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This paper proposes interaction analysis as an alternative scoring procedure in assessment centers (ACs). Interaction analysis allows for a more fine-grained scoring approach by which candidate behaviors are captured as they actually happen, thus avoiding judgment errors typically associated with traditional scoring procedures. We describe interaction analysis and explain how this procedure can improve the validity of ACs. In a short research example, we showcase how interaction analysis can be implemented in AC settings. Finally, we integrate our arguments in terms of three key propositions which we hope will inspire future research on more dynamic scoring procedures.

The metaverse offers new technological possibilities for conducting 3D immersive meetings with head-mounted displays that can enrich virtual teamwork. To conceptualize this new interaction space, we synthesize interdisciplinary findings from human-computer interaction literature, group research, and meeting science. We develop a conceptual framework of 3D immersive group meetings that integrates technological design characteristics, subjective attendee experiences, mediating mechanisms, and meeting outcomes. As a first empirical glimpse into this framework, we include a pilot study of group member's self-reported experiences and observed group dynamics in the metaverse. Building on our framework and first empirical insights, we discuss implications for future investigations of group dynamics in the metaverse. [ABSTRACT FROM AUTHOR]

Informal interactions are vital in daily workplace communication, shaping performance, well-being, collaboration, and overall organizational functioning. However, as the ongoing trends of remote and hybrid work present significant changes and challenges to these interactions, supporting remote informal communication (RIC) becomes urgent. To address this pressing issue, we adopt a socio-technical systems perspective to gain in-depth insights into the enabling and constraining factors related to RIC. Our findings of semi-structured interviews with working adults (N=33) reveal important changes and challenges related to informal communication in the remote work context. We identify eleven interrelated socio-contextual, intrapersonal, and technological factors, culminating in a comprehensive conceptual framework of RIC. Based on our findings, we provide practical recommendations to effectively facilitate RIC in organizations and discuss future research avenues for understanding effective RIC in organizations. [ABSTRACT FROM AUTHOR]

Emergent states are temporal group phenomena that arise from collective affective, behavioral, and cognitive processes shared among the group's members during their interactions. Cohesion is one such state, mainly conceptualized by scholars as affective in nature, and frequently distinguished into the two dimensions social and task cohesion. Whereas social cohesion is related to the need of belonging to a group, task cohesion is related to the group's goals and tasks. In this article, we emphasize the importance of behavioral interaction dynamics to predict cohesion's dynamics. Drawing from Social Science insights, we investigate the interplay between social and task cohesion to predict their dynamics across group tasks from nonverbal behavioral features. Three computational architectures exploiting transfer learning are presented. Transfer learning capitalizes on information learnt by a model for a specific dimension to predict the dynamics of the other dimension. Results show that integrating the influence of social cohesion to predict dynamics of task cohesion outperforms state-of-the-art. To predict dynamics of social cohesion, a model integrating the reciprocal impact of social and task cohesion significantly improves performance with respect to the state-of-the-art and a model only integrating the impact of task cohesion on dynamics of social cohesion. [ABSTRACT FROM AUTHOR]

Social signal processing develops automated approaches to detect, analyze, and synthesize social signals in human–human as well as human–machine interactions by means of machine learning and sensor data processing. Most works analyze individual or dyadic behavior, while the analysis of group or team interactions remains limited. We present a case study of an interdisciplinary work process for social signal processing that can develop automatized measures of complex team interaction dynamics, using team task and social cohesion as an example. In a field sample of 25 real project team meetings, we obtained sensor data from cameras, microphones, and a smart ID badge measuring acceleration. We demonstrate how fine-grained behavioral expressions of task and social cohesion in team meetings can be extracted and processed from sensor data by capturing dyadic coordination patterns that are then aggregated to the team level. The extracted patterns act as proxies for behavioral synchrony and mimicry of speech and body behavior which map onto verbal expressions of task and social cohesion in the observed team meetings. We reflect on opportunities for future interdisciplinary or collaboration that can move beyond a simple producer–consumer model. [ABSTRACT FROM AUTHOR]

