Beyond the Barrier View of Risk: Content Quality, Trust and Informed Adoption in an Extended UTAUT Model of MOOC Acceptance among Educational Personnel

Abstract

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.