Current Practices for Product Usability Testing in Web and Mobile Applications

Software usability testing is a key methodology that ensures applications are intuitive and easy to use for the target audience. Usability testing has direct benefits for companies as usability improvements often are fundamental to the success of a product. A standard usability test study includes the following five steps: obtain suitable participants, design test scripts, conduct usability sessions, interpret test outcomes, and produce recommendations. Due to the increasing importance for more usable applications, effective techniques to develop usable products, as well as technologies to improve usability testing, have been widely utilized. However, as companies are developing more cross-platform web and mobile apps, traditional single-platform usability testing has shortcomings with respect to ensuring a uniform user experience. In this report, a new strategy is proposed to promote a consistent user experience across all application versions and platforms. This method integrates the testing of different application versions, e.g., the website, mobile app, mobile website. Participants are recruited with a better-defined criterion according to their preferred devices. The usability session is conducted iteratively on several different devices, and the test results of individual application versions are compared on a per-device basis to improve the test outcomes. This strategy is expected to extend on current practices for usability testing by incorporating cross-platform consistency of software versions on most devices

Encouraging Privacy-Aware Smartphone App Installation: Finding out what the Technically-Adept Do

Smartphone apps can harvest very personal details from the phone with ease. This is a particular privacy concern. Unthinking installation of untrustworthy apps constitutes risky behaviour. This could be due to poor awareness or a lack of knowhow: knowledge of how to go about protecting privacy. It seems that Smartphone owners proceed with installation, ignoring any misgivings they might have, and thereby irretrievably sacrifice their privacy

Third Party Tracking in the Mobile Ecosystem

Third party tracking allows companies to identify users and track their behaviour across multiple digital services. This paper presents an empirical study of the prevalence of third-party trackers on 959,000 apps from the US and UK Google Play stores. We find that most apps contain third party tracking, and the distribution of trackers is long-tailed with several highly dominant trackers accounting for a large portion of the coverage. The extent of tracking also differs between categories of apps; in particular, news apps and apps targeted at children appear to be amongst the worst in terms of the number of third party trackers associated with them. Third party tracking is also revealed to be a highly trans-national phenomenon, with many trackers operating in jurisdictions outside the EU. Based on these findings, we draw out some significant legal compliance challenges facing the tracking industry.Comment: Corrected missing company info (Linkedin owned by Microsoft). Figures for Microsoft and Linkedin re-calculated and added to Table

Why Do People Adopt, or Reject, Smartphone Password Managers?

People use weak passwords for a variety of reasons, the most prescient of these being memory load and inconvenience. The motivation to choose weak passwords is even more compelling on Smartphones because entering complex passwords is particularly time consuming and arduous on small devices. Many of the memory- and inconvenience-related issues can be ameliorated by using a password manager app. Such an app can generate, remember and automatically supply passwords to websites and other apps on the phone. Given this potential, it is unfortunate that these applications have not enjoyed widespread adoption. We carried out a study to find out why this was so, to investigate factors that impeded or encouraged password manager adoption. We found that a number of factors mediated during all three phases of adoption: searching, deciding and trialling. The study’s findings will help us to market these tools more effectively in order to encourage future adoption of password managers

apk2vec: Semi-supervised multi-view representation learning for profiling Android applications

Building behavior profiles of Android applications (apps) with holistic, rich and multi-view information (e.g., incorporating several semantic views of an app such as API sequences, system calls, etc.) would help catering downstream analytics tasks such as app categorization, recommendation and malware analysis significantly better. Towards this goal, we design a semi-supervised Representation Learning (RL) framework named apk2vec to automatically generate a compact representation (aka profile/embedding) for a given app. More specifically, apk2vec has the three following unique characteristics which make it an excellent choice for largescale app profiling: (1) it encompasses information from multiple semantic views such as API sequences, permissions, etc., (2) being a semi-supervised embedding technique, it can make use of labels associated with apps (e.g., malware family or app category labels) to build high quality app profiles, and (3) it combines RL and feature hashing which allows it to efficiently build profiles of apps that stream over time (i.e., online learning). The resulting semi-supervised multi-view hash embeddings of apps could then be used for a wide variety of downstream tasks such as the ones mentioned above. Our extensive evaluations with more than 42,000 apps demonstrate that apk2vec's app profiles could significantly outperform state-of-the-art techniques in four app analytics tasks namely, malware detection, familial clustering, app clone detection and app recommendation.Comment: International Conference on Data Mining, 201

Understanding and supporting app developers towards designing privacy-friendly apps for children

