Transparency Matters: Psychological Ownership and Trust as Mediators of Explainable Artificial Intelligence Effectiveness.

• 
  By Kirti Sharma
  Associate Professor

  Co-Authors
  Suresh Malodia,
  Yupal Shukla,
  Anil Bilgihan,
  

  Journal : Psychology & Marketing
  Publisher : Willey

Article citation: Malodia, S., Y. Shukla, K. Sharma, and A. Bilgihan. 2026. “Transparency Matters: Psychological Ownership and Trust as Mediators of Explainable Artificial Intelligence Effectiveness.” Psychology & Marketing 43: 1578–1589. https://doi.org/10.1002/mar.70129.

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.