INNOVATION-DRIVEN GOVERNANCE IN HIGHER EDUCATION INSTITUTIONS: A FRAMEWORK FOR ADAPTIVE DECISION-MAKING AND ORGANIZATIONAL PERFORMANCE
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innovation##common.commaListSeparator## governance##common.commaListSeparator## adaptivity##common.commaListSeparator## decision-making##common.commaListSeparator## performance##common.commaListSeparator## universities##common.commaListSeparator## autonomy##common.commaListSeparator## experimentation##common.commaListSeparator## simulation##common.commaListSeparator## UzbekistanAnnotatsiya
Higher education systems that expand and devolve at the same time face a governance paradox: institutions receive new decision rights just as ratings and performance-based funding make poor decisions more visible. This article develops and tests an innovation-driven governance framework for higher education institutions (HEIs) that links governance architecture, adaptive decision-making and organizational performance. Uzbekistan is used as a case of rapid transition: the number of HEIs grew from 69 in 2015/2016 to 222 in 2024/2025 and declined to 215 in 2025/2026, while enrolment reached 1.535 million. The design combined (1) a content analysis of five regulatory acts adopted in 2021–2026, coded against the four autonomy dimensions of the European University Association scorecard; (2) conceptual modelling of a three-layer framework comprising a decision-rights architecture, an adaptive decision loop and a performance system; and (3) a Monte Carlo experiment with 10,000 replications comparing hierarchical, data-informed and innovation-driven adaptive decision regimes. The regulatory analysis reveals “steered autonomy”: staffing and academic decision rights are largely devolved to financially independent HEIs, whereas executive appointments are centrally controlled and tuition fees are capped. In the simulation, datainformed selection raised the mean five-year performance gain by 42% relative to hierarchical decision-making and halved the share of harmful initiatives brought to full scale. Adaptive experimentation halved that share again and improved downside outcomes, but its expectedperformance advantage emerged only under higher volatility or longer horizons, and piloting every initiative reduced the gain by 21%. The findings support explicit decision rights, proportional experimentation and leading indicators as design principles for HEI governance.
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