Artificial intelligence (AI) is increasingly deployed in organizations, allowing information systems (IS) to incorporate self-learning mechanisms. Machine learning (ML) is commonly used as the underlying technology, as it enables IS to derive patters from collected data and perform tasks that were previously reserved for humans. While organizations hope to increase their efficiency and effectivity through adopting AI, the actual linkage between AI use and performance impacts for individuals remains largely overlooked in IS research so far. Therefore, we employ a qualitative research approach to develop a theoretical model for this relationship. In detail, we conduct expert interviews and build on the widely used “task-technology-fit” (TTF) theory. We identify relevant dimensions for the main theory constructs and expand the theory with further components to fit the AI context. Our findings enable future empirical research regarding performance impacts of AI use. Practitioners can use our model to evaluate use cases for AI adoption by considering task, data, and technology characteristics.
Sturm, Timo and Peters, Felix, "The Impact of Artificial Intelligence on Individual Performance: Exploring the Fit between Task, Data, and Technology" (2020). ECIS 2020 Research Papers. 200.
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