The Next Generation of Research on IS Use: A Theoretical Framework of Delegation to and from Agentic IS Artifacts

Information systems (IS) use, the dominant theoretical paradigm for explaining how users apply IS artifacts toward goal attainment, gives primacy to human agency in the user–IS artifact relationship. Models and theorizing in the IS use research stream tend to treat the IS artifact as a passive tool; lacking in the ability to initiate action and accept rights and responsibilities for achieving optimal outcomes under uncertainty. We argue that a new generation of “agentic” IS artifacts requires revisiting the human agency primacy assumption. Agentic IS artifacts are no longer passive tools waiting to be used, are no longer always subordinate to the human agent, and can now assume responsibility for tasks with ambiguous requirements and for seeking optimal outcomes under uncertainty. To move our theorizing forward, we introduce delegation, based on agent interaction theories, as a foundational and powerful lens through which to understand and explain the human– agentic IS artifact relationship. While delegation has always been central to human–IS artifact interactions, it has yet to be explicitly recognized in IS use theorizing. We explicitly theorize IS delegation by developing an IS delegation theoretical framework. This framework provides a scaffolding which can guide future IS delegation theorizing and focuses on the human–agentic IS artifact dyad as the elemental unit of analysis. The framework specifically reveals the importance of agent attributes relevant to delegation (endowments, preferences, and roles) as well as foundational mechanisms of delegation (appraisal, distribution, and coordination). Guidelines are proposed to demonstrate how this theoretical framework can be applied toward generation of testable models. We conclude by outlining a roadmap for mobilizing future research.

• 저널MIS Quarterly · 45(1) · 315–341
• 토픽Information Systems Theories and Implementation · Sociology and Political Science
• DOI10.25300/misq/2021/15882
• 저자Aaron Baird, Likoebe M. Maruping