Relative Advantage and Compatibility as Drivers of ChatGPT Adoption in Latin American Higher Education: A PLS SEM Study Towards Sustainable Digital Education

Juana Beatriz Vargas Bernuy, Marco A. Nolasco-Mamani, Norma C. Velásquez Rodríguez, Renza L. Gambetta Quelopana, Ana N. Martinez Valdivia and Sam M. Espinoza Vidaurre ()
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Juana Beatriz Vargas Bernuy: Faculty of Civil Engineering, Architecture and Geotechnics, Jorge Basadre Grohmann National University, Tacna 23000, Peru
Marco A. Nolasco-Mamani: Private University of Tacna, Tacna 23001, Peru
Norma C. Velásquez Rodríguez: Faculty of Economics and Commercial Sciences, Sedes Sapientiae Catholic University, Los Olivos 15301, Peru
Renza L. Gambetta Quelopana: Private University of Tacna, Tacna 23001, Peru
Ana N. Martinez Valdivia: Private University of Tacna, Tacna 23001, Peru
Sam M. Espinoza Vidaurre: Private University of Tacna, Tacna 23001, Peru

Sustainability, 2025, vol. 17, issue 18, 1-26

Abstract: As Latin American universities pursue digitally and environmentally sustainable teaching models, understanding why students adopt generative AI is essential. We analyzed data from undergraduate students (n = 792) across five Latin American countries (Peru, Chile, Bolivia, Argentina, and Colombia). Grounded in the diffusion of innovations theory, the study evaluated the effects of relative advantage, compatibility, complexity, trialability, and observability on attitudes towards ChatGPT and examined the effect of attitude on intention to use among higher education students in the region. The reliability and validity of the measurement scale were confirmed, and structural relationships were tested using partial least squares structural equation modeling (PLS-SEM). The model explained 58.1% of the variance in attitude: relative advantage (β = 0.247) and compatibility (β = 0.246) exerted the largest effects, followed by trialability (β = 0.223) and observability (β = 0.167); complexity showed a weaker yet significant effect (β = 0.118). Attitude strongly predicted the intention to use ChatGPT (β = 0.777), accounting for 60.4% of its variance. All paths were significant ( p < 0.001), and psychometric indicators exceeded recommended thresholds. These findings indicate that student adoption is driven more by perceived academic benefits and alignment with existing learning routines than by technical ease. Highlighting concrete, ethically delineated use cases and providing guided institutional spaces for experimentation may accelerate the responsible, long-term adoption of generative AI in quality higher education.

Keywords: ChatGPT; diffusion of Innovations; higher education; Latin America; technology adoption; SDG 4 (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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