Why Process-Based Explanations Foster Algorithmic Trust: A Procedural Justice Account of E-Commerce Recommendations

E-commerce platforms increasingly rely on recommendation systems whose internal logic is often opaque, making explanation design important for consumer evaluation. Drawing on procedural justice theory, this study examines whether process-based explanations function as procedural justice cues in e-commerce recommendations and how they relate to algorithmic trust and continuance intention. In a between-subjects online experiment with 394 Chinese consumers (197 per condition), participants received either an outcome-based recommendation or a process-disclosure package that disclosed data inputs and reasoning and therefore bundled procedural content with greater specificity and informational richness. Relative to outcome-based explanations, this package increased perceived procedural justice and was associated with higher trust in the algorithm and greater continuance intention. Perceived procedural justice and trust formed a theoretically ordered indirect pathway, but this ordering should be read as theory-grounded rather than causally established because the mediators and outcome were measured contemporaneously. Exploratory moderation analyses suggested that responsiveness to process-based explanations reflected broader self-reported digital interpretive capacity rather than algorithm-specific literacy alone. Robustness checks further indicated that the procedural justice pathway was not eliminated by explanation clarity, cognitive load, scenario realism, product attractiveness, or privacy intrusiveness. The findings position process-disclosure packages as practical transparency tools while cautioning that their benefits depend on consumers’ interpretive capacity and processing costs.