Research indicates that smartphone users often speculate about notifications upon sensing their arrival, aiding their decision to attend to them. This speculation, however, relies on the presence of sufficient clues to associate with the notification, which are not always available. To address this challenge, through an experience sampling study, we investigated the effectiveness of delivering user-assigned alerts in influencing users' speculation accuracy, attendance effectiveness, and perceived disturbance. Our findings suggest that while user-assigned alerts enhanced the accuracy of speculation and improved participants' decisions to attend to notifications, the increased notification awareness sometimes led participants to view their decision to ignore notifications as less favorable. Moreover, we found that sporadic alert delivery disrupted the association between the alert and the notification, leading to no reduction in perceived disturbance nor improvement in speculation accuracy. In assigning alerts to notifications, participants considered five strategies: familiarity, distinctiveness, disturbance, emotional resonance, and dimension representation.

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.

The proliferation of influencers on social-media platforms has drawn considerable research attention, particularly in the field of marketing. Nevertheless, there is limited understanding among HCI and communication researchers of what leads these social-media influencers’ (SMIs’) audiences to favor and choose their content over traditional media. To fill this gap, we conducted semi-structured interviews with 45 SMI audience members. Our findings revealed a total of eight categories of SMIs’ appeals, i.e., factors that made the interviewees favor their content over traditional media. These appeals can further be grouped into three categories: content, presentation, and closeness. In particular, we identified the key role of SMIs’ perceived high autonomy and independence, which led both their content and their presentation styles to be seen as distinct from and more appealing than traditional media. Likewise, four closeness appeals made our participants feel emotionally attached to SMIs, resulting in sustained engagement.

Mobile users commonly multitask during travel, but doing so on public transit can be challenging due to the dynamic nature of the environment as well as long-standing lack of infrastructural support. Nevertheless, HCI scholars and practitioners have devoted relatively little attention to developing technology for enhancing travel multitasking. To facilitate such development, we sought to understand travel multitaskers’ practices and challenges while on public transit, and to that end, conducted a multi-methods study that involved shadowing and interviewing 30 of them. We identified four travel-multitasking patterns, characterized by distinct motives that affected these travelers’ multitasking practices, receptivity to environmental stimuli, and task persistence. The two main challenges they encountered during travel multitasking resulted from mutual interference from their tasks and from the dynamic nature of transit environments. Based on these findings, design recommendations for public-transit agencies and mobile services are also provided.