## 1 Introduction  
Artificial intelligence (AI) has become integrated into society, serving as essential support in virtually all areas of life. It is a tool that extends across economic sectors and enhances economic productivity and quality of life [1]. Beyond its professional use in industry 4.0 [2] and complex cognitive tasks [3], it is increasingly used in domestic settings. AI facilitates home management through smart devices that optimize energy consumption, security, and comfort [4]. It also plays a key role in home healthcare, health monitoring, and personalized medical assistance [5]. Additionally, its presence is common in educational environments [6; 7]. Among the various types of AI, generative artificial intelligence (GAI) is prominent. Unlike other AI types that focus on classification or data analysis, GAI has the ability to create original content, such as text, images, music, code, or designs [8]. Models such as ChatGPT, Copilot, and Gemini are widely recognized applications that are capable of generating results that traditionally require human intervention. Their potential spans from creative assistance in the arts and communication to advanced applications in medicine, education, and product design, opening new avenues for research in human-machine interaction [9]. However, the deployment of AI presents challenges, particularly in the case of GAL. One of the most significant concerns is the quality and accuracy of the generated content, as this technology does not always distinguish between accurate and erroneous information, which can lead to misleading or incorrect results [8]. There is also the risk of algorithmic bias--if training data contain prejudices, these can be replicated or even amplified in the generated outputs. These limitations can exacerbate informational risks, especially if GAI is used deliberately to spread misinformation, impersonate identities [10], or normalize practices such as automated plagiarism [11]. Additionally, there are unresolved legal uncertainties regarding intellectual property and the copyright of generated content [12]. Ethical and social concerns have also been raised, such as the potential replacement of human creative labor and the urgent need for regulatory frameworks that balance innovation with responsibility [13]. These challenges highlight the need to promote the ethical, transparent, and supervised use of GAI [14]. The aim of this study is to analyze the determinants of generative AI (GAI) usage in Spain and its social relevance, using data from the National Survey conducted by the Centro de Investigaciones Sociologicas (Sociology Research Centre) [15] on the use of generative artificial intelligence in Spanish society. This study focuses specifically on general-purpose GAI, that is, tools designed for multiple functions and cognitive tasks, rather than specialized applications such as image, music, or art generation. These generalist tools are widely accessible to the public and include popular platforms such as ChatGPT (OpenAI), Copilot (Microsoft), Gemini (Google), and Perplexity AI--all of which share the common feature of processing natural language and assisting users across a broad range of general tasks. This study adopts the Theory of Planned Behavior (TPB) [16] as a theoretical framework, which identifies three categories of behavior predictors: attitude, perceived behavioral control, and subjective norms. This conceptual framework has proven effective in explaining a wide range of behaviors, including the acceptance and use of GAI [17]. Figure 1 illustrates the proposed theoretical model. Attitude is defined as the positive or negative evaluation a person makes about performing a specific behavior [16]. In technology adoption models, such as the Technology Acceptance Model (TAM) [18], the main antecedent of attitude is perceived usefulness, such as increased productivity [19]. Additionally, a predisposition to using new technologies has proven particularly relevant in shaping attitudes toward AI usage [20].