The tool also provided reflective value. Participants reported that it helped articulate what matters to them and why. Beyond research settings, individuals can use the framework to audit which dimensions drive their own sense of ownership, select AI tools that respect those priorities (e.g., suggestion-only assistance for high-Control creators), and mediate collaboration by visualizing divergent ownership profiles when teammates disagree about contribution and credit.

For HCI, the immediate use is practical: report ownership as a profile rather than a single score, state construct boundaries, and use the dimensions as design levers (e.g., decision rights for Control, intent alignment for Intentionality, attribution for Recognition, modality-aware workflows for Production/Abstraction, and role clarity for Interdependence).

Methodologically, we recommend reporting an ownership profile rather than a single score and explicitly stating construct boundaries. A brief "ownership design card" in Methods—specifying manipulated versus measured dimensions, expected moderators (e.g., medium tangibility, employment context), and anticipated trade-offs—would improve interpretability and comparability.

A potential risk is profile drift under sustained high-automation use (e.g., declines in perceived Effort or Control). Because the framework is lightweight, it can function as a periodic check-in to track such changes and recommend countermeasures (e.g., adding decision checkpoints or narrowing automation scope).

The framework yields actionable implications for system design. Treating ownership as a first-class experience goal positions each dimension as a design lever. Control can be protected by making decision rights explicit, keeping suggestions reversible, and attaching rationales to consequential edits. Intentionality can be supported through periodic intent check-ins and visual diffs that surface drift from initial goals. Recognition benefits from attribution by default. Production and Abstraction suggest modality-aware workflows (concept-first versus material-first), and Interdependence calls for role visibility and decision traceability in collaborative tools. The aim is not to prescribe features but to make ownership designable: systems can be tuned to the ownership profile a context demands.