AI-TAM: a model to investigate user acceptance and collaborative intention in human-in-the-loop AI applications (Human Computation, 2022)
More and more frequently, digital applications make use of Artificial Intelligence (AI) capabilities to provide advanced features; on the other hand, human-in-the-loop approaches are on the rise to involve people in AI-powered pipelines for data collection, results validation and decision-making.Does the introduction of AI features affect user acceptance? Does the AI result quality affect people willingness to use such applications? Does the additional user effort required in human-in-the-loop mechanisms change the application adoption and use?This study aims to provide a reference approach to answer those questions. We propose a model that extends the Technology Acceptance Model (TAM) with further constructs explicitly related to AI (user trust in AI and perceived quality of AI output, from XAI literature) and collaborative intention (willingness to contribute to AI pipelines).We tested the proposed model with an application for car damage claim reporting with AI-powered damage estimation for insurance customers. The results showed that the XAI related factors have a strong and positive effect on the behavioural intention, the perceived usefulness and the ease of use of the application. Moreover, there is a strong link between the behavioural intention and the collaborative intention, indicating that indeed human-in-the-loop approaches can be successfully adopted in final user applications.