Generative large language models continue to redefine how organizations produce, verify, and utilize information resources. Existing governance arrangements assume that data assets are relatively stable and that system behavior is observable. This assumption no longer holds for outputs that are generated probabilistically, cannot be traced to a source, and are produced jointly by humans and machines. Therefore, this paper proposes an integrative framework in which actors, instruments, and mechanisms interlock. A generative model occupies multiple positions simultaneously within the governance relationship: It is subject to governance arrangements, it is employed as an instrument of governance, and it is a force capable of shaping decisions while remaining beyond accountability. Authority and responsibility are first redistributed among information professionals, business units, technology vendors, and algorithmic systems. Instruments are considered next, and three distinct types must not be conflated: technical devices that discipline inputs, means of governing via generative models, and arrangements that oversee the first two. At the level of mechanisms, structural, procedural, relational, and consequential designs are integrated. The primary challenge lies not in the capability of these systems, but in how long-standing weaknesses – namely ambiguous ownership, evidence that escapes review, and judgment displaced by procedure – are amplified to the point where they can no longer be ignored.
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Share and Cite
Xie, C. (2026) Research on the Collaborative Governance Framework for Organizational Information Resources in the Era of Generative Large Language Models: An Integrative Design of Actors, Instruments, and Mechanisms. Journal of Social Development and History, 2(4), 1-7. https://doi.org/10.71052/jsdh/WAAE3047