Many digital platforms operate as two-sided markets, facilitating the matching between sellers and buyers. The Amazon case is particularly interesting because of the dual role the tech giant plays: Amazon both controls the rules of the game, through platform design choices, and participates as a player itself, operating as a seller. What do consumers know about Amazon’s platform functioning? Would they behave differently if they knew how the type and the amount of information they are provided with is filtered/manipulated? This paper focuses on a peculiar and distinctive algorithmic-driven Amazon’s feature: the Buy-Box. Through a framed field experiment, we collect individual-level data on real consumers’ search and purchase behavior on the marketplace in a controlled environment, where we can manipulate the amount of information disclosed on the functioning (and the hidden risks) of Amazon’s Buy-Box. Experimental data reveal that - in the absence of any information provision - more than 80% of the participants do not even inspect other than the Buy-Box (default) deal. Providing participants with (full) information on the Buy-Box functioning and the availability of alternative deals by competing sellers significantly increases the probability of inspecting alternative deals before finalizing the purchase, and the propensity to select a seller other than the one in the default Buy-Box position. At the same time, the average time spent to finalize the purchase is not significantly different.