Massive open online courses (MOOCs) are increasingly positioned as infrastructure for continuing professional development, yet the mechanisms through which educational personnel accept and use them remain incompletely specified. Drawing on the unified theory of acceptance and use of technology (UTAUT), the information systems success framework, and the trust–risk perspective, this study develops and tests an extended acceptance model that incorporates perceived content quality, trust, perceived risk, self-regulated learning, and digital–AI literacy. We collected survey data from 290 educational personnel in Thailand and screened for insufficient-effort responding, yielding an analytical sample of 270. We estimated the model using partial least squares structural equation modeling with 10,000 bootstrap subsamples, and assessed its predictive capability using PLSpredict and the cross-validated predictive ability test. The model explained 51.7% of the variance in behavioral intention and 42.1% in use behavior. Perceived content quality functioned as the principal upstream driver, exerting a very large effect on self-regulated learning and substantial effects on trust and effort expectancy. Trust was the strongest determinant of behavioral intention and significantly reduced perceived risk. Contrary to the conventional barrier view, perceived risk exerted significant positive effects on both behavioral intention and use behavior, a pattern interpreted as informed adoption. Effort expectancy influenced intention entirely through performance expectancy. This study discusses theoretical and practical implications for platform design and institutional policy.
The increasing adoption of Industry 4.0 technologies has intensified the need for industrial engineering graduates to acquire competency in Programmable Logic Controller (PLC) programming and industrial automation. Despite the growing implementation of PLC simulation software and digital laboratories, limited empirical research has examined the factors influencing students' acceptance of PLC learning. This study applied the Technology Acceptance Model (TAM) to investigate the relationships among perceived ease of use (PEOU), perceived usefulness (PU), attitude toward PLC learning (ATT), and behavioral intention to use PLC learning systems (BI). A quantitative cross-sectional survey was conducted with 399 undergraduate students from Sichuan Vocational and Technical College of Communications in China who had experience in PLC-related courses. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4. The measurement model demonstrated satisfactory reliability and validity, with outer loadings ranging from 0.797 to 0.886, composite reliability values between 0.882 and 0.911, and average variance extracted (AVE) values between 0.651 and 0.773. Both the Fornell-Larcker criterion and the heterotrait-monotrait ratio confirmed adequate discriminant validity. The structural model supported all proposed hypotheses. Perceived ease of use significantly influenced perceived usefulness (β = 0.416, p < .001) and attitude (β = 0.196, p < .001), perceived usefulness positively affected attitude (β = 0.372, p < .001), and attitude exerted the strongest influence on behavioral intention (β = 0.511, p < .001). The model explained 17.3% of the variance in perceived usefulness, 23.8% in attitude, and 26.1% in behavioral intention. The findings indicate that students' acceptance of PLC learning is most strongly associated with the perceived educational value of PLC instruction, while positive attitudes serve as the most important predictor of continued intention to engage with PLC learning. Multi-group analysis, preceded by measurement invariance testing, indicated that the effect of perceived ease of use on perceived usefulness was significantly stronger for female than for male students, while the model was otherwise invariant across gender, year of study, and prior PLC experience. These results provide practical implications for designing learner-centered PLC curricula that integrate user-friendly learning environments with authentic industrial automation experiences.
Generative artificial intelligence (GenAI) has entered classrooms faster than most institutions have been able to formulate policy, yet its instructional value ultimately depends on whether teachers choose to use it. This study examined the determinants of teachers' behavioral intention (BI) to use GenAI in teaching, drawing on an integrated Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology (UTAUT) framework. Five predictors were specified: perceived ease of use (PEoU), perceived usefulness (PU), social influence (SI), facilitating conditions (FC), and anxiety (ANX). A cross-sectional survey was administered to 500 in-service teachers drawn from three Chinese educational institutions spanning medical higher education, vocational higher education, and primary education. Each construct was operationalized as a composite mean of its constituent items, and the model was estimated using the Regression module of SmartPLS 4 with bootstrapping (5,000 subsamples) to obtain confidence intervals. Collinearity diagnostics were acceptable (VIF = 1.046-1.238). The model accounted for 32.7% of the variance in behavioral intention (adjusted R² = .320). Perceived usefulness was the strongest predictor (β = .228, p < .001), followed by facilitating conditions (β = .206, p < .001), perceived ease of use (β = .198, p < .001), and social influence (β = .195, p < .001). Contrary to expectations, anxiety exerted no significant effect (β = .018, p = .631), with a bootstrap confidence interval that spanned zero. The findings indicate that teachers' adoption decisions are jointly driven by instrumental value and institutional provisioning, and that generalized technology anxiety is not, in itself, a barrier among teachers who already have practical exposure to GenAI. Implications for professional development design and institutional AI policy are discussed.
