Faculty governance in higher education depends on transparent participation, reliable quorum enforcement, accountable record keeping, and strict alignment with institutional regulations. Conventional departmental council processes provide formal authority and academic deliberation, but they often rely on manual documentation, fragmented records, and procedural enforcement that is difficult to verify after the fact. This work presents an integrated hybrid Decentralized Autonomous Organization (DAO) framework for faculty governance that combines regulatory alignment analysis, a working smart-contract prototype, and scenario-based simulation. The framework is designed for university departmental councils and is structured across three layers: off-chain community governance, on-chain protocol governance, and off-chain execution governance. It expands prior conceptual work by incorporating governance dimensions related to roles, incentives, membership, communication, decision-making, identity, auditability, conflict-of-interest handling, and institutional ratification. The evaluation simulates 1488 proposals across twelve scenarios covering four faculty sizes (15, 30, 50, and 100 members) and three adoption levels (low, moderate, and high). Scenario results indicate that adoption intensity is the dominant driver of governance performance: mean participation increases from about 33% under low usage to about 85% under high usage, quorum achievement rises from about 6% to about 96%, and execution rises from about 19% to about 70%. Relative to a modeled conventional workflow baseline, the DAO-supported process reduces decision-cycle time by about 76%, improves audit completeness by about 30%, and increases traceability from about 0.63 to 1.00. The results indicate that DAO-assisted faculty governance can strengthen transparency, procedural consistency, and auditability while preserving legally mandated university authority, but its practical value depends on sustained participation, privacy safeguards, cost control, and clearly defined hybrid control points.