Organizations are increasingly looking for ways to reap the benefits of cognitive diversity for problem solving. A major unanswered question concerns the implications of cognitive diversity for longer-term outcomes such as team learning, with its broader effects on organizational learning and productivity. We study how cognitive style diversity in teams—or diversity in the way that team members encode, organize and process information—indirectly influences team learning through collective intelligence, or the general ability of a team to work together across a wide (...) array of tasks. Synthesizing several perspectives, we predict and find that cognitive style diversity has a curvilinear—inverted U-shaped—relationship with collective intelligence. Collective intelligence is further positively related to the rate at which teams learn, and is a mechanism guiding the indirect relationship between cognitive style diversity and team learning. We test the predictions in 98 teams using ten rounds of the minimum-effort tacit coordination game. Overall, this research advances our understanding of the implications of cognitive diversity for organizations and why some teams demonstrate high levels of team learning in dynamic situations while others do not. (shrink)

Management of effort is one of the biggest challenges in any team, and is particularly difficult in distributed teams, where behavior is relatively invisible to teammates. Awareness systems, which provide real-time visual feedback about team members’ behavior, may serve as an effective intervention tool for mitigating various sources of process-loss in teams, including team effort. However, most of the research on visualization tools has been focusing on team communication and learning, and their impact on team effort and consequently team performance (...) has been hardly studied. Furthermore, this line of research has rarely addressed the way visualization tool may interact with team composition, while comprehension of this interaction may facilitate a conceptualization of more effective interventions. In this article we review the research on feedback in distributed teams and integrate it with the research on awareness systems. Focusing on team effort, we examine the effect of an effort visualization tool on team performance in 72 geographically distributed virtual project teams. In addition, we test the moderating effect of team composition, specifically team members’ conscientiousness, on the effectiveness of the effort visualization tool. Our findings demonstrate that the effort visualization tool increases team effort and improves the performance in teams with a low proportion of highly conscientious members, but not in teams with a high proportion of highly conscientious members. We discuss the theoretical and practical implications of our findings, and suggest the need of future research to address the way technological advances may contribute to management and research of team processes. (shrink)

In recent years, we have experienced rapid development of advanced technology, machine learning, and artificial intelligence (AI), intended to interact with and augment the abilities of humans in practically every area of life. With the rapid growth of new capabilities, such as those enabled by generative AI (e.g., ChatGPT), AI is increasingly at the center of human communication and collaboration, resulting in a growing recognition of the need to understand how humans and AI can integrate their inputs in collaborative teams. (...) However, there are many unanswered questions regarding how human–AI collective intelligence will emerge and what the barriers might be. Truly integrated collaboration between humans and intelligent agents may result in a different way of working that looks nothing like what we know now, and it is important to keep the essential goal of human societal well-being and prosperity a priority. In this special issue, we begin to scope out the underpinnings of a socio-cognitive architecture for Collective HUman-MAchine INtelligence (COHUMAIN), which is the study of the capability of an integrated human and machine (i.e., intelligent technology) system to achieve goals in a wide range of environments. This topic consists of nine papers including a description of the conceptual foundation for a socio-cognitive architecture for COHUMAIN, empirical tests of some aspects of this architecture, research on proposed representations of intelligent agents that can jointly interact with humans, empirical tests of human–human and human–machine interactions, and philosophical and ethical issues to consider as we develop these systems. (shrink)

Artificial Intelligence (AI) powered machines are increasingly mediating our work and many of our managerial, economic, and cultural interactions. While technology enhances individual capability in many ways, how do we know that the sociotechnical system as a whole, consisting of a complex web of hundreds of human–machine interactions, is exhibiting collective intelligence? Research on human–machine interactions has been conducted within different disciplinary silos, resulting in social science models that underestimate technology and vice versa. Bringing together these different perspectives and methods (...) at this juncture is critical. To truly advance our understanding of this important and quickly evolving area, we need vehicles to help research connect across disciplinary boundaries.This paper advocates for establishing an interdisciplinary research domain—Collective Human-Machine Intelligence (COHUMAIN). It outlines a research agenda for a holistic approach to designing and developing the dynamics of sociotechnical systems. In illustrating the kind of approach, we envision in this domain, we describe recent work on a sociocognitive architecture, the transactive systems model of collective intelligence, that articulates the critical processes underlying the emergence and maintenance of collective intelligence and extend it to human–AI systems. We connect this with synergistic work on a compatible cognitive architecture, instance-based learning theory and apply it to the design of AI agents that collaborate with humans. We present this work as a call to researchers working on related questions to not only engage with our proposal but also develop their own sociocognitive architectures and unlock the real potential of human–machine intelligence. (shrink)

Dominant paradigms in science foster integration of research findings, but at what cost? Forcing convergence requires centralizing decision-making authority, and risks reducing the diversity of methods and contributors, both of which are essential for the breakthrough ideas that advance science.