Collaboration Engineering Methodology: Horizontal Extension to Accommodate Project and Program Concerns

A Collaboration Engineering Methodology (CEM) comprises a set of defined, standardized, documented, and discoverable objectives, deliverables, key actions, tools/templates, principles and policies for establishing effective, efficient, satisfying collaborative work practices for high-value organizational tasks. First-generation CEMs address design and development CE solutions. Existing CEMs, though, focus on the design/build phase, but lack the pre-design and post-build elements that are common to methodologies for adjacent disciplines. We use Design Science Research to situate existing design/build CEMs in the larger context of CE programs and projects. We develop and validate an extended CEM in four phases: 1) Opportunity Assessment, 2) Development, 3) Deployment, and 4) Improvement (ODDI). Phase 1 concerns CE portfolio management and CE project planning; Phase 2 encapsulates existing design/build CEMs; Phase 3 concerns roll-out planning, change management, and implementation; Phase 4 concerns continuous optimization of a deployed work practice. The ODDI model advances CE another step towards becoming a fully realized professional practice, but more research is still required to derive a complete a design theory for CE

Machines as Teammates: A Collaboration Research Agenda

Humans will soon need to adapt to a collaborative setting in which technology becomes a smart collaboration partner that works with a group to achieve its goals. It is therefore time for collaboration researchers to explore the vast opportunities afforded by smart technology and to test its utility for enhancing team processes and outcomes. In this paper, we take a long view on the implications of smart technology for collaboration process design, and propose a research agenda for the next decade of collaboration research. We create a reference model to frame the research agenda

Machines as teammates: A research agenda on AI in team collaboration

What if artificial intelligence (AI) machines became teammates rather than tools? This paper reports on an international initiative by 65 collaboration scientists to develop a research agenda for exploring the potential risks and benefits of machines as teammates (MaT). They generated 819 research questions. A subteam of 12 converged them to a research agenda comprising three design areas – Machine artifact, Collaboration, and Institution – and 17 dualities – significant effects with the potential for benefit or harm. The MaT research agenda offers a structure and archetypal research questions to organize early thought and research in this new area of study