In this exploratory study we created a digital twin of a Japanese kindergarten and placed it in a metaverse platform, proposing a new interaction paradigm called Parallel-Twin-Worlds where an extended digital twin of the physical environment is created in virtual reality. Seven 5-6-year-old children participated in a treasure hunt, where they first conducted a task in the metaverse environment and then conducted the same task in their kindergarten. Our findings show that children quite effortlessly understood the link between the virtual and real worlds. We assume that this is due to recreating the environment as a digital twin, where the visual correspondence with reality made it easier for users to understand the correlation without the need for special lectures. We propose using the Parallel-Twin-Worlds paradigm for teaching the digital twin concept to children.

Previous research has shown that workplace social norms influence employee well-being. However, such norms vary based on the cultures in which workplaces are embedded, suggesting that cultural differences may influence perceived norms about when and where work should occur. These differences, in turn, could impact employee well-being. Accordingly, through the lenses of cultural tightness-looseness and individualism-collectivism, this paper investigates cultural differences in perceived social norms, and the relationship between those norms and hybrid workers’ well-being. We conducted a survey of 1,000 Japanese and 1,000 American hybrid workers. Results indicated that American respondents perceived stronger norms and demonstrated a higher willingness to conform to norms compared to Japanese respondents. Additionally, strong injunctive norms were positively associated with well-being among Americans but not among Japanese. Interviews (N = 24) showed that Japanese perceived injunctive norms negatively, while Americans saw them positively. We discuss implications for future remote-collaboration technologies in hybrid-work settings.

Previous studies have suggested that organizational social norms can positively affect employee well-being. However, such social norms have not been well developed during the post-COVID-19 transition to hybrid work, which combines office and remote work, and it is unclear how employees’ perceptions of social norms for hybrid work affect their well-being. In this study, we investigated the impact of social norms for hybrid work on the well-being of hybrid workers living in Japan through a mixed-method approach consisting of an online survey (n = 212) and semi-structured interviews (n = 20). The results indicate that hybrid workers who feel subject to strong social norms have lower well-being. Conversely, those who are more willing to conform to social norms have higher well-being. Given our findings, we discuss implications for the design of systems to help hybrid workers conform to organizational social norms and to improve their well-being.

A novel model is proposed to predict interlocutors’ subjective impressions during group meetings based on their head movements. The goal is to explore the potential of the communicative functions performed through head movements. To this end, this paper focuses on ten frequent functions, such as speaker emphasis and listener back-channel responses, which are detected using convolutional neural networks (CNNs) from head-pose sequences. Regarding the prediction target, this study employs four items of subjective impressions—atmosphere, enjoyment, willingness, and concentration—which are scored at every two-minute interval by the interlocutors themselves in four-party meetings with 17 groups. From the detected head-movement functions, this paper newly defines the functional features, including function occurrence rates and composition ratios, in addition to the kinetic features representing head-movement activity. Using these features as the input, random forest regressors predict the impression scores. Compared with the baseline model using only kinetic features, the prediction model using both kinetic and functional features improved prediction performance. The percentage of moderate or higher correlations (≥ 0.5) between reported scores and predictions increased from 25% to 41% in all groups and all items. These results suggest that head-movement functions could be effective cues for predicting interlocutors’ subjective impressions.

With the widespread use of fitness trackers, predicting the future of individuals’ health is becoming easier. However, little is known about how presenting a prediction of an individual's future impacts one's behavior. In this study, we targeted walking behavior and aimed to clarify the impact of presenting a prediction of the number of steps on one's behavior. We conducted a five-week experiment with 36 participants using the application “StepUp Forecast”, which presents prediction on the basis of past lifelogs. We found that self-efficacy and the number of steps increased significantly when the predictions were presented compared with when only records of steps were shown. This was because people were motivated to exceed the predicted value. Furthermore, when additional steps were presented along with the step prediction, neither self-efficacy nor the number of steps increased. Our findings suggest that the prediction should be an achievable value in which the user can exceed, and that a positive feedback loop could be possible by enhancing self-efficacy through the experience of achievement.

Clearly picturing user behavior is one of the key requirements when designing successful interactive software. However, covering all possible user behaviors with one UI is a complex challenge. The Scenario-based Interactive UI Design tool is designed to support the characterization of user behavior based on scenarios and then using the information in UI design. Scenarios make it easy to understand and share user behavior even if we have little design knowledge. However, they have two big weaknesses; 1) integrating several scenarios in one UI is difficult, even if we can create appropriate scenarios, 2) maintaining the links between scenarios and the UI is a heavy task in iterative design. Our tool solves the above problems through its hierarchical scenario structure and visualized overview of scenarios. It enhances the designer's skill in writing scenarios and designing UIs smoothly and easily.

The authors employ behavioral theories of human motivation and affect and present an explanation for why some CSCW experience is satisfying and engaging for a user. In a longitudinal experiment, participants were divided into four groups and solved two open-ended problems together using a video-conference system. Traditional metrics of usability and product acceptance were examined with respect to psychological variables such as personality, background knowledge, mindsets (i.e., implicit beliefs) and feelings toward group members. The results show that group-level mutual affect and implicit beliefs on one's ability (e.g., whether intelligence is fixed or malleable) are strong predictors of system usability and acceptability judgments. It is proposed that evaluating one's experience with a CSCW system is a meaning-making process and that the variables that modulate this process also influence subjective judgments of usability and acceptability of a complex collaborative system.