Understanding Performance and Effort Expectancy in Generative AI Use Through Data Mining Models
DOI:
https://doi.org/10.24191/n1z23h53Keywords:
Generative Artificial Intelligence, Performance Expectancy, Effort Expectancy, UTAUT, Data Mining ModelsAbstract
This study examines how Generative Artificial Intelligence (AI) utilization predicts users’ Performance Expectancy and Effort Expectancy within the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. Using data from 480 Indonesian university students, the research employed data mining models in Orange to classify expectancy perceptions based on generative AI usage. Seven algorithms were tested, with Naïve Bayes achieving the highest predictive accuracy. Results indicate that generative AI use moderately predicts both performance and effort expectancy, suggesting that frequent interaction enhances users’ perceptions of effectiveness and ease. The findings extend UTAUT into a post-adoption context, confirming that expectancy beliefs evolve through experiential learning. Practically, the study emphasizes the importance of exposure and guided practice in fostering AI familiarity among students. Future research should expand across user groups and explore other generative AI modalities beyond text-based applications.
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