In this paper, we investigate the challenges users face with a user-centric context-aware intervention system. Users often face gaps when the system’s responses do not align with their goals and intentions. We explore these gaps through a prototype system that enables users to specify context-action intervention rules as they desire. We conducted a lab study to understand how users perceive and cope with gaps while translating their intentions as rules, revealing that users experience context-mapping and context-recognition uncertainties (instant evaluation cycle). We also performed a field study to explore how users perceive gaps and make adaptations of rules when the operation of specified rules in real-world settings (delayed evaluation cycle). This research highlights the dynamic nature of user interaction with context-aware systems and suggests the potential of such systems in supporting digital well-being. It provides insights into user adaptation processes and offers guidance for designing user-centric context-aware applications.

A semi-automated manufacturing system that entails human intervention in the middle of the process is a representative collaborative system that requires active interaction between humans and machines. User behavior induced by the operator’s decision-making process greatly impacts system operation and performance in such an environment that requires human-machine collaboration. There has been room for utilizing machine-generated data for a fine-grained understanding of the relationship between the behavior and performance of operators in the industrial domain, while multiple streams of data have been collected from manufacturing machines. In this study, we propose a large-scale data-analysis methodology that comprises data contextualization and performance modeling to understand the relationship between operator behavior and performance. For a case study, we collected machine-generated data over 6-months periods from a highly automated machine in a large tire manufacturing facility. We devised a set of metrics consisting of six human-machine interaction factors and four work environment factors as independent variables, and three performance factors as dependent variables. Our modeling results reveal that the performance variations can be explained by the interaction and work environment factors (R2 = 0.502, 0.356, and 0.500 for the three performance factors, respectively). Finally, we discuss future research directions for the realization of context-aware computing in semi-automated systems by leveraging machine-generated data as a new modality in human-machine collaboration.

Techno-spiritual practices refer to the use of digital technologies to support various spiritual activities, such as scripture reading. While prior human-computer interaction studies largely focus on understanding techno-spiritual practices in both personal and ministry environments, there is a lack of design research that explores novel design opportunities, based on longitudinal field deployment. As an important techno-spiritual practice, this work focuses on scripture reading and investigates the design space of "social scripture reading," as it is often organized into small groups for successful behavior maintenance. We designed and evaluated BibleCell, a social scripture reading tool that supports personalized reading plans, scripture reading, and social sharing. After the third year of deployment, we performed a two-month user study in Korean Protestant churches to deepen our understanding of techno-spiritual practices in social contexts via in-depth interviews (n = 27). We report the major themes of social techno-spiritual practices, such as social motivators, social interaction patterns, and leadership roles. We discuss our findings using a novel design concept of social-spiritual awareness that considers both the social and spiritual aspects of interactions in social computing systems.

As people around the world are spending increasing amounts of time online, the question of how online experiences are linked to health and well-being is essential. This paper presents how activities on Facebook are associated with the depressive states of users. Based on online logs of 212 young adults, we show not only the sheer size of the network but also the frequency and diversity of interactions on social networks have close associations with depression. Depressed individuals reported smaller involved networks regarding comments and likes, the two popular forms of interactions. In contrast to the decreased level of interactions, depressed individuals showed an increase in the wall post rates and were active online during midday, which can be interpreted as an endemic behavior linked to the perceived degree of loneliness among young adults who are avid users of social media. We discuss these findings from theoretical, empirical, and subjective perspectives.