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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, Faculty turnover segmentation for the retention of academic talent(Centro de Información Tecnológica, 2024-01-01)This study aims at defining typologies of higher education professors by identifying the most important factors that determine job satisfaction to improve their retention. The methodology is quantitative, non-experimental, and descriptive. For this purpose, a sample of 163 professors from an educational institution located in Lima (Peru) is surveyed to assess their perceptions, preferences, and job expectations. Applying a K-means cluster analysis results in three teacher profiles: promoters, indifferent, and critical. In these three groups, there are differentiated assessments on the importance each assigns to organizational factors (salary, benefits, career development, networking, and institutional prestige) when choosing a new employer or when assessing their current employer. In conclusion, it is important to design and implement differentiated professor retention policies for each group in order to respond more effectively to their career expectations.