AI governance readiness as institutional capital: a capability-orchestration framework for student retention intentions in Kazakhstan's higher education

Assel Kenesovna Jumasseitova, Rajasekhara Mouly Potluri, Maira Khussainova

Frontiers in Education, 2026

Full text Open access: Gold Metadata (CSL-JSON)
DOI 10.3389/feduc.2026.2002812
Data confidence 70 medium
How this score is calculated

It is built from checkable properties of the record itself: whether the article has a DOI, how many independent sources confirmed its data, whether the required fields are filled in, and how recent the latest confirmation is. Disagreement between sources and a missing identifier lower it. A value of 80 or above means the record was published without further checking; 60 to 79 means it is published at reduced confidence, with a manual check advisable. A record verified by an editor by hand is never pushed below 90 by a recalculation. This scores our data about the article, not the article itself.

Abstract

This study reconceptualizes AI Governance Readiness (AIGR) as a form of institutional capital, representing an institution's capacity to establish, coordinate, and operationalize responsible AI governance arrangements in ways associated with student retention intentions through capability orchestration rather than technology adoption alone. Grounded in the Resource-Based View and Dynamic Capabilities Theory, the study develops and tests a structural model linking AIGR with Administrative Efficiency through AI (AEAI) and AI Fluency Orientation (AIFO), while examining Equity Perception (EPS) as a moderator. A quantitative cross-sectional survey of 897 respondents from public and private higher education institutions in Kazakhstan was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with 5,000 bootstrap resamples. The results substantiate six of the seven proposed relationships. AI Governance Readiness is positively associated with Administrative Efficiency through AI ( β = 0.591, p < 0.001) and AI Fluency Orientation ( β = 0.699, p < 0.001). AI Fluency Orientation ( β = 0.367, p < 0.001) and Administrative Efficiency through AI ( β = 0.404, p < 0.001) are positively associated with Student Retention Intentions. AI Fluency Orientation and Administrative Efficiency through AI significantly mediate the association between AI Governance Readiness and Student Retention Intentions, with indirect effects of β = 0.433 and β = 0.375, respectively. Equity Perception does not significantly moderate the Administrative Efficiency through AI–Student Retention Intentions relationship ( β = −0.011, p = 0.444). The findings position AI Governance Readiness as an institutional capability that coordinates governance arrangements, human competencies, and administrative processes to support student-centered outcomes while reinforcing fairness, transparency, accountability, and responsible AI use. The cross-sectional, geographically concentrated sample limits causal inference and generalizability, highlighting the need for longitudinal and comparative research.