Integrating Machine Learning Techniques and the Theory of Planned Behavior to Assess the Drivers of and Barriers to the Use of Generative Artificial Intelligence: Evidence in Spain

Identificador:  imarina:9463669

Handle: https://hdl.handle.net/20.500.11797/imarina9463669

Autores:  Pérez-Portabella, A; de Andrés-Sánchez, J; Arias-Oliva, M; Souto-Romero, M

Resumen:
Generative artificial intelligence (GAI) is emerging as a disruptive force, both economically and socially, with its use spanning from the provision of goods and services to everyday activities such as healthcare and household management. This study analyzes the enabling and inhibiting factors of GAI use in Spain based on a large-scale survey conducted by the Spanish Center for Sociological Research on the use and perception of artificial intelligence. The proposed model is based on the Theory of Planned Behavior and is fitted using machine learning techniques, specifically decision trees, Random Forest extensions, and extreme gradient boosting. While decision trees allow for detailed visualization of how variables interact to explain usage, Random Forest provides an excellent model fit (R2 close to 95%) and predictive performance. The use of Shapley Additive Explanations reveals that knowledge about artificial intelligence, followed by innovation orientation, is the main explanatory variable of GAI use. Among sociodemographic variables, Generation X and Z stood out as the most relevant. It is also noteworthy that the perceived privacy risk does not show a clear inhibitory influence on usage. Factors representing the positive consequences of GAI, such as performance expectancy and social utility, exert a stronger influence than the negative impact of hindering factors such as perceived privacy or social risks.