Adoption of blended learning: Chinese university students' perspectives

The study examines university students’ adoption of blended learning in the post-COVID-19 context in China, where online and blended modes became widespread. It addresses two research questions: (RQ1) the extent to which an integrated conceptual adoption framework combining TAM and TPB can explain adoption, and (RQ2) which factors influence students’ intention to adopt blended learning (IABL). Blended learning is positioned as the “new normal,” offering flexibility and enhanced interaction, yet challenges remain regarding satisfaction and continued use. The study aims to develop and validate a model integrating TAM (perceived usefulness, perceived ease of use) and TPB (attitudes, subjective norms, perceived behavioral control) to explain IABL among Chinese university students.

The literature outlines blended learning as a combination of face-to-face and technology-mediated activities that can enhance interaction, flexibility, learning depth, and participation. In China, blended learning is considered a suitable approach due to large student populations and resource constraints. The Technology Acceptance Model (TAM) posits perceived usefulness (PU) and perceived ease of use (PEU) as key beliefs affecting attitudes and behavioral intention. The Theory of Planned Behavior (TPB) adds attitudes, subjective norms (SN), and perceived behavioral control (PBC) as determinants of intention and behavior. Prior work suggests integrating TAM and TPB improves explanatory power for technology adoption. Based on this, the study proposes an integrated framework with the following hypotheses: H1: PU → LA (+); H2: PU → IABL (+); H3: PEU → LA (+); H4: PEU → PU (+); H5: PEU → IABL (+); H6: LA → IABL (+); H7: SN → LA (+); H8: SN → PBC (+); H9: SN → IABL (+); H10: PBC → IABL (+); H11: LA mediates PU → IABL; H12: PU mediates PEU → IABL.

Design: Quantitative, cross-sectional survey with structural equation modeling (SEM) to validate an integrated TAM–TPB model explaining intention to adopt blended learning (IABL). Sampling and participants: Convenience sampling of university students across mainland China who had taken at least one blended course. Data were collected Feb–Apr 2022 via paper/online questionnaires circulated through instructors. Of 233 responses, 201 valid cases remained after removing 32 incomplete/outlier surveys (effective rate 86%). Sample: 40.8% male, 59.2% female; 77.6% undergraduates, 22.4% postgraduates; disciplines included social sciences, arts and humanities, engineering, business and economics, medical sciences. Instrument: Items adapted from established TAM and TPB measures (Davis et al., Venkatesh et al., Ajzen and Fishbein, Wu and Liu), back-translated (Chinese–English). Six latent constructs: PEU (4 items), PU (4), LA (4), SN (3), PBC (4), IABL (3). Responses on 7-point Likert scales (1–7). Analysis: Reliability and validity assessed via CFA using SPSS 27 and AMOS 26. Convergent validity supported (all standardized factor loadings > 0.50; CR > 0.70; AVE > 0.50). Discriminant validity met via Fornell–Larcker criterion (square roots of AVE exceeded inter-construct correlations). VIF values < 3 indicated acceptable collinearity. Model fit indices indicated acceptable/good fit: χ2=366.740, df=198, χ2/df=1.852; GFI=0.861; AGFI=0.822; CFI=0.940; TLI=0.931; RMSEA=0.065; SRMR=0.077. Mediation was tested via bootstrapping (5,000 samples, bias-corrected and percentile 95% CIs).

• The integrated TAM–TPB model explained 67.6% of the variance in intention to adopt blended learning (IABL). - Significant paths: PU → LA (β=0.301, p<0.001); PEU → LA (β=0.291, p<0.05); SN → LA (β=0.324, p<0.001); PEU → PU (β=0.647, p<0.001; R2=0.418); SN → PBC (β=0.711, p<0.001; R2=0.506); PU → IABL (β=0.229, p<0.01); LA → IABL (β=0.226, p<0.05). - Non-significant paths to IABL: PEU → IABL (β=0.197, p=0.082), SN → IABL (β=0.201, p=0.102), PBC → IABL (β=0.145, p=0.094). - Attitudes (LA) were explained 62% by PU, PEU, and SN (R2=0.620), with SN having the strongest impact on LA. - Mediation: LA significantly mediated PU → IABL (standardized indirect effect=0.299, p<0.001; H11 supported). PU significantly mediated PEU → IABL (standardized indirect effect=0.267, p<0.001; H12 supported). - Overall, usefulness and attitudes directly drive intention; ease of use and social norms act indirectly via PU and LA, while PBC did not directly influence intention in this context.

The findings address RQ1 by showing that an integrated TAM–TPB framework robustly explains students’ intention to adopt blended learning (67.6% variance explained). For RQ2, perceived usefulness (PU) and learning attitudes (LA) are the primary direct determinants of IABL, while perceived ease of use (PEU) and subjective norms (SN) shape attitudes and usefulness rather than directly influencing intention. SN strongly influences LA, underscoring the role of the social environment in shaping positive attitudes but not necessarily immediate intentions. Results suggest PU functions as a motivational factor while PEU behaves like a hygiene factor; students accustomed to technology may prioritize practical utility over ease. These insights indicate that improving perceived learning gains and fostering positive attitudes are more critical for boosting adoption than emphasizing usability or external pressures. The integrated model clarifies how technical perceptions and social influences combine to shape adoption in a post-pandemic higher education context.

This study provides an empirically validated integrated TAM–TPB model explaining Chinese university students’ adoption of blended learning. Contributions include: (1) applying an integrated TAM–TPB framework to blended learning adoption among Chinese undergraduates; (2) identifying that PU and LA directly predict intention, while PEU and SN act indirectly via PU and LA; (3) establishing strong effects of SN on LA and PEU on PU; and (4) demonstrating significant mediations (LA for PU → IABL; PU for PEU → IABL). Practical recommendations emphasize prioritizing course designs that enhance perceived usefulness, strengthening student-centered blended experiences, building supportive social environments, and improving platforms and resources to cultivate positive attitudes. Future research could integrate additional theories (e.g., TTF, UTAUT, TSR, ECM), examine diverse contexts with longitudinal designs, and include perspectives of instructors and administrators.

• Generalizability: Data were collected via convenience sampling with a small-scale, monocultural sample centered in Guangzhou, China. - Cross-sectional design: Limits causal inference and understanding of dynamics over time. - Scope: Focused on students; instructors’ and administrators’ perspectives not included. - Measurement context: Self-reported intentions may not translate to actual behavior. Future studies should use longitudinal designs, expand to diverse institutions and programs, incorporate qualitative methods, and explore additional variables and stakeholder perspectives.