Decomposing responses to mobile notifications

Notifications from mobile devices frequently prompt us with information, either to merely inform us or to elicit a reaction. This has led to increasing research interest in considering an individual’s interruptibility prior to issuing notifications, in order for them to be positively received. To achieve this, predictive models need to be built from previous response behaviour where the individual’s interruptibility is known. However, there are several degrees of freedom in achieving this, from different definitions in what it means to be interruptible and a notification to be successful, to various methods for collecting data, and building predictive models. The primary focus of this thesis is to improve upon the typical convention used for labelling interruptibility, an area which has had limited direct attention. This includes the proposal of a flexible framework, called the decision-on-information-gain model, which passively observes response behaviour in order to support various interruptibility definitions. In contrast, previous studies have largely surrounded the investigation of influential contextual factors on predicting interruptibility, using a broad labelling convention that relies on notifications being responded to fully and potentially a survey needing to be completed. The approach is supported through two in-the-wild studies of Android notifications, one with 11,000 notifications across 90 users, and another with 32,000,000 across 3000 users. Analysis of these datasets shows that: a) responses to notifications is a decisionmaking process, whereby individuals can be reachable but not receptive to their content, supporting the premise of the approach; b) the approach is implementable on typical Android devices and capable of adapting to different notification designs and user preferences; and c) the different labels produced by the model are predictable using data sources that do not require invasive permissions or persistent background monitoring; however there are notable performance differences between different machine learning strategies for training and evaluation

Patterns of multi-device use with the smartphone a video-ethnographic study of young adults’ multi-device use with smartphones in naturally occurring contexts

Using multiple devices at the same time is becoming increasingly common in the daily lives of users, be it for work or for leisure. This paper presents in situ qualitative and quantitative evidence of multi-device use from a dataset of over 200h of first-person and interview recordings (n = 41). We discuss three different ‘patterns’ of multi device use (work, leisure, mixed use) and illustrate the user experience in detail with three participant journeys. We find that the smartphone was always ‘in the mix’; we did not observe multi-device use without the smartphone, or isolated use of other devices. Overall, we suggest that looking at transitions between activities users engage in rather than devices they use is more effective to understand multi-device use. Based on this analysis, we highlight issues around the patterns and experiences of multi-device use in everyday life and provide recommendations for design and further research

The influence of concurrent mobile notifications on individual responses

Notifications on mobile devices punctuate our daily lives to provide information and prompt for further engagement. Investigations into the cognitive processes involved in consuming notifications are common across the literature, however most research to date investigates notifications in isolation of one another. In reality, notifications often coexist together, forming a “stack”, however the behavioural implications of this on the response towards individual notifications has received limited attention. Through an in-the-wild study of 1,889 Android devices, we observe user behaviour in a stream of 30 million notifications from over 6,000 applications. We find distinct strategies for user management of the notification stack within usage sessions, beyond the behaviour patterns observable from responses to individual notifications. From the analysis, we make recommendations for collecting and reporting data from mobile applications to improve validity through timely responses, and capture potential confounding features

AwareNotifications: Multi-Device Semantic Notification Handling with User-Defined Preferences

With the increase of connected devices and online services, the number of notifications received by each person is growing. Although notifications are useful to inform users about important information such as new messages and events, the continuous interruptions, the notification duplication, and the rigid delivery can be sources of discomfort. To overcome these issues, we present AwareNotifications, an intelligent system based on user-defined preferences to manage multi-device notifications. AwareNotifications is powered by Semantic Web technologies. By directly exploiting user preferences in the semantic reasoning process, the system is able to identify suitable device(s), modality, and moment(s) to deliver the incoming user notifications. We evaluated AwareNotifications in a user study with 15 participants, in which we compared our system with the "traditional" notification delivery system. The study confirms the perceived effectiveness of AwareNotifications, and provides insights to further improve the system