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.     

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.     

Mobile apps usage and dynamic capabilities: A structural equation model of SMEs in Lagos,Nigeria

Significant knowledge exists regarding the application of dynamic capability (DC) frameworks in large firms, but their impact on smaller organisations is yet to be fully researched. This study surveyed 1162 small and medium sized enterprises (SMEs) in Lagos in an effort to understand how SMEs in developing country contexts use mobile apps to enhance their businesses through DCs. Through the use of the covariance-based structural equation modelling (SEM) technique, the study explored the fitness of a conceptual formative model for SMEs. The model assembled 7 latent variables namely: mobile app usage, adaptive capability, absorptive capability, innovative capability, opportunity sensing ability, opportunity shaping ability and opportunity seizing ability. Subsequently, 15 hypotheses aimed at testing the relationships between the latent variables were developed and tested. The findings revealed that mobile app usage increases the adaptive, absorptive and innovative capabilities of SMEs. Absorptive capabilities help SMEs to maximise opportunities, while innovative capabilities negatively influence SMEs’ tendency to maximise opportunities. The results failed to establish a direct relationship between mobile app usage and SMEs’ ability to maximise opportunities. The research outcomes indicate that SMEs in Lagos respond to opportunities innovatively but they seldom exhibit innovation in order to create opportunities. The heterogeneous nature of SMEs complicates any clear-cut narrative as to how SMEs in Lagos should employ mobile apps to create and maximise opportunities. However, mobile apps could induce innovation and, as such, impact significantly when developed and applied to the contextual requirements of SMEs. The research revealed the untapped potential of SMEs’ mobile app usage in Lagos.     

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.     

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.     

The role of regulatory focus in decision making of mobile app download: A study of Chinese college students

This paper aims to map out college consumers’ decision making process in mobile application download. Regulatory focus (RF) is examined as an important factor affecting gratifications sought (GS) and information processing (IP), app selection style and the ultimate mobile app download behavior. Results show that RF has effects on hedonic GS, and heuristic IP and systematic IP. Hedonic GS positively predicts equally both heuristic processing and systematic processing. Both heuristic processing and systematic processing predict selection style. Although selection style does not affect download app type diversity, impulsive style consumers are more likely to download the app. The study also shows college consumers in general have high likelihood in downloading an app after they considered it. Hedonic GS mediates the relation between RF and both heuristic processing and systematic processing. Systematic processing mediates hedonic GS and selection style. Hence mobile app download decision involves both heuristic and systematic processing.     

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.     

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.     

Personal and social predictors of use and non-use of fitness/diet app: Application of Random Forest algorithm

This study investigated the various groups of factors that predict individuals’ use and non-use of fitness and diet apps on smartphones. Unlike previous research on fitness and diet apps which have mainly studied individuals’ intentions to use the apps, this study focused on the prediction accuracy of various factors that lead people to use fitness and diet apps through analysis of data collected from users as well as non-users of these apps. To examine prediction accuracy, this study applied the Random Forest algorithm. According to the findings, prediction accuracy higher than that of 70 percent was observed for nine factors: age, annual income, education, perceived obesity, dieting efforts, number of smartphone apps currently used, daily time spent with smartphone apps, perceived benefits from exercise, and social influence. A major contribution of this study is its detection of those factors predicting actual behavioral decisions regarding use of fitness and diet apps, as opposed to future intentions..     

A New Location-Aware Calendar-Based Application for Dynamic Minimum Path Trip Planning

The convergence of several trends, including the proliferation of mobile, cloud technologies, social media, and socio-economic trends such as bring your own device, have led to not only the democratization of computing but also to information overload. This creates an opportunity for pattern recognition and ‘Big Data’ technologies to support mobile ‘context-aware’ computing where technology understands human intentions, and effectively ‘disappears’. In this short paper we explore, through the development of an Android-based test application, one of the capabilities of this computing paradigm, which to the best of our knowledge has not been explored. Namely, we explore the complexity of dynamic calendar based minimum path computing.     

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.     

