The authors investigate app publishers’ decisions to offer free, paid, or both versions of an app over an app’s lifetime by taking into account the interplays between the demand for the free and paid ...versions and publishers’ consideration of future profit streams. Their empirical analyses are based on a comprehensive model of publishers’ versioning decisions calibrated on a data set of 584 top-downloaded apps on Google Play. They find contemporaneous cannibalization but positive intertemporal cross-effects on new users’ demand between the two versions. In addition, the free version’s active user base and in-app purchase and advertising revenues are reduced by the presence of a paid version, but not vice versa. Among the three options, offering the paid version first is the most common optimal launch strategy and applies to 40% of apps in the data. The evolutionary patterns of optimal versioning decisions vary by app category and are related to apps’ abilities to monetize different revenue sources. This study provides insights on how to strategically manage the versioning decision over an app’s lifetime and shows how publishers can make their free version apps more profitable via the deployment or elimination of the paid version.
An examination of dating app culture in China, across user demographics—straight women, straight men, queer women, and queer men. In this exploration of dating app culture in China, Lik Sam Chan ...argues that these popular mobile apps are not merely a platform for personal relationships but also an emerging arena for gender and queer politics. Chan examines the opportunities dating apps present for women's empowerment and men's performances of masculinity, and he links experiences of queer dating app users with their vulnerable position as sexual minorities. He finds that dating apps are both portals to an exciting virtual world of relational possibilities and sites of power dynamics that reflect the heteronormativity and patriarchy of Chinese society. Drawing on in-depth interviews with urban users of such dating apps as Momo, Tantan, Blued, Aloha, Rela, and Lesdo, Chan proposes “networked sexual publics” as a unifying concept to capture the dynamics of dating app culture. Devoting a chapter to each of four publics—straight women, straight men, queer men, and queer women—Chan documents how relationships are shaped and transfigured by this emerging technology. He considers whether dating apps can be a feminist tool; explores straight men's self-presentation on the apps and their interactions with women they meet there; discusses the constant cycle of deleting and installing the same apps seen among queer men; and examines how popular lesbian dating apps may connect queer women to their communities. Finally, Chan maps possible paths for future intersectional, queer, and feminist scholarship on emerging communication technologies.
‘Super apps’ are on the rise. This study explores the characteristics, origins, and manifestations of these apps worldwide, presenting the concept of ‘super-appification’ to describe processes of ...conglomeration in the global digital economy. Super apps aim to become deeply integrated into people’s everyday lives, capturing and monetising essential activities. By analysing 41 super apps, we identify four distinct types of ‘super-app constellations’, showcasing different patterns and dynamics of conglomeration: ‘Swiss-Army Knife’ apps that consolidate services in one app, ‘Family’ apps that expand through subsidiaries, and ‘Host’ and ‘Hub’-style apps that leverage external developers. This typology offers a comprehensive understanding of the conglomeration patterns underpinning the rise of super apps, involving corporate, development and international expansion strategies. Ultimately, super-appification represents an intensified form of ‘appification’, as these apps increasingly pervade and commodify various aspects of everyday life, such as payment, insurance, grocery delivery, mobility and travel, with significant sociopolitical implications.
Traffic Classification (TC), consisting in how to infer applications generating network traffic, is currently the enabler for valuable profiling information, other than being the workhorse for ...service differentiation/blocking. Further, TC is fostered by the blooming of mobile (mostly encrypted) traffic volumes, fueled by the huge adoption of hand-held devices. While researchers and network operators still rely on machine learning to pursue accurate inference, we envision Deep Learning (DL) paradigm as the stepping stone toward the design of practical (and effective) mobile traffic classifiers based on automatically-extracted features, able to operate with encrypted traffic, and reflecting complex traffic patterns. In this context, the paper contribution is fourfold. First, it provides a taxonomy of the key network traffic analysis subjects where DL is foreseen as attractive. Secondly, it delves into the non-trivial adoption of DL to mobile TC, surfacing potential gains. Thirdly, to capitalize such gains, it proposes and validates a general framework for DL-based encrypted TC. Two concrete instances originating from our framework are then experimentally evaluated on three mobile datasets of human users’ activity. Lastly, our framework is leveraged to point to future research perspectives.
