Notifications are commonly considered a distraction when they arrive during a task, and consequently, prior research has consistently sought effective ways of deferring their arrival until task transitions. However, many smartphone users still interact with notifications during tasks. In our qualitative study combining diary study and semi-structured interviews, we examined 34 research participants' motivations for interacting with smartphone notifications at different times, including during tasks. Our findings resulted in a human-notification interaction framework comprised of 12 unique motivations frequently associated with three activity timings for interacting with notifications, including before-task, during-task, and after-task. Notably, participants frequently perceived interaction with notifications as a tool for improving or optimizing task performance, making the most of their time, and promoting personal well-being, rather than only as a distraction. The before-the-task timing, in particular, has received little attention in previous research and deserves more attention as it was related to specific user motivations for notification interaction.
People struggle with the overflow of smartphone notifications but often face two challenges: (1) prioritizing the informative notifications as they wish and (2) retaining the delivered information as long as they want to utilize it.
In this paper, we present DataHalo, a customizable notification visualization system that represents notifications as prolonged ambient visualizations on the home screen. DataHalo supports keyword-based filtering and categorization, and draws graphical marks based on time-varying importance model to enable longitudinal interaction with the notifications. We evaluated DataHalo through a usability study ($N$ = 17), from which we improved the interface. We then conducted a three-week deployment study ($N$ = 12) to assess how people use DataHalo in their domestic contexts. Our study revealed that people generated various visualization settings for different kinds of apps. Drawing on both quantitative and qualitative findings, we discussed implications for supporting effective notification management through customizable ambient visualizations.
Time-killing on smartphones has become a pervasive activity, and could be opportune for delivering content to their users. This research is believed to be the first attempt at time-killing detection, which leverages the fusion of phone-sensor and screenshot data. We collected nearly one million user-annotated screenshots from 36 Android users. Using this dataset, we built a deep-learning fusion model, which achieved a precision of 0.83 and an AUROC of 0.72. We further employed a two-stage clustering approach to separate users into four groups according to the patterns of their phone-usage behaviors, and then built a fusion model for each group. The performance of the four models, though diverse, yielded better average precision of 0.87 and AUROC of 0.76, and was superior to that of the general/unified model shared among all users. We investigated and discussed the features of the four time-killing behavior clusters that explain why the models’ performance differ.
Persuasive tactics intend to encourage users to open advertising emails. However, these tactics can overwhelm users, which makes them frustrated and leads to lower open rates. This paper intends to understand which persuasive tactics are used and how they are perceived by users. We first developed a categorization of inbox-level persuasive tactics in permission-based advertising emails. We then asked participants to interact with an email inbox prototype, combined with interviews (N=32), to investigate their opinions towards advertising emails and underlying persuasive tactics. Our qualitative findings reveal poor user experience with advertising emails, which was related to feeling surveilled by companies, forced subscription, high prior knowledge about persuasive tactics, and a desire for more agency. We also found that using certain persuasive tactics on the inbox level is perceived as ethically inappropriate. Based on these insights, we provide design recommendations to improve advertising communication and make such emails more valuable to users.
People nowadays can use multiple devices to interact with notifications, whether via noticing, glancing, reading, or acting upon them. Prior research has focused on actual usage or on device preferences. However, users’ ideal experience of cross-device notification-interaction might differ from their current practices (due to situational limitations) and/or across the four notification-interaction stages. We therefore conducted an experience-sampling method study with multi-device users to investigate these gaps and the influence of device context. Our results reveal that nearly half of the time, the non-phone devices the participants had ranked as their top preferences for notification-interaction were not actually used, due to the devices’ context. Beyond device context, the participants’ choices of devices for notification-interaction were heavily determined by 1) their preferences that particular notification-interaction stages to take place (or not) on particular devices; and 2) the device on which they had undertaken the former stage.
The concept of phubbing (generally defined as a practice of ignoring co-present others by focusing on one’s mobile device) is now widely used in studies aiming to understand the effects of smartphone use on co-present interactions. However, most of these studies are quantitative in nature and fail to grasp the interactional context of smartphone use. Drawing on video recordings and utilizing multimodal interaction analysis, the present study examines phubbing in naturally occurring interactions among young adults. Contrary to most previous research, the analysis reveals that disengagement often precedes self-initiated smartphone use rather than follows it. The study identifies factors that affect whether phubbing is reciprocated and whether it is oriented to as problematic. As a result of the analysis, an alternative conceptualization of phubbing is offered. By reflecting on participants’ ways of managing phubbing and its consequences, we discuss design solutions for supporting them in this task.