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。     

Why do people purchase virtual goods? A uses and gratification (U&G) theory perspective

Few literature studies have investigated the relationships between different uses and gratifications (U&Gs) of mobile instant messaging (MIM) apps, continuation, and purchase intentions. To address this gap, the researchers aimed to examine the influence of the content, social, process, and technology U&Gs of MIM on continuation intentions toward MIMs, and purchase intentions toward virtual goods available on MIMs. A comprehensive research model was developed based on the U&G theory, which was tested using cross-sectional data from 309 Japanese MIM users. The study considered six different U&Gs of MIM as independent variables and purchase intentions towards stickers and continuation intentions towards MIM as dependent variables. The study results suggest that exposure U&G has a significant positive association with MIM sticker purchase intentions. The entertainment and affection U&G are positively associated with continuation intentions towards MIM use. The study contributes to the literature by investigating U&Gs that motivate MIM users to have both positive purchase intentions toward virtual goods, such as stickers, and continuation intentions toward MIMs. The study has significant theoretical and practical implications for both researchers and practitioners who are interested in virtual goods, the virtual economy, MIM apps, social media, new media, and the service economy.     

Religion-focused dating apps: A Q methodology study on the uses of mutual

Dating apps have become an increasingly viable option for individuals seeking interpersonal romantic relationships. While there is significant research regarding user motivation on dating apps such as Bumble, Tinder, and Match.com, there is no published research that discusses the motivations of Mutual app users. Developed as a dating app to target members of The Church of Jesus Christ of Latter-Day Saints, Mutual allows users to find potential mates who share their religious background and specify their relationship readiness (from “Into Dating I Guess” to “Ready for a Ring”). This research aims to illuminate the various motivations, attitudes, and opinions of Mutual app users through Q methodology, which identifies perceptual groups among homogeneous populations through a factor analysis of participants’ agreement with similar statements regarding Mutual use. Findings indicated four factor groups: the Relationship Readies (i.e., those serious about dating), the Swipeaholics (i.e., those looking for entertainment), the Faithless (i.e., those who felt pressured to use Mutual), and the Eligible Optimists (i.e., those who saw the app as a convenient, entertaining way to date). Different from other research on dating apps, this study indicates that people may use a niche religion-focused dating app to find individuals with similar moral values or due to external pressure from others. Results warrant further investigation into niche dating apps.     

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.     

Microcoordination 2.0: Social Coordination in the Age of Smartphones and Messaging Apps

This paper examines how mobile messaging apps have changed the way that people microcoordinate. It is based on five focus groups of young adults in Singapore and Taiwan. Originally, microcoordination usually assumed dyadic interaction using either SMS or mobile voice calls. Increasingly, mediated communication uses mobile messaging apps that allow multisided interactions that facilitate task‐based chat groups. Groups are easily formed but can be difficult to manage. This paper advances our understanding of microcoordination via the use of messaging apps. Specifically, it provides insights into the dual roles of instrumental and expressive interaction integral to the functioning of these messaging groups, ambient‐mediated sociation in the form of readily available communication partners in groups, and the emergence of meme‐based coordination.     

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.     

Repackaging Attack on Android Banking Applications and Its Countermeasures

Although anyone can easily publish Android applications (or apps) in an app marketplace according to an open policy, decompiling the apps is also easy due to the structural characteristics of the app building process, making them very vulnerable to forgery or modification attacks. In particular, users may suffer direct financial loss if this vulnerability is exploited in security-critical private and business applications, such as online banking. In this paper, some of the major Android-based smartphone banking apps in Korea being distributed on either the Android Market or the third party market were tested to verify whether a money transfer could be made to an unintended recipient. The experimental results with real Android banking apps showed that an attack of this kind is possible without having to illegally obtain any of the sender’s personal information, such as the senders public key certificate, the password to their bank account, or their security card. In addition, the cause of this vulnerability is analyzed and some technical countermeasures 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.     

Extending the theory of planned behavior: A study of lifestyles,contextual factors,mobile viewing habits,TV content interest,and intention to adopt mobile TV

This study applied the theory of planned behavior (TPB) to explain the intentions of Hong Kong consumers to adopt mobile TV and their interests in its content. Using a probability sample of 644 respondents, this study not only demonstrated the robustness of TPB in explaining consumer behavior but also showed that channel deficiency, mobile viewing habits (which were moderated by perceived behavioral control), and content interest could significantly influence consumers’ intentions to adopt mobile TV services. Although lifestyle types were not found a significant predictor of behavioral intention, it had strong effect on content interests in mobile TV. Limitations and practical implications are provided.