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Item type:Publication, Unlocking innovation: How enjoyment drives GenAI use in higher education(Frontiers Media SA, 2024-01-01)Generative Artificial Intelligence (Gen AI) is rapidly transforming education holds immense potential for enhancing learning experiences and fostering innovation skills crucial for success in today’s rapidly changing job market. However, successful integration depends on student adoption. This study investigates factors influencing business students’ intention to use Gen AI in Innovation courses, focusing on the role of Perceived Enjoyment. Method: A cross-sectional predictive analysis was conducted using data from 92 business undergraduate students in a Peruvian higher education institution. A survey questionnaire, adapted from Teo and Noyes, was used to measure perceived enjoyment, usefulness, ease of use, attitude toward, and intention to use Gen AI tools. Results: The study found a strong positive relationship between Perceived Enjoyment and the intention to use Gen AI in Innovation courses. Furthermore, Perceived Enjoyment was positively associated with perceived ease of use. Interestingly, perceived usefulness did not show a significant effect on the intention to use Gen AI. Conclusion: Our finding challenges the traditional emphasis on perceived usefulness as the primary driver of technology acceptance. Instead, our results suggest that prioritizing user enjoyment and ease of use in the design and implementation of Gen AI tools may be a more effective strategy for promoting their adoption in educational settings. This shift in focus from utility to experience could be crucial in unlocking the full potential of Gen AI to transform education.1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Use of technological resources in the era of artificial intelligence and its effect on academic performance(Frontiers Media, 2026-07-08)The advancement of digital technologies and generative artificial intelligence (GenAI) has transformed learning environments in higher education; while tools such as ChatGPT offer personalized experiences and immediate feedback, their relationship with academic performance remains insufficiently understood in university contexts. This study analyzed the use of technological resources in the AI era, considering frequency of use, perceived usefulness, familiarity, and type of use, as well as their contribution to university students' academic performance and identifying technological and contextual factors associated with it. A data-mining approach was employed, applying 10 supervised regression algorithms using 10-fold cross-validation, complemented by interpretability analysis using SHAP values to identify relevant predictors. Regularized regression models (Elastic Net; Lasso) showed the best relative predictive performance. The SHAP analysis revealed that the field of study (? 0.41) and study cycle (? 0.34) are the dominant predictors, while the use of AI for academic writing (? 0.10) emerged as the technological variable with the greatest positive contribution to academic GPA within the model. Academic performance was mainly associated with structural factors related to students' educational trajectories, while AI tools showed a complementary role. The predictive contribution of technology appears to depend more on pedagogical integration than on frequency of use alone. These findings suggest the need to integrate digital strategies into higher education, implementing them in a differentiated manner by discipline and academic level, and promoting the guided and purposeful use of AI and technological resources in learning processes.1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, The research landscape on generative artificial intelligence: a bibliometric analysis of transformer-based models(Emerald Publishing, 2026-02-24)Purpose – The aim of this study is to offer valuable insights to businesses and facilitate better understanding on transformer-based models (TBMs), which are among the widely employed generative artificial intelligence (GAI) models, garnering substantial attention due to their ability to process and generate complex data. Design/methodology/approach – Existing studies on TBMs tend to be limited in scope, either focusing on specific fields or being highly technical. To bridge this gap, this study conducts robust bibliometric analysis to explore the trends across journals, authors, affiliations, countries and research trajectories using science mapping techniques – co-citation, co-words and strategic diagram analysis. Findings – Identified research gaps encompass the evolution of new closed and open-source TBMs; limited exploration across industries like education and disciplines like marketing; a lack of in-depth exploration on TBMs' adoption in the health sector; scarcity of research on TBMs' ethical considerations and potential TBMs' performance research in diverse applications, like image processing. Originality/value – The study offers an updated TBMs landscape and proposes a theoretical framework for TBMs' adoption in organizations. Implications for managers and researchers along with suggested research questions to guide future investigations are provided.1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Fostering entrepreneurial success from the classroom: unleashing the potential of generative AI through technology-to-performance chain. A multi-case study approach(Springer Science+Business Media, 2025-07-01)This study presents an in-depth examination of the role of Generative Artificial Intelligence in enhancing entrepreneurial success, situated within the educational context of a leading business school in Peru. Utilizing the Technology-to-Performance Chain framework, the research integrates both qualitative and quantitative methodologies to explore the alignment and impact of Generative AI on entrepreneurial tasks. Through a multi-case study approach involving 78 early-stage entrepreneurs, the study delves into how Generative AI tools influence individual performance outcomes in entrepreneurial endeavors. The qualitative findings reveal a consensus among entrepreneurs about the