PurposeIn a world where different communication technologies support social connection, managing unavailability is as important as, if not more important than, managing availability. The need to manage unavailability becomes increasingly critical when users employ several communication tools to interact with various ties. A person's availability information disclosure may depend on different social relationships and the technologies used by the person. The study contributes to the literature by drawing on privacy management theory to investigate how users practice availability management and use its deceptive form, which is sometimes called a butler lie, with various ties across different messaging applications (apps) as part of their online privacy. Relevant factors in mediated communication, including facework, common ground, and interpersonal trust, are included in the developed model.Design/methodology/approachThe authors conducted an online survey (n = 475) to explore the relationship between one's contact with different interactants (significant others, family members, close friends, acquaintances, groups of friends, and groups of acquaintances) and one's practice of availability management and use of butler lies with these interactants at different size levels on various messaging apps.FindingsFactors such as facework, privacy related to technology, and privacy related to social relationships affect the practice of availability management and the use of butler lies. Notably, butler lies are used most frequently with acquaintances and groups of acquaintances and least frequently with significant others. Moreover, the practice of availability management and the use of butler lies are negatively moderated by people's conversational grounding and trust.Originality/valueThe study examined the practice of cross-app availability management with diverse social ties on mobile technologies, which is a socio-informatic practice that is widely adopted in the contemporary digital landscape but on which limited scientific and theoretic research has been conducted. No research has directly investigated users' availability management across multiple apps from a relational perspective. Building on the theoretical framework of privacy management, the paper aims to bridge the gap in the relevant literature. The results of this study can serve as a reference for library professionals to develop information literacy programs according to users' availability management needs. The results also provide insights to system designers for developing messaging tools.

Informal interactions are a key element of group work, and many theoretical frameworks and systems have been developed to understand and support these conversations in distributed workgroups. In particular, systems used in several recent experiments provided information about others' current activities so that their availability for conversation could be assessed, and interruptions could be timed strategically. One issue with these experimental systems, though, is that many do not notify the observed party that these observations are taking place. There is reason to believe that such notification could be valuable to users, and that it could alter observers' behavior. Moreover, factors such as the perceived urgency of the interruption could affect willingness to violate social norms in gathering information. We report on an experiment assessing the impact of perceived visibility and task urgency on awareness checking behavior. Results suggest that people check more often when they believe their partners do not know they are checking, and more often when the task is time-constrained than when it is not.

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Artificial intelligence (AI) has been widely used in various industries. In this work, we concentrate on what AI is capable of doing in manufacturing, in the form of a chatbot. We designed a chatbot that helps users complete an assembly task that simulates those in manufacturing settings. In order to recreate this setting, we have users assemble a Meccanoid robot through multiple stages, with the help of an interactive dialogue system. Based on classifying users' intent, the chatbot is able to provide answers or instructions to the user when the user encounters problems during the assembly process. Our goal is to improve our system so that it can capture users' needs by detecting their intent and therefore provide relevant and helpful information to the user. However, in a multiple-step task, we cannot rely on intent classification with user question utterance as the only input, as user questions raised from different steps may share the same intent but require different responses. In this paper, we proposed two methods to address this problem. One is that we capture not only textual features but also visual features through the YOLO-based Masker with CNN (YMC) model. Another is the usage of an Autoencoder to encode multi-modal features for user intent classification. By incorporating visual information, we have significantly improved the chatbot's performance from the experiments conducted on different dataset.INDEX TERMS chatbot, human-robot interaction, multi-modal intent classification

Behavioral accommodation, the adjustment of one's own behavior to match that of other people, is prevalent in human communication, but people differ in the extent to which they accommodate each other. This paper presents a laboratory study examining how cultural background affects behavioral accommodation in awareness information gathering behaviors. Results suggested that members of collectivistic cultures (e.g., China) adjusted their behaviors to match those of their partners, when they were working with someone from other culture, whereas members of individualistic cultures (e.g.: the United States) did not accommodate when in the same situation. Our results suggest that accommodation exists even in online collaborations where no linguistic elements are involved, but this existence is affected by one's cultural background.