With the increasing ubiquity of personal devices, social acceptability of human-machine interactions has gained relevance and growing interest from the HCI community. Yet, there are no best practices or established methods for evaluating social acceptability. Design strategies for increasing social acceptability have been described and employed, but so far not been holistically appraised and evaluated. We offer a systematic literature analysis (N=69) of social acceptability in HCI and contribute a better understanding of current research practices, namely, methods employed, measures and design strategies. Our review identified an unbalanced distribution of study approaches, shortcomings in employed measures, and a lack of interweaving between empirical and artifact-creating approaches. The latter causes a discrepancy between design recommendations based on user research, and design strategies employed in artifact creation. Our survey lays the groundwork for a more nuanced evaluation of social acceptability, the development of best practices, and a future research agenda.
Studies in psychology have shown that framing effects, where the positive or negative attributes of logically equivalent choices are emphasised, influence people's decisions. When outcomes are uncertain, framing effects also induce patterns of choice reversal, where decisions tend to be risk averse when gains are emphasised and risk seeking when losses are emphasised. Studies of these effects typically use potent framing stimuli, such as the mortality of people suffering from diseases or personal financial standing. We examine whether these effects arise in users' decisions about interface features, which typically have less visceral consequences, using a crowd-sourced study based on snap-to-grid drag-and-drop tasks (n = 842). The study examined several framing conditions: those similar to prior psychological research, and those similar to typical interaction choices (enabling/disabling features). Results indicate that attribute framing strongly influences users' decisions, that these decisions conform to patterns of risk seeking for losses, and that patterns of choice reversal occur.
We examine the association between user interactions with a checklist and task performance in a time-critical medical setting. By comparing 98 logs from a digital checklist for trauma resuscitation with activity logs generated by video review, we identified three non-compliant checklist use behaviors: failure to check items for completed tasks, falsely checking items when tasks were not performed, and inaccurately checking items for incomplete tasks. Using video review, we found that user perceptions of task completion were often misaligned with clinical practices that guided activity coding, thereby contributing to non-compliant check-offs. Our analysis of associations between different contexts and the timing of check-offs showed longer delays when (1) checklist users were absent during patient arrival, (2) patients had penetrating injuries, and (3) resuscitations were assigned to the highest acuity. We discuss opportunities for reconsidering checklist designs to reduce non-compliant checklist use.
Ruan et al. found transcribing short phrases with speech recognition nearly 200% faster than typing on a smartphone. We extend this comparison to a novel composition task, using a protocol that enables a controlled comparison with transcription. Results show that both composing and transcribing with speech is faster than typing. But, the magnitude of this difference is lower with composition, and speech has a lower error rate than keyboard during composition, but not during transcription. When transcribing, speech outperformed typing in most NASA-TLX measures, but when composing, there were no significant differences between typing and speech for any measure except physical demand.
Collecting accurate and precise emotion ground truth labels for mobile video watching is essential for ensuring meaningful predictions. However, video-based emotion annotation techniques either rely on post-stimulus discrete self-reports, or allow real-time, continuous emotion annotations (RCEA) only for desktop settings. Following a user-centric approach, we designed an RCEA technique for mobile video watching, and validated its usability and reliability in a controlled, indoor (N=12) and later outdoor (N=20) study. Drawing on physiological measures, interaction logs, and subjective workload reports, we show that (1) RCEA is perceived to be usable for annotating emotions while mobile video watching, without increasing users' mental workload (2) the resulting time-variant annotations are comparable with intended emotion attributes of the video stimuli (classification error for valence: 8.3%; arousal: 25%). We contribute a validated annotation technique and associated annotation fusion method, that is suitable for collecting fine-grained emotion annotations while users watch mobile videos.