## 2 Theoretical Background
### Subjective Norms  
Subjective norms, also referred to as social influence in models such as the Unified Theory of Acceptance and Use of Technology (UTAUT) [27], have been extensively reported as a relevant variable for understanding the adoption of AI [20; 61] and GAI in both societal [37; 47] and industrial [39] contexts, as well as for academic purposes [62; 63].  
Various sources have highlighted the numerous benefits of GAI for society [8], as well as their significant risks, which must be carefully managed [10]. Among the main positive social utilities (SU) are their potential to increase productivity and improve decision-making in key sectors, such as healthcare, education, and industry [8]. GAI has already been used in medicine to predict health risks and in mining to perform hazardous tasks, reduce costs, and improve operational efficiency [49]. Its capacity to support innovation and solve complex problems is widely acknowledged, contributing to both economic and social development [13]. It supports personalized learning and facilitates inclusive education [7; 64].  
However, there are substantial social risks (SR) associated with GAI. Economically, several studies have warned that GAI may increase income and wealth inequality. While highly skilled workers may benefit from higher wages, lower-skilled workers face a greater risk of job displacement and unemployment [65], potentially exacerbating existing social divides. Ethically, GAI raises concerns regarding algorithmic bias, transparency, and privacy [10].  
Public trust in these technologies has been limited. A study analyzing social media comments revealed that negative emotions such as anger and disgust are prevalent in attitudes toward GAI, with frequent concerns about misinformation, loss of privacy, and existential risks [13]. In education, GAI use may undermine academic integrity more than traditional plagiarism does by enabling undetectable cheating, fostering overreliance, and a lack of collective creativity [11; 66]. Therefore, we propose the following:  
**Hypothesis 5 (H5):**_Greater perception of social utility (SU) of GAI leads to its increased use._  
**Hypothesis 6 (H6):**_Greater perception of social risks (SRs) of GAI leads to its decreased use._The growing perception of the need for regulation (REG) of AI and GAI largely stems from the ethical, social, and legal risks posed by technology and its negative externalities. Its widespread use has sparked concerns regarding privacy, misinformation, security, and algorithmic discrimination [10]. Many individuals lack trust in AI systems, and nearly half of them admit that their automation projects have been slowed by uncertainties regarding the inner workings of these tools [49]. These concerns are especially relevant in GAI systems trained on massive datasets, including potentially sensitive information, increasing the risk of data leaks, or misuse [67; 68].  
There is also mounting social and political pressure for regulations to ensure principles such as transparency, fairness, data protection, and respect for human rights [13]. The academic community has emphasized that the opacity, potential bias, and audit challenges associated with these models demand urgent measures to protect individual rights and institutional integrity [10; 69].  
**Hypothesis 7 (H7):**_Greater perception of the need for regulation (REG) of GAI leads to its decreased use._