Institutional structure and governance capability in universities: an empirical study from the perspectives of time, space, and quantity dimensions

China’s expanding higher education sector faces financial constraints, demographic shifts, and innovation pressures, increasing the salience of effective governance and adaptive institutional structures. Governance capability refers to an institution’s ability to make and implement strategic decisions, allocate resources efficiently, and respond to stakeholders. Prior governance models in Chinese universities range from hierarchical to decentralized, but their current effectiveness is debated. This study advances a three-dimensional lens—time (temporal adaptability and path evolution), space (context-specific governance aligned to political-cultural environments), and quantity (scale, resources, and stakeholder scope)—and asks: can institutional arrangements influence governance capacity? Which layouts across these dimensions enhance capability? Using a new survey instrument and high-dimensional fixed-effects analyses, the study evaluates how institutional layouts and governance element configurations shape governance capacity and explores non-linear (threshold) effects of member tenure.

Drawing on institutional theory, governance structures reflect sectoral norms and historical trajectories; resource dependency theory highlights external resource influence on autonomy and strategic choices; and stakeholder theory underscores balancing competing interests among students, faculty, government, and industry. Empirical literature shows shifts from collegial to entrepreneurial/corporatized models, and cross-national spatial differences in governance complexity. Quantity-related studies connect institutional scale with bureaucratic complexity and resource coordination challenges. Domestic discourse often centers on reducing unit counts (streamlining) or power rebalancing (decentralization), which have mixed outcomes (e.g., mergers without functional change; strengthened academic power but weakened coordination). This study reframes the issue through governance elements—policies, space, funding, staffing, titles, incentives—across three dimensions: time (timeliness), space (mobility/discreteness), and quantity (scarcity), linking them to capabilities of recognition-grasping, convergence-integration, and exploration-extension.

Design: Developed a custom “University Governance Capacity Questionnaire” aligning with three dimensions (time, space, quantity) of governance elements. Used a 7-point Likert scale. Initial 40 items refined to 32 items after expert review and psychometric screening. Time dimension: 10 items (e.g., “My department can promptly follow up on directives from higher authorities”). Space dimension: 10 items (e.g., “There are no issues of shirking responsibility or shifting blame among departments when advancing reforms”). Quantity dimension: 8 items (e.g., frequent introduction of new practices/resources). Criterion tool: 4 single overall assessment items measuring responsiveness, coordination, expansiveness, and institutional alignment; maximal rotation showed a common factor (communalities 0.709–0.8133; loadings 0.842–0.901) explaining 77.884% variance. Sampling and data collection: Faculty and staff from higher education institutions across 30 provinces/autonomous regions/municipalities (excluding Qinghai) in China. Multi-wave data collection to mitigate common method bias: June, September, October, November 2022, and January 2023 via an online platform. 1,491 responses collected; after quality controls (completion time <3 minutes, straight-lining, patterned answers), 742 valid cases (effective rate 49.77%). Sample covers diverse titles, administrative levels, roles, and university types (comprehensive, S&T, normal, agriculture/forestry, language, sports, medical, finance, law/politics, art, ethnic, others) and affiliations (Ministry of Education, other ministries, provincial/municipal, local municipal). Measures and descriptives: Overall governance capacity (mean 3.1043, SD 0.9918, range 0–6). Time layout (mean 3.9394, SD 0.9713, range 0.6–6). Space layout (mean 3.9918, SD 1.2175, range 0.7–6.375). Quantity layout (mean 3.7370, SD 0.9358, range 0.875–7). Work experience in years (mean 12.2251, SD 9.6312, range 1–42). Administrative position (coded 4–7). Professional titles (3–5). Reliability and validity: Cronbach’s alpha for time 0.852, space 0.898, quantity 0.867, overall assessment 0.905. Split-half alphas: 0.860, 0.858, 0.822, 0.905. McDonald’s omega: 0.917, 0.915, 0.810, 0.934. Factor loadings >0.7; AVE >0.5; CR >0.8. Pearson correlations: factor-total 0.726–0.841; inter-factor 0.602–0.724. AVE square roots: time 0.748, space 0.724, quantity 0.635, overall 0.841. Harman’s single-factor: 8 factors; largest 36.738% (<40%); CFA four-factor fit better than single-factor (χ²/df=1.982, TLI=0.929, CFI=0.932, IFI=0.906, RMSEA=0.089 vs. χ²/df=10.394, TLI=0.577, CFI=0.608, IFI=0.609, RMSEA=0.158), indicating limited common method bias. Models: Baseline high-dimensional fixed-effects regression: Y_iuds = β0 + β1X_iuds + β2Controls_iuds + α_u + λ_d + γ_s + δ_t + ε_iuds, where Y is governance capacity; X is institutional layout (time, space, quantity); controls: work years, administrative position, professional title; fixed effects by university type, department, and identity. Multicollinearity check: mean VIF=1.97 (<10). Threshold regression (Hansen, 1999, 2000): single-threshold models with work experience as threshold q, indicator functions for regimes; significant thresholds identified for each dimension (time 7 years; space 3 years; quantity 21 years). Mechanism analysis: interaction terms between layout dimensions and single-item factors (responsiveness, coordination, expansion, institutional conformance) to test moderating channels. Ethics: Approved by relevant ethics committees (HNU-2024030). Informed consent obtained.

