Assessing the factors influencing the intention to use information and communication technology implementation and acceptance in China's education sector

The study addresses low and uneven adoption of ICT in China’s education sector by examining factors that drive employees’ intention to use ICT (ITUICT). Drawing on literature identifying social (social influence), individual (trustworthiness), technological (information acquisition, perceived awareness), and environmental (regulatory support) factors, the study sets two goals: (1) to test the effects of information acquisition, perceived awareness, social influence, and regulatory support on ITUICT among university employees in China; and (2) to test whether trustworthiness moderates these relationships. The work is motivated by persistent barriers to technology acceptance among educators and the need to extend established models (notably UTAUT) to educational settings in China to inform policy and practice.

The study is grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT), emphasizing social influence and facilitating conditions (operationalized as regulatory support). The framework integrates additional determinants: information acquisition (exposure to and quality of ICT-related knowledge and data) and perceived awareness (self-perceived knowledge and confidence regarding ICT). The authors posit: H1: Social influence → ITUICT (positive); H2: Information acquisition → ITUICT (positive); H3: Perceived awareness → ITUICT (positive); H4: Regulatory support → ITUICT (positive). Trustworthiness (confidence in system security, privacy, reliability, and supportive institutions) is theorized to have a direct positive effect on ITUICT (H5) and to positively moderate each of the four determinant–intention paths: H5a (SI×TW), H5b (IA×TW), H5c (PA×TW), and H5d (RS×TW). Prior literature across technology adoption contexts supports roles for social norms, information and awareness, and policy support, with trust enhancing perceived usefulness, reducing risk, and strengthening normative and informational influences.

Design: Quantitative, cross-sectional survey analyzed via PLS-SEM using SmartPLS 4. Setting and sampling: Six top-ranking universities in Beijing (Times Higher Education list) were targeted; convenience sampling of university employees. Data collection: 451 questionnaires distributed (on campus and online); 381 usable responses retained after screening (84% response rate). Sample profile: 57% male, 43% female; ages 20–26 (20%), 27–35 (52%), >35 (28%); education: bachelor’s (17%), master’s (49%), PhD (34%); positions: faculty (50%), teaching assistants (19%), researchers using ICT (16%), others/clerical (15%); experience: ≤5 years (34%), 6–10 (46%), 11–20 (20%). Measures: 5-point Likert scales (1=strongly disagree to 5=strongly agree). ITUICT (5 items; Kim et al., 2019); Trustworthiness (5; Fakhoury & Aubert, 2015); Social influence (5; Martins et al., 2014); Information acquisition (5; Irfan & Ahmad, 2022); Perceived awareness (4; Bozdoğan & Özen, 2014); Regulatory support (4; Chohan & Hu, 2020). Measurement model: Indicator loadings ≥0.70; reliability: Cronbach’s alpha and composite reliability ≥0.70; convergent validity: AVE ≥0.50; discriminant validity: Fornell–Larcker and HTMT <0.90 satisfied; multicollinearity acceptable (VIF <5). Structural model: Bootstrapping with 5,000 resamples; model fit indices reported: R²(ITUICT)=0.861; NFI=0.811; SRMR=0.068; Chi-square=2678.235. Moderation tested by interaction terms (e.g., SI×TW).

- Model explanatory power: R² for ITUICT = 0.861 (86.1% variance explained). - Direct effects on ITUICT (all p<0.05): Social influence β=0.195 (t=5.909); Information acquisition β=0.483 (t=8.473); Perceived awareness β=0.134 (t=3.622); Regulatory support β=0.374 (t=6.697); Trustworthiness β=0.211 (t=4.489). - Moderation by trustworthiness (all p<0.05): SI×TW β=0.125 (t=2.508); IA×TW β=0.171 (t=3.563); PA×TW β=0.082 (t=2.562); RS×TW β=0.163 (t=7.409). Effects of SI, IA, PA, and RS on ITUICT are stronger at higher trustworthiness. - Model fit: SRMR=0.068; NFI=0.811; Chi-square=2678.235, supporting acceptable fit. - Measurement quality: All constructs demonstrated satisfactory reliability (α and CR ≥0.79) and convergent/discriminant validity (AVE ≥0.60; HTMT <0.90).

Findings support the UTAUT-informed framework in a Chinese higher-education context: social norms and support (social influence), access to high-quality ICT information (information acquisition), and users’ perceived awareness significantly raise intention to use ICT. Regulatory support—reflecting policies, resources, and institutional facilitation—substantially enhances ITUICT, underscoring the importance of policy-level enablers. Crucially, trustworthiness strengthens all determinant–intention links, indicating that perceptions of security, privacy, and institutional reliability reduce uncertainty and amplify the impacts of social, informational, perceptual, and regulatory drivers. These results suggest that policy and institutional strategies that build trust alongside information provision, training, and supportive regulations will more effectively boost ICT adoption among university employees, thereby improving teaching, research, and administrative outcomes.

The study validates a multifactor model of ICT acceptance in Chinese universities. Social influence, information acquisition, perceived awareness, and regulatory support each positively predict intention to use ICT; trustworthiness both directly increases ITUICT and positively moderates all four determinant relationships. Practically, enhancing ICT training and digital literacy, improving access to reliable information and resources, cultivating supportive policies and infrastructure, and strengthening trust in systems and governance can accelerate ICT uptake. Theoretically, the work extends UTAUT by integrating trustworthiness as a key moderator and by applying this model to an educational workforce in China. Future research should incorporate additional constructs (e.g., self-efficacy, attitudes), adopt mixed methods (including interviews), expand to broader and cross-national samples, and evaluate cost–benefit aspects across educational sectors.

- Construct coverage: The model does not include other potentially influential factors (e.g., self-efficacy, attitudes), limiting comprehensiveness. - Method: Cross-sectional survey only; absence of qualitative insights (e.g., interviews) may constrain depth of understanding. - Generalizability: Sample restricted to Chinese universities in Beijing; findings may not generalize to other regions or countries. - Sample composition: Focus on faculty and researchers; excludes students and other educational categories that may differ in ICT adoption. - Moderator scope: Only trustworthiness examined as a moderator; other moderators (e.g., technology self-efficacy) warrant testing in future work.