Although generative artificial intelligence (GenAI) rapidly integrated in higher education, the existing literature has ignored to explain the impact of GenAI attributes in developing psychological experiences, in terms of GenAI techno-eustress and related outcomes. To fill this gap, we used affordance–actualization theory as a theoretical foundation and developed and tested a research framework. This study aims to investigate the relationship between GenAI attributes – GenAI techno-exploration and GenAI techno-adaptability – and GenAI techno-eustress and subsequently work engagement and academic performance. This work examines the indirect relationships between GenAI techno-exploration and GenAI techno-adaptability, and work engagement and academic performance through GenAI techno-eustress.
We collected data using Prolific Academic platform from 302 respondents from higher education faculty at UK and USA. We used survey questionnaire using purposive sampling technique to approach relevant sampling. Data analyzed was performed using PLS-SEM via SmartPLS 4.0.
The present finding showed that GenAI techno-exploration and GenAI techno-adaptability have positive association with GenAI techno-eustress, which in turn has a positive association with work engagement and academic performance. This study revealed a positive relationship between work engagement and academic performance. This research identified that predictors and outcomes relationship is completely mediated by GenAI techno-eustress. This work found a serial mediation of GenAI techno-eustress and work engagement between predictors and academic performance relationships.
In practice, the findings provide insight into how universities can leverage GenAI to their benefit by reducing stress and create work engagement. By promoting techno-exploration and flexibility, institutions would be able to create specific training programs, digital infrastructures and governance systems to support faculty engagement and performance.
This study is unique in exploring the positive stress (GenAI techno-eustress) that GenAI can induce in faculty at higher education institutions. This research provides significant theoretical insights based on the actualization theory of affordance in human–AI interaction. This study also provides insight into the asymmetrical effects of AI use, indicating that GenAI is primarily function as a motivational and capability enhancing mechanism, instead of a stress inducing one, thereby increasing more comprehensive understanding of AI-enabled work.