In the present research, we developed NegotiAct, a comprehensive coding scheme for negotiations, comprising 47 mutually exclusive behavioral codes. NegotiAct was derived by systematically integrating (i) 89 extant coding schemes for negotiations, (ii) pertinent findings from negotiation research, and (iii) specific interaction behaviors that were previously not considered in coding schemes for negotiations (e.g., active listening). To facilitate the application of NegotiAct, we designed a coding manual with precise instructions and with definitions and examples for every code. NegotiAct can be customized to address many research questions in experimental settings as well as field research by splitting codes into more specific behaviors. Thereby, differentiated codes can always be traced back to the original codes, preserving comparability across studies and facilitating cumulative research. In combination with interaction analytical methods, NegotiAct enables scholars to detect and investigate specific communication patterns across the negotiation process. As a first empirical validation of NegotiAct, we demonstrate a substantial interrater reliability for 18 videotaped negotiations (κ =.80) and conduct an exploratory validation analysis, studying the relation of multi-issue offers, active listening, and joint gains. [ABSTRACT FROM AUTHOR]

In this diary study, we consider shared leadership and team workload as antecedents of team mental health. We draw on conservation of resources theory to theorize how linear change trajectories of shared leadership are related to change trajectories in team members' shared well-being and emotional exhaustion. Furthermore, we investigate the interaction between change trajectories of shared leadership and team workload, predicting that change in shared leadership will be more strongly related to change in team mental health when team workload increases. 265 team members nested in 77 teams completed a daily diary survey over five consecutive workdays. As hypothesized, an increase in shared leadership was associated with an increase in team well-being and a decrease in emotional exhaustion over time. Further, shared leadership interacted with team workload, such that an increase in shared leadership was more strongly associated with a decrease in shared emotional exhaustion when team workload increased. However, team member well-being was not affected by such an interaction. These findings address the missing link between shared leadership and team well-being and exhaustion, establish shared leadership as an important team resource, and contribute a temporal perspective on shared leadership as a dynamic team phenomenon. [ABSTRACT FROM AUTHOR]

Insights into the behavioral profile of groups during meetings help us understand why some groups outperform others on meeting and work tasks. The presented studies investigate behavior-based group profiles in meetings and their relation to group performance. A total of 101 problem-solving meetings took place in two studies in a laboratory setting; data were coded using the act4teams coding scheme and analyzed using INTERACT software. The findings indicate there are four distinct group profile clusters: story-telling, well-organized networking, solution-focused, and problem-focused profiles. These behavior-based group profiles were meaningfully and differentially linked to group performance in the context of a meeting task. [ABSTRACT FROM AUTHOR]

To train machine learning algorithms to predict emotional expressions in terms of arousal and valence, annotated datasets are needed. However, as different people perceive others’ emotional expressions differently, their annotations are subjective. To account for this, annotations are typically collected from multiple annotators and averaged to obtain ground-truth labels. However, when exclusively trained on this averaged ground-truth, the model is agnostic to the inherent subjectivity in emotional expressions. In this work, we therefore propose an end-to-end Bayesian neural network capable of being trained on a distribution of annotations to also capture the subjectivity-based label uncertainty. Instead of a Gaussian, we model the annotation distribution using Student's $t$ t -distribution, which also accounts for the number of annotations available. We derive the corresponding Kullback-Leibler divergence loss and use it to train an estimator for the annotation distribution, from which the mean and uncertainty can be inferred. We validate the proposed method using two in-the-wild datasets. We show that the proposed $t$ t -distribution based approach achieves state-of-the-art uncertainty modeling results in speech emotion recognition, and also consistent results in cross-corpora evaluations. Furthermore, analyses reveal that the advantage of a $t$ t -distribution over a Gaussian grows with increasing inter-annotator correlation and a decreasing number of annotations available. [ABSTRACT FROM AUTHOR]