The success of mobile apps in improving the lifestyle of patients with noncommunicable diseases through self-management interventions is contingent upon the emerging growth in this field. While users ...of mobile health (mHealth) apps continue to grow in number, little is known about the quality of available apps that provide self-management for common noncommunicable diseases such as diabetes, hypertension, and obesity.
We aimed to investigate the availability, characteristics, and quality of mHealth apps for common noncommunicable disease health management that included dietary aspects (based on the developer's description), as well as their features for promoting health outcomes and self-monitoring.
A systematic search of English-language apps on the Google Play Store (Google LLC) and Apple App Store (Apple Inc) was conducted between August 7, 2022, and September 13, 2022. The search terms used included weight management, obesity, diabetes, hypertension, cardiovascular diseases, stroke, and diet. The selected mHealth apps' titles and content were screened based on the description that was provided. Apps that were not designed with self-management features were excluded. We analyzed the mHealth apps by category and whether they involved health care professionals, were based on scientific testing, and had self-monitoring features. A validated and multidimensional tool, the Mobile App Rating Scale (MARS), was used to evaluate each mHealth app's quality based on a 5-point Likert scale from 1 (inadequate) to 5 (excellent).
Overall, 42 apps were identified. Diabetes-specific mHealth apps accounted for 7% (n=3) of the market, hypertension apps for 12% (n=5), and general noncommunicable disease management apps for 21% (n=9). About 38% (n=16) of the apps were for managing chronic diseases, while 74% (n=31) were for weight management. Self-management features such as weight tracking, BMI calculators, diet tracking, and fluid intake tracking were seen in 86% (n=36) of the apps. Most mHealth apps (n=37, 88%) did not indicate whether there was involvement of health professionals in app development. Additionally, none of the apps reported scientific evidence demonstrating their efficacy in managing health. The overall mean MARS score was 3.2 of 5, with a range of 2.0 to 4.1. Functionality was the best-rated category (mean score 3.9, SD 0.5), followed by aesthetics (mean score 3.2, SD 0.9), information (mean score 3.1, SD 0.7), and engagement (mean score 2.9, SD 0.6).
The quality of mHealth apps for managing chronic diseases was heterogeneous, with roughly half of them falling short of acceptable standards for both quality and content. The majority of apps contained scant information about scientific evidence and the developer's history. To increase user confidence and accomplish desired health outcomes, mHealth apps should be optimized with the help of health care professionals. Future studies on mHealth content analysis should focus on other diseases as well.
Mobile Traffic Classification (TC) has become nowadays the enabler for valuable profiling information, other than being the workhorse for service differentiation or blocking. Nonetheless, a main ...hindrance in the design of accurate classifiers is the adoption of encrypted protocols, compromising the effectiveness of deep packet inspection. Also, the evolving nature of mobile network traffic makes solutions with Machine Learning (ML), based on manually- and expert-originated features, unable to keep its pace. These limitations clear the way to Deep Learning (DL) as a viable strategy to design traffic classifiers based on automatically-extracted features, reflecting the complex patterns distilled from the multifaceted traffic nature, implicitly carrying information in “multimodal” fashion. Multi-modality in TC allows to inspect the traffic from complementary views, thus providing an effective solution to the mobile scenario. Accordingly, a novel multimodal DL framework for encrypted TC is proposed, named MIMETIC, able to capitalize traffic data heterogeneity (by learning both intra- and inter-modality dependences), overcome performance limitations of existing (myopic) single-modality DL-based TC proposals, and support the challenging mobile scenario. Using three (human-generated) datasets of mobile encrypted traffic, we demonstrate performance improvement of MIMETIC over (a) single-modality DL-based counterparts, (b) state-of-the-art ML-based (mobile) traffic classifiers, and (c) classifier fusion techniques.
In 2013, the global mobile app market was estimated at over US$50 billion and is expected to grow to $150 billion in the next two years. In this paper, we build a structural econometric model to ...quantify the vibrant platform competition between mobile (smartphone and tablet) apps on the Apple iOS and Google Android platforms and estimate consumer preferences toward different mobile app characteristics. We find that app demand increases with the in-app purchase option wherein a user can complete transactions within the app. On the contrary, app demand decreases with the in-app advertisement option where consumers are shown ads while they are engaging with the app. The direct effects on app revenue from the inclusion of an in-app purchase option and an in-app advertisement option are equivalent to offering a 28% price discount and increasing the price by 8%, respectively. We also find that a price discount strategy results in a greater increase of app demand in Google Play compared with Apple App Store, and app developers can maximize their revenue by providing a 50% discount on their paid apps. Using the estimated demand function, we find that mobile apps have enhanced consumer surplus by approximately $33.6 billion annually in the United States, and we discuss various implications for mobile marketing analytics, app pricing, and app design strategies.