positive influence of Generative AI on task performance, highlighting aspects such as Work Compatibility, Ease of Use, and Information Quality. Complementing this, the quantitative analysis quantifies the relationships between these dimensions of Generative AI use and outcomes like tool utilization and performance impact, corroborating and enriching the qualitative insights. The study discovers that Work Compatibility is a significant predictor of Generative AI tool utilization, indicating that when entrepreneurs perceive a strong alignment between their tasks and the AI tools' capabilities, they are more likely to use these tools extensively. Furthermore, the research elucidates the positive correlations between tool Utilization and Performance Impact, underlining the importance of user-friendly technology and high-quality information output in entrepreneurial ventures. This study contributes significantly to the literature on entrepreneurship and Generative AI, offering valuable insights for educators, policymakers, and entrepreneurs. It emphasizes the need for aligning technology with tasks for enhancing performance, laying the groundwork for integrating advanced technologies into entrepreneurship education and practice. The findings highlight the immense potential of Generative AI in shaping the future of entrepreneurship, advocating for its strategic inclusion in educational curricula and entrepreneurial ventures.1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Empowering educators: A multi-case study investigating the transformative integration of generative AI tools for teaching in business education(Taylor & Francis, 2025-01-01)This study investigates the transformative role of Generative AI (GenAI) tools in enhancing educators' tasks and performance within higher education, utilizing the Technology-to-Performance Chain (TPC) model as a theoretical framework. The research focuses on how dimensions of Work Compatibility, Ease of Use, Ease of Learning, and Information Quality influence the adoption and perceived impact of GenAI on teaching effectiveness. Data were gathered through quantitative surveys and qualitative interviews with 26 educators from a graduate business school in Peru. Results indicate that perceived performance improvements and the quality of AI-generated information are pivotal for successful adoption, while ease of use and learning, though valued, play a secondary role. The findings provide critical insights into the integration of emerging technologies in academic contexts, offering practical recommendations to educators, administrators, and policymakers aiming to leverage AI-driven tools for pedagogical innovation.1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Digital transformation of humanities teaching, from traditional methods to generative artificial intelligence: a systematic review(SciELO, 2026-06-01)The main objective of this study is to explore the digital transformation of teaching in the humanities, from traditional methods to generative artificial intelligence (GAI). This is a systematic review of the literature on Scopus, Springer Link, Refseek, Eric, and Dialnet databases from the years 2020 to 2025, according to the PRISMA statement. A total of 1031 documents are generated, which were then reduced to 19 articles for in-depth analysis. The results show that the year 2024 has the highest number of publications on the subject. Eleven articles are reviews, three are mixed, and about 79% of the studies examined belong to Scopus. The United Kingdom, Spain, Switzerland, and the United States are the countries with the highest scientific output. It is concluded that, with the use of IAG tools and an adjustment to the current instructional design, educators can deliver personalized and enriching educational experiences. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, An analytics framework for secure and intelligent telemedicine in sustainable healthcare(Elsevier BV, 2026-06-01)Telemedicine has significantly transformed healthcare delivery by expanding access to medical services and enabling more personalized care. Despite these advances, healthcare organizations continue to face challenges related to digital literacy, uneven access to technology, data privacy, platform interoperability, and the integration of advanced digital solutions into clinical and operational workflows. This paper proposes an analytics-driven telemedicine framework that emphasizes secure data governance, interoperability, and decision support in healthcare organizations. The framework integrates permissioned blockchain services to provide immutable logging, identity and access management, and end-to-end traceability of clinical and pharmaceutical activities, while federated learning supports decentralized model training without centralizing sensitive patient data. In addition, large language models are incorporated to enhance clinical text understanding and enable multimodal decision making. The framework is evaluated through controlled experiments across two complementary dimensions. An operational simulation of healthcare service indicators demonstrates meaningful performance gains under controlled conditions, reducing consultation response times from 24 to 72 h to 1 to 4 h, shortening diagnostic delays from 3 to 7 days to 2 to 12 h, and decreasing medication traceability errors from 2 to 8 percent annually to below 0.5 percent. A multimodal analytics assessment using 15,000 paired medical images and clinical reports related to breast, liver, and lung cancer shows strong predictive performance, with the best configuration achieving 93.5% accuracy, 94.5% precision, and 91.5% recall, along with low cross entropy values that indicate reliable classification and coherent report generation. Overall, the results indicate that the integration of advanced analytics with secure and auditable data infrastructure can improve efficiency, data integrity, and clinical decision support in telemedicine systems. The framework is particularly relevant for healthcare environments where institutional fragmentation and infrastructure limitations require scalable, privacy-preserving, and interoperable digital solutions.