Susannah B. F. Paletz

In complex domains such as intelligence analysis and healthcare, teams often need to continuously work toward long-term goals. These scenarios involve sequential work, with teammates performing their roles in shifts. Existing teamwork research acknowledges the myriad factors that influence how information is cognitively processed and collaboratively deliberated to arrive at a shared decision. Yet, much of this research describes synchronous and/or reciprocal interdependence rather than sequential interdependence. This paper presents a descriptive model of collective judgment formation (CJF) in sequential teams. CJF involves the development of judgments by drawing on information from diverse sources, including both humans and AI, such that a mosaic comes into form even as pieces appear and others change shape. CJF also often entails situations with an overwhelming variety, volume, and velocity of information. Our model can be applied to numerous sequential scenarios that incorporate shift handovers and processing data into intelligence.

Teams are increasingly the locus of our work and leisure, with the study of such groups still important for human flourishing. This essay focuses on four aspects of studying team diversity: topics (e.g., language, human-AI teams), theory, multidisciplinary approaches, and funding/conducting such research. Not only are different types of heterogeneity fruitful to study, but the next decade could see a diversity of theories, approaches, and ways to support such research.

Team meetings are a key part of professional life which are experienced as sociotechnical, interpersonal phenomena. This interview study examines the intersection of modality, disability, and technology in the team meeting setting. Persistent concerns emerged across meeting modalities: participation as a sociotechnical challenge; social/teamwork considerations; stigma, disclosure, and expectations regarding professionalism; a lack of information regarding meetings; productivity problems; and dueling accommodations between disabled individuals. These themes represent challenges about taskwork, teamwork and social perception, and sociotechnical aspects. Findings suggest that flexibility to choose the modality of attendance based on individual context, along with flexibility to choose in-meeting supports, can help with dynamic needs. The accessibility interventions most desired by participants are both readily available and benefit all workers regardless of disability status, but require simultaneous infrastructural, technological, and process interventions to address.

Color theory plays a crucial role in design, technology, and art. This study examined how the color groups, warm and cool, affect individuals’ emotional and physiological states as measured using the Positive and Negative Affect Schedule and heart rate monitoring. Twenty participants viewed their assigned color simulation for 10 min within a virtual reality (VR) headset. We found a significant decrease in heart rate before, during, and after VR exposure. The interaction between time and the color condition was significant, such that the decrease in heart rate was steeper for those in the cool conditions. There were no statistically significant differences in self-reported positive affect; however, there was a significant decrease in self-reported Negative Affect for both the Warm and Cool groups after the VR exposure. Understanding how colors affect users through the outcomes of this research can help designers and developers make more informed decisions in the domains of UI/UX and front-end application development.

Artificial intelligence (AI) is suggested as a support for virtual collaboration. We conducted a between-subjects experiment ( N = 151) to compare two AI summarizers using the Human-Agent Teaming on Intelligence Tasks (HATIT) platform. Participants reviewed study-created documents, with the HATIT AI summarizer providing either an informative or an indicative summary. Dispositional trust in AI influenced learned trust in the AI summarizer and greater trust developed in the informative condition. Compared to the indicative condition, participants in the informative condition also displayed some evidence of faster but less frequent attention to newly arrived teammate information. Problem-solving accuracy was comparable across conditions.

While emotional content predicts social media post sharing, competing theories of emotion imply different predictions about how emotional content will influence the virality of social media posts. We tested and compared these theoretical frameworks. Teams of annotators assessed more than 4000 multimedia posts from Polish and Lithuanian Facebook for more than 20 emotions. We found that, drawing on semantic space theory, modeling discrete emotions independently was superior to models examining valence (positive or negative), activation/arousal (high or low), or clusters of emotions and was on par with but had more explanatory power than a seven basic emotion model. Certain discrete emotions were associated with post sharing, including both positive and negative and relatively lower and higher activation/arousal emotions (e.g., amusement, cute/kama muta, anger, and sadness) even when controlling for number of followers, time up, topic, and Facebook angry reactions. These results provide key insights into better understanding of social media post virality.

The proper measurement of emotion is vital to understanding the relationship between emotional expression in social media and other factors, such as online

Intelligence analysts pore over myriad sources to identify key facts, make sense of them, and use that “sensemaking” to inform mission-critical action. Often these operations are conducted 24/7 with analysts working in shifts. To understand these processes in situ , we conducted interviews with seven former and current United States intelligence analysts. We develop theory and advance a descriptive model of shift handover processes in this domain, which takes into account sensemaking, the communication process, and the features of big data. Our qualitative analysis reveals the following tensions analysts face in shift handovers (1) narrowing versus broadening one’s own sensemaking, (2) sensemaking versus encoding for the subsequent analyst, and (3) trusting versus validating the prior analyst’s sensemaking. In addition, we describe several practical takeaways.