Drivers and Barriers to the Use of Generative Artificial Intelligence in the Spanish Active Population: Insights from Artificial Neural Network Modeling and Shapley Additive Explanations

This study analyzes the determinants of generative artificial intelligence (GAI) use intensity among the Spanish working population, as well as the possible existence of gender gaps in its adoption. To this end, a conceptual model is proposed that incorporates perceived economic and productive usefulness (PEU), perceived social usefulness (PSU), three dimensions of the Technology Readiness Index—technological optimism (OPTI), innovativeness (INNOV), and insecurity (INSEC)—and three sociodemographic variables: entrepreneurial status, gender, and generational cohort. The model is implemented using artificial neural networks (ANNs) endowed with explanatory capability through Shapley Additive Explanations (SHAP). The application of SHAP enables the assessment of both the global and local importance of the explanatory variables, as well as the potential existence of gender biases in their contribution to GAI use. The results indicate that the most relevant variables are PEU, generational cohort, and INNOV. Although gender does not rank among the most important variables in terms of global importance, women exhibit lower levels of GAI use, and gender-related differences are also observed in the contribution of several explanatory variables. In particular, substantive effect sizes are observed for PSU, OPTI, INSEC, entrepreneurial status, and membership in Generation Y. By contrast, differences associated with especially relevant variables such as PEU and INNOV, as well as membership in Generation Z, do not exhibit meaningful effect sizes.