A Mobile App Search Engine

With the popularity of mobile apps on mobile devices based on iOS, Android, Blackberry and Windows Phone operating systems, the numbers of mobile apps in each of the respective native app stores are increasing in leaps and bounds. Currently there are close to one million mobile apps across these four major native app stores. Due to the enormous number of apps, both the constituents in the app ecosytem, consumers and app developers, face problems in ‘app discovery’. For consumers, it is a daunting task to discover the apps they like and need among the huge number of available apps. Likewise, for developers, enabling their apps to be discovered is a challenge. To address these issues, Mobilewalla (MW) an app search engine provides an independent unbiased search for mobile apps with semantic search capabilities. It has also developed an objective scoring mechanism based on user and developer involvement with an app. The scoring mechanism enables MW to provide a number of other ways to discover apps—such as dynamically maintained ‘hot’ lists and ‘fast rising’ lists. In this paper, we describe the challenges of developing the MW platform and how these challenges have been mitigated. Lastly, we demonstrate some of the key functionalities of MW.     

Automatic labeling of mobile apps by the type of psychological needs they satisfy

App usage is now a ubiquitous phenomenon, but little is known about what types of psychological needs are met by which apps. We proposed a method to label automatically mobile apps in terms of whether and to what extent they can satisfy users’ particular psychological needs. First, using the grounded theory approach, we conducted semi-structured in-depth interviews to identify types of needs associated with app usage. Substantive and theoretical coding of the data from the interviews as well as data from samples of app reviews yielded eight types of psychological needs app users had: utilitarian, low-cost, security, health, hedonic, social, cognitive, and self-actualization needs. Second, using the needs corpus (words and phrases) generated above, a classifier was trained using latent Dirichlet allocation (LDA) and support vector machine (SVM) algorithms to filter reviews in terms of whether they included needs-related comments. The classifier showed good performance. Finally, Labeled-LDA was used to automatically provide each review with multiple labels of the types of needs mentioned and the apps were analyzed for the different types of needs they satisfied.     

Download or swipe left: The role of complexity,future-oriented emotions and feature overload

With the intense competition in the mobile applications (apps) market, it is imperative for app providers to understand how visual stimuli from their apps can create a positive first impression and enhance the app download rates. In the present study, the mechanism through which visual complexity influences mobile app download intention is examined. Using a combination of convenience and snowball sampling, 218 participants were recruited to take part in a single-group post-test only quasi-experimental design. The findings of the study showed that visual complexity influences mobile app download intentions through the mediating role of two future-oriented emotions (i.e. hope and anticipated regret). Additionally, the study showed that the indirect effect of visual complexity on download intentions was moderated by feature overload with the importance of visual complexity significantly reducing as perceptions of feature overload increases. The proposed model explained 46% variance in the intentions to download a mobile app. The findings not only provide practical insights for mobile app developers and publishers but also theoretical insights on consumer decision making in the pre-use context of mobile apps.     

AdaptiveMutate: a technique for privacy preservation

Mobile apps are known to be rich sources for gathering privacy-sensitive information about smartphone users. Despite the presence of encryption, passive network adversaries who have access to the network infrastructure can eavesdrop on the traffic and therefore fingerprint a user’s app by means of packet-level traffic analysis. Since it is difficult to prevent the adversaries from accessing the network, providing secrecy in hostile environments becomes a serious concern.In this study, we propose AdaptiveMutate, a privacy-leak thwarting technique to defend against the statistical traffic analysis of apps. First, we present a method for the identification of mobile apps using traffic analysis. Further, we propose a confusion system in which we obfuscate packet lengths, and/or inter-arrival time information leaked by the mobile traffic to make it hard for intruders to differentiate between the altered app traffic and the actual one using statistical analysis. Our aim is to shape one class of app traffic to obscure its features with the minimum overhead. Our system strives to dynamically maximize its efficiency by matching each app with the corresponding most dissimilar app. Also, AdaptiveMutate has an adaptive capability that allows it to choose the most suitable feature to mutate, depending on the type of apps analyzed and the classifier used, if known.We evaluate the efficiency of our model by conducting a comprehensive simulation analysis that mutates different apps to each other using AdaptiveMutate. We conclude that our algorithm is most efficient when we mutate a feature of one app to its most dissimilar one in another app. When applying the identification technique, we achieve a classification accuracy of 91.1%. Then, using our obfuscation technique, we are able to reduce this accuracy to 7%. Also, we test our algorithm against a recently published approach for mobile apps classification and we are able to reduce its accuracy from 94.8% to 17.9%. Additionally, we analyze the tradeoff between the shaping cost and traffic privacy protection, specifically, the associated overhead and the feasibility for real-time implementation.     

