## 2 Theoretical Background
### Variables Associated with Behavioral Control  
Perceived behavioral control, in the context of GAI, refers to an individual's perceived ability to use this tool effectively [17]. One consequence of the efficacy of using a technology is its positive impact on perceived ease of use [40], which has been identified as a relevant factor in AI acceptance at work [39], in home environments [20], and in the use of GAI [40; 43].  
As shown in Figure 1, this study considers two factors within this dimension: the loss of control over personal information, which creates privacy risks (PRIs), and the level of knowledge (KNOW) of users about GAI.  
Using the Internet entails risks related to the privacy of digital consumers' personal data [48]. GAI users are no exception, as they are ultimately online consumers and are thus exposed to these risks. Moreover, these risks are intensified by the inherent characteristics of AI: massive data collection, limited control over reuse, and difficulty in auditing algorithms, all of which increase public concern and regulatory demand [49].  
The rapid evolution of AI, particularly generative models, presents significant privacy challenges. GAI systems often operate with large volumes of data, including sensitive information, which increases risks, such as unauthorized identification of individuals or biased, opaque automated decision-making [50]. In particular, unauthorized access--by internal or external third parties--to personal data without consent violates fundamental principles of confidentiality and user autonomy, and may lead to serious consequences such as identity theft or fraud [51].  
Empirical findings on the impact of PRI on AI adoption are mixed. While some studies argue that a higher perceived risk discourages the use of AI and GAI [46; 52], others find no significant relationship between privacy concerns and GAI acceptance in educational contexts [53]. Therefore, the following hypothesis is proposed:  
**Hypothesis 3 (H3):**_Privacy risk (PRI) negatively impacts the use of GAI._  
Prior knowledge about a technology can significantly strengthen users' perceived self-efficacy, understood as their belief in their ability to effectively use the technology [54]. This psychological component directly influences the perceived ease of use, which is a key determinant of technology acceptance [32]. In fact, ease of use not only affects intention to use directly but also acts as a predictor of perceived usefulness [26; 55].  
In this regard, prior knowledge of AI (KNOW), whether general education or domain-specific expertise, is associated with more favorable expectations of its usefulness and lower perceived risk [56]. [PERSON] et al. [57] supported this relationship by showing that higher levels of AI knowledge, especially among young people with higher education, enhance acceptance in healthcare contexts. Similarly, in health education settings, lack of AI training hinders adoption among students and professionals, whereas greater understanding promotes acceptance [58; 59]. Higher self-efficacy in using GAI has also been shown to foster acceptance [40; 43; 60]. So:  
**Hypothesis 4 (H4):**_Greater knowledge (KNOW) of GAI leads to its greater use._