Bayesian Restructuring Technology Acceptance Model (TAM) with Moderating Lecture Self-Managing in E-Learning Adoption - Elok Fitriani Rafikasari; Nur Iriawan; Bambang Widjanarko Otok (Decision Making: Applications in Management and Engineering, 2025)
High-quality lecturing is pivotal in advancing the Sustainable Development Goals (SDGs) through both traditional and digital education approaches. In E-learning, lecturers play a fundamental role in influencing adoption and effectiveness. This study introduces a restructured Technology Acceptance Model (TAM) by incorporating Lecture Self-Managing (LSM) as a moderating variable, enhancing its applicability to E-learning contexts. Bayesian Structural Equation Modelling (SEM) was employed to assess the performance of the restructured TAM compared to the original framework. The revised TAM was implemented within an E-learning system, with data collected through structured surveys from lecturers actively engaged in E-learning over one semester. The findings revealed that LSM significantly moderated the relationships between Subjective Norm (SN) and Perceived Ease of Use (PE), as well as SN and Perceived Usefulness (PU), with a BIC of 11,974.18—lower than the BIC of 12,009.42 when LSM was considered an external variable. These results indicate that integrating LSM as a moderating factor within TAM enhances the assessment of E-learning effectiveness. The innovation of lecturer-managed learning content within LSM can enrich and improve the success of E-learning implementation.