## 3 Model Construction and Underlying Assumptions  
Employee intentions toward online learning are influenced by their acceptance of technology modules, leading to the proposal of four main technology adoption models. Initially, the Theory of Reasoned Action (TRA) [38] and its subsequent extension, the Theory of Planned Behavior (TPB) [39], were introduced. These frameworks posit that an individual's intention to engage in a behavior is the primary predictor of that behavior. TPB expands upon TRA by incorporating perceived behavioral control as an influential factor in behavior determination. The second model, the Technology Acceptance Model (TAM) [40], identifies perceived usefulness (PU) and perceived ease of use (PEU) as key drivers of the intention to use technology, subsequently affecting acceptance behaviors. The third model, TAM2, builds on TAM by including factors such as social influence (subjective norm) and cognitive instrumental processes [18]. Lastly, the Innovation Diffusion Theory (IDT) [30] outlines five key factors influencing innovation adoption: relative advantage, complexity, compatibility, trialability, and observability. These models collectively address the direct and indirect determinants of actual usage behavior. [PERSON] et al. [41] advocate for the usefulness of the TAM, recommending its integration into a more expansive framework encompassing elements related to human and social change processes and innovation adoption models. [PERSON] et al. [42] synthesized these elements along with other models (such as the motivation model and PC utilization) to develop a comprehensive theory known as the Unified Theory of Acceptance and Use of Technology (UTAUT). The UTAUT model facilitates the understanding of human acceptance behavior across various fields. As depicted in Figure 1, the model identifies performance expectancy, effort expectancy, and social influence as critical drivers of behavioral intentions, while facilitating conditions are linked to actual usage based on the intention to use. In additionally, the model specifies gender, age, experience, and voluntariness as moderating factors influencing behavioral intentions. Inevitably, the use of UTAUT in research brings with it strengths and limitations. In terms of the merits of UTAUT, its comprehensive methodology stands out for elucidating the intricate interplay among various psychological and social determinants affecting IT adoption. Furthermore, the model is noted for its consistent validity and reliability in relation to the data gathered [43]. However, [PERSON] and [PERSON] [44] contend that UTAUT lacks brevity, necessitating numerous variables to account for significant variance. Existing studies have applied the model in the context of online learning. [PERSON] et al. [45] modified it by adding perceived security as a factor they considered significantly related to intention to use. [PERSON] et al. [46] used the extended UTAUT model to study online learning in developing countries. [PERSON] [47] investigated the impact of college  
Figure 1: UTAUT model. students' behavioral intention to study online through self-regulation and acceptance in conjunction with UTAUT and found that performance expectations and effort expectations had a significant direct effect on student's intention to use online courses. [PERSON] [48] conducted a case study of a distance learning project using an adapted version of the UTAUT model to examine the factors influencing the adoption of webinars. [PERSON] et al. [49] verified that social influences and attitudes towards use are important determinants of intention to learn online and that intention strongly and significant influence actual use. This research applied the UTAUT to investigate employees' intentions towards sustainable learning through online modules. The determinants of acceptance behavior examined encompassed performance expectancy, effort expectancy, social influence, and facilitating conditions. [PERSON] et al.