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University sports-management platforms are perceived as most beneficial when system quality and ease of use align: a survey of 1,887 Chinese users finds six experience profiles and the highest ratings for high-quality, easy-to-use systems; results describe associations in perceptions, not causal effects.

Linking digital teaching management platform quality to perceived net benefits: a hybrid SEM–LPA study in university sports club-based teaching
Juntong Liu, Zhihui Zhang · August 05, 2026 · Frontiers in Psychology
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In a cross-sectional survey of 1,887 university sports-management platform users in China, higher perceived system quality and ease of use were associated with greater perceived usefulness, satisfaction, and teaching-management net benefits, and six distinct SQ–PEOU experience profiles were identified with high-SQ/high-PEOU reporting the most favorable evaluations.

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Introduction Digital teaching management platforms in university sports club-based teaching coordinate course selection, club allocation, venue use, attendance, assessment, and administrative communication. This study integrated the information systems success model and the technology acceptance model to examine system quality (SQ), perceived ease of use (PEOU), perceived usefulness (PU), user satisfaction (SAT), and perceived teaching management net benefits (NB). Methods Data from 1,887 adult platform users were collected in a cross-sectional survey. Structural equation modeling (SEM) estimated sample-wide associations, and latent profile analysis (LPA) identified SQ-PEOU experience configurations. BCH procedures compared PU, SAT, and NB across the profiles. Results SEM showed that SQ was positively associated with NB and that PEOU, PU, and SAT formed a significant sequential indirect association. LPA identified six configurations: low-SQ/low-PEOU, moderate-SQ/high-PEOU, low-SQ/high-PEOU, moderate-SQ/low-PEOU, high-SQ/low-PEOU, and high-SQ/high-PEOU. BCH comparisons showed significant differences in PU, SAT, and NB, with the high-SQ/high-PEOU configuration reporting the most favorable evaluations. Profile membership was not significantly associated with role in platform use, years of platform use, frequency of platform use, or main platform function used. Discussion Average statistical associations coexisted with heterogeneous SQ-PEOU experience configurations. Because the data were cross-sectional and self-reported, the findings should be interpreted as associations rather than causal processes.

Summary

Main Finding

System quality and perceived ease of use jointly shape users’ evaluations of a university sports club teaching management platform: higher system quality predicts greater perceived net benefits both directly and indirectly via a sequential path (PEOU → perceived usefulness → satisfaction), and six distinct SQ–PEOU experience profiles exist — the high-SQ/high-PEOU profile reports the best usefulness, satisfaction, and perceived net benefits.

Key Points

  • Study: Liu J and Zhang Z (2026). Linking digital teaching management platform quality to perceived net benefits: a hybrid SEM–LPA study in university sports club-based teaching. Frontiers in Psychology. DOI: 10.3389/fpsyg.2026.1903344.
  • Theoretical integration: combined DeLone & McLean information systems success model (system quality, satisfaction, net benefits) with Technology Acceptance Model (perceived ease of use [PEOU], perceived usefulness [PU]).
  • Hypotheses tested (main results):
    • H1: System quality (SQ) positively associated with perceived net benefits (NB) — supported.
    • H2: SQ positively associated with PEOU — supported.
    • H3: PEOU positively associated with PU — supported.
    • H4: PU positively associated with satisfaction (SAT) — supported.
    • H5 & H6: PU and SAT positively associated with NB — supported.
    • H7: Sequential indirect path SQ → PEOU → PU → SAT → NB was significant (interpreted as a cross-sectional association).
  • Person-centered analysis (LPA) found six SQ–PEOU configurations:
  • Low-SQ / Low-PEOU
  • Moderate-SQ / High-PEOU
  • Low-SQ / High-PEOU
  • Moderate-SQ / Low-PEOU
  • High-SQ / Low-PEOU
  • High-SQ / High-PEOU
    • High-SQ/High-PEOU reported the highest PU, SAT, and NB.
    • Profile membership was not meaningfully associated with users’ role, years of use, frequency of use, or main platform function.
  • Limitations: cross-sectional, self-reported measures; associations should not be read as causal; sample limited to adult administrative/teaching users in Sichuan province, China.

Data & Methods

  • Sample: N = 1,887 adult users (physical education teachers, club coaches/instructors, administrators, platform admins) from 15 universities in Sichuan Province, China.
  • Data collection: online cross-sectional survey (Wenjuanxing), Jan–Feb 2026; ethical approval obtained.
  • Key constructs (self-report scales): system quality (SQ), perceived ease of use (PEOU), perceived usefulness (PU), user satisfaction (SAT), perceived teaching-management net benefits (NB).
  • Analytic approach:
    • Structural Equation Modeling (SEM) to test sample‑wide associations among SQ, PEOU, PU, SAT, and NB.
    • Latent Profile Analysis (LPA) using SQ and PEOU as profile indicators to identify subgroups of platform experience.
    • BCH procedures to compare distal outcomes (PU, SAT, NB) across the latent profiles.
  • Robustness: combined variable-centered (SEM) and person-centered (LPA) strategy to reveal both average associations and heterogeneity in user experience.

