Decoding the algorithmic black box: how post-hoc explanations shape consumer receptivity toward personalized recommendations of branded content

Purpose This research examines how post-hoc explanations in the algorithm-driven personalization process influence consumer receptivity, defined as consumers’ willingness to allow a brand to use personal information for personalized recommendations. Drawing on HAII-TIME and PKM, we distinguish retrospective post-hoc explanations, which clarify how user data generate current recommendations, from prospective post-hoc explanations, which clarify how user actions influence future recommendations. Design/methodology/approach Three experiments using consumer-triggered micro-interactions test the proposed framework. Study 1 employs a 3 × 2 design to examine the joint effect of retrospective post-hoc explanations and prospective post-hoc explanations. Study 2 tests instrumental motive inference as the mediator. Study 3 examines algorithmic awareness as a moderator of the normative motive-inference pathway. Findings Consumer receptivity depends on explanation configurations. It is highest when retrospective post-hoc explanations are acceptable and prospective post-hoc explanations are available, and lowest when retrospective post-hoc explanations are unacceptable and prospective post-hoc explanations are unavailable. This joint effect operates asymmetrically through motive inference: unfavorable configurations increase instrumental motive inference, whereas normative motive inference emerges conditionally under higher algorithmic awareness. Originality/value This research shifts attention from explanation presence to temporal post-hoc explanation configurations in personalized recommendation encounters. It identifies asymmetric motive inference as the mechanism linking explanation interfaces to consumer receptivity and positions explanation interfaces as communication tools for sustaining consumer participation in algorithm-driven personalization of branded content.