開始時刻
前のセッションの直後
119. Nudging, Recommending, Notifying
発表数
4
時間
2分30秒
発表担当
目次
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Designing for Noticeability: The Impact of Visual Importance on Desktop Notifications
説明
Desktop notifications should be noticeable but are also subject to a number of design choices, e.g. concerning their size, placement, or opacity. It is currently unknown, however, how these choices interact with the desktop background and their influence on noticeability. To address this limitation, we introduce a software tool to automatically synthesize realistically looking desktop images for major operating systems and applications. Using these images, we present a user study (N=34) to investigate the noticeability of notifications during a primary task. We are first to show that visual importance of the background at the notification location significantly impacts whether users detect notifications. We analyse the utility of visual importance to compensate for suboptimal design choices with respect to noticeability, e.g. small notification size. Finally, we introduce noticeability maps - 2D maps encoding the predicted noticeability across the desktop and inform designers how to trade-off notification design and noticeability.
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Situational Recommender: Are You On the Spot, Refining Plans, or Just Bored?
説明
When people engage in urban exploration, the tool they are most likely to use today is a mobile phone. In this paper, we present observations of users’ “home” and “away” conducted to refine our understanding of situational Point-of-Interest (POI) needs. Our findings suggest three distinct categories of situations in which users seek POI information: On-the-spot, Refining plans, and Moments of boredom. Based on the similarities and differences of these three situations in five observed underlying constraints – distance of interest, engagement threshold, ambiguity of the search, profile matching, and other imperative constraints, we derive implications for designing and ranking POIs for a Situational Recommender. To further access our concept, we designed and prototyped Situational Recommender by providing an interactional representation of the situation, and ran a Wizard-of-Oz concept validation study. Our results suggest that participants understood the concept without much effort and appreciated its usefulness.
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Will AI Console Me when I Lose my Pet? Understanding Perceptions of AI-Mediated Email Writing
説明
Large language models are increasingly mediating, modifying, and even generating messages for users, but the receivers of these messages may not be aware of the involvement of AI. To examine this emerging direction of AI-Mediated Communication (AI-MC), we investigate people’s perceptions of AI written messages. We analyze how such perceptions change in accordance with the interpersonal emphasis of a given message. We conducted both large-scale surveys and in-depth interviews to investigate how a diverse set of factors influence people's perceived trust in AI-mediated writing of emails. We found that people's trust in email writers decreased when they were told that AI was involved in the writing process. Surprisingly trust increased when AI was used for writing more interpersonal emails (as opposed to more transactional ones). Our study provides insights regarding how people perceive AI-MC and has practical design implications on building AI-based products to aid human interlocutors in communication\footnote{https://github.com/OscarLiu2000ATL/AI-Social-Implication}.
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Nudging Behavior Change: Using In-Group and Out-Group Social Comparisons to Encourage Healthier Choices
説明
This paper revisits concepts of nudge in the context of helping consumers to make healthier food choices. We introduce a novel form of social influence nudge not yet investigated by HCI scholars, the out-group social comparison, and test whether this form of nudging works at the point of checkout rather than the more conventional point of product consideration. Across two online experiments, we measure the effectiveness of using nutritional information nudges with added in-group (people like you) and out-group (people not like you) social comparisons. Our preliminary findings suggest that out-group social comparison nudges can be effective in encouraging both normal weight and overweight adults to reduce calories, even when these adults indicate that they do not typically change their diet behaviors. This research has implications for digital information design, interactive marketing, and public health.
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