Examining the antecedents and consequences of mobile app engagement

Understanding how and why consumers engage with mobile apps is critical to the success of ubiquitous mobile marketing. This study proposed and tested a structural model to investigate the antecedents and consequences of mobile app engagement. Results show that time convenience, interactivity, and compatibility positively influenced mobile app engagement, in turn leading to strong relationship commitment and self-brand connections. Furthermore, informational and experiential mobile apps moderated the effects of time convenience, interactivity, and compatibility on mobile app engagement. Theoretical and practical implications for effective app engagement strategies are discussed.     

Mobile city applications for Brussels citizens: Smart City trends,challenges and a reality check

This article combines quantitative and qualitative methods to take a first look at the app economy and mobile services landscape in the City and Region of Brussels, capital of Belgium and Europe. By scraping the iTunes App Store and Google Play market places we get a view on platform distribution, pricing, public vs. commercial, adoption, appreciation and popular categories of Brussels apps aimed at citizens, as well as a view on the app economy in the city. This data is then complemented by qualitative expert interviews with actors in the field, such as cities, interest groups and developers. In the context of the current debate surrounding what constitutes a Smart City and the importance of smartphones and mobile in this area, we perform a reality check, using Brussels as a case. We find that the laggard position Brussels is currently in could be an opportunity to leapfrog in the field of mobile services, but that a focused vision and clear mobile strategy, while thinking of the city as a local innovation platform built on open data, is quintessential to achieving this.     

A data reduction scheme for active authentication of legitimate smartphone owner using informative apps ranking

Smartphones are vulnerable to fraudulent use despite having strong authentication mechanisms. Active authentication based on behavioral biometrics is a solution to protect the privacy of data in smart devices. Machine-learning-based frameworks are effective for active authentication. However, the success of any machine-learning-based techniques depends highly on the relevancy of the data in hand for training. In addition, the training time should be very efficient. Keeping in view both issues, we’ve explored a novel fraudulent user detection method based solely on the app usage patterns of legitimate users. We hypothesized that every user has a unique pattern hidden in his/her usage of apps. Motivated by this observation, we’ve designed a way to obtain training data, which can be used by any machine learning model for effective authentication. To achieve better accuracy with reduced training time, we removed data instances related to any specific user from the training samples which did not contain any apps from the user-specific priority list. An information theoretic app ranking scheme was used to prepare a user-targeted apps priority list. Predictability of each instance related to a candidate app was calculated by using a knockout approach. Finally, a weighted rank was calculated for each app specific to every user. Instances with low ranked apps were removed to derive the reduced training set. Two datasets as well as seven classifiers for experimentation revealed that our reduced training data significantly lowered the prediction error rates in the context of classifying the legitimate user of a smartphone.     

Does country culture influence consumers' perceptions toward mobile banking? A comparison between Egypt and the United States

This study provides insights into the potential impact of country culture on consumers’ perceptions toward usage of new innovative technological services. Focusing on mobile banking (m-banking), this work compares responses from three distinct consumer segments, including– 1) consumers living in Egypt, 2) consumers from Egypt who are living in the U.S. and 3) U.S. consumers. The study utilizes constructs from the Technology Acceptance Model (TAM) including, perceived ease of use and perceived usefulness, along with perceived risk, trust and social influence to examine the differences between these three distinct consumer segments’ usage intentions toward mobile banking. The hypothesized model was tested using structural equation modeling (SEM). Results indicate that country culture (both primary and secondary) can, to some degree, influence consumers’ perceptions and intentions toward mobile banking. Implications and future research suggestions are provided.     