- Institutional layout significantly improves governance capability across all three dimensions. - Time dimension: Strong positive effects at 1% level (coefficients ≈ 0.8283 and 0.8190). Emphasizes timely information capture, trend foresight, and policy evaluation/correction. - Space dimension: Positive effects at 1% level (coefficients ≈ 0.5801 and 0.5686). Enhancing element mobility and reducing discreteness (e.g., via interdisciplinary centers) boosts aggregation-integration. - Quantity dimension: Positive effects at 1% level (coefficients ≈ 0.7693 and 0.7560). Transforming and expanding resources to overcome scarcity enhances exploration-extension capability. - Controls: Longer tenure and higher professional titles associate with more critical assessments (negative covariate effects). Administrative position effects vary: limited in time dimension; positive in space and quantity (some at 10% significance), reflecting resource mobilization capacity at higher ranks. - Mechanisms: Responsiveness, coordination, expansion, and organizational conformance significantly moderate the effects of institutional layouts across dimensions, providing channels through which layouts translate into improved governance. - Threshold effects (years of work experience): • Time: single threshold at 7 years. <7 years shows high motivation and strong positive effects; >7 years shows diminishing enthusiasm and reduced gains. • Space: threshold at 3 years. <3 years lack experience to integrate elements; >3 years network/experience enable effective transfer/linkage. • Quantity: threshold at 21 years. <21 years active integration and development of resources; >21 years path dependence and reduced innovation (inverted U-shape). - Heterogeneity: Effects vary by institutional affiliation (Ministry of Education-affiliated, other ministries, provincial/municipal, state/municipal). Generally, non-MoE-affiliated and local institutions benefit substantially from optimized layouts, with administrative positions aiding resource integration. - Psychometrics: High reliability (alphas ≥0.852; omegas ≥0.810), adequate validity (AVE square roots time 0.748, space 0.724, quantity 0.635; overall 0.841). Limited common method bias. - Multicollinearity: Not a concern (mean VIF 1.97).

The findings affirm that governance capability is shaped by how institutions configure governance elements over time, across spaces, and in quantities. Time-sensitive structures enable rapid recognition and seizing of opportunities (recognition–grasping), spatial designs that enhance element mobility and reduce fragmentation foster synergy (convergence–integration), and quantitative strategies that transform and expand resources overcome scarcity (exploration–extension). Moderating pathways through responsiveness, coordination, expansion, and organizational conformance show that institutional layout works by improving systemwide dynamism and coherence. Threshold results explain mixed perceptions by tenure: early-career staff drive timeliness; mid-career experience supports spatial integration; very long tenure risks path dependence in resource development. Heterogeneity by affiliation suggests context-specific strategies: institutions with fewer preferential policies may rely more on administrative networks to mobilize elements. Overall, aligning institutional layouts with the dynamics of governance elements enhances efficacy and addresses practical governance problems beyond simple unit counts or power redistribution.

This study proposes and empirically validates a three-dimensional framework (time, space, quantity) linking institutional layout to university governance capability. Using a novel scale and high-dimensional fixed-effects models on a broad national sample (N=742), it demonstrates that strategic layouts significantly enhance governance, with mechanisms operating through responsiveness, coordination, expansion, and organizational conformance, and with notable threshold effects by tenure. Contributions include: (1) reframing institutional arrangements via governance elements; (2) developing a reliable measurement instrument; (3) identifying non-linear tenure thresholds; and (4) showing affiliation-based heterogeneity. Policy implications: streamline layers to boost timeliness and responsiveness; reduce spatial barriers and departmentalism to promote integration; maintain openness and innovation to expand resources and avoid idleness. Encourage training, rotation, and incentives to manage tenure-related motivation and build networks for element flow. Future research: extend beyond China to test generalizability, incorporate external drivers (policy shifts, market demands, political-economic contexts), and examine technology’s role in governance element flow and integration.

- Geographic scope limited to Chinese universities, constraining generalizability. - Primary reliance on self-reported surveys may omit external influences (policy, market, political-economic factors) and introduce perception bias despite multi-wave design. - Some discriminant validity between subdimensions is average (AVE square roots for time, space, quantity lower than some inter-factor correlations). - Cross-sectional panel of waves limits causal inference; future longitudinal and mixed-methods designs could strengthen claims.