Figure 1 - uploaded by Bo Hao Su
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A complete schematic of our Graph Interlocutor Acoustic Network (G-IAN). It applies modified attention mechanism controlled by group-level personality, and models the inter-group relationship of personality with a graph convolutional layer for the recognition task.
Citations
... The effects of conscientiousness and agreeableness are two-fold. On one hand, they contribute positively to the performance of tasks in stable environments [37]. On the other hand, these traits may impede individuals and organizations from engaging in creative activities and negatively affect their responses to dynamic and evolving environments [38,39]. ...
Formalistic tasks are widely utilized in modern companies due to their ability to increase productivity and contribute to the achievement of corporate goals at a lower cost. However, these tasks are often meet with resistance from individuals because they do not provide direct short-term rewards for their efforts. Drawing on social cognitive theory, this study examined the influence of individual quality and organizational attachment on the completion of formalistic tasks. To address this, the study conducted a questionnaire survey to collect data from 602 Chinese respondents and built a structural equation model for data analysis. Through empirical research, the study confirmed the positive role of individual quality, including knowledge and personality, in the completion of formalistic tasks. Furthermore, the study proved that avoidant attachment could significantly weaken the effect of some components of individual quality on formalistic task completion. This paper is the first to reveal the influence of individual and environmental factors on individuals’ completion of formalistic tasks, progressing from bottom to top. The implications of these results are discussed.
... This research has identified a variety of factors found to predict team-level task performance [29]. One prominent research stream focuses on evaluating social signals, such as proxemic and paralinguistic behavior, during team interactions [19,22,41]. However, few studies have investigated the association between different modes and social signals on performance. ...
Collaborative creativity is an essential part of modern teamwork and is often supported by formal techniques, such as design thinking. Current support tools are often limited in scope as understanding the time-varying nature and structure of team communication is insufficient. We investigate how collaborative creative activities in new product development teams can be digitally supported while maintaining face-to-face communication. This work analyzes to what extent paralinguistic and proxemic features of team interaction relate to performance in new product development teams and if and how this relationship differs for different stages in the design process. This is investigated by applying multilevel modeling on data collected during a four-week new product development cycle. The cycle was completed by four teams, during which data were collected automatically using sociometric badges that capture social signals of team interactions. In addition, the data are combined with survey-based measurements on the team’s daily design process and periodic performance evaluations. The current paper provides evidence that social signals are related to team performance and that this relationship varies across the stages in the product design process. Certain social signals contribute positively in one stage but less in other stages, showing the importance of using multimodal signals when modeling high-level collaborative patterns. This research contributes to the literature by providing a better understanding of relevant factors when designing supporting tools or methods for collaborative creative problem solving.
... Batch size is fixed as [16,32], the max epoch is 1000, and optimizer is ADAMAX [29]. Additionally, we follow [65,66], which are the closest studies to us, to use an unweighted average recall (UAR) as our final evaluation metric. Zhong et al. [65,66] modeled the group-level personality composition for group performance classification. ...
... Additionally, we follow [65,66], which are the closest studies to us, to use an unweighted average recall (UAR) as our final evaluation metric. Zhong et al. [65,66] modeled the group-level personality composition for group performance classification. Finally, the whole framework is implemented using the Pytorch toolkit [49]. ...
Physiological synchrony is a particular phenomenon of physiological responses during a face-face conversation. However, while many previous studies had proposed various physiological synchrony measures between interlocutors in dyadic conversations, there are very few works on computing physiological synchrony in small groups (three or more people). Besides, belongingness and satisfaction are two important factors for the human to decide which group they want to stay. Therefore, in this preliminary work, we want to investigate and reveal the relationship between physiological synchrony and belongingness/satisfaction under group conversation. We feed the physiology of group members into a designed learnable graph structure with the group-level physiological synchrony and heart-related features computed from Photoplethysmography (PPG) signals. We then devise a Group-modulated Attentive Bi-directional Long Short-Term Memory (GGA-BLSTM) model to recognize three-levels of belongingness and satisfaction (low, middle, and high) in groups. Finally, we evaluate the proposed method on our recently collected multimodal group interaction corpus (never published before), NTUBA, and the results show that (1) the models trained jointly with the group-level physiological synchrony and the conventional heart-related features consistently outperforms the model only trained with the conventional features, and (2) the proposed model with a Graph-structure Group-modulated Attention mechanism (GGA), GGA-BLSTM, performs better than the strong baseline model, the attentive BLSTM. Finally, the GGA-BLSTM achieves a promising unweighted average recall (UAR) of 73.3% and 82.1% on group satisfaction and belongingness classification tasks respectively. In further analyses, we reveal the relationships between physiological synchrony and group satisfaction/belongingness.