Systematic evaluation of mobile fitness apps: Apps as the Tutor,Recorder, Game Companion,and Cheerleader

Mobile fitness applications are innovating the ways in which smartphone users self-manage their health. Prior research found that app functions may impact app efficacy. However, research to date has not sought to systematically investigate how different combinations of app functions impact user response to apps, especially adoption intent. This article describes two studies on mobile fitness app characteristics and user attitudes. Study One used content analysis and hierarchical cluster analysis on 98 iPhone fitness apps and identified four app clusters: “Tutor”, “Recorder”, “Game Companion”, and “Cheerleader.” Tracking was the predominant function in current market, but tracking-focused Recorder apps received lowest user ratings among all app clusters. Users favored Tutor apps that combine exercise education and tracking, and Game Companion apps that combine gamification, tracking, and social functions. Function combinations, rather than standalone functions, impact app success. Following a Reasoned Action Approach, Study Two found various effects of individual differences (age, gender, BMI, eHealth literacy, smartphone experience, function preference) on user attitude toward different fitness app types. A comparison between two studies demonstrated a mismatch between market offerings and user needs regarding app functions. Implications of results for mobile fitness app design to improve consumer health and for theories are discussed.     

Facilitation of consumer loyalty toward branded applications: The dual-route perspective

Although many brands develop mobile applications (apps) to build relationships with consumers, most branded apps fail to retain consumers’ loyalty. This study examines the facilitation of consumer loyalty toward branded apps (continuance intention, in-app purchase intention, and word-of-mouth intention) from the dual-route perspective. One route is the affective (relationship) route, where brand benefits (functional benefits, experiential benefits, symbolic benefits, and monetary benefits) drive parasocial interactions between consumers and the brand, which, in turn, influences branded app loyalty. The other route is the utility route, where system characteristics (system quality and information quality) affect perceived usefulness, which, in turn, facilitates branded app loyalty. An online survey was conducted, and the research model was empirically tested using partial least squares structural equation modeling. The findings support the dual-route perspective according to which both affective and utilitarian paths facilitate branded app loyalty. The key theoretical contribution of this study is that it moves beyond the utilitarian path and finds the affective (relationship) path to give a more complete picture of the facilitation of consumer loyalty in the branded app context. A strategy is provided to suggest to practitioners how to design branded apps to facilitate consumer loyalty.     

目标作战能力建模与分析

为满足联合作战对目标作战能力定量化认知与分析的作战需求,在打击毁伤、生存自卫、信息支援、指挥控制、机动投送、综合保障六大目标作战能力研究框架基础上,完善了六大作战能力的概念内涵和理论外延。提出了基于能力要素的目标作战能力建模与分析方法,同时研究了基于作战场景的目标作战能力要素表征技术以及能力分析数据可视化技术,力求构建标准统一、要素齐全、功能完善的目标作战能力建模与分析理论方法体系,实现对目标作战能力的精细化建模与定量化分析。     

创新审计实践教学理念深化审计实验教学改革

审计专业实践教学的强化使其不断推进实验模式和实验内容的创新与改革,培养了广义创新的精神和推行能力,更好更快的培养学生的推行动手实力、分析和解决问题的实力。激发学生的潜能力,所以当今审计教育的目标就是提高以创新能力为更好核心的全面素质。因此我们应当不断创新审计实践教学理念,深化审计实验教学改革,从而培养出符合当今社会需求的具有实践创新能力的审计人才。     

业务能力开放标杆分析及趋势探讨

移动互联网和云计算创造了新的商业模式,对业务能力开放提出了新的需求,差异化能力成为运营商、互联网企业、IT企业争夺新市场的核心竞争力。本文介绍了业务能力开放的现状,重点分析标杆企业在此领域的实践案例,基于案例的共性和特性总结能力开放的3类模式,最后展望其发展趋势。     

基于数字证书的自有APP签名保护方案

为应对移动应用(APP)盗版、仿冒、篡改风险,保证自有APP来源可信、内容完 整,本文提出了一套基于数字证书的APP签名保护与安全控制技术方案,实现了对 自有应用进行签名认证和全流程安全管控。该方案规范了自有APP的签名证书,明 确了正版标识;采用可信副署签名技术,加强代码保护,实现被篡改应用可识别可 追溯;通过中央监控平台,及时发现并限制异常应用,实现了APP的全流程实时安全管控。     

