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Item type:Publication, The Double Side of IA: How Automated Decision Making Challenges Confidence and Increases AnxietyArtificial intelligence has begun to permeate managerial decision-making, yet many organizations remain hesitant to rely on algorithms when choices carry significant consequences. To clarify the roots of this hesitation, we conducted a systematic review of 37 peer-reviewed studies published between 1970 and 2024, following the PRISMA-2020 protocol. The evidence was organized into seven interrelated themes spanning technical design, organizational structures, ethical safeguards, and the psychological climate in which managers operate. Results reveal that adoption is driven less by raw performance claims than by the degree of trust managers place in AI tools. That trust rests on three pillars: transparent and reliable system behavior, a clear perception of usefulness and ease of use, and explicit protections against bias. When any pillar is weak, anxiety about ceding control grows, dampening adoption intentions. Drawing these strands together, we propose an integrative framework that pairs cost-efficient, explainable architectures with user-centered training and governance mechanisms. The framework not only lowers technical barriers but also addresses the human and ethical concerns that most often stall implementation. By illuminating how technological, organizational, and psychological factors interact, the study offers a pragmatic roadmap for managers seeking to harness AI while safeguarding stakeholder confidence. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Algoritmos de aprendizaje para predecir el uso de la inteligencia artificial generativa en estudiantes universitariosThe rise of generative artificial intelligence has transformed educational processes in higher education, generating both opportunities and challenges in university training. This study aimed to analyze machine learning algorithms to predict the use of ChatGPT and Gemini among students at the Escuela Superior Politécnica de Chimborazo in Ecuador. A quantitative, non-experimental, cross-sectional, and correlational design was employed, with a sample of 699 students selected through simple random sampling. Data were collected using a statistically validated questionnaire and processed using SPSS and Orange Data Mining. The results showed that sociodemographic variables (sex, age, religion, and academic semester) did not present significant associations with usage preference, indicating low explanatory power. In contrast, the frequency of generative artificial intelligence use was a relevant predictor, highlighting the importance of habit as a determinant of adoption. Furthermore, the Random Forest and AdaBoost algorithms achieved the highest levels of accuracy, confirming the effectiveness of ensemble methods. It is concluded that socio-educational conditions better explain student behavior than demographic factors, consolidating ChatGPT as the most used tool. Licencia de Creative Commons Atribución 4.0 Internacional (CC BY 4.0)