Collaborative intelligence and innovation in knowledge work

Knowledge work addresses complex problems whose solutions emerge through cycles of exploration, experimentation, and refinement. Increasingly, these problems are solved through collaboration between humans and technologies, with interactions that shift and adapt over time. This dissertation investigates how collaborative dynamics shape intelligence and innovation in knowledge work through the context of the digital production pipeline. Study 1 examines how knowledge workers interact with generative AI (GenAI) tools to create and evaluate solutions to complex problems. Focusing on the design and development phases, it uncovers a credibility–quality paradox in which GenAI outputs are often rated higher even when human solutions are more credible. Randomized experiments reveal four interaction patterns and show that solution quality peaks under effective Prompting-as-Process (PAP), a mechanism through which collaborative intelligence is cultivated. Study 2 asks how developers’ boundary-resource configurations, specifically, the choice between cross-platform SDKs (CPSDKs) and native platform SDKs (NPSDKs), affect app quality in multihoming contexts. Using mobile app data in a staggered difference-in-differences design, it demonstrates a cross-platform learning effect: multihoming enhances home-platform quality only under CPSDK use, with the strongest improvements when both home and target platforms adopt CPSDKs. These findings show that platform boundary-resource configurations govern the direction and magnitude of knowledge spillovers, advancing understanding of collaborative innovation. Taken together, this dissertation positions collaborative intelligence and innovation as dynamic, mutually reinforcing capabilities that accumulate through sustained engagement across the digital production pipeline. It enriches MIS scholarship by revealing how patterns of human–technology interaction transform technological potential into enduring performance gains, offering a foundation for future research on orchestrating human–technology collaboration that continually expands the capacity to think, learn, and innovate.