The integration of digital technology in contemporary society has led to children being exposed to and using mobile devices at younger ages. These devices have become an integral part of their daily routines and experiences, playing a crucial role in their socialisation and development. However, the use of these devices is not without drawbacks. The underlying infrastructure of many of the apps available on such devices heavily relies on a vast and intricate data-driven ecosystem. The proliferation of mobile app developers and numerous third-party and fourth-party entities heavily relies on the collection, sharing, transmission, and analysis of personal data, including that of children. The breach of privacy resulting from the extensive data tracking is prevalent and has detrimental effects on children, including the loss of autonomy and trust. In this thesis, we investigate this problem from the perspective of app developers. We begin by conducting a critical examination of the privacy landscape of popular children's apps in the UK market. In conjunction with a systematic literature review, we develop a research-driven method for evaluating privacy practices in mobile applications. By applying this methodology to a dataset of 137 'expert-approved' children's apps, we reveal that these apps extensively tracked children's data, while providing insufficient user-facing support for children to manage and negotiate these privacy behaviours. This finding raises the crucial question of barriers to designing privacy-friendly mobile apps for children. To explore this issue, we first conduct a mixed-method study with developers of children's apps, comprising 134 surveys and 20 interviews. Our findings show that while the developers are invested in the best interests of children, they encounter difficulties in navigating the complex data-driven ecosystem, understanding the behaviour of third-party libraries and trackers, as well as the pressure to monetise their apps through privacy-friendly alternatives. In light of these findings, we carry out a Research through Design approach to elicit latent needs from children's app developers, using a set of 12 ideas, generated through a workshop with design expert, aimed at addressing the identified challenges. These ideas are evaluated with a sample of 20 children's app developers to uncover a set of latent requirements for support, including a demand for increased transparency regarding third-party libraries and easy-to-adopt compliance checking against regulatory guidelines. Utilising the requirements gathered from the developers, we develop a web-based application that aims to provide transparency about the privacy behaviours of commonly used SDKs and third-party libraries for app developers. We ask a sample of 12 children's app developers to evaluate how features in our application may incentivise developers to consider privacy-friendly alternatives to commonly used SDKs, how they may plan to use it in their development practices, and how it may be improved in the future. The research in this thesis casts a crucial new perspective upon the current state of privacy in the mobile ecosystem, through carefully-designed observations and attempts to disrupt existing practices of app developers for children. Through this journey, we contribute to the HCI research community and related designers and regulatory bodies with fresh and original insights into the design and development of privacy-friendly mobile applications for children

Three Essays On Interfirm Interdependence And Firm Performance

This dissertation explicitly examines the structure of interdependencies that firms are subjected to within a platform-based ecosystem and its implications for firm performance. Two theoretical themes emerge from this dissertation: (1) a firm’s interdependence with other actors in the ecosystem matters both for its performance and the sustainability of its superior performance; and (2) a manager’s understanding of these interdependencies can have significant implications on firm performance and the choice of governance structures. The first essay explores how a firm’s innovation differs with respect to its interdependence with various elements of the ecosystem and examines its implications on the innovation’s commercialization success. The core set of data is based on all the apps that were launched in the Apple iPhone ecosystem from 2008 to 2013. The results suggest that firms can enhance the value of their innovation by drawing on the broader set of complementary technologies that are available in the ecosystem. But, these complementarities also subject firms to an array of bottlenecks limiting their innovation’s value creation. The second essay examines how ecosystem-level interdependencies affect the extent to which firms can sustain their value creation in a platform-based ecosystem. The analysis is based on a panel dataset of top-performing app developers in the iOS and Android ecosystems from January 2012 to January 2014. The results suggest that a firm’s ability to sustain its superior performance is facilitated by the technological interdependence faced by its innovation within an ecosystem and the experience gained within the ecosystem, but hampered by technological transitions initiated by the central firm. The third essay addresses the performance consequences of misrepresentation of interdependence structures in the alliance context using an agent-based simulation. The results suggest that the misrepresentation of interdependence structures plays an important role in determining performance consequences of various governance modes to manage the alliance relationship. Specifically, overrepresentation of interdependence structures requires fully integrated or more hierarchical governance modes, whereas underrepresentation of interdependence structures requires more decentralized governance modes. Collectively, these essays contribute to the literature on ecosystems and alliances, shedding new light on the role of structure of interdependence ins shaping firm’s performance

Navigating the data avalanche: towards supporting developers in developing privacy-friendly children's apps

This paper critically examines the intersection of privacy concerns in children’s apps and the support required by developers to effectively address these concerns. Third-party libraries and software development kits (SDKs) are widely used in mobile app development, however, these libraries are commonly known for posing significant data privacy risks to users. Recent research has shown that app developers for children are particularly struggling with the lack of support in navigating the complex market of third-party SDKs. The support needed for developers to build privacy-friendly apps is largely understudied. Motivated by the needs of developers and an empirical analysis of 137 ‘expert-approved’ children’s apps, we designed DataAvalanche.io, a web-based tool to support app developers in navigating the privacy and legal implications associated with common third-party SDKs on the market. Through semi-structured interviews with 12 app developers for children, we demonstrate that app developers largely perceive the transparency supported by our tool positively. However, they raised several barriers, including the challenges of adopting privacy-friendly alternatives and the struggle to safeguard their own legal interests when facing the imbalance of power in the app market. We contribute to our understanding of the open challenges and barriers faced by app developers in creating privacy-friendly apps for children and provide critical future design and policy directions