Analyzing knowledge in relation to generative artificial intelligence tools' coolness to foster continuous engagement

Abstract

Over the course of the last few years, advancements in generative artificial intelligence (GAI) have democratized AI use, sparking global interest from both experts and the general public in exploring its potential. Today, after the initial success of popular GAI tools (e.g., ChatGPT), the challenge is to ensure they do not become a passing trend, requiring a better understanding of how to keep users continuously engaged. To address this, we propose and examine a model that integrates the hedonic-motivation system adoption model, technology continuance theory, and coolness theory. Considering a final data set of 249 regular users of GAI tools (from once or twice a month: 28,1 %; once a week: 17,7 %; more than once a week: 36,6 %; every day: 17,7 %), we validated the model using PLS-SEM. Our findings highlight the influence of hedonic and emotional factors in shaping users’ perceptions and, subsequently, driving continuous engagement with generative AI tools for task-oriented purposes. In this paper, we investigated and empirically validated continuous engagement as a bidirectional relationship between work engagement and continuance intention. This bidirectional approach, combined with the integration of theories, offers a novel perspective for the IS literature and provides organizations with new insights on how to increase employees’ continuous engagement by supporting generative AI tools for task-oriented purposes.

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