移动运营商用户运营之痛——移动互联时代用户运营核心能力分析

文中分别介绍了移动运营商在通信网环境下的用户运营核心能力和互联网企业在移动互联网环境下用户运营体系的核心能力,总结出真实身份、安全交易环境、支付的三大用户运营核心能力。通过对两种用户运营核心能力的对比,指出移动运营商在移动互联网时代丧失了通信网时代建立的核心能力,推荐移动运营商建立基于公钥基础设施的用户运营核心能力符合移动互联网特征,赢得与互联网企业的用户运营能力的竞争。     

Untangling the effect of cognitive trust and perceived value on health-related information seeking,sharing and psychological well-being: Motivations sought perspective

Previous literature has documented that mobile application utilization increased exponentially throughout the ongoing COVID-19 epidemic. To comprehend the possible antecedents of individuals’ psychological well-being, this research invokes motivation theory and associated literature on perceived assessment (cognitive trust, perceived value, and perceived threat) to comprehensively investigate the determinants of factors influencing mobile app users’ health-related information behaviors and psychological outcomes. Data were gathered collected from 898 users of mobile apps, and this article adopted structural equation modeling (SEM) to evaluate the hypothesis generation model. Obtained findings demonstrated that cognitive trust and perceived value positively impacted seeking and sharing health-related information, which subsequently benefited individuals’ psychological well-being during the public health crisis. Furthermore, perceived threat exerted a negative impact on seeking and sharing health-related information. These results contribute to existing studies on psychological well-being by broadening the antecedence domain of information practices and revealing the underlying psychological mechanism behind this dynamic process. This study could also benefit practitioners by providing insights into embedded system and mobile app development, which would play a pivotal role in enhancing user experiences that optimize psychological well-being.     

Innovative capabilities of a firm and the use of technicalalliances

Relationships between types of innovative capabilities of firms, the amount and nature of technical alliance usage, and the extent and types of problems associated with those alliances are explored with firms in the semiconductor industry. Results show that, although firms need radical and/or incremental product and process innovation capabilities to compete successfully, they are likely to have core capabilities which are either product focused or process focused. Firms can reconcile radical and incremental R&D cultures, but find it more difficult to bridge the product-versus-process divide. Firms with strong capabilities are found to engage in more technical alliances. This is particularly true of firms with radical innovation capabilities. The study also finds that firms engage in technical alliances more often to supplement rather than complement their capabilities. Firms experience more problems in acquiring product innovation capabilities through alliances meant for new technology development than they do in acquiring process innovation capabilities     

Exploring the effect of compulsive social app usage on technostress and academic performance: Perspectives from personality traits

With the rapid development of mobile communications technologies, social apps (e.g., Line, WeChat) have emerged as important communication tools. Although social apps provide people with additional convenience, overuse of such applications may have negative life effects, such as technostress and distraction. Past research has indicated that personality attributes contribute to compulsive usage. This study explores the relationships between personality attributes and compulsive usage of social apps, and examines the impact of technostress on academic performance. A total of 136 valid questionnaires were collected from university students through an online survey. Fourteen proposed hypotheses were examined using SmartPLS software. The results indicate that extraversion, agreeableness, and neuroticism have significant effects on compulsive usage of mobile social applications. Compulsive usage had a significant positive impact on technostress but did not negatively affect academic self-perception and course grades. In addition, conscientiousness significantly influenced academic self-perception. Unexpectedly, gender and number of friends had little influence on technostress or compulsive usage. The implications of these findings are discussed and directions for future research are offered.     

Predicting numeric ratings for Google apps using text features and ensemble learning

Application (app) ratings are feedback provided voluntarily by users and serve as important evaluation criteria for apps. However, these ratings can often be biased owing to insufficient or missing votes. Additionally, significant differences have been observed between numeric ratings and user reviews. This study aims to predict the numeric ratings of Google apps using machine learning classifiers. It exploits numeric app ratings provided by users as training data and returns authentic mobile app ratings by analyzing user reviews. An ensemble learning model is proposed for this purpose that considers term frequency/inverse document frequency (TF/IDF) features. Three TF/IDF features, including unigrams, bigrams, and trigrams, were used. The dataset was scraped from the Google Play store, extracting data from 14 different app categories. Biased and unbiased user ratings were discriminated using TextBlob analysis to formulate the ground truth, from which the classifier prediction accuracy was then evaluated. The results demonstrate the high potential for machine learning-based classifiers to predict authentic numeric ratings based on actual user reviews.