Before revision:  Here we develop an experimental methodology where participants play a web-based game, during which they receive advice from either a human or algorithmic advisor, accompanied with explanations that vary in nature between experimental conditions. We use a reference-dependent decision-making framework, evaluate the game results over time, and in various key situations, to determine whether the different types of explanations affect the readiness to adopt, willingness to pay and trust a financial AI consultant.
After revision:  Here we develop an experimental methodology where participants play a web-based game, during which they receive advice from either a human or algorithmic advisor, accompanied by five types of Local and Global explanation labelings, has an effect on the readiness to adopt, willingness to pay and trust a financial AI consultant.