This paper was accepted by Alok Gupta, special issue on business analytics
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The massive adoption of hand-held devices has led to the explosion of mobile traffic volumes traversing home and enterprise networks, as well as the Internet. Traffic classification (TC), i.e., the ...set of procedures for inferring (mobile) applications generating such traffic, has become nowadays the enabler for highly valuable profiling information (with certain privacy downsides), other than being the workhorse for service differentiation/blocking. Nonetheless, the design of accurate classifiers is exacerbated by the raising adoption of encrypted protocols (such as TLS), hindering the suitability of (effective) deep packet inspection approaches. Also, the fast-expanding set of apps and the moving-target nature of mobile traffic makes design solutions with usual machine learning, based on manually and expert-originated features, outdated and unable to keep the pace. For these reasons deep learning (DL) is here proposed, for the first time, as a viable strategy to design practical mobile traffic classifiers based on automatically extracted features, able to cope with encrypted traffic, and reflecting their complex traffic patterns. To this end, different state-of-the-art DL techniques from (standard) TC are here reproduced, dissected (highlighting critical choices), and set into a systematic framework for comparison, including also a performance evaluation workbench. The latter outcome, although declined in the mobile context, has the applicability appeal to the wider umbrella of encrypted TC tasks. Finally, the performance of these DL classifiers is critically investigated based on an exhaustive experimental validation (based on three mobile datasets of real human users' activity), highlighting the related pitfalls, design guidelines, and challenges.
Abstract Social media allow citizens to express their opinions on all aspects of life and society, and this trend has been enhanced during the COVID-19 crisis, when more “traditional” ways of opinion ...expression were not possible. To get the feeling of Twitter users’ opinions on topics of importance we analysed tweets and combined them with relevant news, thus allowing for potential event detection. We showcase the prototypical framework that we have developed with our findings about European COVID-19 mobile contact tracing apps in tweets posted between 09/07/2020 and 10/07/2021. We obtained both high-level results (for example, trending twitter activity, sentiment polarisation of important hashtags, etc.) and more specific ones (such as, the spatial distribution of tweets regarding a specific app), which indicate that our approach can be applied in the future to get useful insights on topics of public interest that result in active discussions on social media platforms.
In vivo exposure therapy is the most effective treatment for phobias but is often impractical. Virtual reality exposure therapy (VRET) can help overcome critical barriers to in vivo exposure therapy. ...However, accessible mobile software related to VRET is not well understood.
The purpose of our study is to describe the landscape of accessible smartphone apps with potential utility for clinical VRET.
We conducted a content analysis of publicly available smartphone apps related to virtual reality on the Google Play Store and the Apple App Store as of March 2020.
The initial search yielded 525 apps, with 84 apps (52 on the Google Play Store and 32 on the Apple App Store) included for analysis. The most common phobic stimulus depicted was bodies of water or weather events (25/84, 29.8%), followed by heights (24/84, 28.6%), and animals (23/84, 27.4%). More than half of the apps were visually abstract (39/84, 53.5%). Most apps were free to use (48/84, 57.1%), while the rest were free to try (22/84, 26.2%) or required payment for use (14/84, 16.7%), with the highest cost for use being US $6. The average overall app rating was 2.9 stars out of 5, but the number of ratings ranged from 0 to 49,233. None of the 84 apps advertised compliance with the Health Insurance Portability and Accountability Act, offered the ability to monitor data, provided clinician control over variables in the app experiences, or explicitly stated use by or development with clinicians.
None of the smartphone apps reviewed were explicitly developed for phobia therapy. However, 16 of the 84 included apps were considered ideal candidates to investigate further as part of treatment due to their accessibility, depiction of phobia-relevant stimuli, low or no cost, and high user scores. Most of these apps were visually abstract and free to use, making them accessible and potentially flexible as part of clinical exposure hierarchies. However, none of the apps were designed for clinical use, nor did they provide tools for clinician workflows. Formal evaluation of these accessible smartphone apps is needed to understand the clinical potential of accessible VRET solutions.
