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Correlational and Configurational Perspectives on the Determinants of Generative AI Adoption Among Spanish Zoomers and Millennials

Author

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  • Antonio Pérez-Portabella

    (Departament d’Estudis de Comunicació, Universitat Rovira i Virgili, 43002 Tarragona, Spain
    Facultad de Ciencias de la Información, Universidad Complutense de Madrid, 28040 Madrid, Spain)

  • Mario Arias-Oliva

    (Marketing Department, Faculty of Business & Economy, University Complutense of Madrid, Campus de Somosaguas, 28223 Madrid, Spain)

  • Graciela Padilla-Castillo

    (Journalism and New Media Department, Faculty of Information Sciences, University Complutense of Madrid, Avenida Complutense 3, 28040 Madrid, Spain)

  • Jorge de Andrés-Sánchez

    (Social and Business Research Laboratory, University Rovira i Virgili, Campus de Bellissens, 43204 Reus, Spain)

Abstract

Generative Artificial Intelligence (GAI) has become a topic of increasing societal and academic relevance, with its rapid diffusion reshaping public debate, policymaking, and scholarly inquiry across diverse disciplines. Building on this context, the present study explores the factors influencing GAI adoption among Spanish digital natives (Millennials and Zoomers), using data from a large national survey of 1533 participants (average age = 33.51 years). The theoretical foundation of this research is the Theory of Planned Behavior (TPB). Accordingly, the study examines how perceived usefulness (USEFUL), innovativeness (INNOV), privacy concerns (PRI), knowledge (KNOWL), perceived social performance (SPER), and perceived need for regulation (NREG), along with gender (FEM) and generational identity (GENZ), influence the frequency of GAI use. A mixed-methods design combines ordered logistic regression to assess average effects and fuzzy set qualitative comparative analysis (fsQCA) to uncover multiple causal paths. The results show that USEFUL, INNOV, KNOWL, and GENZ positively influence GAI use, whereas NREG discourages it. PRI and SPER show no statistically significant differences. The fsQCA reveals 17 configurations leading to GAI use and eight to non-use, confirming an asymmetric pattern in which all variables, including PRI, SPER, and FEM, are relevant in specific combinations. These insights highlight the multifaceted nature of GAI adoption and suggest tailored educational, communication, and policy strategies to promote responsible and inclusive use.

Suggested Citation

  • Antonio Pérez-Portabella & Mario Arias-Oliva & Graciela Padilla-Castillo & Jorge de Andrés-Sánchez, 2025. "Correlational and Configurational Perspectives on the Determinants of Generative AI Adoption Among Spanish Zoomers and Millennials," Societies, MDPI, vol. 15(10), pages 1-24, October.
    • Handle: RePEc:gam:jsoctx:v:15:y:2025:i:10:p:285-:d:1768889
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    References listed on IDEAS

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    1. Ritu Agarwal & Jayesh Prasad, 1998. "A Conceptual and Operational Definition of Personal Innovativeness in the Domain of Information Technology," Information Systems Research, INFORMS, vol. 9(2), pages 204-215, June.
    2. Remus Runcan & Vasile Hațegan & Ovidiu Toderici & Gabriel Croitoru & Mihaela Gavrila-Ardelean & Lavinia Denisia Cuc & Dana Rad & Alina Costin & Tiberiu Dughi, 2025. "Ethical AI in Social Sciences Research: Are We Gatekeepers or Revolutionaries?," Societies, MDPI, vol. 15(3), pages 1-15, March.
    3. Khizar, Hafiz Muhammad Usman & Ashraf, Aqsa & Yuan, Jingbo & Al-Waqfi, Mohammed, 2025. "Insights into ChatGPT adoption (or resistance) in research practices: The behavioral reasoning perspective," Technological Forecasting and Social Change, Elsevier, vol. 215(C).
    4. Ajzen, Icek, 1991. "The theory of planned behavior," Organizational Behavior and Human Decision Processes, Elsevier, vol. 50(2), pages 179-211, December.
    5. Camilleri, Mark Anthony, 2024. "Factors affecting performance expectancy and intentions to use ChatGPT: Using SmartPLS to advance an information technology acceptance framework," Technological Forecasting and Social Change, Elsevier, vol. 201(C).
    6. Moravec, Vaclav & Hynek, Nik & Skare, Marinko & Gavurova, Beata & Kubak, Matus, 2024. "Human or machine? The perception of artificial intelligence in journalism, its socio-economic conditions, and technological developments toward the digital future," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
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