ABSTRACT AI‐based recommender systems shape many consumer decisions, but users often have limited information about why a recommendation is presented. This paper examines how explainable AI (XAI) design influences consumers' follow‐through, and when these effects are stronger. We conceptualize XAI design along two dimensions: explanation level (high vs. low diagnostic detail) and explanation type (process‐oriented vs. outcome‐oriented), and we examine boundary conditions across recommendation context (product vs. content). Four scenario‐based, between‐subjects experiments were conducted with Prolific participants who reported familiarity with recommendation systems (total N = 1080). Study 1 establishes the baseline effect: high (vs. low) explanation level increases intention to follow the recommendation, and the effect is robust under divided attention. Study 2 shows that the benefit of higher explanation level is context‐dependent, with stronger effects in content recommendations than in product recommendations. Study 3 shows that explanation type also shapes the effect of explanation level on follow‐through, with process‐oriented explanations producing a larger advantage for high (vs. low) explanation level than outcome‐oriented explanations. Study 4 tests the proposed mechanisms in a 2 × 2 × 2 design and finds that explanation level affects follow‐through primarily through trust and psychological ownership, with these indirect effects stronger in content (vs. product) contexts and under process‐oriented (vs. outcome‐oriented) explanations. Together, the findings specify how explanation level, explanation type, and context jointly determine when XAI increases follow‐through, and they identify trust and psychological ownership as mechanisms through which explanation design translates into consumer action.
Malodia et al. (Tue,) studied this question.