Smart Mobile Apps Research Articles

It’s the End of the Competition: When Social Comparison Is Not Always Motivating for Goal Achievement

Abstract Nowadays consumers can easily connect with others who are pursuing similar goals via smart devices and mobile apps. This technology also enables them to compare how well they are doing relative to others in a variety of contexts, ranging from online gaming to losing weight to loyalty programs. This research investigates consumers’ motivation to achieve a goal when they compare themselves with a superior other who has already attained the goal. Building on the literature on social comparison, and on competition in particular, we find that consumers are less motivated when the superior other has attained the goal compared to when the superior other is just ahead, keeping the relative distance equal. This negative effect on motivation is evident even in situations in which consumers can still attain the same goal as the superior other. We argue and demonstrate that this effect occurs because the other’s goal attainment limits consumers’ prospect to compete and overtake the superior other. Six experimental studies show evidence for this effect in hypothetical loyalty programs and behavioral task completion. These findings provide a deeper understanding of the motivational effect of social comparison, which have implications for marketing managers and public policy makers.

Deep Learning Based Inference of Private Information Using Embedded Sensors in Smart Devices

Smart mobile devices and mobile apps have been rolling out at swift speeds over the last decade, turning these devices into convenient and general-purpose computing platforms. Sensory data from smart devices are important resources to nourish mobile services, and they are regarded as innocuous information that can be obtained without user permissions. In this article, we show that this seemingly innocuous information could cause serious privacy issues. First, we demonstrate that users' tap positions on the screens of smart devices can be identified based on sensory data by employing some deep learning techniques. Second, it is shown that tap stream profiles for each type of apps can be collected, so that a user's app usage habit can be accurately inferred. In our experiments, the sensory data and mobile app usage information of 102 volunteers are collected. The experiment results demonstrate that the prediction accuracy of tap position inference can be at least 90 percent by utilizing convolutional neural networks. Furthermore, based on the inferred tap position information, users' app usage habits and passwords may be inferred with high accuracy.

Learnability of a Configurator Empowering End Users to Create Mobile Data Collection Instruments: Usability Study.

BackgroundMany research domains still heavily rely on paper-based data collection procedures, despite numerous associated drawbacks. The QuestionSys framework is intended to empower researchers as well as clinicians without programming skills to develop their own smart mobile apps in order to collect data for their specific scenarios.ObjectiveIn order to validate the feasibility of this model-driven, end-user programming approach, we conducted a study with 80 participants.MethodsAcross 2 sessions (7 days between Session 1 and Session 2), participants had to model 10 data collection instruments (5 at each session) with the developed configurator component of the framework. In this context, performance measures like the time and operations needed as well as the resulting errors were evaluated. Participants were separated into two groups (ie, novices vs experts) based on prior knowledge in process modeling, which is one fundamental pillar of the QuestionSys framework.ResultsStatistical analysis (t tests) revealed that novices showed significant learning effects for errors (P=.04), operations (P<.001), and time (P<.001) from the first to the last use of the configurator. Experts showed significant learning effects for operations (P=.001) and time (P<.001), but not for errors as the experts’ errors were already very low at the first modeling of the data collection instrument. Moreover, regarding the time and operations needed, novices got significantly better at the third modeling task than experts were at the first one (t tests; P<.001 for time and P=.002 for operations). Regarding errors, novices did not get significantly better at working with any of the 10 data collection instruments than experts were at the first modeling task, but novices’ error rates for all 5 data collection instruments at Session 2 were not significantly different anymore from those of experts at the first modeling task. After 7 days of not using the configurator (from Session 1 to Session 2), the experts’ learning effect at the end of Session 1 remained stable at the beginning of Session 2, but the novices’ learning effect at the end of Session 1 showed a significant decay at the beginning of Session 2 regarding time and operations (t tests; P<.001 for time and P=.03 for operations).ConclusionsIn conclusion, novices were able to use the configurator properly and showed fast (but unstable) learning effects, resulting in their performances becoming as good as those of experts (which were already good) after having little experience with the configurator. Following this, researchers and clinicians can use the QuestionSys configurator to develop data collection apps for smart mobile devices on their own.

Smart mobile apps for supporting product design and decision-making in the era of mass customisation

Nowadays, manufacturing industries face the need to rapidly and effectively adapt to the fast changing market demands that are affected by globalisation, economic instability and customer needs towards higher product variety. Rigid centralised decision-making and IT infrastructures are no longer a viable solution for a company to withstand the globalised market pressure. Information on core activities of a company, such as the manufacturing network design, needs to be constantly available to managers and planners to increase awareness and supervision efficiency. Mobility and remote decision-making is steadily gaining ground as the standard practice in the inter-connected business world. Moreover, the personalisation of products tailored to the individual needs and preferences of the customers can now be achieved through mobile applications, namely apps. Yet, the establishment of mobile apps in the manufacturing domain is still premature. The work proposed in this article presents a set of mobile apps developed to support the customer integration in the product design phase and subsequently the design of the manufacturing network. The applicability of the developed mobile apps is demonstrated through a pilot case from the automotive sector, and specifically, from the customisation of accessories and car aesthetics.

Device Nimbus: An Intelligent Middleware for Smarter Services for Health and Fitness

The world is experiencing an unprecedented explosion in the number of smart devices and mobile apps that are available to users. In the health and fitness domains, many of the devices and technologies in the market place are restricted to proprietary platforms, typically working in isolation with fixed hardware settings. Consequently, an important challenge is to investigate techniques for combining and analyzing data encapsulated by these ubiquitous technologies. In this paper, we introduce an intelligent middleware—Device Nimbus—to meet this challenge. The middleware supports the integration of distributed and heterogeneous mobile sensor data, enabling both context and predictive analysis. We describe a minimum viable product of Device Nimbus and report the results of preliminary tests spanning multiple data sources focused on fitness apps, illustrating the efficacy of the middleware.