents. The implications for app providers and policymakers are discussed.

     

Field experience with obfuscating million-user iOS apps in large enterprise mobile development

In recent years, mobile apps have become the infrastructure of many popular Internet services. It is now common that a mobile app serves millions of users across the globe. By examining the code of these apps, reverse engineers can learn various knowledge about the design and implementation of the apps. Real-world cases have shown that the disclosed critical information allows malicious parties to abuse or exploit the app-provided services for unrightful profits, leading to significant financial losses. One of the most viable mitigations against malicious reverse engineering is to obfuscate the apps. Despite that security by obscurity is typically considered to be an unsound protection methodology, software obfuscation can indeed increase the cost of reverse engineering, thus delivering practical merits for protecting mobile apps. In this paper, we share our experience of applying obfuscation to multiple commercial iOS apps, each of which has millions of users. We discuss the necessity of adopting obfuscation for protecting modern mobile business, the challenges of software obfuscation on the iOS platform, and our efforts in overcoming these obstacles. We especially focus on factors that are unique to mobile software development that may affect the design and deployment of obfuscation techniques. We report the outcome of our obfuscation with empirical experiments. We additionally elaborate on the follow-up case studies about how our obfuscation affected the app publication process and how we responded to the negative impacts. This experience report can benefit mobile developers, security service providers, and Apple as the administrator of the iOS ecosystem.     

Influence of Expectation Confirmation,Network Externalities,and Flow on Use of Mobile Shopping Apps

ABSTRACT

The purpose of the current research is to examine the influence of expectation confirmation, network externalities, and flow on word-of-mouth intention and continued use of mobile shopping apps. A structured online survey questionnaire is used to collect data from 363 users of mobile shopping apps. Structural equation modeling is used to analyze the research model. The findings reveal that indirect network externalities, i.e., perceived complementarity, influence perceived usefulness of the mobile shopping app. Users’ confirmation of expectations significantly influences perceived usefulness, satisfaction, and continuance intention to use mobile apps. Satisfaction is found to be a significant predictor of continuance intention and word-of-mouth intention. Flow influences satisfaction of users, perceived usefulness, and continuance intention. Word-of-mouth intention is found to be an important post-adoption behavioral outcome. The results provide valuable theoretical insights for academics and managerial implications for providers of mobile shopping apps.     

Network-based detection of Android malicious apps

Users leverage mobile devices for their daily Internet needs by running various mobile applications (apps) such as social networking, e-mailing, news-reading, and video/audio streaming. Mobile device have become major targets for malicious apps due to their heavy network activity and is a research challenge in the current era. The majority of the research reported in the literature is focused on host-based systems rather than the network-based; unable to detect malicious activities occurring on mobile device through the Internet. This paper presents a detection app model for classification of apps. We investigate the accuracy of various machine learning models, in the context of known and unknown apps, benign and normal apps, with or without encrypted message-based app, and operating system version independence of classification. The best resulted machine learning(ML)-based model is embedded into the detection app for efficient and effective detection. We collect a dataset of network activities of 18 different malware families-based apps and 14 genuine apps and use it to develop ML-based detectors. We show that, it is possible to detect malicious app using network traces with the traditional ML techniques, and results revealed the accuracy (95–99.9 %) in detection of apps in different scenarios. The model proposed is proved efficient and suitable for mobile devices. Due to the widespread penetration of Android OS into the market, it has become the main target for the attackers. Hence, the proposed system is deployed on Android environment.     

An empirical study of Android Wear user complaints

Wearable apps are becoming increasingly popular in recent years. Nevertheless, to date, very few studies have examined the issues that wearable apps face. Prior studies showed that user reviews contain a plethora of insights that can be used to understand quality issues and help developers build better quality mobile apps. Therefore, in this paper, we mine user reviews in order to understand the user complaints about wearable apps. We manually sample and categorize 2,667 reviews from 19 Android wearable apps. Additionally, we examine the replies posted by developers in response to user complaints. This allows us to determine the type of complaints that developers care about the most, and to identify problems that despite being important to users, do not receive a proper response from developers. Our findings indicate that the most frequent complaints are related to Functional Errors, Cost, and Lack of Functionality, whereas the most negatively impacting complaints are related to Installation Problems, Device Compatibility, and Privacy & Ethical Issues. We also find that developers mostly reply to complaints related to Privacy & Ethical Issues, Performance Issues, and notification related issues. Furthermore, we observe that when developers reply, they tend to provide a solution, request more details, or let the user know that they are working on a solution. Lastly, we compare our findings on wearable apps with the study done by Khalid et al. (2015) on handheld devices. From this, we find that some complaint types that appear in handheld apps also appear in wearable apps; though wearable apps have unique issues related to Lack of Functionality, Installation Problems, Connection & Sync, Spam Notifications, and Missing Notifications. Our results highlight the issues that users of wearable apps face the most, and the issues to which developers should pay additional attention to due to their negative impact.     

A study of the relation of mobile device attributes with the user-perceived quality of Android apps

The number of mobile applications (apps) and mobile devices has increased considerably over the past few years. Online app markets, such as the Google Play Store, use a star-rating mechanism to quantify the user-perceived quality of mobile apps. Users may rate apps on a five point (star) scale where a five star-rating is the highest rating. Having considered the importance of a high star-rating to the success of an app, recent studies continue to explore the relationship between the app attributes, such as User Interface (UI) complexity, and the user-perceived quality. However, the user-perceived quality reflects the users’ experience using an app on a particular mobile device. Hence, the user-perceived quality of an app is not solely determined by app attributes. In this paper, we study the relation of both device attributes and app attributes with the user-perceived quality of Android apps from the Google Play Store. We study 20 device attributes, such as the CPU and the display size, and 13 app attributes, such as code size and UI complexity. Our study is based on data from 30 types of Android mobile devices and 280 Android apps. We use linear mixed effect models to identify the device attributes and app attributes with the strongest relationship with the user-perceived quality. We find that the code size has the strongest relationship with the user-perceived quality. However, some device attributes, such as the CPU, have stronger relationships with the user-perceived quality than some app attributes, such as the number of UI inputs and outputs of an app. Our work helps both device manufacturers and app developers. Manufacturers can focus on the attributes that have significant relationships with the user-perceived quality. Moreover, app developers should be careful about the devices for which they make their apps available because the device attributes have a strong relationship with the ratings that users give to apps.