Generative Artificial Intelligence (AI) Technology Adoption Model for Entrepreneurs: Case of ChatGPT

Abstract

This article presents an extensive Generative AI Technology Adoption Model intended to elucidate the complex process that entrepreneurs and other innovation ecosystem actors, for instance, libraries, go through for its adoption. The model suggests that the adoption process happens in three stages: Pre-Perception & Perception, Assessment, and Outcome. During the Pre-Perception & Perception Phase, entrepreneurs initiate their technology exploration by navigating social factors, domain experience, technological familiarity, system quality, training and support, interaction convenience, and anthropomorphism; with utilitarian value and hedonic values playing an important role. As they transition to the Assessment Stage, perceived usefulness, ease of use, and a novel addition, perceived enjoyment, shape their evaluations, leading to generations of emotions toward it, with utilitarian value overweighting hedonic values. The model finishes with the Outcome Stage, where emotions developed in the Assessment Stage become tangible intentions to switch (use technology or switch to human services). The adoption model highlights the adoption factors (also called latent variables) and their relationships grounded on researcher’s professional experiences and need to be further empirically validated. Entrepreneurial implications highlight the strategic insights of the model, providing a decision-making roadmap and highlighting the interaction between utilitarian and hedonistic values. Entrepreneurs can create well-informed technological integrations that are in line with business objectives by using the incremental decision-making process. The model’s focus on comparative evaluations gives entrepreneurs the ability to strategically map the usability of technology for the best possible commercial results. The Generative AI Technology Adoption Model offers a nuanced understanding of entrepreneurs’ technology adoption processes, which is also applicable to other actors in the innovation ecosystem.

Keywords:

• Chat generative pre-trained transformer (ChatGPT)
• co-creation
• entrepreneurs
• generative artificial intelligence
• libraries
• service innovation
• technology adoption

Author contributions

Conceptualization, V.G., and H.Y; methodology, V.G; validation, V.G; investigation, V.G; resources, V.G; data curation, V.G; writing—original draft preparation, V.G; writing—review and editing, V.G; visualization, V.G.; supervision, V.G, and H.Y; project administration, V.G, and H.Y; funding acquisition, V.G., and H.Y. Both authors have read and agreed to the published version of the manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research is funded by Winning Scientific Management, Portugal (Project ID: WINBUSMOD007/2023). This publication solely reflects the views of the authors, and the Agency cannot be held responsible for any use made of the information contained herein.