Investigating the factors affecting the acceptance of generative artificial intelligence in business intelligence applications

Based on previous technology acceptance and adoption literature, this research aims to understand and empirically test the critical factors for generative AI acceptance in business intelligence applications. Artificially Intelligent Device Use Acceptance and Task-Oriented Artificial Intelligence Acceptance models were used as theoretical foundations to interpret users' acceptance when interacting with generative AI services in business intelligence applications. For data collection, an online survey was developed by adapting validated measurement scales from other AI adoption literature. According to the survey results, data were collected from 149 participants. The effects on the extent of acceptance were experimentally tested using the PLS-SEM analysis. The results showed that utilitarian motivation, continuous improvement, conversational intelligence, perceived usefulness, flow experience, trait curiosity, and trust played significant roles in the acceptance of generative AI technology. However, there was no support for the influence of task-technology fit and performance risk factors. Considering the rapid development of AI technologies, developing and empirically testing an adoption model will guide researchers, technology providers, and users to adopt and use this technology successfully.