Implications for AI Economics

  • Complementarity of functionality and usability matters for perceived value:
    • Investments in platform functionality (features, integrations, reliability) generate larger perceived returns when accompanied by usability (low effort to use). From an AI-economics standpoint, returns to upgrading AI-driven features (automation, recommendation, optimization) will be limited if user-facing usability is poor.
  • Heterogeneous user experience implies heterogeneous returns:
    • Six distinct SQ–PEOU profiles suggest that average estimates of platform value mask important variation. Economic evaluations (cost–benefit, ROI) and pricing strategies should account for user segments (e.g., “high-high” adopters vs. “high-SQ/low-PEOU” who may under-utilize advanced features).
  • Adoption and scaling policies should target both supply- and demand-side frictions:
    • Technical improvements alone (e.g., deploying new AI modules) may not raise perceived net benefits without parallel investments in UX, training, and workflows. Subsidies or procurement criteria should weigh ease-of-use metrics alongside functional capability.
  • Measurement and evaluation recommendations for AI-enabled education platforms:
    • Combine SEM-like average-effect estimation with person-centered methods (LPA) to capture heterogeneity in realized economic gains and inform targeted interventions.
    • Move beyond perceptions: future economic analyses should integrate objective performance metrics (time saved, scheduling conflicts resolved, resource utilization), administrative cost reductions, and downstream outcomes to estimate true economic returns.
  • Market design and pricing:
    • Vendors could adopt tiered offerings or modular upgrades that pair advanced AI features with onboarding/usability services; willingness-to-pay will likely vary by profile.
  • Labor and organizational impacts:
    • Platforms that are functionally strong but not easy to use may create hidden labor costs (more staff time, workarounds), reducing net productivity gains. Evaluations of AI automation should include these interaction costs.
  • Policy and regulation:
    • Procurement and evaluation frameworks for educational AI should include standardized measures of system quality and ease of use, along with heterogeneity analysis, to avoid over-investing in functionality with low marginal benefit.
  • Research directions for AI economics:
    • Longitudinal or experimental studies to estimate causal effects of AI-enabled features on objective management outcomes and economic returns.
    • Cost-effectiveness analysis comparing investments in AI capabilities versus usability/training.
    • Demand-side heterogeneity studies to model diffusion, pricing, and investment thresholds across user segments.
    • Examination of externalities (data privacy, labor displacement, equity of access) and their economic implications in institutional procurement of AI education systems.

If you want, I can draft an outline for a follow-up empirical study to estimate causal economic returns (with suggested outcome measures, experimental designs, and cost metrics) or translate these implications into procurement/pricing principles for AI-enabled educational platforms.

Assessment

Paper Typecorrelational Evidence Strengthmedium — Large sample (n=1,887) and appropriate multivariate methods (SEM, LPA, BCH) support internal consistency of associations, but evidence is limited by cross-sectional, self-reported data, single-source measurement (common-method bias), and absence of objective performance or longitudinal outcomes. Methods Rigormedium — Methods are appropriate for the research questions (validated theoretical models, SEM for pathway estimation, LPA for heterogeneity, BCH for distal comparisons). However, reliance on cross-sectional self-report measures, lack of experimental or quasi-experimental identification, potential measurement and selection biases, and limited control for confounders reduce causal interpretability and overall rigor. SampleCross-sectional online survey of 1,887 adult users (physical education teachers, sports club instructors/coaches, teaching administrators, academic affairs administrators, platform administrators, and other staff) from 15 universities in Sichuan Province, China; data collected Jan–Feb 2026 via Wenjuanxing; measures were self-reported perceptions of system quality, perceived ease of use, perceived usefulness, satisfaction, and perceived teaching-management net benefits. Themesadoption org_design IdentificationCross-sectional observational associations estimated using structural equation modeling (SEM) and latent profile analysis (LPA); no causal identification or longitudinal design (authors explicitly treat findings as associations). GeneralizabilityGeographic: sample limited to Sichuan Province, China — may not generalize to other regions or countries., Sector-specific: focused on university sports club-based teaching; findings may not transfer to general e-learning systems or other educational domains., User-population: excludes students and focuses on adult staff roles, limiting applicability to learner outcomes or broader stakeholder perspectives., Measurement: relies on self-reported perceptions rather than objective management or productivity metrics., Design: cross-sectional data precludes causal or temporal generalization., Platform heterogeneity: results may depend on specific platform implementations and China’s national digital education context.

Notes