Complex problem solving is challenging and a high-level cognitive process for individuals. When analyzing complex problem solving in teams, an additional, new dimension has to be considered, as teamwork processes increase the requirements already put on individual team members. After introducing an idealized teamwork process model, that complex problem solving teams pass through, and integrating the relevant teamwork skills for interdependently working teams into the model and combining it with the four kinds of team processes (transition, action, interpersonal and learning processes), the paper demonstrates the importance of fulfilling team process demands for successful complex problem solving within teams. Therefore, results from a controlled team study within complex situations are presented. The study focused on factors that influence action processes, like coordination, such as emergent states like collective orientation, cohesion and trust and that dynamically enable effective teamwork in complex situations. Before conducting the experiments, participants were divided by median split into two-person teams with either high (n=58) or low (n=58) collective orientation values. The study was conducted with the microworld C³Fire, simulating dynamic decision making and acting in complex situations within a teamwork context. The microworld includes interdependent tasks such as extinguishing forest fires or protecting houses. Two firefighting scenarios had been developed, which takes a maximum of 15 minutes each. All teams worked on these two scenarios. Coordination within the team and the resulting team performance were calculated based on a log-file analysis. The results show that no relationships between trust and action processes and team performance exist. Likewise, no relationships were found for cohesion. Only collective orientation of team members positively influences team performance in complex environments mediated by action processes such as coordination within the team. The results are discussed in relation to previous empirical findings and to learning processes within the team with a focus on feedback strategies.

As part of the Special Issue topic “Human-Centered AI at Work: Common Ground in Theories and Methods,” we present a perspective article that looks at human-AI teamwork from a team-centered AI perspective, i. e., we highlight important design aspects that the technology needs to fulfill in order to be accepted by humans and to be fully utilized in the role of a team member in teamwork. Drawing from the model of an idealized teamwork process, we discuss the teamwork requirements for successful human-AI teaming in interdependent and complex work domains, including e.g., responsiveness, situation awareness, and flexible decision-making. We emphasize the need for team-centered AI that aligns goals, communication, and decision making with humans, and outline the requirements for such team-centered AI from a technical perspective, such as cognitive competence, reinforcement learning, and semantic communication. In doing so, we highlight the challenges and open questions associated with its implementation that need to be solved in order to enable effective human-AI teaming.

The purpose of this study is to evaluate the effects of a tailor-made, non-technical skills seminar on medical student’s behaviour, attitudes, and performance during simulated patient treatment.

Purpose – The purpose of the present study is to introduce the elements characterising the work context of high responsibility teams (HRTs) operating in high reliability contexts such as medicine or aviation. Based on these elements, the authors reflected on the function of teamwork in these contexts, which is strongly dominated by a notion of flexibility under complexity, based on the technical, normative, and governance dimensions of teamwork. Design/methodology/approach – Problem-centred interviews (n=11) based on semi-structured guidelines were conducted. Subsequently, a survey was conducted using a questionnaire inventory in six different HRT work contexts (n=551). Findings – The interviews and survey results show significant differences regarding, for example, hierarchy or stress posed on the HRTs. However, they also demonstrate relevant similarities regarding, for instance, dimensions of complexity occurring in the teamwork contexts. Both differences and similarities influence how the support systems of the teamwork dimensions should be set up. Research limitations/implications – The study provided an excellent overview of similar and differing characteristics of the work context of different HRTs. However, it represents six specific HRTs and might not be generalisable to teams in other high reliability organisations, such as in the energy sector. Practical implications – It is recommended that the characteristics of work contexts in HRTs should be taken into account in order to set up support systems of teamwork dimensions that enable teams to transfer the prevalent safety discourse into safety practice. Originality/value – The innovative approach, which combines qualitative and quantitative data, provided insights that can be used to support team functioning in the team's specific work context.

Purpose: Digital technologies comprehensively change work processes and working conditions. However, the use of digital technologies and the modes of collaboration between technologies and human workers differ in terms of specific work organization and automatization. Referring to the job demands-resources model (JD-R), this paper investigates job demands and resources from the workers' perspectives and develops a digital work typology according to dimensions of digitalization and forms of human–computer interaction (HCI).Design/methodology/approach: The authors conducted a qualitative-empirical study with 49 interviews in four German production and logistics organizations, emphasizing different job demands and job resources for five digital work types identified.Findings: The results indicate that job demands and resources are to be differentiated in relation to specific work contexts. In this sense, this paper presents an analysis of dimensions of technology use and the impact of technology use on working conditions through empirically analyzing job demands and resources in digital work settings.Originality/value: The contribution of this paper is to empirically analyze job demands and resources in digital work settings from the workers' perspectives and to develop a digital work typology based on the dimensions of digitalization and form of HCI. This typology can set the basis for further research insights as well as management practice measures in human resources management (HRM).