## 4 Discussion  
### Theoretical Implications  
The findings from this review have several theoretical implications for understanding the adoption of ChatGPT in higher education.  
First, the inconsistent impact of demographic factors, such as age and gender, suggests that traditional models like the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT), which often incorporate these variables as moderators, may require refinement. Specifically, out of the ten studies that examined demographic factors, only two found them to have any significant effect on adoption behavior (e.g., [52]). In comparison, the majority (eight studies) found them to be insignificant, such as in [27; 36]. This suggests that demographic factors may play a limited role in explaining ChatGPT adoption and may not fully capture the complexity of user behavior. Instead, a broader range of psychological factors (e.g., intrinsic motivation and individual attitudes) and contextual factors (e.g., institutional policies and cultural norms) should be considered to offer a more comprehensive understanding of why and how users adopt ChatGPT.  
Additionally, a crucial finding is the confirmed significance of intrinsic motivations--such as perceived usefulness and ease of use--over extrinsic motivations (e.g., external rewards or social pressure). Out of the eleven studies that explored perceived usefulness, ten studies confirmed it as a significant factor (e.g., [13; 20]), while only one study found it to be insignificant. Similarly, perceived ease of use was found to be significant in seven studies, while one study found it to be insignificant. This emphasizes integrating Self-Determination Theory (SDT) into the existing frameworks. Theories such as SDT focus on intrinsic motivation, competence, and relatedness, which could explain why users are more likely to adopt ChatGPT for academic purposes when they perceive it to be useful and easy to use.  
Moreover, the variability in the influence of constructs such as trust, privacy, and security suggests that these elements might interact in complex ways with other constructs in the adoption models. For example, six studies found trust to be a significant factor influencing adoption (e.g., [22]), while two studies found it to be insignificant, including [4]. This variability suggests that privacy and security concerns may mediate or moderate the impact of trust on adoption. Future theoretical models should explore these interactions using a systems thinking approach to understand the interconnected nature of technology adoption better, recognizing that users' decisions are influenced by a network of factors rather than isolated variables.  
Lastly, the observed differences in the significance of the facilitating conditions and effort expectancy across studies highlight the importance of context-specific adaptations of theoretical models. The facilitating conditions were found to be significant in six studies, including [53] and [27], while three studies found them to be insignificant (e.g., [39]). Similarly, effort expectancy was found to be significant in six studies, but three studies reported it as insignificant, such as [32]. These inconsistencies suggest that the availability of technical resources and the complexity of the educational tasks might impact the perceived ease of use of ChatGPT differently across contexts. Researchers should account for these contextual factors when applying and adapting models like the TAM and UTAUT in different educational environments, ensuring that the models remain relevant to the specific conditions and needs of the user population.  
By addressing these theoretical implications, future research can develop more robust and context-sensitive models that provide a deeper understanding of the adoption of ChatGPT in higher education.